Genome wide in silico characterization of Dof gene families of pigeonpea (Cajanus cajan (L) Millsp.).

PubMed

Malviya, N; Gupta, S; Singh, V K; Yadav, M K; Bisht, N C; Sarangi, B K; Yadav, D

2015-02-01

The DNA binding with One Finger (Dof) protein is a plant specific transcription factor involved in the regulation of wide range of processes. The analysis of whole genome sequence of pigeonpea has identified 38 putative Dof genes (CcDof) distributed on 8 chromosomes. A total of 17 out of 38 CcDof genes were found to be intronless. A comprehensive in silico characterization of CcDof gene family including the gene structure, chromosome location, protein motif, phylogeny, gene duplication and functional divergence has been attempted. The phylogenetic analysis resulted in 3 major clusters with closely related members in phylogenetic tree revealed common motif distribution. The in silico cis-regulatory element analysis revealed functional diversity with predominance of light responsive and stress responsive elements indicating the possibility of these CcDof genes to be associated with photoperiodic control and biotic and abiotic stress. The duplication pattern showed that tandem duplication is predominant over segmental duplication events. The comparative phylogenetic analysis of these Dof proteins along with 78 soybean, 36 Arabidopsis and 30 rice Dof proteins revealed 7 major clusters. Several groups of orthologs and paralogs were identified based on phylogenetic tree constructed. Our study provides useful information for functional characterization of CcDof genes.

WEbcoli: an interactive and asynchronous web application for in silico design and analysis of genome-scale E.coli model.

PubMed

Jung, Tae-Sung; Yeo, Hock Chuan; Reddy, Satty G; Cho, Wan-Sup; Lee, Dong-Yup

2009-11-01

WEbcoli is a WEb application for in silico designing, analyzing and engineering Escherichia coli metabolism. It is devised and implemented using advanced web technologies, thereby leading to enhanced usability and dynamic web accessibility. As a main feature, the WEbcoli system provides a user-friendly rich web interface, allowing users to virtually design and synthesize mutant strains derived from the genome-scale wild-type E.coli model and to customize pathways of interest through a graph editor. In addition, constraints-based flux analysis can be conducted for quantifying metabolic fluxes and charactering the physiological and metabolic states under various genetic and/or environmental conditions. WEbcoli is freely accessible at http://webcoli.org. cheld@nus.edu.sg.

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 Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting

PubMed Central

DeJournett, Leon; DeJournett, Jeremy

2016-01-01

Background: Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)–based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. Method: We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient’s glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. Results: For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%. Conclusions: This in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting. PMID:27301982

In Silico Testing of an Artificial-Intelligence-Based Artificial Pancreas Designed for Use in the Intensive Care Unit Setting.

PubMed

DeJournett, Leon; DeJournett, Jeremy

2016-11-01

Effective glucose control in the intensive care unit (ICU) setting has the potential to decrease morbidity and mortality rates which should in turn lead to decreased health care expenditures. Current ICU-based glucose controllers are mathematically derived, and tend to be based on proportional integral derivative (PID) or model predictive control (MPC). Artificial intelligence (AI)-based closed loop glucose controllers may have the ability to achieve control that improves on the results achieved by either PID or MPC controllers. We conducted an in silico analysis of an AI-based glucose controller designed for use in the ICU setting. This controller was tested using a mathematical model of the ICU patient's glucose-insulin system. A total of 126 000 unique 5-day simulations were carried out, resulting in 107 million glucose values for analysis. For the 7 control ranges tested, with a sensor error of ±10%, the following average results were achieved: (1) time in control range, 94.2%, (2) time in range 70-140 mg/dl, 97.8%, (3) time in hyperglycemic range (>140 mg/dl), 2.1%, and (4) time in hypoglycemic range (<70 mg/dl), 0.09%. In addition, the average coefficient of variation (CV) was 11.1%. This in silico study of an AI-based closed loop glucose controller shows that it may be able to improve on the results achieved by currently existing ICU-based PID/MPC controllers. If these results are confirmed in clinical testing, this AI-based controller could be used to create an artificial pancreas system for use in the ICU setting. © 2016 Diabetes Technology Society.

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.

High-Throughput and Rapid Screening of Novel ACE Inhibitory Peptides from Sericin Source and Inhibition Mechanism by Using in Silico and in Vitro Prescriptions.

PubMed

Sun, Huaju; Chang, Qing; Liu, Long; Chai, Kungang; Lin, Guangyan; Huo, Qingling; Zhao, Zhenxia; Zhao, Zhongxing

2017-11-22

Several novel peptides with high ACE-I inhibitory activity were successfully screened from sericin hydrolysate (SH) by coupling in silico and in vitro approaches for the first time. Most screening processes for ACE-I inhibitory peptides were achieved through high-throughput in silico simulation followed by in vitro verification. QSAR model based predicted results indicated that the ACE-I inhibitory activity of these SH peptides and six chosen peptides exhibited moderate high ACE-I inhibitory activities (log IC 50 values: 1.63-2.34). Moreover, two tripeptides among the chosen six peptides were selected for ACE-I inhibition mechanism analysis which based on Lineweaver-Burk plots indicated that they behave as competitive ACE-I inhibitors. The C-terminal residues of short-chain peptides that contain more H-bond acceptor groups could easily form hydrogen bonds with ACE-I and have higher ACE-I inhibitory activity. Overall, sericin protein as a strong ACE-I inhibition source could be deemed a promising agent for antihypertension applications.

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

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.

Hybrid In Silico/In Vitro Approaches for the Identification of Functional Cholesterol-Binding Domains in Membrane Proteins.

PubMed

Di Scala, Coralie; Fantini, Jacques

2017-01-01

In eukaryotic cells, cholesterol is an important regulator of a broad range of membrane proteins, including receptors, transporters, and ion channels. Understanding how cholesterol interacts with membrane proteins is a difficult task because structural data of these proteins complexed with cholesterol are scarce. Here, we describe a dual approach based on in silico studies of protein-cholesterol interactions, combined with physico-chemical measurements of protein insertion into cholesterol-containing monolayers. Our algorithm is validated through careful analysis of the effect of key mutations within and outside the predicted cholesterol-binding site. Our method is illustrated by a complete analysis of cholesterol-binding to Alzheimer's β-amyloid peptide, a protein that penetrates the plasma membrane of brain cells through a cholesterol-dependent process.

LC-MS-Based Lipidomics and Automated Identification of Lipids Using the LipidBlast In-Silico MS/MS Library.

PubMed

Cajka, Tomas; Fiehn, Oliver

2017-01-01

This protocol describes the analysis, specifically the identification, of blood plasma lipids. Plasma lipids are extracted using methyl tert-butyl ether (MTBE), methanol, and water followed by separation and data acquisition of isolated lipids using reversed-phase liquid chromatography coupled to quadrupole/time-of-flight mass spectrometry (RPLC-QTOFMS) operated in MS/MS mode. For lipid identification, acquired MS/MS spectra are converted to the mascot generic format (MGF) followed by library search using the in-silico MS/MS library LipidBlast. Using this approach, lipid classes, carbon-chain lengths, and degree of unsaturation of fatty-acid components are annotated.

Development of predictive pharmacophore model for in silico screening, and 3D QSAR CoMFA and CoMSIA studies for lead optimization, for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

NASA Astrophysics Data System (ADS)

Murumkar, Prashant Revan; Zambre, Vishal Prakash; Yadav, Mange Ram

2010-02-01

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.

An in silico analysis of oxygen uptake of a mild COPD patient during rest and exercise using a portable oxygen concentrator

PubMed Central

Katz, Ira; Pichelin, Marine; Montesantos, Spyridon; Kang, Min-Yeong; Sapoval, Bernard; Zhu, Kaixian; Thevenin, Charles-Philippe; McCoy, Robert; Martin, Andrew R; Caillibotte, Georges

2016-01-01

Oxygen treatment based on intermittent-flow devices with pulse delivery modes available from portable oxygen concentrators (POCs) depends on the characteristics of the delivered pulse such as volume, pulse width (the time of the pulse to be delivered), and pulse delay (the time for the pulse to be initiated from the start of inhalation) as well as a patient’s breathing characteristics, disease state, and respiratory morphology. This article presents a physiological-based analysis of the performance, in terms of blood oxygenation, of a commercial POC at different settings using an in silico model of a COPD patient at rest and during exercise. The analysis encompasses experimental measurements of pulse volume, width, and time delay of the POC at three different settings and two breathing rates related to rest and exercise. These experimental data of device performance are inputs to a physiological-based model of oxygen uptake that takes into account the real dynamic nature of gas exchange to illustrate how device- and patient-specific factors can affect patient oxygenation. This type of physiological analysis that considers the true effectiveness of oxygen transfer to the blood, as opposed to delivery to the nose (or mouth), can be instructive in applying therapies and designing new devices. PMID:27729783

Integration of parallel 13 C-labeling experiments and in silico pathway analysis for enhanced production of ascomycin.

PubMed

Qi, Haishan; Lv, Mengmeng; Song, Kejing; Wen, Jianping

2017-05-01

Herein, the hyper-producing strain for ascomycin was engineered based on 13 C-labeling experiments and elementary flux modes analysis (EFMA). First, the metabolism of non-model organism Streptomyces hygroscopicus var. ascomyceticus SA68 was investigated and an updated network model was reconstructed using 13 C- metabolic flux analysis. Based on the precise model, EFMA was further employed to predict genetic targets for higher ascomycin production. Chorismatase (FkbO) and pyruvate carboxylase (Pyc) were predicted as the promising overexpression and deletion targets, respectively. The corresponding mutant TD-FkbO and TD-ΔPyc exhibited the consistency effects between model prediction and experimental results. Finally, the combined genetic manipulations were performed, achieving a high-yield ascomycin engineering strain TD-ΔPyc-FkbO with production up to 610 mg/L, 84.8% improvement compared with the parent strain SA68. These results manifested that the integration of 13 C-labeling experiments and in silico pathway analysis could serve as a promising concept to enhance ascomycin production, as well as other valuable products. Biotechnol. Bioeng. 2017;114: 1036-1044. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

In vitro, in vivo and in silico analysis of the anticancer and estrogen-like activity of guava leaf extracts.

