silico tools predict: Topics by Science.gov

Sample records for silico tools predict

Assessment of the predictive accuracy of five in silico prediction tools, alone or in combination, and two metaservers to classify long QT syndrome gene mutations.

PubMed

Leong, Ivone U S; Stuckey, Alexander; Lai, Daniel; Skinner, Jonathan R; Love, Donald R

2015-05-13

Long QT syndrome (LQTS) is an autosomal dominant condition predisposing to sudden death from malignant arrhythmia. Genetic testing identifies many missense single nucleotide variants of uncertain pathogenicity. Establishing genetic pathogenicity is an essential prerequisite to family cascade screening. Many laboratories use in silico prediction tools, either alone or in combination, or metaservers, in order to predict pathogenicity; however, their accuracy in the context of LQTS is unknown. We evaluated the accuracy of five in silico programs and two metaservers in the analysis of LQTS 1-3 gene variants. The in silico tools SIFT, PolyPhen-2, PROVEAN, SNPs&GO and SNAP, either alone or in all possible combinations, and the metaservers Meta-SNP and PredictSNP, were tested on 312 KCNQ1, KCNH2 and SCN5A gene variants that have previously been characterised by either in vitro or co-segregation studies as either "pathogenic" (283) or "benign" (29). The accuracy, sensitivity, specificity and Matthews Correlation Coefficient (MCC) were calculated to determine the best combination of in silico tools for each LQTS gene, and when all genes are combined. The best combination of in silico tools for KCNQ1 is PROVEAN, SNPs&GO and SIFT (accuracy 92.7%, sensitivity 93.1%, specificity 100% and MCC 0.70). The best combination of in silico tools for KCNH2 is SIFT and PROVEAN or PROVEAN, SNPs&GO and SIFT. Both combinations have the same scores for accuracy (91.1%), sensitivity (91.5%), specificity (87.5%) and MCC (0.62). In the case of SCN5A, SNAP and PROVEAN provided the best combination (accuracy 81.4%, sensitivity 86.9%, specificity 50.0%, and MCC 0.32). When all three LQT genes are combined, SIFT, PROVEAN and SNAP is the combination with the best performance (accuracy 82.7%, sensitivity 83.0%, specificity 80.0%, and MCC 0.44). Both metaservers performed better than the single in silico tools; however, they did not perform better than the best performing combination of in silico tools. The combination of in silico tools with the best performance is gene-dependent. The in silico tools reported here may have some value in assessing variants in the KCNQ1 and KCNH2 genes, but caution should be taken when the analysis is applied to SCN5A gene variants.
BRCA1/2 missense mutations and the value of in-silico analyses.

PubMed

Sadowski, Carolin E; Kohlstedt, Daniela; Meisel, Cornelia; Keller, Katja; Becker, Kerstin; Mackenroth, Luisa; Rump, Andreas; Schröck, Evelin; Wimberger, Pauline; Kast, Karin

2017-11-01

The clinical implications of genetic variants in BRCA1/2 in healthy and affected individuals are considerable. Variant interpretation, however, is especially challenging for missense variants. The majority of them are classified as variants of unknown clinical significance (VUS). Computational (in-silico) predictive programs are easy to access, but represent only one tool out of a wide range of complemental approaches to classify VUS. With this single-center study, we aimed to evaluate the impact of in-silico analyses in a spectrum of different BRCA1/2 missense variants. We conducted mutation analysis of BRCA1/2 in 523 index patients with suspected hereditary breast and ovarian cancer (HBOC). Classification of the genetic variants was performed according to the German Consortium (GC)-HBOC database. Additionally, all missense variants were classified by the following three in-silico prediction tools: SIFT, Mutation Taster (MT2) and PolyPhen2 (PPH2). Overall 201 different variants, 68 of which constituted missense variants were ranked as pathogenic, neutral, or unknown. The classification of missense variants by in-silico tools resulted in a higher amount of pathogenic mutations (25% vs. 13.2%) compared to the GC-HBOC-classification. Altogether, more than fifty percent (38/68, 55.9%) of missense variants were ranked differently. Sensitivity of in-silico-tools for mutation prediction was 88.9% (PPH2), 100% (SIFT) and 100% (MT2). We found a relevant discrepancy in variant classification by using in-silico prediction tools, resulting in potential overestimation and/or underestimation of cancer risk. More reliable, notably gene-specific, prediction tools and functional tests are needed to improve clinical counseling. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
Performance of in silico prediction tools for the classification of rare BRCA1/2 missense variants in clinical diagnostics.

PubMed

Ernst, Corinna; Hahnen, Eric; Engel, Christoph; Nothnagel, Michael; Weber, Jonas; Schmutzler, Rita K; Hauke, Jan

2018-03-27

The use of next-generation sequencing approaches in clinical diagnostics has led to a tremendous increase in data and a vast number of variants of uncertain significance that require interpretation. Therefore, prediction of the effects of missense mutations using in silico tools has become a frequently used approach. Aim of this study was to assess the reliability of in silico prediction as a basis for clinical decision making in the context of hereditary breast and/or ovarian cancer. We tested the performance of four prediction tools (Align-GVGD, SIFT, PolyPhen-2, MutationTaster2) using a set of 236 BRCA1/2 missense variants that had previously been classified by expert committees. However, a major pitfall in the creation of a reliable evaluation set for our purpose is the generally accepted classification of BRCA1/2 missense variants using the multifactorial likelihood model, which is partially based on Align-GVGD results. To overcome this drawback we identified 161 variants whose classification is independent of any previous in silico prediction. In addition to the performance as stand-alone tools we examined the sensitivity, specificity, accuracy and Matthews correlation coefficient (MCC) of combined approaches. PolyPhen-2 achieved the lowest sensitivity (0.67), specificity (0.67), accuracy (0.67) and MCC (0.39). Align-GVGD achieved the highest values of specificity (0.92), accuracy (0.92) and MCC (0.73), but was outperformed regarding its sensitivity (0.90) by SIFT (1.00) and MutationTaster2 (1.00). All tools suffered from poor specificities, resulting in an unacceptable proportion of false positive results in a clinical setting. This shortcoming could not be bypassed by combination of these tools. In the best case scenario, 138 families would be affected by the misclassification of neutral variants within the cohort of patients of the German Consortium for Hereditary Breast and Ovarian Cancer. We show that due to low specificities state-of-the-art in silico prediction tools are not suitable to predict pathogenicity of variants of uncertain significance in BRCA1/2. Thus, clinical consequences should never be based solely on in silico forecasts. However, our data suggests that SIFT and MutationTaster2 could be suitable to predict benignity, as both tools did not result in false negative predictions in our analysis.
In Silico Strategies for Modeling Stereoselective Metabolism of Pyrethroids

EPA Science Inventory

In silico methods are invaluable tools to researchers seeking to understand and predict metabolic processes within PBPK models. Even though these methods have been successfully utilized to predict and quantify metabolic processes, there are many challenges involved. Stereochemica...
Evaluation of in silico tools to predict the skin sensitization potential of chemicals.

PubMed

Verheyen, G R; Braeken, E; Van Deun, K; Van Miert, S

2017-01-01

Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE Ultra) or rule based (OECD Toolbox, Toxtree, Derek Nexus). In practice, several of these in silico tools are used in gap filling and read-across, but here their use was limited to make predictions based on presence/absence of structural features associated to sensitization. The top 400 ranking substances of the ATSDR 2011 Priority List of Hazardous Substances were selected as a starting point. Experimental information was identified for 160 chemically diverse substances (82 positive and 78 negative). The prediction for skin sensitization potential was compared with the experimental data. Rule-based tools perform slightly better, with accuracies ranging from 0.6 (OECD Toolbox) to 0.78 (Derek Nexus), compared with statistical tools that had accuracies ranging from 0.48 (Vega) to 0.73 (CASE Ultra - LLNA weak model). Combining models increased the performance, with positive and negative predictive values up to 80% and 84%, respectively. However, the number of substances that were predicted positive or negative for skin sensitization in both models was low. Adding more substances to the dataset will increase the confidence in the conclusions reached. The insights obtained in this evaluation are incorporated in a web database www.asopus.weebly.com that provides a potential end user context for the scope and performance of different in silico tools with respect to a common dataset of curated skin sensitization data.
Advances in In Vitro and In Silico Tools for Toxicokinetic Dose Modeling and Predictive Toxicology (WC10)

EPA Science Inventory

Recent advances in vitro assays, in silico tools, and systems biology approaches provide opportunities for refined mechanistic understanding for chemical safety assessment that will ultimately lead to reduced reliance on animal-based methods. With the U.S. commercial chemical lan...
In silico prediction of splice-altering single nucleotide variants in the human genome.

PubMed

Jian, Xueqiu; Boerwinkle, Eric; Liu, Xiaoming

2014-12-16

In silico tools have been developed to predict variants that may have an impact on pre-mRNA splicing. The major limitation of the application of these tools to basic research and clinical practice is the difficulty in interpreting the output. Most tools only predict potential splice sites given a DNA sequence without measuring splicing signal changes caused by a variant. Another limitation is the lack of large-scale evaluation studies of these tools. We compared eight in silico tools on 2959 single nucleotide variants within splicing consensus regions (scSNVs) using receiver operating characteristic analysis. The Position Weight Matrix model and MaxEntScan outperformed other methods. Two ensemble learning methods, adaptive boosting and random forests, were used to construct models that take advantage of individual methods. Both models further improved prediction, with outputs of directly interpretable prediction scores. We applied our ensemble scores to scSNVs from the Catalogue of Somatic Mutations in Cancer database. Analysis showed that predicted splice-altering scSNVs are enriched in recurrent scSNVs and known cancer genes. We pre-computed our ensemble scores for all potential scSNVs across the human genome, providing a whole genome level resource for identifying splice-altering scSNVs discovered from large-scale sequencing studies.
Incorporation of in silico biodegradability screening in early drug development--a feasible approach?

PubMed

Steger-Hartmann, Thomas; Länge, Reinhard; Heuck, Klaus

2011-05-01

The concentration of a pharmaceutical found in the environment is determined by the amount used by the patient, the excretion and metabolism pattern, and eventually by its persistence. Biological degradation or persistence of a pharmaceutical is experimentally tested rather late in the development of a pharmaceutical, often shortly before submission of the dossier to regulatory authorities. To investigate whether the aspect of persistence of a compound could be assessed early during drug development, we investigated whether biodegradation of pharmaceuticals could be predicted with the help of in silico tools. To assess the value of in silico prediction, we collected results for the OECD 301 degradation test ("ready biodegradability") of 42 drugs or drug synthesis intermediates and compared them to the prediction of the in silico tool BIOWIN. Of these compounds, 38 were predictable with BIOWIN, which is a module of the Estimation Programs Interface (EPI) Suite™ provided by the US EPA. The program failed to predict the two drugs which proved to be readily biodegradable in the degradation tests. On the other hand, BIOWIN predicted two compounds to be readily biodegradable which, however, proved to be persistent in the test setting. The comparison of experimental data with the predicted one resulted in a specificity of 94% and a sensitivity of 0%. The results of this study do not indicate that application of the biodegradation prediction tool BIOWIN is a feasible approach to assess the ready biodegradability during early drug development.
In silico site-directed mutagenesis informs species-specific predictions of chemical susceptibility derived from the Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool

EPA Science Inventory

The Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS) tool was developed to address needs for rapid, cost effective methods of species extrapolation of chemical susceptibility. Specifically, the SeqAPASS tool compares the primary sequence (Level 1), functiona...
In silico pharmacology for drug discovery: applications to targets and beyond

PubMed Central

Ekins, S; Mestres, J; Testa, B

2007-01-01

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets. PMID:17549046
In silico environmental chemical science: properties and processes from statistical and computational modelling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tratnyek, Paul G.; Bylaska, Eric J.; Weber, Eric J.

2017-01-01

Quantitative structure–activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with “in silico” results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs usingmore » descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for “in silico environmental chemical science” are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.« less
In-silico wear prediction for knee replacements--methodology and corroboration.

PubMed

Strickland, M A; Taylor, M

2009-07-22

The capability to predict in-vivo wear of knee replacements is a valuable pre-clinical analysis tool for implant designers. Traditionally, time-consuming experimental tests provided the principal means of investigating wear. Today, computational models offer an alternative. However, the validity of these models has not been demonstrated across a range of designs and test conditions, and several different formulas are in contention for estimating wear rates, limiting confidence in the predictive power of these in-silico models. This study collates and retrospectively simulates a wide range of experimental wear tests using fast rigid-body computational models with extant wear prediction algorithms, to assess the performance of current in-silico wear prediction tools. The number of tests corroborated gives a broader, more general assessment of the performance of these wear-prediction tools, and provides better estimates of the wear 'constants' used in computational models. High-speed rigid-body modelling allows a range of alternative algorithms to be evaluated. Whilst most cross-shear (CS)-based models perform comparably, the 'A/A+B' wear model appears to offer the best predictive power amongst existing wear algorithms. However, the range and variability of experimental data leaves considerable uncertainty in the results. More experimental data with reduced variability and more detailed reporting of studies will be necessary to corroborate these models with greater confidence. With simulation times reduced to only a few minutes, these models are ideally suited to large-volume 'design of experiment' or probabilistic studies (which are essential if pre-clinical assessment tools are to begin addressing the degree of variation observed clinically and in explanted components).
In Silico PCR Tools for a Fast Primer, Probe, and Advanced Searching.

PubMed

Kalendar, Ruslan; Muterko, Alexandr; Shamekova, Malika; Zhambakin, Kabyl

2017-01-01

The polymerase chain reaction (PCR) is fundamental to molecular biology and is the most important practical molecular technique for the research laboratory. The principle of this technique has been further used and applied in plenty of other simple or complex nucleic acid amplification technologies (NAAT). In parallel to laboratory "wet bench" experiments for nucleic acid amplification technologies, in silico or virtual (bioinformatics) approaches have been developed, among which in silico PCR analysis. In silico NAAT analysis is a useful and efficient complementary method to ensure the specificity of primers or probes for an extensive range of PCR applications from homology gene discovery, molecular diagnosis, DNA fingerprinting, and repeat searching. Predicting sensitivity and specificity of primers and probes requires a search to determine whether they match a database with an optimal number of mismatches, similarity, and stability. In the development of in silico bioinformatics tools for nucleic acid amplification technologies, the prospects for the development of new NAAT or similar approaches should be taken into account, including forward-looking and comprehensive analysis that is not limited to only one PCR technique variant. The software FastPCR and the online Java web tool are integrated tools for in silico PCR of linear and circular DNA, multiple primer or probe searches in large or small databases and for advanced search. These tools are suitable for processing of batch files that are essential for automation when working with large amounts of data. The FastPCR software is available for download at http://primerdigital.com/fastpcr.html and the online Java version at http://primerdigital.com/tools/pcr.html .
Genetic Epidemiology of Glucose-6-Dehydrogenase Deficiency in the Arab World.

PubMed

Doss, C George Priya; Alasmar, Dima R; Bux, Reem I; Sneha, P; Bakhsh, Fadheela Dad; Al-Azwani, Iman; Bekay, Rajaa El; Zayed, Hatem

2016-11-17

A systematic search was implemented using four literature databases (PubMed, Embase, Science Direct and Web of Science) to capture all the causative mutations of Glucose-6-phosphate dehydrogenase (G6PD) deficiency (G6PDD) in the 22 Arab countries. Our search yielded 43 studies that captured 33 mutations (23 missense, one silent, two deletions, and seven intronic mutations), in 3,430 Arab patients with G6PDD. The 23 missense mutations were then subjected to phenotypic classification using in silico prediction tools, which were compared to the WHO pathogenicity scale as a reference. These in silico tools were tested for their predicting efficiency using rigorous statistical analyses. Of the 23 missense mutations, p.S188F, p.I48T, p.N126D, and p.V68M, were identified as the most common mutations among Arab populations, but were not unique to the Arab world, interestingly, our search strategy found four other mutations (p.N135T, p.S179N, p.R246L, and p.Q307P) that are unique to Arabs. These mutations were exposed to structural analysis and molecular dynamics simulation analysis (MDSA), which predicting these mutant forms as potentially affect the enzyme function. The combination of the MDSA, structural analysis, and in silico predictions and statistical tools we used will provide a platform for future prediction accuracy for the pathogenicity of genetic mutations.
In Silico Approaches for Predicting Adme Properties

NASA Astrophysics Data System (ADS)

Madden, Judith C.

A drug requires a suitable pharmacokinetic profile to be efficacious in vivo in humans. The relevant pharmacokinetic properties include the absorption, distribution, metabolism, and excretion (ADME) profile of the drug. This chapter provides an overview of the definition and meaning of key ADME properties, recent models developed to predict these properties, and a guide as to how to select the most appropriate model(s) for a given query. Many tools using the state-of-the-art in silico methodology are now available to users, and it is anticipated that the continual evolution of these tools will provide greater ability to predict ADME properties in the future. However, caution must be exercised in applying these tools as data are generally available only for "successful" drugs, i.e., those that reach the marketplace, and little supplementary information, such as that for drugs that have a poor pharmacokinetic profile, is available. The possibilities of using these methods and possible integration into toxicity prediction are explored.
An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data.

PubMed

Braaksma, Machtelt; Martens-Uzunova, Elena S; Punt, Peter J; Schaap, Peter J

2010-10-19

The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions.
An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

PubMed Central

2010-01-01

Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions. PMID:20959013
A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.

PubMed

Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

2013-11-02

An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.
A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms

PubMed Central

2013-01-01

Background An in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets. Results The results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally. Conclusions Vaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory. PMID:24180526
Family-Based Benchmarking of Copy Number Variation Detection Software.

PubMed

Nutsua, Marcel Elie; Fischer, Annegret; Nebel, Almut; Hofmann, Sylvia; Schreiber, Stefan; Krawczak, Michael; Nothnagel, Michael

2015-01-01

The analysis of structural variants, in particular of copy-number variations (CNVs), has proven valuable in unraveling the genetic basis of human diseases. Hence, a large number of algorithms have been developed for the detection of CNVs in SNP array signal intensity data. Using the European and African HapMap trio data, we undertook a comparative evaluation of six commonly used CNV detection software tools, namely Affymetrix Power Tools (APT), QuantiSNP, PennCNV, GLAD, R-gada and VEGA, and assessed their level of pair-wise prediction concordance. The tool-specific CNV prediction accuracy was assessed in silico by way of intra-familial validation. Software tools differed greatly in terms of the number and length of the CNVs predicted as well as the number of markers included in a CNV. All software tools predicted substantially more deletions than duplications. Intra-familial validation revealed consistently low levels of prediction accuracy as measured by the proportion of validated CNVs (34-60%). Moreover, up to 20% of apparent family-based validations were found to be due to chance alone. Software using Hidden Markov models (HMM) showed a trend to predict fewer CNVs than segmentation-based algorithms albeit with greater validity. PennCNV yielded the highest prediction accuracy (60.9%). Finally, the pairwise concordance of CNV prediction was found to vary widely with the software tools involved. We recommend HMM-based software, in particular PennCNV, rather than segmentation-based algorithms when validity is the primary concern of CNV detection. QuantiSNP may be used as an additional tool to detect sets of CNVs not detectable by the other tools. Our study also reemphasizes the need for laboratory-based validation, such as qPCR, of CNVs predicted in silico.

In silico toxicology for the pharmaceutical sciences

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valerio, Luis G., E-mail: Luis.Valerio@fda.hhs.go

2009-12-15

The applied use of in silico technologies (a.k.a. computational toxicology, in silico toxicology, computer-assisted tox, e-tox, i-drug discovery, predictive ADME, etc.) for predicting preclinical toxicological endpoints, clinical adverse effects, and metabolism of pharmaceutical substances has become of high interest to the scientific community and the public. The increased accessibility of these technologies for scientists and recent regulations permitting their use for chemical risk assessment supports this notion. The scientific community is interested in the appropriate use of such technologies as a tool to enhance product development and safety of pharmaceuticals and other xenobiotics, while ensuring the reliability and accuracy ofmore » in silico approaches for the toxicological and pharmacological sciences. For pharmaceutical substances, this means active and impurity chemicals in the drug product may be screened using specialized software and databases designed to cover these substances through a chemical structure-based screening process and algorithm specific to a given software program. A major goal for use of these software programs is to enable industry scientists not only to enhance the discovery process but also to ensure the judicious use of in silico tools to support risk assessments of drug-induced toxicities and in safety evaluations. However, a great amount of applied research is still needed, and there are many limitations with these approaches which are described in this review. Currently, there is a wide range of endpoints available from predictive quantitative structure-activity relationship models driven by many different computational software programs and data sources, and this is only expected to grow. For example, there are models based on non-proprietary and/or proprietary information specific to assessing potential rodent carcinogenicity, in silico screens for ICH genetic toxicity assays, reproductive and developmental toxicity, theoretical prediction of human drug metabolism, mechanisms of action for pharmaceuticals, and newer models for predicting human adverse effects. How accurate are these approaches is both a statistical issue and challenge in toxicology. In this review, fundamental concepts and the current capabilities and limitations of this technology will be critically addressed.« less
"In silico" mechanistic studies as predictive tools in microwave-assisted organic synthesis.

PubMed

Rodriguez, A M; Prieto, P; de la Hoz, A; Díaz-Ortiz, A

2011-04-07

Computational calculations can be used as a predictive tool in Microwave-Assisted Organic Synthesis (MAOS). A DFT study on Intramolecular Diels-Alder reactions (IMDA) indicated that the activation energy of the reaction and the polarity of the stationary points are two fundamental parameters to determine "a priori" if a reaction can be improved by using microwave irradiation.
Experimental Assessment of Splicing Variants Using Expression Minigenes and Comparison with In Silico Predictions

PubMed Central

Sharma, Neeraj; Sosnay, Patrick R.; Ramalho, Anabela S.; Douville, Christopher; Franca, Arianna; Gottschalk, Laura B.; Park, Jeenah; Lee, Melissa; Vecchio-Pagan, Briana; Raraigh, Karen S.; Amaral, Margarida D.; Karchin, Rachel; Cutting, Garry R.

2015-01-01

Assessment of the functional consequences of variants near splice sites is a major challenge in the diagnostic laboratory. To address this issue, we created expression minigenes (EMGs) to determine the RNA and protein products generated by splice site variants (n = 10) implicated in cystic fibrosis (CF). Experimental results were compared with the splicing predictions of eight in silico tools. EMGs containing the full-length Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) coding sequence and flanking intron sequences generated wild-type transcript and fully processed protein in Human Embryonic Kidney (HEK293) and CF bronchial epithelial (CFBE41o-) cells. Quantification of variant induced aberrant mRNA isoforms was concordant using fragment analysis and pyrosequencing. The splicing patterns of c.1585−1G>A and c.2657+5G>A were comparable to those reported in primary cells from individuals bearing these variants. Bioinformatics predictions were consistent with experimental results for 9/10 variants (MES), 8/10 variants (NNSplice), and 7/10 variants (SSAT and Sroogle). Programs that estimate the consequences of mis-splicing predicted 11/16 (HSF and ASSEDA) and 10/16 (Fsplice and SplicePort) experimentally observed mRNA isoforms. EMGs provide a robust experimental approach for clinical interpretation of splice site variants and refinement of in silico tools. PMID:25066652
In silico aided thoughts on mitochondrial vitamin C transport.

PubMed

Szarka, András; Balogh, Tibor

2015-01-21

The huge demand of mitochondria as the quantitatively most important sources of ROS in the majority of heterotrophic cells for vitamin C is indisputable. The reduced form of the vitamin, l-ascorbic acid, is imported by an active mechanism requiring two sodium-dependent vitamin C transporters (SVCT1 and SVCT2). The oxidized form, dehydroascorbate is taken up by different members of the GLUT family. Because of the controversial experimental results the picture on mitochondrial vitamin C transport became quite obscure by the spring of 2014. Thus in silico prediction tools were applied in aid of the support of in vitro and in vivo results. The role of GLUT1 as a mitochondrial dehydroascorbate transporter could be reinforced by in silico predictions however the mitochondrial presence of GLUT10 is not likely since this transport protein got far the lowest mitochondrial localization scores. Furthermore the possible roles of GLUT9 and 11 in mitochondrial vitamin C transport can be proposed leastwise on the base of their computational localization analysis. In good concordance with the newest experimental observations on SVCT2 the mitochondrial presence of this transporter could also be supported by the computational prediction tools. Copyright © 2014 Elsevier Ltd. All rights reserved.
Bio-AIMS Collection of Chemoinformatics Web Tools based on Molecular Graph Information and Artificial Intelligence Models.

PubMed

Munteanu, Cristian R; Gonzalez-Diaz, Humberto; Garcia, Rafael; Loza, Mabel; Pazos, Alejandro

2015-01-01

The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction models for specific biological properties of molecules. These models connect the molecular structure information such as atom connectivity (molecular graphs) or physical-chemical properties of an atom/group of atoms to the molecular activity (Quantitative Structure - Activity Relationship, QSAR). Due to the complexity of the proteins, the prediction of their activity is a complicated task and the interpretation of the models is more difficult. The current review presents a series of 11 prediction models for proteins, implemented as free Web tools on an Artificial Intelligence Model Server in Biosciences, Bio-AIMS (http://bio-aims.udc.es/TargetPred.php). Six tools predict protein activity, two models evaluate drug - protein target interactions and the other three calculate protein - protein interactions. The input information is based on the protein 3D structure for nine models, 1D peptide amino acid sequence for three tools and drug SMILES formulas for two servers. The molecular graph descriptor-based Machine Learning models could be useful tools for in silico screening of new peptides/proteins as future drug targets for specific treatments.
Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation.

PubMed

Klein, Julie; Eales, James; Zürbig, Petra; Vlahou, Antonia; Mischak, Harald; Stevens, Robert

2013-04-01

In this study, we have developed Proteasix, an open-source peptide-centric tool that can be used to predict in silico the proteases involved in naturally occurring peptide generation. We developed a curated cleavage site (CS) database, containing 3500 entries about human protease/CS combinations. On top of this database, we built a tool, Proteasix, which allows CS retrieval and protease associations from a list of peptides. To establish the proof of concept of the approach, we used a list of 1388 peptides identified from human urine samples, and compared the prediction to the analysis of 1003 randomly generated amino acid sequences. Metalloprotease activity was predominantly involved in urinary peptide generation, and more particularly to peptides associated with extracellular matrix remodelling, compared to proteins from other origins. In comparison, random sequences returned almost no results, highlighting the specificity of the prediction. This study provides a tool that can facilitate linking of identified protein fragments to predicted protease activity, and therefore into presumed mechanisms of disease. Experiments are needed to confirm the in silico hypotheses; nevertheless, this approach may be of great help to better understand molecular mechanisms of disease, and define new biomarkers, and therapeutic targets. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Propagating annotations of molecular networks using in silico fragmentation

PubMed Central

da Silva, Ricardo R.; Wang, Mingxun; Fox, Evan; Balunas, Marcy J.; Klassen, Jonathan L.; Dorrestein, Pieter C.

2018-01-01

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp. PMID:29668671
Propagating annotations of molecular networks using in silico fragmentation.

PubMed

da Silva, Ricardo R; Wang, Mingxun; Nothias, Louis-Félix; van der Hooft, Justin J J; Caraballo-Rodríguez, Andrés Mauricio; Fox, Evan; Balunas, Marcy J; Klassen, Jonathan L; Lopes, Norberto Peporine; Dorrestein, Pieter C

2018-04-01

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.
Prediction of pharmacokinetic and toxicological parameters of a 4-phenylcoumarin isolated from geopropolis: In silico and in vitro approaches.

PubMed

da Cunha, Marcos Guilherme; Franco, Gilson César Nobre; Franchin, Marcelo; Beutler, John A; de Alencar, Severino Matias; Ikegaki, Masaharu; Rosalen, Pedro Luiz

2016-11-30

In silico and in vitro methodologies have been used as important tools in the drug discovery process, including from natural sources. The aim of this study was to predict pharmacokinetic and toxicity (ADME/Tox) properties of a coumarin isolated from geopropolis using in silico and in vitro approaches. Cinnamoyloxy-mammeisin (CNM) isolated from Brazilian M. scutellaris geopropolis was evaluated for its pharmacokinetic parameters by in silico models (ACD/Percepta™ and MetaDrug™ software). Genotoxicity was assessed by in vitro DNA damage signaling PCR array. CNM did not pass all parameters of Lipinski's rule of five, with a predicted low oral bioavailability and high plasma protein binding, but with good predicted blood brain barrier penetration. CNM was predicted to show low affinity to cytochrome P450 family members. Furthermore, the predicted Ames test indicated potential mutagenicity of CNM. Also, the probability of toxicity for organs and tissues was classified as moderate and high for liver and kidney, and moderate and low for skin and eye irritation, respectively. The PCR array analysis showed that CNM significantly upregulated about 7% of all DNA damage-related genes. By exploring the biological function of these genes, it was found that the predicted CNM genotoxicity is likely to be mediated by apoptosis. The predicted ADME/Tox profile suggests that external use of CNM may be preferable to systemic exposure, while its genotoxicity was characterized by the upregulation of apoptosis-related genes after treatment. The combined use of in silico and in vitro approaches to evaluate these parameters generated useful hypotheses to guide further preclinical studies. Copyright Â© 2016 Elsevier Ireland Ltd. All rights reserved.
Pharmacological validation of in-silico guided novel nootropic potential of Achyranthes aspera L.

PubMed

Gawande, Dinesh Yugraj; Goel, Rajesh Kumar

2015-12-04

Achyranthes aspera (A. aspera) has been used as a brain tonic in folk medicine. Although, ethnic use of medicinal plant has been basis for drug discovery from medicinal plants, but the available in-silico tools can be useful to find novel pharmacological uses of medicinal plants beyond their ethnic use. To validate in-silico prediction for novel nootropic effect of A. aspera by employing battery of tests in mice. Phytoconstituents of A. aspera reported in Dictionary of Natural Product were subjected to in-silico prediction using PASS and Pharmaexpert. The nootropic activity predicted for A. aspera was assessed using radial arm maze, passive shock avoidance and novel object recognition tests in mice. After behavioral evaluation animals were decapitated and their brains were collected and stored for estimation of glutamate levels and acetylcholinesterase activity. In-silico activity spectrum for majority of A. aspera phytoconstituents exhibited excellent prediction score for nootropic activity of this plant. A. aspera extract treatment significantly improved the learning and memory as evident by decreased working memory errors, reference memory errors and latency time in radial arm maze, step through latency in passive shock avoidance and increased recognition index in novel object recognition were observed, moreover significantly enhanced glutamate levels and reduced acetylcholinesterase activity in hippocampus and cortex were observed as compared to the saline treated group. In-silico and in-vivo results suggest that A. aspera plant may improve the learning and memory by modulating the brain glutamatergic and cholinergic neurotransmission. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Environmental metabarcodes for insects: in silico PCR reveals potential for taxonomic bias.

PubMed

Clarke, Laurence J; Soubrier, Julien; Weyrich, Laura S; Cooper, Alan

2014-11-01

Studies of insect assemblages are suited to the simultaneous DNA-based identification of multiple taxa known as metabarcoding. To obtain accurate estimates of diversity, metabarcoding markers ideally possess appropriate taxonomic coverage to avoid PCR-amplification bias, as well as sufficient sequence divergence to resolve species. We used in silico PCR to compare the taxonomic coverage and resolution of newly designed insect metabarcodes (targeting 16S) with that of existing markers [16S and cytochrome oxidase c subunit I (COI)] and then compared their efficiency in vitro. Existing metabarcoding primers amplified in silico <75% of insect species with complete mitochondrial genomes available, whereas new primers targeting 16S provided >90% coverage. Furthermore, metabarcodes targeting COI appeared to introduce taxonomic PCR-amplification bias, typically amplifying a greater percentage of Lepidoptera and Diptera species, while failing to amplify certain orders in silico. To test whether bias predicted in silico was observed in vitro, we created an artificial DNA blend containing equal amounts of DNA from 14 species, representing 11 insect orders and one arachnid. We PCR-amplified the blend using five primer sets, targeting either COI or 16S, with high-throughput amplicon sequencing yielding more than 6 million reads. In vitro results typically corresponded to in silico PCR predictions, with newly designed 16S primers detecting 11 insect taxa present, thus providing equivalent or better taxonomic coverage than COI metabarcodes. Our results demonstrate that in silico PCR is a useful tool for predicting taxonomic bias in mixed template PCR and that researchers should be wary of potential bias when selecting metabarcoding markers. © 2014 John Wiley & Sons Ltd.
Calibration of Multiple In Silico Tools for Predicting Pathogenicity of Mismatch Repair Gene Missense Substitutions

PubMed Central

Thompson, Bryony A.; Greenblatt, Marc S.; Vallee, Maxime P.; Herkert, Johanna C.; Tessereau, Chloe; Young, Erin L.; Adzhubey, Ivan A.; Li, Biao; Bell, Russell; Feng, Bingjian; Mooney, Sean D.; Radivojac, Predrag; Sunyaev, Shamil R.; Frebourg, Thierry; Hofstra, Robert M.W.; Sijmons, Rolf H.; Boucher, Ken; Thomas, Alun; Goldgar, David E.; Spurdle, Amanda B.; Tavtigian, Sean V.

2015-01-01

Classification of rare missense substitutions observed during genetic testing for patient management is a considerable problem in clinical genetics. The Bayesian integrated evaluation of unclassified variants is a solution originally developed for BRCA1/2. Here, we take a step toward an analogous system for the mismatch repair (MMR) genes (MLH1, MSH2, MSH6, and PMS2) that confer colon cancer susceptibility in Lynch syndrome by calibrating in silico tools to estimate prior probabilities of pathogenicity for MMR gene missense substitutions. A qualitative five-class classification system was developed and applied to 143 MMR missense variants. This identified 74 missense substitutions suitable for calibration. These substitutions were scored using six different in silico tools (Align-Grantham Variation Grantham Deviation, multivariate analysis of protein polymorphisms [MAPP], Mut-Pred, PolyPhen-2.1, Sorting Intolerant From Tolerant, and Xvar), using curated MMR multiple sequence alignments where possible. The output from each tool was calibrated by regression against the classifications of the 74 missense substitutions; these calibrated outputs are interpretable as prior probabilities of pathogenicity. MAPP was the most accurate tool and MAPP + PolyPhen-2.1 provided the best-combined model (R2 = 0.62 and area under receiver operating characteristic = 0.93). The MAPP + PolyPhen-2.1 output is sufficiently predictive to feed as a continuous variable into the quantitative Bayesian integrated evaluation for clinical classification of MMR gene missense substitutions. PMID:22949387
Screening of mutations affecting protein stability and dynamics of FGFR1—A simulation analysis

PubMed Central

Doss, C. George Priya; Rajith, B.; Garwasis, Nimisha; Mathew, Pretty Raju; Raju, Anand Solomon; Apoorva, K.; William, Denise; Sadhana, N.R.; Himani, Tanwar; Dike, IP.

2012-01-01

Single amino acid substitutions in Fibroblast Growth Factor Receptor 1 (FGFR1) destabilize protein and have been implicated in several genetic disorders like various forms of cancer, Kallamann syndrome, Pfeiffer syndrome, Jackson Weiss syndrome, etc. In order to gain functional insight into mutation caused by amino acid substitution to protein function and expression, special emphasis was laid on molecular dynamics simulation techniques in combination with in silico tools such as SIFT, PolyPhen 2.0, I-Mutant 3.0 and SNAP. It has been estimated that 68% nsSNPs were predicted to be deleterious by I-Mutant, slightly higher than SIFT (37%), PolyPhen 2.0 (61%) and SNAP (58%). From the observed results, P722S mutation was found to be most deleterious by comparing results of all in silico tools. By molecular dynamics approach, we have shown that P722S mutation leads to increase in flexibility, and deviated more from the native structure which was supported by the decrease in the number of hydrogen bonds. In addition, biophysical analysis revealed a clear insight of stability loss due to P722S mutation in FGFR1 protein. Majority of mutations predicted by these in silico tools were in good concordance with the experimental results. PMID:27896051
Screening of mutations affecting protein stability and dynamics of FGFR1-A simulation analysis.

PubMed

Doss, C George Priya; Rajith, B; Garwasis, Nimisha; Mathew, Pretty Raju; Raju, Anand Solomon; Apoorva, K; William, Denise; Sadhana, N R; Himani, Tanwar; Dike, I P

2012-12-01

Single amino acid substitutions in Fibroblast Growth Factor Receptor 1 ( FGFR1 ) destabilize protein and have been implicated in several genetic disorders like various forms of cancer, Kallamann syndrome, Pfeiffer syndrome, Jackson Weiss syndrome, etc. In order to gain functional insight into mutation caused by amino acid substitution to protein function and expression, special emphasis was laid on molecular dynamics simulation techniques in combination with in silico tools such as SIFT, PolyPhen 2.0, I-Mutant 3.0 and SNAP. It has been estimated that 68% nsSNPs were predicted to be deleterious by I-Mutant, slightly higher than SIFT (37%), PolyPhen 2.0 (61%) and SNAP (58%). From the observed results, P722S mutation was found to be most deleterious by comparing results of all in silico tools. By molecular dynamics approach, we have shown that P722S mutation leads to increase in flexibility, and deviated more from the native structure which was supported by the decrease in the number of hydrogen bonds. In addition, biophysical analysis revealed a clear insight of stability loss due to P722S mutation in FGFR1 protein. Majority of mutations predicted by these in silico tools were in good concordance with the experimental results.
In Vitro and in Silico Tools To Assess Extent of Cellular Uptake and Lysosomal Sequestration of Respiratory Drugs in Human Alveolar Macrophages.

PubMed

Ufuk, Ayşe; Assmus, Frauke; Francis, Laura; Plumb, Jonathan; Damian, Valeriu; Gertz, Michael; Houston, J Brian; Galetin, Aleksandra

2017-04-03

Accumulation of respiratory drugs in human alveolar macrophages (AMs) has not been extensively studied in vitro and in silico despite its potential impact on therapeutic efficacy and/or occurrence of phospholipidosis. The current study aims to characterize the accumulation and subcellular distribution of drugs with respiratory indication in human AMs and to develop an in silico mechanistic AM model to predict lysosomal accumulation of investigated drugs. The data set included 9 drugs previously investigated in rat AM cell line NR8383. Cell-to-unbound medium concentration ratio (K p,cell ) of all drugs (5 μM) was determined to assess the magnitude of intracellular accumulation. The extent of lysosomal sequestration in freshly isolated human AMs from multiple donors (n = 5) was investigated for clarithromycin and imipramine (positive control) using an indirect in vitro method (±20 mM ammonium chloride, NH 4 Cl). The AM cell parameters and drug physicochemical data were collated to develop an in silico mechanistic AM model. Three in silico models differing in their description of drug membrane partitioning were evaluated; model (1) relied on octanol-water partitioning of drugs, model (2) used in vitro data to account for this process, and model (3) predicted membrane partitioning by incorporating AM phospholipid fractions. In vitro K p,cell ranged >200-fold for respiratory drugs, with the highest accumulation seen for clarithromycin. A good agreement in K p,cell was observed between human AMs and NR8383 (2.45-fold bias), highlighting NR8383 as a potentially useful in vitro surrogate tool to characterize drug accumulation in AMs. The mean K p,cell of clarithromycin (81, CV = 51%) and imipramine (963, CV = 54%) were reduced in the presence of NH 4 Cl by up to 67% and 81%, respectively, suggesting substantial contribution of lysosomal sequestration and intracellular binding in the accumulation of these drugs in human AMs. The in vitro data showed variability in drug accumulation between individual human AM donors due to possible differences in lysosomal abundance, volume, and phospholipid content, which may have important clinical implications. Consideration of drug-acidic phospholipid interactions significantly improved the performance of the in silico models; use of in vitro K p,cell obtained in the presence of NH 4 Cl as a surrogate for membrane partitioning (model (2)) captured the variability in clarithromycin and imipramine K p,cell observed in vitro and showed the best ability to predict correctly positive and negative lysosomotropic properties. The developed mechanistic AM model represents a useful in silico tool to predict lysosomal and cellular drug concentrations based on drug physicochemical data and system specific properties, with potential application to other cell types.
Advances in In Vitro and In Silico Tools for Toxicokinetic Dose ...

EPA Pesticide Factsheets

Recent advances in vitro assays, in silico tools, and systems biology approaches provide opportunities for refined mechanistic understanding for chemical safety assessment that will ultimately lead to reduced reliance on animal-based methods. With the U.S. commercial chemical landscape encompassing thousands of chemicals with limited data, safety assessment strategies that reliably predict in vivo systemic exposures and subsequent in vivo effects efficiently are a priority. Quantitative in vitro-in vivo extrapolation (QIVIVE) is a methodology that facilitates the explicit and quantitative application of in vitro experimental data and in silico modeling to predict in vivo system behaviors and can be applied to predict chemical toxicokinetics, toxicodynamics and also population variability. Tiered strategies that incorporate sufficient information to reliably inform the relevant decision context will facilitate acceptance of these alternative data streams for safety assessments. This abstract does not necessarily reflect U.S. EPA policy. This talk will provide an update to an international audience on the state of science being conducted within the EPA’s Office of Research and Development to develop and refine approaches that estimate internal chemical concentrations following a given exposure, known as toxicokinetics. Toxicokinetic approaches hold great potential in their ability to link in vitro activities or toxicities identified during high-throughput screen
An in silico pipeline to filter the Toxoplasma gondii proteome for proteins that could traffic to the host cell nucleus and influence host cell epigenetic regulation.

PubMed

Syn, Genevieve; Blackwell, Jenefer M; Jamieson, Sarra E; Francis, Richard W

2018-01-01

Toxoplasma gondii uses epigenetic mechanisms to regulate both endogenous and host cell gene expression. To identify genes with putative epigenetic functions, we developed an in silico pipeline to interrogate the T. gondii proteome of 8313 proteins. Step 1 employs PredictNLS and NucPred to identify genes predicted to target eukaryotic nuclei. Step 2 uses GOLink to identify proteins of epigenetic function based on Gene Ontology terms. This resulted in 611 putative nuclear localised proteins with predicted epigenetic functions. Step 3 filtered for secretory proteins using SignalP, SecretomeP, and experimental data. This identified 57 of the 611 putative epigenetic proteins as likely to be secreted. The pipeline is freely available online, uses open access tools and software with user-friendly Perl scripts to automate and manage the results, and is readily adaptable to undertake any such in silico search for genes contributing to particular functions.
Java web tools for PCR, in silico PCR, and oligonucleotide assembly and analysis.

PubMed

Kalendar, Ruslan; Lee, David; Schulman, Alan H

2011-08-01

The polymerase chain reaction is fundamental to molecular biology and is the most important practical molecular technique for the research laboratory. We have developed and tested efficient tools for PCR primer and probe design, which also predict oligonucleotide properties based on experimental studies of PCR efficiency. The tools provide comprehensive facilities for designing primers for most PCR applications and their combinations, including standard, multiplex, long-distance, inverse, real-time, unique, group-specific, bisulphite modification assays, Overlap-Extension PCR Multi-Fragment Assembly, as well as a programme to design oligonucleotide sets for long sequence assembly by ligase chain reaction. The in silico PCR primer or probe search includes comprehensive analyses of individual primers and primer pairs. It calculates the melting temperature for standard and degenerate oligonucleotides including LNA and other modifications, provides analyses for a set of primers with prediction of oligonucleotide properties, dimer and G-quadruplex detection, linguistic complexity, and provides a dilution and resuspension calculator. Copyright © 2011 Elsevier Inc. All rights reserved.
Integrating in silico models to enhance predictivity for developmental toxicity.

PubMed

Marzo, Marco; Kulkarni, Sunil; Manganaro, Alberto; Roncaglioni, Alessandra; Wu, Shengde; Barton-Maclaren, Tara S; Lester, Cathy; Benfenati, Emilio

2016-08-31

Application of in silico models to predict developmental toxicity has demonstrated limited success particularly when employed as a single source of information. It is acknowledged that modelling the complex outcomes related to this endpoint is a challenge; however, such models have been developed and reported in the literature. The current study explored the possibility of integrating the selected public domain models (CAESAR, SARpy and P&G model) with the selected commercial modelling suites (Multicase, Leadscope and Derek Nexus) to assess if there is an increase in overall predictive performance. The results varied according to the data sets used to assess performance which improved upon model integration relative to individual models. Moreover, because different models are based on different specific developmental toxicity effects, integration of these models increased the applicable chemical and biological spaces. It is suggested that this approach reduces uncertainty associated with in silico predictions by achieving a consensus among a battery of models. The use of tools to assess the applicability domain also improves the interpretation of the predictions. This has been verified in the case of the software VEGA, which makes freely available QSAR models with a measurement of the applicability domain. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Computational approach to analyze isolated ssDNA aptamers against angiotensin II.

PubMed

Heiat, Mohammad; Najafi, Ali; Ranjbar, Reza; Latifi, Ali Mohammad; Rasaee, Mohammad Javad

2016-07-20

Aptamers are oligonucleotides with highly structured molecules that can bind to their targets through specific 3-D conformation. Commonly, not all the nucleotides such as primer binding fixed region and some other sequences are vital for aptamers folding and interaction. Elimination of unnecessary regions needs trustworthy prediction tools to reduce experimental efforts and errors. Here we introduced a manipulated in-silico approach to predict the 3-D structure of aptamers and their target interactions. To design an approach for computational analysis of isolated ssDNA aptamers (FLC112, FLC125 and their truncated core region including CRC112 and CRC125), their secondary and tertiary structures were modeled by Mfold and RNA composer respectively. Output PDB files were modified from RNA to DNA in the discovery studio visualizer software. Using ZDOCK server, the aptamer-target interactions were predicted. Finally, the interaction scores were compared with the experimental results. In-silico interaction scores and the experimental outcomes were in the same descending arrangement of FLC112>CRC125>CRC112>FLC125 with similar intensity. The consistent results of innovative in-silico method with experimental outputs, affirmed that the present method may be a reliable approach. Also, it showed that the exact in-silico predictions can be utilized as a credible reference to find aptameric fragments binding potency. Copyright © 2016 Elsevier B.V. All rights reserved.

In silico study of breast cancer associated gene 3 using LION Target Engine and other tools.

PubMed

León, Darryl A; Cànaves, Jaume M

2003-12-01

Sequence analysis of individual targets is an important step in annotation and validation. As a test case, we investigated human breast cancer associated gene 3 (BCA3) with LION Target Engine and with other bioinformatics tools. LION Target Engine confirmed that the BCA3 gene is located on 11p15.4 and that the two most likely splice variants (lacking exon 3 and exons 3 and 5, respectively) exist. Based on our manual curation of sequence data, it is proposed that an additional variant (missing only exon 5) published in a public sequence repository, is a prediction artifact. A significant number of new orthologs were also identified, and these were the basis for a high-quality protein secondary structure prediction. Moreover, our research confirmed several distinct functional domains as described in earlier reports. Sequence conservation from multiple sequence alignments, splice variant identification, secondary structure predictions, and predicted phosphorylation sites suggest that the removal of interaction sites through alternative splicing might play a modulatory role in BCA3. This in silico approach shows the depth and relevance of an analysis that can be accomplished by including a variety of publicly available tools with an integrated and customizable life science informatics platform.
CADRE-SS, an in Silico Tool for Predicting Skin Sensitization Potential Based on Modeling of Molecular Interactions.

PubMed

Kostal, Jakub; Voutchkova-Kostal, Adelina

2016-01-19

Using computer models to accurately predict toxicity outcomes is considered to be a major challenge. However, state-of-the-art computational chemistry techniques can now be incorporated in predictive models, supported by advances in mechanistic toxicology and the exponential growth of computing resources witnessed over the past decade. The CADRE (Computer-Aided Discovery and REdesign) platform relies on quantum-mechanical modeling of molecular interactions that represent key biochemical triggers in toxicity pathways. Here, we present an external validation exercise for CADRE-SS, a variant developed to predict the skin sensitization potential of commercial chemicals. CADRE-SS is a hybrid model that evaluates skin permeability using Monte Carlo simulations, assigns reactive centers in a molecule and possible biotransformations via expert rules, and determines reactivity with skin proteins via quantum-mechanical modeling. The results were promising with an overall very good concordance of 93% between experimental and predicted values. Comparison to performance metrics yielded by other tools available for this endpoint suggests that CADRE-SS offers distinct advantages for first-round screenings of chemicals and could be used as an in silico alternative to animal tests where permissible by legislative programs.
Predicted MHC peptide binding promiscuity explains MHC class I 'hotspots' of antigen presentation defined by mass spectrometry eluted ligand data.

PubMed

Jappe, Emma Christine; Kringelum, Jens; Trolle, Thomas; Nielsen, Morten

2018-02-15

Peptides that bind to and are presented by MHC class I and class II molecules collectively make up the immunopeptidome. In the context of vaccine development, an understanding of the immunopeptidome is essential, and much effort has been dedicated to its accurate and cost-effective identification. Current state-of-the-art methods mainly comprise in silico tools for predicting MHC binding, which is strongly correlated with peptide immunogenicity. However, only a small proportion of the peptides that bind to MHC molecules are, in fact, immunogenic, and substantial work has been dedicated to uncovering additional determinants of peptide immunogenicity. In this context, and in light of recent advancements in mass spectrometry (MS), the existence of immunological hotspots has been given new life, inciting the hypothesis that hotspots are associated with MHC class I peptide immunogenicity. We here introduce a precise terminology for defining these hotspots and carry out a systematic analysis of MS and in silico predicted hotspots. We find that hotspots defined from MS data are largely captured by peptide binding predictions, enabling their replication in silico. This leads us to conclude that hotspots, to a great degree, are simply a result of promiscuous HLA binding, which disproves the hypothesis that the identification of hotspots provides novel information in the context of immunogenic peptide prediction. Furthermore, our analyses demonstrate that the signal of ligand processing, although present in the MS data, has very low predictive power to discriminate between MS and in silico defined hotspots. © 2018 John Wiley & Sons Ltd.
An "in silico" Bioinformatics Laboratory Manual for Bioscience Departments: "Prediction of Glycosylation Sites in Phosphoethanolamine Transferases"

ERIC Educational Resources Information Center

Alyuruk, Hakan; Cavas, Levent

2014-01-01

Genomics and proteomics projects have produced a huge amount of raw biological data including DNA and protein sequences. Although these data have been stored in data banks, their evaluation is strictly dependent on bioinformatics tools. These tools have been developed by multidisciplinary experts for fast and robust analysis of biological data.…
In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy

PubMed Central

Echigoya, Yusuke; Mouly, Vincent; Garcia, Luis; Yokota, Toshifumi; Duddy, William

2015-01-01

The use of antisense ‘splice-switching’ oligonucleotides to induce exon skipping represents a potential therapeutic approach to various human genetic diseases. It has achieved greatest maturity in exon skipping of the dystrophin transcript in Duchenne muscular dystrophy (DMD), for which several clinical trials are completed or ongoing, and a large body of data exists describing tested oligonucleotides and their efficacy. The rational design of an exon skipping oligonucleotide involves the choice of an antisense sequence, usually between 15 and 32 nucleotides, targeting the exon that is to be skipped. Although parameters describing the target site can be computationally estimated and several have been identified to correlate with efficacy, methods to predict efficacy are limited. Here, an in silico pre-screening approach is proposed, based on predictive statistical modelling. Previous DMD data were compiled together and, for each oligonucleotide, some 60 descriptors were considered. Statistical modelling approaches were applied to derive algorithms that predict exon skipping for a given target site. We confirmed (1) the binding energetics of the oligonucleotide to the RNA, and (2) the distance in bases of the target site from the splice acceptor site, as the two most predictive parameters, and we included these and several other parameters (while discounting many) into an in silico screening process, based on their capacity to predict high or low efficacy in either phosphorodiamidate morpholino oligomers (89% correctly predicted) and/or 2’O Methyl RNA oligonucleotides (76% correctly predicted). Predictions correlated strongly with in vitro testing for sixteen de novo PMO sequences targeting various positions on DMD exons 44 (R2 0.89) and 53 (R2 0.89), one of which represents a potential novel candidate for clinical trials. We provide these algorithms together with a computational tool that facilitates screening to predict exon skipping efficacy at each position of a target exon. PMID:25816009
Use of physiologically relevant biopharmaceutics tools within the pharmaceutical industry and in regulatory sciences: Where are we now and what are the gaps?

PubMed

Flanagan, Talia; Van Peer, Achiel; Lindahl, Anders

2016-08-25

Regulatory interactions are an important part of the drug development and licensing process. A survey on the use of biopharmaceutical tools for regulatory purposes has been carried out within the industry community of the EU project OrBiTo within Innovative Medicines Initiative (IMI). The aim was to capture current practice and experience in using in vitro and in silico biopharmaceutics tools at various stages of development, what barriers exist or are perceived, and to understand the current gaps in regulatory biopharmaceutics. The survey indicated that biorelevant dissolution testing and physiologically based modelling and simulation are widely applied throughout development to address a number of biopharmaceutics issues. However, data from these in vitro and in silico predictive biopharmaceutics tools are submitted to regulatory authorities far less often than they are used for internal risk assessment and decision making. This may prevent regulators from becoming familiar with these tools and how they are applied in industry, and limits the opportunities for biopharmaceutics scientists working in industry to understand the acceptability of these tools in the regulatory environment. It is anticipated that the advanced biopharmaceutics tools and understanding delivered in the next years by OrBiTo and other initiatives in the area of predictive tools will also be of value in the regulatory setting, and provide a basis for more informed and confident biopharmaceutics risk assessment and regulatory decision making. To enable the regulatory potential of predictive biopharmaceutics tools to be realized, further scientific dialogue is needed between industry, regulators and scientists in academia, and more examples need to be published to demonstrate the applicability of these tools. Copyright © 2016 Elsevier B.V. All rights reserved.
Bitterness prediction in-silico: A step towards better drugs.

PubMed

Bahia, Malkeet Singh; Nissim, Ido; Niv, Masha Y

2018-02-05

Bitter taste is innately aversive and thought to protect against consuming poisons. Bitter taste receptors (Tas2Rs) are G-protein coupled receptors, expressed both orally and extra-orally and proposed as novel targets for several indications, including asthma. Many clinical drugs elicit bitter taste, suggesting the possibility of drugs re-purposing. On the other hand, the bitter taste of medicine presents a major compliance problem for pediatric drugs. Thus, efficient tools for predicting, measuring and masking bitterness of active pharmaceutical ingredients (APIs) are required by the pharmaceutical industry. Here we highlight the BitterDB database of bitter compounds and survey the main computational approaches to prediction of bitter taste based on compound's chemical structure. Current in silico bitterness prediction methods provide encouraging results, can be constantly improved using growing experimental data, and present a reliable and efficient addition to the APIs development toolbox. Copyright © 2017 Elsevier B.V. All rights reserved.
Literature Mining and Knowledge Discovery Tools for Virtual Tissues

EPA Science Inventory

Virtual Tissues (VTs) are in silico models that simulate the cellular fabric of tissues to analyze complex relationships and predict multicellular behaviors in specific biological systems such as the mature liver (v-Liver™) or developing embryo (v-Embryo™). VT models require inpu...
Comparison of in silico models for prediction of mutagenicity.

PubMed

Bakhtyari, Nazanin G; Raitano, Giuseppa; Benfenati, Emilio; Martin, Todd; Young, Douglas

2013-01-01

Using a dataset with more than 6000 compounds, the performance of eight quantitative structure activity relationships (QSAR) models was evaluated: ACD/Tox Suite, Absorption, Distribution, Metabolism, Elimination, and Toxicity of chemical substances (ADMET) predictor, Derek, Toxicity Estimation Software Tool (T.E.S.T.), TOxicity Prediction by Komputer Assisted Technology (TOPKAT), Toxtree, CEASAR, and SARpy (SAR in python). In general, the results showed a high level of performance. To have a realistic estimate of the predictive ability, the results for chemicals inside and outside the training set for each model were considered. The effect of applicability domain tools (when available) on the prediction accuracy was also evaluated. The predictive tools included QSAR models, knowledge-based systems, and a combination of both methods. Models based on statistical QSAR methods gave better results.
Physiologically Based Pharmacokinetic Models: Integration of In Silico Approaches with Micro Cell Culture Analogues

PubMed Central

Chen, A.; Yarmush, M.L.; Maguire, T.

2014-01-01

There is a large emphasis within the pharmaceutical industry to provide tools that will allow early research and development groups to better predict dose ranges for and metabolic responses of candidate molecules in a high throughput manner, prior to entering clinical trials. These tools incorporate approaches ranging from PBPK, QSAR, and molecular dynamics simulations in the in silico realm, to micro cell culture analogue (CCAs)s in the in vitro realm. This paper will serve to review these areas of high throughput predictive research, and highlight hurdles and potential solutions. In particular we will focus on CCAs, as their incorporation with PBPK modeling has the potential to replace animal testing, with a more predictive assay that can combine multiple organ analogs on one microfluidic platform in physiologically correct volume ratios. While several advantages arise from the current embodiments of CCAS in a microfluidic format that can be exploited for realistic simulations of drug absorption, metabolism and action, we explore some of the concerns with these systems, and provide a potential path forward to realizing animal-free solutions. Furthermore we envision that, together with theoretical modeling, CCAs may produce reliable predictions of the efficacy of newly developed drugs. PMID:22571482
Polymer physics predicts the effects of structural variants on chromatin architecture.

PubMed

Bianco, Simona; Lupiáñez, Darío G; Chiariello, Andrea M; Annunziatella, Carlo; Kraft, Katerina; Schöpflin, Robert; Wittler, Lars; Andrey, Guillaume; Vingron, Martin; Pombo, Ana; Mundlos, Stefan; Nicodemi, Mario

2018-05-01

Structural variants (SVs) can result in changes in gene expression due to abnormal chromatin folding and cause disease. However, the prediction of such effects remains a challenge. Here we present a polymer-physics-based approach (PRISMR) to model 3D chromatin folding and to predict enhancer-promoter contacts. PRISMR predicts higher-order chromatin structure from genome-wide chromosome conformation capture (Hi-C) data. Using the EPHA4 locus as a model, the effects of pathogenic SVs are predicted in silico and compared to Hi-C data generated from mouse limb buds and patient-derived fibroblasts. PRISMR deconvolves the folding complexity of the EPHA4 locus and identifies SV-induced ectopic contacts and alterations of 3D genome organization in homozygous or heterozygous states. We show that SVs can reconfigure topologically associating domains, thereby producing extensive rewiring of regulatory interactions and causing disease by gene misexpression. PRISMR can be used to predict interactions in silico, thereby providing a tool for analyzing the disease-causing potential of SVs.
In silico tools for sharing data and knowledge on toxicity and metabolism: derek for windows, meteor, and vitic.

PubMed

Marchant, Carol A; Briggs, Katharine A; Long, Anthony

2008-01-01

ABSTRACT Lhasa Limited is a not-for-profit organization that exists to promote the sharing of data and knowledge in chemistry and the life sciences. It has developed the software tools Derek for Windows, Meteor, and Vitic to facilitate such sharing. Derek for Windows and Meteor are knowledge-based expert systems that predict the toxicity and metabolism of a chemical, respectively. Vitic is a chemically intelligent toxicity database. An overview of each software system is provided along with examples of the sharing of data and knowledge in the context of their development. These examples include illustrations of (1) the use of data entry and editing tools for the sharing of data and knowledge within organizations; (2) the use of proprietary data to develop nonconfidential knowledge that can be shared between organizations; (3) the use of shared expert knowledge to refine predictions; (4) the sharing of proprietary data between organizations through the formation of data-sharing groups; and (5) the use of proprietary data to validate predictions. Sharing of chemical toxicity and metabolism data and knowledge in this way offers a number of benefits including the possibilities of faster scientific progress and reductions in the use of animals in testing. Maximizing the accessibility of data also becomes increasingly crucial as in silico systems move toward the prediction of more complex phenomena for which limited data are available.
Domain-Specific QSAR Model for Identifying Potential Estrogenic Activity of Phenols (ASCCT annual meeting)

EPA Science Inventory

Humans are potentially exposed to tens of thousands of man-made chemicals in the environment, some of which may mimic natural endocrine hormones and thus have the potential to be endocrine disruptors. Predictive in silico tools can be used to quickly and efficiently evaluate thes...
Mixture toxicology in the 21st century: Pathway-based concepts and tools

EPA Science Inventory

The past decade has witnessed notable evolution of approaches focused on predicting chemical hazards and risks in the absence of empirical data from resource-intensive in vivo toxicity tests. In silico models, in vitro high-throughput toxicity assays, and short-term in vivo tests...
GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

PubMed

Jayaraj, P B; Ajay, Mathias K; Nufail, M; Gopakumar, G; Jaleel, U C A

2016-01-01

In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases.
An ensemble model of QSAR tools for regulatory risk assessment.

PubMed

Pradeep, Prachi; Povinelli, Richard J; White, Shannon; Merrill, Stephen J

2016-01-01

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflicting predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa ( κ ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. This feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.
An ensemble model of QSAR tools for regulatory risk assessment

DOE PAGES

Pradeep, Prachi; Povinelli, Richard J.; White, Shannon; ...

2016-09-22

Quantitative structure activity relationships (QSARs) are theoretical models that relate a quantitative measure of chemical structure to a physical property or a biological effect. QSAR predictions can be used for chemical risk assessment for protection of human and environmental health, which makes them interesting to regulators, especially in the absence of experimental data. For compatibility with regulatory use, QSAR models should be transparent, reproducible and optimized to minimize the number of false negatives. In silico QSAR tools are gaining wide acceptance as a faster alternative to otherwise time-consuming clinical and animal testing methods. However, different QSAR tools often make conflictingmore » predictions for a given chemical and may also vary in their predictive performance across different chemical datasets. In a regulatory context, conflicting predictions raise interpretation, validation and adequacy concerns. To address these concerns, ensemble learning techniques in the machine learning paradigm can be used to integrate predictions from multiple tools. By leveraging various underlying QSAR algorithms and training datasets, the resulting consensus prediction should yield better overall predictive ability. We present a novel ensemble QSAR model using Bayesian classification. The model allows for varying a cut-off parameter that allows for a selection in the desirable trade-off between model sensitivity and specificity. The predictive performance of the ensemble model is compared with four in silico tools (Toxtree, Lazar, OECD Toolbox, and Danish QSAR) to predict carcinogenicity for a dataset of air toxins (332 chemicals) and a subset of the gold carcinogenic potency database (480 chemicals). Leave-one-out cross validation results show that the ensemble model achieves the best trade-off between sensitivity and specificity (accuracy: 83.8 % and 80.4 %, and balanced accuracy: 80.6 % and 80.8 %) and highest inter-rater agreement [kappa (κ): 0.63 and 0.62] for both the datasets. The ROC curves demonstrate the utility of the cut-off feature in the predictive ability of the ensemble model. In conclusion, this feature provides an additional control to the regulators in grading a chemical based on the severity of the toxic endpoint under study.« less
Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.

PubMed

Crosara, Karla Tonelli Bicalho; Moffa, Eduardo Buozi; Xiao, Yizhi; Siqueira, Walter Luiz

2018-01-16

Protein-protein interaction is a common physiological mechanism for protection and actions of proteins in an organism. The identification and characterization of protein-protein interactions in different organisms is necessary to better understand their physiology and to determine their efficacy. In a previous in vitro study using mass spectrometry, we identified 43 proteins that interact with histatin 1. Six previously documented interactors were confirmed and 37 novel partners were identified. In this tutorial, we aimed to demonstrate the usefulness of the STRING database for studying protein-protein interactions. We used an in-silico approach along with the STRING database (http://string-db.org/) and successfully performed a fast simulation of a novel constructed histatin 1 protein-protein network, including both the previously known and the predicted interactors, along with our newly identified interactors. Our study highlights the advantages and importance of applying bioinformatics tools to merge in-silico tactics with experimental in vitro findings for rapid advancement of our knowledge about protein-protein interactions. Our findings also indicate that bioinformatics tools such as the STRING protein network database can help predict potential interactions between proteins and thus serve as a guide for future steps in our exploration of the Human Interactome. Our study highlights the usefulness of the STRING protein database for studying protein-protein interactions. The STRING database can collect and integrate data about known and predicted protein-protein associations from many organisms, including both direct (physical) and indirect (functional) interactions, in an easy-to-use interface. Copyright © 2017 Elsevier B.V. All rights reserved.
Impact of gastrointestinal disease states on oral drug absorption - implications for formulation design - a PEARRL review.

PubMed

Effinger, Angela; O'Driscoll, Caitriona M; McAllister, Mark; Fotaki, Nikoletta

2018-05-16

Drug product performance in patients with gastrointestinal (GI) diseases can be altered compared to healthy subjects due to pathophysiological changes. In this review, relevant differences in patients with inflammatory bowel diseases, coeliac disease, irritable bowel syndrome and short bowel syndrome are discussed and possible in vitro and in silico tools to predict drug product performance in this patient population are assessed. Drug product performance was altered in patients with GI diseases compared to healthy subjects, as assessed in a limited number of studies for some drugs. Underlying causes can be observed pathophysiological alterations such as the differences in GI transit time, the composition of the GI fluids and GI permeability. Additionally, alterations in the abundance of metabolising enzymes and transporter systems were observed. The effect of the GI diseases on each parameter is not always evident as it may depend on the location and the state of the disease. The impact of the pathophysiological change on drug bioavailability depends on the physicochemical characteristics of the drug, the pharmaceutical formulation and drug metabolism. In vitro and in silico methods to predict drug product performance in patients with GI diseases are currently limited but could be a useful tool to improve drug therapy. Development of suitable in vitro dissolution and in silico models for patients with GI diseases can improve their drug therapy. The likeliness of the models to provide accurate predictions depends on the knowledge of pathophysiological alterations, and thus, further assessment of physiological differences is essential. © 2018 Royal Pharmaceutical Society.
The Topology Prediction of Membrane Proteins: A Web-Based Tutorial.

PubMed

Kandemir-Cavas, Cagin; Cavas, Levent; Alyuruk, Hakan

2018-06-01

There is a great need for development of educational materials on the transfer of current bioinformatics knowledge to undergraduate students in bioscience departments. In this study, it is aimed to prepare an example in silico laboratory tutorial on the topology prediction of membrane proteins by bioinformatics tools. This laboratory tutorial is prepared for biochemistry lessons at bioscience departments (biology, chemistry, biochemistry, molecular biology and genetics, and faculty of medicine). The tutorial is intended for students who have not taken a bioinformatics course yet or already have taken a course as an introduction to bioinformatics. The tutorial is based on step-by-step explanations with illustrations. It can be applied under supervision of an instructor in the lessons, or it can be used as a self-study guide by students. In the tutorial, membrane-spanning regions and α-helices of membrane proteins were predicted by internet-based bioinformatics tools. According to the results achieved from internet-based bioinformatics tools, the algorithms and parameters used were effective on the accuracy of prediction. The importance of this laboratory tutorial lies on the facts that it provides an introduction to the bioinformatics and that it also demonstrates an in silico laboratory application to the students at natural sciences. The presented example education material is applicable easily at all departments that have internet connection. This study presents an alternative education material to the students in biochemistry laboratories in addition to classical laboratory experiments.

DR2DI: a powerful computational tool for predicting novel drug-disease associations

NASA Astrophysics Data System (ADS)

Lu, Lu; Yu, Hua

2018-05-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.
DR2DI: a powerful computational tool for predicting novel drug-disease associations

NASA Astrophysics Data System (ADS)

Lu, Lu; Yu, Hua

2018-04-01

Finding the new related candidate diseases for known drugs provides an effective method for fast-speed and low-risk drug development. However, experimental identification of drug-disease associations is expensive and time-consuming. This motivates the need for developing in silico computational methods that can infer true drug-disease pairs with high confidence. In this study, we presented a novel and powerful computational tool, DR2DI, for accurately uncovering the potential associations between drugs and diseases using high-dimensional and heterogeneous omics data as information sources. Based on a unified and extended similarity kernel framework, DR2DI inferred the unknown relationships between drugs and diseases using Regularized Kernel Classifier. Importantly, DR2DI employed a semi-supervised and global learning algorithm which can be applied to uncover the diseases (drugs) associated with known and novel drugs (diseases). In silico global validation experiments showed that DR2DI significantly outperforms recent two approaches for predicting drug-disease associations. Detailed case studies further demonstrated that the therapeutic indications and side effects of drugs predicted by DR2DI could be validated by existing database records and literature, suggesting that DR2DI can be served as a useful bioinformatic tool for identifying the potential drug-disease associations and guiding drug repositioning. Our software and comparison codes are freely available at https://github.com/huayu1111/DR2DI.
RNA-SSPT: RNA Secondary Structure Prediction Tools.

PubMed

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; Din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes.
RNA-SSPT: RNA Secondary Structure Prediction Tools

PubMed Central

Ahmad, Freed; Mahboob, Shahid; Gulzar, Tahsin; din, Salah U; Hanif, Tanzeela; Ahmad, Hifza; Afzal, Muhammad

2013-01-01

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis provides the best solution. But secondary structure prediction of a single RNA sequence is challenging. RNA-SSPT is a tool that computationally predicts secondary structure of a single RNA sequence. Most of the RNA secondary structure prediction tools do not allow pseudoknots in the structure or are unable to locate them. Nussinov dynamic programming algorithm has been implemented in RNA-SSPT. The current studies shows only energetically most favorable secondary structure is required and the algorithm modification is also available that produces base pairs to lower the total free energy of the secondary structure. For visualization of RNA secondary structure, NAVIEW in C language is used and modified in C# for tool requirement. RNA-SSPT is built in C# using Dot Net 2.0 in Microsoft Visual Studio 2005 Professional edition. The accuracy of RNA-SSPT is tested in terms of Sensitivity and Positive Predicted Value. It is a tool which serves both secondary structure prediction and secondary structure visualization purposes. PMID:24250115
Cross-species extrapolation of mammalian-based ToxCast Data using Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS)

EPA Science Inventory

In vitro high-throughput screening (HTS) and in silico technologies have emerged as 21st century tools for chemical hazard identification. In 2007 the U.S. Environmental Protection Agency (EPA) launched the ToxCast Program, which has screened thousands of chemicals in hundreds of...
In silico and in vitro inhibition of cytochrome P450 3A by synthetic stilbenoids.