PubMed

Rizzo, L Y; Longato, G B; Ruiz, A Lt G; Tinti, S V; Possenti, A; Vendramini-Costa, D B; Sartoratto, A; Figueira, G M; Silva, F L N; Eberlin, M N; Souza, T A C B; Murakami, M T; Rizzo, E; Foglio, M A; Kiessling, F; Lammers, T; Carvalho, J E

2014-01-01

Anticancer drug research based on natural compounds enabled the discovery of many drugs currently used in cancer therapy. Here, we report the in vitro, in vivo and in silico anticancer and estrogen-like activity of Psidium guajava L. (guava) extracts and enriched mixture containing the meroterpenes guajadial, psidial A and psiguadial A and B. All samples were evaluated in vitro for anticancer activity against nine human cancer lines: K562 (leukemia), MCF7 (breast), NCI/ADR-RES (resistant ovarian cancer), NCI-H460 (lung), UACC-62 (melanoma), PC-3 (prostate), HT-29 (colon), OVCAR-3 (ovarian) and 786-0 (kidney). Psidium guajava's active compounds displayed similar physicochemical properties to estradiol and tamoxifen, as in silico molecular docking studies demonstrated that they fit into the estrogen receptors (ERs). The meroterpene-enriched fraction was also evaluated in vivo in a Solid Ehrlich murine breast adenocarcinoma model, and showed to be highly effective in inhibiting tumor growth, also demonstrating uterus increase in comparison to negative controls. The ability of guajadial, psidial A and psiguadials A and B to reduce tumor growth and stimulate uterus proliferation, as well as their in silico docking similarity to tamoxifen, suggest that these compounds may act as Selective Estrogen Receptors Modulators (SERMs), therefore holding significant potential for anticancer therapy.

In Silico Assigned Resistance Genes Confer Bifidobacterium with Partial Resistance to Aminoglycosides but Not to Β-Lactams

PubMed Central

Fouhy, Fiona; O’Connell Motherway, Mary; Fitzgerald, Gerald F.; Ross, R. Paul; Stanton, Catherine; van Sinderen, Douwe; Cotter, Paul D.

2013-01-01

Bifidobacteria have received significant attention due to their contribution to human gut health and the use of specific strains as probiotics. It is thus not surprising that there has also been significant interest with respect to their antibiotic resistance profile. Numerous culture-based studies have demonstrated that bifidobacteria are resistant to the majority of aminoglycosides, but are sensitive to β-lactams. However, limited research exists with respect to the genetic basis for the resistance of bifidobacteria to aminoglycosides. Here we performed an in-depth in silico analysis of putative Bifidobacterium-encoded aminoglycoside resistance proteins and β-lactamases and assess the contribution of these proteins to antibiotic resistance. The in silico-based screen detected putative aminoglycoside and β-lactam resistance proteins across the Bifidobacterium genus. Laboratory-based investigations of a number of representative bifidobacteria strains confirmed that despite containing putative β-lactamases, these strains were sensitive to β-lactams. In contrast, all strains were resistant to the aminoglycosides tested. To assess the contribution of genes encoding putative aminoglycoside resistance proteins in Bifidobacterium sp. two genes, namely Bbr_0651 and Bbr_1586, were targeted for insertional inactivation in B. breve UCC2003. As compared to the wild-type, the UCC2003 insertion mutant strains exhibited decreased resistance to gentamycin, kanamycin and streptomycin. This study highlights the associated risks of relying on the in silico assignment of gene function. Although several putative β-lactam resistance proteins are located in bifidobacteria, their presence does not coincide with resistance to these antibiotics. In contrast however, this approach has resulted in the identification of two loci that contribute to the aminoglycoside resistance of B. breve UCC2003 and, potentially, many other bifidobacteria. PMID:24324818

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 .

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.

Genetic Profiles of Korean Patients With Glucose-6-Phosphate Dehydrogenase Deficiency

PubMed Central

Lee, Jaewoong; Choi, Hayoung; Kim, Jiyeon; Kwon, Ahlm; Jang, Woori; Chae, Hyojin; Kim, Myungshin; Kim, Yonggoo; Lee, Jae Wook; Chung, Nack-Gyun

2017-01-01

Background We describe the genetic profiles of Korean patients with glucose-6-phosphate dehydrogenase (G6PD) deficiencies and the effects of G6PD mutations on protein stability and enzyme activity on the basis of in silico analysis. Methods In parallel with a genetic analysis, the pathogenicity of G6PD mutations detected in Korean patients was predicted in silico. The simulated effects of G6PD mutations were compared to the WHO classes based on G6PD enzyme activity. Four previously reported mutations and three newly diagnosed patients with missense mutations were estimated. Results One novel mutation (p.Cys385Gly, labeled G6PD Kangnam) and two known mutations [p.Ile220Met (G6PD São Paulo) and p.Glu416Lys (G6PD Tokyo)] were identified in this study. G6PD mutations identified in Koreans were also found in Brazil (G6PD São Paulo), Poland (G6PD Seoul), United States of America (G6PD Riley), Mexico (G6PD Guadalajara), and Japan (G6PD Tokyo). Several mutations occurred at the same nucleotide, but resulted in different amino acid residue changes in different ethnic populations (p.Ile380 variant, G6PD Calvo Mackenna; p.Cys385 variants, Tomah, Madrid, Lynwood; p.Arg387 variant, Beverly Hills; p.Pro396 variant, Bari; and p.Pro396Ala in India). On the basis of the in silico analysis, Class I or II mutations were predicted to be highly deleterious, and the effects of one Class IV mutation were equivocal. Conclusions The genetic profiles of Korean individuals with G6PD mutations indicated that the same mutations may have arisen by independent mutational events, and were not derived from shared ancestral mutations. The in silico analysis provided insight into the role of G6PD mutations in enzyme function and stability. PMID:28028996

Genetic Profiles of Korean Patients With Glucose-6-Phosphate Dehydrogenase Deficiency.

PubMed

Lee, Jaewoong; Park, Joonhong; Choi, Hayoung; Kim, Jiyeon; Kwon, Ahlm; Jang, Woori; Chae, Hyojin; Kim, Myungshin; Kim, Yonggoo; Lee, Jae Wook; Chung, Nack Gyun; Cho, Bin

2017-03-01

We describe the genetic profiles of Korean patients with glucose-6-phosphate dehydrogenase (G6PD) deficiencies and the effects of G6PD mutations on protein stability and enzyme activity on the basis of in silico analysis. In parallel with a genetic analysis, the pathogenicity of G6PD mutations detected in Korean patients was predicted in silico. The simulated effects of G6PD mutations were compared to the WHO classes based on G6PD enzyme activity. Four previously reported mutations and three newly diagnosed patients with missense mutations were estimated. One novel mutation (p.Cys385Gly, labeled G6PD Kangnam) and two known mutations [p.Ile220Met (G6PD São Paulo) and p.Glu416Lys (G6PD Tokyo)] were identified in this study. G6PD mutations identified in Koreans were also found in Brazil (G6PD São Paulo), Poland (G6PD Seoul), United States of America (G6PD Riley), Mexico (G6PD Guadalajara), and Japan (G6PD Tokyo). Several mutations occurred at the same nucleotide, but resulted in different amino acid residue changes in different ethnic populations (p.Ile380 variant, G6PD Calvo Mackenna; p.Cys385 variants, Tomah, Madrid, Lynwood; p.Arg387 variant, Beverly Hills; p.Pro396 variant, Bari; and p.Pro396Ala in India). On the basis of the in silico analysis, Class I or II mutations were predicted to be highly deleterious, and the effects of one Class IV mutation were equivocal. The genetic profiles of Korean individuals with G6PD mutations indicated that the same mutations may have arisen by independent mutational events, and were not derived from shared ancestral mutations. The in silico analysis provided insight into the role of G6PD mutations in enzyme function and stability.

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 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, ...

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.

In-silico Metabolome Target Analysis Towards PanC-based Antimycobacterial Agent Discovery.

PubMed

Khoshkholgh-Sima, Baharak; Sardari, Soroush; Izadi Mobarakeh, Jalal; Khavari-Nejad, Ramezan Ali

2015-01-01

Mycobacterium tuberculosis, the main cause of tuberculosis (TB), has still remained a global health crisis especially in developing countries. Tuberculosis treatment is a laborious and lengthy process with high risk of noncompliance, cytotoxicity adverse events and drug resistance in patient. Recently, there has been an alarming rise of drug resistant in TB. In this regard, it is an unmet need to develop novel antitubercular medicines that target new or more effective biochemical pathways to prevent drug resistant Mycobacterium. Integrated study of metabolic pathways through in-silico approach played a key role in antimycobacterial design process in this study. Our results suggest that pantothenate synthetase (PanC), anthranilate phosphoribosyl transferase (TrpD) and 3-isopropylmalate dehydratase (LeuD) might be appropriate drug targets. In the next step, in-silico ligand analysis was used for more detailed study of chemical tractability of targets. This was helpful to identify pantothenate synthetase (PanC, Rv3602c) as the best target for antimycobacterial design procedure. Virtual library screening on the best ligand of PanC was then performed for inhibitory ligand design. At the end, five chemical intermediates showed significant inhibition of Mycobacterium bovis with good selectivity indices (SI) ≥10 according to Tuberculosis Antimicrobial Acquisition & Coordinating Facility of US criteria for antimycobacterial screening programs.

In silico characterization and expression analysis of the multigene family encoding the Bowman-Birk protease inhibitor in soybean.

PubMed

de Almeida Barros, Beatriz; da Silva, Wiliane Garcia; Moreira, Maurilio Alves; de Barros, Everaldo Gonçalves

2012-01-01

The Bowman-Birk (BBI) protease inhibitors can be used as source of sulfur amino acids, can regulate endogenous protease activity during seed germination and during the defense response of plants to pathogens. In soybean this family has not been fully described. The goal of this work was to characterize in silico and analyze the expression of the members of this family in soybean. We identified 11 potential BBI genes in the soybean genome. In each one of them at least a characteristic BBI conserved domain was detected in addition to a potential signal peptide. The sequences have been positioned in the soybean physical map and the promoter regions were analyzed with respect to known regulatory elements. Elements related to seed-specific expression and also to response to biotic and abiotic stresses have been identified. Based on the in silico analysis and also on quantitative RT-PCR data it was concluded that BBI-A, BBI-CII and BBI-DII are expressed specifically in the seed. The expression profiles of these three genes are similar along seed development. Their expressions reach a maximum in the intermediate stages and decrease as the seed matures. The BBI-DII transcripts are the most abundant ones followed by those of BBI-A and BBI-CII.

Simultaneous use of in silico design and a correlated mutation network as a tool to efficiently guide enzyme engineering.

PubMed

Nobili, Alberto; Tao, Yifeng; Pavlidis, Ioannis V; van den Bergh, Tom; Joosten, Henk-Jan; Tan, Tianwei; Bornscheuer, Uwe T

2015-03-23

In order to improve the efficiency of directed evolution experiments, in silico multiple-substrate clustering was combined with an analysis of the variability of natural enzymes within a protein superfamily. This was applied to a Pseudomonas fluorescens esterase (PFE I) targeting the enantioselective hydrolysis of 3-phenylbutyric acid esters. Data reported in the literature for nine substrates were used for the clustering meta-analysis of the docking conformations in wild-type PFE I, and this highlighted a tryptophan residue (W28) as an interesting target. Exploration of the most frequently, naturally occurring amino acids at this position suggested that the reduced flexibility observed in the case of the W28F variant leads to enhancement of the enantioselectivity. This mutant was subsequently combined with mutations identified in a library based on analysis of a correlated mutation network. By interrogation of <80 variants a mutant with 15-fold improved enantioselectivity was found. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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 ...

Combining structure-based pharmacophore modeling, virtual screening, and in silico ADMET analysis to discover novel tetrahydro-quinoline based pyruvate kinase isozyme M2 activators with antitumor activity

PubMed Central

Chen, Can; Wang, Ting; Wu, Fengbo; Huang, Wei; He, Gu; Ouyang, Liang; Xiang, Mingli; Peng, Cheng; Jiang, Qinglin

2014-01-01

Compared with normal differentiated cells, cancer cells upregulate the expression of pyruvate kinase isozyme M2 (PKM2) to support glycolytic intermediates for anabolic processes, including the synthesis of nucleic acids, amino acids, and lipids. In this study, a combination of the structure-based pharmacophore modeling and a hybrid protocol of virtual screening methods comprised of pharmacophore model-based virtual screening, docking-based virtual screening, and in silico ADMET (absorption, distribution, metabolism, excretion and toxicity) analysis were used to retrieve novel PKM2 activators from commercially available chemical databases. Tetrahydroquinoline derivatives were identified as potential scaffolds of PKM2 activators. Thus, the hybrid virtual screening approach was applied to screen the focused tetrahydroquinoline derivatives embedded in the ZINC database. Six hit compounds were selected from the final hits and experimental studies were then performed. Compound 8 displayed a potent inhibitory effect on human lung cancer cells. Following treatment with Compound 8, cell viability, apoptosis, and reactive oxygen species (ROS) production were examined in A549 cells. Finally, we evaluated the effects of Compound 8 on mice xenograft tumor models in vivo. These results may provide important information for further research on novel PKM2 activators as antitumor agents. PMID:25214764

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.

Advanced continuous cultivation methods for systems microbiology.

PubMed

Adamberg, Kaarel; Valgepea, Kaspar; Vilu, Raivo

2015-09-01

Increasing the throughput of systems biology-based experimental characterization of in silico-designed strains has great potential for accelerating the development of cell factories. For this, analysis of metabolism in the steady state is essential as only this enables the unequivocal definition of the physiological state of cells, which is needed for the complete description and in silico reconstruction of their phenotypes. In this review, we show that for a systems microbiology approach, high-resolution characterization of metabolism in the steady state--growth space analysis (GSA)--can be achieved by using advanced continuous cultivation methods termed changestats. In changestats, an environmental parameter is continuously changed at a constant rate within one experiment whilst maintaining cells in the physiological steady state similar to chemostats. This increases the resolution and throughput of GSA compared with chemostats, and, moreover, enables following of the dynamics of metabolism and detection of metabolic switch-points and optimal growth conditions. We also describe the concept, challenge and necessary criteria of the systematic analysis of steady-state metabolism. Finally, we propose that such systematic characterization of the steady-state growth space of cells using changestats has value not only for fundamental studies of metabolism, but also for systems biology-based metabolic engineering of cell factories.

The antioxidant property of chitosan green tea polyphenols complex induces transglutaminase activation in wound healing.

PubMed

Qin, Yao; Guo, Xing Wei; Li, Lei; Wang, Hong Wei; Kim, Wook

2013-06-01

The present study examined, for the first time, the in vitro wound healing potential of chitosan green tea polyphenols (CGP) complex based on the activation of transglutaminase (TGM) genes in epidermal morphogenesis. Response surface methodology was applied to determine the optimal processing condition that gave maximum extraction of green tea polyphenols. The antioxidant activity, scavenging ability, and chelating ability were studied and expressed as average EC50 values of CGP and other treatments. In silico analysis and gene coexpression network was subjected to the TGM sequences analysis. The temporal expressions of TGMs were profiled by semi-quantitative reverse transcription (RT)-PCR technology within 10 days after wounding and 2 days postwounding. CGP showed the effectiveness of antioxidant properties, and the observations of histopathological photography showed advanced tissue granulation and epithelialization formation by CGP treatment. In silico and coexpression analysis confirmed the regulation via TGM gene family in dermatological tissues. RT-PCR demonstrated increased levels of TGM1-3 expression induced by CGP treatment. The efficacy of CGP in wound healing based on these results may be ascribed to its antioxidant properties and activation of the expression of TGMs, and is, thus, essential for the facilitated repair of skin injury.

Practical application of in silico fragmentation based residue screening with ion mobility high-resolution mass spectrometry.

PubMed

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

2017-07-15

A screening concept for residues in complex matrices based on liquid chromatography coupled to ion mobility high-resolution mass spectrometry LC/IMS-HRMS is presented. The comprehensive four-dimensional data (chromatographic retention time, drift time, mass-to-charge and ion abundance) obtained in data-independent acquisition (DIA) mode was used for data mining. An in silico fragmenter utilizing a molecular structure database was used for suspect screening, instead of targeted screening with reference substances. The utilized data-independent acquisition mode relies on the MS E concept; where two constantly alternating HRMS scans (low and high fragmentation energy) are acquired. Peak deconvolution and drift time alignment of ions from the low (precursor ion) and high (product ion) energy scan result in relatively clean product ion spectra. A bond dissociation in silico fragmenter (MassFragment) supplied with mol files of compounds of interest was used to explain the observed product ions of each extracted candidate component (chromatographic peak). Two complex matrices (fish and bovine liver extract) were fortified with 98 veterinary drugs. Out of 98 screened compounds 94 could be detected with the in silico based screening approach. The high correlation among drift time and m/z value of equally charged ions was utilized for an orthogonal filtration (ranking). Such an orthogonal ion mobility based filter removes multiply charged ions (e.g. peptides and proteins from the matrix) as well as noise and artefacts. Most significantly, this filtration dramatically reduces false positive findings but hardly increases false negative findings. The proposed screening approach may offer new possibilities for applications where reference compounds are hardly or not at all commercially available. Such areas may be the analysis of metabolites of drugs, pyrrolizidine alkaloids, marine toxins, derivatives of sildenafil or novel designer drugs (new psychoactive substances). Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening.

PubMed

Fukunishi, Yoshifumi; Mikami, Yoshiaki; Nakamura, Haruki

2005-09-01

We developed a new method to evaluate the distances and similarities between receptor pockets or chemical compounds based on a multi-receptor versus multi-ligand docking affinity matrix. The receptors were classified by a cluster analysis based on calculations of the distance between receptor pockets. A set of low homologous receptors that bind a similar compound could be classified into one cluster. Based on this line of reasoning, we proposed a new in silico screening method. According to this method, compounds in a database were docked to multiple targets. The new docking score was a slightly modified version of the multiple active site correction (MASC) score. Receptors that were at a set distance from the target receptor were not included in the analysis, and the modified MASC scores were calculated for the selected receptors. The choice of the receptors is important to achieve a good screening result, and our clustering of receptors is useful to this purpose. This method was applied to the analysis of a set of 132 receptors and 132 compounds, and the results demonstrated that this method achieves a high hit ratio, as compared to that of a uniform sampling, using a receptor-ligand docking program, Sievgene, which was newly developed with a good docking performance yielding 50.8% of the reconstructed complexes at a distance of less than 2 A RMSD.

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.

In Silico Pattern-Based Analysis of the Human Cytomegalovirus Genome

PubMed Central

Rigoutsos, Isidore; Novotny, Jiri; Huynh, Tien; Chin-Bow, Stephen T.; Parida, Laxmi; Platt, Daniel; Coleman, David; Shenk, Thomas

2003-01-01

More than 200 open reading frames (ORFs) from the human cytomegalovirus genome have been reported as potentially coding for proteins. We have used two pattern-based in silico approaches to analyze this set of putative viral genes. With the help of an objective annotation method that is based on the Bio-Dictionary, a comprehensive collection of amino acid patterns that describes the currently known natural sequence space of proteins, we have reannotated all of the previously reported putative genes of the human cytomegalovirus. Also, with the help of MUSCA, a pattern-based multiple sequence alignment algorithm, we have reexamined the original human cytomegalovirus gene family definitions. Our analysis of the genome shows that many of the coded proteins comprise amino acid combinations that are unique to either the human cytomegalovirus or the larger group of herpesviruses. We have confirmed that a surprisingly large portion of the analyzed ORFs encode membrane proteins, and we have discovered a significant number of previously uncharacterized proteins that are predicted to be G-protein-coupled receptor homologues. The analysis also indicates that many of the encoded proteins undergo posttranslational modifications such as hydroxylation, phosphorylation, and glycosylation. ORFs encoding proteins with similar functional behavior appear in neighboring regions of the human cytomegalovirus genome. All of the results of the present study can be found and interactively explored online (http://cbcsrv.watson.ibm.com/virus/). PMID:12634390

In silico pattern-based analysis of the human cytomegalovirus genome.