PubMed

Basheer, Loai; Schultz, Keren; Guttman, Yelena; Kerem, Zohar

2017-12-15

Inhibition of cytochrome P450 3A4 (CYP3A4), the major drug metabolizing enzyme, by dietary compounds has recently attracted increased attention. Evaluating the potency of the many known inhibitory compounds is a tedious and time consuming task, yet it can be achieved using computing tools. Here, CDOCKER and Glide served to design model inhibitors in order to characterize molecular features of an inhibitor. Assessing nitro-stilbenoids, both approaches suggested nitrostilbene to be a weaker inhibitor of CYP3A4 than resveratrol, and stronger than dimethoxy-nitrostilbene. Nitrostilbene and resveratrol, but not dimethoxy-nitrostilbene, engage electrostatic interactions in the enzyme cavity, and with the haem. In vitro assessment of the inhibitory capacity supported the in silico predictions, suggesting that evaluating the electrostatic interactions of a compound with the prosthetic group allows the prediction of inhibitory potency. Since both programs yielded related results, it is suggested that for CYP3A4, computing tools may allow rapid identification of potent dietary inhibitors. Copyright © 2017 Elsevier Ltd. All rights reserved.
Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome

DOE PAGES

Marino, Simeone; Gideon, Hannah P.; Gong, Chang; ...

2016-04-11

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2-year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identifiedmore » T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.« less
Computational and empirical studies predict Mycobacterium tuberculosis-specific T cells as a biomarker for infection outcome

DOE Office of Scientific and Technical Information (OSTI.GOV)

Marino, Simeone; Gideon, Hannah P.; Gong, Chang

Identifying biomarkers for tuberculosis (TB) is an ongoing challenge in developing immunological correlates of infection outcome and protection. Biomarker discovery is also necessary for aiding design and testing of new treatments and vaccines. To effectively predict biomarkers for infection progression in any disease, including TB, large amounts of experimental data are required to reach statistical power and make accurate predictions. We took a two-pronged approach using both experimental and computational modeling to address this problem. We first collected 200 blood samples over a 2-year period from 28 non-human primates (NHP) infected with a low dose of Mycobacterium tuberculosis. We identifiedmore » T cells and the cytokines that they were producing (single and multiple) from each sample along with monkey status and infection progression data. Machine learning techniques were used to interrogate the experimental NHP datasets without identifying any potential TB biomarker. In parallel, we used our extensive novel NHP datasets to build and calibrate a multi-organ computational model that combines what is occurring at the site of infection (e.g., lung) at a single granuloma scale with blood level readouts that can be tracked in monkeys and humans. We then generated a large in silico repository of in silico granulomas coupled to lymph node and blood dynamics and developed an in silico tool to scale granuloma level results to a full host scale to identify what best predicts Mycobacterium tuberculosis (Mtb) infection outcomes. The analysis of in silico blood measures identifies Mtb-specific frequencies of effector T cell phenotypes at various time points post infection as promising indicators of infection outcome. As a result, we emphasize that pairing wetlab and computational approaches holds great promise to accelerate TB biomarker discovery.« less
Highly predictive and interpretable models for PAMPA permeability.

PubMed

Sun, Hongmao; Nguyen, Kimloan; Kerns, Edward; Yan, Zhengyin; Yu, Kyeong Ri; Shah, Pranav; Jadhav, Ajit; Xu, Xin

2017-02-01

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.
An integrated computational approach can classify VHL missense mutations according to risk of clear cell renal carcinoma

PubMed Central

Gossage, Lucy; Pires, Douglas E. V.; Olivera-Nappa, Álvaro; Asenjo, Juan; Bycroft, Mark; Blundell, Tom L.; Eisen, Tim

2014-01-01

Mutations in the von Hippel–Lindau (VHL) gene are pathogenic in VHL disease, congenital polycythaemia and clear cell renal carcinoma (ccRCC). pVHL forms a ternary complex with elongin C and elongin B, critical for pVHL stability and function, which interacts with Cullin-2 and RING-box protein 1 to target hypoxia-inducible factor for polyubiquitination and proteasomal degradation. We describe a comprehensive database of missense VHL mutations linked to experimental and clinical data. We use predictions from in silico tools to link the functional effects of missense VHL mutations to phenotype. The risk of ccRCC in VHL disease is linked to the degree of destabilization resulting from missense mutations. An optimized binary classification system (symphony), which integrates predictions from five in silico methods, can predict the risk of ccRCC associated with VHL missense mutations with high sensitivity and specificity. We use symphony to generate predictions for risk of ccRCC for all possible VHL missense mutations and present these predictions, in association with clinical and experimental data, in a publically available, searchable web server. PMID:24969085
Promzea: a pipeline for discovery of co-regulatory motifs in maize and other plant species and its application to the anthocyanin and phlobaphene biosynthetic pathways and the Maize Development Atlas.

PubMed

Liseron-Monfils, Christophe; Lewis, Tim; Ashlock, Daniel; McNicholas, Paul D; Fauteux, François; Strömvik, Martina; Raizada, Manish N

2013-03-15

The discovery of genetic networks and cis-acting DNA motifs underlying their regulation is a major objective of transcriptome studies. The recent release of the maize genome (Zea mays L.) has facilitated in silico searches for regulatory motifs. Several algorithms exist to predict cis-acting elements, but none have been adapted for maize. A benchmark data set was used to evaluate the accuracy of three motif discovery programs: BioProspector, Weeder and MEME. Analysis showed that each motif discovery tool had limited accuracy and appeared to retrieve a distinct set of motifs. Therefore, using the benchmark, statistical filters were optimized to reduce the false discovery ratio, and then remaining motifs from all programs were combined to improve motif prediction. These principles were integrated into a user-friendly pipeline for motif discovery in maize called Promzea, available at http://www.promzea.org and on the Discovery Environment of the iPlant Collaborative website. Promzea was subsequently expanded to include rice and Arabidopsis. Within Promzea, a user enters cDNA sequences or gene IDs; corresponding upstream sequences are retrieved from the maize genome. Predicted motifs are filtered, combined and ranked. Promzea searches the chosen plant genome for genes containing each candidate motif, providing the user with the gene list and corresponding gene annotations. Promzea was validated in silico using a benchmark data set: the Promzea pipeline showed a 22% increase in nucleotide sensitivity compared to the best standalone program tool, Weeder, with equivalent nucleotide specificity. Promzea was also validated by its ability to retrieve the experimentally defined binding sites of transcription factors that regulate the maize anthocyanin and phlobaphene biosynthetic pathways. Promzea predicted additional promoter motifs, and genome-wide motif searches by Promzea identified 127 non-anthocyanin/phlobaphene genes that each contained all five predicted promoter motifs in their promoters, perhaps uncovering a broader co-regulated gene network. Promzea was also tested against tissue-specific microarray data from maize. An online tool customized for promoter motif discovery in plants has been generated called Promzea. Promzea was validated in silico by its ability to retrieve benchmark motifs and experimentally defined motifs and was tested using tissue-specific microarray data. Promzea predicted broader networks of gene regulation associated with the historic anthocyanin and phlobaphene biosynthetic pathways. Promzea is a new bioinformatics tool for understanding transcriptional gene regulation in maize and has been expanded to include rice and Arabidopsis.
Computational approaches to metabolic engineering utilizing systems biology and synthetic biology.

PubMed

Fong, Stephen S

2014-08-01

Metabolic engineering modifies cellular function to address various biochemical applications. Underlying metabolic engineering efforts are a host of tools and knowledge that are integrated to enable successful outcomes. Concurrent development of computational and experimental tools has enabled different approaches to metabolic engineering. One approach is to leverage knowledge and computational tools to prospectively predict designs to achieve the desired outcome. An alternative approach is to utilize combinatorial experimental tools to empirically explore the range of cellular function and to screen for desired traits. This mini-review focuses on computational systems biology and synthetic biology tools that can be used in combination for prospective in silico strain design.
Computational insights of K1444N substitution in GAP-related domain of NF1 gene associated with neurofibromatosis type 1 disease: a molecular modeling and dynamics approach.

PubMed

Agrahari, Ashish Kumar; Muskan, Meghana; George Priya Doss, C; Siva, R; Zayed, Hatem

2018-05-27

The NF1 gene encodes for neurofibromin protein, which is ubiquitously expressed, but most highly in the central nervous system. Non-synonymous SNPs (nsSNPs) in the NF1 gene were found to be associated with Neurofibromatosis Type 1 disease, which is characterized by the growth of tumors along nerves in the skin, brain, and other parts of the body. In this study, we used several in silico predictions tools to analyze 16 nsSNPs in the RAS-GAP domain of neurofibromin, the K1444N (K1423N) mutation was predicted as the most pathogenic. The comparative molecular dynamic simulation (MDS; 50 ns) between the wild type and the K1444N (K1423N) mutant suggested a significant change in the electrostatic potential. In addition, the RMSD, RMSF, Rg, hydrogen bonds, and PCA analysis confirmed the loss of flexibility and increase in compactness of the mutant protein. Further, SASA analysis revealed exchange between hydrophobic and hydrophilic residues from the core of the RAS-GAP domain to the surface of the mutant domain, consistent with the secondary structure analysis that showed significant alteration in the mutant protein conformation. Our data concludes that the K1444N (K1423N) mutant lead to increasing the rigidity and compactness of the protein. This study provides evidence of the benefits of the computational tools in predicting the pathogenicity of genetic mutations and suggests the application of MDS and different in silico prediction tools for variant assessment and classification in genetic clinics.
Experimental validation of predicted cancer genes using FRET

NASA Astrophysics Data System (ADS)

Guala, Dimitri; Bernhem, Kristoffer; Ait Blal, Hammou; Jans, Daniel; Lundberg, Emma; Brismar, Hjalmar; Sonnhammer, Erik L. L.

2018-07-01

Huge amounts of data are generated in genome wide experiments, designed to investigate diseases with complex genetic causes. Follow up of all potential leads produced by such experiments is currently cost prohibitive and time consuming. Gene prioritization tools alleviate these constraints by directing further experimental efforts towards the most promising candidate targets. Recently a gene prioritization tool called MaxLink was shown to outperform other widely used state-of-the-art prioritization tools in a large scale in silico benchmark. An experimental validation of predictions made by MaxLink has however been lacking. In this study we used Fluorescence Resonance Energy Transfer, an established experimental technique for detection of protein-protein interactions, to validate potential cancer genes predicted by MaxLink. Our results provide confidence in the use of MaxLink for selection of new targets in the battle with polygenic diseases.
Translating New Science Into the Drug Review Process

PubMed Central

Rouse, Rodney; Kruhlak, Naomi; Weaver, James; Burkhart, Keith; Patel, Vikram; Strauss, David G.

2017-01-01

In 2011, the US Food and drug Administration (FDA) developed a strategic plan for regulatory science that focuses on developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of FDA-regulated products. In line with this, the Division of Applied Regulatory Science was created to move new science into the Center for Drug Evaluation and Research (CDER) review process and close the gap between scientific innovation and drug review. The Division, located in the Office of Clinical Pharmacology, is unique in that it performs mission-critical applied research and review across the translational research spectrum including in vitro and in vivo laboratory research, in silico computational modeling and informatics, and integrated clinical research covering clinical pharmacology, experimental medicine, and postmarket analyses. The Division collaborates with Offices throughout CDER, across the FDA, other government agencies, academia, and industry. The Division is able to rapidly form interdisciplinary teams of pharmacologists, biologists, chemists, computational scientists, and clinicians to respond to challenging regulatory questions for specific review issues and for longer-range projects requiring the development of predictive models, tools, and biomarkers to speed the development and regulatory evaluation of safe and effective drugs. This article reviews the Division’s recent work and future directions, highlighting development and validation of biomarkers; novel humanized animal models; translational predictive safety combining in vitro, in silico, and in vivo clinical biomarkers; chemical and biomedical informatics tools for safety predictions; novel approaches to speed the development of complex generic drugs, biosimilars, and antibiotics; and precision medicine. PMID:29568713
In silico free energy predictions for ionic liquid-assisted exfoliation of a graphene bilayer into individual graphene nanosheets.

PubMed

Kamath, Ganesh; Baker, Gary A

2012-06-14

Free energies for graphene exfoliation from bilayer graphene using ionic liquids based on various cations paired with the bis(trifluoromethylsulfonyl)imide anion were determined from adaptive bias force-molecular dynamics (ABF-MD) simulation and fall in excellent qualitative agreement with experiment. This method has notable potential as an a priori screening tool for performance based rank order prediction of novel ionic liquids for the dispersion and exfoliation of various nanocarbons and inorganic graphene analogues.
Effect of the pulmonary deposition and in vitro permeability on the prediction of plasma levels of inhaled budesonide formulation.

PubMed

Salar-Behzadi, Sharareh; Wu, Shengqian; Mercuri, Annalisa; Meindl, Claudia; Stranzinger, Sandra; Fröhlich, Eleonore

2017-10-30

The growing interest in the inhalable pharmaceutical products requires advanced approaches to safe and fast product development, such as in silico tools that can be used for estimating the bioavailability and toxicity of developed formulation. GastroPlus™ is one of the few available software packages for in silico simulation of PBPK profile of inhalable products. It contains a complementary module for calculating the lung deposition, the permeability and the systemic absorption of inhalable products. Experimental values of lung deposition and permeability can also be used. This study aims to assess the efficiency of simulation by applying experimental permeability and deposition values, using budesonide as a model substance. The lung deposition values were obtained from the literature, the lung permeability data were experimentally determined by culturing Calu-3 cells under air-liquid interface and submersed conditions to morphologically resemble bronchial and alveolar epithelial cells, respectively. A two-compartment PK model was created for i.v. administration and used as a background for the in silico simulation of the plasma profile of budesonide after inhalation. The predicted plasma profile was compared with the in vivo data from the literature and the effects of experimental lung deposition and permeability on prediction were assessed. The developed model was significantly improved by using realistic lung deposition data combined with experimental data for peripheral permeability. Copyright © 2017 Elsevier B.V. All rights reserved.
Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.
In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part II: The body in a Hilbertian space.

PubMed

Jacob, Alexandre; Pratuangdejkul, Jaturong; Buffet, Sébastien; Launay, Jean-Marie; Manivet, Philippe

2009-04-01

We have broken old surviving dogmas and concepts used in computational chemistry and created an efficient in silico ADME-T pharmacological properties modeling and prediction toolbox for any xenobiotic. With the help of an innovative and pragmatic approach combining various in silico techniques, like molecular modeling, quantum chemistry and in-house developed algorithms, the interactions between drugs and those enzymes, transporters and receptors involved in their biotransformation can be studied. ADME-T pharmacological parameters can then be predicted after in vitro and in vivo validations of in silico models.
Tools for in silico target fishing.

PubMed

Cereto-Massagué, Adrià; Ojeda, María José; Valls, Cristina; Mulero, Miquel; Pujadas, Gerard; Garcia-Vallve, Santiago

2015-01-01

Computational target fishing methods are designed to identify the most probable target of a query molecule. This process may allow the prediction of the bioactivity of a compound, the identification of the mode of action of known drugs, the detection of drug polypharmacology, drug repositioning or the prediction of the adverse effects of a compound. The large amount of information regarding the bioactivity of thousands of small molecules now allows the development of these types of methods. In recent years, we have witnessed the emergence of many methods for in silico target fishing. Most of these methods are based on the similarity principle, i.e., that similar molecules might bind to the same targets and have similar bioactivities. However, the difficult validation of target fishing methods hinders comparisons of the performance of each method. In this review, we describe the different methods developed for target prediction, the bioactivity databases most frequently used by these methods, and the publicly available programs and servers that enable non-specialist users to obtain these types of predictions. It is expected that target prediction will have a large impact on drug development and on the functional food industry. Copyright © 2014 Elsevier Inc. All rights reserved.

ProTox: a web server for the in silico prediction of rodent oral toxicity

PubMed Central

Drwal, Malgorzata N.; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R.; Preissner, Robert

2014-01-01

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein–ligand-based pharmacophore models (‘toxicophores’) for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. PMID:24838562
In silico toxicology protocols.

PubMed

Myatt, Glenn J; Ahlberg, Ernst; Akahori, Yumi; Allen, David; Amberg, Alexander; Anger, Lennart T; Aptula, Aynur; Auerbach, Scott; Beilke, Lisa; Bellion, Phillip; Benigni, Romualdo; Bercu, Joel; Booth, Ewan D; Bower, Dave; Brigo, Alessandro; Burden, Natalie; Cammerer, Zoryana; Cronin, Mark T D; Cross, Kevin P; Custer, Laura; Dettwiler, Magdalena; Dobo, Krista; Ford, Kevin A; Fortin, Marie C; Gad-McDonald, Samantha E; Gellatly, Nichola; Gervais, Véronique; Glover, Kyle P; Glowienke, Susanne; Van Gompel, Jacky; Gutsell, Steve; Hardy, Barry; Harvey, James S; Hillegass, Jedd; Honma, Masamitsu; Hsieh, Jui-Hua; Hsu, Chia-Wen; Hughes, Kathy; Johnson, Candice; Jolly, Robert; Jones, David; Kemper, Ray; Kenyon, Michelle O; Kim, Marlene T; Kruhlak, Naomi L; Kulkarni, Sunil A; Kümmerer, Klaus; Leavitt, Penny; Majer, Bernhard; Masten, Scott; Miller, Scott; Moser, Janet; Mumtaz, Moiz; Muster, Wolfgang; Neilson, Louise; Oprea, Tudor I; Patlewicz, Grace; Paulino, Alexandre; Lo Piparo, Elena; Powley, Mark; Quigley, Donald P; Reddy, M Vijayaraj; Richarz, Andrea-Nicole; Ruiz, Patricia; Schilter, Benoit; Serafimova, Rositsa; Simpson, Wendy; Stavitskaya, Lidiya; Stidl, Reinhard; Suarez-Rodriguez, Diana; Szabo, David T; Teasdale, Andrew; Trejo-Martin, Alejandra; Valentin, Jean-Pierre; Vuorinen, Anna; Wall, Brian A; Watts, Pete; White, Angela T; Wichard, Joerg; Witt, Kristine L; Woolley, Adam; Woolley, David; Zwickl, Craig; Hasselgren, Catrin

2018-07-01

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Assessment of blood-brain barrier penetration: in silico, in vitro and in vivo.

PubMed

Feng, Meihua Rose

2002-12-01

The amount of drug achieved and maintained in the brain after systemic administration is determined by the agent's permeability at blood-brain barrier (BBB), potential involvement of transport systems, and the distribution, metabolism and elimination properties. Passive diffusion permeability may be predicted by an in silico method based on a molecule's structure property. In vitro cell culture is another useful tool for the assessment of passive permeability and BBB transports (e.g. PGP, MRP). In situ or in vivo techniques like carotid artery single injection or perfusion, brain microdialysis, autoradiography, and others are used at various stages of drug discovery and development to estimate CNS penetration and PK/PD correlation. Each technique has its own application with specific advantages and limitations.
Tools for Early Prediction of Drug Loading in Lipid-Based Formulations

PubMed Central

2015-01-01

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R2 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R2 0.85; Polysorbate 80, R2 0.90; Cremophor EL, R2 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R2 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R2 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug. PMID:26568134
Tools for Early Prediction of Drug Loading in Lipid-Based Formulations.

PubMed

Alskär, Linda C; Porter, Christopher J H; Bergström, Christel A S

2016-01-04

Identification of the usefulness of lipid-based formulations (LBFs) for delivery of poorly water-soluble drugs is at date mainly experimentally based. In this work we used a diverse drug data set, and more than 2,000 solubility measurements to develop experimental and computational tools to predict the loading capacity of LBFs. Computational models were developed to enable in silico prediction of solubility, and hence drug loading capacity, in the LBFs. Drug solubility in mixed mono-, di-, triglycerides (Maisine 35-1 and Capmul MCM EP) correlated (R(2) 0.89) as well as the drug solubility in Carbitol and other ethoxylated excipients (PEG400, R(2) 0.85; Polysorbate 80, R(2) 0.90; Cremophor EL, R(2) 0.93). A melting point below 150 °C was observed to result in a reasonable solubility in the glycerides. The loading capacity in LBFs was accurately calculated from solubility data in single excipients (R(2) 0.91). In silico models, without the demand of experimentally determined solubility, also gave good predictions of the loading capacity in these complex formulations (R(2) 0.79). The framework established here gives a better understanding of drug solubility in single excipients and of LBF loading capacity. The large data set studied revealed that experimental screening efforts can be rationalized by solubility measurements in key excipients or from solid state information. For the first time it was shown that loading capacity in complex formulations can be accurately predicted using molecular information extracted from calculated descriptors and thermal properties of the crystalline drug.
ReactPRED: a tool to predict and analyze biochemical reactions.

PubMed

Sivakumar, Tadi Venkata; Giri, Varun; Park, Jin Hwan; Kim, Tae Yong; Bhaduri, Anirban

2016-11-15

Biochemical pathways engineering is often used to synthesize or degrade target chemicals. In silico screening of the biochemical transformation space allows predicting feasible reactions, constituting these pathways. Current enabling tools are customized to predict reactions based on pre-defined biochemical transformations or reaction rule sets. Reaction rule sets are usually curated manually and tailored to specific applications. They are not exhaustive. In addition, current systems are incapable of regulating and refining data with an aim to tune specificity and sensitivity. A robust and flexible tool that allows automated reaction rule set creation along with regulated pathway prediction and analyses is a need. ReactPRED aims to address the same. ReactPRED is an open source flexible and customizable tool enabling users to predict biochemical reactions and pathways. The tool allows automated reaction rule creation from a user defined reaction set. Additionally, reaction rule degree and rule tolerance features allow refinement of predicted data. It is available as a flexible graphical user interface and a console application. ReactPRED is available at: https://sourceforge.net/projects/reactpred/ CONTACT: anirban.b@samsung.com or ty76.kim@samsung.comSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
The identification of protein domains that mediate functional interactions between Rab-GTPases and RabGAPs using 3D protein modeling.

PubMed

Davie, Jeremiah J; Faitar, Silviu L

2017-01-01

Currently, time-consuming serial in vitro experimentation involving immunocytochemistry or radiolabeled materials is required to identify which of the numerous Rab-GTPases (Rab) and Rab-GTPase activating proteins (RabGAP) are capable of functional interactions. These interactions are essential for numerous cellular functions, and in silico methods of reducing in vitro trial and error would accelerate the pace of research in cell biology. We have utilized a combination of three-dimensional protein modeling and protein bioinformatics to identify domains present in Rab proteins that are predictive of their functional interaction with a specific RabGAP. The RabF2 and RabSF1 domains appear to play functional roles in mediating the interaction between Rabs and RabGAPs. Moreover, the RabSF1 domain can be used to make in silico predictions of functional Rab/RabGAP pairs. This method is expected to be a broadly applicable tool for predicting protein-protein interactions where existing crystal structures for homologs of the proteins of interest are available.
Using the genome aggregation database, computational pathogenicity prediction tools, and patch clamp heterologous expression studies to demote previously published long QT syndrome type 1 mutations from pathogenic to benign.

PubMed

Clemens, Daniel J; Lentino, Anne R; Kapplinger, Jamie D; Ye, Dan; Zhou, Wei; Tester, David J; Ackerman, Michael J

2018-04-01

Mutations in the KCNQ1-encoded Kv7.1 potassium channel cause long QT syndrome (LQTS) type 1 (LQT1). It has been suggested that ∼10%-20% of rare LQTS case-derived variants in the literature may have been published erroneously as LQT1-causative mutations and may be "false positives." The purpose of this study was to determine which previously published KCNQ1 case variants are likely false positives. A list of all published, case-derived KCNQ1 missense variants (MVs) was compiled. The occurrence of each MV within the Genome Aggregation Database (gnomAD) was assessed. Eight in silico tools were used to predict each variant's pathogenicity. Case-derived variants that were either (1) too frequently found in gnomAD or (2) absent in gnomAD but predicted to be pathogenic by ≤2 tools were considered potential false positives. Three of these variants were characterized functionally using whole-cell patch clamp technique. Overall, there were 244 KCNQ1 case-derived MVs. Of these, 29 (12%) were seen in ≥10 individuals in gnomAD and are demotable. However, 157 of 244 MVs (64%) were absent in gnomAD. Of these, 7 (4%) were predicted to be pathogenic by ≤2 tools, 3 of which we characterized functionally. There was no significant difference in current density between heterozygous KCNQ1-F127L, -P477L, or -L619M variant-containing channels compared to KCNQ1-WT. This study offers preliminary evidence for the demotion of 32 (13%) previously published LQT1 MVs. Of these, 29 were demoted because of their frequent sighting in gnomAD. Additionally, in silico analysis and in vitro functional studies have facilitated the demotion of 3 ultra-rare MVs (F127L, P477L, L619M). Copyright © 2017 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.
Assessment of in vivo organ-uptake and in silico prediction of CYP mediated metabolism of DA-Phen, a new dopaminergic agent.

PubMed

Sutera, Flavia Maria; Giannola, Libero Italo; Murgia, Denise; De Caro, Viviana

2017-12-01

The drug development process strives to predict metabolic fate of a drug candidate, together with its uptake in major organs, whether they act as target, deposit or metabolism sites, to the aim of establish a relationship between the pharmacodynamics and the pharmacokinetics and highlight the potential toxicity of the drug candidate. The present study was aimed at evaluating the in vivo uptake of 2-Amino-N-[2-(3,4-dihydroxy-phenyl)-ethyl]-3-phenyl-propionamide (DA-Phen) - a new dopaminergic neurotransmission modulator, in target and non-target organs of animal subjects and integrating these data with SMARTCyp results, an in silico method that predicts the sites of cytochrome P450-mediated metabolism of drug-like molecules. Wistar rats, subjected to two different behavioural studies in which DA-Phen was intraperitoneally administrated at a dose equal to 0.03mmol/kg, were sacrificed after the experimental protocols and their major organs were analysed to quantify the drug uptake. The data obtained were integrated with in silico prediction of potential metabolites of DA-Phen using the SmartCYP predictive tool. DA-Phen reached quantitatively the Central Nervous System and the results showed that the amide bond of the DA-Phen is scarcely hydrolysed as it was found intact in analyzed organs. As a consequence, it is possible to assume that DA-Phen acts as dopaminergic modulator per se and not as a Dopamine prodrug, thus avoiding peripheral release and toxic side effects due to the endogenous neurotransmitter. Furthermore the identification of potential metabolites related to biotransformation of the drug candidate leads to a more careful evaluation of the appropriate route of administration for future intended therapeutic aims and potential translation into clinical studies. Copyright © 2017 Elsevier Ltd. All rights reserved.
Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.
A comparative in silico linear B-cell epitope prediction and characterization for South American and African Trypanosoma vivax strains.

PubMed

Guedes, Rafael Lucas Muniz; Rodrigues, Carla Monadeli Filgueira; Coatnoan, Nicolas; Cosson, Alain; Cadioli, Fabiano Antonio; Garcia, Herakles Antonio; Gerber, Alexandra Lehmkuhl; Machado, Rosangela Zacarias; Minoprio, Paola Marcella Camargo; Teixeira, Marta Maria Geraldes; de Vasconcelos, Ana Tereza Ribeiro

2018-02-27

Trypanosoma vivax is a parasite widespread across Africa and South America. Immunological methods using recombinant antigens have been developed aiming at specific and sensitive detection of infections caused by T. vivax. Here, we sequenced for the first time the transcriptome of a virulent T. vivax strain (Lins), isolated from an outbreak of severe disease in South America (Brazil) and performed a computational integrated analysis of genome, transcriptome and in silico predictions to identify and characterize putative linear B-cell epitopes from African and South American T. vivax. A total of 2278, 3936 and 4062 linear B-cell epitopes were respectively characterized for the transcriptomes of T. vivax LIEM-176 (Venezuela), T. vivax IL1392 (Nigeria) and T. vivax Lins (Brazil) and 4684 for the genome of T. vivax Y486 (Nigeria). The results presented are a valuable theoretical source that may pave the way for highly sensitive and specific diagnostic tools. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects

PubMed Central

Cronin, Mark T.D.; Enoch, Steven J.; Mellor, Claire L.; Przybylak, Katarzyna R.; Richarz, Andrea-Nicole; Madden, Judith C.

2017-01-01

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given. PMID:28744348
In Silico Prediction of Organ Level Toxicity: Linking Chemistry to Adverse Effects.

PubMed

Cronin, Mark T D; Enoch, Steven J; Mellor, Claire L; Przybylak, Katarzyna R; Richarz, Andrea-Nicole; Madden, Judith C

2017-07-01

In silico methods to predict toxicity include the use of (Quantitative) Structure-Activity Relationships ((Q)SARs) as well as grouping (category formation) allowing for read-across. A challenging area for in silico modelling is the prediction of chronic toxicity and the No Observed (Adverse) Effect Level (NO(A)EL) in particular. A proposed solution to the prediction of chronic toxicity is to consider organ level effects, as opposed to modelling the NO(A)EL itself. This review has focussed on the use of structural alerts to identify potential liver toxicants. In silico profilers, or groups of structural alerts, have been developed based on mechanisms of action and informed by current knowledge of Adverse Outcome Pathways. These profilers are robust and can be coded computationally to allow for prediction. However, they do not cover all mechanisms or modes of liver toxicity and recommendations for the improvement of these approaches are given.
In silico characterization of a novel pathogenic deletion mutation identified in XPA gene in a Pakistani family with severe xeroderma pigmentosum.

PubMed

Nasir, Muhammad; Ahmad, Nafees; Sieber, Christian M K; Latif, Amir; Malik, Salman Akbar; Hameed, Abdul

2013-09-24

Xeroderma Pigmentosum (XP) is a rare skin disorder characterized by skin hypersensitivity to sunlight and abnormal pigmentation. The aim of this study was to investigate the genetic cause of a severe XP phenotype in a consanguineous Pakistani family and in silico characterization of any identified disease-associated mutation. The XP complementation group was assigned by genotyping of family for known XP loci. Genotyping data mapped the family to complementation group A locus, involving XPA gene. Mutation analysis of the candidate XP gene by DNA sequencing revealed a novel deletion mutation (c.654del A) in exon 5 of XPA gene. The c.654del A, causes frameshift, which pre-maturely terminates protein and result into a truncated product of 222 amino acid (aa) residues instead of 273 (p.Lys218AsnfsX5). In silico tools were applied to study the likelihood of changes in structural motifs and thus interaction of mutated protein with binding partners. In silico analysis of mutant protein sequence, predicted to affect the aa residue which attains coiled coil structure. The coiled coil structure has an important role in key cellular interactions, especially with DNA damage-binding protein 2 (DDB2), which has important role in DDB-mediated nucleotide excision repair (NER) system. Our findings support the fact of genetic and clinical heterogeneity in XP. The study also predicts the critical role of DDB2 binding region of XPA protein in NER pathway and opens an avenue for further research to study the functional role of the mutated protein domain.
Functional examination of MLH1, MSH2, and MSH6 intronic mutations identified in Danish colorectal cancer patients.

PubMed

Petersen, Sanne M; Dandanell, Mette; Rasmussen, Lene J; Gerdes, Anne-Marie; Krogh, Lotte N; Bernstein, Inge; Okkels, Henrik; Wikman, Friedrik; Nielsen, Finn C; Hansen, Thomas V O

2013-10-03

Germ-line mutations in the DNA mismatch repair genes MLH1, MSH2, and MSH6 predispose to the development of colorectal cancer (Lynch syndrome or hereditary nonpolyposis colorectal cancer). These mutations include disease-causing frame-shift, nonsense, and splicing mutations as well as large genomic rearrangements. However, a large number of mutations, including missense, silent, and intronic variants, are classified as variants of unknown clinical significance. Intronic MLH1, MSH2, or MSH6 variants were investigated using in silico prediction tools and mini-gene assay to asses the effect on splicing. We describe in silico and in vitro characterization of nine intronic MLH1, MSH2, or MSH6 mutations identified in Danish colorectal cancer patients, of which four mutations are novel. The analysis revealed aberrant splicing of five mutations (MLH1 c.588 + 5G > A, MLH1 c.677 + 3A > T, MLH1 c.1732-2A > T, MSH2 c.1276 + 1G > T, and MSH2 c.1662-2A > C), while four mutations had no effect on splicing compared to wild type (MLH1 c.117-34A > T, MLH1 c.1039-8 T > A, MSH2 c.2459-18delT, and MSH6 c.3439-16C > T). In conclusion, we classify five MLH1/MSH2 mutations as pathogenic, whereas four MLH1/MSH2/MSH6 mutations are classified as neutral. This study supports the notion that in silico prediction tools and mini-gene assays are important for the classification of intronic variants, and thereby crucial for the genetic counseling of patients and their family members.
In silico regenerative medicine: how computational tools allow regulatory and financial challenges to be addressed in a volatile market

PubMed Central

Geris, L.; Guyot, Y.; Schrooten, J.; Papantoniou, I.

2016-01-01

The cell therapy market is a highly volatile one, due to the use of disruptive technologies, the current economic situation and the small size of the market. In such a market, companies as well as academic research institutes are in need of tools to advance their understanding and, at the same time, reduce their R&D costs, increase product quality and productivity, and reduce the time to market. An additional difficulty is the regulatory path that needs to be followed, which is challenging in the case of cell-based therapeutic products and should rely on the implementation of quality by design (QbD) principles. In silico modelling is a tool that allows the above-mentioned challenges to be addressed in the field of regenerative medicine. This review discusses such in silico models and focuses more specifically on the bioprocess. Three (clusters of) examples related to this subject are discussed. The first example comes from the pharmaceutical engineering field where QbD principles and their implementation through the use of in silico models are both a regulatory and economic necessity. The second example is related to the production of red blood cells. The described in silico model is mainly used to investigate the manufacturing process of the cell-therapeutic product, and pays special attention to the economic viability of the process. Finally, we describe the set-up of a model capturing essential events in the development of a tissue-engineered combination product in the context of bone tissue engineering. For each of the examples, a short introduction to some economic aspects is given, followed by a description of the in silico tool or tools that have been developed to allow the implementation of QbD principles and optimal design. PMID:27051516
In silico regenerative medicine: how computational tools allow regulatory and financial challenges to be addressed in a volatile market.

PubMed

Geris, L; Guyot, Y; Schrooten, J; Papantoniou, I

2016-04-06

The cell therapy market is a highly volatile one, due to the use of disruptive technologies, the current economic situation and the small size of the market. In such a market, companies as well as academic research institutes are in need of tools to advance their understanding and, at the same time, reduce their R&D costs, increase product quality and productivity, and reduce the time to market. An additional difficulty is the regulatory path that needs to be followed, which is challenging in the case of cell-based therapeutic products and should rely on the implementation of quality by design (QbD) principles. In silico modelling is a tool that allows the above-mentioned challenges to be addressed in the field of regenerative medicine. This review discusses such in silico models and focuses more specifically on the bioprocess. Three (clusters of) examples related to this subject are discussed. The first example comes from the pharmaceutical engineering field where QbD principles and their implementation through the use of in silico models are both a regulatory and economic necessity. The second example is related to the production of red blood cells. The described in silico model is mainly used to investigate the manufacturing process of the cell-therapeutic product, and pays special attention to the economic viability of the process. Finally, we describe the set-up of a model capturing essential events in the development of a tissue-engineered combination product in the context of bone tissue engineering. For each of the examples, a short introduction to some economic aspects is given, followed by a description of the in silico tool or tools that have been developed to allow the implementation of QbD principles and optimal design.
RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.
ProTox: a web server for the in silico prediction of rodent oral toxicity.

PubMed

Drwal, Malgorzata N; Banerjee, Priyanka; Dunkel, Mathias; Wettig, Martin R; Preissner, Robert

2014-07-01

Animal trials are currently the major method for determining the possible toxic effects of drug candidates and cosmetics. In silico prediction methods represent an alternative approach and aim to rationalize the preclinical drug development, thus enabling the reduction of the associated time, costs and animal experiments. Here, we present ProTox, a web server for the prediction of rodent oral toxicity. The prediction method is based on the analysis of the similarity of compounds with known median lethal doses (LD50) and incorporates the identification of toxic fragments, therefore representing a novel approach in toxicity prediction. In addition, the web server includes an indication of possible toxicity targets which is based on an in-house collection of protein-ligand-based pharmacophore models ('toxicophores') for targets associated with adverse drug reactions. The ProTox web server is open to all users and can be accessed without registration at: http://tox.charite.de/tox. The only requirement for the prediction is the two-dimensional structure of the input compounds. All ProTox methods have been evaluated based on a diverse external validation set and displayed strong performance (sensitivity, specificity and precision of 76, 95 and 75%, respectively) and superiority over other toxicity prediction tools, indicating their possible applicability for other compound classes. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.
IMHOTEP—a composite score integrating popular tools for predicting the functional consequences of non-synonymous sequence variants

PubMed Central

Knecht, Carolin; Mort, Matthew; Junge, Olaf; Cooper, David N.; Krawczak, Michael

2017-01-01

Abstract The in silico prediction of the functional consequences of mutations is an important goal of human pathogenetics. However, bioinformatic tools that classify mutations according to their functionality employ different algorithms so that predictions may vary markedly between tools. We therefore integrated nine popular prediction tools (PolyPhen-2, SNPs&GO, MutPred, SIFT, MutationTaster2, Mutation Assessor and FATHMM as well as conservation-based Grantham Score and PhyloP) into a single predictor. The optimal combination of these tools was selected by means of a wide range of statistical modeling techniques, drawing upon 10 029 disease-causing single nucleotide variants (SNVs) from Human Gene Mutation Database and 10 002 putatively ‘benign’ non-synonymous SNVs from UCSC. Predictive performance was found to be markedly improved by model-based integration, whilst maximum predictive capability was obtained with either random forest, decision tree or logistic regression analysis. A combination of PolyPhen-2, SNPs&GO, MutPred, MutationTaster2 and FATHMM was found to perform as well as all tools combined. Comparison of our approach with other integrative approaches such as Condel, CoVEC, CAROL, CADD, MetaSVM and MetaLR using an independent validation dataset, revealed the superiority of our newly proposed integrative approach. An online implementation of this approach, IMHOTEP (‘Integrating Molecular Heuristics and Other Tools for Effect Prediction’), is provided at http://www.uni-kiel.de/medinfo/cgi-bin/predictor/. PMID:28180317

Automated benchmarking of peptide-MHC class I binding predictions.

PubMed

Trolle, Thomas; Metushi, Imir G; Greenbaum, Jason A; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

2015-07-01

Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. mniel@cbs.dtu.dk or bpeters@liai.org Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Automated benchmarking of peptide-MHC class I binding predictions

PubMed Central

Trolle, Thomas; Metushi, Imir G.; Greenbaum, Jason A.; Kim, Yohan; Sidney, John; Lund, Ole; Sette, Alessandro; Peters, Bjoern; Nielsen, Morten

2015-01-01

Motivation: Numerous in silico methods predicting peptide binding to major histocompatibility complex (MHC) class I molecules have been developed over the last decades. However, the multitude of available prediction tools makes it non-trivial for the end-user to select which tool to use for a given task. To provide a solid basis on which to compare different prediction tools, we here describe a framework for the automated benchmarking of peptide-MHC class I binding prediction tools. The framework runs weekly benchmarks on data that are newly entered into the Immune Epitope Database (IEDB), giving the public access to frequent, up-to-date performance evaluations of all participating tools. To overcome potential selection bias in the data included in the IEDB, a strategy was implemented that suggests a set of peptides for which different prediction methods give divergent predictions as to their binding capability. Upon experimental binding validation, these peptides entered the benchmark study. Results: The benchmark has run for 15 weeks and includes evaluation of 44 datasets covering 17 MHC alleles and more than 4000 peptide-MHC binding measurements. Inspection of the results allows the end-user to make educated selections between participating tools. Of the four participating servers, NetMHCpan performed the best, followed by ANN, SMM and finally ARB. Availability and implementation: Up-to-date performance evaluations of each server can be found online at http://tools.iedb.org/auto_bench/mhci/weekly. All prediction tool developers are invited to participate in the benchmark. Sign-up instructions are available at http://tools.iedb.org/auto_bench/mhci/join. Contact: mniel@cbs.dtu.dk or bpeters@liai.org Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25717196
Exonic Splicing Mutations Are More Prevalent than Currently Estimated and Can Be Predicted by Using In Silico Tools

PubMed Central

Soukarieh, Omar; Gaildrat, Pascaline; Hamieh, Mohamad; Drouet, Aurélie; Baert-Desurmont, Stéphanie; Frébourg, Thierry; Tosi, Mario; Martins, Alexandra

2016-01-01

The identification of a causal mutation is essential for molecular diagnosis and clinical management of many genetic disorders. However, even if next-generation exome sequencing has greatly improved the detection of nucleotide changes, the biological interpretation of most exonic variants remains challenging. Moreover, particular attention is typically given to protein-coding changes often neglecting the potential impact of exonic variants on RNA splicing. Here, we used the exon 10 of MLH1, a gene implicated in hereditary cancer, as a model system to assess the prevalence of RNA splicing mutations among all single-nucleotide variants identified in a given exon. We performed comprehensive minigene assays and analyzed patient’s RNA when available. Our study revealed a staggering number of splicing mutations in MLH1 exon 10 (77% of the 22 analyzed variants), including mutations directly affecting splice sites and, particularly, mutations altering potential splicing regulatory elements (ESRs). We then used this thoroughly characterized dataset, together with experimental data derived from previous studies on BRCA1, BRCA2, CFTR and NF1, to evaluate the predictive power of 3 in silico approaches recently described as promising tools for pinpointing ESR-mutations. Our results indicate that ΔtESRseq and ΔHZEI-based approaches not only discriminate which variants affect splicing, but also predict the direction and severity of the induced splicing defects. In contrast, the ΔΨ-based approach did not show a compelling predictive power. Our data indicates that exonic splicing mutations are more prevalent than currently appreciated and that they can now be predicted by using bioinformatics methods. These findings have implications for all genetically-caused diseases. PMID:26761715
In Vivo Predictive Dissolution (IPD) and Biopharmaceutical Modeling and Simulation: Future Use of Modern Approaches and Methodologies in a Regulatory Context.

PubMed

Lennernäs, H; Lindahl, A; Van Peer, A; Ollier, C; Flanagan, T; Lionberger, R; Nordmark, A; Yamashita, S; Yu, L; Amidon, G L; Fischer, V; Sjögren, E; Zane, P; McAllister, M; Abrahamsson, B

2017-04-03

The overall objective of OrBiTo, a project within Innovative Medicines Initiative (IMI), is to streamline and optimize the development of orally administered drug products through the creation and efficient application of biopharmaceutics tools. This toolkit will include both experimental and computational models developed on improved understanding of the highly dynamic gastrointestinal (GI) physiology relevant to the GI absorption of drug products in both fasted and fed states. A part of the annual OrBiTo meeting in 2015 was dedicated to the presentation of the most recent progress in the development of the regulatory use of PBPK in silico modeling, in vivo predictive dissolution (IPD) tests, and their application to biowaivers. There are still several areas for improvement of in vitro dissolution testing by means of generating results relevant for the intraluminal conditions in the GI tract. The major opportunity is probably in combining IPD testing and physiologically based in silico models where the in vitro data provide input to the absorption predictions. The OrBiTo project and other current research projects include definition of test media representative for the more distal parts of the GI tract, models capturing supersaturation and precipitation phenomena, and influence of motility waves on shear and other forces of hydrodynamic origin, addressing the interindividual variability in composition and characteristics of GI fluids, food effects, definition of biorelevant buffer systems, and intestinal water volumes. In conclusion, there is currently a mismatch between the extensive industrial usage of modern in vivo predictive tools and very limited inclusion of such data in regulatory files. However, there is a great interest among all stakeholders to introduce recent progresses in prediction of in vivo GI drug absorption into regulatory context.
The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies.

PubMed

Yoshida, Catherine E; Kruczkiewicz, Peter; Laing, Chad R; Lingohr, Erika J; Gannon, Victor P J; Nash, John H E; Taboada, Eduardo N

2016-01-01

For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.
Flux analysis and metabolomics for systematic metabolic engineering of microorganisms.

PubMed

Toya, Yoshihiro; Shimizu, Hiroshi

2013-11-01

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering. Copyright © 2013 Elsevier Inc. All rights reserved.
In silico, in vitro and in vivo analyses of dipeptidyl peptidase IV inhibitory activity and the antidiabetic effect of sodium caseinate hydrolysate.

PubMed

Hsieh, Cheng-Hong; Wang, Tzu-Yuan; Hung, Chuan-Chuan; Jao, Chia-Ling; Hsieh, You-Liang; Wu, Si-Xian; Hsu, Kuo-Chiang

2016-02-01

The frequency (A), a novel in silico parameter, was developed by calculating the ratio of the number of truncated peptides with Xaa-proline and Xaa-alanine to all peptide fragments from a protein hydrolyzed with a specific protease. The highest in vitro DPP-IV inhibitory activity (72.7%) was observed in the hydrolysate of sodium caseinate by bromelain (Cas/BRO), and the constituent proteins of bovine casein also had relatively high A values (0.10-0.17) with BRO hydrolysis. 1CBR (the <1 kDa fraction of Cas/BRO) showed the greatest in vitro DPP-IV inhibitory activity of 77.5% and was used for in vivo test by high-fat diet-fed and low-dose streptozotocin-induced diabetic rats. The daily administration of 1CBR for 6 weeks was effective to improve glycaemic control in diabetic rats. The results indicate that the novel in silico method has the potential as a screening tool to predict dietary proteins to generate DPP-IV inhibitory and antidiabetic peptides.
Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

PubMed

Huang, Ying; Chen, Shi-Yi; Deng, Feilong

2016-01-01

In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools.
A novel QSAR model of Salmonella mutagenicity and its application in the safety assessment of drug impurities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valencia, Antoni; Prous, Josep; Mora, Oscar

As indicated in ICH M7 draft guidance, in silico predictive tools including statistically-based QSARs and expert analysis may be used as a computational assessment for bacterial mutagenicity for the qualification of impurities in pharmaceuticals. To address this need, we developed and validated a QSAR model to predict Salmonella t. mutagenicity (Ames assay outcome) of pharmaceutical impurities using Prous Institute's Symmetry℠, a new in silico solution for drug discovery and toxicity screening, and the Mold2 molecular descriptor package (FDA/NCTR). Data was sourced from public benchmark databases with known Ames assay mutagenicity outcomes for 7300 chemicals (57% mutagens). Of these data, 90%more » was used to train the model and the remaining 10% was set aside as a holdout set for validation. The model's applicability to drug impurities was tested using a FDA/CDER database of 951 structures, of which 94% were found within the model's applicability domain. The predictive performance of the model is acceptable for supporting regulatory decision-making with 84 ± 1% sensitivity, 81 ± 1% specificity, 83 ± 1% concordance and 79 ± 1% negative predictivity based on internal cross-validation, while the holdout dataset yielded 83% sensitivity, 77% specificity, 80% concordance and 78% negative predictivity. Given the importance of having confidence in negative predictions, an additional external validation of the model was also carried out, using marketed drugs known to be Ames-negative, and obtained 98% coverage and 81% specificity. Additionally, Ames mutagenicity data from FDA/CFSAN was used to create another data set of 1535 chemicals for external validation of the model, yielding 98% coverage, 73% sensitivity, 86% specificity, 81% concordance and 84% negative predictivity. - Highlights: • A new in silico QSAR model to predict Ames mutagenicity is described. • The model is extensively validated with chemicals from the FDA and the public domain. • Validation tests show desirable high sensitivity and high negative predictivity. • The model predicted 14 reportedly difficult to predict drug impurities with accuracy. • The model is suitable to support risk evaluation of potentially mutagenic compounds.« less
FireProt: web server for automated design of thermostable proteins

PubMed Central

Musil, Milos; Stourac, Jan; Brezovsky, Jan; Prokop, Zbynek; Zendulka, Jaroslav; Martinek, Tomas

2017-01-01

Abstract There is a continuous interest in increasing proteins stability to enhance their usability in numerous biomedical and biotechnological applications. A number of in silico tools for the prediction of the effect of mutations on protein stability have been developed recently. However, only single-point mutations with a small effect on protein stability are typically predicted with the existing tools and have to be followed by laborious protein expression, purification, and characterization. Here, we present FireProt, a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core. FireProt utilizes sixteen tools and three protein engineering strategies for making reliable protein designs. The server is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostable mutants. FireProt is freely available at http://loschmidt.chemi.muni.cz/fireprot. PMID:28449074
The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS)

PubMed Central

2016-01-01

Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (Rs) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for Rs prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict Rs. Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d–1 (RTD-model) and 0.03 ± 0.03 L d–1 (GSD-model) relative to experimentally determined Rs. Prediction variability in replicated models was the same or less than for measured Rs. Networks were externally validated using a measured Rs data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d–1 and 0.0437 ± 0.02 L d–1, respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for Rs measurement. The use of in silico tools for Rs determination represents a more economical approach than laboratory calibrations. PMID:27363449
An evaluation of selected in silico models for the assessment ...

EPA Pesticide Factsheets

Skin sensitization remains an important endpoint for consumers, manufacturers and regulators. Although the development of alternative approaches to assess skin sensitization potential has been extremely active over many years, the implication of regulations such as REACH and the Cosmetics Directive in EU has provided a much stronger impetus to actualize this research into practical tools for decision making. Thus there has been considerable focus on the development, evaluation, and integration of alternative approaches for skin sensitization hazard and risk assessment. This includes in silico approaches such as (Q)SARs and expert systems. This study aimed to evaluate the predictive performance of a selection of in silico models and then to explore whether combining those models led to an improvement in accuracy. A dataset of 473 substances that had been tested in the local lymph node assay (LLNA) was compiled. This comprised 295 sensitizers and 178 non-sensitizers. Four freely available models were identified - 2 statistical models VEGA and MultiCASE model A33 for skin sensitization (MCASE A33) from the Danish National Food Institute and two mechanistic models Toxtree’s Skin sensitization Reaction domains (Toxtree SS Rxn domains) and the OASIS v1.3 protein binding alerts for skin sensitization from the OECD Toolbox (OASIS). VEGA and MCASE A33 aim to predict sensitization as a binary score whereas the mechanistic models identified reaction domains or structura
Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform.

PubMed

Marshall-Colon, Amy; Long, Stephen P; Allen, Douglas K; Allen, Gabrielle; Beard, Daniel A; Benes, Bedrich; von Caemmerer, Susanne; Christensen, A J; Cox, Donna J; Hart, John C; Hirst, Peter M; Kannan, Kavya; Katz, Daniel S; Lynch, Jonathan P; Millar, Andrew J; Panneerselvam, Balaji; Price, Nathan D; Prusinkiewicz, Przemyslaw; Raila, David; Shekar, Rachel G; Shrivastava, Stuti; Shukla, Diwakar; Srinivasan, Venkatraman; Stitt, Mark; Turk, Matthew J; Voit, Eberhard O; Wang, Yu; Yin, Xinyou; Zhu, Xin-Guang

2017-01-01

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.
Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform

PubMed Central

Marshall-Colon, Amy; Long, Stephen P.; Allen, Douglas K.; Allen, Gabrielle; Beard, Daniel A.; Benes, Bedrich; von Caemmerer, Susanne; Christensen, A. J.; Cox, Donna J.; Hart, John C.; Hirst, Peter M.; Kannan, Kavya; Katz, Daniel S.; Lynch, Jonathan P.; Millar, Andrew J.; Panneerselvam, Balaji; Price, Nathan D.; Prusinkiewicz, Przemyslaw; Raila, David; Shekar, Rachel G.; Shrivastava, Stuti; Shukla, Diwakar; Srinivasan, Venkatraman; Stitt, Mark; Turk, Matthew J.; Voit, Eberhard O.; Wang, Yu; Yin, Xinyou; Zhu, Xin-Guang

2017-01-01

Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop. PMID:28555150
Membrane-assisted extraction of monoterpenes: from in silico solvent screening towards biotechnological process application

PubMed Central

2018-01-01

This work focuses on the process development of membrane-assisted solvent extraction of hydrophobic compounds such as monoterpenes. Beginning with the choice of suitable solvents, quantum chemical calculations with the simulation tool COSMO-RS were carried out to predict the partition coefficient (logP) of (S)-(+)-carvone and terpinen-4-ol in various solvent–water systems and validated afterwards with experimental data. COSMO-RS results show good prediction accuracy for non-polar solvents such as n-hexane, ethyl acetate and n-heptane even in the presence of salts and glycerol in an aqueous medium. Based on the high logP value, n-heptane was chosen for the extraction of (S)-(+)-carvone in a lab-scale hollow-fibre membrane contactor. Two operation modes are investigated where experimental and theoretical mass transfer values, based on their related partition coefficients, were compared. In addition, the process is evaluated in terms of extraction efficiency and overall product recovery, and its biotechnological application potential is discussed. Our work demonstrates that the combination of in silico prediction by COSMO-RS with membrane-assisted extraction is a promising approach for the recovery of hydrophobic compounds from aqueous solutions. PMID:29765654
A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine.

PubMed

Christodoulou, Nikolaos A; Tousert, Nikolaos E; Georgiadi, Eleni Ch; Argyri, Katerina D; Misichroni, Fay D; Stamatakos, Georgios S

2016-01-01

The plethora of available disease prediction models and the ongoing process of their application into clinical practice - following their clinical validation - have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. In addition, the end users should be provided with all the necessary interfaces for model execution and effortless result retrieval. We therefore propose a software infrastructure, based on a tool, model and data repository that handles the storage of models and pertinent execution-related data, along with functionalities for execution management, communication with third-party applications, user-friendly interfaces to access and use the infrastructure with minimal effort and basic security features.
A Modular Repository-based Infrastructure for Simulation Model Storage and Execution Support in the Context of In Silico Oncology and In Silico Medicine

PubMed Central

Christodoulou, Nikolaos A.; Tousert, Nikolaos E.; Georgiadi, Eleni Ch.; Argyri, Katerina D.; Misichroni, Fay D.; Stamatakos, Georgios S.

2016-01-01

The plethora of available disease prediction models and the ongoing process of their application into clinical practice – following their clinical validation – have created new needs regarding their efficient handling and exploitation. Consolidation of software implementations, descriptive information, and supportive tools in a single place, offering persistent storage as well as proper management of execution results, is a priority, especially with respect to the needs of large healthcare providers. At the same time, modelers should be able to access these storage facilities under special rights, in order to upgrade and maintain their work. In addition, the end users should be provided with all the necessary interfaces for model execution and effortless result retrieval. We therefore propose a software infrastructure, based on a tool, model and data repository that handles the storage of models and pertinent execution-related data, along with functionalities for execution management, communication with third-party applications, user-friendly interfaces to access and use the infrastructure with minimal effort and basic security features. PMID:27812280
A Genome-Scale Metabolic Reconstruction of Mycoplasma genitalium, iPS189

PubMed Central

Suthers, Patrick F.; Dasika, Madhukar S.; Kumar, Vinay Satish; Denisov, Gennady; Glass, John I.; Maranas, Costas D.

2009-01-01

With a genome size of ∼580 kb and approximately 480 protein coding regions, Mycoplasma genitalium is one of the smallest known self-replicating organisms and, additionally, has extremely fastidious nutrient requirements. The reduced genomic content of M. genitalium has led researchers to suggest that the molecular assembly contained in this organism may be a close approximation to the minimal set of genes required for bacterial growth. Here, we introduce a systematic approach for the construction and curation of a genome-scale in silico metabolic model for M. genitalium. Key challenges included estimation of biomass composition, handling of enzymes with broad specificities, and the lack of a defined medium. Computational tools were subsequently employed to identify and resolve connectivity gaps in the model as well as growth prediction inconsistencies with gene essentiality experimental data. The curated model, M. genitalium iPS189 (262 reactions, 274 metabolites), is 87% accurate in recapitulating in vivo gene essentiality results for M. genitalium. Approaches and tools described herein provide a roadmap for the automated construction of in silico metabolic models of other organisms. PMID:19214212
In silico characterization of a novel pathogenic deletion mutation identified in XPA gene in a Pakistani family with severe xeroderma pigmentosum

PubMed Central

2013-01-01

Background Xeroderma Pigmentosum (XP) is a rare skin disorder characterized by skin hypersensitivity to sunlight and abnormal pigmentation. The aim of this study was to investigate the genetic cause of a severe XP phenotype in a consanguineous Pakistani family and in silico characterization of any identified disease-associated mutation. Results The XP complementation group was assigned by genotyping of family for known XP loci. Genotyping data mapped the family to complementation group A locus, involving XPA gene. Mutation analysis of the candidate XP gene by DNA sequencing revealed a novel deletion mutation (c.654del A) in exon 5 of XPA gene. The c.654del A, causes frameshift, which pre-maturely terminates protein and result into a truncated product of 222 amino acid (aa) residues instead of 273 (p.Lys218AsnfsX5). In silico tools were applied to study the likelihood of changes in structural motifs and thus interaction of mutated protein with binding partners. In silico analysis of mutant protein sequence, predicted to affect the aa residue which attains coiled coil structure. The coiled coil structure has an important role in key cellular interactions, especially with DNA damage-binding protein 2 (DDB2), which has important role in DDB-mediated nucleotide excision repair (NER) system. Conclusions Our findings support the fact of genetic and clinical heterogeneity in XP. The study also predicts the critical role of DDB2 binding region of XPA protein in NER pathway and opens an avenue for further research to study the functional role of the mutated protein domain. PMID:24063568
Surface proteome mining for identification of potential vaccine candidates against Campylobacter jejuni: an in silico approach.

PubMed

Mehla, Kusum; Ramana, Jayashree

2017-01-01

Campylobacter jejuni remains a major cause of human gastroenteritis with estimated annual incidence rate of 450 million infections worldwide. C. jejuni is a major burden to public health in both socioeconomically developing and industrialized nations. Virulence determinants involved in C. jejuni pathogenesis are multifactorial in nature and not yet fully understood. Despite the completion of the first C. jejuni genome project in 2000, there are currently no vaccines in the market against this pathogen. Traditional vaccinology approach is an arduous and time extensive task. Omics techniques coupled with sequencing data have engaged researcher's attention to reduce the time and resources applied in the process of vaccine development. Recently, there has been remarkable increase in development of in silico analysis tools for efficiently mining biological information obscured in the genome. In silico approaches have been crucial for combating infectious diseases by accelerating the pace of vaccine development. This study employed a range of bioinformatics approaches for proteome scale identification of peptide vaccine candidates. Whole proteome of C. jejuni was investigated for varied properties like antigenicity, allergenicity, major histocompatibility class (MHC)-peptide interaction, immune cell processivity, HLA distribution, conservancy, and population coverage. Predicted epitopes were further tested for binding in MHC groove using computational docking studies. The predicted epitopes were conserved; covered more than 80 % of the world population and were presented by MHC-I supertypes. We conclude by underscoring that the epitopes predicted are believed to expedite the development of successful vaccines to control or prevent C. jejuni infections albeit the results need to be experimentally validated.

Definition and characterization of a "trypsinosome" from specific peptide characteristics by nano-HPLC-MS/MS and in silico analysis of complex protein mixtures.

PubMed

Le Bihan, Thierry; Robinson, Mark D; Stewart, Ian I; Figeys, Daniel

2004-01-01

Although HPLC-ESI-MS/MS is rapidly becoming an indispensable tool for the analysis of peptides in complex mixtures, the sequence coverage it affords is often quite poor. Low protein expression resulting in peptide signal intensities that fall below the limit of detection of the MS system in combination with differences in peptide ionization efficiency plays a significant role in this. A second important factor stems from differences in physicochemical properties of each peptide and how these properties relate to chromatographic retention and ultimate detection. To identify and understand those properties, we compared data from experimentally identified peptides with data from peptides predicted by in silico digest of all corresponding proteins in the experimental set. Three different complex protein mixtures extracted were used to define a training set to evaluate the amino acid retention coefficients based on linear regression analysis. The retention coefficients were also compared with other previous hydrophobic and retention scale. From this, we have constructed an empirical model that can be readily used to predict peptides that are likely to be observed on our HPLC-ESI-MS/MS system based on their physicochemical properties. Finally, we demonstrated that in silico prediction of peptides and their retention coefficients can be used to generate an inclusion list for a targeted mass spectrometric identification of low abundance proteins in complex protein samples. This approach is based on experimentally derived data to calibrate the method and therefore may theoretically be applied to any HPLC-MS/MS system on which data are being generated.
Incorporation of absorption and metabolism into liver toxicity prediction for phytochemicals: A tiered in silico QSAR approach.

PubMed

Liu, Yitong

2018-05-18

An increased use of herbal dietary supplements has been associated with adverse liver effects such as elevated serum enzymes and liver failure. The safety assessment for herbal dietary supplements is challenging since they often contain complex mixtures of phytochemicals, most of which have unknown pharmacokinetic and toxicological properties. Rapid tools are needed to evaluate large numbers of phytochemicals for potential liver toxicity. The current study demonstrates a tiered approach combining identification of phytochemicals in liver toxic botanicals, followed by in silico quantitative structure-activity relationship (QSAR) evaluation of these phytochemicals for absorption (e.g. permeability), metabolism (cytochromes P450) and liver toxicity (e.g. elevated transaminases). First, 255 phytochemicals from 20 botanicals associated with clinical liver injury were identified, and the phytochemical structures were subsequently used for QSAR evaluation. Among these identified phytochemicals, 193 were predicted to be absorbed and then used to generate metabolites, which were both used to predict liver toxicity. Forty-eight phytochemicals were predicted as liver toxic, either due to parent phytochemicals or metabolites. Among them, nineteen phytochemicals have previous evidence of liver toxicity (e.g. pyrrolizidine alkaloids), while the majority were newly discovered (e.g. sesquiterpenoids). These findings help reveal new toxic phytochemicals in herbal dietary supplements and prioritize future toxicological testing. Published by Elsevier Ltd.
Unlocking the potential of publicly available microarray data using inSilicoDb and inSilicoMerging R/Bioconductor packages.

PubMed

Taminau, Jonatan; Meganck, Stijn; Lazar, Cosmin; Steenhoff, David; Coletta, Alain; Molter, Colin; Duque, Robin; de Schaetzen, Virginie; Weiss Solís, David Y; Bersini, Hugues; Nowé, Ann

2012-12-24

With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [https://insilicodb.org/app/].
Enhancing bioactive peptide release and identification using targeted enzymatic hydrolysis of milk proteins.

PubMed

Nongonierma, Alice B; FitzGerald, Richard J

2018-06-01

Milk proteins have been extensively studied for their ability to yield a range of bioactive peptides following enzymatic hydrolysis/digestion. However, many hurdles still exist regarding the widespread utilization of milk protein-derived bioactive peptides as health enhancing agents for humans. These mostly arise from the fact that most milk protein-derived bioactive peptides are not highly potent. In addition, they may be degraded during gastrointestinal digestion and/or have a low intestinal permeability. The targeted release of bioactive peptides during the enzymatic hydrolysis of milk proteins may allow the generation of particularly potent bioactive hydrolysates and peptides. Therefore, the development of milk protein hydrolysates capable of improving human health requires, in the first instance, optimized targeted release of specific bioactive peptides. The targeted hydrolysis of milk proteins has been aided by a range of in silico tools. These include peptide cutters and predictive modeling linking bioactivity to peptide structure [i.e., molecular docking, quantitative structure activity relationship (QSAR)], or hydrolysis parameters [design of experiments (DOE)]. Different targeted enzymatic release strategies employed during the generation of milk protein hydrolysates are reviewed herein and their limitations are outlined. In addition, specific examples are provided to demonstrate how in silico tools may help in the identification and discovery of potent milk protein-derived peptides. It is anticipated that the development of novel strategies employing a range of in silico tools may help in the generation of milk protein hydrolysates containing potent and bioavailable peptides, which in turn may be used to validate their health promoting effects in humans. Graphical abstract The targeted enzymatic hydrolysis of milk proteins may allow the generation of highly potent and bioavailable bioactive peptides.
SeqAPASS: Sequence alignment to predict across-species ...

EPA Pesticide Factsheets

Efforts to shift the toxicity testing paradigm from whole organism studies to those focused on the initiation of toxicity and relevant pathways have led to increased utilization of in vitro and in silico methods. Hence the emergence of high through-put screening (HTS) programs, such as U.S. EPA ToxCast, and application of the adverse outcome pathway (AOP) framework for identifying and defining biological key events triggered upon perturbation of molecular initiating events and leading to adverse outcomes occuring at a level of organization relevant for risk assessment [1]. With these recent initiatives to harness the power of “the pathway” in describing and evaluating toxicity comes the need to extrapolate data beyond the model species. Sequence alignment to predict across-species susceptibilty (SeqAPASS) is a web-based tool that allows the user to begin to understand how broadly HTS data or AOP constructs may plausibly be extrapolated across species, while describing the relative intrinsic susceptibiltiy of different taxa to chemicals with known modes of action (e.g., pharmaceuticals and pesticides). The tool rapidly and strategically assesses available molecular target information to describe protein sequence similarity at the primary amino acid sequence, conserved domain, and individual amino acid residue levels. This in silico approach to species extrapolation was designed to automate and streamline the relatively complex and time-consuming process of co
Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0

PubMed Central

Schellenberger, Jan; Que, Richard; Fleming, Ronan M. T.; Thiele, Ines; Orth, Jeffrey D.; Feist, Adam M.; Zielinski, Daniel C.; Bordbar, Aarash; Lewis, Nathan E.; Rahmanian, Sorena; Kang, Joseph; Hyduke, Daniel R.; Palsson, Bernhard Ø.

2012-01-01

Over the past decade, a growing community of researchers has emerged around the use of COnstraint-Based Reconstruction and Analysis (COBRA) methods to simulate, analyze and predict a variety of metabolic phenotypes using genome-scale models. The COBRA Toolbox, a MATLAB package for implementing COBRA methods, was presented earlier. Here we present a significant update of this in silico ToolBox. Version 2.0 of the COBRA Toolbox expands the scope of computations by including in silico analysis methods developed since its original release. New functions include: (1) network gap filling, (2) 13C analysis, (3) metabolic engineering, (4) omics-guided analysis, and (5) visualization. As with the first version, the COBRA Toolbox reads and writes Systems Biology Markup Language formatted models. In version 2.0, we improved performance, usability, and the level of documentation. A suite of test scripts can now be used to learn the core functionality of the Toolbox and validate results. This Toolbox lowers the barrier of entry to use powerful COBRA methods. PMID:21886097
An integrated in silico approach for functional and structural impact of non- synonymous SNPs in the MYH1 gene in Jeju Native Pigs.

PubMed

Ghosh, Mrinmoy; Sodhi, Simrinder Singh; Sharma, Neelesh; Mongre, Raj Kumar; Kim, Nameun; Singh, Amit Kumar; Lee, Sung Jin; Kim, Dae Cheol; Kim, Sung Woo; Lee, Hak Kyo; Song, Ki-Duk; Jeong, Dong Kee

2016-02-04

This study was performed to identify the non- synonymous polymorphisms in the myosin heavy chain 1 gene (MYH1) association with skeletal muscle development in economically important Jeju Native Pig (JNP) and Berkshire breeds. Herein, we present an in silico analysis, with a focus on (a) in silico approaches to predict the functional effect of non-synonymous SNP (nsSNP) in MYH1 on growth, and (b) molecular docking and dynamic simulation of MYH1 to predict the effects of those nsSNP on protein-protein association. The NextGENe (V 2.3.4.) tool was used to identify the variants in MYH1 from JNP and Berkshire using RNA seq. Gene ontology analysis of MYH1 revealed significant association with muscle contraction and muscle organ development. The 95 % confidence intervals clearly indicate that the mRNA expression of MYH1 is significantly higher in the Berkshire longissimus dorsi muscle samples than JNP breed. Concordant in silico analysis of MYH1, the open-source software tools identified 4 potential nsSNP (L884T, K972C, N981G, and Q1285C) in JNP and 1 nsSNP (H973G) in Berkshire pigs. Moreover, protein-protein interactions were studied to investigate the effect of MYH1 mutations on association with hub proteins, and MYH1 was found to be closely associated with the protein myosin light chain, phosphorylatable, fast skeletal muscle MYLPF. The results of molecular docking studies on MYH1 (native and 4 mutants) and MYLFP demonstrated that the native complex showed higher electrostatic energy (-466.5 Kcal mol(-1)), van der Walls energy (-87.3 Kcal mol(-1)), and interaction energy (-835.7 Kcal mol(-1)) than the mutant complexes. Furthermore, the molecular dynamic simulation revealed that the native complex yielded a higher root-mean-square deviation (0.2-0.55 nm) and lower root-mean-square fluctuation (approximately 0.08-0.3 nm) as compared to the mutant complexes. The results suggest that the variants at L884T, K972C, N981G, and Q1285C in MYH1 in JNP might represent a cause for the poor growth performance for this breed. This study is a pioneering in-depth in silico analysis of polymorphic MYH1 and will serve as a valuable resource for further targeted molecular diagnosis and population-based studies conducted for improving the growth performance of JNP.
Engineering Proteins for Thermostability with iRDP Web Server

PubMed Central

Ghanate, Avinash; Ramasamy, Sureshkumar; Suresh, C. G.

2015-01-01

Engineering protein molecules with desired structure and biological functions has been an elusive goal. Development of industrially viable proteins with improved properties such as stability, catalytic activity and altered specificity by modifying the structure of an existing protein has widely been targeted through rational protein engineering. Although a range of factors contributing to thermal stability have been identified and widely researched, the in silico implementation of these as strategies directed towards enhancement of protein stability has not yet been explored extensively. A wide range of structural analysis tools is currently available for in silico protein engineering. However these tools concentrate on only a limited number of factors or individual protein structures, resulting in cumbersome and time-consuming analysis. The iRDP web server presented here provides a unified platform comprising of iCAPS, iStability and iMutants modules. Each module addresses different facets of effective rational engineering of proteins aiming towards enhanced stability. While iCAPS aids in selection of target protein based on factors contributing to structural stability, iStability uniquely offers in silico implementation of known thermostabilization strategies in proteins for identification and stability prediction of potential stabilizing mutation sites. iMutants aims to assess mutants based on changes in local interaction network and degree of residue conservation at the mutation sites. Each module was validated using an extensively diverse dataset. The server is freely accessible at http://irdp.ncl.res.in and has no login requirements. PMID:26436543
Engineering Proteins for Thermostability with iRDP Web Server.