PubMed

Rigoutsos, Isidore; Novotny, Jiri; Huynh, Tien; Chin-Bow, Stephen T; Parida, Laxmi; Platt, Daniel; Coleman, David; Shenk, Thomas

2003-04-01

More than 200 open reading frames (ORFs) from the human cytomegalovirus genome have been reported as potentially coding for proteins. We have used two pattern-based in silico approaches to analyze this set of putative viral genes. With the help of an objective annotation method that is based on the Bio-Dictionary, a comprehensive collection of amino acid patterns that describes the currently known natural sequence space of proteins, we have reannotated all of the previously reported putative genes of the human cytomegalovirus. Also, with the help of MUSCA, a pattern-based multiple sequence alignment algorithm, we have reexamined the original human cytomegalovirus gene family definitions. Our analysis of the genome shows that many of the coded proteins comprise amino acid combinations that are unique to either the human cytomegalovirus or the larger group of herpesviruses. We have confirmed that a surprisingly large portion of the analyzed ORFs encode membrane proteins, and we have discovered a significant number of previously uncharacterized proteins that are predicted to be G-protein-coupled receptor homologues. The analysis also indicates that many of the encoded proteins undergo posttranslational modifications such as hydroxylation, phosphorylation, and glycosylation. ORFs encoding proteins with similar functional behavior appear in neighboring regions of the human cytomegalovirus genome. All of the results of the present study can be found and interactively explored online (http://cbcsrv.watson.ibm.com/virus/).

In vitro fatigue tests and in silico finite element analysis of dental implants with different fixture/abutment joint types using computer-aided design models.

PubMed

Yamaguchi, Satoshi; Yamanishi, Yasufumi; Machado, Lucas S; Matsumoto, Shuji; Tovar, Nick; Coelho, Paulo G; Thompson, Van P; Imazato, Satoshi

2018-01-01

The aim of this study was to evaluate fatigue resistance of dental fixtures with two different fixture-abutment connections by in vitro fatigue testing and in silico three-dimensional finite element analysis (3D FEA) using original computer-aided design (CAD) models. Dental implant fixtures with external connection (EX) or internal connection (IN) abutments were fabricated from original CAD models using grade IV titanium and step-stress accelerated life testing was performed. Fatigue cycles and loads were assessed by Weibull analysis, and fatigue cracking was observed by micro-computed tomography and a stereomicroscope with high dynamic range software. Using the same CAD models, displacement vectors of implant components were also analyzed by 3D FEA. Angles of the fractured line occurring at fixture platforms in vitro and of displacement vectors corresponding to the fractured line in silico were compared by two-way ANOVA. Fatigue testing showed significantly greater reliability for IN than EX (p<0.001). Fatigue crack initiation was primarily observed at implant fixture platforms. FEA demonstrated that crack lines of both implant systems in vitro were observed in the same direction as displacement vectors of the implant fixtures in silico. In silico displacement vectors in the implant fixture are insightful for geometric development of dental implants to reduce complex interactions leading to fatigue failure. Copyright © 2017 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.

Is Increased Susceptibility to Balkan Endemic Nephropathy in Carriers of Common GSTA1 (*A/*B) Polymorphism Linked with the Catalytic Role of GSTA1 in Ochratoxin A Biotransformation? Serbian Case Control Study and In Silico Analysis

PubMed Central

Reljic, Zorica; Zlatovic, Mario; Savic-Radojevic, Ana; Pekmezovic, Tatjana; Djukanovic, Ljubica; Matic, Marija; Pljesa-Ercegovac, Marija; Mimic-Oka, Jasmina; Opsenica, Dejan; Simic, Tatjana

2014-01-01

Although recent data suggest aristolochic acid as a putative cause of Balkan endemic nephropathy (BEN), evidence also exists in favor of ochratoxin A (OTA) exposure as risk factor for the disease. The potential role of xenobiotic metabolizing enzymes, such as the glutathione transferases (GSTs), in OTA biotransformation is based on OTA glutathione adducts (OTHQ-SG and OTB-SG) in blood and urine of BEN patients. We aimed to analyze the association between common GSTA1, GSTM1, GSTT1, and GSTP1 polymorphisms and BEN susceptibility, and thereafter performed an in silico simulation of particular GST enzymes potentially involved in OTA transformations. GSTA1, GSTM1, GSTT1 and GSTP1 genotypes were determined in 207 BEN patients and 138 non-BEN healthy individuals from endemic regions by polymerase chain reaction (PCR). Molecular modeling in silico was performed for GSTA1 protein. Among the GST polymorphisms tested, only GSTA1 was significantly associated with a higher risk of BEN. Namely, carriers of the GSTA1*B gene variant, associated with lower transcriptional activation, were at a 1.6-fold higher BEN risk than those carrying the homozygous GSTA1*A/*A genotype (OR = 1.6; p = 0.037). In in silico modeling, we found four structures, two OTB-SG and two OTHQ-SG, bound in a GSTA1 monomer. We found that GSTA1 polymorphism was associated with increased risk of BEN, and suggested, according to the in silico simulation, that GSTA1-1 might be involved in catalyzing the formation of OTHQ-SG and OTB-SG conjugates. PMID:25111321

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.

Genererating a core cluster of Fasciola hepatica virulence and immunomodulation-related genes using a comparative in silico approach.

PubMed

Haçarız, Orçun; Sayers, Gearóid P

2018-04-01

A total of 71 virulence and immunomodulation-related transcripts (VIRs) of Fasciola hepatica have been previously proposed (Haçarız et al., 2015). In an attempt to further refine this cohort, an in silico meta analysis approach was carried out using publicly available sequence data of related liver flukes, Clonorchis sinensis and Opisthorchis viverrini. Data of both liver flukes were investigated in terms of sequential homology with data of non-parasitic organisms, pathogens and VIRs of F. hepatica, directional selection (Ka/Ks), and cytokine signaling relation (protein motif based). Some VIRs of F. hepatica [showing homology with immune receptors (for toll/interleukin-1, TGF-β or TNF-α), TGF-β, TNF-α, CD147, or relation with suppressors of cytokine signaling/IKBKE 1 or stimulation of TGF-β (through thrombospondin similarity)] were found to be orthologous with those of both C. sinensis and O. viverrini. The in silico analysis indicates that on the basis of genetic commonality, a total of 30 VIRs of F. hepatica are highlighted as of foremost importance in the parasite evasion strategy, through controlling of host immune system. Findings in this study could be important to further enhance our understanding of the parasitic mechanisms and develop effective control strategies against F. hepatica and other related parasites. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

MTHFR-Ala222Val and male infertility: a study in Iranian men, an updated meta-analysis and an in silico-analysis.

PubMed

Nikzad, Hossein; Karimian, Mohammad; Sareban, Kobra; Khoshsokhan, Maryam; Hosseinzadeh Colagar, Abasalt

2015-11-01

Methylenetetrahydrofolate reductase (MTHFR) functions as a main regulatory enzyme in folate metabolism. The association of MTHFR gene Ala222Val polymorphism with male infertility in an Iranian population was investigated by undertaking a meta-analysis and in-silico approach. A genetic association study included 497 men; 242 had unexplained infertility and 255 were healthy controls. Polymerase chain reaction restriction fragment length polymorphism was used for genotyping MTHFR-Ala222Val. OpenMeta[Analyst] software was used to conduct the analysis; 22 studies were identified by searching PubMed and the currently reported genetic association study. A novel in-silico approach was used to analyse the effects of Ala222Val substitution on the structure of mRNA and protein. Genetic association study revealed a significant association of MTHFR-222Val/Val genotype with oligozoospermia (OR 2.32; 95% CI, 1.12 to 4.78; P = 0.0451) and azoospermia (OR 2.59; 95% CI 1.09 to 6.17; P = 0.0314). Meta-analysis for allelic, dominant and codominant models showed a significant association between Ala222Val polymorphism and the risk of male infertility (P < 0.001). In silico-analysis showed MTHFR-Ala222Val affects enzyme structure and could also change the mRNA properties (P = 0.1641; P < 0.2 is significant). The meta-analysis suggested significant association of MTHFR-Ala222Val with risk of male infertility, especially in Asian populations. Copyright © 2015 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Structure-guided fragment-based in silico drug design of dengue protease inhibitors.

PubMed

Knehans, Tim; Schüller, Andreas; Doan, Danny N; Nacro, Kassoum; Hill, Jeffrey; Güntert, Peter; Madhusudhan, M S; Weil, Tanja; Vasudevan, Subhash G

2011-03-01

An in silico fragment-based drug design approach was devised and applied towards the identification of small molecule inhibitors of the dengue virus (DENV) NS2B-NS3 protease. Currently, no DENV protease co-crystal structure with bound inhibitor and fully formed substrate binding site is available. Therefore a homology model of DENV NS2B-NS3 protease was generated employing a multiple template spatial restraints method and used for structure-based design. A library of molecular fragments was derived from the ZINC screening database with help of the retrosynthetic combinatorial analysis procedure (RECAP). 150,000 molecular fragments were docked to the DENV protease homology model and the docking poses were rescored using a target-specific scoring function. High scoring fragments were assembled to small molecule candidates by an implicit linking cascade. The cascade included substructure searching and structural filters focusing on interactions with the S1 and S2 pockets of the protease. The chemical space adjacent to the promising candidates was further explored by neighborhood searching. A total of 23 compounds were tested experimentally and two compounds were discovered to inhibit dengue protease (IC(50) = 7.7 μM and 37.9 μM, respectively) and the related West Nile virus protease (IC(50) = 6.3 μM and 39.0 μM, respectively). This study demonstrates the successful application of a structure-guided fragment-based in silico drug design approach for dengue protease inhibitors providing straightforward hit generation using a combination of homology modeling, fragment docking, chemical similarity and structural filters.

Structure-guided fragment-based in silico drug design of dengue protease inhibitors

NASA Astrophysics Data System (ADS)

Knehans, Tim; Schüller, Andreas; Doan, Danny N.; Nacro, Kassoum; Hill, Jeffrey; Güntert, Peter; Madhusudhan, M. S.; Weil, Tanja; Vasudevan, Subhash G.

2011-03-01

An in silico fragment-based drug design approach was devised and applied towards the identification of small molecule inhibitors of the dengue virus (DENV) NS2B-NS3 protease. Currently, no DENV protease co-crystal structure with bound inhibitor and fully formed substrate binding site is available. Therefore a homology model of DENV NS2B-NS3 protease was generated employing a multiple template spatial restraints method and used for structure-based design. A library of molecular fragments was derived from the ZINC screening database with help of the retrosynthetic combinatorial analysis procedure (RECAP). 150,000 molecular fragments were docked to the DENV protease homology model and the docking poses were rescored using a target-specific scoring function. High scoring fragments were assembled to small molecule candidates by an implicit linking cascade. The cascade included substructure searching and structural filters focusing on interactions with the S1 and S2 pockets of the protease. The chemical space adjacent to the promising candidates was further explored by neighborhood searching. A total of 23 compounds were tested experimentally and two compounds were discovered to inhibit dengue protease (IC50 = 7.7 μM and 37.9 μM, respectively) and the related West Nile virus protease (IC50 = 6.3 μM and 39.0 μM, respectively). This study demonstrates the successful application of a structure-guided fragment-based in silico drug design approach for dengue protease inhibitors providing straightforward hit generation using a combination of homology modeling, fragment docking, chemical similarity and structural filters.

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.

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

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.

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

HLA-B*15:21 and carbamazepine-induced Stevens-Johnson syndrome: pooled-data and in silico analysis

NASA Astrophysics Data System (ADS)

Jaruthamsophon, Kanoot; Tipmanee, Varomyalin; Sangiemchoey, Antida; Sukasem, Chonlaphat; Limprasert, Pornprot

2017-03-01

HLA-B*15:02 screening before carbamazepine (CBZ) prescription in Asian populations is the recommended practice to prevent CBZ-induced Stevens-Johnson syndrome (CBZ-SJS). However, a number of patients have developed CBZ-SJS even having no HLA-B*15:02. Herein, we present the case of a Thai patient who had a negative HLA-B*15:02 screening result but later developed CBZ-SJS. Further HLA typing revealed HLA-B*15:21/B*13:01. HLA-B*15:21 is a member of the HLA-B75 serotype and is commonly found in Southeast Asian populations. Based on this case, we hypothesised that if all HLA-B*15:02 carriers were prevented from CBZ prescription, another common HLA-B75 serotype marker would show its association with CBZ-SJS. To test this hypothesis, we pooled data from previous association studies in Asian populations, excluded all cases with HLA-B*15:02, and analysed the association significance of HLA-B75 serotype markers. A significant association was found between CBZ-SJS and HLA-B*15:21 and HLA-B*15:11. We also applied an in silico analysis and found that all HLA-B75 serotype molecules shared similar capability in binding the CBZ molecule. In summary, this report provides the first evidence of a positive association between HLA-B*15:21 and CBZ-SJS and the first in silico analysis of CBZ binding sites and details of the molecular behaviour of HLA-B75 molecule to explain its molecular action.

Discovery of Novel Anti-prion Compounds Using In Silico and In Vitro Approaches

PubMed Central

Hyeon, Jae Wook; Choi, Jiwon; Kim, Su Yeon; Govindaraj, Rajiv Gandhi; Jam Hwang, Kyu; Lee, Yeong Seon; An, Seong Soo A.; Lee, Myung Koo; Joung, Jong Young; No, Kyoung Tai; Lee, Jeongmin

2015-01-01

Prion diseases are associated with the conformational conversion of the physiological form of cellular prion protein (PrPC) to the pathogenic form, PrPSc. Compounds that inhibit this process by blocking conversion to the PrPSc could provide useful anti-prion therapies. However, no suitable drugs have been identified to date. To identify novel anti-prion compounds, we developed a combined structure- and ligand-based virtual screening system in silico. Virtual screening of a 700,000-compound database, followed by cluster analysis, identified 37 compounds with strong interactions with essential hotspot PrP residues identified in a previous study of PrPC interaction with a known anti-prion compound (GN8). These compounds were tested in vitro using a multimer detection system, cell-based assays, and surface plasmon resonance. Some compounds effectively reduced PrPSc levels and one of these compounds also showed a high binding affinity for PrPC. These results provide a promising starting point for the development of anti-prion compounds. PMID:26449325

VIRTUAL LIVER: AN IN SILICO FRAMEWORK FOR ANALYZING CHEMICAL-INDUCED HEPATOTOXICITY

EPA Science Inventory

The US EPA Virtual Liver (v-LiverTM) is an in silico framework for the dose-dependent perturbation of normal hepatic functions by chemicals using in vitro data. The framework consists of a computable knowledge-base (KB) to infer putative pathways in hepatotoxicity and a cellular...

Development of a Computational (in silico) Model of Ocular Teratogenesis

EPA Science Inventory

EPA’s ToxCast™ project is profiling the in vitro bioactivity of chemical compounds to assess pathway-level and cell-based signatures that are highly correlated with observed in vivo toxicity. In silico models provide a framework for interpreting the in vitro results and for simul...

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...

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.

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.

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

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts

PubMed Central

Paraskevopoulou, Maria D.; Vlachos, Ioannis S.; Karagkouni, Dimitra; Georgakilas, Georgios; Kanellos, Ilias; Vergoulis, Thanasis; Zagganas, Konstantinos; Tsanakas, Panayiotis; Floros, Evangelos; Dalamagas, Theodore; Hatzigeorgiou, Artemis G.

2016-01-01

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads. PMID:26612864

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.

Novel approaches for targeting the adenosine A2A receptor.

PubMed

Yuan, Gengyang; Gedeon, Nicholas G; Jankins, Tanner C; Jones, Graham B

2015-01-01

The adenosine A2A receptor (A2AR) represents a drug target for a wide spectrum of diseases. Approaches for targeting this membrane-bound protein have been greatly advanced by new stabilization techniques. The resulting X-ray crystal structures and subsequent analyses provide deep insight to the A2AR from both static and dynamic perspectives. Application of this, along with other biophysical methods combined with fragment-based drug design (FBDD), has become a standard approach in targeting A2AR. Complementarities of in silico screening based- and biophysical screening assisted- FBDD are likely to feature in future approaches in identifying novel ligands against this key receptor. This review describes evolution of the above approaches for targeting A2AR and highlights key modulators identified. It includes a review of: adenosine receptor structures, homology modeling, X-ray structural analysis, rational drug design, biophysical methods, FBDD and in silico screening. As a drug target, the A2AR is attractive as its function plays a role in a wide spectrum of diseases including oncologic, inflammatory, Parkinson's and cardiovascular diseases. Although traditional approaches such as high-throughput screening and homology model-based virtual screening (VS) have played a role in targeting A2AR, numerous shortcomings have generally restricted their applications to specific ligand families. Using stabilization methods for crystallization, X-ray structures of A2AR have greatly accelerated drug discovery and influenced development of biophysical-in silico hybrid screening methods. Application of these new methods to other ARs and G-protein-coupled receptors is anticipated in the future.

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.

In-silico identification of miRNAs and their regulating target functions in Ocimum basilicum.

PubMed

Singh, Noopur; Sharma, Ashok

2014-12-01

microRNA is known to play an important role in growth and development of the plants and also in environmental stress. Ocimum basilicum (Basil) is a well known herb for its medicinal properties. In this study, we used in-silico approaches to identify miRNAs and their targets regulating different functions in O. basilicum using EST approach. Additionally, functional annotation, gene ontology and pathway analysis of identified target transcripts were also done. Seven miRNA families were identified. Meaningful regulations of target transcript by identified miRNAs were computationally evaluated. Four miRNA families have been reported by us for the first time from the Lamiaceae. Our results further confirmed that uracil was the predominant base in the first positions of identified mature miRNA sequence, while adenine and uracil were predominant in pre-miRNA sequences. Phylogenetic analysis was carried out to determine the relation between O. basilicum and other plant pre-miRNAs. Thirteen potential targets were evaluated for 4 miRNA families. Majority of the identified target transcripts regulated by miRNAs showed response to stress. miRNA 5021 was also indicated for playing an important role in the amino acid metabolism and co-factor metabolism in this plant. To the best of our knowledge this is the first in silico study describing miRNAs and their regulation in different metabolic pathways of O. basilicum. Copyright © 2014 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

In Silico Evaluation of the Potential Impact of Bioanalytical Bias Difference between Two Therapeutic Protein Formulations for Pharmacokinetic Assessment in a Biocomparability Study.

PubMed

Thway, Theingi M; Macaraeg, Chris; Eschenberg, Michael; Ma, Mark

2015-05-01

Formulation changes at later stages of biotherapeutics development require biocomparability (BC) assessment. Using simulation, this study aims to determine the potential effect of bias difference observed between the two formulations after spiking into serum in passing or failing of a critical BC study. An ELISA method with 20% total error was used to assess any bias differences between a reference (RF) and test formulations (TF) in serum. During bioanalytical comparison of these formulations, a 9% difference in bias was observed between the two formulations in sera. To determine acceptable level of bias difference between the RF and TF bioanalytically, two in silico simulations were performed. The in silico analysis showed that the likelihood of the study meeting the BC criteria was >90% when the bias difference between RF and TF in serum was 9% and the number of subjects was ≥20 per treatment arm. An additional simulation showed that when the bias difference was increased to 13% and the number of subjects was <40, the likelihood of meeting the BC criteria decreased to 80%. The result from in silico analysis allowed the bioanalytical laboratory to proceed with sample analysis using a single calibrator and quality controls made from the reference formulation. This modeling approach can be applied to other BC studies with similar situations.

Toxicological evaluation in silico and in vivo of secondary metabolites of Cissampelos sympodialis in Mus musculus mice following inhalation.