PubMed

Panigrahi, Priyabrata; Sule, Manas; Ghanate, Avinash; Ramasamy, Sureshkumar; Suresh, C G

2015-01-01

Engineering protein molecules with desired structure and biological functions has been an elusive goal. Development of industrially viable proteins with improved properties such as stability, catalytic activity and altered specificity by modifying the structure of an existing protein has widely been targeted through rational protein engineering. Although a range of factors contributing to thermal stability have been identified and widely researched, the in silico implementation of these as strategies directed towards enhancement of protein stability has not yet been explored extensively. A wide range of structural analysis tools is currently available for in silico protein engineering. However these tools concentrate on only a limited number of factors or individual protein structures, resulting in cumbersome and time-consuming analysis. The iRDP web server presented here provides a unified platform comprising of iCAPS, iStability and iMutants modules. Each module addresses different facets of effective rational engineering of proteins aiming towards enhanced stability. While iCAPS aids in selection of target protein based on factors contributing to structural stability, iStability uniquely offers in silico implementation of known thermostabilization strategies in proteins for identification and stability prediction of potential stabilizing mutation sites. iMutants aims to assess mutants based on changes in local interaction network and degree of residue conservation at the mutation sites. Each module was validated using an extensively diverse dataset. The server is freely accessible at http://irdp.ncl.res.in and has no login requirements.
In Silico Studies of the Toxcast Chemicals Interacting with Biomolecular targets

EPA Science Inventory

Molecular docking, a structure-based in silico tool for chemical library pre-screening in drug discovery, can be used to explore the potential toxicity of environmental chemicals acting at specific biomelcular targets.
Metabolism of captopril carboxyl ester derivatives for percutaneous absorption.

PubMed

Gullick, Darren R; Ingram, Matthew J; Pugh, W John; Cox, Paul A; Gard, Paul; Smart, John D; Moss, Gary P

2009-02-01

To determine the metabolism of captopril n-carboxyl derivatives and how this may impact on their use as transdermal prodrugs. The pharmacological activity of the ester derivatives was also characterised in order to compare the angiotensin converting enzyme inhibitory potency of the derivatives compared with the parent drug, captopril. The metabolism rates of the ester derivatives were determined in vitro (using porcine liver esterase and porcine ear skin) and in silico (using molecular modelling to investigate the potential to predict metabolism). Relatively slow pseudo first-order metabolism of the prodrugs was observed, with the ethyl ester displaying the highest rate of metabolism. A strong relationship was established between in-vitro methods, while in-silico methods support the use of in-vitro methods and highlight the potential of in-silico techniques to predict metabolism. All the prodrugs behaved as angiotensin converting enzyme inhibitors, with the methyl ester displaying optimum inhibition. In-vitro porcine liver esterase metabolism rates inform in-vitro skin rates well, and in-silico interaction energies relate well to both. Thus, in-silico methods may be developed that include interaction energies to predict metabolism rates.
IN SILICO METHODOLOGIES FOR PREDICTIVE EVALUATION OF TOXICITY BASED ON INTEGRATION OF DATABASES

EPA Science Inventory

In silico methodologies for predictive evaluation of toxicity based on integration of databases

Chihae Yang1 and Ann M. Richard2, 1LeadScope, Inc. 1245 Kinnear Rd. Columbus, OH. 43212 2National Health & Environmental Effects Research Lab, U.S. EPA, Research Triangle Park, ...
Prediction of bioavailability of selected bisphosphonates using in silico methods towards categorization into a biopharmaceutical classification system.

PubMed

Biernacka, Joanna; Betlejewska-Kielak, Katarzyna; Kłosińska-Szmurło, Ewa; Pluciński, Franciszek A; Mazurek, Aleksander P

2013-01-01

The physicochemical properties relevant to biological activity of selected bisphosphonates such as clodronate disodium salt, etidronate disodium salt, pamidronate disodium salt, alendronate sodium salt, ibandronate sodium salt, risedronate sodium salt and zoledronate disodium salt were determined using in silico methods. The main aim of our research was to investigate and propose molecular determinants thataffect bioavailability of above mentioned compounds. These determinants are: stabilization energy (deltaE), free energy of solvation (deltaG(solv)), electrostatic potential, dipole moment, as well as partition and distribution coefficients estimated by the log P and log D values. Presented values indicate that selected bisphosphonates a recharacterized by high solubility and low permeability. The calculated parameters describing both solubility and permeability through biological membranes seem to be a good bioavailability indicators of bisphosphonates examined and can be a useful tool to include into Biopharmaceutical Classification System (BCS) development.
High-throughput micro-scale cultivations and chromatography modeling: Powerful tools for integrated process development.

PubMed

Baumann, Pascal; Hahn, Tobias; Hubbuch, Jürgen

2015-10-01

Upstream processes are rather complex to design and the productivity of cells under suitable cultivation conditions is hard to predict. The method of choice for examining the design space is to execute high-throughput cultivation screenings in micro-scale format. Various predictive in silico models have been developed for many downstream processes, leading to a reduction of time and material costs. This paper presents a combined optimization approach based on high-throughput micro-scale cultivation experiments and chromatography modeling. The overall optimized system must not necessarily be the one with highest product titers, but the one resulting in an overall superior process performance in up- and downstream. The methodology is presented in a case study for the Cherry-tagged enzyme Glutathione-S-Transferase from Escherichia coli SE1. The Cherry-Tag™ (Delphi Genetics, Belgium) which can be fused to any target protein allows for direct product analytics by simple VIS absorption measurements. High-throughput cultivations were carried out in a 48-well format in a BioLector micro-scale cultivation system (m2p-Labs, Germany). The downstream process optimization for a set of randomly picked upstream conditions producing high yields was performed in silico using a chromatography modeling software developed in-house (ChromX). The suggested in silico-optimized operational modes for product capturing were validated subsequently. The overall best system was chosen based on a combination of excellent up- and downstream performance. © 2015 Wiley Periodicals, Inc.
In silico prediction of potential chemical reactions mediated by human enzymes.

PubMed

Yu, Myeong-Sang; Lee, Hyang-Mi; Park, Aaron; Park, Chungoo; Ceong, Hyithaek; Rhee, Ki-Hyeong; Na, Dokyun

2018-06-13

Administered drugs are often converted into an ineffective or activated form by enzymes in our body. Conventional in silico prediction approaches focused on therapeutically important enzymes such as CYP450. However, there are more than thousands of different cellular enzymes that potentially convert administered drug into other forms. We developed an in silico model to predict which of human enzymes including metabolic enzymes as well as CYP450 family can catalyze a given chemical compound. The prediction is based on the chemical and physical similarity between known enzyme substrates and a query chemical compound. Our in silico model was developed using multiple linear regression and the model showed high performance (AUC = 0.896) despite of the large number of enzymes. When evaluated on a test dataset, it also showed significantly high performance (AUC = 0.746). Interestingly, evaluation with literature data showed that our model can be used to predict not only enzymatic reactions but also drug conversion and enzyme inhibition. Our model was able to predict enzymatic reactions of a query molecule with a high accuracy. This may foster to discover new metabolic routes and to accelerate the computational development of drug candidates by enabling the prediction of the potential conversion of administered drugs into active or inactive forms.
Evaluation of a genome-scale in silico metabolic model for Geobacter metallireducens by using proteomic data from a field biostimulation experiment.

PubMed

Fang, Yilin; Wilkins, Michael J; Yabusaki, Steven B; Lipton, Mary S; Long, Philip E

2012-12-01

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.
hfAIM: A reliable bioinformatics approach for in silico genome-wide identification of autophagy-associated Atg8-interacting motifs in various organisms

PubMed Central

Xie, Qingjun; Tzfadia, Oren; Levy, Matan; Weithorn, Efrat; Peled-Zehavi, Hadas; Van Parys, Thomas; Van de Peer, Yves; Galili, Gad

2016-01-01

ABSTRACT Most of the proteins that are specifically turned over by selective autophagy are recognized by the presence of short Atg8 interacting motifs (AIMs) that facilitate their association with the autophagy apparatus. Such AIMs can be identified by bioinformatics methods based on their defined degenerate consensus F/W/Y-X-X-L/I/V sequences in which X represents any amino acid. Achieving reliability and/or fidelity of the prediction of such AIMs on a genome-wide scale represents a major challenge. Here, we present a bioinformatics approach, high fidelity AIM (hfAIM), which uses additional sequence requirements—the presence of acidic amino acids and the absence of positively charged amino acids in certain positions—to reliably identify AIMs in proteins. We demonstrate that the use of the hfAIM method allows for in silico high fidelity prediction of AIMs in AIM-containing proteins (ACPs) on a genome-wide scale in various organisms. Furthermore, by using hfAIM to identify putative AIMs in the Arabidopsis proteome, we illustrate a potential contribution of selective autophagy to various biological processes. More specifically, we identified 9 peroxisomal PEX proteins that contain hfAIM motifs, among which AtPEX1, AtPEX6 and AtPEX10 possess evolutionary-conserved AIMs. Bimolecular fluorescence complementation (BiFC) results verified that AtPEX6 and AtPEX10 indeed interact with Atg8 in planta. In addition, we show that mutations occurring within or nearby hfAIMs in PEX1, PEX6 and PEX10 caused defects in the growth and development of various organisms. Taken together, the above results suggest that the hfAIM tool can be used to effectively perform genome-wide in silico screens of proteins that are potentially regulated by selective autophagy. The hfAIM system is a web tool that can be accessed at link: http://bioinformatics.psb.ugent.be/hfAIM/. PMID:27071037
Predictive computation of genomic logic processing functions in embryonic development

PubMed Central

Peter, Isabelle S.; Faure, Emmanuel; Davidson, Eric H.

2012-01-01

Gene regulatory networks (GRNs) control the dynamic spatial patterns of regulatory gene expression in development. Thus, in principle, GRN models may provide system-level, causal explanations of developmental process. To test this assertion, we have transformed a relatively well-established GRN model into a predictive, dynamic Boolean computational model. This Boolean model computes spatial and temporal gene expression according to the regulatory logic and gene interactions specified in a GRN model for embryonic development in the sea urchin. Additional information input into the model included the progressive embryonic geometry and gene expression kinetics. The resulting model predicted gene expression patterns for a large number of individual regulatory genes each hour up to gastrulation (30 h) in four different spatial domains of the embryo. Direct comparison with experimental observations showed that the model predictively computed these patterns with remarkable spatial and temporal accuracy. In addition, we used this model to carry out in silico perturbations of regulatory functions and of embryonic spatial organization. The model computationally reproduced the altered developmental functions observed experimentally. Two major conclusions are that the starting GRN model contains sufficiently complete regulatory information to permit explanation of a complex developmental process of gene expression solely in terms of genomic regulatory code, and that the Boolean model provides a tool with which to test in silico regulatory circuitry and developmental perturbations. PMID:22927416
Complementing in vitro screening assays with in silico ...

EPA Pesticide Factsheets

High-throughput in vitro assays offer a rapid, cost-efficient means to screen thousands of chemicals across hundreds of pathway-based toxicity endpoints. However, one main concern involved with the use of in vitro assays is the erroneous omission of chemicals that are inactive under assay conditions but that can generate active metabolites under in vivo conditions. To address this potential issue, a case study will be presented to demonstrate the use of in silico tools to identify inactive parents with the ability to generate active metabolites. This case study used the results from an orthogonal assay designed to improve confidence in the identification of active chemicals tested across eighteen estrogen receptor (ER)-related in vitro assays by accounting for technological limitations inherent within each individual assay. From the 1,812 chemicals tested within the orthogonal assay, 1,398 were considered inactive. These inactive chemicals were analyzed using Chemaxon Metabolizer software to predict the first and second generation metabolites. From the nearly 1,400 inactive chemicals, over 2,200 first-generation (i.e., primary) metabolites and over 5,500 second-generation (i.e., secondary) metabolites were predicted. Nearly 70% of primary metabolites were immediately detoxified or converted to other metabolites, while over 70% of secondary metabolites remained stable. Among these predicted metabolites, those that are most likely to be produced and remain
Inhibition of cytochrome P450 3A by acetoxylated analogues of resveratrol in in vitro and in silico models

NASA Astrophysics Data System (ADS)

Basheer, Loai; Schultz, Keren; Kerem, Zohar

2016-08-01

Many dietary compounds, including resveratrol, are potent inhibitors of CYP3A4. Here we examined the potential to predict inhibition capacity of dietary polyphenolics using an in silico and in vitro approaches and synthetic model compounds. Mono, di, and tri-acetoxy resveratrol were synthesized, a cell line of human intestine origin and microsomes from rat liver served to determine their in vitro inhibition of CYP3A4, and compared to that of resveratrol. Docking simulation served to predict the affinity of the synthetic model compounds to the enzyme. Modelling of the enzyme’s binding site revealed three types of interaction: hydrophobic, electrostatic and H-bonding. The simulation revealed that each of the examined acetylations of resveratrol led to the loss of important interactions of all types. Tri-acetoxy resveratrol was the weakest inhibitor in vitro despite being the more lipophilic and having the highest affinity for the binding site. The simulation demonstrated exclusion of all interactions between tri-acetoxy resveratrol and the heme due to distal binding, highlighting the complexity of the CYP3A4 binding site, which may allow simultaneous accommodation of two molecules. Finally, the use of computational modelling may serve as a quick predictive tool to identify potential harmful interactions between dietary compounds and prescribed drugs.

Predicting dermal penetration for ToxCast chemicals using in silico estimates for diffusion in combination with physiologically based pharmacokinetic (PBPK) modeling.

EPA Science Inventory

Predicting dermal penetration for ToxCast chemicals using in silico estimates for diffusion in combination with physiologically based pharmacokinetic (PBPK) modeling.Evans, M.V., Sawyer, M.E., Isaacs, K.K, and Wambaugh, J.With the development of efficient high-throughput (HT) in ...
IN SILICO APPROACHES TO MECHANISTIC AND PREDICTIVE TOXICOLOGY: AN INTRODUCTION TO BIOINFORMATICS FOR TOXICOLOGISTS. (R827402)

EPA Science Inventory

Abstract
Bioinformatics, or in silico biology, is a rapidly growing field that encompasses the theory and application of computational approaches to model, predict, and explain biological function at the molecular level. This information rich field requires new ...
Response to DNA damage of CHEK2 missense mutations in familial breast cancer

PubMed Central

Roeb, Wendy; Higgins, Jake; King, Mary-Claire

2012-01-01

Comprehensive sequencing of tumor suppressor genes to evaluate inherited predisposition to cancer yields many individually rare missense alleles of unknown functional and clinical consequence. To address this problem for CHEK2 missense alleles, we developed a yeast-based assay to assess in vivo CHEK2-mediated response to DNA damage. Of 25 germline CHEK2 missense alleles detected in familial breast cancer patients, 12 alleles had complete loss of DNA damage response, 8 had partial loss and 5 exhibited a DNA damage response equivalent to that mediated by wild-type CHEK2. Variants exhibiting reduced response to DNA damage were found in all domains of the CHEK2 protein. Assay results were in agreement with epidemiologic assessments of breast cancer risk for those variants sufficiently common for case–control studies to have been undertaken. Assay results were largely concordant with consensus predictions of in silico tools, particularly for damaging alleles in the kinase domain. However, of the 25 variants, 6 were not consistently classifiable by in silico tools. An in vivo assay of cellular response to DNA damage by mutant CHEK2 alleles may complement and extend epidemiologic and genetic assessment of their clinical consequences. PMID:22419737
Response to DNA damage of CHEK2 missense mutations in familial breast cancer.

PubMed

Roeb, Wendy; Higgins, Jake; King, Mary-Claire

2012-06-15

Comprehensive sequencing of tumor suppressor genes to evaluate inherited predisposition to cancer yields many individually rare missense alleles of unknown functional and clinical consequence. To address this problem for CHEK2 missense alleles, we developed a yeast-based assay to assess in vivo CHEK2-mediated response to DNA damage. Of 25 germline CHEK2 missense alleles detected in familial breast cancer patients, 12 alleles had complete loss of DNA damage response, 8 had partial loss and 5 exhibited a DNA damage response equivalent to that mediated by wild-type CHEK2. Variants exhibiting reduced response to DNA damage were found in all domains of the CHEK2 protein. Assay results were in agreement with epidemiologic assessments of breast cancer risk for those variants sufficiently common for case-control studies to have been undertaken. Assay results were largely concordant with consensus predictions of in silico tools, particularly for damaging alleles in the kinase domain. However, of the 25 variants, 6 were not consistently classifiable by in silico tools. An in vivo assay of cellular response to DNA damage by mutant CHEK2 alleles may complement and extend epidemiologic and genetic assessment of their clinical consequences.
Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity.

PubMed

Passini, Elisa; Britton, Oliver J; Lu, Hua Rong; Rohrbacher, Jutta; Hermans, An N; Gallacher, David J; Greig, Robert J H; Bueno-Orovio, Alfonso; Rodriguez, Blanca

2017-01-01

Early prediction of cardiotoxicity is critical for drug development. Current animal models raise ethical and translational questions, and have limited accuracy in clinical risk prediction. Human-based computer models constitute a fast, cheap and potentially effective alternative to experimental assays, also facilitating translation to human. Key challenges include consideration of inter-cellular variability in drug responses and integration of computational and experimental methods in safety pharmacology. Our aim is to evaluate the ability of in silico drug trials in populations of human action potential (AP) models to predict clinical risk of drug-induced arrhythmias based on ion channel information, and to compare simulation results against experimental assays commonly used for drug testing. A control population of 1,213 human ventricular AP models in agreement with experimental recordings was constructed. In silico drug trials were performed for 62 reference compounds at multiple concentrations, using pore-block drug models (IC 50 /Hill coefficient). Drug-induced changes in AP biomarkers were quantified, together with occurrence of repolarization/depolarization abnormalities. Simulation results were used to predict clinical risk based on reports of Torsade de Pointes arrhythmias, and further evaluated in a subset of compounds through comparison with electrocardiograms from rabbit wedge preparations and Ca 2+ -transient recordings in human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs). Drug-induced changes in silico vary in magnitude depending on the specific ionic profile of each model in the population, thus allowing to identify cell sub-populations at higher risk of developing abnormal AP phenotypes. Models with low repolarization reserve (increased Ca 2+ /late Na + currents and Na + /Ca 2+ -exchanger, reduced Na + /K + -pump) are highly vulnerable to drug-induced repolarization abnormalities, while those with reduced inward current density (fast/late Na + and Ca 2+ currents) exhibit high susceptibility to depolarization abnormalities. Repolarization abnormalities in silico predict clinical risk for all compounds with 89% accuracy. Drug-induced changes in biomarkers are in overall agreement across different assays: in silico AP duration changes reflect the ones observed in rabbit QT interval and hiPS-CMs Ca 2+ -transient, and simulated upstroke velocity captures variations in rabbit QRS complex. Our results demonstrate that human in silico drug trials constitute a powerful methodology for prediction of clinical pro-arrhythmic cardiotoxicity, ready for integration in the existing drug safety assessment pipelines.
Virtual Interactomics of Proteins from Biochemical Standpoint

PubMed Central

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel

2012-01-01

Virtual interactomics represents a rapidly developing scientific area on the boundary line of bioinformatics and interactomics. Protein-related virtual interactomics then comprises instrumental tools for prediction, simulation, and networking of the majority of interactions important for structural and individual reproduction, differentiation, recognition, signaling, regulation, and metabolic pathways of cells and organisms. Here, we describe the main areas of virtual protein interactomics, that is, structurally based comparative analysis and prediction of functionally important interacting sites, mimotope-assisted and combined epitope prediction, molecular (protein) docking studies, and investigation of protein interaction networks. Detailed information about some interesting methodological approaches and online accessible programs or databases is displayed in our tables. Considerable part of the text deals with the searches for common conserved or functionally convergent protein regions and subgraphs of conserved interaction networks, new outstanding trends and clinically interesting results. In agreement with the presented data and relationships, virtual interactomic tools improve our scientific knowledge, help us to formulate working hypotheses, and they frequently also mediate variously important in silico simulations. PMID:22928109
Human induced pluripotent stem cell‐derived versus adult cardiomyocytes: an in silico electrophysiological study on effects of ionic current block

PubMed Central

Paci, M; Hyttinen, J; Rodriguez, B

2015-01-01

Background and Purpose Two new technologies are likely to revolutionize cardiac safety and drug development: in vitro experiments on human‐induced pluripotent stem cell‐derived cardiomyocytes (hiPSC‐CMs) and in silico human adult ventricular cardiomyocyte (hAdultV‐CM) models. Their combination was recently proposed as a potential replacement for the present hERG‐based QT study for pharmacological safety assessments. Here, we systematically compared in silico the effects of selective ionic current block on hiPSC‐CM and hAdultV‐CM action potentials (APs), to identify similarities/differences and to illustrate the potential of computational models as supportive tools for evaluating new in vitro technologies. Experimental Approach In silico AP models of ventricular‐like and atrial‐like hiPSC‐CMs and hAdultV‐CM were used to simulate the main effects of four degrees of block of the main cardiac transmembrane currents. Key Results Qualitatively, hiPSC‐CM and hAdultV‐CM APs showed similar responses to current block, consistent with results from experiments. However, quantitatively, hiPSC‐CMs were more sensitive to block of (i) L‐type Ca2+ currents due to the overexpression of the Na+/Ca2+ exchanger (leading to shorter APs) and (ii) the inward rectifier K+ current due to reduced repolarization reserve (inducing diastolic potential depolarization and repolarization failure). Conclusions and Implications In silico hiPSC‐CMs and hAdultV‐CMs exhibit a similar response to selective current blocks. However, overall hiPSC‐CMs show greater sensitivity to block, which may facilitate in vitro identification of drug‐induced effects. Extrapolation of drug effects from hiPSC‐CM to hAdultV‐CM and pro‐arrhythmic risk assessment can be facilitated by in silico predictions using biophysically‐based computational models. PMID:26276951
Using In Silico Fragmentation to Improve Routine Residue Screening in Complex Matrices.

PubMed

Kaufmann, Anton; Butcher, Patrick; Maden, Kathryn; Walker, Stephan; Widmer, Mirjam

2017-12-01

Targeted residue screening requires the use of reference substances in order to identify potential residues. This becomes a difficult issue when using multi-residue methods capable of analyzing several hundreds of analytes. Therefore, the capability of in silico fragmentation based on a structure database ("suspect screening") instead of physical reference substances for routine targeted residue screening was investigated. The detection of fragment ions that can be predicted or explained by in silico software was utilized to reduce the number of false positives. These "proof of principle" experiments were done with a tool that is integrated into a commercial MS vendor instrument operating software (UNIFI) as well as with a platform-independent MS tool (Mass Frontier). A total of 97 analytes belonging to different chemical families were separated by reversed phase liquid chromatography and detected in a data-independent acquisition (DIA) mode using ion mobility hyphenated with quadrupole time of flight mass spectrometry. The instrument was operated in the MS E mode with alternating low and high energy traces. The fragments observed from product ion spectra were investigated using a "chopping" bond disconnection algorithm and a rule-based algorithm. The bond disconnection algorithm clearly explained more analyte product ions and a greater percentage of the spectral abundance than the rule-based software (92 out of the 97 compounds produced ≥1 explainable fragment ions). On the other hand, tests with a complex blank matrix (bovine liver extract) indicated that the chopping algorithm reports significantly more false positive fragments than the rule based software. Graphical Abstract.
Using In Silico Fragmentation to Improve Routine Residue Screening in Complex Matrices

NASA Astrophysics Data System (ADS)

Kaufmann, Anton; Butcher, Patrick; Maden, Kathryn; Walker, Stephan; Widmer, Mirjam

2017-12-01

Targeted residue screening requires the use of reference substances in order to identify potential residues. This becomes a difficult issue when using multi-residue methods capable of analyzing several hundreds of analytes. Therefore, the capability of in silico fragmentation based on a structure database ("suspect screening") instead of physical reference substances for routine targeted residue screening was investigated. The detection of fragment ions that can be predicted or explained by in silico software was utilized to reduce the number of false positives. These "proof of principle" experiments were done with a tool that is integrated into a commercial MS vendor instrument operating software (UNIFI) as well as with a platform-independent MS tool (Mass Frontier). A total of 97 analytes belonging to different chemical families were separated by reversed phase liquid chromatography and detected in a data-independent acquisition (DIA) mode using ion mobility hyphenated with quadrupole time of flight mass spectrometry. The instrument was operated in the MSE mode with alternating low and high energy traces. The fragments observed from product ion spectra were investigated using a "chopping" bond disconnection algorithm and a rule-based algorithm. The bond disconnection algorithm clearly explained more analyte product ions and a greater percentage of the spectral abundance than the rule-based software (92 out of the 97 compounds produced ≥1 explainable fragment ions). On the other hand, tests with a complex blank matrix (bovine liver extract) indicated that the chopping algorithm reports significantly more false positive fragments than the rule based software. [Figure not available: see fulltext.
[Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis].

PubMed

Qiao, Lian-Sheng; Jiang, Lu-di; Luo, Gang-Gang; Lu, Fang; Chen, Yan-Kun; Wang, Ling-Zhi; Li, Gong-Yu; Zhang, Yan-Ling

2017-02-01

Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides. Copyright© by the Chinese Pharmaceutical Association.
Improved, ACMG-Compliant, in silico prediction of pathogenicity for missense substitutions encoded by TP53 variants.

PubMed

Fortuno, Cristina; James, Paul A; Young, Erin L; Feng, Bing; Olivier, Magali; Pesaran, Tina; Tavtigian, Sean V; Spurdle, Amanda B

2018-05-18

Clinical interpretation of germline missense variants represents a major challenge, including those in the TP53 Li-Fraumeni syndrome gene. Bioinformatic prediction is a key part of variant classification strategies. We aimed to optimize the performance of the Align-GVGD tool used for p53 missense variant prediction, and compare its performance to other bioinformatic tools (SIFT, PolyPhen-2) and ensemble methods (REVEL, BayesDel). Reference sets of assumed pathogenic and assumed benign variants were defined using functional and/or clinical data. Area under the curve and Matthews correlation coefficient (MCC) values were used as objective functions to select an optimized protein multi-sequence alignment with best performance for Align-GVGD. MCC comparison of tools using binary categories showed optimized Align-GVGD (C15 cut-off) combined with BayesDel (0.16 cut-off), or with REVEL (0.5 cut-off), to have the best overall performance. Further, a semi-quantitative approach using multiple tiers of bioinformatic prediction, validated using an independent set of non-functional and functional variants, supported use of Align-GVGD and BayesDel prediction for different strength of evidence levels in ACMG/AMP rules. We provide rationale for bioinformatic tool selection for TP53 variant classification, and have also computed relevant bioinformatic predictions for every possible p53 missense variant to facilitate their use by the scientific and medical community. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.
Design, synthesis, pharmacological evaluation and in silico ADMET prediction of novel substituted benzimidazole derivatives as angiotensin II-AT1 receptor antagonists based on predictive 3D QSAR models.

PubMed

Vyas, V K; Gupta, N; Ghate, M; Patel, S

2014-01-01

In this study we designed novel substituted benzimidazole derivatives and predicted their absorption, distribution, metabolism, excretion and toxicity (ADMET) properties, based on a predictive 3D QSAR study on 132 substituted benzimidazoles as AngII-AT1 receptor antagonists. The two best predicted compounds were synthesized and evaluated for AngII-AT1 receptor antagonism. Three different alignment tools for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used. The best 3D QSAR models were obtained using the rigid body (Distill) alignment method. CoMFA and CoMSIA models were found to be statistically significant with leave-one-out correlation coefficients (q(2)) of 0.630 and 0.623, respectively, cross-validated coefficients (r(2)cv) of 0.651 and 0.630, respectively, and conventional coefficients of determination (r(2)) of 0.848 and 0.843, respectively. 3D QSAR models were validated using a test set of 24 compounds, giving satisfactory predicted results (r(2)pred) of 0.727 and 0.689 for the CoMFA and CoMSIA models, respectively. We have identified some key features in substituted benzimidazole derivatives, such as lipophilicity and H-bonding at the 2- and 5-positions of the benzimidazole nucleus, respectively, for AT1 receptor antagonistic activity. We designed 20 novel substituted benzimidazole derivatives and predicted their activity. In silico ADMET properties were also predicted for these designed molecules. Finally, the compounds with best predicted activity were synthesized and evaluated for in vitro angiotensin II-AT1 receptor antagonism.
Three dimensional electron microscopy and in silico tools for macromolecular structure determination

PubMed Central

Borkotoky, Subhomoi; Meena, Chetan Kumar; Khan, Mohammad Wahab; Murali, Ayaluru

2013-01-01

Recently, structural biology witnessed a major tool - electron microscopy - in solving the structures of macromolecules in addition to the conventional techniques, X-ray crystallography and nuclear magnetic resonance (NMR). Three dimensional transmission electron microscopy (3DTEM) is one of the most sophisticated techniques for structure determination of molecular machines. Known to give the 3-dimensional structures in its native form with literally no upper limit on size of the macromolecule, this tool does not need the crystallization of the protein. Combining the 3DTEM data with in silico tools, one can have better refined structure of a desired complex. In this review we are discussing about the recent advancements in three dimensional electron microscopy and tools associated with it. PMID:27092033
Understanding the toxicological potential of aerosol organic compounds using informatics based screening

NASA Astrophysics Data System (ADS)

Topping, David; Decesari, Stefano; Bassan, Arianna; Pavan, Manuela; Ciacci, Andrea

2016-04-01

Exposure to atmospheric particulate matter is responsible for both short-term and long-term adverse health effects. So far, all efforts spent in achieving a systematic epidemiological evidence of specific aerosol compounds determining the overall aerosol toxicity were unsuccessful. The results of the epidemiological studies apparently conflict with the laboratory toxicological analyses which have highlighted very different chemical and toxicological potentials for speciated aerosol compounds. Speciation remains a problem, especially for organic compounds: it is impossible to conduct screening on all possible molecular species. At the same time, research on toxic compounds risks to be biased towards the already known compounds, such as PAHs and dioxins. In this study we present results from an initial assessment of the use of in silico methods (i.e. (Q)SAR, read-across) to predict toxicity of atmospheric organic compounds including evaluation of applicability of a variety of popular tools (e.g. OECD QSAR Toolbox) for selected endpoints (e.g. genotoxicity). Compounds are categorised based on the need of new experimental data for the development of in silico approaches for toxicity prediction covering this specific chemical space, namely the atmospheric aerosols. Whilst only an initial investigation, we present recommendations for continuation of this work.
p.Arg82Leu von Hippel-Lindau (VHL) Gene Mutation among Three Members of a Family with Familial Bilateral Pheochromocytoma in India: Molecular Analysis and In Silico Characterization

PubMed Central

John, Anulekha Mary; C, George Priya Doss; Ebenazer, Andrew; Seshadri, Mandalam Subramaniam; Nair, Aravindan; Rajaratnam, Simon; Pai, Rekha

2013-01-01

Various missense mutations in the VHL gene have been reported among patients with familial bilateral pheochromocytoma. However, the p.Arg82Leu mutation in the VHL gene described here among patients with familial bilateral pheochromocytoma, has never been reported previously in a germline configuration. Interestingly, long-term follow-up of these patients indicated that the mutation might have had little impact on the normal function of the VHL gene, since all of them have remained asymptomatic. We further attempted to correlate this information with the results obtained by in silico analysis of this mutation using SIFT, PhD-SNP SVM profile, MutPred, PolyPhen2, and SNPs&GO prediction tools. To gain, new mechanistic insight into the structural effect, we mapped the mutation on to 3D structure (PDB ID 1LM8). Further, we analyzed the structural level changes in time scale level with respect to native and mutant protein complexes by using 12 ns molecular dynamics simulation method. Though these methods predict the mutation to have a pathogenic potential, it remains to be seen if these patients will eventually develop symptomatic disease. PMID:23626751
Accessing biological actions of Ganoderma secondary metabolites by in silico profiling

PubMed Central

Grienke, Ulrike; Kaserer, Teresa; Pfluger, Florian; Mair, Christina E.; Langer, Thierry; Schuster, Daniela; Rollinger, Judith M.

2016-01-01

The species complex around the medicinal fungus Ganoderma lucidum Karst. (Ganodermataceae) is widely known in traditional medicines as well as in modern applications such as functional food or nutraceuticals. A considerable number of publications reflects its abundance and variety in biological actions either provoked by primary metabolites such as polysaccharides or secondary metabolites such as lanostane-type triterpenes. However, due to this remarkable amount of information, a rationalization of the individual Ganoderma constituents to biological actions on a molecular level is quite challenging. To overcome this issue, a database was generated containing meta-information, i.e. chemical structures and biological actions of hitherto identified Ganoderma constituents (279). This was followed by a computational approach subjecting this 3D multi-conformational molecular dataset to in silico parallel screening against an in-house collection of validated structure- and ligand-based 3D pharmacophore models. The predictive power of the evaluated in silico tools and hints from traditional application fields served as criteria for the model selection. Thus, we focused on representative druggable targets in the field of viral infections (5) and diseases related to the metabolic syndrome (22). The results obtained from this in silico approach were compared to bioactivity data available from the literature to distinguish between true and false positives or negatives. 89 and 197 Ganoderma compounds were predicted as ligands of at least one of the selected pharmacological targets in the antiviral and the metabolic syndrome screening, respectively. Among them only a minority of individual compounds (around 10%) has ever been investigated on these targets or for the associated biological activity. Accordingly, this study discloses putative ligand target interactions for a plethora of Ganoderma constituents in the empirically manifested field of viral diseases and metabolic syndrome which serve as a basis for future applications to access yet undiscovered biological actions of Ganoderma secondary metabolites on a molecular level. PMID:25457486
Oral biopharmaceutics tools - time for a new initiative - an introduction to the IMI project OrBiTo.

PubMed

Lennernäs, H; Aarons, L; Augustijns, P; Beato, S; Bolger, M; Box, K; Brewster, M; Butler, J; Dressman, J; Holm, R; Julia Frank, K; Kendall, R; Langguth, P; Sydor, J; Lindahl, A; McAllister, M; Muenster, U; Müllertz, A; Ojala, K; Pepin, X; Reppas, C; Rostami-Hodjegan, A; Verwei, M; Weitschies, W; Wilson, C; Karlsson, C; Abrahamsson, B

2014-06-16

OrBiTo is a new European project within the IMI programme in the area of oral biopharmaceutics tools that includes world leading scientists from nine European universities, one regulatory agency, one non-profit research organization, four SMEs together with scientists from twelve pharmaceutical companies. The OrBiTo project will address key gaps in our knowledge of gastrointestinal (GI) drug absorption and deliver a framework for rational application of predictive biopharmaceutics tools for oral drug delivery. This will be achieved through novel prospective investigations to define new methodologies as well as refinement of existing tools. Extensive validation of novel and existing biopharmaceutics tools will be performed using active pharmaceutical ingredient (API), formulations and supporting datasets from industry partners. A combination of high quality in vitro or in silico characterizations of API and formulations will be integrated into physiologically based in silico biopharmaceutics models capturing the full complexity of GI drug absorption. This approach gives an unparalleled opportunity to initiate a transformational change in industrial research and development to achieve model-based pharmaceutical product development in accordance with the Quality by Design concept. Benefits include an accelerated and more efficient drug candidate selection, formulation development process, particularly for challenging projects such as low solubility molecules (BCS II and IV), enhanced and modified-release formulations, as well as allowing optimization of clinical product performance for patient benefit. In addition, the tools emerging from OrBiTo are expected to significantly reduce demand for animal experiments in the future as well as reducing the number of human bioequivalence studies required to bridge formulations after manufacturing or composition changes. Copyright © 2013 Elsevier B.V. All rights reserved.
Predictive and Experimental Approaches for Elucidating Protein–Protein Interactions and Quaternary Structures

PubMed Central

Nealon, John Oliver; Philomina, Limcy Seby

2017-01-01

The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development. PMID:29206185
Metabolic Network Modeling for Computer-Aided Design of Microbial Interactions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Song, Hyun-Seob; Nelson, William C.; Lee, Joon-Yong

Interest in applying microbial communities to biotechnology continues to increase. Successful engineering of microbial communities requires a fundamental shift in focus from enhancing metabolic capabilities in individual organisms to promoting synergistic interspecies interactions. This goal necessitates in silico tools that provide a predictive understanding of how microorganisms interact with each other and their environments. In this regard, we highlight a need for a new concept that we have termed biological computer-aided design of interactions (BioCADi). We ground this discussion within the context of metabolic network modeling.
In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

PubMed

Christiansen, Eric M; Yang, Samuel J; Ando, D Michael; Javaherian, Ashkan; Skibinski, Gaia; Lipnick, Scott; Mount, Elliot; O'Neil, Alison; Shah, Kevan; Lee, Alicia K; Goyal, Piyush; Fedus, William; Poplin, Ryan; Esteva, Andre; Berndl, Marc; Rubin, Lee L; Nelson, Philip; Finkbeiner, Steven

2018-04-19

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire. Copyright © 2018 Elsevier Inc. All rights reserved.

In silico prediction of cytochrome P450-mediated drug metabolism.

PubMed

Zhang, Tao; Chen, Qi; Li, Li; Liu, Limin Angela; Wei, Dong-Qing

2011-06-01

The application of combinatorial chemistry and high-throughput screening technique enables the large number of chemicals to be generated and tested simultaneously, which will facilitate the drug development and discovery. At the same time, it brings about a challenge of how to efficiently identify the potential drug candidates from thousands of compounds. A way used to deal with the challenge is to consider the drug pharmacokinetic properties, such as absorption, distribution, metabolism and excretion (ADME), in the early stage of drug development. Among ADME properties, metabolism is of importance due to the strong association with efficacy and safety of drug. The review will focus on in silico approaches for prediction of Cytochrome P450-mediated drug metabolism. We will describe these predictive methods from two aspects, structure-based and data-based. Moreover, the applications and limitations of various methods will be discussed. Finally, we provide further direction toward improving the predictive accuracy of these in silico methods.
Next-generation genome-scale models for metabolic engineering.

PubMed

King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

2015-12-01

Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering. Copyright © 2014 Elsevier Ltd. All rights reserved.
Mutagenicity in a Molecule: Identification of Core Structural Features of Mutagenicity Using a Scaffold Analysis

PubMed Central

Hsu, Kuo-Hsiang; Su, Bo-Han; Tu, Yi-Shu; Lin, Olivia A.; Tseng, Yufeng J.

2016-01-01

With advances in the development and application of Ames mutagenicity in silico prediction tools, the International Conference on Harmonisation (ICH) has amended its M7 guideline to reflect the use of such prediction models for the detection of mutagenic activity in early drug safety evaluation processes. Since current Ames mutagenicity prediction tools only focus on functional group alerts or side chain modifications of an analog series, these tools are unable to identify mutagenicity derived from core structures or specific scaffolds of a compound. In this study, a large collection of 6512 compounds are used to perform scaffold tree analysis. By relating different scaffolds on constructed scaffold trees with Ames mutagenicity, four major and one minor novel mutagenic groups of scaffold are identified. The recognized mutagenic groups of scaffold can serve as a guide for medicinal chemists to prevent the development of potentially mutagenic therapeutic agents in early drug design or development phases, by modifying the core structures of mutagenic compounds to form non-mutagenic compounds. In addition, five series of substructures are provided as recommendations, for direct modification of potentially mutagenic scaffolds to decrease associated mutagenic activities. PMID:26863515
Meeting report: applied biopharmaceutics and quality by design for dissolution/release specification setting: product quality for patient benefit.

PubMed

Selen, Arzu; Cruañes, Maria T; Müllertz, Anette; Dickinson, Paul A; Cook, Jack A; Polli, James E; Kesisoglou, Filippos; Crison, John; Johnson, Kevin C; Muirhead, Gordon T; Schofield, Timothy; Tsong, Yi

2010-09-01

A biopharmaceutics and Quality by Design (QbD) conference was held on June 10-12, 2009 in Rockville, Maryland, USA to provide a forum and identify approaches for enhancing product quality for patient benefit. Presentations concerned the current biopharmaceutical toolbox (i.e., in vitro, in silico, pre-clinical, in vivo, and statistical approaches), as well as case studies, and reflections on new paradigms. Plenary and breakout session discussions evaluated the current state and envisioned a future state that more effectively integrates QbD and biopharmaceutics. Breakout groups discussed the following four topics: Integrating Biopharmaceutical Assessment into the QbD Paradigm, Predictive Statistical Tools, Predictive Mechanistic Tools, and Predictive Analytical Tools. Nine priority areas, further described in this report, were identified for advancing integration of biopharmaceutics and support a more fundamentally based, integrated approach to setting product dissolution/release acceptance criteria. Collaboration among a broad range of disciplines and fostering a knowledge sharing environment that places the patient's needs as the focus of drug development, consistent with science- and risk-based spirit of QbD, were identified as key components of the path forward.
The Proteasix Ontology.

PubMed

Arguello Casteleiro, Mercedes; Klein, Julie; Stevens, Robert

2016-06-04

The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides) The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis. The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results. The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone.
BioJazz: in silico evolution of cellular networks with unbounded complexity using rule-based modeling.

PubMed

Feng, Song; Ollivier, Julien F; Swain, Peter S; Soyer, Orkun S

2015-10-30

Systems biologists aim to decipher the structure and dynamics of signaling and regulatory networks underpinning cellular responses; synthetic biologists can use this insight to alter existing networks or engineer de novo ones. Both tasks will benefit from an understanding of which structural and dynamic features of networks can emerge from evolutionary processes, through which intermediary steps these arise, and whether they embody general design principles. As natural evolution at the level of network dynamics is difficult to study, in silico evolution of network models can provide important insights. However, current tools used for in silico evolution of network dynamics are limited to ad hoc computer simulations and models. Here we introduce BioJazz, an extendable, user-friendly tool for simulating the evolution of dynamic biochemical networks. Unlike previous tools for in silico evolution, BioJazz allows for the evolution of cellular networks with unbounded complexity by combining rule-based modeling with an encoding of networks that is akin to a genome. We show that BioJazz can be used to implement biologically realistic selective pressures and allows exploration of the space of network architectures and dynamics that implement prescribed physiological functions. BioJazz is provided as an open-source tool to facilitate its further development and use. Source code and user manuals are available at: http://oss-lab.github.io/biojazz and http://osslab.lifesci.warwick.ac.uk/BioJazz.aspx. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Best of both worlds: combining pharma data and state of the art modeling technology to improve in Silico pKa prediction.

PubMed

Fraczkiewicz, Robert; Lobell, Mario; Göller, Andreas H; Krenz, Ursula; Schoenneis, Rolf; Clark, Robert D; Hillisch, Alexander

2015-02-23

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.
Large Dataset of Acute Oral Toxicity Data Created for Testing ...

EPA Pesticide Factsheets

Acute toxicity data is a common requirement for substance registration in the US. Currently only data derived from animal tests are accepted by regulatory agencies, and the standard in vivo tests use lethality as the endpoint. Non-animal alternatives such as in silico models are being developed due to animal welfare and resource considerations. We compiled a large dataset of oral rat LD50 values to assess the predictive performance currently available in silico models. Our dataset combines LD50 values from five different sources: literature data provided by The Dow Chemical Company, REACH data from eChemportal, HSDB (Hazardous Substances Data Bank), RTECS data from Leadscope, and the training set underpinning TEST (Toxicity Estimation Software Tool). Combined these data sources yield 33848 chemical-LD50 pairs (data points), with 23475 unique data points covering 16439 compounds. The entire dataset was loaded into a chemical properties database. All of the compounds were registered in DSSTox and 59.5% have publically available structures. Compounds without a structure in DSSTox are currently having their structures registered. The structural data will be used to evaluate the predictive performance and applicable chemical domains of three QSAR models (TIMES, PROTOX, and TEST). Future work will combine the dataset with information from ToxCast assays, and using random forest modeling, assess whether ToxCast assays are useful in predicting acute oral toxicity. Pre
Inhibition of cytochrome P450 3A by acetoxylated analogues of resveratrol in in vitro and in silico models

PubMed Central

Basheer, Loai; Schultz, Keren; Kerem, Zohar

2016-01-01

Many dietary compounds, including resveratrol, are potent inhibitors of CYP3A4. Here we examined the potential to predict inhibition capacity of dietary polyphenolics using an in silico and in vitro approaches and synthetic model compounds. Mono, di, and tri-acetoxy resveratrol were synthesized, a cell line of human intestine origin and microsomes from rat liver served to determine their in vitro inhibition of CYP3A4, and compared to that of resveratrol. Docking simulation served to predict the affinity of the synthetic model compounds to the enzyme. Modelling of the enzyme’s binding site revealed three types of interaction: hydrophobic, electrostatic and H-bonding. The simulation revealed that each of the examined acetylations of resveratrol led to the loss of important interactions of all types. Tri-acetoxy resveratrol was the weakest inhibitor in vitro despite being the more lipophilic and having the highest affinity for the binding site. The simulation demonstrated exclusion of all interactions between tri-acetoxy resveratrol and the heme due to distal binding, highlighting the complexity of the CYP3A4 binding site, which may allow simultaneous accommodation of two molecules. Finally, the use of computational modelling may serve as a quick predictive tool to identify potential harmful interactions between dietary compounds and prescribed drugs. PMID:27530542
CisMiner: Genome-Wide In-Silico Cis-Regulatory Module Prediction by Fuzzy Itemset Mining

PubMed Central

Navarro, Carmen; Lopez, Francisco J.; Cano, Carlos; Garcia-Alcalde, Fernando; Blanco, Armando

2014-01-01

Eukaryotic gene control regions are known to be spread throughout non-coding DNA sequences which may appear distant from the gene promoter. Transcription factors are proteins that coordinately bind to these regions at transcription factor binding sites to regulate gene expression. Several tools allow to detect significant co-occurrences of closely located binding sites (cis-regulatory modules, CRMs). However, these tools present at least one of the following limitations: 1) scope limited to promoter or conserved regions of the genome; 2) do not allow to identify combinations involving more than two motifs; 3) require prior information about target motifs. In this work we present CisMiner, a novel methodology to detect putative CRMs by means of a fuzzy itemset mining approach able to operate at genome-wide scale. CisMiner allows to perform a blind search of CRMs without any prior information about target CRMs nor limitation in the number of motifs. CisMiner tackles the combinatorial complexity of genome-wide cis-regulatory module extraction using a natural representation of motif combinations as itemsets and applying the Top-Down Fuzzy Frequent- Pattern Tree algorithm to identify significant itemsets. Fuzzy technology allows CisMiner to better handle the imprecision and noise inherent to regulatory processes. Results obtained for a set of well-known binding sites in the S. cerevisiae genome show that our method yields highly reliable predictions. Furthermore, CisMiner was also applied to putative in-silico predicted transcription factor binding sites to identify significant combinations in S. cerevisiae and D. melanogaster, proving that our approach can be further applied genome-wide to more complex genomes. CisMiner is freely accesible at: http://genome2.ugr.es/cisminer. CisMiner can be queried for the results presented in this work and can also perform a customized cis-regulatory module prediction on a query set of transcription factor binding sites provided by the user. PMID:25268582
In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

PubMed

Abbasitabar, Fatemeh; Zare-Shahabadi, Vahid

2017-04-01

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for R training 2 and R test 2 , respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for R training 2 and R test 2 , respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (R training 2 and R test 2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615. Copyright © 2016 Elsevier Ltd. All rights reserved.
COMPUTER-AIDED DRUG DISCOVERY AND DEVELOPMENT (CADDD): in silico-chemico-biological approach

PubMed Central

Kapetanovic, I.M.

2008-01-01

It is generally recognized that drug discovery and development are very time and resources consuming processes. There is an ever growing effort to apply computational power to the combined chemical and biological space in order to streamline drug discovery, design, development and optimization. In biomedical arena, computer-aided or in silico design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, optimize the absorption, distribution, metabolism, excretion and toxicity profile and avoid safety issues. Commonly used computational approaches include ligand-based drug design (pharmacophore, a 3-D spatial arrangement of chemical features essential for biological activity), structure-based drug design (drug-target docking), and quantitative structure-activity and quantitative structure-property relationships. Regulatory agencies as well as pharmaceutical industry are actively involved in development of computational tools that will improve effectiveness and efficiency of drug discovery and development process, decrease use of animals, and increase predictability. It is expected that the power of CADDD will grow as the technology continues to evolve. PMID:17229415
Pharmacokinetic properties and in silico ADME modeling in drug discovery.

PubMed

Honório, Kathia M; Moda, Tiago L; Andricopulo, Adriano D

2013-03-01

The discovery and development of a new drug are time-consuming, difficult and expensive. This complex process has evolved from classical methods into an integration of modern technologies and innovative strategies addressed to the design of new chemical entities to treat a variety of diseases. The development of new drug candidates is often limited by initial compounds lacking reasonable chemical and biological properties for further lead optimization. Huge libraries of compounds are frequently selected for biological screening using a variety of techniques and standard models to assess potency, affinity and selectivity. In this context, it is very important to study the pharmacokinetic profile of the compounds under investigation. Recent advances have been made in the collection of data and the development of models to assess and predict pharmacokinetic properties (ADME--absorption, distribution, metabolism and excretion) of bioactive compounds in the early stages of drug discovery projects. This paper provides a brief perspective on the evolution of in silico ADME tools, addressing challenges, limitations, and opportunities in medicinal chemistry.
InSilico DB genomic datasets hub: an efficient starting point for analyzing genome-wide studies in GenePattern, Integrative Genomics Viewer, and R/Bioconductor.

PubMed

Coletta, Alain; Molter, Colin; Duqué, Robin; Steenhoff, David; Taminau, Jonatan; de Schaetzen, Virginie; Meganck, Stijn; Lazar, Cosmin; Venet, David; Detours, Vincent; Nowé, Ann; Bersini, Hugues; Weiss Solís, David Y

2012-11-18

Genomics datasets are increasingly useful for gaining biomedical insights, with adoption in the clinic underway. However, multiple hurdles related to data management stand in the way of their efficient large-scale utilization. The solution proposed is a web-based data storage hub. Having clear focus, flexibility and adaptability, InSilico DB seamlessly connects genomics dataset repositories to state-of-the-art and free GUI and command-line data analysis tools. The InSilico DB platform is a powerful collaborative environment, with advanced capabilities for biocuration, dataset sharing, and dataset subsetting and combination. InSilico DB is available from https://insilicodb.org.
An integrated molecular docking and rescoring method for predicting the sensitivity spectrum of various serine hydrolases to organophosphorus pesticides.

PubMed

Yang, Ling-Ling; Yang, Xiao; Li, Guo-Bo; Fan, Kai-Ge; Yin, Peng-Fei; Chen, Xiang-Gui

2016-04-01

The enzymatic chemistry method is currently the most widely used method for the rapid detection of organophosphorus (OP) pesticides, but the enzymes used, such as cholinesterases, lack sufficient sensitivity to detect low concentrations of OP pesticides present in given samples. Serine hydrolase is considered an ideal enzyme source in seeking high-sensitivity enzymes used for OP pesticide detection. However, it is difficult to systematically evaluate sensitivities of various serine hydrolases to OP pesticides by in vitro experiments. This study aimed to establish an in silico method to predict the sensitivity spectrum of various serine hydrolases to OP pesticides. A serine hydrolase database containing 219 representative serine hydrolases was constructed. Based on this database, an integrated molecular docking and rescoring method was established, in which the AutoDock Vina program was used to produce the binding poses of OP pesticides to various serine hydrolases and the ID-Score method developed recently by us was adopted as a rescoring method to predict their binding affinities. In retrospective case studies, this method showed good performance in predicting the sensitivities of known serine hydrolases to two OP pesticides: paraoxon and diisopropyl fluorophosphate. The sensitivity spectrum of the 219 collected serine hydrolases to 37 commonly used OP pesticides was finally obtained using this method. Overall, this study presented a promising in silico tool to predict the sensitivity spectrum of various serine hydrolases to OP pesticides, which will help in finding high-sensitivity serine hydrolases for OP pesticide detection. © 2015 Society of Chemical Industry.
In silico optimization of pharmacokinetic properties and receptor binding affinity simultaneously: a 'parallel progression approach to drug design' applied to β-blockers.

PubMed

Advani, Poonam; Joseph, Blessy; Ambre, Premlata; Pissurlenkar, Raghuvir; Khedkar, Vijay; Iyer, Krishna; Gabhe, Satish; Iyer, Radhakrishnan P; Coutinho, Evans

2016-01-01

The present work exploits the potential of in silico approaches for minimizing attrition of leads in the later stages of drug development. We propose a theoretical approach, wherein 'parallel' information is generated to simultaneously optimize the pharmacokinetics (PK) and pharmacodynamics (PD) of lead candidates. β-blockers, though in use for many years, have suboptimal PKs; hence are an ideal test series for the 'parallel progression approach'. This approach utilizes molecular modeling tools viz. hologram quantitative structure activity relationships, homology modeling, docking, predictive metabolism, and toxicity models. Validated models have been developed for PK parameters such as volume of distribution (log Vd) and clearance (log Cl), which together influence the half-life (t1/2) of a drug. Simultaneously, models for PD in terms of inhibition constant pKi have been developed. Thus, PK and PD properties of β-blockers were concurrently analyzed and after iterative cycling, modifications were proposed that lead to compounds with optimized PK and PD. We report some of the resultant re-engineered β-blockers with improved half-lives and pKi values comparable with marketed β-blockers. These were further analyzed by the docking studies to evaluate their binding poses. Finally, metabolic and toxicological assessment of these molecules was done through in silico methods. The strategy proposed herein has potential universal applicability, and can be used in any drug discovery scenario; provided that the data used is consistent in terms of experimental conditions, endpoints, and methods employed. Thus the 'parallel progression approach' helps to simultaneously fine-tune various properties of the drug and would be an invaluable tool during the drug development process.
Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

PubMed

Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

2015-12-15

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.
Probing the toxicity of nanoparticles: a unified in silico machine learning model based on perturbation theory.

PubMed

Concu, Riccardo; Kleandrova, Valeria V; Speck-Planche, Alejandro; Cordeiro, M Natália D S

2017-09-01

Nanoparticles (NPs) are part of our daily life, having a wide range of applications in engineering, physics, chemistry, and biomedicine. However, there are serious concerns regarding the harmful effects that NPs can cause to the different biological systems and their ecosystems. Toxicity testing is an essential step for assessing the potential risks of the NPs, but the experimental assays are often very expensive and usually too slow to flag the number of NPs that may cause adverse effects. In silico models centered on quantitative structure-activity/toxicity relationships (QSAR/QSTR) are alternative tools that have become valuable supports to risk assessment, rationalizing the search for safer NPs. In this work, we develop a unified QSTR-perturbation model based on artificial neural networks, aimed at simultaneously predicting general toxicity profiles of NPs under diverse experimental conditions. The model is derived from 54,371 NP-NP pair cases generated by applying the perturbation theory to a set of 260 unique NPs, and showed an accuracy higher than 97% in both training and validation sets. Physicochemical interpretation of the different descriptors in the model are additionally provided. The QSTR-perturbation model is then employed to predict the toxic effects of several NPs not included in the original dataset. The theoretical results obtained for this independent set are strongly consistent with the experimental evidence found in the literature, suggesting that the present QSTR-perturbation model can be viewed as a promising and reliable computational tool for probing the toxicity of NPs.
Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens Using Proteomic Data from a Field Biostimulation Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.

2012-12-12

Biomass and shotgun global proteomics data that reflected relative protein abundances from samples collected during the 2008 experiment at the U.S. Department of Energy Integrated Field-Scale Subsurface Research Challenge site in Rifle, Colorado, provided an unprecedented opportunity to validate a genome-scale metabolic model of Geobacter metallireducens and assess its performance with respect to prediction of metal reduction, biomass yield, and growth rate under dynamic field conditions. Reconstructed from annotated genomic sequence, biochemical, and physiological data, the constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes.more » Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low fluxes through amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.« less
Prediction of anti-cancer drug response by kernelized multi-task learning.

PubMed

Tan, Mehmet

2016-10-01

Chemotherapy or targeted therapy are two of the main treatment options for many types of cancer. Due to the heterogeneous nature of cancer, the success of the therapeutic agents differs among patients. In this sense, determination of chemotherapeutic response of the malign cells is essential for establishing a personalized treatment protocol and designing new drugs. With the recent technological advances in producing large amounts of pharmacogenomic data, in silico methods have become important tools to achieve this aim. Data produced by using cancer cell lines provide a test bed for machine learning algorithms that try to predict the response of cancer cells to different agents. The potential use of these algorithms in drug discovery/repositioning and personalized treatments motivated us in this study to work on predicting drug response by exploiting the recent pharmacogenomic databases. We aim to improve the prediction of drug response of cancer cell lines. We propose to use a method that employs multi-task learning to improve learning by transfer, and kernels to extract non-linear relationships to predict drug response. The method outperforms three state-of-the-art algorithms on three anti-cancer drug screen datasets. We achieved a mean squared error of 3.305 and 0.501 on two different large scale screen data sets. On a recent challenge dataset, we obtained an error of 0.556. We report the methodological comparison results as well as the performance of the proposed algorithm on each single drug. The results show that the proposed method is a strong candidate to predict drug response of cancer cell lines in silico for pre-clinical studies. The source code of the algorithm and data used can be obtained from http://mtan.etu.edu.tr/Supplementary/kMTrace/. Copyright © 2016 Elsevier B.V. All rights reserved.

Advancing Predictive Hepatotoxicity at the Intersection of Experimental, in Silico, and Artificial Intelligence Technologies.

PubMed

Fraser, Keith; Bruckner, Dylan M; Dordick, Jonathan S

2018-06-18

Adverse drug reactions, particularly those that result in drug-induced liver injury (DILI), are a major cause of drug failure in clinical trials and drug withdrawals. Hepatotoxicity-mediated drug attrition occurs despite substantial investments of time and money in developing cellular assays, animal models, and computational models to predict its occurrence in humans. Underperformance in predicting hepatotoxicity associated with drugs and drug candidates has been attributed to existing gaps in our understanding of the mechanisms involved in driving hepatic injury after these compounds perfuse and are metabolized by the liver. Herein we assess in vitro, in vivo (animal), and in silico strategies used to develop predictive DILI models. We address the effectiveness of several two- and three-dimensional in vitro cellular methods that are frequently employed in hepatotoxicity screens and how they can be used to predict DILI in humans. We also explore how humanized animal models can recapitulate human drug metabolic profiles and associated liver injury. Finally, we highlight the maturation of computational methods for predicting hepatotoxicity, the untapped potential of artificial intelligence for improving in silico DILI screens, and how knowledge acquired from these predictions can shape the refinement of experimental methods.
In silico Mechano-Chemical Model of Bone Healing for the Regeneration of Critical Defects: The Effect of BMP-2

PubMed Central

2015-01-01

The healing of bone defects is a challenge for both tissue engineering and modern orthopaedics. This problem has been addressed through the study of scaffold constructs combined with mechanoregulatory theories, disregarding the influence of chemical factors and their respective delivery devices. Of the chemical factors involved in the bone healing process, bone morphogenetic protein-2 (BMP-2) has been identified as one of the most powerful osteoinductive proteins. The aim of this work is to develop and validate a mechano-chemical regulatory model to study the effect of BMP-2 on the healing of large bone defects in silico. We first collected a range of quantitative experimental data from the literature concerning the effects of BMP-2 on cellular activity, specifically proliferation, migration, differentiation, maturation and extracellular matrix production. These data were then used to define a model governed by mechano-chemical stimuli to simulate the healing of large bone defects under the following conditions: natural healing, an empty hydrogel implanted in the defect and a hydrogel soaked with BMP-2 implanted in the defect. For the latter condition, successful defect healing was predicted, in agreement with previous in vivo experiments. Further in vivo comparisons showed the potential of the model, which accurately predicted bone tissue formation during healing, bone tissue distribution across the defect and the quantity of bone inside the defect. The proposed mechano-chemical model also estimated the effect of BMP-2 on cells and the evolution of healing in large bone defects. This novel in silico tool provides valuable insight for bone tissue regeneration strategies. PMID:26043112
UV-vis degradation of α-tocopherol in a model system and in a cosmetic emulsion-Structural elucidation of photoproducts and toxicological consequences.

PubMed

De Vaugelade, Ségolène; Nicol, Edith; Vujovic, Svetlana; Bourcier, Sophie; Pirnay, Stéphane; Bouchonnet, Stéphane

2017-09-29

The UV-vis photodegradation of α-tocopherol was investigated in a model system and in a cosmetic emulsion. Both gas chromatography coupled with tandem mass spectrometry (GC-MS/MS) and high performance liquid chromatography coupled with ultrahigh resolution Fourier transform ion cyclotron resonance mass spectrometry (LC-UHR-MS) were used for photoproducts structural identification. Nine photoproduct families were detected and identified based on their mass spectra and additional experiments with α-tocopherol-d 9 ; phototransformation mechanisms were postulated to rationalize their formation under irradiation. In silico QSAR (Quantitative Structure Activity Relationship) toxicity predictions were conducted with the Toxicity Estimation Software Tool (T.E.S.T.). Low oral rat LD50 values of 466.78mgkg -1 and 467.9mgkg -1 were predicted for some photoproducts, indicating a potential toxicity more than 10 times greater that of α-tocopherol (5742.54mgkg -1 ). In vitro assays on Vibrio fischeri bacteria showed that the global ecotoxicity of the α-tocopherol solution significantly increases with irradiation time. One identified product should contribute to this ecotoxicity enhancement since in silico estimations for D. magna provide a LC50 value 4 times lower than that of the parent molecule. Copyright © 2017. Published by Elsevier B.V.
Recovering actives in multi-antitarget and target design of analogs of the myosin II inhibitor blebbistatin

NASA Astrophysics Data System (ADS)

Roman, Bart I.; Guedes, Rita C.; Stevens, Christian V.; García-Sosa, Alfonso T.

2018-05-01

In multitarget drug design, it is critical to identify active and inactive compounds against a variety of targets and antitargets. Multitarget strategies thus test the limits of available technology, be that in screening large databases of compounds versus a large number of targets, or in using in silico methods for understanding and reliably predicting these pharmacological outcomes. In this paper, we have evaluated the potential of several in silico approaches to predict the target, antitarget and physicochemical profile of (S)-blebbistatin, the best-known myosin II ATPase inhibitor, and a series of analogs thereof. Standard and augmented structure-based design techniques could not recover the observed activity profiles. A ligand-based method using molecular fingerprints was, however, able to select actives for myosin II inhibition. Using further ligand- and structure-based methods, we also evaluated toxicity through androgen receptor binding, affinity for an array of antitargets and the ADME profile (including assay-interfering compounds) of the series. In conclusion, in the search for (S)-blebbistatin analogs, the dissimilarity distance of molecular fingerprints to known actives and the computed antitarget and physicochemical profile of the molecules can be used for compound design for molecules with potential as tools for modulating myosin II and motility-related diseases.
Performing SELEX experiments in silico

NASA Astrophysics Data System (ADS)

Wondergem, J. A. J.; Schiessel, H.; Tompitak, M.

2017-11-01

Due to the sequence-dependent nature of the elasticity of DNA, many protein-DNA complexes and other systems in which DNA molecules must be deformed have preferences for the type of DNA sequence they interact with. SELEX (Systematic Evolution of Ligands by EXponential enrichment) experiments and similar sequence selection experiments have been used extensively to examine the (indirect readout) sequence preferences of, e.g., nucleosomes (protein spools around which DNA is wound for compactification) and DNA rings. We show how recently developed computational and theoretical tools can be used to emulate such experiments in silico. Opening up this possibility comes with several benefits. First, it allows us a better understanding of our models and systems, specifically about the roles played by the simulation temperature and the selection pressure on the sequences. Second, it allows us to compare the predictions made by the model of choice with experimental results. We find agreement on important features between predictions of the rigid base-pair model and experimental results for DNA rings and interesting differences that point out open questions in the field. Finally, our simulations allow application of the SELEX methodology to systems that are experimentally difficult to realize because they come with high energetic costs and are therefore unlikely to form spontaneously, such as very short or overwound DNA rings.
Exploring root symbiotic programs in the model legume Medicago truncatula using EST analysis.

PubMed

Journet, Etienne-Pascal; van Tuinen, Diederik; Gouzy, Jérome; Crespeau, Hervé; Carreau, Véronique; Farmer, Mary-Jo; Niebel, Andreas; Schiex, Thomas; Jaillon, Olivier; Chatagnier, Odile; Godiard, Laurence; Micheli, Fabienne; Kahn, Daniel; Gianinazzi-Pearson, Vivienne; Gamas, Pascal

2002-12-15

We report on a large-scale expressed sequence tag (EST) sequencing and analysis program aimed at characterizing the sets of genes expressed in roots of the model legume Medicago truncatula during interactions with either of two microsymbionts, the nitrogen-fixing bacterium Sinorhizobium meliloti or the arbuscular mycorrhizal fungus Glomus intraradices. We have designed specific tools for in silico analysis of EST data, in relation to chimeric cDNA detection, EST clustering, encoded protein prediction, and detection of differential expression. Our 21 473 5'- and 3'-ESTs could be grouped into 6359 EST clusters, corresponding to distinct virtual genes, along with 52 498 other M.truncatula ESTs available in the dbEST (NCBI) database that were recruited in the process. These clusters were manually annotated, using a specifically developed annotation interface. Analysis of EST cluster distribution in various M.truncatula cDNA libraries, supported by a refined R test to evaluate statistical significance and by 'electronic northern' representation, enabled us to identify a large number of novel genes predicted to be up- or down-regulated during either symbiotic root interaction. These in silico analyses provide a first global view of the genetic programs for root symbioses in M.truncatula. A searchable database has been built and can be accessed through a public interface.
Effect of Different Sampling Schedules on Results of Bioavailability and Bioequivalence Studies: Evaluation by Means of Monte Carlo Simulations.

PubMed

Kano, Eunice Kazue; Chiann, Chang; Fukuda, Kazuo; Porta, Valentina

2017-08-01

Bioavailability and bioequivalence study is one of the most frequently performed investigations in clinical trials. Bioequivalence testing is based on the assumption that 2 drug products will be therapeutically equivalent when they are equivalent in the rate and extent to which the active drug ingredient or therapeutic moiety is absorbed and becomes available at the site of drug action. In recent years there has been a significant growth in published papers that use in silico studies based on mathematical simulations to analyze pharmacokinetic and pharmacodynamic properties of drugs, including bioavailability and bioequivalence aspects. The goal of this study is to evaluate the usefulness of in silico studies as a tool in the planning of bioequivalence, bioavailability and other pharmacokinetic assays, e.g., to determine an appropriate sampling schedule. Monte Carlo simulations were used to define adequate blood sampling schedules for a bioequivalence assay comparing 2 different formulations of cefadroxil oral suspensions. In silico bioequivalence studies comparing different formulation of cefadroxil oral suspensions using various sampling schedules were performed using models. An in vivo study was conducted to confirm in silico results. The results of in silico and in vivo bioequivalence studies demonstrated that schedules with fewer sampling times are as efficient as schedules with larger numbers of sampling times in the assessment of bioequivalence, but only if T max is included as a sampling time. It was also concluded that in silico studies are useful tools in the planning of bioequivalence, bioavailability and other pharmacokinetic in vivo assays. © Georg Thieme Verlag KG Stuttgart · New York.
Comparative Genomics of Oral Isolates of Streptococcus mutans by in silico Genome Subtraction Does Not Reveal Accessory DNA Associated with Severe Early Childhood Caries

PubMed Central

Argimón, Silvia; Konganti, Kranti; Chen, Hao; Alekseyenko, Alexander V.; Brown, Stuart; Caufield, Page W.

2014-01-01

Comparative genomics is a popular method for the identification of microbial virulence determinants, especially since the sequencing of a large number of whole bacterial genomes from pathogenic and non-pathogenic strains has become relatively inexpensive. The bioinformatics pipelines for comparative genomics usually include gene prediction and annotation and can require significant computer power. To circumvent this, we developed a rapid method for genome-scale in silico subtractive hybridization, based on blastn and independent of feature identification and annotation. Whole genome comparisons by in silico genome subtraction were performed to identify genetic loci specific to Streptococcus mutans strains associated with severe early childhood caries (S-ECC), compared to strains isolated from caries-free (CF) children. The genome similarity of the 20 S. mutans strains included in this study, calculated by Simrank k-mer sharing, ranged from 79.5 to 90.9%, confirming this is a genetically heterogeneous group of strains. We identified strain-specific genetic elements in 19 strains, with sizes ranging from 200 bp to 39 kb. These elements contained protein-coding regions with functions mostly associated with mobile DNA. We did not, however, identify any genetic loci consistently associated with dental caries, i.e., shared by all the S-ECC strains and absent in the CF strains. Conversely, we did not identify any genetic loci specific with the healthy group. Comparison of previously published genomes from pathogenic and carriage strains of Neisseria meningitidis with our in silico genome subtraction yielded the same set of genes specific to the pathogenic strains, thus validating our method. Our results suggest that S. mutans strains derived from caries active or caries free dentitions cannot be differentiated based on the presence or absence of specific genetic elements. Our in silico genome subtraction method is available as the Microbial Genome Comparison (MGC) tool, with a user-friendly JAVA graphical interface. PMID:24291226
Systems Biology-Driven Hypotheses Tested In Vivo: The Need to Advancing Molecular Imaging Tools.

PubMed

Verma, Garima; Palombo, Alessandro; Grigioni, Mauro; La Monaca, Morena; D'Avenio, Giuseppe

2018-01-01

Processing and interpretation of biological images may provide invaluable insights on complex, living systems because images capture the overall dynamics as a "whole." Therefore, "extraction" of key, quantitative morphological parameters could be, at least in principle, helpful in building a reliable systems biology approach in understanding living objects. Molecular imaging tools for system biology models have attained widespread usage in modern experimental laboratories. Here, we provide an overview on advances in the computational technology and different instrumentations focused on molecular image processing and analysis. Quantitative data analysis through various open source software and algorithmic protocols will provide a novel approach for modeling the experimental research program. Besides this, we also highlight the predictable future trends regarding methods for automatically analyzing biological data. Such tools will be very useful to understand the detailed biological and mathematical expressions under in-silico system biology processes with modeling properties.
Results of a round-robin exercise on read-across.

PubMed

Benfenati, E; Belli, M; Borges, T; Casimiro, E; Cester, J; Fernandez, A; Gini, G; Honma, M; Kinzl, M; Knauf, R; Manganaro, A; Mombelli, E; Petoumenou, M I; Paparella, M; Paris, P; Raitano, G

2016-05-01

A round-robin exercise was conducted within the CALEIDOS LIFE project. The participants were invited to assess the hazard posed by a substance, applying in silico methods and read-across approaches. The exercise was based on three endpoints: mutagenicity, bioconcentration factor and fish acute toxicity. Nine chemicals were assigned for each endpoint and the participants were invited to complete a specific questionnaire communicating their conclusions. The interesting aspect of this exercise is the justification behind the answers more than the final prediction in itself. Which tools were used? How did the approach selected affect the final answer?
Musite, a tool for global prediction of general and kinase-specific phosphorylation sites.

PubMed

Gao, Jianjiong; Thelen, Jay J; Dunker, A Keith; Xu, Dong

2010-12-01

Reversible protein phosphorylation is one of the most pervasive post-translational modifications, regulating diverse cellular processes in various organisms. High throughput experimental studies using mass spectrometry have identified many phosphorylation sites, primarily from eukaryotes. However, the vast majority of phosphorylation sites remain undiscovered, even in well studied systems. Because mass spectrometry-based experimental approaches for identifying phosphorylation events are costly, time-consuming, and biased toward abundant proteins and proteotypic peptides, in silico prediction of phosphorylation sites is potentially a useful alternative strategy for whole proteome annotation. Because of various limitations, current phosphorylation site prediction tools were not well designed for comprehensive assessment of proteomes. Here, we present a novel software tool, Musite, specifically designed for large scale predictions of both general and kinase-specific phosphorylation sites. We collected phosphoproteomics data in multiple organisms from several reliable sources and used them to train prediction models by a comprehensive machine-learning approach that integrates local sequence similarities to known phosphorylation sites, protein disorder scores, and amino acid frequencies. Application of Musite on several proteomes yielded tens of thousands of phosphorylation site predictions at a high stringency level. Cross-validation tests show that Musite achieves some improvement over existing tools in predicting general phosphorylation sites, and it is at least comparable with those for predicting kinase-specific phosphorylation sites. In Musite V1.0, we have trained general prediction models for six organisms and kinase-specific prediction models for 13 kinases or kinase families. Although the current pretrained models were not correlated with any particular cellular conditions, Musite provides a unique functionality for training customized prediction models (including condition-specific models) from users' own data. In addition, with its easily extensible open source application programming interface, Musite is aimed at being an open platform for community-based development of machine learning-based phosphorylation site prediction applications. Musite is available at http://musite.sourceforge.net/.
In silico modeling to predict drug-induced phospholipidosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Choi, Sydney S.; Kim, Jae S.; Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov

2013-06-01

Drug-induced phospholipidosis (DIPL) is a preclinical finding during pharmaceutical drug development that has implications on the course of drug development and regulatory safety review. A principal characteristic of drugs inducing DIPL is known to be a cationic amphiphilic structure. This provides evidence for a structure-based explanation and opportunity to analyze properties and structures of drugs with the histopathologic findings for DIPL. In previous work from the FDA, in silico quantitative structure–activity relationship (QSAR) modeling using machine learning approaches has shown promise with a large dataset of drugs but included unconfirmed data as well. In this study, we report the constructionmore » and validation of a battery of complementary in silico QSAR models using the FDA's updated database on phospholipidosis, new algorithms and predictive technologies, and in particular, we address high performance with a high-confidence dataset. The results of our modeling for DIPL include rigorous external validation tests showing 80–81% concordance. Furthermore, the predictive performance characteristics include models with high sensitivity and specificity, in most cases above ≥ 80% leading to desired high negative and positive predictivity. These models are intended to be utilized for regulatory toxicology applied science needs in screening new drugs for DIPL. - Highlights: • New in silico models for predicting drug-induced phospholipidosis (DIPL) are described. • The training set data in the models is derived from the FDA's phospholipidosis database. • We find excellent predictivity values of the models based on external validation. • The models can support drug screening and regulatory decision-making on DIPL.« less
How Not To Drown in Data: A Guide for Biomaterial Engineers.

PubMed

Vasilevich, Aliaksei S; Carlier, Aurélie; de Boer, Jan; Singh, Shantanu

2017-08-01

High-throughput assays that produce hundreds of measurements per sample are powerful tools for quantifying cell-material interactions. With advances in automation and miniaturization in material fabrication, hundreds of biomaterial samples can be rapidly produced, which can then be characterized using these assays. However, the resulting deluge of data can be overwhelming. To the rescue are computational methods that are well suited to these problems. Machine learning techniques provide a vast array of tools to make predictions about cell-material interactions and to find patterns in cellular responses. Computational simulations allow researchers to pose and test hypotheses and perform experiments in silico. This review describes approaches from these two domains that can be brought to bear on the problem of analyzing biomaterial screening data. Copyright © 2017 Elsevier Ltd. All rights reserved.
Differentiation of Toxocara canis and Toxocara cati based on PCR-RFLP analyses of rDNA-ITS and mitochondrial cox1 and nad1 regions.

PubMed

Mikaeili, Fattaneh; Mathis, Alexander; Deplazes, Peter; Mirhendi, Hossein; Barazesh, Afshin; Ebrahimi, Sepideh; Kia, Eshrat Beigom

2017-09-26

The definitive genetic identification of Toxocara species is currently based on PCR/sequencing. The objectives of the present study were to design and conduct an in silico polymerase chain reaction-restriction fragment length polymorphism method for identification of Toxocara species. In silico analyses using the DNASIS and NEBcutter softwares were performed with rDNA internal transcribed spacers, and mitochondrial cox1 and nad1 sequences obtained in our previous studies along with relevant sequences deposited in GenBank. Consequently, RFLP profiles were designed and all isolates of T. canis and T. cati collected from dogs and cats in different geographical areas of Iran were investigated with the RFLP method using some of the identified suitable enzymes. The findings of in silico analyses predicted that on the cox1 gene only the MboII enzyme is appropriate for PCR-RFLP to reliably distinguish the two species. No suitable enzyme for PCR-RFLP on the nad1 gene was identified that yields the same pattern for all isolates of a species. DNASIS software showed that there are 241 suitable restriction enzymes for the differentiation of T. canis from T. cati based on ITS sequences. RsaI, MvaI and SalI enzymes were selected to evaluate the reliability of the in silico PCR-RFLP. The sizes of restriction fragments obtained by PCR-RFLP of all samples consistently matched the expected RFLP patterns. The ITS sequences are usually conserved and the PCR-RFLP approach targeting the ITS sequence is recommended for the molecular differentiation of Toxocara species and can provide a reliable tool for identification purposes particularly at the larval and egg stages.
In vitro, in silico and in vivo studies of ursolic acid as an anti-filarial agent.

PubMed

Kalani, Komal; Kushwaha, Vikas; Sharma, Pooja; Verma, Richa; Srivastava, Mukesh; Khan, Feroz; Murthy, P K; Srivastava, Santosh Kumar

2014-01-01

As part of our drug discovery program for anti-filarial agents from Indian medicinal plants, leaves of Eucalyptus tereticornis were chemically investigated, which resulted in the isolation and characterization of an anti-filarial agent, ursolic acid (UA) as a major constituent. Antifilarial activity of UA against the human lymphatic filarial parasite Brugia malayi using in vitro and in vivo assays, and in silico docking search on glutathione-s-transferase (GST) parasitic enzyme were carried out. The UA was lethal to microfilariae (mf; LC100: 50; IC50: 8.84 µM) and female adult worms (LC100: 100; IC50: 35.36 µM) as observed by motility assay; it exerted 86% inhibition in MTT reduction potential of the adult parasites. The selectivity index (SI) of UA for the parasites was found safe. This was supported by the molecular docking studies, which showed adequate docking (LibDock) scores for UA (-8.6) with respect to the standard antifilarial drugs, ivermectin (IVM -8.4) and diethylcarbamazine (DEC-C -4.6) on glutathione-s-transferase enzyme. Further, in silico pharmacokinetic and drug-likeness studies showed that UA possesses drug-like properties. Furthermore, UA was evaluated in vivo in B. malayi-M. coucha model (natural infection), which showed 54% macrofilaricidal activity, 56% female worm sterility and almost unchanged microfilaraemia maintained throughout observation period with no adverse effect on the host. Thus, in conclusion in vitro, in silico and in vivo results indicate that UA is a promising, inexpensive, widely available natural lead, which can be designed and developed into a macrofilaricidal drug. To the best of our knowledge this is the first ever report on the anti-filarial potential of UA from E. tereticornis, which is in full agreement with the Thomson Reuter's 'Metadrug' tool screening predictions.
In Silico Identification of Epitopes in Mycobacterium avium subsp. paratuberculosis Proteins That Were Upregulated under Stress Conditions

PubMed Central

Gurung, Ratna B.; Purdie, Auriol C.; Begg, Douglas J.

2012-01-01

Johne's disease in ruminants is caused by Mycobacterium avium subsp. paratuberculosis. Diagnosis of M. avium subsp. paratuberculosis infection is difficult, especially in the early stages. To date, ideal antigen candidates are not available for efficient immunization or immunodiagnosis. This study reports the in silico selection and subsequent analysis of epitopes of M. avium subsp. paratuberculosis proteins that were found to be upregulated under stress conditions as a means to identify immunogenic candidate proteins. Previous studies have reported differential regulation of proteins when M. avium subsp. paratuberculosis is exposed to stressors which induce a response similar to dormancy. Dormancy may be involved in evading host defense mechanisms, and the host may also mount an immune response against these proteins. Twenty-five M. avium subsp. paratuberculosis proteins that were previously identified as being upregulated under in vitro stress conditions were analyzed for B and T cell epitopes by use of the prediction tools at the Immune Epitope Database and Analysis Resource. Major histocompatibility complex class I T cell epitopes were predicted using an artificial neural network method, and class II T cell epitopes were predicted using the consensus method. Conformational B cell epitopes were predicted from the relevant three-dimensional structure template for each protein. Based on the greatest number of predicted epitopes, eight proteins (MAP2698c [encoded by desA2], MAP2312c [encoded by fadE19], MAP3651c [encoded by fadE3_2], MAP2872c [encoded by fabG5_2], MAP3523c [encoded by oxcA], MAP0187c [encoded by sodA], and the hypothetical proteins MAP3567 and MAP1168c) were identified as potential candidates for study of antibody- and cell-mediated immune responses within infected hosts. PMID:22496492
Screening of pharmacokinetic properties of fifty dihydropyrimidin(thi)one derivatives using a combo of in vitro and in silico assays.

PubMed

Matias, Mariana; Fortuna, Ana; Bicker, Joana; Silvestre, Samuel; Falcão, Amílcar; Alves, Gilberto

2017-11-15

The heterocycles dihydropyrimidin(thi)ones have been under intensive pharmacological research, but their pharmacokinetic properties remain almost unknown. Herein, fifty dihydropyrimidin(thi)ones were submitted to in vitro screening tests using parallel artificial membrane permeability assays (PAMPA) to evaluate their apparent permeability (Papp) through intestinal membrane and blood-brain barrier models, and cell-based assays to assess their interference on the efflux transporter P-glycoprotein (P-gp). Moreover, a set of kinetic and toxicological parameters was also estimated employing a new computational tool, the pkCSM. The in vitro results suggested that 82% of the test compounds have good intestinal permeability (Papp>1.1×10 -6 cm/s), and 66% of these are also expected to exhibit good permeability through blood-brain barrier (Papp>2.0×10 -6 cm/s); these findings are consistent with a high transport rate by passive transcellular pathway. In both PAMPA models, thiourea derivatives presented higher Papp values than the respective urea analogues, which were further corroborated by in silico predictions. The in vitro results also suggested a low extent of plasma protein binding for all compounds (Papp<1.0×10 -5 cm/s), and these findings were also supported by in silico data (unbound fraction ranging from 0.13 to 0.59). In addition, although approximately half of the compounds did not modulate P-gp at the tested concentrations (10 and 50μM), nine of them presented a trend to induce P-gp and particularly the chlorinated compounds exhibited a marked P-gp inhibition at 50μM. Furthermore, the in silico predictions suggested that half of the compounds have hepatotoxic potential. Overall, within this group of compounds, the thiourea derivatives containing an unsubstituted or a monosubstituted (NO 2 , CH 3 , OCH 3 ) phenyl ring attached to the position 4 of the dihydropyrimidine ring represented the most promising structures and should be considered in the subsequent studies of the development of new structurally related drug candidates. Copyright © 2017 Elsevier B.V. All rights reserved.
Non-animal methods to predict skin sensitization (II): an assessment of defined approaches *.

PubMed

Kleinstreuer, Nicole C; Hoffmann, Sebastian; Alépée, Nathalie; Allen, David; Ashikaga, Takao; Casey, Warren; Clouet, Elodie; Cluzel, Magalie; Desprez, Bertrand; Gellatly, Nichola; Göbel, Carsten; Kern, Petra S; Klaric, Martina; Kühnl, Jochen; Martinozzi-Teissier, Silvia; Mewes, Karsten; Miyazawa, Masaaki; Strickland, Judy; van Vliet, Erwin; Zang, Qingda; Petersohn, Dirk

2018-05-01

Skin sensitization is a toxicity endpoint of widespread concern, for which the mechanistic understanding and concurrent necessity for non-animal testing approaches have evolved to a critical juncture, with many available options for predicting sensitization without using animals. Cosmetics Europe and the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods collaborated to analyze the performance of multiple non-animal data integration approaches for the skin sensitization safety assessment of cosmetics ingredients. The Cosmetics Europe Skin Tolerance Task Force (STTF) collected and generated data on 128 substances in multiple in vitro and in chemico skin sensitization assays selected based on a systematic assessment by the STTF. These assays, together with certain in silico predictions, are key components of various non-animal testing strategies that have been submitted to the Organization for Economic Cooperation and Development as case studies for skin sensitization. Curated murine local lymph node assay (LLNA) and human skin sensitization data were used to evaluate the performance of six defined approaches, comprising eight non-animal testing strategies, for both hazard and potency characterization. Defined approaches examined included consensus methods, artificial neural networks, support vector machine models, Bayesian networks, and decision trees, most of which were reproduced using open source software tools. Multiple non-animal testing strategies incorporating in vitro, in chemico, and in silico inputs demonstrated equivalent or superior performance to the LLNA when compared to both animal and human data for skin sensitization.
Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach.

PubMed

Kashyap, Manju; Jaiswal, Varun; Farooq, Umar

2017-09-01

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants. Copyright © 2017 Elsevier B.V. All rights reserved.
Investigation of metabolic objectives in cultured hepatocytes.

PubMed

Uygun, Korkut; Matthew, Howard W T; Huang, Yinlun

2007-06-15

Using optimization based methods to predict fluxes in metabolic flux balance models has been a successful approach for some microorganisms, enabling construction of in silico models and even inference of some regulatory motifs. However, this success has not been translated to mammalian cells. The lack of knowledge about metabolic objectives in mammalian cells is a major obstacle that prevents utilization of various metabolic engineering tools and methods for tissue engineering and biomedical purposes. In this work, we investigate and identify possible metabolic objectives for hepatocytes cultured in vitro. To achieve this goal, we present a special data-mining procedure for identifying metabolic objective functions in mammalian cells. This multi-level optimization based algorithm enables identifying the major fluxes in the metabolic objective from MFA data in the absence of information about critical active constraints of the system. Further, once the objective is determined, active flux constraints can also be identified and analyzed. This information can be potentially used in a predictive manner to improve cell culture results or clinical metabolic outcomes. As a result of the application of this method, it was found that in vitro cultured hepatocytes maximize oxygen uptake, coupling of urea and TCA cycles, and synthesis of serine and urea. Selection of these fluxes as the metabolic objective enables accurate prediction of the flux distribution in the system given a limited amount of flux data; thus presenting a workable in silico model for cultured hepatocytes. It is observed that an overall homeostasis picture is also emergent in the findings.

Identification of widespread adenosine nucleotide binding in Mycobacterium tuberculosis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ansong, Charles; Ortega, Corrie; Payne, Samuel H.

The annotation of protein function is almost completely performed by in silico approaches. However, computational prediction of protein function is frequently incomplete and error prone. In Mycobacterium tuberculosis (Mtb), ~25% of all genes have no predicted function and are annotated as hypothetical proteins. This lack of functional information severely limits our understanding of Mtb pathogenicity. Current tools for experimental functional annotation are limited and often do not scale to entire protein families. Here, we report a generally applicable chemical biology platform to functionally annotate bacterial proteins by combining activity-based protein profiling (ABPP) and quantitative LC-MS-based proteomics. As an example ofmore » this approach for high-throughput protein functional validation and discovery, we experimentally annotate the families of ATP-binding proteins in Mtb. Our data experimentally validate prior in silico predictions of >250 ATPases and adenosine nucleotide-binding proteins, and reveal 73 hypothetical proteins as novel ATP-binding proteins. We identify adenosine cofactor interactions with many hypothetical proteins containing a diversity of unrelated sequences, providing a new and expanded view of adenosine nucleotide binding in Mtb. Furthermore, many of these hypothetical proteins are both unique to Mycobacteria and essential for infection, suggesting specialized functions in mycobacterial physiology and pathogenicity. Thus, we provide a generally applicable approach for high throughput protein function discovery and validation, and highlight several ways in which application of activity-based proteomics data can improve the quality of functional annotations to facilitate novel biological insights.« less
Accurate in silico prediction of species-specific methylation sites based on information gain feature optimization.

PubMed

Wen, Ping-Ping; Shi, Shao-Ping; Xu, Hao-Dong; Wang, Li-Na; Qiu, Jian-Ding

2016-10-15

As one of the most important reversible types of post-translational modification, protein methylation catalyzed by methyltransferases carries many pivotal biological functions as well as many essential biological processes. Identification of methylation sites is prerequisite for decoding methylation regulatory networks in living cells and understanding their physiological roles. Experimental methods are limitations of labor-intensive and time-consuming. While in silicon approaches are cost-effective and high-throughput manner to predict potential methylation sites, but those previous predictors only have a mixed model and their prediction performances are not fully satisfactory now. Recently, with increasing availability of quantitative methylation datasets in diverse species (especially in eukaryotes), there is a growing need to develop a species-specific predictor. Here, we designed a tool named PSSMe based on information gain (IG) feature optimization method for species-specific methylation site prediction. The IG method was adopted to analyze the importance and contribution of each feature, then select the valuable dimension feature vectors to reconstitute a new orderly feature, which was applied to build the finally prediction model. Finally, our method improves prediction performance of accuracy about 15% comparing with single features. Furthermore, our species-specific model significantly improves the predictive performance compare with other general methylation prediction tools. Hence, our prediction results serve as useful resources to elucidate the mechanism of arginine or lysine methylation and facilitate hypothesis-driven experimental design and validation. The tool online service is implemented by C# language and freely available at http://bioinfo.ncu.edu.cn/PSSMe.aspx CONTACT: jdqiu@ncu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.

PubMed

Zhang, Yan-Yan; Liu, Houfu; Summerfield, Scott G; Luscombe, Christopher N; Sahi, Jasminder

2016-05-02

Estimation of uptake across the blood-brain barrier (BBB) is key to designing central nervous system (CNS) therapeutics. In silico approaches ranging from physicochemical rules to quantitative structure-activity relationship (QSAR) models are utilized to predict potential for CNS penetration of new chemical entities. However, there are still gaps in our knowledge of (1) the relationship between marketed human drug derived CNS-accessible chemical space and preclinical neuropharmacokinetic (neuroPK) data, (2) interpretability of the selected physicochemical descriptors, and (3) correlation of the in vitro human P-glycoprotein (P-gp) efflux ratio (ER) and in vivo rodent unbound brain-to-blood ratio (Kp,uu), as these are assays routinely used to predict clinical CNS exposure, during drug discovery. To close these gaps, we explored the CNS druglike property boundaries of 920 market oral drugs (315 CNS and 605 non-CNS) and 846 compounds (54 CNS drugs and 792 proprietary GlaxoSmithKline compounds) with available rat Kp,uu data. The exact permeability coefficient (Pexact) and P-gp ER were determined for 176 compounds from the rat Kp,uu data set. Receiver operating characteristic curves were performed to evaluate the predictive power of human P-gp ER for rat Kp,uu. Our data demonstrates that simple physicochemical rules (most acidic pKa ≥ 9.5 and TPSA < 100) in combination with P-gp ER < 1.5 provide mechanistic insights for filtering BBB permeable compounds. For comparison, six classification modeling methods were investigated using multiple sets of in silico molecular descriptors. We present a random forest model with excellent predictive power (∼0.75 overall accuracy) using the rat neuroPK data set. We also observed good concordance between the structural interpretation results and physicochemical descriptor importance from the Kp,uu classification QSAR model. In summary, we propose a novel, hybrid in silico/in vitro approach and an in silico screening model for the effective development of chemical series with the potential to achieve optimal CNS exposure.
Crops in silico: A community wide multi-scale computational modeling framework of plant canopies

NASA Astrophysics Data System (ADS)

Srinivasan, V.; Christensen, A.; Borkiewic, K.; Yiwen, X.; Ellis, A.; Panneerselvam, B.; Kannan, K.; Shrivastava, S.; Cox, D.; Hart, J.; Marshall-Colon, A.; Long, S.

2016-12-01

Current crop models predict a looming gap between supply and demand for primary foodstuffs over the next 100 years. While significant yield increases were achieved in major food crops during the early years of the green revolution, the current rates of yield increases are insufficient to meet future projected food demand. Furthermore, with projected reduction in arable land, decrease in water availability, and increasing impacts of climate change on future food production, innovative technologies are required to sustainably improve crop yield. To meet these challenges, we are developing Crops in silico (Cis), a biologically informed, multi-scale, computational modeling framework that can facilitate whole plant simulations of crop systems. The Cis framework is capable of linking models of gene networks, protein synthesis, metabolic pathways, physiology, growth, and development in order to investigate crop response to different climate scenarios and resource constraints. This modeling framework will provide the mechanistic details to generate testable hypotheses toward accelerating directed breeding and engineering efforts to increase future food security. A primary objective for building such a framework is to create synergy among an inter-connected community of biologists and modelers to create a realistic virtual plant. This framework advantageously casts the detailed mechanistic understanding of individual plant processes across various scales in a common scalable framework that makes use of current advances in high performance and parallel computing. We are currently designing a user friendly interface that will make this tool equally accessible to biologists and computer scientists. Critically, this framework will provide the community with much needed tools for guiding future crop breeding and engineering, understanding the emergent implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment.
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

NASA Astrophysics Data System (ADS)

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-02-01

For a drug, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

PubMed Central

Yang, Hongbin; Sun, Lixia; Li, Weihua; Liu, Guixia; Tang, Yun

2018-01-01

During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future. PMID:29515993
Whole-exome sequencing analysis of Waardenburg syndrome in a Chinese family.

PubMed

Chen, Dezhong; Zhao, Na; Wang, Jing; Li, Zhuoyu; Wu, Changxin; Fu, Jie; Xiao, Han

2017-01-01

Waardenburg syndrome (WS) is a dominantly inherited, genetically heterogeneous auditory-pigmentary syndrome characterized by non-progressive sensorineural hearing loss and iris discoloration. By whole-exome sequencing (WES), we identified a nonsense mutation (c.598C>T) in PAX3 gene, predicted to be disease causing by in silico analysis. This is the first report of genetically diagnosed case of WS PAX3 c.598C>T nonsense mutation in Chinese ethnic origin by WES and in silico functional prediction methods.
Whole-exome sequencing analysis of Waardenburg syndrome in a Chinese family

PubMed Central

Chen, Dezhong; Zhao, Na; Wang, Jing; Li, Zhuoyu; Wu, Changxin; Fu, Jie; Xiao, Han

2017-01-01

Waardenburg syndrome (WS) is a dominantly inherited, genetically heterogeneous auditory-pigmentary syndrome characterized by non-progressive sensorineural hearing loss and iris discoloration. By whole-exome sequencing (WES), we identified a nonsense mutation (c.598C>T) in PAX3 gene, predicted to be disease causing by in silico analysis. This is the first report of genetically diagnosed case of WS PAX3 c.598C>T nonsense mutation in Chinese ethnic origin by WES and in silico functional prediction methods. PMID:28690861
LipidFrag: Improving reliability of in silico fragmentation of lipids and application to the Caenorhabditis elegans lipidome

PubMed Central

Neumann, Steffen; Schmitt-Kopplin, Philippe

2017-01-01

Lipid identification is a major bottleneck in high-throughput lipidomics studies. However, tools for the analysis of lipid tandem MS spectra are rather limited. While the comparison against spectra in reference libraries is one of the preferred methods, these libraries are far from being complete. In order to improve identification rates, the in silico fragmentation tool MetFrag was combined with Lipid Maps and lipid-class specific classifiers which calculate probabilities for lipid class assignments. The resulting LipidFrag workflow was trained and evaluated on different commercially available lipid standard materials, measured with data dependent UPLC-Q-ToF-MS/MS acquisition. The automatic analysis was compared against manual MS/MS spectra interpretation. With the lipid class specific models, identification of the true positives was improved especially for cases where candidate lipids from different lipid classes had similar MetFrag scores by removing up to 56% of false positive results. This LipidFrag approach was then applied to MS/MS spectra of lipid extracts of the nematode Caenorhabditis elegans. Fragments explained by LipidFrag match known fragmentation pathways, e.g., neutral losses of lipid headgroups and fatty acid side chain fragments. Based on prediction models trained on standard lipid materials, high probabilities for correct annotations were achieved, which makes LipidFrag a good choice for automated lipid data analysis and reliability testing of lipid identifications. PMID:28278196
FutureTox II: In vitro Data and In Silico Models for Predictive Toxicology

PubMed Central

Knudsen, Thomas B.; Keller, Douglas A.; Sander, Miriam; Carney, Edward W.; Doerrer, Nancy G.; Eaton, David L.; Fitzpatrick, Suzanne Compton; Hastings, Kenneth L.; Mendrick, Donna L.; Tice, Raymond R.; Watkins, Paul B.; Whelan, Maurice

2015-01-01

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. PMID:25628403
[In silico identification of molecular mimicry between T-cell epitopes of Neisseria meningitidis B and the human proteome].

PubMed

Batista-Duharte, Alexander; Téllez, Bruno; Tamayo, Maybia; Portuondo, Deivys; Cabrera, Osmir; Sierra, Gustavo; Pérez, Oliver

2013-07-01

The objective of the study was to determine the T-cell epitopes of four of the most frequent antigenic proteins of the outer membrane of Neisseria meningitidis B, and to identify the most relevant sites for molecular mimicry with T-cell epitopes in humans. In order to do so, an in silico study -a type of study that uses bioinformatic tools- was carried out using SWISS-PROT/TrEMBL, SYFPEITHI and FASTA databases, which helped to determine the protein sequences, CD4 and CD8 T-cell epitope prediction, as well as the molecular mimicry with humans, respectively. Molecular similarity was found in several human proteins present in different organs and tissues such as: liver, skin and epithelial tissues, brain, lymphatic system and testicles. Of these, those found in testicles were more similar, showing the highest frequency of mimetic sequences. This finding shed light on the success of N. meningitidis B to colonize human tissues and the failure of certain vaccines against this bacterium, and it even helps to explain possible autoimmune reactions associated with the infection or vaccination.
The in silico drug discovery toolbox: applications in lead discovery and optimization.

PubMed

Bruno, Agostino; Costantino, Gabriele; Sartori, Luca; Radi, Marco

2017-11-06

Discovery and development of a new drug is a long lasting and expensive journey that takes around 15 years from starting idea to approval and marketing of new medication. Despite the R&D expenditures have been constantly increasing in the last few years, number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. From this point of view, it is clear that if we want to increase drug-discovery success rate and reduce costs associated with development of a new drug, a comprehensive evaluation/prediction of potential safety issues should be conducted as soon as possible during early drug discovery phase. In the present review, we will analyse the early steps of drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
In silico polypharmacology of natural products.

PubMed

Fang, Jiansong; Liu, Chuang; Wang, Qi; Lin, Ping; Cheng, Feixiong

2017-04-27

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.
CisSERS: Customizable in silico sequence evaluation for restriction sites

DOE PAGES

Sharpe, Richard M.; Koepke, Tyson; Harper, Artemus; ...

2016-04-12

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Here, data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated tomore » enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERSenable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3’UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERSand results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.« less
CisSERS: Customizable in silico sequence evaluation for restriction sites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sharpe, Richard M.; Koepke, Tyson; Harper, Artemus

High-throughput sequencing continues to produce an immense volume of information that is processed and assembled into mature sequence data. Here, data analysis tools are urgently needed that leverage the embedded DNA sequence polymorphisms and consequent changes to restriction sites or sequence motifs in a high-throughput manner to enable biological experimentation. CisSERS was developed as a standalone open source tool to analyze sequence datasets and provide biologists with individual or comparative genome organization information in terms of presence and frequency of patterns or motifs such as restriction enzymes. Predicted agarose gel visualization of the custom analyses results was also integrated tomore » enhance the usefulness of the software. CisSERS offers several novel functionalities, such as handling of large and multiple datasets in parallel, multiple restriction enzyme site detection and custom motif detection features, which are seamlessly integrated with real time agarose gel visualization. Using a simple fasta-formatted file as input, CisSERS utilizes the REBASE enzyme database. Results from CisSERSenable the user to make decisions for designing genotyping by sequencing experiments, reduced representation sequencing, 3’UTR sequencing, and cleaved amplified polymorphic sequence (CAPS) molecular markers for large sample sets. CisSERS is a java based graphical user interface built around a perl backbone. Several of the applications of CisSERS including CAPS molecular marker development were successfully validated using wet-lab experimentation. Here, we present the tool CisSERSand results from in-silico and corresponding wet-lab analyses demonstrating that CisSERS is a technology platform solution that facilitates efficient data utilization in genomics and genetics studies.« less
Systems metabolic engineering: genome-scale models and beyond.

PubMed

Blazeck, John; Alper, Hal

2010-07-01

The advent of high throughput genome-scale bioinformatics has led to an exponential increase in available cellular system data. Systems metabolic engineering attempts to use data-driven approaches--based on the data collected with high throughput technologies--to identify gene targets and optimize phenotypical properties on a systems level. Current systems metabolic engineering tools are limited for predicting and defining complex phenotypes such as chemical tolerances and other global, multigenic traits. The most pragmatic systems-based tool for metabolic engineering to arise is the in silico genome-scale metabolic reconstruction. This tool has seen wide adoption for modeling cell growth and predicting beneficial gene knockouts, and we examine here how this approach can be expanded for novel organisms. This review will highlight advances of the systems metabolic engineering approach with a focus on de novo development and use of genome-scale metabolic reconstructions for metabolic engineering applications. We will then discuss the challenges and prospects for this emerging field to enable model-based metabolic engineering. Specifically, we argue that current state-of-the-art systems metabolic engineering techniques represent a viable first step for improving product yield that still must be followed by combinatorial techniques or random strain mutagenesis to achieve optimal cellular systems.
Utilization of Gastrointestinal Simulator, an in Vivo Predictive Dissolution Methodology, Coupled with Computational Approach To Forecast Oral Absorption of Dipyridamole.

PubMed

Matsui, Kazuki; Tsume, Yasuhiro; Takeuchi, Susumu; Searls, Amanda; Amidon, Gordon L

2017-04-03

Weakly basic drugs exhibit a pH-dependent dissolution profile in the gastrointestinal (GI) tract, which makes it difficult to predict their oral absorption profile. The aim of this study was to investigate the utility of the gastrointestinal simulator (GIS), a novel in vivo predictive dissolution (iPD) methodology, in predicting the in vivo behavior of the weakly basic drug dipyridamole when coupled with in silico analysis. The GIS is a multicompartmental dissolution apparatus, which represents physiological gastric emptying in the fasted state. Kinetic parameters for drug dissolution and precipitation were optimized by fitting a curve to the dissolved drug amount-time profiles in the United States Pharmacopeia apparatus II and GIS. Optimized parameters were incorporated into mathematical equations to describe the mass transport kinetics of dipyridamole in the GI tract. By using this in silico model, intraluminal drug concentration-time profile was simulated. The predicted profile of dipyridamole in the duodenal compartment adequately captured observed data. In addition, the plasma concentration-time profile was also predicted using pharmacokinetic parameters following intravenous administration. On the basis of the comparison with observed data, the in silico approach coupled with the GIS successfully predicted in vivo pharmacokinetic profiles. Although further investigations are still required to generalize, these results indicated that incorporating GIS data into mathematical equations improves the predictability of in vivo behavior of weakly basic drugs like dipyridamole.
Integration of Molecular Networking and In-Silico MS/MS Fragmentation for Natural Products Dereplication.

PubMed

Allard, Pierre-Marie; Péresse, Tiphaine; Bisson, Jonathan; Gindro, Katia; Marcourt, Laurence; Pham, Van Cuong; Roussi, Fanny; Litaudon, Marc; Wolfender, Jean-Luc

2016-03-15

Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.
VerSeDa: vertebrate secretome database

PubMed Central

Cortazar, Ana R.; Oguiza, José A.

2017-01-01

Based on the current tools, de novo secretome (full set of proteins secreted by an organism) prediction is a time consuming bioinformatic task that requires a multifactorial analysis in order to obtain reliable in silico predictions. Hence, to accelerate this process and offer researchers a reliable repository where secretome information can be obtained for vertebrates and model organisms, we have developed VerSeDa (Vertebrate Secretome Database). This freely available database stores information about proteins that are predicted to be secreted through the classical and non-classical mechanisms, for the wide range of vertebrate species deposited at the NCBI, UCSC and ENSEMBL sites. To our knowledge, VerSeDa is the only state-of-the-art database designed to store secretome data from multiple vertebrate genomes, thus, saving an important amount of time spent in the prediction of protein features that can be retrieved from this repository directly. Database URL: VerSeDa is freely available at http://genomics.cicbiogune.es/VerSeDa/index.php PMID:28365718
VerSeDa: vertebrate secretome database.

PubMed

Cortazar, Ana R; Oguiza, José A; Aransay, Ana M; Lavín, José L

2017-01-01

Based on the current tools, de novo secretome (full set of proteins secreted by an organism) prediction is a time consuming bioinformatic task that requires a multifactorial analysis in order to obtain reliable in silico predictions. Hence, to accelerate this process and offer researchers a reliable repository where secretome information can be obtained for vertebrates and model organisms, we have developed VerSeDa (Vertebrate Secretome Database). This freely available database stores information about proteins that are predicted to be secreted through the classical and non-classical mechanisms, for the wide range of vertebrate species deposited at the NCBI, UCSC and ENSEMBL sites. To our knowledge, VerSeDa is the only state-of-the-art database designed to store secretome data from multiple vertebrate genomes, thus, saving an important amount of time spent in the prediction of protein features that can be retrieved from this repository directly. VerSeDa is freely available at http://genomics.cicbiogune.es/VerSeDa/index.php. © The Author(s) 2017. Published by Oxford University Press.

Energy-based culture medium design for biomanufacturing optimization: A case study in monoclonal antibody production by GS-NS0 cells.

PubMed

Quiroga-Campano, Ana L; Panoskaltsis, Nicki; Mantalaris, Athanasios

2018-03-02

Demand for high-value biologics, a rapidly growing pipeline, and pressure from competition, time-to-market and regulators, necessitate novel biomanufacturing approaches, including Quality by Design (QbD) principles and Process Analytical Technologies (PAT), to facilitate accelerated, efficient and effective process development platforms that ensure consistent product quality and reduced lot-to-lot variability. Herein, QbD and PAT principles were incorporated within an innovative in vitro-in silico integrated framework for upstream process development (UPD). The central component of the UPD framework is a mathematical model that predicts dynamic nutrient uptake and average intracellular ATP content, based on biochemical reaction networks, to quantify and characterize energy metabolism and its adaptive response, metabolic shifts, to maintain ATP homeostasis. The accuracy and flexibility of the model depends on critical cell type/product/clone-specific parameters, which are experimentally estimated. The integrated in vitro-in silico platform and the model's predictive capacity reduced burden, time and expense of experimentation resulting in optimal medium design compared to commercially available culture media (80% amino acid reduction) and a fed-batch feeding strategy that increased productivity by 129%. The framework represents a flexible and efficient tool that transforms, improves and accelerates conventional process development in biomanufacturing with wide applications, including stem cell-based therapies. Copyright © 2018. Published by Elsevier Inc.
In silico identification of essential proteins in Corynebacterium pseudotuberculosis based on protein-protein interaction networks.

PubMed

Folador, Edson Luiz; de Carvalho, Paulo Vinícius Sanches Daltro; Silva, Wanderson Marques; Ferreira, Rafaela Salgado; Silva, Artur; Gromiha, Michael; Ghosh, Preetam; Barh, Debmalya; Azevedo, Vasco; Röttger, Richard

2016-11-04

Corynebacterium pseudotuberculosis (Cp) is a gram-positive bacterium that is classified into equi and ovis serovars. The serovar ovis is the etiological agent of caseous lymphadenitis, a chronic infection affecting sheep and goats, causing economic losses due to carcass condemnation and decreased production of meat, wool, and milk. Current diagnosis or treatment protocols are not fully effective and, thus, require further research of Cp pathogenesis. Here, we mapped known protein-protein interactions (PPI) from various species to nine Cp strains to reconstruct parts of the potential Cp interactome and to identify potentially essential proteins serving as putative drug targets. On average, we predict 16,669 interactions for each of the nine strains (with 15,495 interactions shared among all strains). An in silico sanity check suggests that the potential networks were not formed by spurious interactions but have a strong biological bias. With the inferred Cp networks we identify 181 essential proteins, among which 41 are non-host homologous. The list of candidate interactions of the Cp strains lay the basis for developing novel hypotheses and designing according wet-lab studies. The non-host homologous essential proteins are attractive targets for therapeutic and diagnostic proposes. They allow for searching of small molecule inhibitors of binding interactions enabling modern drug discovery. Overall, the predicted Cp PPI networks form a valuable and versatile tool for researchers interested in Corynebacterium pseudotuberculosis.
GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods.

PubMed

Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

2011-08-15

Over the last decade, numerous methods have been developed for inference of regulatory networks from gene expression data. However, accurate and systematic evaluation of these methods is hampered by the difficulty of constructing adequate benchmarks and the lack of tools for a differentiated analysis of network predictions on such benchmarks. Here, we describe a novel and comprehensive method for in silico benchmark generation and performance profiling of network inference methods available to the community as an open-source software called GeneNetWeaver (GNW). In addition to the generation of detailed dynamical models of gene regulatory networks to be used as benchmarks, GNW provides a network motif analysis that reveals systematic prediction errors, thereby indicating potential ways of improving inference methods. The accuracy of network inference methods is evaluated using standard metrics such as precision-recall and receiver operating characteristic curves. We show how GNW can be used to assess the performance and identify the strengths and weaknesses of six inference methods. Furthermore, we used GNW to provide the international Dialogue for Reverse Engineering Assessments and Methods (DREAM) competition with three network inference challenges (DREAM3, DREAM4 and DREAM5). GNW is available at http://gnw.sourceforge.net along with its Java source code, user manual and supporting data. Supplementary data are available at Bioinformatics online. dario.floreano@epfl.ch.
Crysalis: an integrated server for computational analysis and design of protein crystallization.

PubMed

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I; Lin, Donghai; Song, Jiangning

2016-02-24

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/.
Crysalis: an integrated server for computational analysis and design of protein crystallization

PubMed Central

Wang, Huilin; Feng, Liubin; Zhang, Ziding; Webb, Geoffrey I.; Lin, Donghai; Song, Jiangning

2016-01-01

The failure of multi-step experimental procedures to yield diffraction-quality crystals is a major bottleneck in protein structure determination. Accordingly, several bioinformatics methods have been successfully developed and employed to select crystallizable proteins. Unfortunately, the majority of existing in silico methods only allow the prediction of crystallization propensity, seldom enabling computational design of protein mutants that can be targeted for enhancing protein crystallizability. Here, we present Crysalis, an integrated crystallization analysis tool that builds on support-vector regression (SVR) models to facilitate computational protein crystallization prediction, analysis, and design. More specifically, the functionality of this new tool includes: (1) rapid selection of target crystallizable proteins at the proteome level, (2) identification of site non-optimality for protein crystallization and systematic analysis of all potential single-point mutations that might enhance protein crystallization propensity, and (3) annotation of target protein based on predicted structural properties. We applied the design mode of Crysalis to identify site non-optimality for protein crystallization on a proteome-scale, focusing on proteins currently classified as non-crystallizable. Our results revealed that site non-optimality is based on biases related to residues, predicted structures, physicochemical properties, and sequence loci, which provides in-depth understanding of the features influencing protein crystallization. Crysalis is freely available at http://nmrcen.xmu.edu.cn/crysalis/. PMID:26906024
Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

Ngo, Trieu-Du; Tran, Thanh-Dao; Le, Minh-Tri; Thai, Khac-Minh

2016-11-01

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.
Prediction of functionally significant single nucleotide polymorphisms in PTEN tumor suppressor gene: An in silico approach.

PubMed

Khan, Imran; Ansari, Irfan A; Singh, Pratichi; Dass J, Febin Prabhu

2017-09-01

The phosphatase and tensin homolog (PTEN) gene plays a crucial role in signal transduction by negatively regulating the PI3K signaling pathway. It is the most frequent mutated gene in many human-related cancers. Considering its critical role, a functional analysis of missense mutations of PTEN gene was undertaken in this study. Thirty five nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of the PTEN gene were selected for our in silico investigation, and five nsSNPs (G129E, C124R, D252G, H61D, and R130G) were found to be deleterious based on combinatorial predictions of different computational tools. Moreover, molecular dynamics (MD) simulation was performed to investigate the conformational variation between native and all the five mutant PTEN proteins having predicted deleterious nsSNPs. The results of MD simulation of all mutant models illustrated variation in structural attributes such as root-mean-square deviation, root-mean-square fluctuation, radius of gyration, and total energy; which depicts the structural stability of PTEN protein. Furthermore, mutant PTEN protein structures also showed a significant variation in the solvent accessible surface area and hydrogen bond frequencies from the native PTEN structure. In conclusion, results of this study have established the deleterious effect of the all the five predicted nsSNPs on the PTEN protein structure. Thus, results of the current study can pave a new platform to sort out nsSNPs that can be undertaken for the confirmation of their phenotype and their correlation with diseased status in case of control studies. © 2016 International Union of Biochemistry and Molecular Biology, Inc.
Genetic interaction networks: better understand to better predict

PubMed Central

Boucher, Benjamin; Jenna, Sarah

2013-01-01

A genetic interaction (GI) between two genes generally indicates that the phenotype of a double mutant differs from what is expected from each individual mutant. In the last decade, genome scale studies of quantitative GIs were completed using mainly synthetic genetic array technology and RNA interference in yeast and Caenorhabditis elegans. These studies raised questions regarding the functional interpretation of GIs, the relationship of genetic and molecular interaction networks, the usefulness of GI networks to infer gene function and co-functionality, the evolutionary conservation of GI, etc. While GIs have been used for decades to dissect signaling pathways in genetic models, their functional interpretations are still not trivial. The existence of a GI between two genes does not necessarily imply that these two genes code for interacting proteins or that the two genes are even expressed in the same cell. In fact, a GI only implies that the two genes share a functional relationship. These two genes may be involved in the same biological process or pathway; or they may also be involved in compensatory pathways with unrelated apparent function. Considering the powerful opportunity to better understand gene function, genetic relationship, robustness and evolution, provided by a genome-wide mapping of GIs, several in silico approaches have been employed to predict GIs in unicellular and multicellular organisms. Most of these methods used weighted data integration. In this article, we will review the later knowledge acquired on GI networks in metazoans by looking more closely into their relationship with pathways, biological processes and molecular complexes but also into their modularity and organization. We will also review the different in silico methods developed to predict GIs and will discuss how the knowledge acquired on GI networks can be used to design predictive tools with higher performances. PMID:24381582
Predicting the extent of metabolism using in vitro permeability rate measurements and in silico permeability rate predictions

PubMed Central

Hosey, Chelsea M; Benet, Leslie Z

2015-01-01

The Biopharmaceutics Drug Disposition Classification System (BDDCS) can be utilized to predict drug disposition, including interactions with other drugs and transporter or metabolizing enzyme effects based on the extent of metabolism and solubility of a drug. However, defining the extent of metabolism relies upon clinical data. Drugs exhibiting high passive intestinal permeability rates are extensively metabolized. Therefore, we aimed to determine if in vitro measures of permeability rate or in silico permeability rate predictions could predict the extent of metabolism, to determine a reference compound representing the permeability rate above which compounds would be expected to be extensively metabolized, and to predict the major route of elimination of compounds in a two-tier approach utilizing permeability rate and a previously published model predicting the major route of elimination of parent drug. Twenty-two in vitro permeability rate measurement data sets in Caco-2 and MDCK cell lines and PAMPA were collected from the literature, while in silico permeability rate predictions were calculated using ADMET Predictor™ or VolSurf+. The potential for permeability rate to differentiate between extensively and poorly metabolized compounds was analyzed with receiver operating characteristic curves. Compounds that yielded the highest sensitivity-specificity average were selected as permeability rate reference standards. The major route of elimination of poorly permeable drugs was predicted by our previously published model and the accuracies and predictive values were calculated. The areas under the receiver operating curves were >0.90 for in vitro measures of permeability rate and >0.80 for the VolSurf+ model of permeability rate, indicating they were able to predict the extent of metabolism of compounds. Labetalol and zidovudine predicted greater than 80% of extensively metabolized drugs correctly and greater than 80% of poorly metabolized drugs correctly in Caco-2 and MDCK, respectively, while theophylline predicted greater than 80% of extensively and poorly metabolized drugs correctly in PAMPA. A two-tier approach predicting elimination route predicts 72±9%, 49±10%, and 66±7% of extensively metabolized, biliarily eliminated, and renally eliminated parent drugs correctly when the permeability rate is predicted in silico and 74±7%, 85±2%, and 73±8% of extensively metabolized, biliarily eliminated, and renally eliminated parent drugs correctly, respectively when the permeability rate is determined in vitro. PMID:25816851
In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details.

PubMed

Lee, William; Windley, Monique J; Vandenberg, Jamie I; Hill, Adam P

2017-01-01

Acquired long QT syndrome, mostly as a result of drug block of the Kv11. 1 potassium channel in the heart, is characterized by delayed cardiac myocyte repolarization, prolongation of the T interval on the ECG, syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). In recent years, efforts are underway through the Comprehensive in vitro proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on in silico simulations of pharmacological disruption of repolarization. However, drug binding to Kv11.1 is more complex than a simple binary molecular reaction, meaning simple steady state measures of potency are poor surrogates for risk. As a result, there is a plethora of mechanistic detail describing the drug/Kv11.1 interaction-such as drug binding kinetics, state preference, temperature dependence and trapping-that needs to be considered when developing in silico models for risk prediction. In addition to this, other factors, such as multichannel pharmacological profile and the nature of the ventricular cell models used in simulations also need to be considered in the search for the optimum in silico approach. Here we consider how much of mechanistic detail needs to be included for in silico models to accurately predict risk and further, how much of this detail can be retrieved from protocols that are practical to implement in high throughout screens as part of next generation of preclinical in silico drug screening approaches?
In silico modeling on ADME properties of natural products: Classification models for blood-brain barrier permeability, its application to traditional Chinese medicine and in vitro experimental validation.

PubMed

Zhang, Xiuqing; Liu, Ting; Fan, Xiaohui; Ai, Ni

2017-08-01

In silico modeling of blood-brain barrier (BBB) permeability plays an important role in early discovery of central nervous system (CNS) drugs due to its high-throughput and cost-effectiveness. Natural products (NP) have demonstrated considerable therapeutic efficacy against several CNS diseases. However, BBB permeation property of NP is scarcely evaluated both experimentally and computationally. It is well accepted that significant difference in chemical spaces exists between NP and synthetic drugs, which calls into doubt on suitability of available synthetic chemical based BBB permeability models for the evaluation of NP. Herein poor discriminative performance on BBB permeability of NP are first confirmed using internal constructed and previously published drug-derived computational models, which warrants the need for NP-oriented modeling. Then a quantitative structure-property relationship (QSPR) study on a NP dataset was carried out using four different machine learning methods including support vector machine, random forest, Naïve Bayes and probabilistic neural network with 67 selected features. The final consensus model was obtained with approximate 90% overall accuracy for the cross-validation study, which is further taken to predict passive BBB permeability of a large dataset consisting of over 10,000 compounds from traditional Chinese medicine (TCM). For 32 selected TCM molecules, their predicted BBB permeability were evaluated by in vitro parallel artificial membrane permeability assay and overall accuracy for in vitro experimental validation is around 81%. Interestingly, our in silico model successfully predicted different BBB permeation potentials of parent molecules and their known in vivo metabolites. Finally, we found that the lipophilicity, the number of hydrogen bonds and molecular polarity were important molecular determinants for BBB permeability of NP. Our results suggest that the consensus model proposed in current work is a reliable tool for prioritizing potential CNS active NP across the BBB, which would accelerate their development and provide more understanding on their mechanisms, especially those with pharmacologically active metabolites. Copyright © 2017 Elsevier Inc. All rights reserved.
The effect of in silico targeting Pseudomonas aeruginosa patatin-like protein D, for immunogenic administration.

PubMed

Chirani, Alireza Salimi; Majidzadeh, Robabeh; Pouriran, Ramin; Heidary, Mohsen; Nasiri, Mohammad Javad; Gholami, Mehrdad; Goudarzi, Mehdi; Omrani, Vahid Fallah

2018-02-05

The vaccine candidates that have been introduced for immunization against Pseudomonas aeruginosa (P. aeruginosa) strains are quite diverse. In fact, there has been no proper antigen to act as an effective immunogenic substance against this ubiquitous pathogen in the market as yet. The complications caused by this bacterium due to the rapid development of multiple drug resistant strains have led to clinical problems worldwide. P. aeruginosa encodes many specific virulence elements that could be used as appropriate vaccine candidates. Type Vd secretion system, also known as patatin-like protein D, is a novel P. aeruginosa auto-transporter system. It is known that cellular or humoral immune responses could be elevated by chimeric proteins carrying epitopes. It has been recognized that in silico tools are essential for the evaluation of new chimeric antigens. In this study, we have considered the patatin-like protein D (PlpD) molecule from P. aeruginosa and predicted some immunogenic properties of this strong cytotoxic phospholipase A2 with the use of in-depth computational and immunoinformatics assessment methods The novelty of our in silico study is the modeling and assessment of both humoral and cellular immune potential against the PlpD molecule. The molecule was considered by multiple sequence alignment and homology valuation. The extremely conserved regions in the PlpD were predicted. The allergenic and physicochemical property predictions on the PlpD state that the molecule is a non-allergic and stable molecule. High-resolution secondary and tertiary conformations were created. Indeed, the B-cell and T-cell epitope mapping on the chimeric target protein confirmed that the engineered protein contained a tremendous number of both B-cell and T-cell corresponding epitopes. This investigation magnificently attained the chimeric molecule as being a potent lipolytic enzyme composed of numerous B-cell and T-cell restricted epitopes and could induce both humoral and cellular immune responses. The results indicated that this molecule has therapeutic potential against several potent pathogenic P. aeruginosa strains. Copyright © 2018. Published by Elsevier Ltd.
Structural Prediction and In Silico Physicochemical Characterization for Mouse Caltrin I and Bovine Caltrin Proteins

PubMed Central

Grasso, Ernesto J.; Sottile, Adolfo E.; Coronel, Carlos E.

2016-01-01

It is known that caltrin (calcium transport inhibitor) protein binds to sperm cells during ejaculation and inhibits extracellular Ca2+ uptake. Although the sequence and some biological features of mouse caltrin I and bovine caltrin are known, their physicochemical properties and tertiary structure are mainly unknown. We predicted the 3D structures of mouse caltrin I and bovine caltrin by molecular homology modeling and threading. Surface electrostatic potentials and electric fields were calculated using the Poisson–Boltzmann equation. Several different bioinformatics tools and available web servers were used to thoroughly analyze the physicochemical characteristics of both proteins, such as their Kyte and Doolittle hydropathy scores and helical wheel projections. The results presented in this work significantly aid further understanding of the molecular mechanisms of caltrin proteins modulating physiological processes associated with fertilization. PMID:27812283
In silico analysis of subtilisin from Glaciozyma antarctica PI12

NASA Astrophysics Data System (ADS)

Mustafha, Siti Mardhiah; Murad, Abdul Munir Abdul; Mahadi, Nor Muhammad; Kamaruddin, Shazilah; Bakar, Farah Diba Abu

2015-09-01

Subtilisin constitute as a major player in industrial enzymes that has a wide range of application especially in the detergent industry. In this study, a cDNA encoding for subtilisin (GaSUBT) was extracted from the psychrophilic yeast, Glaciozyma antarctica PI12, PCR amplified and sequenced. Various bioinformatics tools were used to characterize the GaSUBT. GaSUBT contains 1587 bp nucleotides encoding for 529 amino acids. The predicted molecular weight of the deduced protein is 55.34 kDa with an isoelectric point of 6.25. GaSUBT was predicted to possess a signal peptide and pro-peptide consisting of a peptidase inhibitor I9 sequence. From the sequence alignment analysis of deduced amino acids with other subtilisins in the NCBI database showed that the sequences surrounding the catalytic triad that forms the catalytic domain are well conserved.
In silico modelling of radiation effects towards personalised treatment in radiotherapy

NASA Astrophysics Data System (ADS)

Marcu, Loredana G.; Marcu, David

2017-12-01

In silico models applied in medical physics are valuable tools to assist in treatment optimization and personalization, which represent the ultimate goal of today's radiotherapy. Next to several biological and biophysical factors that influence tumour response to ionizing radiation, hypoxia and cancer stem cells are critical parameters that dictate the final outcome. The current work presents the results of an in silico model of tumour growth and response to radiation developed using Monte Carlo techniques. We are presenting the impact of partial oxygen tension and repopulation via cancer stem cells on tumour control after photon irradiation, highlighting some of the gaps that clinical research needs to fill for better customized treatment.
Evaluation of Bioinformatic Programmes for the Analysis of Variants within Splice Site Consensus Regions

PubMed Central

Tang, Rongying; Prosser, Debra O.; Love, Donald R.

2016-01-01

The increasing diagnostic use of gene sequencing has led to an expanding dataset of novel variants that lie within consensus splice junctions. The challenge for diagnostic laboratories is the evaluation of these variants in order to determine if they affect splicing or are merely benign. A common evaluation strategy is to use in silico analysis, and it is here that a number of programmes are available online; however, currently, there are no consensus guidelines on the selection of programmes or protocols to interpret the prediction results. Using a collection of 222 pathogenic mutations and 50 benign polymorphisms, we evaluated the sensitivity and specificity of four in silico programmes in predicting the effect of each variant on splicing. The programmes comprised Human Splice Finder (HSF), Max Entropy Scan (MES), NNSplice, and ASSP. The MES and ASSP programmes gave the highest performance based on Receiver Operator Curve analysis, with an optimal cut-off of score reduction of 10%. The study also showed that the sensitivity of prediction is affected by the level of conservation of individual positions, with in silico predictions for variants at positions −4 and +7 within consensus splice sites being largely uninformative. PMID:27313609
Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity

NASA Astrophysics Data System (ADS)

Chen, Huanhuan; Yao, Xin

Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.
The acceptance of in silico models for REACH: Requirements, barriers, and perspectives

PubMed Central

2011-01-01

In silico models have prompted considerable interest and debate because of their potential value in predicting the properties of chemical substances for regulatory purposes. The European REACH legislation promotes innovation and encourages the use of alternative methods, but in practice the use of in silico models is still very limited. There are many stakeholders influencing the regulatory trajectory of quantitative structure-activity relationships (QSAR) models, including regulators, industry, model developers and consultants. Here we outline some of the issues and challenges involved in the acceptance of these methods for regulatory purposes. PMID:21982269
Rational assignment of key motifs for function guides in silico enzyme identification.

PubMed

Höhne, Matthias; Schätzle, Sebastian; Jochens, Helge; Robins, Karen; Bornscheuer, Uwe T

2010-11-01

Biocatalysis has emerged as a powerful alternative to traditional chemistry, especially for asymmetric synthesis. One key requirement during process development is the discovery of a biocatalyst with an appropriate enantiopreference and enantioselectivity, which can be achieved, for instance, by protein engineering or screening of metagenome libraries. We have developed an in silico strategy for a sequence-based prediction of substrate specificity and enantiopreference. First, we used rational protein design to predict key amino acid substitutions that indicate the desired activity. Then, we searched protein databases for proteins already carrying these mutations instead of constructing the corresponding mutants in the laboratory. This methodology exploits the fact that naturally evolved proteins have undergone selection over millions of years, which has resulted in highly optimized catalysts. Using this in silico approach, we have discovered 17 (R)-selective amine transaminases, which catalyzed the synthesis of several (R)-amines with excellent optical purity up to >99% enantiomeric excess.
Prediction of the Hydrogen Peroxide-Induced Methionine Oxidation Propensity in Monoclonal Antibodies.

PubMed

Agrawal, Neeraj J; Dykstra, Andrew; Yang, Jane; Yue, Hai; Nguyen, Xichdao; Kolvenbach, Carl; Angell, Nicolas

2018-05-01

Methionine oxidation in therapeutic antibodies can impact the product's stability, clinical efficacy, and safety and hence it is desirable to address the methionine oxidation liability during antibody discovery and development phase. Although the current experimental approaches can identify the oxidation-labile methionine residues, their application is limited mostly to the development phase. We demonstrate an in silico method that can be used to predict oxidation-labile residues based solely on the antibody sequence and structure information. Since antibody sequence information is available in the discovery phase, the in silico method can be applied very early on to identify the oxidation-labile methionine residues and subsequently address the oxidation liability. We believe that the in silico method for methionine oxidation liability assessment can aid in antibody discovery and development phase to address the liability in a more rational way. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Predicting skin sensitisation using a decision tree integrated testing strategy with an in silico model and in chemico/in vitro assays.

PubMed

Macmillan, Donna S; Canipa, Steven J; Chilton, Martyn L; Williams, Richard V; Barber, Christopher G

2016-04-01

There is a pressing need for non-animal methods to predict skin sensitisation potential and a number of in chemico and in vitro assays have been designed with this in mind. However, some compounds can fall outside the applicability domain of these in chemico/in vitro assays and may not be predicted accurately. Rule-based in silico models such as Derek Nexus are expert-derived from animal and/or human data and the mechanism-based alert domain can take a number of factors into account (e.g. abiotic/biotic activation). Therefore, Derek Nexus may be able to predict for compounds outside the applicability domain of in chemico/in vitro assays. To this end, an integrated testing strategy (ITS) decision tree using Derek Nexus and a maximum of two assays (from DPRA, KeratinoSens, LuSens, h-CLAT and U-SENS) was developed. Generally, the decision tree improved upon other ITS evaluated in this study with positive and negative predictivity calculated as 86% and 81%, respectively. Our results demonstrate that an ITS using an in silico model such as Derek Nexus with a maximum of two in chemico/in vitro assays can predict the sensitising potential of a number of chemicals, including those outside the applicability domain of existing non-animal assays. Copyright © 2016 Elsevier Inc. All rights reserved.
In Silico Modeling Approach for the Evaluation of Gastrointestinal Dissolution, Supersaturation, and Precipitation of Posaconazole.

PubMed

Hens, Bart; Pathak, Shriram M; Mitra, Amitava; Patel, Nikunjkumar; Liu, Bo; Patel, Sanjaykumar; Jamei, Masoud; Brouwers, Joachim; Augustijns, Patrick; Turner, David B

2017-12-04

The aim of this study was to evaluate gastrointestinal (GI) dissolution, supersaturation, and precipitation of posaconazole, formulated as an acidified (pH 1.6) and neutral (pH 7.1) suspension. A physiologically based pharmacokinetic (PBPK) modeling and simulation tool was applied to simulate GI and systemic concentration-time profiles of posaconazole, which were directly compared with intraluminal and systemic data measured in humans. The Advanced Dissolution Absorption and Metabolism (ADAM) model of the Simcyp Simulator correctly simulated incomplete gastric dissolution and saturated duodenal concentrations of posaconazole in the duodenal fluids following administration of the neutral suspension. In contrast, gastric dissolution was approximately 2-fold higher after administration of the acidified suspension, which resulted in supersaturated concentrations of posaconazole upon transfer to the upper small intestine. The precipitation kinetics of posaconazole were described by two precipitation rate constants, extracted by semimechanistic modeling of a two-stage medium change in vitro dissolution test. The 2-fold difference in exposure in the duodenal compartment for the two formulations corresponded with a 2-fold difference in systemic exposure. This study demonstrated for the first time predictive in silico simulations of GI dissolution, supersaturation, and precipitation for a weakly basic compound in part informed by modeling of in vitro dissolution experiments and validated via clinical measurements in both GI fluids and plasma. Sensitivity analysis with the PBPK model indicated that the critical supersaturation ratio (CSR) and second precipitation rate constant (sPRC) are important parameters of the model. Due to the limitations of the two-stage medium change experiment the CSR was extracted directly from the clinical data. However, in vitro experiments with the BioGIT transfer system performed after completion of the in silico modeling provided an almost identical CSR to the clinical study value; this had no significant impact on the PBPK model predictions.
Advancing alternatives analysis: The role of predictive toxicology in selecting safer chemical products and processes.

PubMed

Malloy, Timothy; Zaunbrecher, Virginia; Beryt, Elizabeth; Judson, Richard; Tice, Raymond; Allard, Patrick; Blake, Ann; Cote, Ila; Godwin, Hilary; Heine, Lauren; Kerzic, Patrick; Kostal, Jakub; Marchant, Gary; McPartland, Jennifer; Moran, Kelly; Nel, Andre; Ogunseitan, Oladele; Rossi, Mark; Thayer, Kristina; Tickner, Joel; Whittaker, Margaret; Zarker, Ken

2017-09-01

Alternatives analysis (AA) is a method used in regulation and product design to identify, assess, and evaluate the safety and viability of potential substitutes for hazardous chemicals. It requires toxicological data for the existing chemical and potential alternatives. Predictive toxicology uses in silico and in vitro approaches, computational models, and other tools to expedite toxicological data generation in a more cost-effective manner than traditional approaches. The present article briefly reviews the challenges associated with using predictive toxicology in regulatory AA, then presents 4 recommendations for its advancement. It recommends using case studies to advance the integration of predictive toxicology into AA, adopting a stepwise process to employing predictive toxicology in AA beginning with prioritization of chemicals of concern, leveraging existing resources to advance the integration of predictive toxicology into the practice of AA, and supporting transdisciplinary efforts. The further incorporation of predictive toxicology into AA would advance the ability of companies and regulators to select alternatives to harmful ingredients, and potentially increase the use of predictive toxicology in regulation more broadly. Integr Environ Assess Manag 2017;13:915-925. © 2017 SETAC. © 2017 SETAC.
In silico prediction of a disease-associated STIL mutant and its affect on the recruitment of centromere protein J (CENPJ).

PubMed

Kumar, Ambuj; Rajendran, Vidya; Sethumadhavan, Rao; Purohit, Rituraj

2012-01-01

Human STIL (SCL/TAL1 interrupting locus) protein maintains centriole stability and spindle pole localisation. It helps in recruitment of CENPJ (Centromere protein J)/CPAP (centrosomal P4.1-associated protein) and other centrosomal proteins. Mutations in STIL protein are reported in several disorders, especially in deregulation of cell cycle cascades. In this work, we examined the non-synonymous single nucleotide polymorphisms (nsSNPs) reported in STIL protein for their disease association. Different SNP prediction tools were used to predict disease-associated nsSNPs. Our evaluation technique predicted rs147744459 (R242C) as a highly deleterious disease-associated nsSNP and its interaction behaviour with CENPJ protein. Molecular modelling, docking and molecular dynamics simulation were conducted to examine the structural consequences of the predicted disease-associated mutation. By molecular dynamic simulation we observed structural consequences of R242C mutation which affects interaction of STIL and CENPJ functional domains. The result obtained in this study will provide a biophysical insight into future investigations of pathological nsSNPs using a computational platform.
De-MetaST-BLAST: A Tool for the Validation of Degenerate Primer Sets and Data Mining of Publicly Available Metagenomes

PubMed Central

Gulvik, Christopher A.; Effler, T. Chad; Wilhelm, Steven W.; Buchan, Alison

2012-01-01

Development and use of primer sets to amplify nucleic acid sequences of interest is fundamental to studies spanning many life science disciplines. As such, the validation of primer sets is essential. Several computer programs have been created to aid in the initial selection of primer sequences that may or may not require multiple nucleotide combinations (i.e., degeneracies). Conversely, validation of primer specificity has remained largely unchanged for several decades, and there are currently few available programs that allows for an evaluation of primers containing degenerate nucleotide bases. To alleviate this gap, we developed the program De-MetaST that performs an in silico amplification using user defined nucleotide sequence dataset(s) and primer sequences that may contain degenerate bases. The program returns an output file that contains the in silico amplicons. When De-MetaST is paired with NCBI’s BLAST (De-MetaST-BLAST), the program also returns the top 10 nr NCBI database hits for each recovered in silico amplicon. While the original motivation for development of this search tool was degenerate primer validation using the wealth of nucleotide sequences available in environmental metagenome and metatranscriptome databases, this search tool has potential utility in many data mining applications. PMID:23189198
Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.
Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.

PubMed

Braido, Fulvio; Santus, Pierachille; Corsico, Angelo Guido; Di Marco, Fabiano; Melioli, Giovanni; Scichilone, Nicola; Solidoro, Paolo

2018-01-01

The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive lung disease (COLD). A questionnaire and a WebFlex code were developed and validated in silico. An expert panel pilot validation on 60 cases and a clinical validation on 241 cases were performed. The developed questionnaire and code validated in silico resulted in a suitable tool to support the medical diagnosis. The clinical validation of the ES was performed in an academic setting that included six different reference centers for respiratory diseases. The results of the ES expressed as a score associated with the risk of suffering from COLD were matched and compared with the final clinical diagnoses. A set of 60 patients were evaluated by a pilot expert panel validation with the aim of calculating the sample size for the clinical validation study. The concordance analysis between these preliminary ES scores and diagnoses performed by the experts indicated that the accuracy was 94.7% when both experts and the system confirmed the COLD diagnosis and 86.3% when COLD was excluded. Based on these results, the sample size of the validation set was established in 240 patients. The clinical validation, performed on 241 patients, resulted in ES accuracy of 97.5%, with confirmed COLD diagnosis in 53.6% of the cases and excluded COLD diagnosis in 32% of the cases. In 11.2% of cases, a diagnosis of COLD was made by the experts, although the imaging results showed a potential concomitant disorder. The ES presented here (COLD ES ) is a safe and robust supporting tool for COLD diagnosis in primary care settings.
Green Toxicology: a strategy for sustainable chemical and material development.

PubMed

Crawford, Sarah E; Hartung, Thomas; Hollert, Henner; Mathes, Björn; van Ravenzwaay, Bennard; Steger-Hartmann, Thomas; Studer, Christoph; Krug, Harald F

2017-01-01

Green Toxicology refers to the application of predictive toxicology in the sustainable development and production of new less harmful materials and chemicals, subsequently reducing waste and exposure. Built upon the foundation of "Green Chemistry" and "Green Engineering", "Green Toxicology" aims to shape future manufacturing processes and safe synthesis of chemicals in terms of environmental and human health impacts. Being an integral part of Green Chemistry, the principles of Green Toxicology amplify the role of health-related aspects for the benefit of consumers and the environment, in addition to being economical for manufacturing companies. Due to the costly development and preparation of new materials and chemicals for market entry, it is no longer practical to ignore the safety and environmental status of new products during product development stages. However, this is only possible if toxicologists and chemists work together early on in the development of materials and chemicals to utilize safe design strategies and innovative in vitro and in silico tools. This paper discusses some of the most relevant aspects, advances and limitations of the emergence of Green Toxicology from the perspective of different industry and research groups. The integration of new testing methods and strategies in product development, testing and regulation stages are presented with examples of the application of in silico, omics and in vitro methods. Other tools for Green Toxicology, including the reduction of animal testing, alternative test methods, and read-across approaches are also discussed.
Using proteomic data to assess a genome-scale "in silico" model of metal reducing bacteria in the simulation of field-scale uranium bioremediation

NASA Astrophysics Data System (ADS)

Yabusaki, S.; Fang, Y.; Wilkins, M. J.; Long, P.; Rifle IFRC Science Team

2011-12-01

A series of field experiments in a shallow alluvial aquifer at a former uranium mill tailings site have demonstrated that indigenous bacteria can be stimulated with acetate to catalyze the conversion of hexavalent uranium in a groundwater plume to immobile solid-associated uranium in the +4 oxidation state. While this bioreduction of uranium has been shown to lower groundwater concentrations below actionable standards, a viable remediation methodology will need a mechanistic, predictive and quantitative understanding of the microbially-mediated reactions that catalyze the reduction of uranium in the context of site-specific processes, properties, and conditions. At the Rifle IFRC site, we are investigating the impacts on uranium behavior of pulsed acetate amendment, acetate-oxidizing iron and sulfate reducing bacteria, seasonal water table variation, spatially-variable physical (hydraulic conductivity, porosity) and geochemical (reactive surface area) material properties. The simulation of three-dimensional, variably saturated flow and biogeochemical reactive transport during a uranium bioremediation field experiment includes a genome-scale in silico model of Geobacter sp. to represent the Fe(III) terminal electron accepting process (TEAP). The Geobacter in silico model of cell-scale physiological metabolic pathways is comprised of hundreds of intra-cellular and environmental exchange reactions. One advantage of this approach is that the TEAP reaction stoichiometry and rate are now functions of the metabolic status of the microorganism. The linkage of in silico model reactions to specific Geobacter proteins has enabled the use of groundwater proteomic analyses to assess the accuracy of the model under evolving hydrologic and biogeochemical conditions. In this case, the largest predicted fluxes through in silico model reactions generally correspond to high abundances of proteins linked to those reactions (e.g. the condensation reaction catalyzed by the protein citrate synthase that generates citrate from acetyl-CoA and oxaloacetate). Model discrepancies with the proteomic data, such as the prediction of shifts associated with nitrogen limitation, revealed pathways in the in silico code that could be modified to more accurately predict metabolic processes that occur in the subsurface. The potential outcome of this approach is the engineering of electron donor (e.g., acetate), terminal electron acceptor [e.g., U(VI)], and biogeochemical conditions that enhance the desired metabolic pathways of the target microorganism(s) to effect cost-effective uranium bioreduction.
Limitations of in silico predictability of specificity of co-immobilised cytochromes P450 and mimics in food-bioprocessing.