PubMed

Alves, Mateus Feitosa; Ferreira, Larissa Adilis Maria Paiva; Gadelha, Francisco Allysson Assis Ferreira; Ferreira, Laércia Karla Diega Paiva; Felix, Mayara Barbalho; Scotti, Marcus Tullius; Scotti, Luciana; de Oliveira, Kardilândia Mendes; Dos Santos, Sócrates Golzio; Diniz, Margareth de Fátima Formiga Melo

2017-12-04

The ethanolic extract of the leaves of Cissampelos sympodialis showed great pharmacological potential, with inflammatory and immunomodulatory activities, however, it showed some toxicological effects. Therefore, this study aims to verify the toxicological potential of alkaloids of the genus Cissampelos through in silico methodologies, to develop a method in LC-MS/MS verifying the presence of alkaloids in the infusion and to evaluate the toxicity of the infusion of the leaves of C. sympodialis when inhaled by Swiss mice. Results in silico showed that alkaloid 93 presented high toxicological potential along with the products of its metabolism. LC-MS/MS results showed that the infusion of the leaves of this plant contained the alkaloids warifteine and methylwarifteine. Finally, the in vivo toxicological analysis of the C. sympodialis infusion showed results, both in biochemistry, organ weights and histological analysis, that the infusion of C. sympodialis leaves presents a low toxicity.

In silico design and screening of hypothetical MOF-74 analogs and their experimental synthesis

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

Witman, Matthew; Ling, Sanliang; Anderson, Samantha

2016-01-01

We present thein silico designof MOFs exhibiting 1-dimensional rod topologies by enumerating MOF-74-type analogs based on the PubChem Compounds database. We simulate the adsorption behavior of CO 2in the generated analogs and experimentally validate a novel MOF-74 analog, Mg 2(olsalazine).

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.

‘Candidatus Phytoplasma luffae’, a novel taxon associated with a witches’-broom disease of loofah, Luffa aegyptica Mill

USDA-ARS?s Scientific Manuscript database

The phytoplasma associated with witches’ broom disease of loofah (Luffa aegyptica Mill., syn. L.uffa cylindrica (L.) M.J. Roem.) in Taiwan was classified in group 16SrVIII, subgroup A (16SrVIII-A), based on results from actual and in silico RFLP analysis of 16S rRNA gene sequences. Nucleotide sequ...

An in silico method to identify computer-based protocols worthy of clinical study: An insulin infusion protocol use case

PubMed Central

Wong, Anthony F; Pielmeier, Ulrike; Haug, Peter J; Andreassen, Steen

2016-01-01

Objective Develop an efficient non-clinical method for identifying promising computer-based protocols for clinical study. An in silico comparison can provide information that informs the decision to proceed to a clinical trial. The authors compared two existing computer-based insulin infusion protocols: eProtocol-insulin from Utah, USA, and Glucosafe from Denmark. Materials and Methods The authors used eProtocol-insulin to manage intensive care unit (ICU) hyperglycemia with intravenous (IV) insulin from 2004 to 2010. Recommendations accepted by the bedside clinicians directly link the subsequent blood glucose values to eProtocol-insulin recommendations and provide a unique clinical database. The authors retrospectively compared in silico 18 984 eProtocol-insulin continuous IV insulin infusion rate recommendations from 408 ICU patients with those of Glucosafe, the candidate computer-based protocol. The subsequent blood glucose measurement value (low, on target, high) was used to identify if the insulin recommendation was too high, on target, or too low. Results Glucosafe consistently provided more favorable continuous IV insulin infusion rate recommendations than eProtocol-insulin for on target (64% of comparisons), low (80% of comparisons), or high (70% of comparisons) blood glucose. Aggregated eProtocol-insulin and Glucosafe continuous IV insulin infusion rates were clinically similar though statistically significantly different (Wilcoxon signed rank test P = .01). In contrast, when stratified by low, on target, or high subsequent blood glucose measurement, insulin infusion rates from eProtocol-insulin and Glucosafe were statistically significantly different (Wilcoxon signed rank test, P < .001), and clinically different. Discussion This in silico comparison appears to be an efficient nonclinical method for identifying promising computer-based protocols. Conclusion Preclinical in silico comparison analytical framework allows rapid and inexpensive identification of computer-based protocol care strategies that justify expensive and burdensome clinical trials. PMID:26228765

Comprehensive in silico allergenicity assessment of novel protein engineered chimeric Cry proteins for safe deployment in crops.

PubMed

Rathinam, Maniraj; Singh, Shweta; Pattanayak, Debasis; Sreevathsa, Rohini

2017-08-02

Development of chimeric Cry toxins by protein engineering of known and validated proteins is imperative for enhancing the efficacy and broadening the insecticidal spectrum of these genes. Expression of novel Cry proteins in food crops has however created apprehensions with respect to the safety aspects. To clarify this, premarket evaluation consisting of an array of analyses to evaluate the unintended effects is a prerequisite to provide safety assurance to the consumers. Additionally, series of bioinformatic tools as in silico aids are being used to evaluate the likely allergenic reaction of the proteins based on sequence and epitope similarity with known allergens. In the present study, chimeric Cry toxins developed through protein engineering were evaluated for allergenic potential using various in silico algorithms. Major emphasis was on the validation of allergenic potential on three aspects of paramount significance viz., sequence-based homology between allergenic proteins, validation of conformational epitopes towards identification of food allergens and physico-chemical properties of amino acids. Additionally, in vitro analysis pertaining to heat stability of two of the eight chimeric proteins and pepsin digestibility further demonstrated the non-allergenic potential of these chimeric toxins. The study revealed for the first time an all-encompassing evaluation that the recombinant Cry proteins did not show any potential similarity with any known allergens with respect to the parameters generally considered for a protein to be designated as an allergen. These novel chimeric proteins hence can be considered safe to be introgressed into plants.

Methodological flaws introduce strong bias into molecular analysis of microbial populations.

PubMed

Krakat, N; Anjum, R; Demirel, B; Schröder, P

2017-02-01

In this study, we report how different cell disruption methods, PCR primers and in silico analyses can seriously bias results from microbial population studies, with consequences for the credibility and reproducibility of the findings. Our results emphasize the pitfalls of commonly used experimental methods that can seriously weaken the interpretation of results. Four different cell lysis methods, three commonly used primer pairs and various computer-based analyses were applied to investigate the microbial diversity of a fermentation sample composed of chicken dung. The fault-prone, but still frequently used, amplified rRNA gene restriction analysis was chosen to identify common weaknesses. In contrast to other studies, we focused on the complete analytical process, from cell disruption to in silico analysis, and identified potential error rates. This identified a wide disagreement of results between applied experimental approaches leading to very different community structures depending on the chosen approach. The interpretation of microbial diversity data remains a challenge. In order to accurately investigate the taxonomic diversity and structure of prokaryotic communities, we suggest a multi-level approach combining DNA-based and DNA-independent techniques. The identified weaknesses of commonly used methods to study microbial diversity can be overcome by a multi-level approach, which produces more reliable data about the fate and behaviour of microbial communities of engineered habitats such as biogas plants, so that the best performance can be ensured. © 2016 The Society for Applied Microbiology.

High-resolution chromatography/time-of-flight MSE with in silico data mining is an information-rich approach to reactive metabolite screening.

PubMed

Barbara, Joanna E; Castro-Perez, Jose M

2011-10-30

Electrophilic reactive metabolite screening by liquid chromatography/mass spectrometry (LC/MS) is commonly performed during drug discovery and early-stage drug development. Accurate mass spectrometry has excellent utility in this application, but sophisticated data processing strategies are essential to extract useful information. Herein, a unified approach to glutathione (GSH) trapped reactive metabolite screening with high-resolution LC/TOF MS(E) analysis and drug-conjugate-specific in silico data processing was applied to rapid analysis of test compounds without the need for stable- or radio-isotope-labeled trapping agents. Accurate mass defect filtering (MDF) with a C-heteroatom dealkylation algorithm dynamic with mass range was compared to linear MDF and shown to minimize false positive results. MS(E) data-filtering, time-alignment and data mining post-acquisition enabled detection of 53 GSH conjugates overall formed from 5 drugs. Automated comparison of sample and control data in conjunction with the mass defect filter enabled detection of several conjugates that were not evident with mass defect filtering alone. High- and low-energy MS(E) data were time-aligned to generate in silico product ion spectra which were successfully applied to structural elucidation of detected GSH conjugates. Pseudo neutral loss and precursor ion chromatograms derived post-acquisition demonstrated 50.9% potential coverage, at best, of the detected conjugates by any individual precursor or neutral loss scan type. In contrast with commonly applied neutral loss and precursor-based techniques, the unified method has the advantage of applicability across different classes of GSH conjugates. The unified method was also successfully applied to cyanide trapping analysis and has potential for application to alternate trapping agents. Copyright © 2011 John Wiley & Sons, Ltd.

In silico prediction of the pathogenic effect of a novel variant of BCKDHA leading to classical maple syrup urine disease identified using clinical exome sequencing.

PubMed

Fernández-Lainez, Cynthia; Aláez-Verson, Carmen; Ibarra-González, Isabel; Enríquez-Flores, Sergio; Carrillo-Sanchez, Karol; Flores-Lagunes, Leonardo; Guillén-López, Sara; Belmont-Martínez, Leticia; Vela-Amieva, Marcela

2018-04-16

Maple syrup urine disease (MSUD) is a metabolic disorder caused by mutations in three of the branched-chain α-keto acid dehydrogenase complex (BCKDC) genes. Classical MSUD symptom can be observed immediately after birth and include ketoacidosis, irritability, lethargy, and coma, which can lead to death or irreversible neurodevelopmental delay in survivors. The molecular diagnosis of MSUD can be time-consuming and difficult to establish using conventional Sanger sequencing because it could be due to pathogenic variants of any of the BCKDC genes. Next-generation sequencing-based methodologies have revolutionized the molecular diagnosis of inborn errors in metabolism and offer a superior approach for genotyping these patients. Here, we report an MSUD case whose molecular diagnosis was performed by clinical exome sequencing (CES), and the possible structural pathogenic effect of a novel E1α subunit pathogenic variant was analyzed using in silico analysis of α and β subunit crystallographic structure. Molecular analysis revealed a new homozygous non-sense c.1267C>T or p.Gln423Ter variant of BCKDHA. The novel BCKDHA variant is considered pathogenic because it caused a premature stop codon that probably led to the loss of the last 22 amino acid residues of the E1α subunit C-terminal end. In silico analysis of this region showed that it is in contact with several residues of the E1β subunit mainly through polar contacts, hydrogen bonds, and hydrophobic interactions. CES strategy could benefit the patients and families by offering precise and prompt diagnosis and better genetic counseling. Copyright © 2018 Elsevier B.V. All rights reserved.