PubMed

Wiseman, Alan

2003-04-01

Cytochromes P450 (EC 1.14.14.1) are mixed function oxidases (oxygenases) that can catalyse redox bioconversions of food components. Also, efficacious removal of undesirable components can be achieved using solid-support immobilised enzyme (IME) of a selection from 2700 isoforms of cytochromes P450 (CYP). Cytochromes P450 co-immobilised with other enzymes, or protein receptors, may be used to confer a secondary order of regio- or stereo-specificity of chiral bioconversion: these can be predictable in silico by utilisation of QSARs (quantitative structure/activity relationships).
High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE.

PubMed

Castellana, Stefano; Fusilli, Caterina; Mazzoccoli, Gianluigi; Biagini, Tommaso; Capocefalo, Daniele; Carella, Massimo; Vescovi, Angelo Luigi; Mazza, Tommaso

2017-06-01

24,189 are all the possible non-synonymous amino acid changes potentially affecting the human mitochondrial DNA. Only a tiny subset was functionally evaluated with certainty so far, while the pathogenicity of the vast majority was only assessed in-silico by software predictors. Since these tools proved to be rather incongruent, we have designed and implemented APOGEE, a machine-learning algorithm that outperforms all existing prediction methods in estimating the harmfulness of mitochondrial non-synonymous genome variations. We provide a detailed description of the underlying algorithm, of the selected and manually curated training and test sets of variants, as well as of its classification ability.
Simulation data for an estimation of the maximum theoretical value and confidence interval for the correlation coefficient.

PubMed

Rocco, Paolo; Cilurzo, Francesco; Minghetti, Paola; Vistoli, Giulio; Pedretti, Alessandro

2017-10-01

The data presented in this article are related to the article titled "Molecular Dynamics as a tool for in silico screening of skin permeability" (Rocco et al., 2017) [1]. Knowledge of the confidence interval and maximum theoretical value of the correlation coefficient r can prove useful to estimate the reliability of developed predictive models, in particular when there is great variability in compiled experimental datasets. In this Data in Brief article, data from purposely designed numerical simulations are presented to show how much the maximum r value is worsened by increasing the data uncertainty. The corresponding confidence interval of r is determined by using the Fisher r → Z transform.
Immunoinformatics: an integrated scenario

PubMed Central

Tomar, Namrata; De, Rajat K

2010-01-01

Genome sequencing of humans and other organisms has led to the accumulation of huge amounts of data, which include immunologically relevant data. A large volume of clinical data has been deposited in several immunological databases and as a result immunoinformatics has emerged as an important field which acts as an intersection between experimental immunology and computational approaches. It not only helps in dealing with the huge amount of data but also plays a role in defining new hypotheses related to immune responses. This article reviews classical immunology, different databases and prediction tools. It also describes applications of immunoinformatics in designing in silico vaccination and immune system modelling. All these efforts save time and reduce cost. PMID:20722763
Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

SYNOPSIS: The question of how tissues and organs are shaped during development is crucial for understanding human birth defects. Data from high-throughput screening assays on human stem cells may be utilized predict developmental toxicity with reasonable accuracy. Other types of models are necessary, however, for mechanism-specific analysis because embryogenesis requires precise timing and control. Agent-based modeling and simulation (ABMS) is an approach to virtually reconstruct these dynamics, cell-by-cell and interaction-by-interaction. Using ABMS, HTS lesions from ToxCast can be integrated with patterning systems heuristically to propagate key events This presentation to FDA-CFSAN will update progress on the applications of in silico modeling tools and approaches for assessing developmental toxicity.
The current status of alternatives to animal testing and predictive toxicology methods using liver microfluidic biochips.

PubMed

Prot, Jean Matthieu; Leclerc, Eric

2012-06-01

In this paper, we will consider new in vitro cell culture platforms and the progress made, based on the microfluidic liver biochips dedicated to pharmacological and toxicological studies. Particular emphasis will be given to recent developments in the microfluidic tools dedicated to cell culture (more particularly liver cell culture), in silico opportunities for Physiologically Based PharmacoKinetic (PBPK) modelling, the challenge of the mechanistic interpretations offered by the approaches resulting from "multi-omics" data (transcriptomics, proteomics, metabolomics, cytomics) and imaging microfluidic platforms. Finally, we will discuss the critical features regarding microfabrication, design and materials, and cell functionality as the key points for the future development of new microfluidic liver biochips.
In Silico Estimation of Skin Concentration Following the Dermal Exposure to Chemicals.

PubMed

Hatanaka, Tomomi; Yoshida, Shun; Kadhum, Wesam R; Todo, Hiroaki; Sugibayashi, Kenji

2015-12-01

To develop an in silico method based on Fick's law of diffusion to estimate the skin concentration following dermal exposure to chemicals with a wide range of lipophilicity. Permeation experiments of various chemicals were performed through rat and porcine skin. Permeation parameters, namely, permeability coefficient and partition coefficient, were obtained by the fitting of data to two-layered and one-layered diffusion models for whole and stripped skin. The mean skin concentration of chemicals during steady-state permeation was calculated using the permeation parameters and compared with the observed values. All permeation profiles could be described by the diffusion models. The estimated skin concentrations of chemicals using permeation parameters were close to the observed levels and most data fell within the 95% confidence interval for complete prediction. The permeability coefficient and partition coefficient for stripped skin were almost constant, being independent of the permeant's lipophilicity. Skin concentration following dermal exposure to various chemicals can be accurately estimated based on Fick's law of diffusion. This method should become a useful tool to assess the efficacy of topically applied drugs and cosmetic ingredients, as well as the risk of chemicals likely to cause skin disorders and diseases.
Report from the EPAA workshop: in vitro ADME in safety testing used by EPAA industry sectors.

PubMed

Schroeder, K; Bremm, K D; Alépée, N; Bessems, J G M; Blaauboer, B; Boehn, S N; Burek, C; Coecke, S; Gombau, L; Hewitt, N J; Heylings, J; Huwyler, J; Jaeger, M; Jagelavicius, M; Jarrett, N; Ketelslegers, H; Kocina, I; Koester, J; Kreysa, J; Note, R; Poth, A; Radtke, M; Rogiers, V; Scheel, J; Schulz, T; Steinkellner, H; Toeroek, M; Whelan, M; Winkler, P; Diembeck, W

2011-04-01

There are now numerous in vitro and in silico ADME alternatives to in vivo assays but how do different industries incorporate them into their decision tree approaches for risk assessment, bearing in mind that the chemicals tested are intended for widely varying purposes? The extent of the use of animal tests is mainly driven by regulations or by the lack of a suitable in vitro model. Therefore, what considerations are needed for alternative models and how can they be improved so that they can be used as part of the risk assessment process? To address these issues, the European Partnership for Alternative Approaches to Animal Testing (EPAA) working group on prioritization, promotion and implementation of the 3Rs research held a workshop in November, 2008 in Duesseldorf, Germany. Participants included different industry sectors such as pharmaceuticals, cosmetics, industrial- and agro-chemicals. This report describes the outcome of the discussions and recommendations (a) to reduce the number of animals used for determining the ADME properties of chemicals and (b) for considerations and actions regarding in vitro and in silico assays. These included: standardisation and promotion of in vitro assays so that they may become accepted by regulators; increased availability of industry in vivo kinetic data for a central database to increase the power of in silico predictions; expansion of the applicability domains of in vitro and in silico tools (which are not necessarily more applicable or even exclusive to one particular sector) and continued collaborations between regulators, academia and industry. A recommended immediate course of action was to establish an expert panel of users, developers and regulators to define the testing scope of models for different chemical classes. It was agreed by all participants that improvement and harmonization of alternative approaches is needed for all sectors and this will most effectively be achieved by stakeholders from different sectors sharing data. Copyright © 2010 Elsevier Ltd. All rights reserved.
In silico SNP analysis of the breast cancer antigen NY-BR-1.

PubMed

Kosaloglu, Zeynep; Bitzer, Julia; Halama, Niels; Huang, Zhiqin; Zapatka, Marc; Schneeweiss, Andreas; Jäger, Dirk; Zörnig, Inka

2016-11-18

Breast cancer is one of the most common malignancies with increasing incidences every year and a leading cause of death among women. Although early stage breast cancer can be effectively treated, there are limited numbers of treatment options available for patients with advanced and metastatic disease. The novel breast cancer associated antigen NY-BR-1 was identified by SEREX analysis and is expressed in the majority (>70%) of breast tumors as well as metastases, in normal breast tissue, in testis and occasionally in prostate tissue. The biological function and regulation of NY-BR-1 is up to date unknown. We performed an in silico analysis on the genetic variations of the NY-BR-1 gene using data available in public SNP databases and the tools SIFT, Polyphen and Provean to find possible functional SNPs. Additionally, we considered the allele frequency of the found damaging SNPs and also analyzed data from an in-house sequencing project of 55 breast cancer samples for recurring SNPs, recorded in dbSNP. Over 2800 SNPs are recorded in the dbSNP and NHLBI ESP databases for the NY-BR-1 gene. Of these, 65 (2.07%) are synonymous SNPs, 191 (6.09%) are non-synoymous SNPs, and 2430 (77.48%) are noncoding intronic SNPs. As a result, 69 non-synoymous SNPs were predicted to be damaging by at least two, and 16 SNPs were predicted as damaging by all three of the used tools. The SNPs rs200639888, rs367841401 and rs377750885 were categorized as highly damaging by all three tools. Eight damaging SNPs are located in the ankyrin repeat domain (ANK), a domain known for its frequent involvement in protein-protein interactions. No distinctive features could be observed in the allele frequency of the analyzed SNPs. Considering these results we expect to gain more insights into the variations of the NY-BR-1 gene and their possible impact on giving rise to splice variants and therefore influence the function of NY-BR-1 in healthy tissue as well as in breast cancer.
Reliable B Cell Epitope Predictions: Impacts of Method Development and Improved Benchmarking

PubMed Central

Kringelum, Jens Vindahl; Lundegaard, Claus; Lund, Ole; Nielsen, Morten

2012-01-01

The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0. PMID:23300419
Reevaluation of RINT1 as a breast cancer predisposition gene.

PubMed

Li, Na; Thompson, Ella R; Rowley, Simone M; McInerny, Simone; Devereux, Lisa; Goode, David; Investigators, LifePool; Wong-Brown, Michelle W; Scott, Rodney J; Trainer, Alison H; Gorringe, Kylie L; James, Paul A; Campbell, Ian G

2016-09-01

Rad50 interactor 1 (RINT1) has recently been reported as an intermediate-penetrance (odds ratio 3.24) breast cancer susceptibility gene, as well as a risk factor for Lynch syndrome. The coding regions and exon-intron boundaries of RINT1 were sequenced in 2024 familial breast cancer cases previously tested negative for BRCA1, BRCA2, and PALB2 mutations and 1886 population-matched cancer-free controls using HaloPlex Targeted Enrichment Assays. Only one RINT1 protein-truncating variant was detected in a control. No excess was observed in the total number of rare variants (truncating and missense) (28, 1.38 %, vs. 27, 1.43 %. P > 0.999) or in the number of variants predicted to be pathogenic by various in silico tools (Condel, Polyphen2, SIFT, and CADD) in the cases compared to the controls. In addition, there was no difference in the incidence of classic Lynch syndrome cancers in RINT1 rare variant-carrying families compared to RINT1 wild-type families. This study had 90 % power to detect an odds ratio of at least 2.06, and the results do not provide any support for RINT1 being a moderate-penetrance breast cancer susceptibility gene, although larger studies will be required to exclude more modest effects. This study emphasizes the need for caution before designating a cancer predisposition role for any gene based on very rare truncating variants and in silico-predicted missense variants.

Arrhythmic hazard map for a 3D whole-ventricles model under multiple ion channel block.

PubMed

Okada, Jun-Ichi; Yoshinaga, Takashi; Kurokawa, Junko; Washio, Takumi; Furukawa, Tetsushi; Sawada, Kohei; Sugiura, Seiryo; Hisada, Toshiaki

2018-05-10

To date, proposed in silico models for preclinical cardiac safety testing are limited in their predictability and usability. We previously reported a multi-scale heart simulation that accurately predicts arrhythmogenic risk for benchmark drugs. We extend this approach and report the first comprehensive hazard map of drug-induced arrhythmia based on the exhaustive in silico electrocardiogram (ECG) database of drug effects, developed using a petaflop computer. A total of 9075 electrocardiograms constitute the five-dimensional hazard map, with coordinates representing the extent of the block of each of the five ionic currents (rapid delayed rectifier potassium current (IKr), fast (INa) and late (INa,L) components of the sodium current, L-type calcium current (ICa,L) and slow delayed rectifier current (IKs)), involved in arrhythmogenesis. Results of the evaluation of arrhythmogenic risk based on this hazard map agreed well with the risk assessments reported in three references. ECG database also suggested that the interval between the J-point and the T-wave peak is a superior index of arrhythmogenicity compared to other ECG biomarkers including the QT interval. Because concentration-dependent effects on electrocardiograms of any drug can be traced on this map based on in vitro current assay data, its arrhythmogenic risk can be evaluated without performing costly and potentially risky human electrophysiological assays. Hence, the map serves as a novel tool for use in pharmaceutical research and development. This article is protected by copyright. All rights reserved.
Airflow and particle deposition simulations in health and emphysema: from in vivo to in silico animal experiments.

PubMed

Oakes, Jessica M; Marsden, Alison L; Grandmont, Celine; Shadden, Shawn C; Darquenne, Chantal; Vignon-Clementel, Irene E

2014-04-01

Image-based in silico modeling tools provide detailed velocity and particle deposition data. However, care must be taken when prescribing boundary conditions to model lung physiology in health or disease, such as in emphysema. In this study, the respiratory resistance and compliance were obtained by solving an inverse problem; a 0D global model based on healthy and emphysematous rat experimental data. Multi-scale CFD simulations were performed by solving the 3D Navier-Stokes equations in an MRI-derived rat geometry coupled to a 0D model. Particles with 0.95 μm diameter were tracked and their distribution in the lung was assessed. Seven 3D-0D simulations were performed: healthy, homogeneous, and five heterogeneous emphysema cases. Compliance (C) was significantly higher (p = 0.04) in the emphysematous rats (C = 0.37 ± 0.14 cm(3)/cmH2O) compared to the healthy rats (C = 0.25 ± 0.04 cm(3)/cmH2O), while the resistance remained unchanged (p = 0.83). There were increases in airflow, particle deposition in the 3D model, and particle delivery to the diseased regions for the heterogeneous cases compared to the homogeneous cases. The results highlight the importance of multi-scale numerical simulations to study airflow and particle distribution in healthy and diseased lungs. The effect of particle size and gravity were studied. Once available, these in silico predictions may be compared to experimental deposition data.
Robustness of atomistic Gō models in predicting native-like folding intermediates

NASA Astrophysics Data System (ADS)

Estácio, S. G.; Fernandes, C. S.; Krobath, H.; Faísca, P. F. N.; Shakhnovich, E. I.

2012-08-01

Gō models are exceedingly popular tools in computer simulations of protein folding. These models are native-centric, i.e., they are directly constructed from the protein's native structure. Therefore, it is important to understand up to which extent the atomistic details of the native structure dictate the folding behavior exhibited by Gō models. Here we address this challenge by performing exhaustive discrete molecular dynamics simulations of a Gō potential combined with a full atomistic protein representation. In particular, we investigate the robustness of this particular type of Gō models in predicting the existence of intermediate states in protein folding. We focus on the N47G mutational form of the Spc-SH3 folding domain (x-ray structure) and compare its folding pathway with that of alternative native structures produced in silico. Our methodological strategy comprises equilibrium folding simulations, structural clustering, and principal component analysis.
Tandem Repeats in Proteins: Prediction Algorithms and Biological Role.

PubMed

Pellegrini, Marco

2015-01-01

Tandem repetitions in protein sequence and structure is a fascinating subject of research which has been a focus of study since the late 1990s. In this survey, we give an overview on the multi-faceted aspects of research on protein tandem repeats (PTR for short), including prediction algorithms, databases, early classification efforts, mechanisms of PTR formation and evolution, and synthetic PTR design. We also touch on the rather open issue of the relationship between PTR and flexibility (or disorder) in proteins. Detection of PTR either from protein sequence or structure data is challenging due to inherent high (biological) signal-to-noise ratio that is a key feature of this problem. As early in silico analytic tools have been key enablers for starting this field of study, we expect that current and future algorithmic and statistical breakthroughs will have a high impact on the investigations of the biological role of PTR.
Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies.

PubMed

Soler, Miguel A; de Marco, Ario; Fortuna, Sara

2016-10-10

Nanobodies (VHHs) have proved to be valuable substitutes of conventional antibodies for molecular recognition. Their small size represents a precious advantage for rational mutagenesis based on modelling. Here we address the problem of predicting how Camelidae nanobody sequences can tolerate mutations by developing a simulation protocol based on all-atom molecular dynamics and whole-molecule docking. The method was tested on two sets of nanobodies characterized experimentally for their biophysical features. One set contained point mutations introduced to humanize a wild type sequence, in the second the CDRs were swapped between single-domain frameworks with Camelidae and human hallmarks. The method resulted in accurate scoring approaches to predict experimental yields and enabled to identify the structural modifications induced by mutations. This work is a promising tool for the in silico development of single-domain antibodies and opens the opportunity to customize single functional domains of larger macromolecules.
Molecular dynamics simulations and docking enable to explore the biophysical factors controlling the yields of engineered nanobodies

NASA Astrophysics Data System (ADS)

Soler, Miguel A.; De Marco, Ario; Fortuna, Sara

2016-10-01

Nanobodies (VHHs) have proved to be valuable substitutes of conventional antibodies for molecular recognition. Their small size represents a precious advantage for rational mutagenesis based on modelling. Here we address the problem of predicting how Camelidae nanobody sequences can tolerate mutations by developing a simulation protocol based on all-atom molecular dynamics and whole-molecule docking. The method was tested on two sets of nanobodies characterized experimentally for their biophysical features. One set contained point mutations introduced to humanize a wild type sequence, in the second the CDRs were swapped between single-domain frameworks with Camelidae and human hallmarks. The method resulted in accurate scoring approaches to predict experimental yields and enabled to identify the structural modifications induced by mutations. This work is a promising tool for the in silico development of single-domain antibodies and opens the opportunity to customize single functional domains of larger macromolecules.
Self-Organizing Maps for In Silico Screening and Data Visualization.

PubMed

Digles, Daniela; Ecker, Gerhard F

2011-10-01

Self-organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self-organizing maps: the use of self-organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
FutureTox II: in vitro data and in silico models for predictive toxicology.

PubMed

Knudsen, Thomas B; Keller, Douglas A; Sander, Miriam; Carney, Edward W; Doerrer, Nancy G; Eaton, David L; Fitzpatrick, Suzanne Compton; Hastings, Kenneth L; Mendrick, Donna L; Tice, Raymond R; Watkins, Paul B; Whelan, Maurice

2015-02-01

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. © The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.
Gastrointestinal Endogenous Proteins as a Source of Bioactive Peptides - An In Silico Study

PubMed Central

Dave, Lakshmi A.; Montoya, Carlos A.; Rutherfurd, Shane M.; Moughan, Paul J.

2014-01-01

Dietary proteins are known to contain bioactive peptides that are released during digestion. Endogenous proteins secreted into the gastrointestinal tract represent a quantitatively greater supply of protein to the gut lumen than those of dietary origin. Many of these endogenous proteins are digested in the gastrointestinal tract but the possibility that these are also a source of bioactive peptides has not been considered. An in silico prediction method was used to test if bioactive peptides could be derived from the gastrointestinal digestion of gut endogenous proteins. Twenty six gut endogenous proteins and seven dietary proteins were evaluated. The peptides present after gastric and intestinal digestion were predicted based on the amino acid sequence of the proteins and the known specificities of the major gastrointestinal proteases. The predicted resultant peptides possessing amino acid sequences identical to those of known bioactive peptides were identified. After gastrointestinal digestion (based on the in silico simulation), the total number of bioactive peptides predicted to be released ranged from 1 (gliadin) to 55 (myosin) for the selected dietary proteins and from 1 (secretin) to 39 (mucin-5AC) for the selected gut endogenous proteins. Within the intact proteins and after simulated gastrointestinal digestion, angiotensin converting enzyme (ACE)-inhibitory peptide sequences were the most frequently observed in both the dietary and endogenous proteins. Among the dietary proteins, after in silico simulated gastrointestinal digestion, myosin was found to have the highest number of ACE-inhibitory peptide sequences (49 peptides), while for the gut endogenous proteins, mucin-5AC had the greatest number of ACE-inhibitory peptide sequences (38 peptides). Gut endogenous proteins may be an important source of bioactive peptides in the gut particularly since gut endogenous proteins represent a quantitatively large and consistent source of protein. PMID:24901416
Navigating through the minefield of read-across tools: A review of in silico tools for grouping

EPA Science Inventory

Read-across is a popular data gap filling technique used within analogue and category approaches for regulatory purposes. In recent years there have been many efforts focused on the challenges involved in read-across development, its scientific justification and documentation. To...
Extrapolating toxicity data across species using U.S. EPA SeqAPASS tool

EPA Science Inventory

In vitro high-throughput screening (HTS) and in silico technologies have emerged as 21st century tools for chemical hazard identification. In 2007 the U.S. Environmental Protection Agency (EPA) launched the ToxCast Program, which has screened thousands of chemicals in hundreds of...
Applicability of predictive toxicology methods for monoclonal antibody therapeutics: status Quo and scope.

PubMed

Kizhedath, Arathi; Wilkinson, Simon; Glassey, Jarka

2017-04-01

Biopharmaceuticals, monoclonal antibody (mAb)-based therapeutics in particular, have positively impacted millions of lives. MAbs and related therapeutics are highly desirable from a biopharmaceutical perspective as they are highly target specific and well tolerated within the human system. Nevertheless, several mAbs have been discontinued or withdrawn based either on their inability to demonstrate efficacy and/or due to adverse effects. Approved monoclonal antibodies and derived therapeutics have been associated with adverse effects such as immunogenicity, cytokine release syndrome, progressive multifocal leukoencephalopathy, intravascular haemolysis, cardiac arrhythmias, abnormal liver function, gastrointestinal perforation, bronchospasm, intraocular inflammation, urticaria, nephritis, neuropathy, birth defects, fever and cough to name a few. The advances made in this field are also impeded by a lack of progress in bioprocess development strategies as well as increasing costs owing to attrition, wherein the lack of efficacy and safety accounts for nearly 60 % of all factors contributing to attrition. This reiterates the need for smarter preclinical development using quality by design-based approaches encompassing carefully designed predictive models during early stages of drug development. Different in vitro and in silico methods are extensively used for predicting biological activity as well as toxicity during small molecule drug development; however, their full potential has not been utilized for biological drug development. The scope of in vitro and in silico tools in early developmental stages of monoclonal antibody-based therapeutics production and how it contributes to lower attrition rates leading to faster development of potential drug candidates has been evaluated. The applicability of computational toxicology approaches in this context as well as the pitfalls and promises of extending such techniques to biopharmaceutical development has been highlighted.
Computational toxicology: Its essential role in reducing drug attrition.

PubMed

Naven, R T; Louise-May, S

2015-12-01

Predictive toxicology plays a critical role in reducing the failure rate of new drugs in pharmaceutical research and development. Despite recent gains in our understanding of drug-induced toxicity, however, it is urgent that the utility and limitations of our current predictive tools be determined in order to identify gaps in our understanding of mechanistic and chemical toxicology. Using recently published computational regression analyses of in vitro and in vivo toxicology data, it will be demonstrated that significant gaps remain in early safety screening paradigms. More strategic analyses of these data sets will allow for a better understanding of their domain of applicability and help identify those compounds that cause significant in vivo toxicity but which are currently mis-predicted by in silico and in vitro models. These 'outliers' and falsely predicted compounds are metaphorical lighthouses that shine light on existing toxicological knowledge gaps, and it is essential that these compounds are investigated if attrition is to be reduced significantly in the future. As such, the modern computational toxicologist is more productively engaged in understanding these gaps and driving investigative toxicology towards addressing them. © The Author(s) 2015.
A modeling assessment of the physicochemical properties and environmental fate of emerging and novel per- and polyfluoroalkyl substances.

PubMed

Gomis, Melissa Ines; Wang, Zhanyun; Scheringer, Martin; Cousins, Ian T

2015-02-01

Long-chain perfluoroalkyl carboxylic acids (PFCAs) and perfluoroalkane sulfonic acids (PFSAs) are persistent, bioaccumulative, and toxic contaminants that are globally present in the environment, wildlife and humans. Phase-out actions and use restrictions to reduce the environmental release of long-chain PFCAs, PFSAs and their precursors have been taken since 2000. In particular, long-chain poly- and perfluoroalkyl substances (PFASs) are being replaced with shorter-chain homologues or other fluorinated or non-fluorinated alternatives. A key question is: are these alternatives, particularly the structurally similar fluorinated alternatives, less hazardous to humans and the environment than the substances they replace? Several fluorinated alternatives including perfluoroether carboxylic acids (PFECAs) and perfluoroether sulfonic acids (PFESAs) have been recently identified. However, the scarcity of experimental data prevents hazard and risk assessments for these substances. In this study, we use state-of-the-art in silico tools to estimate key properties of these newly identified fluorinated alternatives. [i] COSMOtherm and SPARC are used to estimate physicochemical properties. The US EPA EPISuite software package is used to predict degradation half-lives in air, water and soil. [ii] In combination with estimated chemical properties, a fugacity-based multimedia mass-balance unit-world model - the OECD Overall Persistence (POV) and Long-Range Transport Potential (LRTP) Screening Tool - is used to assess the likely environmental fate of these alternatives. Even though the fluorinated alternatives contain some structural differences, their physicochemical properties are not significantly different from those of their predecessors. Furthermore, most of the alternatives are estimated to be similarly persistent and mobile in the environment as the long-chain PFASs. The models therefore predict that the fluorinated alternatives will become globally distributed in the environment similar to their predecessors. Although such in silico methods are coupled with uncertainties, this preliminary assessment provides enough cause for concern to warrant experimental work to better determine the properties of these fluorinated alternatives. Copyright © 2014 Elsevier B.V. All rights reserved.
Provisional in-silico biopharmaceutics classification (BCS) to guide oral drug product development

PubMed Central

Wolk, Omri; Agbaria, Riad; Dahan, Arik

2014-01-01

The main objective of this work was to investigate in-silico predictions of physicochemical properties, in order to guide oral drug development by provisional biopharmaceutics classification system (BCS). Four in-silico methods were used to estimate LogP: group contribution (CLogP) using two different software programs, atom contribution (ALogP), and element contribution (KLogP). The correlations (r2) of CLogP, ALogP and KLogP versus measured LogP data were 0.97, 0.82, and 0.71, respectively. The classification of drugs with reported intestinal permeability in humans was correct for 64.3%–72.4% of the 29 drugs on the dataset, and for 81.82%–90.91% of the 22 drugs that are passively absorbed using the different in-silico algorithms. Similar permeability classification was obtained with the various in-silico methods. The in-silico calculations, along with experimental melting points, were then incorporated into a thermodynamic equation for solubility estimations that largely matched the reference solubility values. It was revealed that the effect of melting point on the solubility is minor compared to the partition coefficient, and an average melting point (162.7°C) could replace the experimental values, with similar results. The in-silico methods classified 20.76% (±3.07%) as Class 1, 41.51% (±3.32%) as Class 2, 30.49% (±4.47%) as Class 3, and 6.27% (±4.39%) as Class 4. In conclusion, in-silico methods can be used for BCS classification of drugs in early development, from merely their molecular formula and without foreknowledge of their chemical structure, which will allow for the improved selection, engineering, and developability of candidates. These in-silico methods could enhance success rates, reduce costs, and accelerate oral drug products development. PMID:25284986
Insights and Perspectives on Emerging Inputs to Weight of Evidence Determinations for Food Safety: Workshop Proceedings

PubMed Central

Bialk, Heidi; Llewellyn, Craig; Kretser, Alison; Canady, Richard; Lane, Richard; Barach, Jeffrey

2013-01-01

This workshop aimed to elucidate the contribution of computational and emerging in vitro methods to the weight of evidence used by risk assessors in food safety assessments. The following issues were discussed: using in silico and high-throughput screening (HTS) data to confirm the safety of approved food ingredients, applying in silico and HTS data in the process of assessing the safety of a new food ingredient, and utilizing in silico and HTS data in communicating the safety of food ingredients while enhancing the public’s trust in the food supply. Perspectives on integrating computational modeling and HTS assays as well as recommendations for optimizing predictive methods for risk assessment were also provided. Given the need to act quickly or proceed cautiously as new data emerge, this workshop also focused on effectively identifying a path forward in communicating in silico and in vitro data. PMID:24296863
Identification and the molecular mechanism of a novel myosin-derived ACE inhibitory peptide.

PubMed

Yu, Zhipeng; Wu, Sijia; Zhao, Wenzhu; Ding, Long; Shiuan, David; Chen, Feng; Li, Jianrong; Liu, Jingbo

2018-01-24

The objective of this work was to identify a novel ACE inhibitory peptide from myosin using a number of in silico methods. Myosin was evaluated as a substrate for use in the generation of ACE inhibitory peptides using BIOPEP and ExPASy PeptideCutter. Then the ACE inhibitory activity prediction of peptides in silico was evaluated using the program peptide ranker, following the database search of known and unknown peptides using the program BIOPEP. In addition, the interaction mechanisms of the peptide and ACE were evaluated by DS. All of the tripeptides were predicted to be nontoxic. Results suggested that the tripeptide NCW exerted potent ACE inhibitory activity with an IC 50 value of 35.5 μM. Furthermore, the results suggested that the peptide NCW comes into contact with Zn 701, Tyr 523, His 383, Glu 384, Glu 411, and His 387. The potential molecular mechanism of the NCW/ACE interaction was investigated. Results confirmed that the higher inhibitory potency of NCW might be attributed to the formation of more hydrogen bonds with the ACE's active site. Therefore, the in silico method is effective to predict and identify novel ACE inhibitory peptides from protein hydrolysates.
In Silico Identification Software (ISIS): A Machine Learning Approach to Tandem Mass Spectral Identification of Lipids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kangas, Lars J.; Metz, Thomas O.; Isaac, Georgis

2012-05-15

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissocia-tion tandem mass spectrometry. A preliminary test of the algorithm with 45 lipidsmore » from a subset of lipid classes shows both high sensitivity and specificity.« less
Prediction of pH dependent absorption using in vitro, in silico, and in vivo rat models: Early liability assessment during lead optimization.

PubMed

Saxena, Ajay; Shah, Devang; Padmanabhan, Shweta; Gautam, Shashyendra Singh; Chowan, Gajendra Singh; Mandlekar, Sandhya; Desikan, Sridhar

2015-08-30

Weakly basic compounds which have pH dependent solubility are liable to exhibit pH dependent absorption. In some cases, a subtle change in gastric pH can significantly modulate the plasma concentration of the drug and can lead to sub-therapeutic exposure of the drug. Evaluating the risk of pH dependent absorption and potential drug-drug interaction with pH modulators are important aspects of drug discovery and development. In order to assess the risk around the extent of decrease in the systemic exposure of drugs co-administered with pH modulators in the clinic, a pH effect study is carried out, typically in higher species, mostly dog. The major limitation of a higher species pH effect study is the resource and material requirement to assess this risk. Hence, these studies are mostly restricted to promising or advanced leads. In our current work, we have used in vitro aqueous solubility, in silico simulations using GastroPlus™ and an in vivo rat pH effect model to provide a qualitative assessment of the pH dependent absorption liability. Here, we evaluate ketoconazole and atazanavir with different pH dependent solubility profiles and based on in vitro, in silico and in vivo results, a different extent of gastric pH effect on absorption is predicted. The prediction is in alignment with higher species and human pH effect study results. This in vitro, in silico and in vivo (IVISIV) correlation is then extended to assess pH absorption mitigation strategy. The IVISIV predicts pH dependent absorption for BMS-582949 whereas its solubility enhancing prodrug, BMS-751324 is predicted to mitigate this liability. Overall, the material requirement for this assessment is substantially low which makes this approach more practical to screen multiple compounds during lead optimization. Copyright © 2015 Elsevier B.V. All rights reserved.
Discovering a vaccine against neosporosis using computers: is it feasible?

PubMed

Goodswen, Stephen J; Kennedy, Paul J; Ellis, John T

2014-08-01

A vaccine is urgently needed to prevent cattle neosporosis. This infectious disease is caused by the parasite Neospora caninum, a complex biological system with multifaceted life cycles. An in silico vaccine discovery approach attempts to transform digital abstractions of this system into adequate knowledge to predict candidates. Researchers need current information to implement such an approach, such as understanding evasion mechanisms of the immune system, type of immune response to elicit, availability of data and prediction programs, and statistical models to analyze predictions. Taken together, an in silico approach involves assembly of an intricate jigsaw of interdisciplinary and interdependent knowledge. In this review, we focus on the approach influencing vaccine development against Neospora caninum, which can be generalized to other pathogenic apicomplexans. Copyright © 2014 Elsevier Ltd. All rights reserved.

Evaluation and validation of de novo and hybrid assembly techniques to derive high quality genome sequences

DOE PAGES

Utturkar, Sagar M.; Klingeman, Dawn Marie; Land, Miriam L.; ...

2014-06-14

Our motivation with this work was to assess the potential of different types of sequence data combined with de novo and hybrid assembly approaches to improve existing draft genome sequences. Our results show Illumina, 454 and PacBio sequencing technologies were used to generate de novo and hybrid genome assemblies for four different bacteria, which were assessed for quality using summary statistics (e.g. number of contigs, N50) and in silico evaluation tools. Differences in predictions of multiple copies of rDNA operons for each respective bacterium were evaluated by PCR and Sanger sequencing, and then the validated results were applied as anmore » additional criterion to rank assemblies. In general, assemblies using longer PacBio reads were better able to resolve repetitive regions. In this study, the combination of Illumina and PacBio sequence data assembled through the ALLPATHS-LG algorithm gave the best summary statistics and most accurate rDNA operon number predictions. This study will aid others looking to improve existing draft genome assemblies. As to availability and implementation–all assembly tools except CLC Genomics Workbench are freely available under GNU General Public License.« less
Computational methods in drug discovery

PubMed Central

Leelananda, Sumudu P

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed. PMID:28144341
Computational methods in drug discovery.

PubMed

Leelananda, Sumudu P; Lindert, Steffen

2016-01-01

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
A community computational challenge to predict the activity of pairs of compounds.

PubMed

Bansal, Mukesh; Yang, Jichen; Karan, Charles; Menden, Michael P; Costello, James C; Tang, Hao; Xiao, Guanghua; Li, Yajuan; Allen, Jeffrey; Zhong, Rui; Chen, Beibei; Kim, Minsoo; Wang, Tao; Heiser, Laura M; Realubit, Ronald; Mattioli, Michela; Alvarez, Mariano J; Shen, Yao; Gallahan, Daniel; Singer, Dinah; Saez-Rodriguez, Julio; Xie, Yang; Stolovitzky, Gustavo; Califano, Andrea

2014-12-01

Recent therapeutic successes have renewed interest in drug combinations, but experimental screening approaches are costly and often identify only small numbers of synergistic combinations. The DREAM consortium launched an open challenge to foster the development of in silico methods to computationally rank 91 compound pairs, from the most synergistic to the most antagonistic, based on gene-expression profiles of human B cells treated with individual compounds at multiple time points and concentrations. Using scoring metrics based on experimental dose-response curves, we assessed 32 methods (31 community-generated approaches and SynGen), four of which performed significantly better than random guessing. We highlight similarities between the methods. Although the accuracy of predictions was not optimal, we find that computational prediction of compound-pair activity is possible, and that community challenges can be useful to advance the field of in silico compound-synergy prediction.
Freely Accessible Chemical Database Resources of Compounds for in Silico Drug Discovery.

PubMed

Yang, JingFang; Wang, Di; Jia, Chenyang; Wang, Mengyao; Hao, GeFei; Yang, GuangFu

2018-05-07

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases are crucial. To the best of our knowledge, this is the first systematic review on this issue. The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review. Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced. In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
In silico prediction of post-translational modifications.

PubMed

Liu, Chunmei; Li, Hui

2011-01-01

Methods for predicting protein post-translational modifications have been developed extensively. In this chapter, we review major post-translational modification prediction strategies, with a particular focus on statistical and machine learning approaches. We present the workflow of the methods and summarize the advantages and disadvantages of the methods.
Understanding the mode-of-action of Cassia auriculata via in silico and in vivo studies towards validating it as a long term therapy for type II diabetes.

PubMed

Mohd Fauzi, Fazlin; John, Cini Mathew; Karunanidhi, Arunkumar; Mussa, Hamse Y; Ramasamy, Rajesh; Adam, Aishah; Bender, Andreas

2017-02-02

Cassia auriculata (CA) is used as an antidiabetic therapy in Ayurvedic and Siddha practice. This study aimed to understand the mode-of-action of CA via combined cheminformatics and in vivo biological analysis. In particular, the effect of 10 polyphenolic constituents of CA in modulating insulin and immunoprotective pathways were studied. In silico target prediction was first employed to predict the probability of the polyphenols interacting with key protein targets related to insulin signalling, based on a model trained on known bioactivity data and chemical similarity considerations. Next, CA was investigated in in vivo studies where induced type 2 diabetic rats were treated with CA for 28 days and the expression levels of genes regulating insulin signalling pathway, glucose transporters of hepatic (GLUT2) and muscular (GLUT4) tissue, insulin receptor substrate (IRS), phosphorylated insulin receptor (AKT), gluconeogenesis (G6PC and PCK-1), along with inflammatory mediators genes (NF-κB, IL-6, IFN-γ and TNF-α) and peroxisome proliferators-activated receptor gamma (PPAR-γ) were determined by qPCR. In silico analysis shows that several of the top 20 enriched targets predicted for the constituents of CA are involved in insulin signalling pathways e.g. PTPN1, PCK-α, AKT2, PI3K-γ. Some of the predictions were supported by scientific literature such as the prediction of PI3K for epigallocatechin gallate. Based on the in silico and in vivo findings, we hypothesized that CA may enhance glucose uptake and glucose transporter expressions via the IRS signalling pathway. This is based on AKT2 and PI3K-γ being listed in the top 20 enriched targets. In vivo analysis shows significant increase in the expression of IRS, AKT, GLUT2 and GLUT4. CA may also affect the PPAR-γ signalling pathway. This is based on the CA-treated groups showing significant activation of PPAR-γ in the liver compared to control. PPAR-γ was predicted by the in silico target prediction with high normalisation rate although it was not in the top 20 most enriched targets. CA may also be involved in the gluconeogenesis and glycogenolysis in the liver based on the downregulation of G6PC and PCK-1 genes seen in CA-treated groups. In addition, CA-treated groups also showed decreased cholesterol, triglyceride, glucose, CRP and Hb1Ac levels, and increased insulin and C-peptide levels. These findings demonstrate the insulin secretagogue and sensitizer effect of CA. Based on both an in silico and in vivo analysis, we propose here that CA mediates glucose/lipid metabolism via the PI3K signalling pathway, and influence AKT thereby causing insulin secretion and insulin sensitivity in peripheral tissues. CA enhances glucose uptake and expression of glucose transporters in particular via the upregulation of GLUT2 and GLUT4. Thus, based on its ability to modulate immunometabolic pathways, CA appears as an attractive long term therapy for T2DM even at relatively low doses. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Influence of Length and Amino Acid Composition on Dimer Formation of Immunoglobulin based Chimera.

PubMed

Manoj, Patidar; Naveen, Yadav; Dalai, Sarat Kumar

2017-10-18

The dimeric immunoglobulin (Ig) chimeras used for drug targeting and delivery are preferred biologics over their monomeric forms. Designing these Ig chimeras involves critical selection of a suitable Ig base that ensures dimer formation. In the present study, we systematically analyzed several factors that influence the formation of dimeric chimera. We designed and predicted 608 cytokine-Ig chimeras where we tested the contributions of (1) different domains of Ig constant heavy chain, (2) length of partner proteins, (3) amino acid (AA) composition and (4) position of cysteine in the formation of homodimer. The sequences of various Ig and cytokines were procured from Uniprot database, fused and submitted to COTH (CO-THreader) server for the prediction of dimer formation. Contributions of different domains of Ig constant heavy chain, length of chimeric proteins, AA composition and position of cysteine were tested to the homodimer formation of 608 cytokine-Ig chimeras. Various in silico approaches were adopted for validating the in silico findings. Experimentally we also validated our approach by expressing in CHO cells the chimeric design of shorter cytokine with Ig domain and analyzing the protein by SDS-PAGE. Our results advocate that while the CH1 region and the Hinge region of Ig heavy chain are critical, the length of partner proteins also crucially influences homodimer formation of the Ig-based chimera. We also report that the CH1 domain of Ig is not required for dimer formation of Ig based chimera in the presence of larger partner proteins. For shorter partner proteins fused to CH2-CH3, however, careful selection of partner sequence is critical, particularly the hydrophobic AA composition, cysteine content & their positions, disulphide bond formation property, and the linker sequences. We validated our in silico observation by various bioinformatics tools and checked the ability of chimeras to bind with the receptors of native protein by docking studies. As a proof of concept, we have expressed the chimeric proteins in CHO cells and found that our design favors the synthesis of dimeric proteins. Our structural prediction study suggests that extra amino acids in the range of 15-20 added to the CH2 domain of Ig is a critical requirement to make homodimer. This information from our study will have implication in designing efficacious homodimeric chimera. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.
SpirPep: an in silico digestion-based platform to assist bioactive peptides discovery from a genome-wide database.

PubMed

Anekthanakul, Krittima; Hongsthong, Apiradee; Senachak, Jittisak; Ruengjitchatchawalya, Marasri

2018-04-20

Bioactive peptides, including biological sources-derived peptides with different biological activities, are protein fragments that influence the functions or conditions of organisms, in particular humans and animals. Conventional methods of identifying bioactive peptides are time-consuming and costly. To quicken the processes, several bioinformatics tools are recently used to facilitate screening of the potential peptides prior their activity assessment in vitro and/or in vivo. In this study, we developed an efficient computational method, SpirPep, which offers many advantages over the currently available tools. The SpirPep web application tool is a one-stop analysis and visualization facility to assist bioactive peptide discovery. The tool is equipped with 15 customized enzymes and 1-3 miscleavage options, which allows in silico digestion of protein sequences encoded by protein-coding genes from single, multiple, or genome-wide scaling, and then directly classifies the peptides by bioactivity using an in-house database that contains bioactive peptides collected from 13 public databases. With this tool, the resulting peptides are categorized by each selected enzyme, and shown in a tabular format where the peptide sequences can be tracked back to their original proteins. The developed tool and webpages are coded in PHP and HTML with CSS/JavaScript. Moreover, the tool allows protein-peptide alignment visualization by Generic Genome Browser (GBrowse) to display the region and details of the proteins and peptides within each parameter, while considering digestion design for the desirable bioactivity. SpirPep is efficient; it takes less than 20 min to digest 3000 proteins (751,860 amino acids) with 15 enzymes and three miscleavages for each enzyme, and only a few seconds for single enzyme digestion. Obviously, the tool identified more bioactive peptides than that of the benchmarked tool; an example of validated pentapeptide (FLPIL) from LC-MS/MS was demonstrated. The web and database server are available at http://spirpepapp.sbi.kmutt.ac.th . SpirPep, a web-based bioactive peptide discovery application, is an in silico-based tool with an overview of the results. The platform is a one-stop analysis and visualization facility; and offers advantages over the currently available tools. This tool may be useful for further bioactivity analysis and the quantitative discovery of desirable peptides.
In Silico Dynamics: computer simulation in a Virtual Embryo (SOT)

EPA Science Inventory

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require preci...
Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

PubMed

Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

2012-04-05

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.
In silico models for the prediction of dose-dependent human hepatotoxicity

NASA Astrophysics Data System (ADS)

Cheng, Ailan; Dixon, Steven L.

2003-12-01

The liver is extremely vulnerable to the effects of xenobiotics due to its critical role in metabolism. Drug-induced hepatotoxicity may involve any number of different liver injuries, some of which lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important dose-limiting considerations in the drug development cycle, yet there remains a serious shortage of methods to predict hepatotoxicity from chemical structure. We discuss our latest findings in this area and present a new, fully general in silico model which is able to predict the occurrence of dose-dependent human hepatotoxicity with greater than 80% accuracy. Utilizing an ensemble recursive partitioning approach, the model classifies compounds as toxic or non-toxic and provides a confidence level to indicate which predictions are most likely to be correct. Only 2D structural information is required and predictions can be made quite rapidly, so this approach is entirely appropriate for data mining applications and for profiling large synthetic and/or virtual libraries.
Critical evaluation of a simple retention time predictor based on LogKow as a complementary tool in the identification of emerging contaminants in water.

PubMed

Bade, Richard; Bijlsma, Lubertus; Sancho, Juan V; Hernández, Felix

2015-07-01

There has been great interest in environmental analytical chemistry in developing screening methods based on liquid chromatography-high resolution mass spectrometry (LC-HRMS) for emerging contaminants. Using HRMS, compound identification relies on the high mass resolving power and mass accuracy attainable by these analyzers. When dealing with wide-scope screening, retention time prediction can be a complementary tool for the identification of compounds, and can also reduce tedious data processing when several peaks appear in the extracted ion chromatograms. There are many in silico, Quantitative Structure-Retention Relationship methods available for the prediction of retention time for LC. However, most of these methods use commercial software to predict retention time based on various molecular descriptors. This paper explores the applicability and makes a critical discussion on a far simpler and cheaper approach to predict retention times by using LogKow. The predictor was based on a database of 595 compounds, their respective LogKow values and a chromatographic run time of 18min. Approximately 95% of the compounds were found within 4.0min of their actual retention times, and 70% within 2.0min. A predictor based purely on pesticides was also made, enabling 80% of these compounds to be found within 2.0min of their actual retention times. To demonstrate the utility of the predictors, they were successfully used as an additional tool in the identification of 30 commonly found emerging contaminants in water. Furthermore, a comparison was made by using different mass extraction windows to minimize the number of false positives obtained. Copyright © 2015 Elsevier B.V. All rights reserved.
Cardio-vascular safety beyond hERG: in silico modelling of a guinea pig right atrium assay

NASA Astrophysics Data System (ADS)

Fenu, Luca A.; Teisman, Ard; De Buck, Stefan S.; Sinha, Vikash K.; Gilissen, Ron A. H. J.; Nijsen, Marjoleen J. M. A.; Mackie, Claire E.; Sanderson, Wendy E.

2009-12-01

As chemists can easily produce large numbers of new potential drug candidates, there is growing demand for high capacity models that can help in driving the chemistry towards efficacious and safe candidates before progressing towards more complex models. Traditionally, the cardiovascular (CV) safety domain plays an important role in this process, as many preclinical CV biomarkers seem to have high prognostic value for the clinical outcome. Throughout the industry, traditional ion channel binding data are generated to drive the early selection process. Although this assay can generate data at high capacity, it has the disadvantage of producing high numbers of false negatives. Therefore, our company applies the isolated guinea pig right atrium (GPRA) assay early-on in discovery. This functional multi-channel/multi-receptor model seems much more predictive in identifying potential CV liabilities. Unfortunately however, its capacity is limited, and there is no room for full automation. We assessed the correlation between ion channel binding and the GPRA's Rate of Contraction (RC), Contractile Force (CF), and effective refractory frequency (ERF) measures assay using over six thousand different data points. Furthermore, the existing experimental knowledge base was used to develop a set of in silico classification models attempting to mimic the GPRA inhibitory activity. The Naïve Bayesian classifier was used to built several models, using the ion channel binding data or in silico computed properties and structural fingerprints as descriptors. The models were validated on an independent and diverse test set of 200 reference compounds. Performances were assessed on the bases of their overall accuracy, sensitivity and specificity in detecting both active and inactive molecules. Our data show that all in silico models are highly predictive of actual GPRA data, at a level equivalent or superior to the ion channel binding assays. Furthermore, the models were interpreted in terms of the descriptors used to highlight the undesirable areas in the explored chemical space, specifically regions of low polarity, high lipophilicity and high molecular weight. In conclusion, we developed a predictive in silico model of a complex physiological assay based on a large and high quality set of experimental data. This model allows high throughput in silico safety screening based on chemical structure within a given chemical space.
Establishing best practise in the application of expert review of mutagenicity under ICH M7.

PubMed

Barber, Chris; Amberg, Alexander; Custer, Laura; Dobo, Krista L; Glowienke, Susanne; Van Gompel, Jacky; Gutsell, Steve; Harvey, Jim; Honma, Masamitsu; Kenyon, Michelle O; Kruhlak, Naomi; Muster, Wolfgang; Stavitskaya, Lidiya; Teasdale, Andrew; Vessey, Jonathan; Wichard, Joerg

2015-10-01

The ICH M7 guidelines for the assessment and control of DNA reactive (mutagenic) impurities in pharmaceuticals allows for the consideration of in silico predictions in place of in vitro studies. This represents a significant advance in the acceptance of (Q)SAR models and has resulted from positive interactions between modellers, regulatory agencies and industry with a shared purpose of developing effective processes to minimise risk. This paper discusses key scientific principles that should be applied when evaluating in silico predictions with a focus on accuracy and scientific rigour that will support a consistent and practical route to regulatory submission. Copyright © 2015 Elsevier Inc. All rights reserved.
Assessing the environmental fate of S-metolachlor, its commercial product Mercantor Gold® and their photoproducts using a water-sediment test and in silico methods.

PubMed

Gutowski, Lukasz; Baginska, Ewelina; Olsson, Oliver; Leder, Christoph; Kümmerer, Klaus

2015-11-01

Pesticides enter surface and groundwater by several routes in which partition to sediment contributes to their fate by abiotic (e.g. photolysis, hydrolysis) and biotic processes. Yet, little is known about S-metolachlor (SM) transformation in water-sediment systems. Therefore, a newly developed screening water-sediment test (WST) was applied to compare biodegradation and sorption processes between pure SM and Mercantor Gold® (MG), a commercial formulation of SM. Photolysis in water was performed by Xe lamp irradiation. Subsequently, the biodegradability of SM and MG photolysis mixtures was examined in WST. The primary elimination of SM from water phase was monitored and structures of its TPs resulting from biotransformation (bio-TPs) were elucidated by LC-MS/MS. SM was extracted from sediment in order to estimate the role of sorption in WST for its elimination. A set of in silico prediction software tools was applied for toxicity assessment of SM and its bio-TPs. Obtained results suggest that the MG adjuvants do not significantly affect biodegradation, but do influence diffusion of SM into sediment. 50% of SM could not be re-extracted from sediment with 0.01 M CaCl2 aqueous solution recommended in OECD test guideline for adsorption. Neither the parent compound nor the photo-TPs were biodegraded. However, new bio-TPs have been generated from SM and MG photo-TPs due to bacterial activity in the water-sediment interphase. Moreover, according to in silico assessment of the bio-TPs the biotransformation might lead to an increased toxicity to the water organisms compared with the SM. This might raise concerns of bio-TPs presence in the environment. Copyright © 2015 Elsevier Ltd. All rights reserved.
A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory.

PubMed

Nogales, Juan; Palsson, Bernhard Ø; Thiele, Ines

2008-09-16

Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic production.
The hOGG1 Ser326Cys Gene Polymorphism and Breast Cancer Risk in Saudi Population.

PubMed

Alanazi, Mohammed; Pathan, Akbar Ali Khan; Shaik, Jilani P; Alhadheq, Abdullah; Khan, Zahid; Khan, Wajahatullah; Al Naeem, Abdulrahman; Parine, Narasimha Reddy

2017-07-01

The purpose of this study was to test the association between human 8-oxoguanine glycosylase 1 (hOGG1) gene polymorphisms and susceptibility to breast cancer in Saudi population. We have also aimed to screen the hOGG1 Ser326Cys polymorphism effect on structural and functional properties of the hOGG1 protein using in silico tools. We have analyzed four SNPs of hOGG1 gene among Saudi breast cancer patients along with healthy controls. Genotypes were screened using TaqMan SNP genotype analysis method. Experimental data was analyzed using Chi-square, t test and logistic regression analysis using SPSS software (v.16). In silco analysis was conducted using discovery studio and HOPE program. Genotypic analysis showed that hOGG1 rs1052133 (Ser326Cys) is significantly associated with breast cancer samples in Saudi population, however rs293795 (T >C), rs2072668 (C>G) and rs2075747 (G >A) did not show any association with breast cancer. The hOGG1 SNP rs1052133 (Ser326Cys) minor allele T showed a significant association with breast cancer samples (OR = 1.78, χ2 = 7.86, p = 0.02024). In silico structural analysis was carried out to compare the wild type (Ser326) and mutant (Cys326) protein structures. The structural prediction studies revealed that Ser326Cys variant may destabilize the protein structure and it may disturb the hOGG1 function. Taken together this is the first In silico study report to confirm Ser326Cys variant effect on structural and functional properties of hOGG1 gene and Ser326Cys role in breast cancer susceptibility in Saudi population.
Size Does Matter: An Integrative In Vivo-In Silico Approach for the Treatment of Critical Size Bone Defects

PubMed Central

Carlier, Aurélie; van Gastel, Nick; Geris, Liesbet; Carmeliet, Geert; Van Oosterwyck, Hans

2014-01-01

Although bone has a unique restorative capacity, i.e., it has the potential to heal scarlessly, the conditions for spontaneous bone healing are not always present, leading to a delayed union or a non-union. In this work, we use an integrative in vivo - in silico approach to investigate the occurrence of non-unions, as well as to design possible treatment strategies thereof. The gap size of the domain geometry of a previously published mathematical model was enlarged in order to study the complex interplay of blood vessel formation, oxygen supply, growth factors and cell proliferation on the final healing outcome in large bone defects. The multiscale oxygen model was not only able to capture the essential aspects of in vivo non-unions, it also assisted in understanding the underlying mechanisms of action, i.e., the delayed vascularization of the central callus region resulted in harsh hypoxic conditions, cell death and finally disrupted bone healing. Inspired by the importance of a timely vascularization, as well as by the limited biological potential of the fracture hematoma, the influence of the host environment on the bone healing process in critical size defects was explored further. Moreover, dependent on the host environment, several treatment strategies were designed and tested for effectiveness. A qualitative correspondence between the predicted outcomes of certain treatment strategies and experimental observations was obtained, clearly illustrating the model's potential. In conclusion, the results of this study demonstrate that due to the complex non-linear dynamics of blood vessel formation, oxygen supply, growth factor production and cell proliferation and the interactions thereof with the host environment, an integrative in silico-in vivo approach is a crucial tool to further unravel the occurrence and treatments of challenging critical sized bone defects. PMID:25375821
Ergot alkaloids: From witchcraft till in silico analysis. Multi-receptor analysis of ergotamine metabolites.

PubMed

Dellafiora, Luca; Dall'Asta, Chiara; Cozzini, Pietro

2015-01-01

The term Ergot is referred to the sclerotium of ascomycetes - a protective kernel produced during resting stage of some fungi - which replaces seeds of susceptible cereals and plants intended for human and animal diet. It contains various composition of tryptophan-derived toxins defined ergot alkaloids. Since sclerotia can be harvested and milled together with cereals, they represent a source of food and feed contamination after breakage and spreading of mycotoxins into the various milling fractions. The effects of ergot alkaloids, including those adverse for human health, have been known since the Middle Ages. Nevertheless, as recently stated by the European Food Safety Authority, further information is needed on metabolism and target receptors-binding of common alkaloids in food. Unfortunately, the experimental investigation is challenging due to the high costs in terms of time and money. This study was thus aimed at assessing whether the in silico modeling can be an effective tool to investigate the interaction between multiple serotonin receptors and a wide set of ergotamine metabolites, including experimentally detected molecules and predicted derivatives. Validated models provided precious insights about the effects exerted by metabolic modifications on the receptor-ligand interaction. Such structural information may be useful to support the design of further experimental analysis.

Predicting human liver microsomal stability with machine learning techniques.

PubMed

Sakiyama, Yojiro; Yuki, Hitomi; Moriya, Takashi; Hattori, Kazunari; Suzuki, Misaki; Shimada, Kaoru; Honma, Teruki

2008-02-01

To ensure a continuing pipeline in pharmaceutical research, lead candidates must possess appropriate metabolic stability in the drug discovery process. In vitro ADMET (absorption, distribution, metabolism, elimination, and toxicity) screening provides us with useful information regarding the metabolic stability of compounds. However, before the synthesis stage, an efficient process is required in order to deal with the vast quantity of data from large compound libraries and high-throughput screening. Here we have derived a relationship between the chemical structure and its metabolic stability for a data set of in-house compounds by means of various in silico machine learning such as random forest, support vector machine (SVM), logistic regression, and recursive partitioning. For model building, 1952 proprietary compounds comprising two classes (stable/unstable) were used with 193 descriptors calculated by Molecular Operating Environment. The results using test compounds have demonstrated that all classifiers yielded satisfactory results (accuracy > 0.8, sensitivity > 0.9, specificity > 0.6, and precision > 0.8). Above all, classification by random forest as well as SVM yielded kappa values of approximately 0.7 in an independent validation set, slightly higher than other classification tools. These results suggest that nonlinear/ensemble-based classification methods might prove useful in the area of in silico ADME modeling.
In silico identification of a therapeutic target for photo-activated disinfection with indocyanine green: Modeling and virtual screening analysis of Arg-gingipain from Porphyromonas gingivalis.

PubMed

Pourhajibagher, Maryam; Bahador, Abbas

2017-06-01

Porphyromonas gingivalis is a momentous bacterial etiological agent associated with periodontitis, peri-implantitis as well as endodontic infections. The potential advantage of Photo-activated disinfection (PAD) as a promising novel approach is the choice of a suitable target site, specific photosensitizer, and wavelength of light for delivery of the light from source to target. Since Arg-gingipain is a cysteine proteinase that is involved in the virulence of P. gingivalis, it was evaluated as a target site for PAD with indocyanine green (ICG) as a photosensitizer. In this study, we used a range of in silico strategies, bioinformatics tools, biological databases, and computer simulation molecular modeling to evaluate the capacity of Arg-gingipain. The predicted structure of Arg-gingipain indicated that it is located outside the cell and has nine domains and 17 ligands, including two calcium ions and three sodium ions with positive charges which can be a site of interaction for anionic ICG. Based on the results of this study, anionic ICG desires to bind and interact with residues of Arg-gingipain during PAD as a main site to enhance the yield of treatment of endo-periodontal lesions. Copyright © 2017 Elsevier B.V. All rights reserved.
Training needs for toxicity testing in the 21st century: a survey-informed analysis.

PubMed

Lapenna, Silvia; Gabbert, Silke; Worth, Andrew

2012-12-01

Current training needs on the use of alternative methods in predictive toxicology, including new approaches based on mode-of-action (MoA) and adverse outcome pathway (AOP) concepts, are expected to evolve rapidly. In order to gain insight into stakeholder preferences for training, the European Commission's Joint Research Centre (JRC) conducted a single-question survey with twelve experts in regulatory agencies, industry, national research organisations, NGOs and consultancies. Stakeholder responses were evaluated by means of theory-based qualitative data analysis. Overall, a set of training topics were identified that relate both to general background information and to guidance for applying alternative testing methods. In particular, for the use of in silico methods, stakeholders emphasised the need for training on data integration and evaluation, in order to increase confidence in applying these methods for regulatory purposes. Although the survey does not claim to offer an exhaustive overview of the training requirements, its findings support the conclusion that the development of well-targeted and tailor-made training opportunities that inform about the usefulness of alternative methods, in particular those that offer practical experience in the application of in silico methods, deserves more attention. This should be complemented by transparent information and guidance on the interpretation of the results generated by these methods and software tools. 2012 FRAME.
New Insights into the In Silico Prediction of HIV Protease Resistance to Nelfinavir

PubMed Central

Antunes, Dinler A.; Rigo, Maurício M.; Sinigaglia, Marialva; de Medeiros, Rúbia M.; Junqueira, Dennis M.; Almeida, Sabrina E. M.; Vieira, Gustavo F.

2014-01-01

The Human Immunodeficiency Virus type 1 protease enzyme (HIV-1 PR) is one of the most important targets of antiretroviral therapy used in the treatment of AIDS patients. The success of protease-inhibitors (PIs), however, is often limited by the emergence of protease mutations that can confer resistance to a specific drug, or even to multiple PIs. In the present study, we used bioinformatics tools to evaluate the impact of the unusual mutations D30V and V32E over the dynamics of the PR-Nelfinavir complex, considering that codons involved in these mutations were previously related to major drug resistance to Nelfinavir. Both studied mutations presented structural features that indicate resistance to Nelfinavir, each one with a different impact over the interaction with the drug. The D30V mutation triggered a subtle change in the PR structure, which was also observed for the well-known Nelfinavir resistance mutation D30N, while the V32E exchange presented a much more dramatic impact over the PR flap dynamics. Moreover, our in silico approach was also able to describe different binding modes of the drug when bound to different proteases, identifying specific features of HIV-1 subtype B and subtype C proteases. PMID:24498124
Classification and virtual screening of androgen receptor antagonists.

PubMed

Li, Jiazhong; Gramatica, Paola

2010-05-24

Computational tools, such as quantitative structure-activity relationship (QSAR), are highly useful as screening support for prioritization of substances of very high concern (SVHC). From the practical point of view, QSAR models should be effective to pick out more active rather than inactive compounds, expressed as sensitivity in classification works. This research investigates the classification of a big data set of endocrine-disrupting chemicals (EDCs)-androgen receptor (AR) antagonists, mainly aiming to improve the external sensitivity and to screen for potential AR binders. The kNN, lazy IB1, and ADTree methods and the consensus approach were used to build different models, which improve the sensitivity on external chemicals from 57.1% (literature) to 76.4%. Additionally, the models' predictive abilities were further validated on a blind collected data set (sensitivity: 85.7%). Then the proposed classifiers were used: (i) to distinguish a set of AR binders into antagonists and agonists; (ii) to screen a combined estrogen receptor binder database to find out possible chemicals that can bind to both AR and ER; and (iii) to virtually screen our in-house environmental chemical database. The in silico screening results suggest: (i) that some compounds can affect the normal endocrine system through a complex mechanism binding both to ER and AR; (ii) new EDCs, which are nonER binders, but can in silico bind to AR, are recognized; and (iii) about 20% of compounds in a big data set of environmental chemicals are predicted as new AR antagonists. The priority should be given to them to experimentally test the binding activities with AR.
In silico structural analysis of group 3, 6 and 9 allergens from Dermatophagoides farinae.

PubMed

Teng, Feixiang; Yu, Lili; Bian, Yonghua; Sun, Jinxia; Wu, Juansong; Ling, Cunbao; Yang, Li; Wang, Yungang; Cui, Yubao

2015-05-01

Dermatophagoides farinae (Hughes; Acari: Pyroglyphidae) are the predominant source of dust mite allergens, which provoke allergic diseases, such as rhinitis, asthma and eczema. Of the 30 allergen groups produced by D. farinae, the Der f 3, Der f 6 and Der f 9 allergens are all trypsin‑associated proteins, however little else is currently known about them. The present study used in silico tools to compare the amino acid sequences, and predict the secondary and tertiary structures of Der f 3, Der f 6 and Der f 9 allergens. Protein sequence alignment detected ~46% identity between Der f 3, Der f 6 and Der f 9. Furthermore, each protein was shown to contain three active sites and two highly conserved trypsin functional domains. Predictions of the secondary and tertiary structure identified α‑helices, β‑sheets and random coils. The active sites of the three proteins appeared to fold onto each other in a three‑dimensional model, constituting the active site of the enzyme. Epitope analysis demonstrated that Der f 3, Der f 6 and Der f 9 have 4‑5 potential epitopes located in random coils, and the epitope sequences of Der f 3, Der f 6 and Der f 9 were shown to overlap in two domains (at amino acids 83‑87 and 179‑180); however the residues in these two domains were not identical. The present study aimed to conduct a biochemical and genetic analysis of these three allergens, and to potentially contribute to the development of vaccines for allergen‑specific immunotherapy.
An overview of bioinformatics tools for epitope prediction: implications on vaccine development.

PubMed

Soria-Guerra, Ruth E; Nieto-Gomez, Ricardo; Govea-Alonso, Dania O; Rosales-Mendoza, Sergio

2015-02-01

Exploitation of recombinant DNA and sequencing technologies has led to a new concept in vaccination in which isolated epitopes, capable of stimulating a specific immune response, have been identified and used to achieve advanced vaccine formulations; replacing those constituted by whole pathogen-formulations. In this context, bioinformatics approaches play a critical role on analyzing multiple genomes to select the protective epitopes in silico. It is conceived that cocktails of defined epitopes or chimeric protein arrangements, including the target epitopes, may provide a rationale design capable to elicit convenient humoral or cellular immune responses. This review presents a comprehensive compilation of the most advantageous online immunological software and searchable, in order to facilitate the design and development of vaccines. An outlook on how these tools are supporting vaccine development is presented. HIV and influenza have been taken as examples of promising developments on vaccination against hypervariable viruses. Perspectives in this field are also envisioned. Copyright © 2014 Elsevier Inc. All rights reserved.
Structure prediction, expression, and antigenicity of c-terminal of GRP78.

PubMed

Aghamollaei, Hossein; Mousavi Gargari, Seyed Latif; Ghanei, Mostafa; Rasaee, Mohamad Javad; Amani, Jafar; Bakherad, Hamid; Farnoosh, Gholamreza

2017-01-01

Glucose-regulated protein 78 (GRP78) is a typical endoplasmic reticulum luminal chaperone having a main role in the activation of the unfolded protein response. Because of hypoxia and nutrient deprivation in the tumor microenvironment, expression of GRP78 in these cells becomes higher than the native cells, which makes it a suitable candidate for cancer targeting. Suppression of survival signals by antibody production against C-terminal domain of GR78 (CGRP) can induce apoptosis of cancer cells. The aim of this study was in silico analysis, recombinant production, and characterization of CGRP in Escherichia coli. Structural prediction of CGRP by bioinformatics tools was done and the construct containing optimized sequence was transferred to E. coli T7 shuffle. Expression was induced by isopropyl-β-d-thiogalactoside, and recombinant protein was purified by Ni-NTA agarose resin. The content of secondary structures was obtained by circular dichroism (CD) spectrum. CGRP immunogenicity was evaluated from the immunized mouse sera. SDS-PAGE analysis showed CGRP expression in E. coli. CD spectrum also confirmed prediction of structures by bioinformatics tools. The enzyme-linked immunosorbent assay using sera from immunized mice revealed CGRP as a good immunogen. The results obtained in this study showed that the structure of truncated CGRP is very similar to its structure in the whole protein context. This protein can be used in cancer researches. © 2015 International Union of Biochemistry and Molecular Biology, Inc.
Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective.

PubMed

Doss, C George Priya; Chakraborty, Chiranjib; Chen, Luonan; Zhu, Hailong

2014-01-01

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype.
Exploration of structural stability in deleterious nsSNPs of the XPA gene: A molecular dynamics approach.

PubMed

Nagasundaram, N; Priya Doss, C George

2011-01-01

Distinguishing the deleterious from the massive number of non-functional nsSNPs that occur within a single genome is a considerable challenge in mutation research. In this approach, we have used the existing in silico methods to explore the mutation-structure-function relationship in the XPAgene. We used the Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping (PolyPhen), I-Mutant 2.0, and the Protein Analysis THrough Evolutionary Relationships methods to predict the effects of deleterious nsSNPs on protein function and evaluated the impact of mutation on protein stability by Molecular Dynamics simulations. By comparing the scores of all the four in silico methods, nsSNP with an ID rs104894131 at position C108F was predicted to be highly deleterious. We extended our Molecular dynamics approach to gain insight into the impact of this non-synonymous polymorphism on structural changes that may affect the activity of the XPAgene. Based on the in silico methods score, potential energy, root-mean-square deviation, and root-mean-square fluctuation, we predict that deleterious nsSNP at position C108F would play a significant role in causing disease by the XPA gene. Our approach would present the application of in silicotools in understanding the functional variation from the perspective of structure, evolution, and phenotype.
Recent advances in the in silico modelling of UDP glucuronosyltransferase substrates.

PubMed

Sorich, Michael J; Smith, Paul A; Miners, John O; Mackenzie, Peter I; McKinnon, Ross A

2008-01-01

UDP glucurononosyltransferases (UGT) are a superfamily of enzymes that catalyse the conjugation of a range of structurally diverse drugs, environmental and endogenous chemicals with glucuronic acid. This process plays a significant role in the clearance and detoxification of many chemicals. Over the last decade the regulation and substrate profiles of UGT isoforms have been increasingly characterised. The resulting data has facilitated the prototyping of ligand based in silico models capable of predicting, and gaining insights into, binding affinity and the substrate- and regio- selectivity of glucuronidation by UGT isoforms. Pharmacophore modelling has produced particularly insightful models and quantitative structure-activity relationships based on machine learning algorithms result in accurate predictions. Simple structural chemical descriptors were found to capture much of the chemical information relevant to UGT metabolism. However, quantum chemical properties of molecules and the nucleophilic atoms in the molecule can enhance both the predictivity and chemical intuitiveness of structure-activity models. Chemical diversity analysis of known substrates has shown some bias towards chemicals with aromatic and aliphatic hydroxyl groups. Future progress in in silico development will depend on larger and more diverse high quality metabolic datasets. Furthermore, improved protein structure data on UGTs will enable the application of structural modelling techniques likely leading to greater insight into the binding and reactive processes of UGT catalysed glucuronidation.
The Virtual Anemia Trial: An Assessment of Model‐Based In Silico Clinical Trials of Anemia Treatment Algorithms in Patients With Hemodialysis

PubMed Central

Topping, Alice; Kappel, Franz; Thijssen, Stephan; Kotanko, Peter

2018-01-01

In silico approaches have been proposed as a novel strategy to increase the repertoire of clinical trial designs. Realistic simulations of clinical trials can provide valuable information regarding safety and limitations of treatment protocols and have been shown to assist in the cost‐effective planning of clinical studies. In this report, we present a blueprint for the stepwise integration of internal, external, and ecological validity considerations in virtual clinical trials (VCTs). We exemplify this approach in the context of a model‐based in silico clinical trial aimed at anemia treatment in patients undergoing hemodialysis (HD). Hemoglobin levels and subsequent anemia treatment were simulated on a per patient level over the course of a year and compared to real‐life clinical data of 79,426 patients undergoing HD. The novel strategies presented here, aimed to improve external and ecological validity of a VCT, significantly increased the predictive power of the discussed in silico trial. PMID:29368434
A Critical Assessment of Combined Ligand-based and Structure-based Approaches to hERG Channel Blocker Modeling

PubMed Central

Du-Cuny, Lei; Chen, Lu; Zhang, Shuxing

2014-01-01

Blockade of hERG channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining QSAR modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high predictive capacity and improved enrichment, and they could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed and molecular docking studies were conducted, resulting in high correlations (R2=0.81) between predicted and experimental binding affinities. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities. PMID:21902220
Using genome-scale metabolic models to compare serovars of the foodborne pathogen Listeria monocytogenes.

PubMed

Metz, Zachary P; Ding, Tong; Baumler, David J

2018-01-01

Listeria monocytogenes is a microorganism of great concern for the food industry and the cause of human foodborne disease. Therefore, novel methods of control are needed, and systems biology is one such approach to identify them. Using a combination of computational techniques and laboratory methods, genome-scale metabolic models (GEMs) can be created, validated, and used to simulate growth environments and discern metabolic capabilities of microbes of interest, including L. monocytogenes. The objective of the work presented here was to generate GEMs for six different strains of L. monocytogenes, and to both qualitatively and quantitatively validate these GEMs with experimental data to examine the diversity of metabolic capabilities of numerous strains from the three different serovar groups most associated with foodborne outbreaks and human disease. Following qualitative validation, 57 of the 95 carbon sources tested experimentally were present in the GEMs, and; therefore, these were the compounds from which comparisons could be drawn. Of these 57 compounds, agreement between in silico predictions and in vitro results for carbon source utilization ranged from 80.7% to 91.2% between strains. Nutrient utilization agreement between in silico predictions and in vitro results were also conducted for numerous nitrogen, phosphorous, and sulfur sources. Additionally, quantitative validation showed that the L. monocytogenes GEMs were able to generate in silico predictions for growth rate and growth yield that were strongly and significantly (p < 0.0013 and p < 0.0015, respectively) correlated with experimental results. These findings are significant because they show that these GEMs for L. monocytogenes are comparable to published GEMs of other organisms for agreement between in silico predictions and in vitro results. Therefore, as with the other GEMs, namely those for Escherichia coli, Staphylococcus aureus, Vibrio vulnificus, and Salmonella spp., they can be used to determine new methods of growth control and disease treatment.
Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

DOE PAGES

Nguyen, Marcus; Brettin, Thomas; Long, S. Wesley; ...

2018-01-11

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies aremore » >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.« less
Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nguyen, Marcus; Brettin, Thomas; Long, S. Wesley

Here, antimicrobial resistant infections are a serious public health threat worldwide. Whole genome sequencing approaches to rapidly identify pathogens and predict antibiotic resistance phenotypes are becoming more feasible and may offer a way to reduce clinical test turnaround times compared to conventional culture-based methods, and in turn, improve patient outcomes. In this study, we use whole genome sequence data from 1668 clinical isolates of Klebsiella pneumoniae to develop a XGBoost-based machine learning model that accurately predicts minimum inhibitory concentrations (MICs) for 20 antibiotics. The overall accuracy of the model, within ± 1 two-fold dilution factor, is 92%. Individual accuracies aremore » >= 90% for 15/20 antibiotics. We show that the MICs predicted by the model correlate with known antimicrobial resistance genes. Importantly, the genome-wide approach described in this study offers a way to predict MICs for isolates without knowledge of the underlying gene content. This study shows that machine learning can be used to build a complete in silico MIC prediction panel for K. pneumoniae and provides a framework for building MIC prediction models for other pathogenic bacteria.« less
Deep-Learning-Based Drug-Target Interaction Prediction.

PubMed

Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

2017-04-07

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.
DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.
Multiscale modeling and simulation of embryogenesis for in silico predictive toxicology (WC9)

EPA Science Inventory

Translating big data from alternative and HTS platforms into hazard identification and risk assessment is an important need for predictive toxicology and for elucidating adverse outcome pathways (AOPs) in developmental toxicity. Understanding how chemical disruption of molecular ...
In silico cancer modeling: is it ready for primetime?

PubMed Central

Deisboeck, Thomas S; Zhang, Le; Yoon, Jeongah; Costa, Jose

2011-01-01

SUMMARY At the dawn of the era of personalized, systems-driven medicine, computational or in silico modeling and the simulation of disease processes is becoming increasingly important for hypothesis generation and data integration in both experiment and clinics alike. Arguably, this is nowhere more visible than in oncology. To illustrate the field’s vast potential as well as its current limitations we briefly review selected works on modeling malignant brain tumors. Implications for clinical practice, including trial design and outcome prediction are also discussed. PMID:18852721

Computational approaches to predict bacteriophage–host relationships

PubMed Central

Edwards, Robert A.; McNair, Katelyn; Faust, Karoline; Raes, Jeroen; Dutilh, Bas E.

2015-01-01

Metagenomics has changed the face of virus discovery by enabling the accurate identification of viral genome sequences without requiring isolation of the viruses. As a result, metagenomic virus discovery leaves the first and most fundamental question about any novel virus unanswered: What host does the virus infect? The diversity of the global virosphere and the volumes of data obtained in metagenomic sequencing projects demand computational tools for virus–host prediction. We focus on bacteriophages (phages, viruses that infect bacteria), the most abundant and diverse group of viruses found in environmental metagenomes. By analyzing 820 phages with annotated hosts, we review and assess the predictive power of in silico phage–host signals. Sequence homology approaches are the most effective at identifying known phage–host pairs. Compositional and abundance-based methods contain significant signal for phage–host classification, providing opportunities for analyzing the unknowns in viral metagenomes. Together, these computational approaches further our knowledge of the interactions between phages and their hosts. Importantly, we find that all reviewed signals significantly link phages to their hosts, illustrating how current knowledge and insights about the interaction mechanisms and ecology of coevolving phages and bacteria can be exploited to predict phage–host relationships, with potential relevance for medical and industrial applications. PMID:26657537
A systematic identification of species-specific protein succinylation sites using joint element features information.

PubMed

Hasan, Md Mehedi; Khatun, Mst Shamima; Mollah, Md Nurul Haque; Yong, Cao; Guo, Dianjing

2017-01-01

Lysine succinylation, an important type of protein posttranslational modification, plays significant roles in many cellular processes. Accurate identification of succinylation sites can facilitate our understanding about the molecular mechanism and potential roles of lysine succinylation. However, even in well-studied systems, a majority of the succinylation sites remain undetected because the traditional experimental approaches to succinylation site identification are often costly, time-consuming, and laborious. In silico approach, on the other hand, is potentially an alternative strategy to predict succinylation substrates. In this paper, a novel computational predictor SuccinSite2.0 was developed for predicting generic and species-specific protein succinylation sites. This predictor takes the composition of profile-based amino acid and orthogonal binary features, which were used to train a random forest classifier. We demonstrated that the proposed SuccinSite2.0 predictor outperformed other currently existing implementations on a complementarily independent dataset. Furthermore, the important features that make visible contributions to species-specific and cross-species-specific prediction of protein succinylation site were analyzed. The proposed predictor is anticipated to be a useful computational resource for lysine succinylation site prediction. The integrated species-specific online tool of SuccinSite2.0 is publicly accessible.
Binary classification of aqueous solubility using support vector machines with reduction and recombination feature selection.

PubMed

Cheng, Tiejun; Li, Qingliang; Wang, Yanli; Bryant, Stephen H

2011-02-28

Aqueous solubility is recognized as a critical parameter in both the early- and late-stage drug discovery. Therefore, in silico modeling of solubility has attracted extensive interests in recent years. Most previous studies have been limited in using relatively small data sets with limited diversity, which in turn limits the predictability of derived models. In this work, we present a support vector machines model for the binary classification of solubility by taking advantage of the largest known public data set that contains over 46 000 compounds with experimental solubility. Our model was optimized in combination with a reduction and recombination feature selection strategy. The best model demonstrated robust performance in both cross-validation and prediction of two independent test sets, indicating it could be a practical tool to select soluble compounds for screening, purchasing, and synthesizing. Moreover, our work may be used for comparative evaluation of solubility classification studies ascribe to the use of completely public resources.
ADMET in silico modelling: towards prediction paradise?

PubMed

van de Waterbeemd, Han; Gifford, Eric

2003-03-01

Following studies in the late 1990s that indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development, it has become widely appreciated that these areas should be considered as early as possible in the drug discovery process. However, in recent years, combinatorial chemistry and high-throughput screening have significantly increased the number of compounds for which early data on absorption, distribution, metabolism, excretion (ADME) and toxicity (T) are needed, which has in turn driven the development of a variety of medium and high-throughput in vitro ADMET screens. Here, we describe how in silico approaches will further increase our ability to predict and model the most relevant pharmacokinetic, metabolic and toxicity endpoints, thereby accelerating the drug discovery process.
In silico prediction of microRNAs on fluoride induced sperm toxicity in mice.

PubMed

Raghunath, Azhwar; Jeyabaskar, Dhivyalakshmi; Sundarraj, Kiruthika; Panneerselvam, Lakshmikanthan; Perumal, Ekambaram

2016-12-01

Fluorosis is an endemic global problem causing male reproductive impairment. F mediates male reproductive toxicity in mice down-regulating 63 genes involved in diverse biological processes - apoptosis, cell cycle, cell signaling, chemotaxis, electron transport, glycolysis, oxidative stress, sperm capacitation and spermatogenesis. We predicted the miRNAs down-regulating these 63 genes using TargetScan, DIANA and MicroCosm. The prediction tools identified 3059 miRNAs targeting 63 genes. Of the predicted interactions, 11 miRNAs (mmu-miR-103, -107, -122, -188a, -199a-5p, -205, -340-5p, -345-3p, -452-5p, -499, -878-3p) were commonly found in the three tools utilized and seven miRNAs (miR-9-5p, miR-511-3p, miR-7b-5p, miR-30e-5p, miR-17-5p, miR-122-5p and miR-541-5p) targeting six genes (Traf3, Rock2, Rgs8, Atp1b2, Cacna2d1 and Aldoa) were already validated experimentally in mice. The miRNA-mRNA network of the predicted miRNAs with its respective targets revealed the complex interaction within a biological process leading to sperm dysfunction on exposure to F. Our findings not only suggest that the predicted miRs furnish evidence, but also have the potential to serve as non-invasive biomarkers on F-induced sperm dysfunction. Our data contribute towards elucidating the function of miRNAs in the fluoride induced infertility. miRNA molecular pathways in F intoxication will open new avenues on the use of antagomirs in recovering fertility. Copyright © 2016 Elsevier Ltd. All rights reserved.
Overview of the status of predictive computer models for skin sensitization (JRC Expert meeting on pre- and pro-haptens )

EPA Science Inventory

No abstract was prepared or requested. This is a short presentation aiming to present a status of what in silico models and approaches exists in the prediction of skin sensitization potential and/or potency.
Monte Carlo simulations guided by imaging to predict the in vitro ranking of radiosensitizing nanoparticles.

PubMed

Retif, Paul; Reinhard, Aurélie; Paquot, Héna; Jouan-Hureaux, Valérie; Chateau, Alicia; Sancey, Lucie; Barberi-Heyob, Muriel; Pinel, Sophie; Bastogne, Thierry

This article addresses the in silico-in vitro prediction issue of organometallic nanoparticles (NPs)-based radiosensitization enhancement. The goal was to carry out computational experiments to quickly identify efficient nanostructures and then to preferentially select the most promising ones for the subsequent in vivo studies. To this aim, this interdisciplinary article introduces a new theoretical Monte Carlo computational ranking method and tests it using 3 different organometallic NPs in terms of size and composition. While the ranking predicted in a classical theoretical scenario did not fit the reference results at all, in contrast, we showed for the first time how our accelerated in silico virtual screening method, based on basic in vitro experimental data (which takes into account the NPs cell biodistribution), was able to predict a relevant ranking in accordance with in vitro clonogenic efficiency. This corroborates the pertinence of such a prior ranking method that could speed up the preclinical development of NPs in radiation therapy.
Development of novel in silico model for developmental toxicity assessment by using naïve Bayes classifier method.

PubMed

Zhang, Hui; Ren, Ji-Xia; Kang, Yan-Li; Bo, Peng; Liang, Jun-Yu; Ding, Lan; Kong, Wei-Bao; Zhang, Ji

2017-08-01

Toxicological testing associated with developmental toxicity endpoints are very expensive, time consuming and labor intensive. Thus, developing alternative approaches for developmental toxicity testing is an important and urgent task in the drug development filed. In this investigation, the naïve Bayes classifier was applied to develop a novel prediction model for developmental toxicity. The established prediction model was evaluated by the internal 5-fold cross validation and external test set. The overall prediction results for the internal 5-fold cross validation of the training set and external test set were 96.6% and 82.8%, respectively. In addition, four simple descriptors and some representative substructures of developmental toxicants were identified. Thus, we hope the established in silico prediction model could be used as alternative method for toxicological assessment. And these obtained molecular information could afford a deeper understanding on the developmental toxicants, and provide guidance for medicinal chemists working in drug discovery and lead optimization. Copyright © 2017 Elsevier Inc. All rights reserved.
RUCS: rapid identification of PCR primers for unique core sequences.

PubMed

Thomsen, Martin Christen Frølund; Hasman, Henrik; Westh, Henrik; Kaya, Hülya; Lund, Ole

2017-12-15

Designing PCR primers to target a specific selection of whole genome sequenced strains can be a long, arduous and sometimes impractical task. Such tasks would benefit greatly from an automated tool to both identify unique targets, and to validate the vast number of potential primer pairs for the targets in silico. Here we present RUCS, a program that will find PCR primer pairs and probes for the unique core sequences of a positive genome dataset complement to a negative genome dataset. The resulting primer pairs and probes are in addition to simple selection also validated through a complex in silico PCR simulation. We compared our method, which identifies the unique core sequences, against an existing tool called ssGeneFinder, and found that our method was 6.5-20 times more sensitive. We used RUCS to design primer pairs that would target a set of genomes known to contain the mcr-1 colistin resistance gene. Three of the predicted pairs were chosen for experimental validation using PCR and gel electrophoresis. All three pairs successfully produced an amplicon with the target length for the samples containing mcr-1 and no amplification products were produced for the negative samples. The novel methods presented in this manuscript can reduce the time needed to identify target sequences, and provide a quick virtual PCR validation to eliminate time wasted on ambiguously binding primers. Source code is freely available on https://bitbucket.org/genomicepidemiology/rucs. Web service is freely available on https://cge.cbs.dtu.dk/services/RUCS. mcft@cbs.dtu.dk. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.
Infinity: An In-Silico Tool for Genome-Wide Prediction of Specific DNA Matrices in miRNA Genomic Loci.

PubMed

Falcone, Emmanuela; Grandoni, Luca; Garibaldi, Francesca; Manni, Isabella; Filligoi, Giancarlo; Piaggio, Giulia; Gurtner, Aymone

2016-01-01

miRNAs are potent regulators of gene expression and modulate multiple cellular processes in physiology and pathology. Deregulation of miRNAs expression has been found in various cancer types, thus, miRNAs may be potential targets for cancer therapy. However, the mechanisms through which miRNAs are regulated in cancer remain unclear. Therefore, the identification of transcriptional factor-miRNA crosstalk is one of the most update aspects of the study of miRNAs regulation. In the present study we describe the development of a fast and user-friendly software, named infinity, able to find the presence of DNA matrices, such as binding sequences for transcriptional factors, on ~65kb (kilobase) of 939 human miRNA genomic sequences, simultaneously. Of note, the power of this software has been validated in vivo by performing chromatin immunoprecipitation assays on a subset of new in silico identified target sequences (CCAAT) for the transcription factor NF-Y on colon cancer deregulated miRNA loci. Moreover, for the first time, we have demonstrated that NF-Y, through its CCAAT binding activity, regulates the expression of miRNA-181a, -181b, -21, -17, -130b, -301b in colon cancer cells. The infinity software that we have developed is a powerful tool to underscore new TF/miRNA regulatory networks. Infinity was implemented in pure Java using Eclipse framework, and runs on Linux and MS Windows machine, with MySQL database. The software is freely available on the web at https://github.com/bio-devel/infinity. The website is implemented in JavaScript, PHP and HTML with all major browsers supported.
Infinity: An In-Silico Tool for Genome-Wide Prediction of Specific DNA Matrices in miRNA Genomic Loci

PubMed Central

Garibaldi, Francesca; Manni, Isabella; Filligoi, Giancarlo; Piaggio, Giulia; Gurtner, Aymone

2016-01-01

Motivation miRNAs are potent regulators of gene expression and modulate multiple cellular processes in physiology and pathology. Deregulation of miRNAs expression has been found in various cancer types, thus, miRNAs may be potential targets for cancer therapy. However, the mechanisms through which miRNAs are regulated in cancer remain unclear. Therefore, the identification of transcriptional factor–miRNA crosstalk is one of the most update aspects of the study of miRNAs regulation. Results In the present study we describe the development of a fast and user-friendly software, named infinity, able to find the presence of DNA matrices, such as binding sequences for transcriptional factors, on ~65kb (kilobase) of 939 human miRNA genomic sequences, simultaneously. Of note, the power of this software has been validated in vivo by performing chromatin immunoprecipitation assays on a subset of new in silico identified target sequences (CCAAT) for the transcription factor NF-Y on colon cancer deregulated miRNA loci. Moreover, for the first time, we have demonstrated that NF-Y, through its CCAAT binding activity, regulates the expression of miRNA-181a, -181b, -21, -17, -130b, -301b in colon cancer cells. Conclusions The infinity software that we have developed is a powerful tool to underscore new TF/miRNA regulatory networks. Availability and Implementation Infinity was implemented in pure Java using Eclipse framework, and runs on Linux and MS Windows machine, with MySQL database. The software is freely available on the web at https://github.com/bio-devel/infinity. The website is implemented in JavaScript, PHP and HTML with all major browsers supported. PMID:27082112
BioVeL: a virtual laboratory for data analysis and modelling in biodiversity science and ecology.

PubMed

Hardisty, Alex R; Bacall, Finn; Beard, Niall; Balcázar-Vargas, Maria-Paula; Balech, Bachir; Barcza, Zoltán; Bourlat, Sarah J; De Giovanni, Renato; de Jong, Yde; De Leo, Francesca; Dobor, Laura; Donvito, Giacinto; Fellows, Donal; Guerra, Antonio Fernandez; Ferreira, Nuno; Fetyukova, Yuliya; Fosso, Bruno; Giddy, Jonathan; Goble, Carole; Güntsch, Anton; Haines, Robert; Ernst, Vera Hernández; Hettling, Hannes; Hidy, Dóra; Horváth, Ferenc; Ittzés, Dóra; Ittzés, Péter; Jones, Andrew; Kottmann, Renzo; Kulawik, Robert; Leidenberger, Sonja; Lyytikäinen-Saarenmaa, Päivi; Mathew, Cherian; Morrison, Norman; Nenadic, Aleksandra; de la Hidalga, Abraham Nieva; Obst, Matthias; Oostermeijer, Gerard; Paymal, Elisabeth; Pesole, Graziano; Pinto, Salvatore; Poigné, Axel; Fernandez, Francisco Quevedo; Santamaria, Monica; Saarenmaa, Hannu; Sipos, Gergely; Sylla, Karl-Heinz; Tähtinen, Marko; Vicario, Saverio; Vos, Rutger Aldo; Williams, Alan R; Yilmaz, Pelin

2016-10-20

Making forecasts about biodiversity and giving support to policy relies increasingly on large collections of data held electronically, and on substantial computational capability and capacity to analyse, model, simulate and predict using such data. However, the physically distributed nature of data resources and of expertise in advanced analytical tools creates many challenges for the modern scientist. Across the wider biological sciences, presenting such capabilities on the Internet (as "Web services") and using scientific workflow systems to compose them for particular tasks is a practical way to carry out robust "in silico" science. However, use of this approach in biodiversity science and ecology has thus far been quite limited. BioVeL is a virtual laboratory for data analysis and modelling in biodiversity science and ecology, freely accessible via the Internet. BioVeL includes functions for accessing and analysing data through curated Web services; for performing complex in silico analysis through exposure of R programs, workflows, and batch processing functions; for on-line collaboration through sharing of workflows and workflow runs; for experiment documentation through reproducibility and repeatability; and for computational support via seamless connections to supporting computing infrastructures. We developed and improved more than 60 Web services with significant potential in many different kinds of data analysis and modelling tasks. We composed reusable workflows using these Web services, also incorporating R programs. Deploying these tools into an easy-to-use and accessible 'virtual laboratory', free via the Internet, we applied the workflows in several diverse case studies. We opened the virtual laboratory for public use and through a programme of external engagement we actively encouraged scientists and third party application and tool developers to try out the services and contribute to the activity. Our work shows we can deliver an operational, scalable and flexible Internet-based virtual laboratory to meet new demands for data processing and analysis in biodiversity science and ecology. In particular, we have successfully integrated existing and popular tools and practices from different scientific disciplines to be used in biodiversity and ecological research.
In silico designing of therapeutic protein enriched with branched-chain amino acids for the dietary treatment of chronic liver disease.

PubMed

L, Sunil; Vasu, Prasanna

2017-09-01

Leucine, isoleucine, and valine are three essential branched-chain amino acids (BCAA) account for 40-45% of total essential amino acids. BCAA stimulates protein synthesis primarily in skeletal muscles, and it can directly transport to circulatory blood stream bypassing the liver. Hence, a protein enriched with BCAA is an important therapeutic target for the dietary treatment of chronic liver disease. The present study is to design a synthetic protein enriched with BCAA and the challenge is to maximize the BCAA content, keeping the balanced ratio of leucine, isoleucine, valine - 2: 1: 1.2 as specified by WHO/UNU/FAO. Here, we turned the general concept of homology modeling and tried to find a suitable scaffold (α-helix) to host an excess amount of BCAA for increased stability and digestibility. A total of 50 protein models were constructed by using SWISS-MODEL, Modeller 9.17, ProtParam tool, and allergen online tools. Out of 50 different protein models, protein model-50 was found to be best, which had a well-defined 3D structure, good in silico digestibility, balanced ratio of BCAA and showed 65.57% structure identity to the template apo-bovine α-lactalbumin (1F6R). Templates search was performed against PDB using PSI-BLAST, SWISS-MODEL, PROFUNC, I-TASSER, and ConSurf. The secondary structure was predicted by PSSPred, PSIPRED, I-TASSER, PORTER, and SPIDER2. The modeled structure of protein Model-50 was validated by PROCHECK, ERRAT, ProSA, and QMEAN. COACH and ProFUNC tools were performed to determine the functional effects of protein model-50. Overall, the BCAA was enriched from 22 to 56.4% with the balanced ratio of Leu: Ile: Val (2: 1: 1.2). The Ramachandran plot showed 97.7% of the amino acid residues in allowed regions with ERRAT score of 86.05. We have successfully modeled the complete three-dimensional structure of the target protein model-50 using highly reputed computational tools. Copyright © 2017 Elsevier Inc. All rights reserved.
Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

PubMed

Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.
Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

NASA Astrophysics Data System (ADS)

Voet, Arnout R. D.; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y. J.

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.
Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

DOE Office of Scientific and Technical Information (OSTI.GOV)

Valerio, Luis G., E-mail: luis.valerio@fda.hhs.gov; Cross, Kevin P.

Control and minimization of human exposure to potential genotoxic impurities found in drug substances and products is an important part of preclinical safety assessments of new drug products. The FDA's 2008 draft guidance on genotoxic and carcinogenic impurities in drug substances and products allows use of computational quantitative structure–activity relationships (QSAR) to identify structural alerts for known and expected impurities present at levels below qualified thresholds. This study provides the information necessary to establish the practical use of a new in silico toxicology model for predicting Salmonella t. mutagenicity (Ames assay outcome) of drug impurities and other chemicals. We describemore » the model's chemical content and toxicity fingerprint in terms of compound space, molecular and structural toxicophores, and have rigorously tested its predictive power using both cross-validation and external validation experiments, as well as case studies. Consistent with desired regulatory use, the model performs with high sensitivity (81%) and high negative predictivity (81%) based on external validation with 2368 compounds foreign to the model and having known mutagenicity. A database of drug impurities was created from proprietary FDA submissions and the public literature which found significant overlap between the structural features of drug impurities and training set chemicals in the QSAR model. Overall, the model's predictive performance was found to be acceptable for screening drug impurities for Salmonella mutagenicity. -- Highlights: ► We characterize a new in silico model to predict mutagenicity of drug impurities. ► The model predicts Salmonella mutagenicity and will be useful for safety assessment. ► We examine toxicity fingerprints and toxicophores of this Ames assay model. ► We compare these attributes to those found in drug impurities known to FDA/CDER. ► We validate the model and find it has a desired predictive performance.« less
(Q)SAR tools for priority setting: A case study with printed paper and board food contact material substances.

PubMed

Van Bossuyt, Melissa; Van Hoeck, Els; Raitano, Giuseppa; Manganelli, Serena; Braeken, Els; Ates, Gamze; Vanhaecke, Tamara; Van Miert, Sabine; Benfenati, Emilio; Mertens, Birgit; Rogiers, Vera

2017-04-01

Over the last years, more stringent safety requirements for an increasing number of chemicals across many regulatory fields (e.g. industrial chemicals, pharmaceuticals, food, cosmetics, …) have triggered the need for an efficient screening strategy to prioritize the substances of highest concern. In this context, alternative methods such as in silico (i.e. computational) techniques gain more and more importance. In the current study, a new prioritization strategy for identifying potentially mutagenic substances was developed based on the combination of multiple (quantitative) structure-activity relationship ((Q)SAR) tools. Non-evaluated substances used in printed paper and board food contact materials (FCM) were selected for a case study. By applying our strategy, 106 out of the 1723 substances were assigned 'high priority' as they were predicted mutagenic by 4 different (Q)SAR models. Information provided within the models allowed to identify 53 substances for which Ames mutagenicity prediction already has in vitro Ames test results. For further prioritization, additional support could be obtained by applying local i.e. specific models, as demonstrated here for aromatic azo compounds, typically found in printed paper and board FCM. The strategy developed here can easily be applied to other groups of chemicals facing the same need for priority ranking. Copyright © 2017 Elsevier Ltd. All rights reserved.
A new in silico classification model for ready biodegradability, based on molecular fragments.

PubMed

Lombardo, Anna; Pizzo, Fabiola; Benfenati, Emilio; Manganaro, Alberto; Ferrari, Thomas; Gini, Giuseppina

2014-08-01

Regulations such as the European REACH (Registration, Evaluation, Authorization and restriction of Chemicals) often require chemicals to be evaluated for ready biodegradability, to assess the potential risk for environmental and human health. Because not all chemicals can be tested, there is an increasing demand for tools for quick and inexpensive biodegradability screening, such as computer-based (in silico) theoretical models. We developed an in silico model starting from a dataset of 728 chemicals with ready biodegradability data (MITI-test Ministry of International Trade and Industry). We used the novel software SARpy to automatically extract, through a structural fragmentation process, a set of substructures statistically related to ready biodegradability. Then, we analysed these substructures in order to build some general rules. The model consists of a rule-set made up of the combination of the statistically relevant fragments and of the expert-based rules. The model gives good statistical performance with 92%, 82% and 76% accuracy on the training, test and external set respectively. These results are comparable with other in silico models like BIOWIN developed by the United States Environmental Protection Agency (EPA); moreover this new model includes an easily understandable explanation. Copyright © 2014 Elsevier Ltd. All rights reserved.
Computer-aided design for metabolic engineering.

PubMed

Fernández-Castané, Alfred; Fehér, Tamás; Carbonell, Pablo; Pauthenier, Cyrille; Faulon, Jean-Loup

2014-12-20

The development and application of biotechnology-based strategies has had a great socio-economical impact and is likely to play a crucial role in the foundation of more sustainable and efficient industrial processes. Within biotechnology, metabolic engineering aims at the directed improvement of cellular properties, often with the goal of synthesizing a target chemical compound. The use of computer-aided design (CAD) tools, along with the continuously emerging advanced genetic engineering techniques have allowed metabolic engineering to broaden and streamline the process of heterologous compound-production. In this work, we review the CAD tools available for metabolic engineering with an emphasis, on retrosynthesis methodologies. Recent advances in genetic engineering strategies for pathway implementation and optimization are also reviewed as well as a range of bionalytical tools to validate in silico predictions. A case study applying retrosynthesis is presented as an experimental verification of the output from Retropath, the first complete automated computational pipeline applicable to metabolic engineering. Applying this CAD pipeline, together with genetic reassembly and optimization of culture conditions led to improved production of the plant flavonoid pinocembrin. Coupling CAD tools with advanced genetic engineering strategies and bioprocess optimization is crucial for enhanced product yields and will be of great value for the development of non-natural products through sustainable biotechnological processes. Copyright © 2014 Elsevier B.V. All rights reserved.
CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool

PubMed Central

del Sol Keyer, Maria; Wittbrodt, Joachim; Mateo, Juan L.

2015-01-01

Engineering of the CRISPR/Cas9 system has opened a plethora of new opportunities for site-directed mutagenesis and targeted genome modification. Fundamental to this is a stretch of twenty nucleotides at the 5’ end of a guide RNA that provides specificity to the bound Cas9 endonuclease. Since a sequence of twenty nucleotides can occur multiple times in a given genome and some mismatches seem to be accepted by the CRISPR/Cas9 complex, an efficient and reliable in silico selection and evaluation of the targeting site is key prerequisite for the experimental success. Here we present the CRISPR/Cas9 target online predictor (CCTop, http://crispr.cos.uni-heidelberg.de) to overcome limitations of already available tools. CCTop provides an intuitive user interface with reasonable default parameters that can easily be tuned by the user. From a given query sequence, CCTop identifies and ranks all candidate sgRNA target sites according to their off-target quality and displays full documentation. CCTop was experimentally validated for gene inactivation, non-homologous end-joining as well as homology directed repair. Thus, CCTop provides the bench biologist with a tool for the rapid and efficient identification of high quality target sites. PMID:25909470

FORUM - FutureTox II: In vitro Data and In Silico Models for ...

EPA Pesticide Factsheets

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo responses from in vitro and in silico data, and to define the goals for the future. Presentations and discussions were held on priority concerns such as predicting and modeling of metabolism, cell growth and differentiation, effects on sensitive subpopulations, and integrating data into risk assessment. Emerging trends in technologies such as stem cell-derived human cells, 3D organotypic culture models, mathematical modeling of cellular processes and morphogenesis, adverse outcome pathway development, and high-content imaging of in vivo systems were discussed. Although advances in moving towards an in vitro/in silico based risk assessment paradigm were apparent, knowledge gaps in these areas and limitations of technologies were identified. Specific recommendations were made for future directions and research needs in the areas of hepatotoxicity, cancer prediction, developmental toxicity, and regulatory toxicology. This article reports on the outcome of FutureTox II1,2, the second in a series of Society of Toxicology (SOT) Contemporary Concepts in Toxicology (CCT) Workshops, which was attended by invitees and participants from governmental and regulatory agencies, research institutes, academ
Exploration of structural stability in deleterious nsSNPs of the XPA gene: A molecular dynamics approach

PubMed Central

NagaSundaram, N; Priya Doss, C George

2011-01-01

Background: Distinguishing the deleterious from the massive number of non-functional nsSNPs that occur within a single genome is a considerable challenge in mutation research. In this approach, we have used the existing in silico methods to explore the mutation-structure-function relationship in the XPAgene. Materials and Methods: We used the Sorting Intolerant From Tolerant (SIFT), Polymorphism Phenotyping (PolyPhen), I-Mutant 2.0, and the Protein Analysis THrough Evolutionary Relationships methods to predict the effects of deleterious nsSNPs on protein function and evaluated the impact of mutation on protein stability by Molecular Dynamics simulations. Results: By comparing the scores of all the four in silico methods, nsSNP with an ID rs104894131 at position C108F was predicted to be highly deleterious. We extended our Molecular dynamics approach to gain insight into the impact of this non-synonymous polymorphism on structural changes that may affect the activity of the XPAgene. Conclusion: Based on the in silico methods score, potential energy, root-mean-square deviation, and root-mean-square fluctuation, we predict that deleterious nsSNP at position C108F would play a significant role in causing disease by the XPA gene. Our approach would present the application of in silicotools in understanding the functional variation from the perspective of structure, evolution, and phenotype. PMID:22190868
In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans.

PubMed

Chedik, Lisa; Mias-Lucquin, Dominique; Bruyere, Arnaud; Fardel, Olivier

2017-06-30

Intestinal absorption and brain permeation constitute key parameters of toxicokinetics for pesticides, conditioning their toxicity, including neurotoxicity. However, they remain poorly characterized in humans. The present study was therefore designed to evaluate human intestine and brain permeation for a large set of pesticides ( n = 338) belonging to various chemical classes, using an in silico graphical BOILED-Egg/SwissADME online method based on lipophilicity and polarity that was initially developed for drugs. A high percentage of the pesticides (81.4%) was predicted to exhibit high intestinal absorption, with a high accuracy (96%), whereas a lower, but substantial, percentage (38.5%) displayed brain permeation. Among the pesticide classes, organochlorines ( n = 30) constitute the class with the lowest percentage of intestine-permeant members (40%), whereas that of the organophosphorus compounds ( n = 99) has the lowest percentage of brain-permeant chemicals (9%). The predictions of the permeations for the pesticides were additionally shown to be significantly associated with various molecular descriptors well-known to discriminate between permeant and non-permeant drugs. Overall, our in silico data suggest that human exposure to pesticides through the oral way is likely to result in an intake of these dietary contaminants for most of them and brain permeation for some of them, thus supporting the idea that they have toxic effects on human health, including neurotoxic effects.
In Silico Prediction for Intestinal Absorption and Brain Penetration of Chemical Pesticides in Humans

PubMed Central

Chedik, Lisa; Mias-Lucquin, Dominique; Bruyere, Arnaud; Fardel, Olivier

2017-01-01

Intestinal absorption and brain permeation constitute key parameters of toxicokinetics for pesticides, conditioning their toxicity, including neurotoxicity. However, they remain poorly characterized in humans. The present study was therefore designed to evaluate human intestine and brain permeation for a large set of pesticides (n = 338) belonging to various chemical classes, using an in silico graphical BOILED-Egg/SwissADME online method based on lipophilicity and polarity that was initially developed for drugs. A high percentage of the pesticides (81.4%) was predicted to exhibit high intestinal absorption, with a high accuracy (96%), whereas a lower, but substantial, percentage (38.5%) displayed brain permeation. Among the pesticide classes, organochlorines (n = 30) constitute the class with the lowest percentage of intestine-permeant members (40%), whereas that of the organophosphorus compounds (n = 99) has the lowest percentage of brain-permeant chemicals (9%). The predictions of the permeations for the pesticides were additionally shown to be significantly associated with various molecular descriptors well-known to discriminate between permeant and non-permeant drugs. Overall, our in silico data suggest that human exposure to pesticides through the oral way is likely to result in an intake of these dietary contaminants for most of them and brain permeation for some of them, thus supporting the idea that they have toxic effects on human health, including neurotoxic effects. PMID:28665355
Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.

PubMed

Jeukens, Julie; Freschi, Luca; Kukavica-Ibrulj, Irena; Emond-Rheault, Jean-Guillaume; Tucker, Nicholas P; Levesque, Roger C

2017-06-02

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic-resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real-time patient management and the prediction of antimicrobial resistance. © 2017 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals Inc. on behalf of The New York Academy of Sciences.
In silico identification and characterization of common epitope-based peptide vaccine for Nipah and Hendra viruses.

PubMed

Saha, Chayan Kumar; Mahbub Hasan, Md; Saddam Hossain, Md; Asraful Jahan, Md; Azad, Abul Kalam

2017-06-01

To explore a common B- and T-cell epitope-based vaccine that can elicit an immune response against encephalitis causing genus Henipaviruses, Hendra virus (HeV) and Nipah virus (NiV). Membrane proteins F, G and M of HeV and NiV were retrieved from the protein database and subjected to different bioinformatics tools to predict antigenic B-cell epitopes. Best B-cell epitopes were then analyzed to predict their T-cell antigenic potentiality. Antigenic B- and T-cell epitopes that shared maximum identity with HeV and NiV were selected. Stability of the selected epitopes was predicted. Finally, the selected epitopes were subjected to molecular docking simulation with HLA-DR to confirm their antigenic potentiality in silico. One epitope from G proteins, one from M proteins and none from F proteins were selected based on their antigenic potentiality. The epitope from the G proteins was stable whereas that from M was unstable. The M-epitope was made stable by adding flanking dipeptides. The 15-mer G-epitope (VDPLRVQWRNNSVIS) showed at least 66% identity with all NiV and HeV G protein sequences, while the 15-mer M-epitope (GKLEFRRNNAIAFKG) with the dipeptide flanking residues showed 73% identity with all NiV and HeV M protein sequences available in the database. Molecular docking simulation with most frequent MHC class-II (MHC II) and class-I (MHC I) molecules showed that these epitopes could bind within HLA binding grooves to elicit an immune response. Data in our present study revealed the notion that the epitopes from G and M proteins might be the target for peptide-based subunit vaccine design against HeV and NiV. However, the biochemical analysis is necessary to experimentally validate the interaction of epitopes individually with the MHC molecules through elucidation of immunity induction. Copyright © 2017 Hainan Medical University. Production and hosting by Elsevier B.V. All rights reserved.
Metabolism of the synthetic cannabinoids AMB-CHMICA and 5C-AKB48 in pooled human hepatocytes and rat hepatocytes analyzed by UHPLC-(IMS)-HR-MSE.

PubMed

Mardal, Marie; Dalsgaard, Petur Weihe; Qi, Bing; Mollerup, Christian Brinch; Annaert, Pieter; Linnet, Kristian

2018-04-15

The main analytical targets of synthetic cannabinoids are often metabolites. With the high number of new psychoactive substances entering the market, suitable workflows are needed for analytical target identification in biological samples. The aims of this study were to identify the main metabolites of the synthetic cannabinoids, AMB-CHMICA and 5C-AKB48, using an in silico-assisted workflow with analytical data acquired using ultra-high-performance liquid chromatography-(ion mobility spectroscopy)-high resolution-mass spectrometry in data-independent acquisition mode (UHPLC-(IMS)-HR-MS E ). The metabolites were identified after incubation with rat and pooled human hepatocytes using UHPLC-HR-MS E , followed by UHPLC-IMS-HR-MS E . Metabolites of AMB-CHMICA and 5C-AKB48 were predicted with Meteor (Lhasa Ltd) and imported to the UNIFI software (Waters). The predicted metabolites were assigned to analytical components supported by the UNIFI in silico fragmentation tool. The main metabolic pathway of AMB-CHMICA was O-demethylation and hydroxylation of the methylhexyl moiety. For 5C-AKB48, the main metabolic pathways were hydroxylation(s) of the adamantyl moiety and oxidative dechlorination with subsequent oxidation to the ω-COOH. The matrix components in the metabolite spectra were reduced with IMS, which improved the accuracy of the spectral interpretation; however, this left fewer fragment ions for assigning sites of metabolism. Meteor was able to predict the majority of the metabolites, with the most notable exception being the oxidative dechlorination and, consequently, all metabolites that underwent that transformation pathway. Oxidative dechlorination of ω-chloroalkanes in humans has not been previously reported in the literature. The postulated metabolites can be used for screening of biological samples, with four-dimensional identification based on retention time, collision cross section, precursor ion, and fragment ions. Copyright © 2018 Elsevier B.V. All rights reserved.
Clinically relevant hypoglycemia prediction metrics for event mitigation.

PubMed

Harvey, Rebecca A; Dassau, Eyal; Zisser, Howard C; Bevier, Wendy; Seborg, Dale E; Jovanovič, Lois; Doyle, Francis J

2012-08-01

The purpose of this study was to develop a method to compare hypoglycemia prediction algorithms and choose parameter settings for different applications, such as triggering insulin pump suspension or alerting for rescue carbohydrate treatment. Hypoglycemia prediction algorithms with different parameter settings were implemented on an ambulatory dataset containing 490 days from 30 subjects with type 1 diabetes mellitus using the Dexcom™ (San Diego, CA) SEVEN™ continuous glucose monitoring system. The performance was evaluated using a proposed set of metrics representing the true-positive ratio, false-positive rate, and distribution of warning times. A prospective, in silico study was performed to show the effect of using different parameter settings to prevent or rescue from hypoglycemia. The retrospective study results suggest the parameter settings for different methods of hypoglycemia mitigation. When rescue carbohydrates are used, a high true-positive ratio, a minimal false-positive rate, and alarms with short warning time are desired. These objectives were met with a 30-min prediction horizon and two successive flags required to alarm: 78% of events were detected with 3.0 false alarms/day and 66% probability of alarms occurring within 30 min of the event. This parameter setting selection was confirmed in silico: treating with rescue carbohydrates reduced the duration of hypoglycemia from 14.9% to 0.5%. However, for a different method, such as pump suspension, this parameter setting only reduced hypoglycemia to 8.7%, as can be expected by the low probability of alarming more than 30 min ahead. The proposed metrics allow direct comparison of hypoglycemia prediction algorithms and selection of parameter settings for different types of hypoglycemia mitigation, as shown in the prospective in silico study in which hypoglycemia was alerted or treated with rescue carbohydrates.
Decreased HIV diversity after allogeneic stem cell transplantation of an HIV-1 infected patient: a case report

PubMed Central

2010-01-01

The human immunodeficiency virus type 1 (HIV-1) coreceptor use and viral evolution were analyzed in blood samples from an HIV-1 infected patient undergoing allogeneic stem cell transplantation (SCT). Coreceptor use was predicted in silico from sequence data obtained from the third variable loop region of the viral envelope gene with two software tools. Viral diversity and evolution was evaluated on the same samples by Bayesian inference and maximum likelihood methods. In addition, phenotypic analysis was done by comparison of viral growth in peripheral blood mononuclear cells and in a CCR5 (R5)-deficient T-cell line which was controlled by a reporter assay confirming viral tropism. In silico coreceptor predictions did not match experimental determinations that showed a consistent R5 tropism. Anti-HIV directed antibodies could be detected before and after the SCT. These preexisting antibodies did not prevent viral rebound after the interruption of antiretroviral therapy during the SCT. Eventually, transplantation and readministration of anti-retroviral drugs lead to sustained increase in CD4 counts and decreased viral load to undetectable levels. Unexpectedly, viral diversity decreased after successful SCT. Our data evidence that only R5-tropic virus was found in the patient before and after transplantation. Therefore, blocking CCR5 receptor during stem cell transplantation might have had beneficial effects and this might apply to more patients undergoing allogeneic stem cell transplantation. Furthermore, we revealed a scenario of HIV-1 dynamic different from the commonly described ones. Analysis of viral evolution shows the decrease of viral diversity even during episodes with bursts in viral load. PMID:20210988
Accurate recognition and feature qualify for flavonoid extracts from Liang-wai Gan Cao by liquid chromatography-high resolution-mass spectrometry and computational MS/MS fragmentation.

PubMed

He, Min; Wu, Hai; Nie, Juan; Yan, Pan; Yang, Tian-Biao; Yang, Zhi-Yu; Pei, Rui

2017-11-30

In this study, Liquid Chromatography (LC) separation combined with quadrupole-Time-Of-Flight Mass Spectrometry (qTOF-MS) detection was used to analyze the characteristic ions of the flavonoids from Liang-wai Gan Cao (Radix Glycyrrhizae uralensis). First, accurate mass measurement and isotope curve optimization could provide reliable molecular prediction after noise deduction, baseline calibration and "ghost peak recognition". Thus, some spectral features in the LC-MS data could be clearly explained. Secondly, the chemical structure of flavonoids was deduced by MS/MS fragment ions, and the in-silico spectra by MS-FINDER program provided strong support for overcoming the bottleneck of phytochemical identification. For a predicted formula and experimental MS/MS spectrum, the MS-FINDER program could sort the candidate compounds in the public database based on a comprehensive weighted score, and we took the first 20 reliable compounds to seek the target compound in an in-house database. Certainly, those fragmentation pathways could also be deduced and described as Retro-Diels-Alder (RDA) fragmentation reaction, losses of C 4 H 8 , C 5 H 8 , CH 3 , CO, CO 2 and others. Accordingly, 63 flavonoids were identified, and their in-silico bioactivity were clearly disclosed by some bioinformatics tools. In this experiment, the flavonoids obtained by the four extraction processes were tested by LC-qTOF-MS. We looked for possible Q-markers from these data matrices and then quantified them; their similarities/differences were also described. The results also indicated that the Macroporous Adsorption Resins (MARs) purification is a low cost, environmentally friendly and effective approach. Copyright © 2017 Elsevier B.V. All rights reserved.
Effects of amlodipine and adenosine on coronary haemodynamics: in vivo study and numerical simulation.

PubMed

De Lazzari, Claudio; L'Abbate, Antonio; Micalizzi, Mauro; Trivella, Maria Giovanna; Neglia, Danilo

2014-11-01

Amlodipine (AMLO) is a calcium channel blocker with vasodilating properties, in which the specific effects on the coronary circulation are not fully known. Coronary flow velocity-pressure (F/P) curves were obtained at rest and during administration of AMLO (10 mg to 20 mg iv) or adenosine (ADO, 1 mg ic) in 10 normal subjects (six women, age 48 ± 14 years). F/P curves were reproduced in a numerical simulator of systemic and coronary circulations (CARDIOSIM(©)) by adjustment of coronary resistance ( > or < 100 μm diameter vessels) and extravascular resistance applied to smaller vessels at endocardial (ENDO), middle and epicardial (EPI) myocardial layers. Best matching of in silico to in vivo curves was achieved by trial and error approach. ADO induced 170% and 250% increase in coronary flow velocity CFV and F/P diastolic slope as compared to 80% and 25-30% increase induced by AMLO, respectively. In the cardiovascular model, AMLO effects were predicted by progressive reduction of>100 μm vessels resistance from EPI to ENDO. ADO effects were mimicked by reducing resistance of both>100 μm and < 100 μm vessels, progressively from EPI to ENDO in the latter. Additional reduction in extravascular resistance avoided to impose a transmural gradient of vasodilating effect for both drugs. Numerical simulation predicts vasodilating effects of AMLO mainly on larger arteries and of ADO on both>and < 100 μm vessels. In vivo F/P loops could be completely reproduced in silico by adding extravascular resistance reduction for both drugs. Numerical simulator is useful tool for exploring the coronary effects of cardioactive drugs.
Glucokinase gene mutations (MODY 2) in Asian Indians.

PubMed

Kanthimathi, Sekar; Jahnavi, Suresh; Balamurugan, Kandasamy; Ranjani, Harish; Sonya, Jagadesan; Goswami, Soumik; Chowdhury, Subhankar; Mohan, Viswanathan; Radha, Venkatesan

2014-03-01

Heterozygous inactivating mutations in the glucokinase (GCK) gene cause a hyperglycemic condition termed maturity-onset diabetes of the young (MODY) 2 or GCK-MODY. This is characterized by mild, stable, usually asymptomatic, fasting hyperglycemia that rarely requires pharmacological intervention. The aim of the present study was to screen for GCK gene mutations in Asian Indian subjects with mild hyperglycemia. Of the 1,517 children and adolescents of the population-based ORANGE study in Chennai, India, 49 were found to have hyperglycemia. These children along with the six patients referred to our center with mild hyperglycemia were screened for MODY 2 mutations. The GCK gene was bidirectionally sequenced using BigDye(®) Terminator v3.1 (Applied Biosystems, Foster City, CA) chemistry. In silico predictions of the pathogenicity were carried out using the online tools SIFT, Polyphen-2, and I-Mutant 2.0 software programs. Direct sequencing of the GCK gene in the patients referred to our Centre revealed one novel mutation, Thr206Ala (c.616A>G), in exon 6 and one previously described mutation, Met251Thr (c.752T>C), in exon 7. In silico analysis predicted the novel mutation to be pathogenic. The highly conserved nature and critical location of the residue Thr206 along with the clinical course suggests that the Thr206Ala is a MODY 2 mutation. However, we did not find any MODY 2 mutations in the 49 children selected from the population-based study. Hence prevalence of GCK mutations in Chennai is <1:1,517. This is the first study of MODY 2 mutations from India and confirms the importance of considering GCK gene mutation screening in patients with mild early-onset hyperglycemia who are negative for β-cell antibodies.
In vitro pepsin resistance of proteins: Effect of non-reduced SDS-PAGE analysis on fragment observation.

PubMed

Pickles, Juliette; Rafiq, Samera; Cochrane, Stella A; Lalljie, Anja

2014-01-01

The introduction of novel proteins to food products carries with it the need to assess the potential allergenicity of such materials. Resistance to in vitro pepsin digestion is one parameter considered in such a risk assessment using a weight of evidence approach; however, the methodology used to investigate this has not been fully standardised. In vitro pepsin resistance assays typically involve SDS-PAGE performed under reducing conditions, with limited published data available on the assay using non-reducing conditions despite the need to consider non-reducing analysis techniques having been highlighted by regulatory bodies such as the European Food Safety Authority (EFSA). The purpose of the work reported here was to investigate the applicability of (and additional insight provided by) non-reducing analyses, by digesting a set of proteins using a ring-trial validated method, with analysis by SDS-PAGE under both reducing and non-reducing conditions. In silico prediction of digest fragments was also investigated. Significant differences were observed between results under reduced and non-reduced conditions for proteins in which disulphide bonds have a major role in protein structure, such as ribulose 1,5-diphosphate carboxylase (RUBISCO) and bovine serum albumin. For proteins with no or few disulphide bonds, no significant differences were seen in the results. Structural information such as disulphide bond numbers and positions should be considered during experimental design, as a non-reduced approach may be appropriate for some proteins. The in silico approach was a useful tool to suggest potential digest fragments, however the predictions were not always confirmed in vitro and should be considered a guide only.
The Virtual Anemia Trial: An Assessment of Model-Based In Silico Clinical Trials of Anemia Treatment Algorithms in Patients With Hemodialysis.

PubMed

Fuertinger, Doris H; Topping, Alice; Kappel, Franz; Thijssen, Stephan; Kotanko, Peter

2018-04-01

In silico approaches have been proposed as a novel strategy to increase the repertoire of clinical trial designs. Realistic simulations of clinical trials can provide valuable information regarding safety and limitations of treatment protocols and have been shown to assist in the cost-effective planning of clinical studies. In this report, we present a blueprint for the stepwise integration of internal, external, and ecological validity considerations in virtual clinical trials (VCTs). We exemplify this approach in the context of a model-based in silico clinical trial aimed at anemia treatment in patients undergoing hemodialysis (HD). Hemoglobin levels and subsequent anemia treatment were simulated on a per patient level over the course of a year and compared to real-life clinical data of 79,426 patients undergoing HD. The novel strategies presented here, aimed to improve external and ecological validity of a VCT, significantly increased the predictive power of the discussed in silico trial. © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics.
rpiCOOL: A tool for In Silico RNA-protein interaction detection using random forest.

PubMed

Akbaripour-Elahabad, Mohammad; Zahiri, Javad; Rafeh, Reza; Eslami, Morteza; Azari, Mahboobeh

2016-08-07

Understanding the principle of RNA-protein interactions (RPIs) is of critical importance to provide insights into post-transcriptional gene regulation and is useful to guide studies about many complex diseases. The limitations and difficulties associated with experimental determination of RPIs, call an urgent need to computational methods for RPI prediction. In this paper, we proposed a machine learning method to detect RNA-protein interactions based on sequence information. We used motif information and repetitive patterns, which have been extracted from experimentally validated RNA-protein interactions, in combination with sequence composition as descriptors to build a model to RPI prediction via a random forest classifier. About 20% of the "sequence motifs" and "nucleotide composition" features have been selected as the informative features with the feature selection methods. These results suggest that these two feature types contribute effectively in RPI detection. Results of 10-fold cross-validation experiments on three non-redundant benchmark datasets show a better performance of the proposed method in comparison with the current state-of-the-art methods in terms of various performance measures. In addition, the results revealed that the accuracy of the RPI prediction methods could vary considerably across different organisms. We have implemented the proposed method, namely rpiCOOL, as a stand-alone tool with a user friendly graphical user interface (GUI) that enables the researchers to predict RNA-protein interaction. The rpiCOOL is freely available at http://biocool.ir/rpicool.html for non-commercial uses. Copyright © 2016 Elsevier Ltd. All rights reserved.
SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli.

PubMed

Whiteside, Matthew D; Laing, Chad R; Manji, Akiff; Kruczkiewicz, Peter; Taboada, Eduardo N; Gannon, Victor P J

2016-04-12

Predictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data. In this study, we present SuperPhy, an online predictive genomics platform ( http://lfz.corefacility.ca/superphy/ ) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype. SuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.
Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsetsmore » driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. - Highlights: • Consensus method to predict ready biodegradability by prioritizing multiple QSARs. • Consensus reduced the amount of unpredictable chemicals to less than 2%. • Performance increased with the number of QSAR models considered. • The absence of 2D atom pairs contributed significantly to the consensus model.« less
Dealing with Diversity in Computational Cancer Modeling

PubMed Central

Johnson, David; McKeever, Steve; Stamatakos, Georgios; Dionysiou, Dimitra; Graf, Norbert; Sakkalis, Vangelis; Marias, Konstantinos; Wang, Zhihui; Deisboeck, Thomas S.

2013-01-01

This paper discusses the need for interconnecting computational cancer models from different sources and scales within clinically relevant scenarios to increase the accuracy of the models and speed up their clinical adaptation, validation, and eventual translation. We briefly review current interoperability efforts drawing upon our experiences with the development of in silico models for predictive oncology within a number of European Commission Virtual Physiological Human initiative projects on cancer. A clinically relevant scenario, addressing brain tumor modeling that illustrates the need for coupling models from different sources and levels of complexity, is described. General approaches to enabling interoperability using XML-based markup languages for biological modeling are reviewed, concluding with a discussion on efforts towards developing cancer-specific XML markup to couple multiple component models for predictive in silico oncology. PMID:23700360
Perspectives on pathway perturbation: Focused research to enhance 3R objectives

EPA Science Inventory

In vitro high-throughput screening (HTS) and in silico technologies are emerging as 21st century tools for hazard identification. Computational methods that strategically examine cross-species conservation of protein sequence/structural information for chemical molecular targets ...
In silico identification of genetic variants in glucocerebrosidase (GBA) gene involved in Gaucher's disease using multiple software tools.

PubMed

Manickam, Madhumathi; Ravanan, Palaniyandi; Singh, Pratibha; Talwar, Priti

2014-01-01

Gaucher's disease (GD) is an autosomal recessive disorder caused by the deficiency of glucocerebrosidase, a lysosomal enzyme that catalyses the hydrolysis of the glycolipid glucocerebroside to ceramide and glucose. Polymorphisms in GBA gene have been associated with the development of Gaucher disease. We hypothesize that prediction of SNPs using multiple state of the art software tools will help in increasing the confidence in identification of SNPs involved in GD. Enzyme replacement therapy is the only option for GD. Our goal is to use several state of art SNP algorithms to predict/address harmful SNPs using comparative studies. In this study seven different algorithms (SIFT, MutPred, nsSNP Analyzer, PANTHER, PMUT, PROVEAN, and SNPs&GO) were used to predict the harmful polymorphisms. Among the seven programs, SIFT found 47 nsSNPs as deleterious, MutPred found 46 nsSNPs as harmful. nsSNP Analyzer program found 43 out of 47 nsSNPs are disease causing SNPs whereas PANTHER found 32 out of 47 as highly deleterious, 22 out of 47 are classified as pathological mutations by PMUT, 44 out of 47 were predicted to be deleterious by PROVEAN server, all 47 shows the disease related mutations by SNPs&GO. Twenty two nsSNPs were commonly predicted by all the seven different algorithms. The common 22 targeted mutations are F251L, C342G, W312C, P415R, R463C, D127V, A309V, G46E, G202E, P391L, Y363C, Y205C, W378C, I402T, S366R, F397S, Y418C, P401L, G195E, W184R, R48W, and T43R.

Unveiling combinatorial regulation through the combination of ChIP information and in silico cis-regulatory module detection

PubMed Central

Sun, Hong; Guns, Tias; Fierro, Ana Carolina; Thorrez, Lieven; Nijssen, Siegfried; Marchal, Kathleen

2012-01-01

Computationally retrieving biologically relevant cis-regulatory modules (CRMs) is not straightforward. Because of the large number of candidates and the imperfection of the screening methods, many spurious CRMs are detected that are as high scoring as the biologically true ones. Using ChIP-information allows not only to reduce the regions in which the binding sites of the assayed transcription factor (TF) should be located, but also allows restricting the valid CRMs to those that contain the assayed TF (here referred to as applying CRM detection in a query-based mode). In this study, we show that exploiting ChIP-information in a query-based way makes in silico CRM detection a much more feasible endeavor. To be able to handle the large datasets, the query-based setting and other specificities proper to CRM detection on ChIP-Seq based data, we developed a novel powerful CRM detection method ‘CPModule’. By applying it on a well-studied ChIP-Seq data set involved in self-renewal of mouse embryonic stem cells, we demonstrate how our tool can recover combinatorial regulation of five known TFs that are key in the self-renewal of mouse embryonic stem cells. Additionally, we make a number of new predictions on combinatorial regulation of these five key TFs with other TFs documented in TRANSFAC. PMID:22422841
Anticancer activity of cow, sheep, goat, mare, donkey and camel milks and their caseins and whey proteins and in silico comparison of the caseins.

PubMed

Shariatikia, Malihe; Behbahani, Mandana; Mohabatkar, Hassan

2017-06-01

The present investigation was carried out to evaluate anticancer activity of cow, goat, sheep, mare, donkey and camel milks and their casein and whey proteins against MCF7 cell line. The structure-based properties of the casein proteins were also investigated, using bioinformatics tools to find explanation for their antitumor activities. The effect of different milks and their casein and whey proteins on MCF7 proliferation was measured using MTT assay at different concentrations (0.5, 1 and 2 mg/ml). The results showed that mare, donkey, cow and camel milks and their casein and whey proteins have potent cytotoxic activity against MCF7 cells in a dose dependent manner while sheep and goat milks and their proteins did not reveal any cytotoxic activity. The in silico results demonstrated that mare, donkey and camel caseins had highest positive and negative charges. The secondary structure prediction indicated that mare and donkey caseins had the maximum percentage of α helix and camel casein had the highest percentage of extended strand. This study suggests that there is a striking correlation between anti-cancer activity of milk caseins and their physicochemical properties such as alpha helix structure and positive and negative charges. In conclusion, the results indicated that mare, camel and donkey milks might be good candidates against breast cancer cells.
Drug-Like Protein–Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

PubMed Central

Villoutreix, Bruno O; Kuenemann, Melaine A; Poyet, Jean-Luc; Bruzzoni-Giovanelli, Heriberto; Labbé, Céline; Lagorce, David; Sperandio, Olivier; Miteva, Maria A

2014-01-01

Fundamental processes in living cells are largely controlled by macromolecular interactions and among them, protein–protein interactions (PPIs) have a critical role while their dysregulations can contribute to the pathogenesis of numerous diseases. Although PPIs were considered as attractive pharmaceutical targets already some years ago, they have been thus far largely unexploited for therapeutic interventions with low molecular weight compounds. Several limiting factors, from technological hurdles to conceptual barriers, are known, which, taken together, explain why research in this area has been relatively slow. However, this last decade, the scientific community has challenged the dogma and became more enthusiastic about the modulation of PPIs with small drug-like molecules. In fact, several success stories were reported both, at the preclinical and clinical stages. In this review article, written for the 2014 International Summer School in Chemoinformatics (Strasbourg, France), we discuss in silico tools (essentially post 2012) and databases that can assist the design of low molecular weight PPI modulators (these tools can be found at www.vls3d.com). We first introduce the field of protein–protein interaction research, discuss key challenges and comment recently reported in silico packages, protocols and databases dedicated to PPIs. Then, we illustrate how in silico methods can be used and combined with experimental work to identify PPI modulators. PMID:25254076
Improving the physiological realism of experimental models.

PubMed

Vinnakota, Kalyan C; Cha, Chae Y; Rorsman, Patrik; Balaban, Robert S; La Gerche, Andre; Wade-Martins, Richard; Beard, Daniel A; Jeneson, Jeroen A L

2016-04-06

The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease.
The Silicon Trypanosome: a test case of iterative model extension in systems biology

PubMed Central

Achcar, Fiona; Fadda, Abeer; Haanstra, Jurgen R.; Kerkhoven, Eduard J.; Kim, Dong-Hyun; Leroux, Alejandro E.; Papamarkou, Theodore; Rojas, Federico; Bakker, Barbara M.; Barrett, Michael P.; Clayton, Christine; Girolami, Mark; Luise Krauth-Siegel, R.; Matthews, Keith R.; Breitling, Rainer

2016-01-01

The African trypanosome, Trypanosoma brucei, is a unicellular parasite causing African Trypanosomiasis (sleeping sickness in humans and nagana in animals). Due to some of its unique properties, it has emerged as a popular model organism in systems biology. A predictive quantitative model of glycolysis in the bloodstream form of the parasite has been constructed and updated several times. The Silicon Trypanosome (SilicoTryp) is a project that brings together modellers and experimentalists to improve and extend this core model with new pathways and additional levels of regulation. These new extensions and analyses use computational methods that explicitly take different levels of uncertainty into account. During this project, numerous tools and techniques have been developed for this purpose, which can now be used for a wide range of different studies in systems biology. PMID:24797926
A novel in silico approach to drug discovery via computational intelligence.

PubMed

Hecht, David; Fogel, Gary B

2009-04-01

A computational intelligence drug discovery platform is introduced as an innovative technology designed to accelerate high-throughput drug screening for generalized protein-targeted drug discovery. This technology results in collections of novel small molecule compounds that bind to protein targets as well as details on predicted binding modes and molecular interactions. The approach was tested on dihydrofolate reductase (DHFR) for novel antimalarial drug discovery; however, the methods developed can be applied broadly in early stage drug discovery and development. For this purpose, an initial fragment library was defined, and an automated fragment assembly algorithm was generated. These were combined with a computational intelligence screening tool for prescreening of compounds relative to DHFR inhibition. The entire method was assayed relative to spaces of known DHFR inhibitors and with chemical feasibility in mind, leading to experimental validation in future studies.
DEKOIS: demanding evaluation kits for objective in silico screening--a versatile tool for benchmarking docking programs and scoring functions.

PubMed

Vogel, Simon M; Bauer, Matthias R; Boeckler, Frank M

2011-10-24

For widely applied in silico screening techniques success depends on the rational selection of an appropriate method. We herein present a fast, versatile, and robust method to construct demanding evaluation kits for objective in silico screening (DEKOIS). This automated process enables creating tailor-made decoy sets for any given sets of bioactives. It facilitates a target-dependent validation of docking algorithms and scoring functions helping to save time and resources. We have developed metrics for assessing and improving decoy set quality and employ them to investigate how decoy embedding affects docking. We demonstrate that screening performance is target-dependent and can be impaired by latent actives in the decoy set (LADS) or enhanced by poor decoy embedding. The presented method allows extending and complementing the collection of publicly available high quality decoy sets toward new target space. All present and future DEKOIS data sets will be made accessible at www.dekois.com.
In Silico Discovery of Potential Uridine-Cytidine Kinase 2 Inhibitors from the Rhizome of Alpinia mutica.

PubMed

Malami, Ibrahim; Abdul, Ahmad Bustamam; Abdullah, Rasedee; Bt Kassim, Nur Kartinee; Waziri, Peter; Christopher Etti, Imaobong

2016-04-08

Uridine-cytidine kinase 2 is implicated in uncontrolled proliferation of abnormal cells and it is a hallmark of cancer, therefore, there is need for effective inhibitors of this key enzyme. In this study, we employed the used of in silico studies to find effective UCK2 inhibitors of natural origin using bioinformatics tools. An in vitro kinase assay was established by measuring the amount of ADP production in the presence of ATP and 5-fluorouridine as a substrate. Molecular docking studies revealed an interesting ligand interaction with the UCK2 protein for both flavokawain B and alpinetin. Both compounds were found to reduce ADP production, possibly by inhibiting UCK2 activity in vitro. In conclusion, we have identified flavokawain B and alpinetin as potential natural UCK2 inhibitors as determined by their interactions with UCK2 protein using in silico molecular docking studies. This can provide information to identify lead candidates for further drug design and development.
In Silico Systems Biology Analysis of Variants of Uncertain Significance in Lynch Syndrome Supports the Prioritization of Functional Molecular Validation.

PubMed

Borras, Ester; Chang, Kyle; Pande, Mala; Cuddy, Amanda; Bosch, Jennifer L; Bannon, Sarah A; Mork, Maureen E; Rodriguez-Bigas, Miguel A; Taggart, Melissa W; Lynch, Patrick M; You, Y Nancy; Vilar, Eduardo

2017-10-01

Lynch syndrome (LS) is a genetic condition secondary to germline alterations in the DNA mismatch repair (MMR) genes with 30% of changes being variants of uncertain significance (VUS). Our aim was to perform an in silico reclassification of VUS from a large single institutional cohort that will help prioritizing functional validation. A total of 54 VUS were detected with 33 (61%) novel variants. We integrated family history, pathology, and genetic information along with supporting evidence from eight different in silico tools at the RNA and protein level. Our assessment allowed us to reclassify 54% (29/54) of the VUS as probably damaging, 13% (7/54) as possibly damaging, and 28% (15/54) as probably neutral. There are more than 1,000 VUS reported in MMR genes and our approach facilitates the prioritization of further functional efforts to assess the pathogenicity to those classified as probably damaging. Cancer Prev Res; 10(10); 580-7. ©2017 AACR . ©2017 American Association for Cancer Research.
FORUM - FutureTox II: In vitro Data and In Silico Models for Predictive Toxicology

EPA Science Inventory

FutureTox II, a Society of Toxicology Contemporary Concepts in Toxicology workshop, was held in January, 2014. The meeting goals were to review and discuss the state of the science in toxicology in the context of implementing the NRC 21st century vision of predicting in vivo resp...
Current and future perspectives on the development, evaluation and application of in silico approaches for predicting toxicity

EPA Science Inventory

Safety-related problems continue to be one of the major reasons of attrition in drug development. Non-testing approaches to predict toxicity could form part of the solution. This review provides a perspective of current status of non-testing approaches available for the predictio...
GeneSilico protein structure prediction meta-server.

PubMed

Kurowski, Michal A; Bujnicki, Janusz M

2003-07-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.
GeneSilico protein structure prediction meta-server

PubMed Central

Kurowski, Michal A.; Bujnicki, Janusz M.

2003-01-01

Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta. PMID:12824313
Isolation and in silico analysis of a novel H+-pyrophosphatase gene orthologue from the halophytic grass Leptochloa fusca

NASA Astrophysics Data System (ADS)

Rauf, Muhammad; Saeed, Nasir A.; Habib, Imran; Ahmed, Moddassir; Shahzad, Khurram; Mansoor, Shahid; Ali, Rashid

2017-02-01

Structure prediction can provide information about function and active sites of protein which helps to design new functional proteins. H+-pyrophosphatase is transmembrane protein involved in establishing proton motive force for active transport of Na+ across membrane by Na+/H+ antiporters. A full length novel H+-pyrophosphatase gene was isolated from halophytic grass Leptochloa fusca using RT-PCR and RACE method. Full length LfVP1 gene sequence of 2292 nucleotides encodes protein of 764 amino acids. DNA and protein sequences were used for characterization using bioinformatics tools. Various important potential sites were predicted by PROSITE webserver. Primary structural analysis showed LfVP1 as stable protein and Grand average hydropathy (GRAVY) indicated that LfVP1 protein has good hydrosolubility. Secondary structure analysis showed that LfVP1 protein sequence contains significant proportion of alpha helix and random coil. Protein membrane topology suggested the presence of 14 transmembrane domains and presence of catalytic domain in TM3. Three dimensional structure from LfVP1 protein sequence also indicated the presence of 14 transmembrane domains and hydrophobicity surface model showed amino acid hydrophobicity. Ramachandran plot showed that 98% amino acid residues were predicted in the favored region.
Discriminative Prediction of A-To-I RNA Editing Events from DNA Sequence

PubMed Central

Sun, Jiangming; Singh, Pratibha; Bagge, Annika; Valtat, Bérengère; Vikman, Petter; Spégel, Peter; Mulder, Hindrik

2016-01-01

RNA editing is a post-transcriptional alteration of RNA sequences that, via insertions, deletions or base substitutions, can affect protein structure as well as RNA and protein expression. Recently, it has been suggested that RNA editing may be more frequent than previously thought. A great impediment, however, to a deeper understanding of this process is the paramount sequencing effort that needs to be undertaken to identify RNA editing events. Here, we describe an in silico approach, based on machine learning, that ameliorates this problem. Using 41 nucleotide long DNA sequences, we show that novel A-to-I RNA editing events can be predicted from known A-to-I RNA editing events intra- and interspecies. The validity of the proposed method was verified in an independent experimental dataset. Using our approach, 203 202 putative A-to-I RNA editing events were predicted in the whole human genome. Out of these, 9% were previously reported. The remaining sites require further validation, e.g., by targeted deep sequencing. In conclusion, the approach described here is a useful tool to identify potential A-to-I RNA editing events without the requirement of extensive RNA sequencing. PMID:27764195
Modeling the effect of 3 missense AGXT mutations on dimerization of the AGT enzyme in primary hyperoxaluria type 1.