FRET imaging and in silico simulation: analysis of the signaling network of nerve growth factor-induced neuritogenesis.

PubMed

Nakamura, Takeshi; Aoki, Kazuhiro; Matsuda, Michiyuki

2008-08-01

Genetically encoded probes based on Förster resonance energy transfer (FRET) enable us to decipher spatiotemporal information encoded in complex tissues such as the brain. Firstly, this review focuses on FRET probes wherein both the donor and acceptor are fluorescence proteins and are incorporated into a single molecule, i.e. unimolecular probes. Advantages of these probes lie in their easy loading into cells, the simple acquisition of FRET images, and the clear evaluation of data. Next, we introduce our recent study which encompasses FRET imaging and in silico simulation. In nerve growth factor-induced neurite outgrowth in PC12 cells, we found positive and negative signaling feedback loops. We propose that these feedback loops determine neurite-budding sites. We would like to emphasize that it is now time to accelerate crossover research in neuroscience, optics, and computational biology.

Identification of novel potential scaffold for class I HDACs inhibition: An in-silico protocol based on virtual screening, molecular dynamics, mathematical analysis and machine learning.

PubMed

Fan, Cong; Huang, Yanxin

2017-09-23

Histone deacetylases (HDACs) family has been widely reported as an important class of enzyme targets for cancer therapy. Much effort has been made in discovery of novel scaffolds for HDACs inhibition besides existing hydroxamic acids, cyclic peptides, benzamides, and short-chain fatty acids. Herein we set up an in-silico protocol which not only could detect potential Zn 2+ chelation bonds but also still adopted non-bonded model to be effective in discovery of Class I HDACs inhibitors, with little human's subjective visual judgment involved. We applied the protocol to screening of Chembridge database and selected out 7 scaffolds, 3 with probability of more than 99%. Biological assay results demonstrated that two of them exhibited HDAC-inhibitory activity and are thus considerable for structure modification to further improve their bio-activity. Copyright © 2017. Published by Elsevier Inc.

Using in vitro/in silico data for consumer safety assessment of feed flavoring additives--A feasibility study using piperine.

PubMed

Thiel, A; Etheve, S; Fabian, E; Leeman, W R; Plautz, J R

2015-10-01

Consumer health risk assessment for feed additives is based on the estimated human exposure to the additive that may occur in livestock edible tissues compared to its hazard. We present an approach using alternative methods for consumer health risk assessment. The aim was to use the fewest possible number of animals to estimate its hazard and human exposure without jeopardizing the safety upon use. As an example we selected the feed flavoring substance piperine and applied in silico modeling for residue estimation, results from literature surveys, and Read-Across to assess metabolism in different species. Results were compared to experimental in vitro metabolism data in rat and chicken, and to quantitative analysis of residues' levels from the in vivo situation in livestock. In silico residue modeling showed to be a worst case: the modeled residual levels were considerably higher than the measured residual levels. The in vitro evaluation of livestock versus rodent metabolism revealed no major differences in metabolism between the species. We successfully performed a consumer health risk assessment without performing additional animal experiments. As shown, the use and combination of different alternative methods supports animal welfare consideration and provides future perspective to reducing the number of animals. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

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.

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 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.

Thermal heterogeneity within aqueous materials quantified by 1H NMR spectroscopy: Multiparametric validation in silico and in vitro

NASA Astrophysics Data System (ADS)

Lutz, Norbert W.; Bernard, Monique

2018-02-01

We recently suggested a new paradigm for statistical analysis of thermal heterogeneity in (semi-)aqueous materials by 1H NMR spectroscopy, using water as a temperature probe. Here, we present a comprehensive in silico and in vitro validation that demonstrates the ability of this new technique to provide accurate quantitative parameters characterizing the statistical distribution of temperature values in a volume of (semi-)aqueous matter. First, line shape parameters of numerically simulated water 1H NMR spectra are systematically varied to study a range of mathematically well-defined temperature distributions. Then, corresponding models based on measured 1H NMR spectra of agarose gel are analyzed. In addition, dedicated samples based on hydrogels or biological tissue are designed to produce temperature gradients changing over time, and dynamic NMR spectroscopy is employed to analyze the resulting temperature profiles at sub-second temporal resolution. Accuracy and consistency of the previously introduced statistical descriptors of temperature heterogeneity are determined: weighted median and mean temperature, standard deviation, temperature range, temperature mode(s), kurtosis, skewness, entropy, and relative areas under temperature curves. Potential and limitations of this method for quantitative analysis of thermal heterogeneity in (semi-)aqueous materials are discussed in view of prospective applications in materials science as well as biology and medicine.

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.

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.

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

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.

DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts.

PubMed

Paraskevopoulou, Maria D; Vlachos, Ioannis S; Karagkouni, Dimitra; Georgakilas, Georgios; Kanellos, Ilias; Vergoulis, Thanasis; Zagganas, Konstantinos; Tsanakas, Panayiotis; Floros, Evangelos; Dalamagas, Theodore; Hatzigeorgiou, Artemis G

2016-01-04

microRNAs (miRNAs) are short non-coding RNAs (ncRNAs) that act as post-transcriptional regulators of coding gene expression. Long non-coding RNAs (lncRNAs) have been recently reported to interact with miRNAs. The sponge-like function of lncRNAs introduces an extra layer of complexity in the miRNA interactome. DIANA-LncBase v1 provided a database of experimentally supported and in silico predicted miRNA Recognition Elements (MREs) on lncRNAs. The second version of LncBase (www.microrna.gr/LncBase) presents an extensive collection of miRNA:lncRNA interactions. The significantly enhanced database includes more than 70 000 low and high-throughput, (in)direct miRNA:lncRNA experimentally supported interactions, derived from manually curated publications and the analysis of 153 AGO CLIP-Seq libraries. The new experimental module presents a 14-fold increase compared to the previous release. LncBase v2 hosts in silico predicted miRNA targets on lncRNAs, identified with the DIANA-microT algorithm. The relevant module provides millions of predicted miRNA binding sites, accompanied with detailed metadata and MRE conservation metrics. LncBase v2 caters information regarding cell type specific miRNA:lncRNA regulation and enables users to easily identify interactions in 66 different cell types, spanning 36 tissues for human and mouse. Database entries are also supported by accurate lncRNA expression information, derived from the analysis of more than 6 billion RNA-Seq reads. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

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

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.

Antibody quantum dot conjugates developed via copper-free click chemistry for rapid analysis of biological samples using a microfluidic microsphere array system.

PubMed

Kotagiri, Nalinikanth; Li, Zhenyu; Xu, Xiaoxiao; Mondal, Suman; Nehorai, Arye; Achilefu, Samuel

2014-07-16

Antibody-based proteomics is an enabling technology that has significant implications for cancer biomarker discovery, diagnostic screening, prognostic and pharmacodynamic evaluation of disease state, and targeted therapeutics. Quantum dot based fluoro-immunoconjugates possess promising features toward realization of this goal such as high photostability, brightness, and multispectral tunability. However, current strategies to generate such conjugates are riddled with complications such as improper orientation of antigen binding sites of the antibody, aggregation, and stability issues. We report a facile yet effective strategy to conjugate anti-epidermal growth factor receptor (EGFR) antibody to quantum dots using copper-free click reaction, and compared them to similar constructs prepared using traditional strategies such as succinimidyl-4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC) and biotin-streptavidin schemes. The Fc and Fab regions of the conjugates retain their binding potential, compared to those generated through the traditional schemes. We further applied the conjugates in testing a novel microsphere array device designed to carry out sensitive detection of cancer biomarkers through fluoroimmunoassays. Using purified EGFR, we determined the limit of detection of the microscopy centric system to be 12.5 ng/mL. The biological assay, in silico, was successfully tested and validated by using tumor cell lysates, as well as human serum from breast cancer patients, and the results were compared to normal serum. A pattern consistent with established clinical data was observed, which further validates the effectiveness of the developed conjugates and its successful implementation both in vitro as well as in silico fluoroimmunoassays. The results suggest the potential development of a high throughput in silico paradigm for predicting the class of patient cancer based on EGFR expression levels relative to normal reference levels in blood.

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.

Text mining-based in silico drug discovery in oral mucositis caused by high-dose cancer therapy.

PubMed

Kirk, Jon; Shah, Nirav; Noll, Braxton; Stevens, Craig B; Lawler, Marshall; Mougeot, Farah B; Mougeot, Jean-Luc C

2018-08-01

Oral mucositis (OM) is a major dose-limiting side effect of chemotherapy and radiation used in cancer treatment. Due to the complex nature of OM, currently available drug-based treatments are of limited efficacy. Our objectives were (i) to determine genes and molecular pathways associated with OM and wound healing using computational tools and publicly available data and (ii) to identify drugs formulated for topical use targeting the relevant OM molecular pathways. OM and wound healing-associated genes were determined by text mining, and the intersection of the two gene sets was selected for gene ontology analysis using the GeneCodis program. Protein interaction network analysis was performed using STRING-db. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in OM. Our analysis identified 447 genes common to both the "OM" and "wound healing" text mining concepts. Gene enrichment analysis yielded 20 genes representing six pathways and targetable by a total of 32 drugs which could possibly be formulated for topical application. A manual search on ClinicalTrials.gov confirmed no relevant pathway/drug candidate had been overlooked. Twenty-five of the 32 drugs can directly affect the PTGS2 (COX-2) pathway, the pathway that has been targeted in previous clinical trials with limited success. Drug discovery using in silico text mining and pathway analysis tools can facilitate the identification of existing drugs that have the potential of topical administration to improve OM treatment.

Triazolopyridinyl-acrylonitrile derivatives as antimicrotubule agents: Synthesis, in vitro and in silico characterization of antiproliferative activity, inhibition of tubulin polymerization and binding thermodynamics.