PubMed

Robbiano, Angela; Frecer, Vladimir; Miertus, Jan; Zadro, Cristina; Ulivi, Sheila; Bevilacqua, Elena; Mandrile, Giorgia; De Marchi, Mario; Miertus, Stanislav; Amoroso, Antonio

2010-01-01

Mutations of the AGXT gene encoding the alanine:glyoxylate aminotransferase liver enzyme (AGT) cause primary hyperoxaluria type 1 (PH1). Here we report a molecular modeling study of selected missense AGXT mutations: the common Gly170Arg and the recently described Gly47Arg and Ser81Leu variants, predicted to be pathogenic using standard criteria. Taking advantage of the refined 3D structure of AGT, we computed the dimerization energy of the wild-type and mutated proteins. Molecular modeling predicted that Gly47Arg affects dimerization with a similar effect to that shown previously for Gly170Arg through classical biochemical approaches. In contrast, no effect on dimerization was predicted for Ser81Leu. Therefore, this probably demonstrates pathogenic properties via a different mechanism, similar to that described for the adjacent Gly82Glu mutation that affects pyridoxine binding. This study shows that the molecular modeling approach can contribute to evaluating the pathogenicity of some missense variants that affect dimerization. However, in silico studies--aimed to assess the relationship between structural change and biological effects--require the integrated use of more than 1 tool.
Binding-Site Compatible Fragment Growing Applied to the Design of β2-Adrenergic Receptor Ligands.

PubMed

Chevillard, Florent; Rimmer, Helena; Betti, Cecilia; Pardon, Els; Ballet, Steven; van Hilten, Niek; Steyaert, Jan; Diederich, Wibke E; Kolb, Peter

2018-02-08

Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β 2 -adrenergic receptor (β 2 AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.
Epitope-based immunoinformatics and molecular docking studies of nucleocapsid protein and ovarian tumor domain of crimean-congo hemorrhagic Fever virus.

PubMed

Srinivasan, Pappu; Kumar, Sivakumar Prasanth; Karthikeyan, Muthusamy; Jeyakanthan, Jeyaram; Jasrai, Yogesh T; Pandya, Himanshu A; Rawal, Rakesh M; Patel, Saumya K

2011-01-01

Crimean-Congo hemorrhagic fever virus (CCHFV), the fatal human pathogen is transmitted to humans by tick bite, or exposure to infected blood or tissues of infected livestock. The CCHFV genome consists of three RNA segments namely, S, M, and L. The unusual large viral L protein has an ovarian tumor (OTU) protease domain located in the N terminus. It is likely that the protein may be autoproteolytically cleaved to generate the active virus L polymerase with additional functions. Identification of the epitope regions of the virus is important for the diagnosis, phylogeny studies, and drug discovery. Early diagnosis and treatment of CCHF infection is critical to the survival of patients and the control of the disease. In this study, we undertook different in silico approaches using molecular docking and immunoinformatics tools to predict epitopes which can be helpful for vaccine designing. Small molecule ligands against OTU domain and protein-protein interaction between a viral and a host protein have been studied using docking tools.
QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.
in silico identification of cross affinity towards Cry1Ac pesticidal protein with receptor enzyme in Bos taurus and sequence, structure analysis of crystal proteins for stability.

PubMed

Ebenezer, King Solomon; Nachimuthu, Ramesh; Thiagarajan, Prabha; Velu, Rajesh Kannan

2013-01-01

Any novel protein introduced into the GM crops need to be evaluated for cross affinity on living organisms. Many researchers are currently focusing on the impact of Bacillus thuringiensis cotton on soil and microbial diversity by field experiments. In spite of this, in silico approach might be helpful to elucidate the impact of cry genes. The crystal a protein which was produced by Bt at the time of sporulation has been used as a biological pesticide to target the insectivorous pests like Cry1Ac for Helicoverpa armigera and Cry2Ab for Spodoptera sp. and Heliothis sp. Here, we present the comprehensive in silico analysis of Cry1Ac and Cry2Ab proteins with available in silico tools, databases and docking servers. Molecular docking of Cry1Ac with procarboxypeptidase from Helicoverpa armigera and Cry1Ac with Leucine aminopeptidase from Bos taurus has showed the 125(th) amino acid position to be the preference site of Cry1Ac protein. The structures were compared with each other and it showed 5% of similarity. The cross affinity of this toxin that have confirmed the earlier reports of ill effects of Bt cotton consumed by cattle.

Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies

PubMed Central

Brito, Rory C. F.; Guimarães, Frederico G.; Velloso, João P. L.; Corrêa-Oliveira, Rodrigo; Ruiz, Jeronimo C.; Reis, Alexandre B.; Resende, Daniela M.

2017-01-01

Leishmaniasis is a wide-spectrum disease caused by parasites from Leishmania genus. There is no human vaccine available and it is considered by many studies as apotential effective tool for disease control. To discover novel antigens, computational programs have been used in reverse vaccinology strategies. In this work, we developed a validation antigen approach that integrates prediction of B and T cell epitopes, analysis of Protein-Protein Interaction (PPI) networks and metabolic pathways. We selected twenty candidate proteins from Leishmania tested in murine model, with experimental outcome published in the literature. The predictions for CD4+ and CD8+ T cell epitopes were correlated with protection in experimental outcomes. We also mapped immunogenic proteins on PPI networks in order to find Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with them. Our results suggest that non-protective antigens have lowest frequency of predicted T CD4+ and T CD8+ epitopes, compared with protective ones. T CD4+ and T CD8+ cells are more related to leishmaniasis protection in experimental outcomes than B cell predicted epitopes. Considering KEGG analysis, the proteins considered protective are connected to nodes with few pathways, including those associated with ribosome biosynthesis and purine metabolism. PMID:28208616
Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies.

PubMed

Brito, Rory C F; Guimarães, Frederico G; Velloso, João P L; Corrêa-Oliveira, Rodrigo; Ruiz, Jeronimo C; Reis, Alexandre B; Resende, Daniela M

2017-02-10

Leishmaniasis is a wide-spectrum disease caused by parasites from Leishmania genus. There is no human vaccine available and it is considered by many studies as apotential effective tool for disease control. To discover novel antigens, computational programs have been used in reverse vaccinology strategies. In this work, we developed a validation antigen approach that integrates prediction of B and T cell epitopes, analysis of Protein-Protein Interaction (PPI) networks and metabolic pathways. We selected twenty candidate proteins from Leishmania tested in murine model, with experimental outcome published in the literature. The predictions for CD4⁺ and CD8⁺ T cell epitopes were correlated with protection in experimental outcomes. We also mapped immunogenic proteins on PPI networks in order to find Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with them. Our results suggest that non-protective antigens have lowest frequency of predicted T CD4⁺ and T CD8⁺ epitopes, compared with protective ones. T CD4⁺ and T CD8⁺ cells are more related to leishmaniasis protection in experimental outcomes than B cell predicted epitopes. Considering KEGG analysis, the proteins considered protective are connected to nodes with few pathways, including those associated with ribosome biosynthesis and purine metabolism.
Prediction of Pan-Specific B-Cell Epitopes From Nucleocapsid Protein of Hantaviruses Causing Hantavirus Cardiopulmonary Syndrome.

PubMed

Kalaiselvan, Sagadevan; Sankar, Sathish; Ramamurthy, Mageshbabu; Ghosh, Asit Ranjan; Nandagopal, Balaji; Sridharan, Gopalan

2017-08-01

Hantaviruses are emerging viral pathogens that causes hantavirus cardiopulmonary syndrome (HCPS) in the Americas, a severe, sometimes fatal, respiratory disease in humans with a case fatality rate of ≥50%. IgM and IgG-based serological detection methods are the most common approaches used for laboratory diagnosis of hantaviruses. Such emerging viral pathogens emphasizes the need for improved rapid diagnostic devices and vaccines incorporating pan-specific epitopes of genotypes. We predicted linear B-cell epitopes for hantaviruses that are specific to genotypes causing HCPS in humans using in silico prediction servers. We modeled the Andes and Sin Nombre hantavirus nucleocapsid protein to locate the identified epitopes. Based on the mean percent prediction probability score, epitope IMASKSVGS/TAEEKLKKKSAF was identified as the best candidate B-cell epitope specific for hantaviruses causing HCPS. Promiscuous epitopes were identified in the C-terminal of the protein. Our study for the first time has reported pan-specific B-cell epitopes for developing immunoassays in the detection of antibodies to hantaviruses causing HCPS. Identification of epitopes with pan-specific recognition of all genotypes causing HCPS could be valuable for the development of immunodiagnositic tools toward pan-detection of hantavirus antibodies in ELISA. J. Cell. Biochem. 118: 2320-2324, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.
Splice Site Mutations in the ATP7A Gene

PubMed Central

Møller, Lisbeth Birk

2011-01-01

Menkes disease (MD) is caused by mutations in the ATP7A gene. We describe 33 novel splice site mutations detected in patients with MD or the milder phenotypic form, Occipital Horn Syndrome. We review these 33 mutations together with 28 previously published splice site mutations. We investigate 12 mutations for their effect on the mRNA transcript in vivo. Transcriptional data from another 16 mutations were collected from the literature. The theoretical consequences of splice site mutations, predicted with the bioinformatics tool Human Splice Finder, were investigated and evaluated in relation to in vivo results. Ninety-six percent of the mutations identified in 45 patients with classical MD were predicted to have a significant effect on splicing, which concurs with the absence of any detectable wild-type transcript in all 19 patients investigated in vivo. Sixty-seven percent of the mutations identified in 12 patients with milder phenotypes were predicted to have no significant effect on splicing, which concurs with the presence of wild-type transcript in 7 out of 9 patients investigated in vivo. Both the in silico predictions and the in vivo results support the hypothesis previously suggested by us and others, that the presence of some wild-type transcript is correlated to a milder phenotype. PMID:21494555
Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability.

PubMed

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-01

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsets driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. Copyright © 2015 Elsevier Inc. All rights reserved.
Design, synthesis, anticancer screening, docking studies and in silico ADME prediction of some β-carboline derivatives.

PubMed

Abdelsalam, Mohamed A; AboulWafa, Omaima M; M Badawey, El-Sayed A; El-Shoukrofy, Mai S; El-Miligy, Mostafa M; Gouda, Noha; Elaasser, Mahmoud M

2018-05-22

Medicinal interest has focused on β-carbolines as anticancer agents. Several β-carbolines were designed, synthesized and evaluated for their cytotoxic activity against MCF-7 and A-549 cancer cell lines using MTT assay. Compounds 13a, 13c, 13d and 20a were the most promising showing high selectivity indices. Compounds 13c and 20a showed potent inhibition of topoisomerase (topo-I) and kinesin spindle protein (KSP/Eg5 ATPase) which was confirmed by their docking results into the active site of both enzymes. In silico physicochemical calculations predicted that compounds 13a, 13d and 20a obeyed Lipinski's rule of five. Compounds 13c and 20a are multitarget anticancer leads that act as potent inhibitors for both topo-I and/or KSP ATPase.
Evaluation of in silico pharmacokinetic properties and in vitro cytotoxic activity of selected newly synthesized N-succinimide derivatives.

PubMed

Milosevic, Natasa P; Kojic, Vesna; Curcic, Jelena; Jakimov, Dimitar; Milic, Natasa; Banjac, Nebojsa; Uscumlic, Gordana; Kaliszan, Roman

2017-04-15

Design of a new drug entity is usually preceded by analysis of quantitative structure activity (properties) relationships, QSA(P)R. Six newly synthesized succinimide derivatives have been determined for (i) in silico physico-chemical descriptors, pharmacokinetic and toxicity predictors, (ii) in vitro biological activity on four different carcinoma cell lines and on normal fetal lung cells and (iii) lipophilicity on liquid chromatography. All compounds observed were predicted for good permeability and solubility, good oral absorption rate and moderate volume of distribution as well as for modest blood brain permeation, followed by acceptable observed toxicity. In silico determined lipophilicity, permeability through jejunum and aqueous solubility were correlated with experimentally obtained lipophilic constants (by use of high pressure liquid chromatography) and linear correlations were obtained. Absorption rate and volume of distribution were predicted by chromatographic lipophilicity measurements while permeation through blood bran barrier was predicted dominantly by molecular size defined with molecular weight. Five compounds have demonstrated antiproliferative activity toward cervix carcinoma HeLa cell lines; three were cytotoxic against breast carcinoma MCF-7 cells, while one inhibited proliferation of colon carcinoma HT-29 cell lines. Only one compound was cytotoxic toward normal cell lines, while other compounds were proven as safe. Antiproliferative potential against HeLa cells was described as exponential function of lipophilicity. Based on obtained results, lead compounds were selected. Copyright © 2017 Elsevier B.V. All rights reserved.
In Silico Detection of Sequence Variations Modifying Transcriptional Regulation

PubMed Central

Andersen, Malin C; Engström, Pär G; Lithwick, Stuart; Arenillas, David; Eriksson, Per; Lenhard, Boris; Wasserman, Wyeth W; Odeberg, Jacob

2008-01-01

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation. PMID:18208319
Reliable differentiation of Meyerozyma guilliermondii from Meyerozyma caribbica by internal transcribed spacer restriction fingerprinting.

PubMed

Romi, Wahengbam; Keisam, Santosh; Ahmed, Giasuddin; Jeyaram, Kumaraswamy

2014-02-28

Meyerozyma guilliermondii (anamorph Candida guilliermondii) and Meyerozyma caribbica (anamorph Candida fermentati) are closely related species of the genetically heterogenous M. guilliermondii complex. Conventional phenotypic methods frequently misidentify the species within this complex and also with other species of the Saccharomycotina CTG clade. Even the long-established sequencing of large subunit (LSU) rRNA gene remains ambiguous. We also faced similar problem during identification of yeast isolates of M. guilliermondii complex from indigenous bamboo shoot fermentation in North East India. There is a need for development of reliable and accurate identification methods for these closely related species because of their increasing importance as emerging infectious yeasts and associated biotechnological attributes. We targeted the highly variable internal transcribed spacer (ITS) region (ITS1-5.8S-ITS2) and identified seven restriction enzymes through in silico analysis for differentiating M. guilliermondii from M. caribbica. Fifty five isolates of M. guilliermondii complex which could not be delineated into species-specific taxonomic ranks by API 20 C AUX and LSU rRNA gene D1/D2 sequencing were subjected to ITS-restriction fragment length polymorphism (ITS-RFLP) analysis. TaqI ITS-RFLP distinctly differentiated the isolates into M. guilliermondii (47 isolates) and M. caribbica (08 isolates) with reproducible species-specific patterns similar to the in silico prediction. The reliability of this method was validated by ITS1-5.8S-ITS2 sequencing, mitochondrial DNA RFLP and electrophoretic karyotyping. We herein described a reliable ITS-RFLP method for distinct differentiation of frequently misidentified M. guilliermondii from M. caribbica. Even though in silico analysis differentiated other closely related species of M. guilliermondii complex from the above two species, it is yet to be confirmed by in vitro analysis using reference strains. This method can be used as a reliable tool for rapid and accurate identification of closely related species of M. guilliermondii complex and for differentiating emerging infectious yeasts of the Saccharomycotina CTG clade.
Ontology-oriented retrieval of putative microRNAs in Vitis vinifera via GrapeMiRNA: a web database of de novo predicted grape microRNAs.

PubMed

Lazzari, Barbara; Caprera, Andrea; Cestaro, Alessandro; Merelli, Ivan; Del Corvo, Marcello; Fontana, Paolo; Milanesi, Luciano; Velasco, Riccardo; Stella, Alessandra

2009-06-29

Two complete genome sequences are available for Vitis vinifera Pinot noir. Based on the sequence and gene predictions produced by the IASMA, we performed an in silico detection of putative microRNA genes and of their targets, and collected the most reliable microRNA predictions in a web database. The application is available at http://www.itb.cnr.it/ptp/grapemirna/. The program FindMiRNA was used to detect putative microRNA genes in the grape genome. A very high number of predictions was retrieved, calling for validation. Nine parameters were calculated and, based on the grape microRNAs dataset available at miRBase, thresholds were defined and applied to FindMiRNA predictions having targets in gene exons. In the resulting subset, predictions were ranked according to precursor positions and sequence similarity, and to target identity. To further validate FindMiRNA predictions, comparisons to the Arabidopsis genome, to the grape Genoscope genome, and to the grape EST collection were performed. Results were stored in a MySQL database and a web interface was prepared to query the database and retrieve predictions of interest. The GrapeMiRNA database encompasses 5,778 microRNA predictions spanning the whole grape genome. Predictions are integrated with information that can be of use in selection procedures. Tools added in the web interface also allow to inspect predictions according to gene ontology classes and metabolic pathways of targets. The GrapeMiRNA database can be of help in selecting candidate microRNA genes to be validated.
GenePattern | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

GenePattern is a genomic analysis platform that provides access to hundreds of tools for the analysis and visualization of multiple data types. A web-based interface provides easy access to these tools and allows the creation of multi-step analysis pipelines that enable reproducible in silico research. A new GenePattern Notebook environment allows users to combine GenePattern analyses with text, graphics, and code to create complete reproducible research narratives.
Educational websites--Bioinformatics Tools II.

PubMed

Lomberk, Gwen

2009-01-01

In this issue, the highlighted websites are a continuation of a series of educational websites; this one in particular from a couple of years ago, Bioinformatics Tools [Pancreatology 2005;5:314-315]. These include sites that are valuable resources for many research needs in genomics and proteomics. Bioinformatics has become a laboratory tool to map sequences to databases, develop models of molecular interactions, evaluate structural compatibilities, describe differences between normal and disease-associated DNA, identify conserved motifs within proteins, and chart extensive signaling networks, all in silico. Copyright 2008 S. Karger AG, Basel and IAP.
Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter.

PubMed

Hirota, Morihiko; Ashikaga, Takao; Kouzuki, Hirokazu

2018-04-01

It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES-M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential. Copyright © 2017 John Wiley & Sons, Ltd.
Predictive Models for Carcinogenicity and Mutagenicity ...

EPA Pesticide Factsheets

Mutagenicity and carcinogenicity are endpoints of major environmental and regulatory concern. These endpoints are also important targets for development of alternative methods for screening and prediction due to the large number of chemicals of potential concern and the tremendous cost (in time, money, animals) of rodent carcinogenicity bioassays. Both mutagenicity and carcinogenicity involve complex, cellular processes that are only partially understood. Advances in technologies and generation of new data will permit a much deeper understanding. In silico methods for predicting mutagenicity and rodent carcinogenicity based on chemical structural features, along with current mutagenicity and carcinogenicity data sets, have performed well for local prediction (i.e., within specific chemical classes), but are less successful for global prediction (i.e., for a broad range of chemicals). The predictivity of in silico methods can be improved by improving the quality of the data base and endpoints used for modelling. In particular, in vitro assays for clastogenicity need to be improved to reduce false positives (relative to rodent carcinogenicity) and to detect compounds that do not interact directly with DNA or have epigenetic activities. New assays emerging to complement or replace some of the standard assays include VitotoxTM, GreenScreenGC, and RadarScreen. The needs of industry and regulators to assess thousands of compounds necessitate the development of high-t
In Silico Models for Ecotoxicity of Pharmaceuticals.

PubMed

Roy, Kunal; Kar, Supratik

2016-01-01

Pharmaceuticals and their active metabolites are one of the significantly emerging environmental toxicants. The major routes of entry of pharmaceuticals into the environment are industries, hospitals, or direct disposal of unwanted or expired drugs made by the patient. The most important and distinct features of pharmaceuticals are that they are deliberately designed to have an explicit mode of action and designed to exert an effect on humans and other living systems. This distinctive feature makes pharmaceuticals and their metabolites different from other chemicals, and this necessitates the evaluation of the direct effects of pharmaceuticals in various environmental compartments as well as to living systems. In this background, the alarming situation of ecotoxicity of diverse pharmaceuticals have forced government and nongovernment regulatory authorities to recommend the application of in silico methods to provide quick information about the risk assessment and fate properties of pharmaceuticals as well as their ecological and indirect human health effects. This chapter aims to offer information regarding occurrence of pharmaceuticals in the environment, their persistence, environmental fate, and toxicity as well as application of in silico methods to provide information about the basic risk management and fate prediction of pharmaceuticals in the environment. Brief ideas about toxicity endpoints, available ecotoxicity databases, and expert systems employed for rapid toxicity predictions of ecotoxicity of pharmaceuticals are also discussed.
Predicting dermal penetration for Expocast chemicals using in silico approaches – should dermal metabolism be considered?

EPA Science Inventory

There are thousands of consumer product chemicals to which humans may be exposed to via direct (e.g. product use) or indirect (e.g. contact with contaminated media) pathways. The US EPA has developed a research program known as ExpoCast to predict exposures to give real-world con...
Meeting Report: FutureTox II: Contemporary Concepts in Toxicology “Pathways to Prediction: In Vitro and In Silico Models for Predictive Toxicology”

EPA Science Inventory

The Society of Toxicology (SOT) held avery successful FutureTox II Contemporary Concepts in Toxicology (CCT) Conference in Chapel Hill, North Carolina, on January 16th and 17th, 2014. There were over 291 attendees representing industry, government and academia; the sessions were ...
Machine learning algorithms for the prediction of hERG and CYP450 binding in drug development.

PubMed

Klon, Anthony E

2010-07-01

The cost of developing new drugs is estimated at approximately $1 billion; the withdrawal of a marketed compound due to toxicity can result in serious financial loss for a pharmaceutical company. There has been a greater interest in the development of in silico tools that can identify compounds with metabolic liabilities before they are brought to market. The two largest classes of machine learning (ML) models, which will be discussed in this review, have been developed to predict binding to the human ether-a-go-go related gene (hERG) ion channel protein and the various CYP isoforms. Being able to identify potentially toxic compounds before they are made would greatly reduce the number of compound failures and the costs associated with drug development. This review summarizes the state of modeling hERG and CYP binding towards this goal since 2003 using ML algorithms. A wide variety of ML algorithms that are comparable in their overall performance are available. These ML methods may be applied regularly in discovery projects to flag compounds with potential metabolic liabilities.
Integrating Crystallography into Early Metabolism Studies

NASA Astrophysics Data System (ADS)

Cruciani, Gabriele; Aristei, Yasmin; Goracci, Laura; Carosati, Emanuele

Since bioavailability, activity, toxicity, distribution, and final elimination all depend on metabolic biotransformations, it would be extremely advantageous if this information to be produced early in the discovery phase. Once obtained, researchers can judge whether or not a potential candidate should be eliminated from the pipeline, or modified to improve chemical stability or safety. The use of in silico methods to predict the site of metabolism in Phase I cytochrome-mediated reactions is a starting point in any metabolic pathway prediction. This paper presents a new method, which provides the site of metabolism for any CYP-mediated reaction acting on unknown substrates. The methodology can be applied automatically to all the cytochromes whose Xray 3D structure is known, but can be also applied to homology model 3D structures. The fully automated procedure can be used to detect positions that should be protected in order to avoid metabolic degradation, or to check the suitability of a new scaffold or pro-drug. Therefore the procedure is also a valuable new tool in early ADME-Tox, where drug-safety and metabolic profile patterns must be evaluated as soon, and as early, as possible.
VDA, a Method of Choosing a Better Algorithm with Fewer Validations

PubMed Central

Kluger, Yuval

2011-01-01

The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power. Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico. VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms. Our VDA software is available at http://sourceforge.net/projects/klugerlab/files/VDA/ PMID:22046256

Profiling deleterious non-synonymous SNPs of smoker's gene CYP1A1.

PubMed

Ramesh, A Sai; Khan, Imran; Farhan, Md; Thiagarajan, Padma

2013-01-01

CYP1A1 gene belongs to the cytochrome P450 family and is known better as smokers' gene due to its hyperactivation as a consequence of long term smoking. The expression of CYP1A1 induces polycyclic aromatic hydrocarbon production in the lungs, which when over expressed, is known to cause smoking related diseases, such as cardiovascular pathologies, cancer, and diabetes. Single nucleotide polymorphisms (SNPs) are the simplest form of genetic variations that occur at a higher frequency, and are denoted as synonymous and non-synonymous SNPs on the basis of their effects on the amino acids. This study adopts a systematic in silico approach to predict the deleterious SNPs that are associated with disease conditions. It is inferred that four SNPs are highly deleterious, among which the SNP with rs17861094 is commonly predicted to be harmful by all tools. Hydrophobic (isoleucine) to hydrophilic (serine) amino acid variation was observed in the candidate gene. Hence, this investigation aims to characterize a candidate gene from 159 SNPs of CYP1A1.
Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.
Heterozygote PCR product melting curve prediction.

PubMed

Dwight, Zachary L; Palais, Robert; Kent, Jana; Wittwer, Carl T

2014-03-01

Melting curve prediction of PCR products is limited to perfectly complementary strands. Multiple domains are calculated by recursive nearest neighbor thermodynamics. However, the melting curve of an amplicon containing a heterozygous single-nucleotide variant (SNV) after PCR is the composite of four duplexes: two matched homoduplexes and two mismatched heteroduplexes. To better predict the shape of composite heterozygote melting curves, 52 experimental curves were compared with brute force in silico predictions varying two parameters simultaneously: the relative contribution of heteroduplex products and an ionic scaling factor for mismatched tetrads. Heteroduplex products contributed 25.7 ± 6.7% to the composite melting curve, varying from 23%-28% for different SNV classes. The effect of ions on mismatch tetrads scaled to 76%-96% of normal (depending on SNV class) and averaged 88 ± 16.4%. Based on uMelt (www.dna.utah.edu/umelt/umelt.html) with an expanded nearest neighbor thermodynamic set that includes mismatched base pairs, uMelt HETS calculates helicity as a function of temperature for homoduplex and heteroduplex products, as well as the composite curve expected from heterozygotes. It is an interactive Web tool for efficient genotyping design, heterozygote melting curve prediction, and quality control of melting curve experiments. The application was developed in Actionscript and can be found online at http://www.dna.utah.edu/hets/. © 2013 WILEY PERIODICALS, INC.
In silico analyses of structural and allergenicity features of sapodilla (Manilkara zapota) acidic thaumatin-like protein in comparison with allergenic plant TLPs.

PubMed

Ashok Kumar, Hassan G; Venkatesh, Yeldur P

2014-02-01

Thaumatin-like proteins (TLPs) belong to the pathogenesis-related family (PR-5) of plant defense proteins. TLPs from only 32 plant genera have been identified as pollen or food allergens. IgE epitopes on allergens play a central role in food allergy by initiating cross-linking of specific IgE on basophils/mast cells. A comparative analysis of pollen- and food-allergenic TLPs is lacking. The main objective of this investigation was to study the structural and allergenicity features of sapodilla (Manilkara zapota) acidic TLP (TLP 1) by in silico methods. The allergenicity prediction of composite sequence of sapodilla TLP 1 (NCBI B3EWX8.1, G5DC91.1) was performed using FARRP, Allermatch and Evaller web tools. A homology model of the protein was generated using banana TLP template (1Z3Q) by HHPRED-MODELLER. B-cell linear epitope prediction was performed using BCpreds and BepiPred. Sapodilla TLP 1 matched significantly with allergenic TLPs from olive, kiwi, bell pepper and banana. IgE epitope prediction as performed using AlgPred indicated the presence of 2 epitopes (epitope 1: residues 36-48; epitope 2: residues 51-63), and a comprehensive analysis of all allergenic TLPs displayed up to 3 additional epitopes on other TLPs. It can be inferred from these analyses that plant allergenic TLPs generally carry 2-3 IgE epitopes. ClustalX alignments of allergenic TLPs indicate that IgE epitopes 1 and 2 are common in food allergenic TLPs, and IgE epitopes 2 and 3 are common in pollen allergenic TLPs; IgE epitope 2 overlaps with a portion of the thaumatin family signature. The secondary structural elements of TLPs vary markedly in regions 1 and 2 which harbor all the predicted IgE epitopes in all food and pollen TLPs in either of the region. Further, based on the number of IgE epitopes, food TLPs are grouped into rosid and non-rosid clades. The number and distribution of the predicted IgE epitopes among the allergenic TLPs may explain the specificity of food or pollen allergy as well as the varied degree of cross-reactivity among plant foods and/or pollens. Copyright © 2013 Elsevier Ltd. All rights reserved.
Application of bioinformatics tools and databases in microbial dehalogenation research (a review).

PubMed

Satpathy, R; Konkimalla, V B; Ratha, J

2015-01-01

Microbial dehalogenation is a biochemical process in which the halogenated substances are catalyzed enzymatically in to their non-halogenated form. The microorganisms have a wide range of organohalogen degradation ability both explicit and non-specific in nature. Most of these halogenated organic compounds being pollutants need to be remediated; therefore, the current approaches are to explore the potential of microbes at a molecular level for effective biodegradation of these substances. Several microorganisms with dehalogenation activity have been identified and characterized. In this aspect, the bioinformatics plays a key role to gain deeper knowledge in this field of dehalogenation. To facilitate the data mining, many tools have been developed to annotate these data from databases. Therefore, with the discovery of a microorganism one can predict a gene/protein, sequence analysis, can perform structural modelling, metabolic pathway analysis, biodegradation study and so on. This review highlights various methods of bioinformatics approach that describes the application of various databases and specific tools in the microbial dehalogenation fields with special focus on dehalogenase enzymes. Attempts have also been made to decipher some recent applications of in silico modeling methods that comprise of gene finding, protein modelling, Quantitative Structure Biodegradibility Relationship (QSBR) study and reconstruction of metabolic pathways employed in dehalogenation research area.
Regulatory assessment of chemical mixtures: Requirements, current approaches and future perspectives.

PubMed

Kienzler, Aude; Bopp, Stephanie K; van der Linden, Sander; Berggren, Elisabet; Worth, Andrew

2016-10-01

This paper reviews regulatory requirements and recent case studies to illustrate how the risk assessment (RA) of chemical mixtures is conducted, considering both the effects on human health and on the environment. A broad range of chemicals, regulations and RA methodologies are covered, in order to identify mixtures of concern, gaps in the regulatory framework, data needs, and further work to be carried out. Also the current and potential future use of novel tools (Adverse Outcome Pathways, in silico tools, toxicokinetic modelling, etc.) in the RA of combined effects were reviewed. The assumptions made in the RA, predictive model specifications and the choice of toxic reference values can greatly influence the assessment outcome, and should therefore be specifically justified. Novel tools could support mixture RA mainly by providing a better understanding of the underlying mechanisms of combined effects. Nevertheless, their use is currently limited because of a lack of guidance, data, and expertise. More guidance is needed to facilitate their application. As far as the authors are aware, no prospective RA concerning chemicals related to various regulatory sectors has been performed to date, even though numerous chemicals are registered under several regulatory frameworks. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
In silico modeling for tumor growth visualization.

PubMed

Jeanquartier, Fleur; Jean-Quartier, Claire; Cemernek, David; Holzinger, Andreas

2016-08-08

Cancer is a complex disease. Fundamental cellular based studies as well as modeling provides insight into cancer biology and strategies to treatment of the disease. In silico models complement in vivo models. Research on tumor growth involves a plethora of models each emphasizing isolated aspects of benign and malignant neoplasms. Biologists and clinical scientists are often overwhelmed by the mathematical background knowledge necessary to grasp and to apply a model to their own research. We aim to provide a comprehensive and expandable simulation tool to visualizing tumor growth. This novel Web-based application offers the advantage of a user-friendly graphical interface with several manipulable input variables to correlate different aspects of tumor growth. By refining model parameters we highlight the significance of heterogeneous intercellular interactions on tumor progression. Within this paper we present the implementation of the Cellular Potts Model graphically presented through Cytoscape.js within a Web application. The tool is available under the MIT license at https://github.com/davcem/cpm-cytoscape and http://styx.cgv.tugraz.at:8080/cpm-cytoscape/ . In-silico methods overcome the lack of wet experimental possibilities and as dry method succeed in terms of reduction, refinement and replacement of animal experimentation, also known as the 3R principles. Our visualization approach to simulation allows for more flexible usage and easy extension to facilitate understanding and gain novel insight. We believe that biomedical research in general and research on tumor growth in particular will benefit from the systems biology perspective.
In vitro and in silico antioxidant and toxicological activities of Achyrocline satureioides.

PubMed

Salgueiro, Andréia C F; Folmer, Vanderlei; da Rosa, Hemerson S; Costa, Márcio T; Boligon, Aline A; Paula, Fávero R; Roos, Daniel H; Puntel, Gustavo O

2016-12-24

Achyrocline satureioides ("macela or marcela") is a medicinal plant, traditionally collected in "Good Friday" before sunrise. In traditional medicine, dried flowers of A. satureioides are used as anti-dyspeptic, antispasmodic and anti-inflammatory. To evaluate the phytochemical profile and to present an in vitro and in silico approach about toxicity and antioxidant potential of A. satureioides flowers extract and its major phytoconstituents. Plant were collected according to the popular tradition. Extract were obtained by infusion and analyzed from high-performance liquid chromatography. Toxicity was evaluated in Artemia salina and human lymphocytes. Extract antioxidant activity was determined with total antioxidant capacity, DPPH • and ABTS +• scavenging, ferric reducing antioxidant power, deoxyribose degradation assay, and thiobarbituric acid reactive substances (TBA-RS) assay. TBA-RS inhibitions were evaluated in brain of rats for A. satureioides extract and its major phytoconstituents. Predictions of activity spectra for substances and in silico toxicity evaluation from major phytoconstituents were performed via computer simulation. Chromatographic data indicated isoquercitrin, quercetin and caffeic acid as main compounds in flowers extract. Toxicity tests demonstrated a very low toxic potential of A. satureioides. Extract exhibited antioxidant activities in low concentrations. Both extract and major phytochemicals standards showed protection against lipid peroxidation in brain of rats. Computer simulations pointed some biological activities in agreement with traditional use, as well as some experimental results found in this work. Moreover, in silico toxic predictions showed that the A. satureioides major compounds had low probability for toxic risk. Our results indicate that A. satureioides infusion possesses low toxicological potential and an effective antioxidant activity. These findings confirm the traditional use of this plant in the folk medicine. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
In silico predicted reproductive endocrine transcriptional regulatory networks during zebrafish (Danio rerio) development.

PubMed

Hala, D

2017-03-21

The interconnected topology of transcriptional regulatory networks (TRNs) readily lends to mathematical (or in silico) representation and analysis as a stoichiometric matrix. Such a matrix can be 'solved' using the mathematical method of extreme pathway (ExPa) analysis, which identifies uniquely activated genes subject to transcription factor (TF) availability. In this manuscript, in silico multi-tissue TRN models of brain, liver and gonad were used to study reproductive endocrine developmental programming in zebrafish (Danio rerio) from 0.25h post fertilization (hpf; zygote) to 90 days post fertilization (dpf; adult life stage). First, properties of TRN models were studied by sequentially activating all genes in multi-tissue models. This analysis showed the brain to exhibit lowest proportion of co-regulated genes (19%) relative to liver (23%) and gonad (32%). This was surprising given that the brain comprised 75% and 25% more TFs than liver and gonad respectively. Such 'hierarchy' of co-regulatory capability (brain
Information theory-based algorithm for in silico prediction of PCR products with whole genomic sequences as templates.

PubMed

Cao, Youfang; Wang, Lianjie; Xu, Kexue; Kou, Chunhai; Zhang, Yulei; Wei, Guifang; He, Junjian; Wang, Yunfang; Zhao, Liping

2005-07-26

A new algorithm for assessing similarity between primer and template has been developed based on the hypothesis that annealing of primer to template is an information transfer process. Primer sequence is converted to a vector of the full potential hydrogen numbers (3 for G or C, 2 for A or T), while template sequence is converted to a vector of the actual hydrogen bond numbers formed after primer annealing. The former is considered as source information and the latter destination information. An information coefficient is calculated as a measure for fidelity of this information transfer process and thus a measure of similarity between primer and potential annealing site on template. Successful prediction of PCR products from whole genomic sequences with a computer program based on the algorithm demonstrated the potential of this new algorithm in areas like in silico PCR and gene finding.
Identification and in silico prediction of metabolites of the model compound, tebufenozide by human CYP3A4 and CYP2C19.

PubMed

Shirotani, Naoki; Togawa, Moe; Ikushiro, Shinichi; Sakaki, Toshiyuki; Harada, Toshiyuki; Miyagawa, Hisashi; Matsui, Masayoshi; Nagahori, Hirohisa; Mikata, Kazuki; Nishioka, Kazuhiko; Hirai, Nobuhiro; Akamatsu, Miki

2015-10-15

The metabolites of tebufenozide, a model compound, formed by the yeast-expressed human CYP3A4 and CYP2C19 were identified to clarify the substrate recognition mechanism of the human cytochrome P450 (CYP) isozymes. We then determined whether tebufenozide metabolites may be predicted in silico. Hydrogen abstraction energies were calculated with the density functional theory method B3LYP/6-31G(∗). A docking simulation was performed using FRED software. Several alkyl sites of tebufenozide were hydroxylated by CYP3A4 whereas only one site was modified by CYP2C19. The accessibility of each site of tebufenozide to the reaction center of CYP enzymes and the susceptibility of each hydrogen atom for metabolism by CYP enzymes were evaluated by a docking simulation and hydrogen abstraction energy estimation, respectively. Copyright © 2015 Elsevier Ltd. All rights reserved.
Improving the physiological realism of experimental models

PubMed Central

Vinnakota, Kalyan C.; Cha, Chae Y.; Rorsman, Patrik; Balaban, Robert S.; La Gerche, Andre; Wade-Martins, Richard; Beard, Daniel A.

2016-01-01

The Virtual Physiological Human (VPH) project aims to develop integrative, explanatory and predictive computational models (C-Models) as numerical investigational tools to study disease, identify and design effective therapies and provide an in silico platform for drug screening. Ultimately, these models rely on the analysis and integration of experimental data. As such, the success of VPH depends on the availability of physiologically realistic experimental models (E-Models) of human organ function that can be parametrized to test the numerical models. Here, the current state of suitable E-models, ranging from in vitro non-human cell organelles to in vivo human organ systems, is discussed. Specifically, challenges and recent progress in improving the physiological realism of E-models that may benefit the VPH project are highlighted and discussed using examples from the field of research on cardiovascular disease, musculoskeletal disorders, diabetes and Parkinson's disease. PMID:27051507
Wisdom of crowds for robust gene network inference

PubMed Central

Marbach, Daniel; Costello, James C.; Küffner, Robert; Vega, Nicci; Prill, Robert J.; Camacho, Diogo M.; Allison, Kyle R.; Kellis, Manolis; Collins, James J.; Stolovitzky, Gustavo

2012-01-01

Reconstructing gene regulatory networks from high-throughput data is a long-standing problem. Through the DREAM project (Dialogue on Reverse Engineering Assessment and Methods), we performed a comprehensive blind assessment of over thirty network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae, and in silico microarray data. We characterize performance, data requirements, and inherent biases of different inference approaches offering guidelines for both algorithm application and development. We observe that no single inference method performs optimally across all datasets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse datasets. Thereby, we construct high-confidence networks for E. coli and S. aureus, each comprising ~1700 transcriptional interactions at an estimated precision of 50%. We experimentally test 53 novel interactions in E. coli, of which 23 were supported (43%). Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks. PMID:22796662
A systematic approach for finding the objective function and active constraints for dynamic flux balance analysis.

PubMed

Nikdel, Ali; Braatz, Richard D; Budman, Hector M

2018-05-01

Dynamic flux balance analysis (DFBA) has become an instrumental modeling tool for describing the dynamic behavior of bioprocesses. DFBA involves the maximization of a biologically meaningful objective subject to kinetic constraints on the rate of consumption/production of metabolites. In this paper, we propose a systematic data-based approach for finding both the biological objective function and a minimum set of active constraints necessary for matching the model predictions to the experimental data. The proposed algorithm accounts for the errors in the experiments and eliminates the need for ad hoc choices of objective function and constraints as done in previous studies. The method is illustrated for two cases: (1) for in silico (simulated) data generated by a mathematical model for Escherichia coli and (2) for actual experimental data collected from the batch fermentation of Bordetella Pertussis (whooping cough).
How to turn a genetic circuit into a synthetic tunable oscillator, or a bistable switch.

PubMed

Marucci, Lucia; Barton, David A W; Cantone, Irene; Ricci, Maria Aurelia; Cosma, Maria Pia; Santini, Stefania; di Bernardo, Diego; di Bernardo, Mario

2009-12-07

Systems and Synthetic Biology use computational models of biological pathways in order to study in silico the behaviour of biological pathways. Mathematical models allow to verify biological hypotheses and to predict new possible dynamical behaviours. Here we use the tools of non-linear analysis to understand how to change the dynamics of the genes composing a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-engineering and Modelling Assessment (IRMA). Guided by previous theoretical results that make the dynamics of a biological network depend on its topological properties, through the use of simulation and continuation techniques, we found that the network can be easily turned into a robust and tunable synthetic oscillator or a bistable switch. Our results provide guidelines to properly re-engineering in vivo the network in order to tune its dynamics.
Toward Fully in Silico Melting Point Prediction Using Molecular Simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Y; Maginn, EJ

2013-03-01

Melting point is one of the most fundamental and practically important properties of a compound. Molecular computation of melting points. However, all of these methods simulation methods have been developed for the accurate need an experimental crystal structure as input, which means that such calculations are not really predictive since the melting point can be measured easily in experiments once a crystal structure is known. On the other hand, crystal structure prediction (CSP) has become an active field and significant progress has been made, although challenges still exist. One of the main challenges is the existence of many crystal structuresmore » (polymorphs) that are very close in energy. Thermal effects and kinetic factors make the situation even more complicated, such that it is still not trivial to predict experimental crystal structures. In this work, we exploit the fact that free energy differences are often small between crystal structures. We show that accurate melting point predictions can be made by using a reasonable crystal structure from CSP as a starting point for a free energy-based melting point calculation. The key is that most crystal structures predicted by CSP have free energies that are close to that of the experimental structure. The proposed method was tested on two rigid molecules and the results suggest that a fully in silico melting point prediction method is possible.« less
QSAR DataBank - an approach for the digital organization and archiving of QSAR model information

PubMed Central

2014-01-01

Background Research efforts in the field of descriptive and predictive Quantitative Structure-Activity Relationships or Quantitative Structure–Property Relationships produce around one thousand scientific publications annually. All the materials and results are mainly communicated using printed media. The printed media in its present form have obvious limitations when they come to effectively representing mathematical models, including complex and non-linear, and large bodies of associated numerical chemical data. It is not supportive of secondary information extraction or reuse efforts while in silico studies poses additional requirements for accessibility, transparency and reproducibility of the research. This gap can and should be bridged by introducing domain-specific digital data exchange standards and tools. The current publication presents a formal specification of the quantitative structure-activity relationship data organization and archival format called the QSAR DataBank (QsarDB for shorter, or QDB for shortest). Results The article describes QsarDB data schema, which formalizes QSAR concepts (objects and relationships between them) and QsarDB data format, which formalizes their presentation for computer systems. The utility and benefits of QsarDB have been thoroughly tested by solving everyday QSAR and predictive modeling problems, with examples in the field of predictive toxicology, and can be applied for a wide variety of other endpoints. The work is accompanied with open source reference implementation and tools. Conclusions The proposed open data, open source, and open standards design is open to public and proprietary extensions on many levels. Selected use cases exemplify the benefits of the proposed QsarDB data format. General ideas for future development are discussed. PMID:24910716
The PILGRIM study: in silico modeling of a predictive low glucose management system and feasibility in youth with type 1 diabetes during exercise.

PubMed

Danne, Thomas; Tsioli, Christiana; Kordonouri, Olga; Blaesig, Sarah; Remus, Kerstin; Roy, Anirban; Keenan, Barry; Lee, Scott W; Kaufman, Francine R

2014-06-01

Predictive low glucose management (PLGM) may help prevent hypoglycemia by stopping insulin pump delivery based on predicted sensor glucose values. Hypoglycemic challenges were simulated using the Food and Drug Administration-accepted glucose simulator with 100 virtual patients. PLGM was then tested with a system composed of a Paradigm(®) insulin pump (Medtronic, Northridge, CA), an Enlite™ glucose sensor (Medtronic), and a BlackBerry(®) (Waterloo, ON, Canada)-based controller. Subjects (n=22) on continuous subcutaneous insulin infusion (five females, 17 males; median [range] age, 15 [range, 14-20] years; median [range] diabetes duration, 7 [2-14] years; median [range] glycated hemoglobin, 8.0% [6.7-10.4%]) exercised until the PLGM system suspended insulin delivery or until the reference blood glucose value (HemoCue(®); HemoCue GmbH, Großostheim, Germany) reached the predictive suspension threshold setting. PLGM reduced hypoglycemia (<70 mg/dL) in silico by 26.7% compared with no insulin suspension, as opposed to a 5.3% reduction in hypoglycemia with use of low glucose suspend (LGS). The median duration of hypoglycemia (time spent <70 mg/dL) with PLGM was significantly less than with LGS (58 min vs. 101 min, respectively; P<0.001). In the clinical trial the hypoglycemic threshold during exercise was reached in 73% of the patients, and hypoglycemia was prevented in 80% of the successful experiments. The mean (±SD) sensor glucose at predictive suspension was 92±7 mg/dL, resulting in a postsuspension nadir (by HemoCue) of 77±22 mg/dL. The suspension lasted for 90±35 (range, 30-120) min, resulting in a sensor glucose level at insulin resumption of 97±19 mg/dL. In silico modeling and early feasibility data demonstrate that PLGM may further reduce the severity of hypoglycemia beyond that already established for algorithms that use a threshold-based suspension.
Ab initio chemical safety assessment: A workflow based on exposure considerations and non-animal methods.

PubMed

Berggren, Elisabet; White, Andrew; Ouedraogo, Gladys; Paini, Alicia; Richarz, Andrea-Nicole; Bois, Frederic Y; Exner, Thomas; Leite, Sofia; Grunsven, Leo A van; Worth, Andrew; Mahony, Catherine

2017-11-01

We describe and illustrate a workflow for chemical safety assessment that completely avoids animal testing. The workflow, which was developed within the SEURAT-1 initiative, is designed to be applicable to cosmetic ingredients as well as to other types of chemicals, e.g. active ingredients in plant protection products, biocides or pharmaceuticals. The aim of this work was to develop a workflow to assess chemical safety without relying on any animal testing, but instead constructing a hypothesis based on existing data, in silico modelling, biokinetic considerations and then by targeted non-animal testing. For illustrative purposes, we consider a hypothetical new ingredient x as a new component in a body lotion formulation. The workflow is divided into tiers in which points of departure are established through in vitro testing and in silico prediction, as the basis for estimating a safe external dose in a repeated use scenario. The workflow includes a series of possible exit (decision) points, with increasing levels of confidence, based on the sequential application of the Threshold of Toxicological (TTC) approach, read-across, followed by an "ab initio" assessment, in which chemical safety is determined entirely by new in vitro testing and in vitro to in vivo extrapolation by means of mathematical modelling. We believe that this workflow could be applied as a tool to inform targeted and toxicologically relevant in vitro testing, where necessary, and to gain confidence in safety decision making without the need for animal testing.
Biochemical profiling in silico--predicting substrate specificities of large enzyme families.

PubMed

Tyagi, Sadhna; Pleiss, Juergen

2006-06-25

A general high-throughput method for in silico biochemical profiling of enzyme families has been developed based on covalent docking of potential substrates into the binding sites of target enzymes. The method has been tested by systematically docking transition state--analogous intermediates of 12 substrates into the binding sites of 20 alpha/beta hydrolases from 15 homologous families. To evaluate the effect of side chain orientations to the docking results, 137 crystal structures were included in the analysis. A good substrate must fulfil two criteria: it must bind in a productive geometry with four hydrogen bonds between the substrate and the catalytic histidine and the oxyanion hole, and a high affinity of the enzyme-substrate complex as predicted by a high docking score. The modelling results in general reproduce experimental data on substrate specificity and stereoselectivity: the differences in substrate specificity of cholinesterases toward acetyl- and butyrylcholine, the changes of activity of lipases and esterases upon the size of the acid moieties, activity of lipases and esterases toward tertiary alcohols, and the stereopreference of lipases and esterases toward chiral secondary alcohols. Rigidity of the docking procedure was the major reason for false positive and false negative predictions, as the geometry of the complex and docking score may sensitively depend on the orientation of individual side chains. Therefore, appropriate structures have to be identified. In silico biochemical profiling provides a time efficient and cost saving protocol for virtual screening to identify the potential substrates of the members of large enzyme family from a library of molecules.

New milk protein-derived peptides with potential antimicrobial activity: an approach based on bioinformatic studies.

PubMed

Dziuba, Bartłomiej; Dziuba, Marta

2014-08-20

New peptides with potential antimicrobial activity, encrypted in milk protein sequences, were searched for with the use of bioinformatic tools. The major milk proteins were hydrolyzed in silico by 28 enzymes. The obtained peptides were characterized by the following parameters: molecular weight, isoelectric point, composition and number of amino acid residues, net charge at pH 7.0, aliphatic index, instability index, Boman index, and GRAVY index, and compared with those calculated for known 416 antimicrobial peptides including 59 antimicrobial peptides (AMPs) from milk proteins listed in the BIOPEP database. A simple analysis of physico-chemical properties and the values of biological activity indicators were insufficient to select potentially antimicrobial peptides released in silico from milk proteins by proteolytic enzymes. The final selection was made based on the results of multidimensional statistical analysis such as support vector machines (SVM), random forest (RF), artificial neural networks (ANN) and discriminant analysis (DA) available in the Collection of Anti-Microbial Peptides (CAMP database). Eleven new peptides with potential antimicrobial activity were selected from all peptides released during in silico proteolysis of milk proteins.
New Milk Protein-Derived Peptides with Potential Antimicrobial Activity: An Approach Based on Bioinformatic Studies

PubMed Central

Dziuba, Bartłomiej; Dziuba, Marta

2014-01-01

New peptides with potential antimicrobial activity, encrypted in milk protein sequences, were searched for with the use of bioinformatic tools. The major milk proteins were hydrolyzed in silico by 28 enzymes. The obtained peptides were characterized by the following parameters: molecular weight, isoelectric point, composition and number of amino acid residues, net charge at pH 7.0, aliphatic index, instability index, Boman index, and GRAVY index, and compared with those calculated for known 416 antimicrobial peptides including 59 antimicrobial peptides (AMPs) from milk proteins listed in the BIOPEP database. A simple analysis of physico-chemical properties and the values of biological activity indicators were insufficient to select potentially antimicrobial peptides released in silico from milk proteins by proteolytic enzymes. The final selection was made based on the results of multidimensional statistical analysis such as support vector machines (SVM), random forest (RF), artificial neural networks (ANN) and discriminant analysis (DA) available in the Collection of Anti-Microbial Peptides (CAMP database). Eleven new peptides with potential antimicrobial activity were selected from all peptides released during in silico proteolysis of milk proteins. PMID:25141106
In silico quantitative structure-toxicity relationship study of aromatic nitro compounds.

PubMed

Pasha, Farhan Ahmad; Neaz, Mohammad Morshed; Cho, Seung Joo; Ansari, Mohiuddin; Mishra, Sunil Kumar; Tiwari, Sharvan

2009-05-01

Small molecules often have toxicities that are a function of molecular structural features. Minor variations in structural features can make large difference in such toxicity. Consequently, in silico techniques may be used to correlate such molecular toxicities with their structural features. Relative to nine different sets of aromatic nitro compounds having known observed toxicities against different targets, we developed ligand-based 2D quantitative structure-toxicity relationship models using 20 selected topological descriptors. The topological descriptors have several advantages such as conformational independency, facile and less time-consuming computation to yield good results. Multiple linear regression analysis was used to correlate variations of toxicity with molecular properties. The information index on molecular size, lopping centric index and Kier flexibility index were identified as fundamental descriptors for different kinds of toxicity, and further showed that molecular size, branching and molecular flexibility might be particularly important factors in quantitative structure-toxicity relationship analysis. This study revealed that topological descriptor-guided quantitative structure-toxicity relationship provided a very useful, cost and time-efficient, in silico tool for describing small-molecule toxicities.
In silico evaluation of gadofosveset pharmacokinetics in different population groups using the Simcyp® simulator platform.

PubMed

Spanakis, Marios; Marias, Kostas

2014-12-01

Gadofosveset is a Gd-based contrast agent used for magnetic resonance imaging (MRI). Gadolinium kinetic distribution models are implemented in T1-weighted dynamic contrast-enhanced perfusion MRI for characterization of lesion sites in the body. Physiology changes in a disease state potentially can influence the pharmacokinetics of drugs and to this respect modify the distribution properties of contrast agents. This work focuses on the in silico modelling of pharmacokinetic properties of gadofosveset in different population groups through the application of physiologically-based pharmacokinetic models (PBPK) embedded in Simcyp® population pharmacokinetics platform. Physicochemical and pharmacokinetic properties of gadofosveset were introduced into Simcyp® simulator platform and a min-PBPK model was applied. In silico clinical trials were generated simulating the administration of the recommended dose for the contrast agent (i.v., 30 mg/kg) in population cohorts of healthy volunteers, obese, renal and liver impairment, and in a generated virtual oncology population. Results were evaluated regarding basic pharmacokinetic parameters of Cmax, AUC and systemic CL and differences were assessed through ANOVA and estimation of ratio of geometric mean between healthy volunteers and the other population groups. Simcyp® predicted a mean Cmax = 551.60 mg/l, a mean AUC = 4079.12 mg/L*h and a mean systemic CL = 0.56 L/h for the virtual population of healthy volunteers. Obese population showed a modulation in Cmax and CL, attributed to increased administered dose. In renal and liver impairment cohorts a significant modulation in Cmax, AUC and CL of gadofosveset is predicted. Oncology population exhibited statistical significant differences regarding AUC when compared with healthy volunteers. This work employed Simcyp® population pharmacokinetics platform in order to compute gadofosveset's pharmacokinetic profiles through PBPK models and in silico clinical trials and evaluate possible differences between population groups. The approach showed promising results that could provide new insights regarding administration of contrast agents in special population cohorts. In silico pharmacokinetics could further be used for evaluating of possible toxicity, interpretation of MRI PK image maps and development of novel contrast agents.
In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.

PubMed

Viira, Birgit; Gendron, Thibault; Lanfranchi, Don Antoine; Cojean, Sandrine; Horvath, Dragos; Marcou, Gilles; Varnek, Alexandre; Maes, Louis; Maran, Uko; Loiseau, Philippe M; Davioud-Charvet, Elisabeth

2016-06-29

Malaria is a parasitic tropical disease that kills around 600,000 patients every year. The emergence of resistant Plasmodium falciparum parasites to artemisinin-based combination therapies (ACTs) represents a significant public health threat, indicating the urgent need for new effective compounds to reverse ACT resistance and cure the disease. For this, extensive curation and homogenization of experimental anti-Plasmodium screening data from both in-house and ChEMBL sources were conducted. As a result, a coherent strategy was established that allowed compiling coherent training sets that associate compound structures to the respective antimalarial activity measurements. Seventeen of these training sets led to the successful generation of classification models discriminating whether a compound has a significant probability to be active under the specific conditions of the antimalarial test associated with each set. These models were used in consensus prediction of the most likely active from a series of curcuminoids available in-house. Positive predictions together with a few predicted as inactive were then submitted to experimental in vitro antimalarial testing. A large majority from predicted compounds showed antimalarial activity, but not those predicted as inactive, thus experimentally validating the in silico screening approach. The herein proposed consensus machine learning approach showed its potential to reduce the cost and duration of antimalarial drug discovery.
20170312 - In Silico Dynamics: computer simulation in a ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or bioche
In Silico Dynamics: computer simulation in a Virtual Embryo ...

EPA Pesticide Factsheets

Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate
Petri Net computational modelling of Langerhans cell Interferon Regulatory Factor Network predicts their role in T cell activation.

PubMed

Polak, Marta E; Ung, Chuin Ying; Masapust, Joanna; Freeman, Tom C; Ardern-Jones, Michael R

2017-04-06

Langerhans cells (LCs) are able to orchestrate adaptive immune responses in the skin by interpreting the microenvironmental context in which they encounter foreign substances, but the regulatory basis for this has not been established. Utilising systems immunology approaches combining in silico modelling of a reconstructed gene regulatory network (GRN) with in vitro validation of the predictions, we sought to determine the mechanisms of regulation of immune responses in human primary LCs. The key role of Interferon regulatory factors (IRFs) as controllers of the human Langerhans cell response to epidermal cytokines was revealed by whole transcriptome analysis. Applying Boolean logic we assembled a Petri net-based model of the IRF-GRN which provides molecular pathway predictions for the induction of different transcriptional programmes in LCs. In silico simulations performed after model parameterisation with transcription factor expression values predicted that human LC activation of antigen-specific CD8 T cells would be differentially regulated by epidermal cytokine induction of specific IRF-controlled pathways. This was confirmed by in vitro measurement of IFN-γ production by activated T cells. As a proof of concept, this approach shows that stochastic modelling of a specific immune networks renders transcriptome data valuable for the prediction of functional outcomes of immune responses.
vEmbryo In Silico Models: Predicting Vascular Developmental Toxicity

EPA Science Inventory

The cardiovascular system is the first to function in the vertebrate embryo, reflecting the critical need for nutrient delivery and waste removal during organogenesis. Blood vessel development occurs by complex interacting signaling networks, including extra-cellular matrix remod...
Experimental Data Extraction and in Silico Prediction of the Estrogenic Activity of Renewable Replacements for Bisphenol A

PubMed Central

Hong, Huixiao; Harvey, Benjamin G.; Palmese, Giuseppe R.; Stanzione, Joseph F.; Ng, Hui Wen; Sakkiah, Sugunadevi; Tong, Weida; Sadler, Joshua M.

2016-01-01

Bisphenol A (BPA) is a ubiquitous compound used in polymer manufacturing for a wide array of applications; however, increasing evidence has shown that BPA causes significant endocrine disruption and this has raised public concerns over safety and exposure limits. The use of renewable materials as polymer feedstocks provides an opportunity to develop replacement compounds for BPA that are sustainable and exhibit unique properties due to their diverse structures. As new bio-based materials are developed and tested, it is important to consider the impacts of both monomers and polymers on human health. Molecular docking simulations using the Estrogenic Activity Database in conjunction with the decision forest were performed as part of a two-tier in silico model to predict the activity of 29 bio-based platform chemicals in the estrogen receptor-α (ERα). Fifteen of the candidates were predicted as ER binders and fifteen as non-binders. Gaining insight into the estrogenic activity of the bio-based BPA replacements aids in the sustainable development of new polymeric materials. PMID:27420082
Bioinformatics Identification of Modules of Transcription Factor Binding Sites in Alzheimer's Disease-Related Genes by In Silico Promoter Analysis and Microarrays

PubMed Central

Augustin, Regina; Lichtenthaler, Stefan F.; Greeff, Michael; Hansen, Jens; Wurst, Wolfgang; Trümbach, Dietrich

2011-01-01

The molecular mechanisms and genetic risk factors underlying Alzheimer's disease (AD) pathogenesis are only partly understood. To identify new factors, which may contribute to AD, different approaches are taken including proteomics, genetics, and functional genomics. Here, we used a bioinformatics approach and found that distinct AD-related genes share modules of transcription factor binding sites, suggesting a transcriptional coregulation. To detect additional coregulated genes, which may potentially contribute to AD, we established a new bioinformatics workflow with known multivariate methods like support vector machines, biclustering, and predicted transcription factor binding site modules by using in silico analysis and over 400 expression arrays from human and mouse. Two significant modules are composed of three transcription factor families: CTCF, SP1F, and EGRF/ZBPF, which are conserved between human and mouse APP promoter sequences. The specific combination of in silico promoter and multivariate analysis can identify regulation mechanisms of genes involved in multifactorial diseases. PMID:21559189
Evolving phenotypic networks in silico.

PubMed

François, Paul

2014-11-01

Evolved gene networks are constrained by natural selection. Their structures and functions are consequently far from being random, as exemplified by the multiple instances of parallel/convergent evolution. One can thus ask if features of actual gene networks can be recovered from evolutionary first principles. I review a method for in silico evolution of small models of gene networks aiming at performing predefined biological functions. I summarize the current implementation of the algorithm, insisting on the construction of a proper "fitness" function. I illustrate the approach on three examples: biochemical adaptation, ligand discrimination and vertebrate segmentation (somitogenesis). While the structure of the evolved networks is variable, dynamics of our evolved networks are usually constrained and present many similar features to actual gene networks, including properties that were not explicitly selected for. In silico evolution can thus be used to predict biological behaviours without a detailed knowledge of the mapping between genotype and phenotype. Copyright © 2014 The Author. Published by Elsevier Ltd.. All rights reserved.
Structure-function studies of BPP-BrachyNH2 and synthetic analogues thereof with Angiotensin I-Converting Enzyme.

PubMed

Arcanjo, Daniel D R; Vasconcelos, Andreanne G; Nascimento, Lucas A; Mafud, Ana Carolina; Plácido, Alexandra; Alves, Michel M M; Delerue-Matos, Cristina; Bemquerer, Marcelo P; Vale, Nuno; Gomes, Paula; Oliveira, Eduardo B; Lima, Francisco C A; Mascarenhas, Yvonne P; Carvalho, Fernando Aécio A; Simonsen, Ulf; Ramos, Ricardo M; Leite, José Roberto S A

2017-10-20

The vasoactive proline-rich oligopeptide termed BPP-BrachyNH 2 (H-WPPPKVSP-NH 2 ) induces in vitro inhibitory activity of angiotensin I-converting enzyme (ACE) in rat blood serum. In the present study, the removal of N-terminal tryptophan or C-terminal proline from BPP-BrachyNH 2 was investigated in order to predict which structural components are important or required for interaction with ACE. Furthermore, the toxicological profile was assessed by in silico prediction and in vitro MTT assay. Two BPP-BrachyNH 2 analogues (des-Trp 1 -BPP-BrachyNH 2 and des-Pro 8 -BPP-BrachyNH 2 ) were synthesized, and in vitro and in silico ACE inhibitory activity and toxicological profile were assessed. The des-Trp 1 -BPP-BrachyNH 2 and des-Pro 8 -BPP-BrachyNH 2 were respectively 3.2- and 29.5-fold less active than the BPP-BrachyNH 2 -induced ACE inhibitory activity. Molecular Dynamic and Molecular Mechanics Poisson-Boltzmann Surface Area simulations (MM-PBSA) demonstrated that the ACE/BBP-BrachyNH 2 complex showed lower binding and van der Wall energies than the ACE/des-Pro 8 -BPP-BrachyNH 2 complex, therefore having better stability. The removal of the N-terminal tryptophan increased the in silico predicted toxicological effects and cytotoxicity when compared with BPP-BrachyNH 2 or des-Pro 8 -BPP-BrachyNH 2 . Otherwise, des-Pro 8 -BPP-BrachyNH 2 was 190-fold less cytotoxic than BPP-BrachyNH 2 . Thus, the removal of C-terminal proline residue was able to markedly decrease both the BPP-BrachyNH 2 -induced ACE inhibitory and cytotoxic effects assessed by in vitro and in silico approaches. In conclusion, the aminoacid sequence of BPP-BrachyNH 2 is essential for its ACE inhibitory activity and associated with an acceptable toxicological profile. The perspective of the interactions of BPP-BrachyNH 2 with ACE found in the present study can be used for development of drugs with differential therapeutic profile than current ACE inhibitors. Copyright © 2017 Elsevier Masson SAS. All rights reserved.
In vivo and in silico determination of essential genes of Campylobacter jejuni.

PubMed

Metris, Aline; Reuter, Mark; Gaskin, Duncan J H; Baranyi, Jozsef; van Vliet, Arnoud H M

2011-11-01

In the United Kingdom, the thermophilic Campylobacter species C. jejuni and C. coli are the most frequent causes of food-borne gastroenteritis in humans. While campylobacteriosis is usually a relatively mild infection, it has a significant public health and economic impact, and possible complications include reactive arthritis and the autoimmune diseases Guillain-Barré syndrome. The rapid developments in "omics" technologies have resulted in the availability of diverse datasets allowing predictions of metabolism and physiology of pathogenic micro-organisms. When combined, these datasets may allow for the identification of potential weaknesses that can be used for development of new antimicrobials to reduce or eliminate C. jejuni and C. coli from the food chain. A metabolic model of C. jejuni was constructed using the annotation of the NCTC 11168 genome sequence, a published model of the related bacterium Helicobacter pylori, and extensive literature mining. Using this model, we have used in silico Flux Balance Analysis (FBA) to determine key metabolic routes that are essential for generating energy and biomass, thus creating a list of genes potentially essential for growth under laboratory conditions. To complement this in silico approach, candidate essential genes have been determined using a whole genome transposon mutagenesis method. FBA and transposon mutagenesis (both this study and a published study) predict a similar number of essential genes (around 200). The analysis of the intersection between the three approaches highlights the shikimate pathway where genes are predicted to be essential by one or more method, and tend to be network hubs, based on a previously published Campylobacter protein-protein interaction network, and could therefore be targets for novel antimicrobial therapy. We have constructed the first curated metabolic model for the food-borne pathogen Campylobacter jejuni and have presented the resulting metabolic insights. We have shown that the combination of in silico and in vivo approaches could point to non-redundant, indispensable genes associated with the well characterised shikimate pathway, and also genes of unknown function specific to C. jejuni, which are all potential novel Campylobacter intervention targets.
Purely in silico BCS classification: science based quality standards for the world's drugs.

PubMed

Dahan, Arik; Wolk, Omri; Kim, Young Hoon; Ramachandran, Chandrasekharan; Crippen, Gordon M; Takagi, Toshihide; Bermejo, Marival; Amidon, Gordon L

2013-11-04

BCS classification is a vital tool in the development of both generic and innovative drug products. The purpose of this work was to provisionally classify the world's top selling oral drugs according to the BCS, using in silico methods. Three different in silico methods were examined: the well-established group contribution (CLogP) and atom contribution (ALogP) methods, and a new method based solely on the molecular formula and element contribution (KLogP). Metoprolol was used as the benchmark for the low/high permeability class boundary. Solubility was estimated in silico using a thermodynamic equation that relies on the partition coefficient and melting point. The validity of each method was affirmed by comparison to reference data and literature. We then used each method to provisionally classify the orally administered, IR drug products found in the WHO Model list of Essential Medicines, and the top-selling oral drug products in the United States (US), Great Britain (GB), Spain (ES), Israel (IL), Japan (JP), and South Korea (KR). A combined list of 363 drugs was compiled from the various lists, and 257 drugs were classified using the different in silico permeability methods and literature solubility data, as well as BDDCS classification. Lastly, we calculated the solubility values for 185 drugs from the combined set using in silico approach. Permeability classification with the different in silico methods was correct for 69-72.4% of the 29 reference drugs with known human jejunal permeability, and for 84.6-92.9% of the 14 FDA reference drugs in the set. The correlations (r(2)) between experimental log P values of 154 drugs and their CLogP, ALogP and KLogP were 0.97, 0.82 and 0.71, respectively. The different in silico permeability methods produced comparable results: 30-34% of the US, GB, ES and IL top selling drugs were class 1, 27-36.4% were class 2, 22-25.5% were class 3, and 5.46-14% were class 4 drugs, while ∼8% could not be classified. The WHO list included significantly less class 1 and more class 3 drugs in comparison to the countries' lists, probably due to differences in commonly used drugs in developing vs industrial countries. BDDCS classified more drugs as class 1 compared to in silico BCS, likely due to the more lax benchmark for metabolism (70%), in comparison to the strict permeability benchmark (metoprolol). For 185 out of the 363 drugs, in silico solubility values were calculated, and successfully matched the literature solubility data. In conclusion, relatively simple in silico methods can be used to estimate both permeability and solubility. While CLogP produced the best correlation to experimental values, even KLogP, the most simplified in silico method that is based on molecular formula with no knowledge of molecular structure, produced comparable BCS classification to the sophisticated methods. This KLogP, when combined with a mean melting point and estimated dose, can be used to provisionally classify potential drugs from just molecular formula, even before synthesis. 49-59% of the world's top-selling drugs are highly soluble (class 1 and class 3), and are therefore candidates for waivers of in vivo bioequivalence studies. For these drugs, the replacement of expensive human studies with affordable in vitro dissolution tests would ensure their bioequivalence, and encourage the development and availability of generic drug products in both industrial and developing countries.
In silico and in vitro prediction of gastrointestinal absorption from potential drug eremantholide C.

PubMed

Caldeira, Tamires G; Saúde-Guimarães, Dênia A; Dezani, André B; Serra, Cristina Helena Dos Reis; de Souza, Jacqueline

2017-11-01

Analysis of the biopharmaceutical properties of eremantholide C, sesquiterpene lactone with proven pharmacological activity and low toxicity, is required to evaluate its potential to become a drug. Preliminary analysis of the physicochemical characteristics of eremantholide C was performed in silico. Equilibrium solubility was evaluated using the shake-flask method, at 37.0 °C, 100 rpm during 72 h in biorelevant media. The permeability was analysed using parallel artificial membrane permeability assay, at 37.0 °C, 50 rpm for 5 h. The donor compartment was composed of an eremantholide C solution in intestinal fluid simulated without enzymes, while the acceptor compartment consisted of phosphate buffer. Physicochemical characteristics predicted in silico indicated that eremantholide C has a low solubility and high permeability. In-vitro data of eremantholide C showed low solubility, with values for the dose/solubility ratio (ml): 9448.82, 10 389.61 e 15 000.00 for buffers acetate (pH 4.5), intestinal fluid simulated without enzymes (pH 6.8) and phosphate (pH 7.4), respectively. Also, it showed high permeability, with effective permeability of 30.4 × 10 -6 cm/s, a higher result compared with propranolol hydrochloride (9.23 × 10 -6 cm/s). The high permeability combined with its solubility, pharmacological activity and low toxicity demonstrate the importance of eremantholide C as a potential drug candidate. © 2017 Royal Pharmaceutical Society.
Estimating Likelihood of Fetal In Vivo Interactions Using In ...

EPA Pesticide Factsheets

Tox21/ToxCast efforts provide in vitro concentration-response data for thousands of compounds. Predicting whether chemical-biological interactions observed in vitro will occur in vivo is challenging. We hypothesize that using a modified model from the FDA guidance for drug interaction studies, Cmax/AC50 (i.e., maximal in vivo blood concentration over the half-maximal in in vitro activity concentration), will give a useful approximation for concentrations where in vivo interactions are likely. Further, for doses where maternal blood concentrations are likely to elicit an interaction (Cmax/AC50>0.1), where do the compounds accumulate in fetal tissues? In order to estimate these doses based on Tox21 data, in silico parameters of chemical fraction unbound in plasma and intrinsic hepatic clearance were estimated from ADMET predictor (Simulations-Plus Inc.) and used in the HTTK R-package to obtain Cmax values from a physiologically-based toxicokinetics model. In silico estimated Cmax values predicted in vivo human Cmax with median absolute error of 0.81 for 93 chemicals, giving confidence in the R-package and in silico estimates. A case example evaluating Cmax/AC50 values for peroxisome proliferator-activated receptor gamma (PPARγ) and glucocorticoid receptor revealed known compounds (glitazones and corticosteroids, respectively) highest on the list at pharmacological doses. Doses required to elicit likely interactions across all Tox21/ToxCast assays were compared to
An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains.

PubMed

Pichon, Christophe; du Merle, Laurence; Caliot, Marie Elise; Trieu-Cuot, Patrick; Le Bouguénec, Chantal

2012-04-01

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli.
An in silico model for identification of small RNAs in whole bacterial genomes: characterization of antisense RNAs in pathogenic Escherichia coli and Streptococcus agalactiae strains

PubMed Central

Pichon, Christophe; du Merle, Laurence; Caliot, Marie Elise; Trieu-Cuot, Patrick; Le Bouguénec, Chantal

2012-01-01

Characterization of small non-coding ribonucleic acids (sRNA) among the large volume of data generated by high-throughput RNA-seq or tiling microarray analyses remains a challenge. Thus, there is still a need for accurate in silico prediction methods to identify sRNAs within a given bacterial species. After years of effort, dedicated software were developed based on comparative genomic analyses or mathematical/statistical models. Although these genomic analyses enabled sRNAs in intergenic regions to be efficiently identified, they all failed to predict antisense sRNA genes (asRNA), i.e. RNA genes located on the DNA strand complementary to that which encodes the protein. The statistical models enabled any genomic region to be analyzed theorically but not efficiently. We present a new model for in silico identification of sRNA and asRNA candidates within an entire bacterial genome. This model was successfully used to analyze the Gram-negative Escherichia coli and Gram-positive Streptococcus agalactiae. In both bacteria, numerous asRNAs are transcribed from the complementary strand of genes located in pathogenicity islands, strongly suggesting that these asRNAs are regulators of the virulence expression. In particular, we characterized an asRNA that acted as an enhancer-like regulator of the type 1 fimbriae production involved in the virulence of extra-intestinal pathogenic E. coli. PMID:22139924
Computational Exploration of a Protein Receptor Binding Space with Student Proposed Peptide Ligands

ERIC Educational Resources Information Center

King, Matthew D.; Phillips, Paul; Turner, Matthew W.; Katz, Michael; Lew, Sarah; Bradburn, Sarah; Andersen, Tim; McDougal, Owen M.

2016-01-01

Computational molecular docking is a fast and effective "in silico" method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The…

Extrapolation of mammalian-based ToxCast assay results to non-mammalian species to evaluate endocrine disruption

EPA Science Inventory

In vitro high-throughput screening (HTS) and in silico technologies have emerged as 21st century tools for chemical hazard identification. In 2007 the U.S. Environmental Protection Agency (EPA) launched the ToxCast Program, which has screened thousands of chemicals in hundreds of...
A molecular dynamics approach for predicting the glass transition temperature and plasticization effect in amorphous pharmaceuticals.

PubMed

Gupta, Jasmine; Nunes, Cletus; Jonnalagadda, Sriramakamal

2013-11-04

The objectives of this study were as follows: (i) To develop an in silico technique, based on molecular dynamics (MD) simulations, to predict glass transition temperatures (Tg) of amorphous pharmaceuticals. (ii) To computationally study the effect of plasticizer on Tg. (iii) To investigate the intermolecular interactions using radial distribution function (RDF). Amorphous sucrose and water were selected as the model compound and plasticizer, respectively. MD simulations were performed using COMPASS force field and isothermal-isobaric ensembles. The specific volumes of amorphous cells were computed in the temperature range of 440-265 K. The characteristic "kink" observed in volume-temperature curves, in conjunction with regression analysis, defined the Tg. The MD computed Tg values were 367 K, 352 K and 343 K for amorphous sucrose containing 0%, 3% and 5% w/w water, respectively. The MD technique thus effectively simulated the plasticization effect of water; and the corresponding Tg values were in reasonable agreement with theoretical models and literature reports. The RDF measurements revealed strong hydrogen bond interactions between sucrose hydroxyl oxygens and water oxygen. Steric effects led to weak interactions between sucrose acetal oxygens and water oxygen. MD is thus a powerful predictive tool for probing temperature and water effects on the stability of amorphous systems during drug development.
Proteome-wide search for functional motifs altered in tumors: Prediction of nuclear export signals inactivated by cancer-related mutations

PubMed Central

Prieto, Gorka; Fullaondo, Asier; Rodríguez, Jose A.

2016-01-01

Large-scale sequencing projects are uncovering a growing number of missense mutations in human tumors. Understanding the phenotypic consequences of these alterations represents a formidable challenge. In silico prediction of functionally relevant amino acid motifs disrupted by cancer mutations could provide insight into the potential impact of a mutation, and guide functional tests. We have previously described Wregex, a tool for the identification of potential functional motifs, such as nuclear export signals (NESs), in proteins. Here, we present an improved version that allows motif prediction to be combined with data from large repositories, such as the Catalogue of Somatic Mutations in Cancer (COSMIC), and to be applied to a whole proteome scale. As an example, we have searched the human proteome for candidate NES motifs that could be altered by cancer-related mutations included in the COSMIC database. A subset of the candidate NESs identified was experimentally tested using an in vivo nuclear export assay. A significant proportion of the selected motifs exhibited nuclear export activity, which was abrogated by the COSMIC mutations. In addition, our search identified a cancer mutation that inactivates the NES of the human deubiquitinase USP21, and leads to the aberrant accumulation of this protein in the nucleus. PMID:27174732
In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses.

PubMed

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-19

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.
Structural details (kinks and non-α conformations) in transmembrane helices are intrahelically determined and can be predicted by sequence pattern descriptors

PubMed Central

Rigoutsos, Isidore; Riek, Peter; Graham, Robert M.; Novotny, Jiri

2003-01-01

One of the promising methods of protein structure prediction involves the use of amino acid sequence-derived patterns. Here we report on the creation of non-degenerate motif descriptors derived through data mining of training sets of residues taken from the transmembrane-spanning segments of polytopic proteins. These residues correspond to short regions in which there is a deviation from the regular α-helical character (i.e. π-helices, 310-helices and kinks). A ‘search engine’ derived from these motif descriptors correctly identifies, and discriminates amongst instances of the above ‘non-canonical’ helical motifs contained in the SwissProt/TrEMBL database of protein primary structures. Our results suggest that deviations from α-helicity are encoded locally in sequence patterns only about 7–9 residues long and can be determined in silico directly from the amino acid sequence. Delineation of such variations in helical habit is critical to understanding the complex structure–function relationships of polytopic proteins and for drug discovery. The success of our current methodology foretells development of similar prediction tools capable of identifying other structural motifs from sequence alone. The method described here has been implemented and is available on the World Wide Web at http://cbcsrv.watson.ibm.com/Ttkw.html. PMID:12888523
Structural details (kinks and non-alpha conformations) in transmembrane helices are intrahelically determined and can be predicted by sequence pattern descriptors.

PubMed

Rigoutsos, Isidore; Riek, Peter; Graham, Robert M; Novotny, Jiri

2003-08-01

One of the promising methods of protein structure prediction involves the use of amino acid sequence-derived patterns. Here we report on the creation of non-degenerate motif descriptors derived through data mining of training sets of residues taken from the transmembrane-spanning segments of polytopic proteins. These residues correspond to short regions in which there is a deviation from the regular alpha-helical character (i.e. pi-helices, 3(10)-helices and kinks). A 'search engine' derived from these motif descriptors correctly identifies, and discriminates amongst instances of the above 'non-canonical' helical motifs contained in the SwissProt/TrEMBL database of protein primary structures. Our results suggest that deviations from alpha-helicity are encoded locally in sequence patterns only about 7-9 residues long and can be determined in silico directly from the amino acid sequence. Delineation of such variations in helical habit is critical to understanding the complex structure-function relationships of polytopic proteins and for drug discovery. The success of our current methodology foretells development of similar prediction tools capable of identifying other structural motifs from sequence alone. The method described here has been implemented and is available on the World Wide Web at http://cbcsrv.watson.ibm.com/Ttkw.html.
NiaoDuQing granules relieve chronic kidney disease symptoms by decreasing renal fibrosis and anemia

PubMed Central

Wang, Xu; Yu, Suyun; Jia, Qi; Chen, Lichuan; Zhong, Jinqiu; Pan, Yanhong; Shen, Peiliang; Shen, Yin; Wang, Siliang; Wei, Zhonghong; Cao, Yuzhu; Lu, Yin

2017-01-01

NiaoDuQing (NDQ) granules, a traditional Chinese medicine, has been clinically used in China for over fourteen years to treat chronic kidney disease (CKD). To elucidate the mechanisms underlying the therapeutic benefits of NDQ, we designed an approach incorporating chemoinformatics, bioinformatics, network biology methods, and cellular and molecular biology experiments. A total of 182 active compounds were identified in NDQ granules, and 397 putative targets associated with different diseases were derived through ADME modelling and target prediction tools. Protein-protein interaction networks of CKD-related and putative NDQ targets were constructed, and 219 candidate targets were identified based on topological features. Pathway enrichment analysis showed that the candidate targets were mostly related to the TGF-β, the p38MAPK, and the erythropoietin (EPO) receptor signaling pathways, which are known contributors to renal fibrosis and/or renal anemia. A rat model of CKD was established to validate the drug-target mechanisms predicted by the systems pharmacology analysis. Experimental results confirmed that NDQ granules exerted therapeutic effects on CKD and its comorbidities, including renal anemia, mainly by modulating the TGF-β and EPO signaling pathways. Thus, the pharmacological actions of NDQ on CKD symptoms correlated well with in silico predictions. PMID:28915563
In Silico Prediction and Experimental Confirmation of HA Residues Conferring Enhanced Human Receptor Specificity of H5N1 Influenza A Viruses

NASA Astrophysics Data System (ADS)

Schmier, Sonja; Mostafa, Ahmed; Haarmann, Thomas; Bannert, Norbert; Ziebuhr, John; Veljkovic, Veljko; Dietrich, Ursula; Pleschka, Stephan

2015-06-01

Newly emerging influenza A viruses (IAV) pose a major threat to human health by causing seasonal epidemics and/or pandemics, the latter often facilitated by the lack of pre-existing immunity in the general population. Early recognition of candidate pandemic influenza viruses (CPIV) is of crucial importance for restricting virus transmission and developing appropriate therapeutic and prophylactic strategies including effective vaccines. Often, the pandemic potential of newly emerging IAV is only fully recognized once the virus starts to spread efficiently causing serious disease in humans. Here, we used a novel phylogenetic algorithm based on the informational spectrum method (ISM) to identify potential CPIV by predicting mutations in the viral hemagglutinin (HA) gene that are likely to (differentially) affect critical interactions between the HA protein and target cells from bird and human origin, respectively. Predictions were subsequently validated by generating pseudotyped retrovirus particles and genetically engineered IAV containing these mutations and characterizing potential effects on virus entry and replication in cells expressing human and avian IAV receptors, respectively. Our data suggest that the ISM-based algorithm is suitable to identify CPIV among IAV strains that are circulating in animal hosts and thus may be a new tool for assessing pandemic risks associated with specific strains.
Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

PubMed

Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

2016-03-14

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
A Permeability-Limited Physiologically Based Pharmacokinetic (PBPK) Model for Perfluorooctanoic acid (PFOA) in Male Rats.

PubMed

Cheng, Weixiao; Ng, Carla A

2017-09-05

Physiologically based pharmacokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicokinetics and tissue distribution of xenobiotic substances, such as perfluorooctanoic acid (PFOA), in organisms. However, most existing PBPK models have been based on the flow-limited assumption and largely rely on in vivo data for parametrization. In this study, we propose a permeability-limited PBPK model to estimate the toxicokinetics and tissue distribution of PFOA in male rats. Our model considers the cellular uptake and efflux of PFOA via both passive diffusion and transport facilitated by various membrane transporters, association with serum albumin in circulatory and extracellular spaces, and association with intracellular proteins in liver and kidney. Model performance is assessed using seven experimental data sets extracted from three different studies. Comparing model predictions with these experimental data, our model successfully predicts the toxicokinetics and tissue distribution of PFOA in rats following exposure via both IV and oral routes. More importantly, rather than requiring in vivo data fitting, all PFOA-related parameters were obtained from in vitro assays. Our model thus provides an effective framework to test in vitro-in vivo extrapolation and holds great promise for predicting toxicokinetics of per- and polyfluorinated alkyl substances in humans.
How and how much does RAD-seq bias genetic diversity estimates?

PubMed

Cariou, Marie; Duret, Laurent; Charlat, Sylvain

2016-11-08

RAD-seq is a powerful tool, increasingly used in population genomics. However, earlier studies have raised red flags regarding possible biases associated with this technique. In particular, polymorphism on restriction sites results in preferential sampling of closely related haplotypes, so that RAD data tends to underestimate genetic diversity. Here we (1) clarify the theoretical basis of this bias, highlighting the potential confounding effects of population structure and selection, (2) confront predictions to real data from in silico digestion of full genomes and (3) provide a proof of concept toward an ABC-based correction of the RAD-seq bias. Under a neutral and panmictic model, we confirm the previously established relationship between the true polymorphism and its RAD-based estimation, showing a more pronounced bias when polymorphism is high. Using more elaborate models, we show that selection, resulting in heterogeneous levels of polymorphism along the genome, exacerbates the bias and leads to a more pronounced underestimation. On the contrary, spatial genetic structure tends to reduce the bias. We confront the neutral and panmictic model to "ideal" empirical data (in silico RAD-sequencing) using full genomes from natural populations of the fruit fly Drosophila melanogaster and the fungus Shizophyllum commune, harbouring respectively moderate and high genetic diversity. In D. melanogaster, predictions fit the model, but the small difference between the true and RAD polymorphism makes this comparison insensitive to deviations from the model. In the highly polymorphic fungus, the model captures a large part of the bias but makes inaccurate predictions. Accordingly, ABC corrections based on this model improve the estimations, albeit with some imprecisions. The RAD-seq underestimation of genetic diversity associated with polymorphism in restriction sites becomes more pronounced when polymorphism is high. In practice, this means that in many systems where polymorphism does not exceed 2 %, the bias is of minor importance in the face of other sources of uncertainty, such as heterogeneous bases composition or technical artefacts. The neutral panmictic model provides a practical mean to correct the bias through ABC, albeit with some imprecisions. More elaborate ABC methods might integrate additional parameters, such as population structure and selection, but their opposite effects could hinder accurate corrections.
Relative stability of DNA as a generic criterion for promoter prediction: whole genome annotation of microbial genomes with varying nucleotide base composition.

PubMed

Rangannan, Vetriselvi; Bansal, Manju

2009-12-01

The rapid increase in genome sequence information has necessitated the annotation of their functional elements, particularly those occurring in the non-coding regions, in the genomic context. Promoter region is the key regulatory region, which enables the gene to be transcribed or repressed, but it is difficult to determine experimentally. Hence an in silico identification of promoters is crucial in order to guide experimental work and to pin point the key region that controls the transcription initiation of a gene. In this analysis, we demonstrate that while the promoter regions are in general less stable than the flanking regions, their average free energy varies depending on the GC composition of the flanking genomic sequence. We have therefore obtained a set of free energy threshold values, for genomic DNA with varying GC content and used them as generic criteria for predicting promoter regions in several microbial genomes, using an in-house developed tool PromPredict. On applying it to predict promoter regions corresponding to the 1144 and 612 experimentally validated TSSs in E. coli (50.8% GC) and B. subtilis (43.5% GC) sensitivity of 99% and 95% and precision values of 58% and 60%, respectively, were achieved. For the limited data set of 81 TSSs available for M. tuberculosis (65.6% GC) a sensitivity of 100% and precision of 49% was obtained.
Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

NASA Astrophysics Data System (ADS)

Costanzi, Stefano; Tikhonova, Irina G.; Harden, T. Kendall; Jacobson, Kenneth A.

2009-11-01

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered.
MAGIA2: from miRNA and genes expression data integrative analysis to microRNA–transcription factor mixed regulatory circuits (2012 update)

PubMed Central

Bisognin, Andrea; Sales, Gabriele; Coppe, Alessandro; Bortoluzzi, Stefania; Romualdi, Chiara

2012-01-01

MAGIA2 (http://gencomp.bio.unipd.it/magia2) is an update, extension and evolution of the MAGIA web tool. It is dedicated to the integrated analysis of in silico target prediction, microRNA (miRNA) and gene expression data for the reconstruction of post-transcriptional regulatory networks. miRNAs are fundamental post-transcriptional regulators of several key biological and pathological processes. As miRNAs act prevalently through target degradation, their expression profiles are expected to be inversely correlated to those of the target genes. Low specificity of target prediction algorithms makes integration approaches an interesting solution for target prediction refinement. MAGIA2 performs this integrative approach supporting different association measures, multiple organisms and almost all target predictions algorithms. Nevertheless, miRNAs activity should be viewed as part of a more complex scenario where regulatory elements and their interactors generate a highly connected network and where gene expression profiles are the result of different levels of regulation. The updated MAGIA2 tries to dissect this complexity by reconstructing mixed regulatory circuits involving either miRNA or transcription factor (TF) as regulators. Two types of circuits are identified: (i) a TF that regulates both a miRNA and its target and (ii) a miRNA that regulates both a TF and its target. PMID:22618880
SH2 Ligand Prediction-Guidance for In-Silico Screening.

PubMed

Li, Shawn S C; Li, Lei

2017-01-01

Systematic identification of binding partners for SH2 domains is important for understanding the biological function of the corresponding SH2 domain-containing proteins. Here, we describe two different web-accessible computer programs, SMALI and DomPep, for predicting binding ligands for SH2 domains. The former was developed using a Scoring Matrix method and the latter based on the Support Vector Machine model.
Public-Private Partnerships in Lead Discovery: Overview and Case Studies.

PubMed

Gottwald, Matthias; Becker, Andreas; Bahr, Inke; Mueller-Fahrnow, Anke

2016-09-01

The pharmaceutical industry is faced with significant challenges in its efforts to discover new drugs that address unmet medical needs. Safety concerns and lack of efficacy are the two main technical reasons for attrition. Improved early research tools including predictive in silico, in vitro, and in vivo models, as well as a deeper understanding of the disease biology, therefore have the potential to improve success rates. The combination of internal activities with external collaborations in line with the interests and needs of all partners is a successful approach to foster innovation and to meet the challenges. Collaboration can take place in different ways, depending on the requirements of the participants. In this review, the value of public-private partnership approaches will be discussed, using examples from the Innovative Medicines Initiative (IMI). These examples describe consortia approaches to develop tools and processes for improving target identification and validation, as well as lead identification and optimization. The project "Kinetics for Drug Discovery" (K4DD), focusing on the adoption of drug-target binding kinetics analysis in the drug discovery decision-making process, is described in more detail. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Minimalist Design of Allosterically Regulated Protein Catalysts.

PubMed

Makhlynets, O V; Korendovych, I V

2016-01-01

Nature facilitates chemical transformations with exceptional selectivity and efficiency. Despite a tremendous progress in understanding and predicting protein function, the overall problem of designing a protein catalyst for a given chemical transformation is far from solved. Over the years, many design techniques with various degrees of complexity and rational input have been developed. Minimalist approach to protein design that focuses on the bare minimum requirements to achieve activity presents several important advantages. By focusing on basic physicochemical properties and strategic placing of only few highly active residues one can feasibly evaluate in silico a very large variety of possible catalysts. In more general terms minimalist approach looks for the mere possibility of catalysis, rather than trying to identify the most active catalyst possible. Even very basic designs that utilize a single residue introduced into nonenzymatic proteins or peptide bundles are surprisingly active. Because of the inherent simplicity of the minimalist approach computational tools greatly enhance its efficiency. No complex calculations need to be set up and even a beginner can master this technique in a very short time. Here, we present a step-by-step protocol for minimalist design of functional proteins using basic, easily available, and free computational tools. © 2016 Elsevier Inc. All rights reserved.
PPARγ regulates exocrine pancreas lipase.

PubMed

Danino, Hila; Naor, Ronny Peri-; Fogel, Chen; Ben-Harosh, Yael; Kadir, Rotem; Salem, Hagit; Birk, Ruth

2016-12-01

Pancreatic lipase (triacylglycerol lipase EC 3.1.1.3) is an essential enzyme in hydrolysis of dietary fat. Dietary fat, especially polyunsaturated fatty acids (PUFA), regulate pancreatic lipase (PNLIP); however, the molecular mechanism underlying this regulation is mostly unknown. As PUFA are known to regulate expression of proliferator-activated receptor gamma (PPARγ), and as we identified in-silico putative PPARγ binding sites within the putative PNLIP promoter sequence, we hypothesized that PUFA regulation of PNLIP might be mediated by PPARγ. We used in silico bioinformatics tools, reporter luciferase assay, PPARγ agonists and antagonists, PPARγ overexpression in exocrine pancreas AR42J and primary cells to study PPARγ regulation of PNLIP. Using in silico bioinformatics tools we mapped PPARγ binding sites (PPRE) to the putative promoter region of PNLIP. Reporter luciferase assay in AR42J rat exocrine pancreas acinar cells transfected with various constructs of the putative PNLIP promoter showed that PNLIP transcription is significantly enhanced by PPARγ dose-dependently, reaching maximal levels with multi PPRE sites. This effect was significantly augmented in the presence of PPARγ agonists and reduced by PPARγ antagonists or mutagenesis abrogating PPRE sites. Over-expression of PPARγ significantly elevated PNLIP transcript and protein levels in AR42J cells and in primary pancreas cells. Moreover, PNLIP expression was up-regulated by PPARγ agonists (pioglitazone and 15dPGJ2) and significantly down-regulated by PPARγ antagonists in non-transfected rat exocrine pancreas AR42J cell line cells. PPARγ transcriptionally regulates PNLIP gene expression. This transcript regulation resolves part of the missing link between dietary PUFA direct regulation of PNLIP. Copyright © 2016 Elsevier B.V. All rights reserved.
PreTIS: A Tool to Predict Non-canonical 5’ UTR Translational Initiation Sites in Human and Mouse

PubMed Central

Reuter, Kerstin; Helms, Volkhard

2016-01-01

Translation of mRNA sequences into proteins typically starts at an AUG triplet. In rare cases, translation may also start at alternative non–AUG codons located in the annotated 5’ UTR which leads to an increased regulatory complexity. Since ribosome profiling detects translational start sites at the nucleotide level, the properties of these start sites can then be used for the statistical evaluation of functional open reading frames. We developed a linear regression approach to predict in–frame and out–of–frame translational start sites within the 5’ UTR from mRNA sequence information together with their translation initiation confidence. Predicted start codons comprise AUG as well as near–cognate codons. The underlying datasets are based on published translational start sites for human HEK293 and mouse embryonic stem cells that were derived by the original authors from ribosome profiling data. The average prediction accuracy of true vs. false start sites for HEK293 cells was 80%. When applied to mouse mRNA sequences, the same model predicted translation initiation sites observed in mouse ES cells with an accuracy of 76%. Moreover, we illustrate the effect of in silico mutations in the flanking sequence context of a start site on the predicted initiation confidence. Our new webservice PreTIS visualizes alternative start sites and their respective ORFs and predicts their ability to initiate translation. Solely, the mRNA sequence is required as input. PreTIS is accessible at http://service.bioinformatik.uni-saarland.de/pretis. PMID:27768687
Functional Studies and In Silico Analyses to Evaluate Non-Coding Variants in Inherited Cardiomyopathies.

PubMed

Frisso, Giulia; Detta, Nicola; Coppola, Pamela; Mazzaccara, Cristina; Pricolo, Maria Rosaria; D'Onofrio, Antonio; Limongelli, Giuseppe; Calabrò, Raffaele; Salvatore, Francesco

2016-11-10

Point mutations are the most common cause of inherited diseases. Bioinformatics tools can help to predict the pathogenicity of mutations found during genetic screening, but they may work less well in determining the effect of point mutations in non-coding regions. In silico analysis of intronic variants can reveal their impact on the splicing process, but the consequence of a given substitution is generally not predictable. The aim of this study was to functionally test five intronic variants ( MYBPC3 -c.506-2A>C, MYBPC3 -c.906-7G>T, MYBPC3 -c.2308+3G>C, SCN5A -c.393-5C>A, and ACTC1 -c.617-7T>C) found in five patients affected by inherited cardiomyopathies in the attempt to verify their pathogenic role. Analysis of the MYBPC3 -c.506-2A>C mutation in mRNA from the peripheral blood of one of the patients affected by hypertrophic cardiac myopathy revealed the loss of the canonical splice site and the use of an alternative splicing site, which caused the loss of the first seven nucleotides of exon 5 ( MYBPC3 -G169AfsX14). In the other four patients, we generated minigene constructs and transfected them in HEK-293 cells. This minigene approach showed that MYBPC3 -c.2308+3G>C and SCN5A -c.393-5C>A altered pre-mRNA processing, thus resulting in the skipping of one exon. No alterations were found in either MYBPC3 -c.906-7G>T or ACTC1 -c.617-7T>C. In conclusion, functional in vitro analysis of the effects of potential splicing mutations can confirm or otherwise the putative pathogenicity of non-coding mutations, and thus help to guide the patient's clinical management and improve genetic counseling in affected families.

Secondary metabolites of Cynodon dactylon as an antagonist to angiotensin II type1 receptor: Novel in silico drug targeting approach for diabetic retinopathy

PubMed Central

Jananie, R. K.; Priya, V.; Vijayalakshmi, K.

2012-01-01

Objectives: To study the ability of the secondary metabolites of Cynodon dactylon to serve as an antagonist to angiotensin II type 1 receptor (AT1); activation of this receptor plays a vital role in diabetic retinopathy (DR). Materials and Methods: In silico methods are mainly harnessed to reduce time, cost and risk associated with drug discovery. Twenty-four compounds were identified as the secondary metabolites of hydroalcoholic extract of C. dactylon using the GCMS technique. These were considered as the ligands or inhibitors that would serve as an antagonist to the AT1. The ACD/Chemsketch tool was used to generate 3D structures of the ligands. A molecular file format converter tool was used to convert the generated data to the PDB format (Protein Data Bank) and was used for docking studies. The AT1 structure was retrieved from the Swissprot data base and PDB and visualized using the Rasmol tool. Domain analysis was carried from the Pfam data base; following this, the active site of the target protein was identified using a Q-site finder tool. The ability of the ligands to bind with the active site of AT1 was studied using the Autodocking tool. The docking results were analyzed using the WebLab viewer tool. Results: Sixteen ligands showed effective binding with the target protein; diazoprogesteron, didodecyl phthalate, and 9,12-octadecadienoyl chloride (z,z) may be considered as compounds that could be used to bind with the active site sequence of AT1. Conclusions: The present study shows that the metabolites of C. dactylon could serve as a natural antagonist to AT1 that could be used to treat diabetic retinopathy. PMID:22368412
Bioinformatics functional analysis of let-7a, miR-34a, and miR-199a/b reveals novel insights into immune system pathways and cancer hallmarks for hepatocellular carcinoma.

PubMed

Soliman, Bangly; Salem, Ahmed; Ghazy, Mohamed; Abu-Shahba, Nourhan; El Hefnawi, Mahmoud

2018-05-01

Let-7a, miR-34a, and miR-199 a/b have gained a great attention as master regulators for cellular processes. In particular, these three micro-RNAs act as potential onco-suppressors for hepatocellular carcinoma. Bioinformatics can reveal the functionality of these micro-RNAs through target prediction and functional annotation analysis. In the current study, in silico analysis using innovative servers (miRror Suite, DAVID, miRGator V3.0, GeneTrail) has demonstrated the combinatorial and the individual target genes of these micro-RNAs and further explored their roles in hepatocellular carcinoma progression. There were 87 common target messenger RNAs (p ≤ 0.05) that were predicted to be regulated by the three micro-RNAs using miRror 2.0 target prediction tool. In addition, the functional enrichment analysis of these targets that was performed by DAVID functional annotation and REACTOME tools revealed two major immune-related pathways, eight hepatocellular carcinoma hallmarks-linked pathways, and two pathways that mediate interconnected processes between immune system and hepatocellular carcinoma hallmarks. Moreover, protein-protein interaction network for the predicted common targets was obtained by using STRING database. The individual analysis of target genes and pathways for the three micro-RNAs of interest using miRGator V3.0 and GeneTrail servers revealed some novel predicted target oncogenes such as SOX4, which we validated experimentally, in addition to some regulated pathways of immune system and hepatocarcinogenesis such as insulin signaling pathway and adipocytokine signaling pathway. In general, our results demonstrate that let-7a, miR-34a, and miR-199 a/b have novel interactions in different immune system pathways and major hepatocellular carcinoma hallmarks. Thus, our findings shed more light on the roles of these miRNAs as cancer silencers.
Computational toxicology and in silico modeling of embryogenesis

EPA Science Inventory

High-throughput screening (HTS) is providing a rich source of in vitro data for predictive toxicology. ToxCast™ HTS data presently covers 1060 broad-use chemicals and captures >650 in vitro features for diverse biochemical and receptor binding activities, multiplexed reporter gen...
Adverse outcome pathways (AOPs): A framework to support predictive toxicology

EPA Science Inventory

High throughput and in silico methods are providing the regulatory toxicology community with capacity to rapidly and cost effectively generate data concerning a chemical’s ability to initiate one or more biological perturbations that may culminate in an adverse ecological o...
Systems Toxicology of Embryo Development (9th Copenhagen Workshop)

EPA Science Inventory

An important consideration for predictive toxicology is to identify developmental hazards utilizing mechanism-based in vitro assays (e.g., ToxCast) and in silico multiscale models. Steady progress has been made with agent-based models that recapitulate morphogenetic drivers for a...
Assessment of the Clinical Relevance of BRCA2 Missense Variants by Functional and Computational Approaches.

PubMed

Guidugli, Lucia; Shimelis, Hermela; Masica, David L; Pankratz, Vernon S; Lipton, Gary B; Singh, Namit; Hu, Chunling; Monteiro, Alvaro N A; Lindor, Noralane M; Goldgar, David E; Karchin, Rachel; Iversen, Edwin S; Couch, Fergus J

2018-01-17

Many variants of uncertain significance (VUS) have been identified in BRCA2 through clinical genetic testing. VUS pose a significant clinical challenge because the contribution of these variants to cancer risk has not been determined. We conducted a comprehensive assessment of VUS in the BRCA2 C-terminal DNA binding domain (DBD) by using a validated functional assay of BRCA2 homologous recombination (HR) DNA-repair activity and defined a classifier of variant pathogenicity. Among 139 variants evaluated, 54 had ≥99% probability of pathogenicity, and 73 had ≥95% probability of neutrality. Functional assay results were compared with predictions of variant pathogenicity from the Align-GVGD protein-sequence-based prediction algorithm, which has been used for variant classification. Relative to the HR assay, Align-GVGD significantly (p < 0.05) over-predicted pathogenic variants. We subsequently combined functional and Align-GVGD prediction results in a Bayesian hierarchical model (VarCall) to estimate the overall probability of pathogenicity for each VUS. In addition, to predict the effects of all other BRCA2 DBD variants and to prioritize variants for functional studies, we used the endoPhenotype-Optimized Sequence Ensemble (ePOSE) algorithm to train classifiers for BRCA2 variants by using data from the HR functional assay. Together, the results show that systematic functional assays in combination with in silico predictors of pathogenicity provide robust tools for clinical annotation of BRCA2 VUS. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
Inroads to predict in vivo toxicology-an introduction to the eTOX Project.

PubMed

Briggs, Katharine; Cases, Montserrat; Heard, David J; Pastor, Manuel; Pognan, François; Sanz, Ferran; Schwab, Christof H; Steger-Hartmann, Thomas; Sutter, Andreas; Watson, David K; Wichard, Jörg D

2012-01-01

There is a widespread awareness that the wealth of preclinical toxicity data that the pharmaceutical industry has generated in recent decades is not exploited as efficiently as it could be. Enhanced data availability for compound comparison ("read-across"), or for data mining to build predictive tools, should lead to a more efficient drug development process and contribute to the reduction of animal use (3Rs principle). In order to achieve these goals, a consortium approach, grouping numbers of relevant partners, is required. The eTOX ("electronic toxicity") consortium represents such a project and is a public-private partnership within the framework of the European Innovative Medicines Initiative (IMI). The project aims at the development of in silico prediction systems for organ and in vivo toxicity. The backbone of the project will be a database consisting of preclinical toxicity data for drug compounds or candidates extracted from previously unpublished, legacy reports from thirteen European and European operation-based pharmaceutical companies. The database will be enhanced by incorporation of publically available, high quality toxicology data. Seven academic institutes and five small-to-medium size enterprises (SMEs) contribute with their expertise in data gathering, database curation, data mining, chemoinformatics and predictive systems development. The outcome of the project will be a predictive system contributing to early potential hazard identification and risk assessment during the drug development process. The concept and strategy of the eTOX project is described here, together with current achievements and future deliverables.
Evolutionary and Functional Relationships in the Truncated Hemoglobin Family.

PubMed

Bustamante, Juan P; Radusky, Leandro; Boechi, Leonardo; Estrin, Darío A; Ten Have, Arjen; Martí, Marcelo A

2016-01-01

Predicting function from sequence is an important goal in current biological research, and although, broad functional assignment is possible when a protein is assigned to a family, predicting functional specificity with accuracy is not straightforward. If function is provided by key structural properties and the relevant properties can be computed using the sequence as the starting point, it should in principle be possible to predict function in detail. The truncated hemoglobin family presents an interesting benchmark study due to their ubiquity, sequence diversity in the context of a conserved fold and the number of characterized members. Their functions are tightly related to O2 affinity and reactivity, as determined by the association and dissociation rate constants, both of which can be predicted and analyzed using in-silico based tools. In the present work we have applied a strategy, which combines homology modeling with molecular based energy calculations, to predict and analyze function of all known truncated hemoglobins in an evolutionary context. Our results show that truncated hemoglobins present conserved family features, but that its structure is flexible enough to allow the switch from high to low affinity in a few evolutionary steps. Most proteins display moderate to high oxygen affinities and multiple ligand migration paths, which, besides some minor trends, show heterogeneous distributions throughout the phylogenetic tree, again suggesting fast functional adaptation. Our data not only deepens our comprehension of the structural basis governing ligand affinity, but they also highlight some interesting functional evolutionary trends.
Evolutionary and Functional Relationships in the Truncated Hemoglobin Family

PubMed Central

Bustamante, Juan P.; Radusky, Leandro; Boechi, Leonardo; Estrin, Darío A.; ten Have, Arjen; Martí, Marcelo A.

2016-01-01

Predicting function from sequence is an important goal in current biological research, and although, broad functional assignment is possible when a protein is assigned to a family, predicting functional specificity with accuracy is not straightforward. If function is provided by key structural properties and the relevant properties can be computed using the sequence as the starting point, it should in principle be possible to predict function in detail. The truncated hemoglobin family presents an interesting benchmark study due to their ubiquity, sequence diversity in the context of a conserved fold and the number of characterized members. Their functions are tightly related to O2 affinity and reactivity, as determined by the association and dissociation rate constants, both of which can be predicted and analyzed using in-silico based tools. In the present work we have applied a strategy, which combines homology modeling with molecular based energy calculations, to predict and analyze function of all known truncated hemoglobins in an evolutionary context. Our results show that truncated hemoglobins present conserved family features, but that its structure is flexible enough to allow the switch from high to low affinity in a few evolutionary steps. Most proteins display moderate to high oxygen affinities and multiple ligand migration paths, which, besides some minor trends, show heterogeneous distributions throughout the phylogenetic tree, again suggesting fast functional adaptation. Our data not only deepens our comprehension of the structural basis governing ligand affinity, but they also highlight some interesting functional evolutionary trends. PMID:26788940
Multiscale modeling of mucosal immune responses

PubMed Central

2015-01-01

Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM. Background Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Implementation Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. Conclusion We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation. PMID:26329787
Multiscale modeling of mucosal immune responses.

PubMed

Mei, Yongguo; Abedi, Vida; Carbo, Adria; Zhang, Xiaoying; Lu, Pinyi; Philipson, Casandra; Hontecillas, Raquel; Hoops, Stefan; Liles, Nathan; Bassaganya-Riera, Josep

2015-01-01

Computational techniques are becoming increasingly powerful and modeling tools for biological systems are of greater needs. Biological systems are inherently multiscale, from molecules to tissues and from nano-seconds to a lifespan of several years or decades. ENISI MSM integrates multiple modeling technologies to understand immunological processes from signaling pathways within cells to lesion formation at the tissue level. This paper examines and summarizes the technical details of ENISI, from its initial version to its latest cutting-edge implementation. Object-oriented programming approach is adopted to develop a suite of tools based on ENISI. Multiple modeling technologies are integrated to visualize tissues, cells as well as proteins; furthermore, performance matching between the scales is addressed. We used ENISI MSM for developing predictive multiscale models of the mucosal immune system during gut inflammation. Our modeling predictions dissect the mechanisms by which effector CD4+ T cell responses contribute to tissue damage in the gut mucosa following immune dysregulation.Computational modeling techniques are playing increasingly important roles in advancing a systems-level mechanistic understanding of biological processes. Computer simulations guide and underpin experimental and clinical efforts. This study presents ENteric Immune Simulator (ENISI), a multiscale modeling tool for modeling the mucosal immune responses. ENISI's modeling environment can simulate in silico experiments from molecular signaling pathways to tissue level events such as tissue lesion formation. ENISI's architecture integrates multiple modeling technologies including ABM (agent-based modeling), ODE (ordinary differential equations), SDE (stochastic modeling equations), and PDE (partial differential equations). This paper focuses on the implementation and developmental challenges of ENISI. A multiscale model of mucosal immune responses during colonic inflammation, including CD4+ T cell differentiation and tissue level cell-cell interactions was developed to illustrate the capabilities, power and scope of ENISI MSM.
Prediction of Hematopoietic Stem Cell Transplantation Related Mortality- Lessons Learned from the In-Silico Approach: A European Society for Blood and Marrow Transplantation Acute Leukemia Working Party Data Mining Study.

PubMed

Shouval, Roni; Labopin, Myriam; Unger, Ron; Giebel, Sebastian; Ciceri, Fabio; Schmid, Christoph; Esteve, Jordi; Baron, Frederic; Gorin, Norbert Claude; Savani, Bipin; Shimoni, Avichai; Mohty, Mohamad; Nagler, Arnon

2016-01-01

Models for prediction of allogeneic hematopoietic stem transplantation (HSCT) related mortality partially account for transplant risk. Improving predictive accuracy requires understating of prediction limiting factors, such as the statistical methodology used, number and quality of features collected, or simply the population size. Using an in-silico approach (i.e., iterative computerized simulations), based on machine learning (ML) algorithms, we set out to analyze these factors. A cohort of 25,923 adult acute leukemia patients from the European Society for Blood and Marrow Transplantation (EBMT) registry was analyzed. Predictive objective was non-relapse mortality (NRM) 100 days following HSCT. Thousands of prediction models were developed under varying conditions: increasing sample size, specific subpopulations and an increasing number of variables, which were selected and ranked by separate feature selection algorithms. Depending on the algorithm, predictive performance plateaued on a population size of 6,611-8,814 patients, reaching a maximal area under the receiver operator characteristic curve (AUC) of 0.67. AUCs' of models developed on specific subpopulation ranged from 0.59 to 0.67 for patients in second complete remission and receiving reduced intensity conditioning, respectively. Only 3-5 variables were necessary to achieve near maximal AUCs. The top 3 ranking variables, shared by all algorithms were disease stage, donor type, and conditioning regimen. Our findings empirically demonstrate that with regards to NRM prediction, few variables "carry the weight" and that traditional HSCT data has been "worn out". "Breaking through" the predictive boundaries will likely require additional types of inputs.
Transcriptomics-based strain optimization tool for designing secondary metabolite overproducing strains of Streptomyces coelicolor.

PubMed

Kim, Minsuk; Yi, Jeong Sang; Lakshmanan, Meiyappan; Lee, Dong-Yup; Kim, Byung-Gee

2016-03-01

In silico model-driven analysis using genome-scale model of metabolism (GEM) has been recognized as a promising method for microbial strain improvement. However, most of the current GEM-based strain design algorithms based on flux balance analysis (FBA) heavily rely on the steady-state and optimality assumptions without considering any regulatory information. Thus, their practical usage is quite limited, especially in its application to secondary metabolites overproduction. In this study, we developed a transcriptomics-based strain optimization tool (tSOT) in order to overcome such limitations by integrating transcriptomic data into GEM. Initially, we evaluated existing algorithms for integrating transcriptomic data into GEM using Streptomyces coelicolor dataset, and identified iMAT algorithm as the only and the best algorithm for characterizing the secondary metabolism of S. coelicolor. Subsequently, we developed tSOT platform where iMAT is adopted to predict the reaction states, and successfully demonstrated its applicability to secondary metabolites overproduction by designing actinorhodin (ACT), a polyketide antibiotic, overproducing strain of S. coelicolor. Mutants overexpressing tSOT targets such as ribulose 5-phosphate 3-epimerase and NADP-dependent malic enzyme showed 2 and 1.8-fold increase in ACT production, thereby validating the tSOT prediction. It is expected that tSOT can be used for solving other metabolic engineering problems which could not be addressed by current strain design algorithms, especially for the secondary metabolite overproductions. © 2015 Wiley Periodicals, Inc.
Stochastic differential equations as a tool to regularize the parameter estimation problem for continuous time dynamical systems given discrete time measurements.

PubMed

Leander, Jacob; Lundh, Torbjörn; Jirstrand, Mats

2014-05-01

In this paper we consider the problem of estimating parameters in ordinary differential equations given discrete time experimental data. The impact of going from an ordinary to a stochastic differential equation setting is investigated as a tool to overcome the problem of local minima in the objective function. Using two different models, it is demonstrated that by allowing noise in the underlying model itself, the objective functions to be minimized in the parameter estimation procedures are regularized in the sense that the number of local minima is reduced and better convergence is achieved. The advantage of using stochastic differential equations is that the actual states in the model are predicted from data and this will allow the prediction to stay close to data even when the parameters in the model is incorrect. The extended Kalman filter is used as a state estimator and sensitivity equations are provided to give an accurate calculation of the gradient of the objective function. The method is illustrated using in silico data from the FitzHugh-Nagumo model for excitable media and the Lotka-Volterra predator-prey system. The proposed method performs well on the models considered, and is able to regularize the objective function in both models. This leads to parameter estimation problems with fewer local minima which can be solved by efficient gradient-based methods. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.
Comparative analysis of amino acid composition in the active site of nirk gene encoding copper-containing nitrite reductase (CuNiR) in bacterial spp.

PubMed

Adhikari, Utpal Kumar; Rahman, M Mizanur

2017-04-01

The nirk gene encoding the copper-containing nitrite reductase (CuNiR), a key catalytic enzyme in the environmental denitrification process that helps to produce nitric oxide from nitrite. The molecular mechanism of denitrification process is definitely complex and in this case a theoretical investigation has been conducted to know the sequence information and amino acid composition of the active site of CuNiR enzyme using various Bioinformatics tools. 10 Fasta formatted sequences were retrieved from the NCBI database and the domain and disordered regions identification and phylogenetic analyses were done on these sequences. The comparative modeling of protein was performed through Modeller 9v14 program and visualized by PyMOL tools. Validated protein models were deposited in the Protein Model Database (PMDB) (PMDB id: PM0080150 to PM0080159). Active sites of nirk encoding CuNiR enzyme were identified by Castp server. The PROCHECK showed significant scores for four protein models in the most favored regions of the Ramachandran plot. Active sites and cavities prediction exhibited that the amino acid, namely Glycine, Alanine, Histidine, Aspartic acid, Glutamic acid, Threonine, and Glutamine were common in four predicted protein models. The present in silico study anticipates that active site analyses result will pave the way for further research on the complex denitrification mechanism of the selected species in the experimental laboratory. Copyright © 2016. Published by Elsevier Ltd.
Approaches for in silico finishing of microbial genome sequences

PubMed Central

Kremer, Frederico Schmitt; McBride, Alan John Alexander; Pinto, Luciano da Silva

2017-01-01

Abstract The introduction of next-generation sequencing (NGS) had a significant effect on the availability of genomic information, leading to an increase in the number of sequenced genomes from a large spectrum of organisms. Unfortunately, due to the limitations implied by the short-read sequencing platforms, most of these newly sequenced genomes remained as “drafts”, incomplete representations of the whole genetic content. The previous genome sequencing studies indicated that finishing a genome sequenced by NGS, even bacteria, may require additional sequencing to fill the gaps, making the entire process very expensive. As such, several in silico approaches have been developed to optimize the genome assemblies and facilitate the finishing process. The present review aims to explore some free (open source, in many cases) tools that are available to facilitate genome finishing. PMID:28898352
Approaches for in silico finishing of microbial genome sequences.

PubMed

Kremer, Frederico Schmitt; McBride, Alan John Alexander; Pinto, Luciano da Silva

The introduction of next-generation sequencing (NGS) had a significant effect on the availability of genomic information, leading to an increase in the number of sequenced genomes from a large spectrum of organisms. Unfortunately, due to the limitations implied by the short-read sequencing platforms, most of these newly sequenced genomes remained as "drafts", incomplete representations of the whole genetic content. The previous genome sequencing studies indicated that finishing a genome sequenced by NGS, even bacteria, may require additional sequencing to fill the gaps, making the entire process very expensive. As such, several in silico approaches have been developed to optimize the genome assemblies and facilitate the finishing process. The present review aims to explore some free (open source, in many cases) tools that are available to facilitate genome finishing.
MobilomeFINDER: web-based tools for in silico and experimental discovery of bacterial genomic islands

PubMed Central

Ou, Hong-Yu; He, Xinyi; Harrison, Ewan M.; Kulasekara, Bridget R.; Thani, Ali Bin; Kadioglu, Aras; Lory, Stephen; Hinton, Jay C. D.; Barer, Michael R.; Rajakumar, Kumar

2007-01-01

MobilomeFINDER (http://mml.sjtu.edu.cn/MobilomeFINDER) is an interactive online tool that facilitates bacterial genomic island or ‘mobile genome’ (mobilome) discovery; it integrates the ArrayOme and tRNAcc software packages. ArrayOme utilizes a microarray-derived comparative genomic hybridization input data set to generate ‘inferred contigs’ produced by merging adjacent genes classified as ‘present’. Collectively these ‘fragments’ represent a hypothetical ‘microarray-visualized genome (MVG)’. ArrayOme permits recognition of discordances between physical genome and MVG sizes, thereby enabling identification of strains rich in microarray-elusive novel genes. Individual tRNAcc tools facilitate automated identification of genomic islands by comparative analysis of the contents and contexts of tRNA sites and other integration hotspots in closely related sequenced genomes. Accessory tools facilitate design of hotspot-flanking primers for in silico and/or wet-science-based interrogation of cognate loci in unsequenced strains and analysis of islands for features suggestive of foreign origins; island-specific and genome-contextual features are tabulated and represented in schematic and graphical forms. To date we have used MobilomeFINDER to analyse several Enterobacteriaceae, Pseudomonas aeruginosa and Streptococcus suis genomes. MobilomeFINDER enables high-throughput island identification and characterization through increased exploitation of emerging sequence data and PCR-based profiling of unsequenced test strains; subsequent targeted yeast recombination-based capture permits full-length sequencing and detailed functional studies of novel genomic islands. PMID:17537813
Improving draft genome contiguity with reference-derived in silico mate-pair libraries.

PubMed

Grau, José Horacio; Hackl, Thomas; Koepfli, Klaus-Peter; Hofreiter, Michael

2018-05-01

Contiguous genome assemblies are a highly valued biological resource because of the higher number of completely annotated genes and genomic elements that are usable compared to fragmented draft genomes. Nonetheless, contiguity is difficult to obtain if only low coverage data and/or only distantly related reference genome assemblies are available. In order to improve genome contiguity, we have developed Cross-Species Scaffolding-a new pipeline that imports long-range distance information directly into the de novo assembly process by constructing mate-pair libraries in silico. We show how genome assembly metrics and gene prediction dramatically improve with our pipeline by assembling two primate genomes solely based on ∼30x coverage of shotgun sequencing data.
In Silico Modeling of Indigo and Tyrian Purple Single-Electron Nano-Transistors Using Density Functional Theory Approach

NASA Astrophysics Data System (ADS)

Shityakov, Sergey; Roewer, Norbert; Förster, Carola; Broscheit, Jens-Albert

2017-07-01

The purpose of this study was to develop and implement an in silico model of indigoid-based single-electron transistor (SET) nanodevices, which consist of indigoid molecules from natural dye weakly coupled to gold electrodes that function in a Coulomb blockade regime. The electronic properties of the indigoid molecules were investigated using the optimized density-functional theory (DFT) with a continuum model. Higher electron transport characteristics were determined for Tyrian purple, consistent with experimentally derived data. Overall, these results can be used to correctly predict and emphasize the electron transport functions of organic SETs, demonstrating their potential for sustainable nanoelectronics comprising the biodegradable and biocompatible materials.

Temperature Effects on Kinetics of KV11.1 Drug Block Have Important Consequences for In Silico Proarrhythmic Risk Prediction.

PubMed

Windley, Monique J; Mann, Stefan A; Vandenberg, Jamie I; Hill, Adam P

2016-07-01

Drug block of voltage-gated potassium channel subtype 11.1 human ether-a-go-go related gene (Kv11.1) (hERG) channels, encoded by the KCNH2 gene, is associated with reduced repolarization of the cardiac action potential and is the predominant cause of acquired long QT syndrome that can lead to fatal cardiac arrhythmias. Current safety guidelines require that potency of KV11.1 block is assessed in the preclinical phase of drug development. However, not all drugs that block KV11.1 are proarrhythmic, meaning that screening on the basis of equilibrium measures of block can result in high attrition of potentially low-risk drugs. The basis of the next generation of drug-screening approaches is set to be in silico risk prediction, informed by in vitro mechanistic descriptions of drug binding, including measures of the kinetics of block. A critical issue in this regard is characterizing the temperature dependence of drug binding. Specifically, it is important to address whether kinetics relevant to physiologic temperatures can be inferred or extrapolated from in vitro data gathered at room temperature in high-throughout systems. Here we present the first complete study of the temperature-dependent kinetics of block and unblock of a proarrhythmic drug, cisapride, to KV11.1. Our data highlight a complexity to binding that manifests at higher temperatures and can be explained by accumulation of an intermediate, non-blocking encounter-complex. These results suggest that for cisapride, physiologically relevant kinetic parameters cannot be simply extrapolated from those measured at lower temperatures; rather, data gathered at physiologic temperatures should be used to constrain in silico models that may be used for proarrhythmic risk prediction. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.
Convergence of models of human ventricular myocyte electrophysiology after global optimization to recapitulate clinical long QT phenotypes.

PubMed

Mann, Stefan A; Imtiaz, Mohammad; Winbo, Annika; Rydberg, Annika; Perry, Matthew D; Couderc, Jean-Philippe; Polonsky, Bronislava; McNitt, Scott; Zareba, Wojciech; Hill, Adam P; Vandenberg, Jamie I

2016-11-01

In-silico models of human cardiac electrophysiology are now being considered for prediction of cardiotoxicity as part of the preclinical assessment phase of all new drugs. We ask the question whether any of the available models are actually fit for this purpose. We tested three models of the human ventricular action potential, the O'hara-Rudy (ORD11), the Grandi-Bers (GB10) and the Ten Tusscher (TT06) models. We extracted clinical QT data for LQTS1 and LQTS2 patients with nonsense mutations that would be predicted to cause 50% loss of function in I Ks and I Kr respectively. We also obtained clinical QT data for LQTS3 patients. We then used a global optimization approach to improve the existing in silico models so that they reproduced all three clinical data sets more closely. We also examined the effects of adrenergic stimulation in the different LQTS subsets. All models, in their original form, produce markedly different and unrealistic predictions of QT prolongation for LQTS1, 2 and 3. After global optimization of the maximum conductances for membrane channels, all models have similar current densities during the action potential, despite differences in kinetic properties of the channels in the different models, and more closely reproduce the prolongation of repolarization seen in all LQTS subtypes. In-silico models of cardiac electrophysiology have the potential to be tremendously useful in complementing traditional preclinical drug testing studies. However, our results demonstrate they should be carefully validated and optimized to clinical data before they can be used for this purpose. Copyright © 2016 Elsevier Ltd. All rights reserved.
RNA-sequence data normalization through in silico prediction of reference genes: the bacterial response to DNA damage as case study.

PubMed

Berghoff, Bork A; Karlsson, Torgny; Källman, Thomas; Wagner, E Gerhart H; Grabherr, Manfred G

2017-01-01

Measuring how gene expression changes in the course of an experiment assesses how an organism responds on a molecular level. Sequencing of RNA molecules, and their subsequent quantification, aims to assess global gene expression changes on the RNA level (transcriptome). While advances in high-throughput RNA-sequencing (RNA-seq) technologies allow for inexpensive data generation, accurate post-processing and normalization across samples is required to eliminate any systematic noise introduced by the biochemical and/or technical processes. Existing methods thus either normalize on selected known reference genes that are invariant in expression across the experiment, assume that the majority of genes are invariant, or that the effects of up- and down-regulated genes cancel each other out during the normalization. Here, we present a novel method, moose 2 , which predicts invariant genes in silico through a dynamic programming (DP) scheme and applies a quadratic normalization based on this subset. The method allows for specifying a set of known or experimentally validated invariant genes, which guides the DP. We experimentally verified the predictions of this method in the bacterium Escherichia coli , and show how moose 2 is able to (i) estimate the expression value distances between RNA-seq samples, (ii) reduce the variation of expression values across all samples, and (iii) to subsequently reveal new functional groups of genes during the late stages of DNA damage. We further applied the method to three eukaryotic data sets, on which its performance compares favourably to other methods. The software is implemented in C++ and is publicly available from http://grabherr.github.io/moose2/. The proposed RNA-seq normalization method, moose 2 , is a valuable alternative to existing methods, with two major advantages: (i) in silico prediction of invariant genes provides a list of potential reference genes for downstream analyses, and (ii) non-linear artefacts in RNA-seq data are handled adequately to minimize variations between replicates.
Integrated stoichiometric, thermodynamic and kinetic modelling of steady state metabolism

PubMed Central

Fleming, R.M.T.; Thiele, I.; Provan, G.; Nasheuer, H.P.

2010-01-01

The quantitative analysis of biochemical reactions and metabolites is at frontier of biological sciences. The recent availability of high-throughput technology data sets in biology has paved the way for new modelling approaches at various levels of complexity including the metabolome of a cell or an organism. Understanding the metabolism of a single cell and multi-cell organism will provide the knowledge for the rational design of growth conditions to produce commercially valuable reagents in biotechnology. Here, we demonstrate how equations representing steady state mass conservation, energy conservation, the second law of thermodynamics, and reversible enzyme kinetics can be formulated as a single system of linear equalities and inequalities, in addition to linear equalities on exponential variables. Even though the feasible set is non-convex, the reformulation is exact and amenable to large-scale numerical analysis, a prerequisite for computationally feasible genome scale modelling. Integrating flux, concentration and kinetic variables in a unified constraint-based formulation is aimed at increasing the quantitative predictive capacity of flux balance analysis. Incorporation of experimental and theoretical bounds on thermodynamic and kinetic variables ensures that the predicted steady state fluxes are both thermodynamically and biochemically feasible. The resulting in silico predictions are tested against fluxomic data for central metabolism in E. coli and compare favourably with in silico prediction by flux balance analysis. PMID:20230840
Knowledge-based identification of soluble biomarkers: hepatic fibrosis in NAFLD as an example.

PubMed

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases.
Knowledge-Based Identification of Soluble Biomarkers: Hepatic Fibrosis in NAFLD as an Example

PubMed Central

Page, Sandra; Birerdinc, Aybike; Estep, Michael; Stepanova, Maria; Afendy, Arian; Petricoin, Emanuel; Younossi, Zobair; Chandhoke, Vikas; Baranova, Ancha

2013-01-01

The discovery of biomarkers is often performed using high-throughput proteomics-based platforms and is limited to the molecules recognized by a given set of purified and validated antigens or antibodies. Knowledge-based, or systems biology, approaches that involve the analysis of integrated data, predominantly molecular pathways and networks may infer quantitative changes in the levels of biomolecules not included by the given assay from the levels of the analytes profiled. In this study we attempted to use a knowledge-based approach to predict biomarkers reflecting the changes in underlying protein phosphorylation events using Nonalcoholic Fatty Liver Disease (NAFLD) as a model. Two soluble biomarkers, CCL-2 and FasL, were inferred in silico as relevant to NAFLD pathogenesis. Predictive performance of these biomarkers was studied using serum samples collected from patients with histologically proven NAFLD. Serum levels of both molecules, in combination with clinical and demographic data, were predictive of hepatic fibrosis in a cohort of NAFLD patients. Our study suggests that (1) NASH-specific disruption of the kinase-driven signaling cascades in visceral adipose tissue lead to detectable changes in the levels of soluble molecules released into the bloodstream, and (2) biomarkers discovered in silico could contribute to predictive models for non-malignant chronic diseases. PMID:23405244
Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus

NASA Astrophysics Data System (ADS)

Lobo, Daniel; Lobikin, Maria; Levin, Michael

2017-01-01

Progress in regenerative medicine requires reverse-engineering cellular control networks to infer perturbations with desired systems-level outcomes. Such dynamic models allow phenotypic predictions for novel perturbations to be rapidly assessed in silico. Here, we analyzed a Xenopus model of conversion of melanocytes to a metastatic-like phenotype only previously observed in an all-or-none manner. Prior in vivo genetic and pharmacological experiments showed that individual animals either fully convert or remain normal, at some characteristic frequency after a given perturbation. We developed a Machine Learning method which inferred a model explaining this complex, stochastic all-or-none dataset. We then used this model to ask how a new phenotype could be generated: animals in which only some of the melanocytes converted. Systematically performing in silico perturbations, the model predicted that a combination of altanserin (5HTR2 inhibitor), reserpine (VMAT inhibitor), and VP16-XlCreb1 (constitutively active CREB) would break the all-or-none concordance. Remarkably, applying the predicted combination of three reagents in vivo revealed precisely the expected novel outcome, resulting in partial conversion of melanocytes within individuals. This work demonstrates the capability of automated analysis of dynamic models of signaling networks to discover novel phenotypes and predictively identify specific manipulations that can reach them.
Mathematics as a Conduit for Translational Research in Post-Traumatic Osteoarthritis

PubMed Central

Ayati, Bruce P.; Kapitanov, Georgi I.; Coleman, Mitchell C.; Anderson, Donald D.; Martin, James A.

2016-01-01

Biomathematical models offer a powerful method of clarifying complex temporal interactions and the relationships among multiple variables in a system. We present a coupled in silico biomathematical model of articular cartilage degeneration in response to impact and/or aberrant loading such as would be associated with injury to an articular joint. The model incorporates fundamental biological and mechanical information obtained from explant and small animal studies to predict post-traumatic osteoarthritis (PTOA) progression, with an eye toward eventual application in human patients. In this sense, we refer to the mathematics as a “conduit of translation”. The new in silico framework presented in this paper involves a biomathematical model for the cellular and biochemical response to strains computed using finite element analysis. The model predicts qualitative responses presently, utilizing system parameter values largely taken from the literature. To contribute to accurate predictions, models need to be accurately parameterized with values that are based on solid science. We discuss a parameter identification protocol that will enable us to make increasingly accurate predictions of PTOA progression using additional data from smaller scale explant and small animal assays as they become available. By distilling the data from the explant and animal assays into parameters for biomathematical models, mathematics can translate experimental data to clinically relevant knowledge. PMID:27653021
Using Free Computational Resources to Illustrate the Drug Design Process in an Undergraduate Medicinal Chemistry Course

ERIC Educational Resources Information Center

Rodrigues, Ricardo P.; Andrade, Saulo F.; Mantoani, Susimaire P.; Eifler-Lima, Vera L.; Silva, Vinicius B.; Kawano, Daniel F.

2015-01-01

Advances in, and dissemination of, computer technologies in the field of drug research now enable the use of molecular modeling tools to teach important concepts of drug design to chemistry and pharmacy students. A series of computer laboratories is described to introduce undergraduate students to commonly adopted "in silico" drug design…
Introduction to Classical Density Functional Theory by a Computational Experiment

ERIC Educational Resources Information Center

Jeanmairet, Guillaume; Levy, Nicolas; Levesque, Maximilien; Borgis, Daniel

2014-01-01

We propose an in silico experiment to introduce the classical density functional theory (cDFT). Density functional theories, whether quantum or classical, rely on abstract concepts that are nonintuitive; however, they are at the heart of powerful tools and active fields of research in both physics and chemistry. They led to the 1998 Nobel Prize in…
Complementing in vitro screening assays with in silico molecular chemistry tools to examine potential in vivo metabolite-mediated effects

EPA Science Inventory

High-throughput in vitro assays offer a rapid, cost-efficient means to screen thousands of chemicals across hundreds of pathway-based toxicity endpoints. However, one main concern involved with the use of in vitro assays is the erroneous omission of chemicals that are inactive un...
Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

PubMed

Marques, Yuri Bento; de Paiva Oliveira, Alcione; Ribeiro Vasconcelos, Ana Tereza; Cerqueira, Fabio Ribeiro

2016-12-15

MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.
Identification of genetic variants predictive of early onset pancreatic cancer through a population science analysis of functional genomic datasets

PubMed Central

Chen, Jinyun; Wu, Xifeng; Huang, Yujing; Chen, Wei; Brand, Randall E.; Killary, Ann M.; Sen, Subrata; Frazier, Marsha L.

2016-01-01

Biomarkers are critically needed for the early detection of pancreatic cancer (PC) are urgently needed. Our purpose was to identify a panel of genetic variants that, combined, can predict increased risk for early-onset PC and thereby identify individuals who should begin screening at an early age. Previously, we identified genes using a functional genomic approach that were aberrantly expressed in early pathways to PC tumorigenesis. We now report the discovery of single nucleotide polymorphisms (SNPs) in these genes associated with early age at diagnosis of PC using a two-phase study design. In silico and bioinformatics tools were used to examine functional relevance of the identified SNPs. Eight SNPs were consistently associated with age at diagnosis in the discovery phase, validation phase and pooled analysis. Further analysis of the joint effects of these 8 SNPs showed that, compared to participants carrying none of these unfavorable genotypes (median age at PC diagnosis 70 years), those carrying 1–2, 3–4, or 5 or more unfavorable genotypes had median ages at diagnosis of 64, 63, and 62 years, respectively (P = 3.0E–04). A gene-dosage effect was observed, with age at diagnosis inversely related to number of unfavorable genotypes (Ptrend = 1.0E–04). Using bioinformatics tools, we found that all of the 8 SNPs were predicted to play functional roles in the disruption of transcription factor and/or enhancer binding sites and most of them were expression quantitative trait loci (eQTL) of the target genes. The panel of genetic markers identified may serve as susceptibility markers for earlier PC diagnosis. PMID:27486767
Adverse outcome pathways (AOPs): A framework to support predictive toxicology (presentation)

EPA Science Inventory

High throughput and in silico methods are providing the regulatory toxicology community with capacity to rapidly and cost effectively generate data concerning a chemical’s ability to initiate one or more biological perturbations that may culminate in an adverse ecological o...
Mechanistic modeling of developmental defects through computational embryology (WC10th)

EPA Science Inventory

Abstract: An important consideration for 3Rs is to identify developmental hazards utilizing mechanism-based in vitro assays (e.g., ToxCast) and in silico predictive models. Steady progress has been made with agent-based models that recapitulate morphogenetic drivers for angiogen...
Protein features as determinants of wild-type glycoside hydrolase thermostability.

PubMed

Geertz-Hansen, Henrik Marcus; Kiemer, Lars; Nielsen, Morten; Stanchev, Kiril; Blom, Nikolaj; Brunak, Søren; Petersen, Thomas Nordahl

2017-11-01

Thermostable enzymes for conversion of lignocellulosic biomass into biofuels have significant advantages over enzymes with more moderate themostability due to the challenging application conditions. Experimental discovery of thermostable enzymes is highly cost intensive, and the development of in-silico methods guiding the discovery process would be of high value. To develop such an in-silico method and provide the data foundation of it, we determined the melting temperatures of 602 fungal glycoside hydrolases from the families GH5, 6, 7, 10, 11, 43, and AA9 (formerly GH61). We, then used sequence and homology modeled structure information of these enzymes to develop the ThermoP melting temperature prediction method. Futhermore, in the context of thermostability, we determined the relative importance of 160 molecular features, such as amino acid frequencies and spatial interactions, and exemplified their biological significance. The presented prediction method is made publicly available at http://www.cbs.dtu.dk/services/ThermoP. © 2017 Wiley Periodicals, Inc.
In silico cloning and B/T cell epitope prediction of triosephosphate isomerase from Echinococcus granulosus.

PubMed

Wang, Fen; Ye, Bin

2016-10-01

Cystic echinococcosis is a worldwide zoonosis caused by Echinococcus granulosus. Because the methods of diagnosis and treatment for cystic echinococcosis were limited, it is still necessary to screen target proteins for the development of new anti-hydatidosis vaccine. In this study, the triosephosphate isomerase gene of E. granulosus was in silico cloned. The B cell and T cell epitopes were predicted by bioinformatics methods. The cDNA sequence of EgTIM was composition of 1094 base pairs, with an open reading frame of 753 base pairs. The deduced amino acid sequences were composed of 250 amino acids. Five cross-reactive epitopes, locating on 21aa-35aa, 43aa-57aa, 94aa-107aa, 115-129aa, and 164aa-183aa, could be expected to serve as candidate epitopes in the development of vaccine against E. granulosus. These results could provide bases for gene cloning, recombinant expression, and the designation of anti-hydatidosis vaccine.
Simulating the drug discovery pipeline: a Monte Carlo approach

PubMed Central

2012-01-01

Background The early drug discovery phase in pharmaceutical research and development marks the beginning of a long, complex and costly process of bringing a new molecular entity to market. As such, it plays a critical role in helping to maintain a robust downstream clinical development pipeline. Despite its importance, however, to our knowledge there are no published in silico models to simulate the progression of discrete virtual projects through a discovery milestone system. Results Multiple variables were tested and their impact on productivity metrics examined. Simulations predict that there is an optimum number of scientists for a given drug discovery portfolio, beyond which output in the form of preclinical candidates per year will remain flat. The model further predicts that the frequency of compounds to successfully pass the candidate selection milestone as a function of time will be irregular, with projects entering preclinical development in clusters marked by periods of low apparent productivity. Conclusions The model may be useful as a tool to facilitate analysis of historical growth and achievement over time, help gauge current working group progress against future performance expectations, and provide the basis for dialogue regarding working group best practices and resource deployment strategies. PMID:23186040
Computational Approaches to Drug Repurposing and Pharmacology

PubMed Central

Hodos, Rachel A; Kidd, Brian A; Khader, Shameer; Readhead, Ben P; Dudley, Joel T

2016-01-01

Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing- finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we rationalize that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. PMID:27080087
Deep sequencing and in silico analysis of small RNA library reveals novel miRNA from leaf Persicaria minor transcriptome.

PubMed

Samad, Abdul Fatah A; Nazaruddin, Nazaruddin; Murad, Abdul Munir Abdul; Jani, Jaeyres; Zainal, Zamri; Ismail, Ismanizan

2018-03-01

In current era, majority of microRNA (miRNA) are being discovered through computational approaches which are more confined towards model plants. Here, for the first time, we have described the identification and characterization of novel miRNA in a non-model plant, Persicaria minor ( P . minor ) using computational approach. Unannotated sequences from deep sequencing were analyzed based on previous well-established parameters. Around 24 putative novel miRNAs were identified from 6,417,780 reads of the unannotated sequence which represented 11 unique putative miRNA sequences. PsRobot target prediction tool was deployed to identify the target transcripts of putative novel miRNAs. Most of the predicted target transcripts (mRNAs) were known to be involved in plant development and stress responses. Gene ontology showed that majority of the putative novel miRNA targets involved in cellular component (69.07%), followed by molecular function (30.08%) and biological process (0.85%). Out of 11 unique putative miRNAs, 7 miRNAs were validated through semi-quantitative PCR. These novel miRNAs discoveries in P . minor may develop and update the current public miRNA database.

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Sample records for silico tools predict

Abstract