PubMed

Briguglio, Irene; Laurini, Erik; Pirisi, Maria Antonietta; Piras, Sandra; Corona, Paola; Fermeglia, Maurizio; Pricl, Sabrina; Carta, Antonio

2017-12-01

In this paper we report the synthesis, in vitro anticancer activity, and the experimental/computational characterization of mechanism of action of a new series of E isomers of triazolo[4,5-b/c]pyridin-acrylonitrile derivatives (6c-g, 7d-e, 8d-e, 9c-f, 10d-e, 11d-e). All new compounds are endowed with moderate to interesting antiproliferative activity against 9 different cancer cell lines derived from solid and hematological human tumors. Fluorescence-based assays prove that these molecules interfere with tubulin polymerization. Furthermore, isothermal titration calorimetry (ITC) provides full tubulin/compound binding thermodynamics, thereby ultimately qualifying and quantifying the interactions of these molecular series with the target protein. Lastly, the analysis based on the tight coupling of in vitro and in silico modeling of the interactions between tubulin and the title compounds allows to propose a molecular rationale for their biological activity. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

In silico and experimental evaluation of DNA-based detection methods for the ability to discriminate almond from other Prunus spp.

PubMed

Brežná, Barbara; Šmíd, Jiří; Costa, Joana; Radvanszky, Jan; Mafra, Isabel; Kuchta, Tomáš

2015-04-01

Ten published DNA-based analytical methods aiming at detecting material of almond (Prunus dulcis) were in silico evaluated for potential cross-reactivity with other stone fruits (Prunus spp.), including peach, apricot, plum, cherry, sour cherry and Sargent cherry. For most assays, the analysis of nucleotide databases suggested none or insufficient discrimination of at least some stone fruits. On the other hand, the assay targeting non-specific lipid transfer protein (Röder et al., 2011, Anal Chim Acta 685:74-83) was sufficiently discriminative, judging from nucleotide alignments. Empirical evaluation was performed for three of the published methods, one modification of a commercial kit (SureFood allergen almond) and one attempted novel method targeting thaumatin-like protein gene. Samples of leaves and kernels were used in the experiments. The empirical results were favourable for the method from Röder et al. (2011) and a modification of SureFood allergen almond kit, both showing cross-reactivity <10(-3) compared to the model almond. Copyright © 2014 Elsevier Ltd. All rights reserved.

Understanding mechanism of in vitro maturation, fertilization and culture of sheep embryoes through in silico analysis.

PubMed

Sreenivas, Dulam; Kaladhar, Dowluru Svgk; Samy, A Palni; Kumar, R Sangeeth

2012-01-01

Protein interations are presently required to understand the mechanisms of in vitro maturation, fertilization and culture of sheep embryoes through in silico analysis. The present work has been conducted on TCM-199 supplemented with epidermal growth factor (EGF), fetal bovine serum (FBS) or wheat peptones The maturation rate of oocyte was significantly higher in the FBS supplemented group when compared with BSA and wheat peptone supplemented groups. The in silico protein interaction studies has shown that the proteins EGFR (epidermal growth factor receptor), CCK (cholecystokinin)- a peptide hormone, Alb - a serum albumin, ESR- estrogen receptor 1, TGFA- transforming growth factor, STAT- signal transducer and FN1- fibronectin 1 has direct interaction and produces cell growth in in vitro culture. Alb is directly activates EGF and promotes MAPK3 that mediates diverse biological functions such as cell growth, adhesion and proliferation. Alb may also involve in stress response signalling and may be in cell cycle control.

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.

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.

Phenytoin-Bovine Serum Albumin interactions - modeling plasma protein - drug binding: A multi-spectroscopy and in silico-based correlation

NASA Astrophysics Data System (ADS)

Suresh, P. K.; Divya, Naik; Nidhi, Shah; Rajasekaran, R.

2018-03-01

The study focused on the analysis of the nature and site of binding of Phenytoin (PHT) -(a model hydrophobic drug) with Bovine Serum Albumin (BSA) (a model protein used as a surrogate for HSA). Interactions with defined amounts of Phenytoin and BSA demonstrated a blue shift (hypsochromic -change in the microenvironment of the tryptophan residue with decrease in the polar environment and more of hydrophobicity) with respect to the albumin protein and a red shift (bathochromic -hydrophobicity and polarity related changes) in the case of the model hydrophobic drug. This shift, albeit lower in magnitude, has been substantiated by a fairly convincing, Phenytoin-mediated quenching of the endogenous fluorophore in BSA. Spectral shifts studied at varying pH, temperatures and incubation periods (at varying concentrations of PHT with a defined/constant BSA concentration) showed no significant differences (data not shown). FTIR analysis provided evidence of the interaction of PHT with BSA with a stretching vibration of 1737.86 cm- 1, apart from the vibrations characteristically associated with the amine and carboxyl groups respectively. Our in vitro findings were extended to molecular docking of BSA with PHT (with the different ionized forms of the drug) and the subsequent LIGPLOT-based analysis. In general, a preponderance of hydrophobic interactions was observed. These hydrophobic interactions corroborate the tryptophan-based spectral shifts and the fluorescence quenching data. These results substantiates our hitherto unreported in vitro/in silico experimental flow and provides a basis for screening other hydrophobic drugs in its class.

Genome-wide investigation and expression analysis of AP2-ERF gene family in salt tolerant common bean

PubMed Central

Kavas, Musa; Kizildogan, Aslihan; Gökdemir, Gökhan; Baloglu, Mehmet Cengiz

2015-01-01

Apetala2-ethylene-responsive element binding factor (AP2-ERF) superfamily with common AP2-DNA binding domain have developmentally and physiologically important roles in plants. Since common bean genome project has been completed recently, it is possible to identify all of the AP2-ERF genes in the common bean genome. In this study, a comprehensive genome-wide in silico analysis identified 180 AP2-ERF superfamily genes in common bean (Phaseolus vulgaris). Based on the amino acid alignment and phylogenetic analyses, superfamily members were classified into four subfamilies: DREB (54), ERF (95), AP2 (27) and RAV (3), as well as one soloist. The physical and chemical characteristics of amino acids, interaction between AP2-ERF proteins, cis elements of promoter region of AP2-ERF genes and phylogenetic trees were predicted and analyzed. Additionally, expression levels of AP2-ERF genes were evaluated by in silico and qRT-PCR analyses. In silico micro-RNA target transcript analyses identified nearly all PvAP2-ERF genes as targets of by 44 different plant species' miRNAs were identified in this study. The most abundant target genes were PvAP2/ERF-20-25-62-78-113-173. miR156, miR172 and miR838 were the most important miRNAs found in targeting and BLAST analyses. Interactome analysis revealed that the transcription factor PvAP2-ERF78, an ortholog of Arabidopsis At2G28550, was potentially interacted with at least 15 proteins, indicating that it was very important in transcriptional regulation. Here we present the first study to identify and characterize the AP2-ERF transcription factors in common bean using whole-genome analysis, and the findings may serve as a references for future functional research on the transcription factors in common bean. PMID:27152109

Cadmium effects on sperm morphology and semenogelin with relates to increased ROS in infertile smokers: An in vitro and in silico approach.

PubMed

Ranganathan, Parameswari; Rao, Kamini A; Sudan, Jesu Jaya; Balasundaram, Sridharan

2018-06-01

Smoking releases cadmium (Cd), the metal toxicant which causes an imbalance in reactive oxygen species level in seminal plasma. This imbalance is envisaged to impair the sperm DNA morphology and thereby result in male infertility. In order to correlate this association, we performed in vitro and in silico studies and evaluated the influence of reactive oxygen species imbalance on sperm morphology impairments due to smoking. The study included 76 infertile smokers, 72 infertile non-smokers, 68 fertile smokers and 74 fertile non-smokers (control). Semen samples were collected at regular intervals from all the subjects. Semen parameters were examined by computer assisted semen analysis, quantification of metal toxicant by atomic absorption spectrophotometer, assessment of antioxidants through enzymatic and non-enzymatic methods, diagnosis of reactive oxygen species by nitro blue tetrazolium method and Cd influence on sperm protein by in vitro and in silico methods. Our analysis revealed that the levels of cigarette toxicants in semen were high, accompanied by low levels of antioxidants in seminal plasma of infertile smoker subjects. In addition the investigation of Cd treated sperm cells through scanning electronic microscope showed the mid piece damage of spermatozoa. The dispersive X-ray analysis to identify the elemental composition further confirmed the presence of Cd. Finally, the in-silico analysis on semenogelin sequences revealed the D-H-D motif which represents a favourable binding site for Cd coordination. Our findings clearly indicated the influence of Cd on reactive oxygen species leading to impaired sperm morphology leading to male infertility. Copyright © 2018 Society for Biology of Reproduction & the Institute of Animal Reproduction and Food Research of Polish Academy of Sciences in Olsztyn. Published by Elsevier B.V. All rights reserved.

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

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

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.

Exploring internal features of 16S rRNA gene for identification of clinically relevant species of the genus Streptococcus

PubMed Central

2011-01-01

Background Streptococcus is an economically important genus as a number of species belonging to this genus are human and animal pathogens. The genus has been divided into different groups based on 16S rRNA gene sequence similarity. The variability observed among the members of these groups is low and it is difficult to distinguish them. The present study was taken up to explore 16S rRNA gene sequence to develop methods that can be used for preliminary identification and can supplement the existing methods for identification of clinically-relevant isolates of the genus Streptococcus. Methods 16S rRNA gene sequences belonging to the isolates of S. dysgalactiae, S. equi, S. pyogenes, S. agalactiae, S. bovis, S. gallolyticus, S. mutans, S. sobrinus, S. mitis, S. pneumoniae, S. thermophilus and S. anginosus were analyzed with the purpose to define genetic variability within each species to generate a phylogenetic framework, to identify species-specific signatures and in-silico restriction enzyme analysis. Results The framework based analysis was used to segregate Streptococcus spp. previously identified upto genus level. This segregation was validated using species-specific signatures and in-silico restriction enzyme analysis. 43 uncharacterized Streptococcus spp. could be identified using this approach. Conclusions The markers generated exploring 16S rRNA gene sequences provided useful tool that can be further used for identification of different species of the genus Streptococcus. PMID:21702978

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

In silico analysis of cacao (Theobroma cacao L.) genes that involved in pathogen and disease responses

NASA Astrophysics Data System (ADS)

Agung, Muhammad Budi; Budiarsa, I. Made; Suwastika, I. Nengah

2017-02-01

Cocoa bean is one of the main commodities from Indonesia for the world, which still have problem regarding yield degradation due to pathogens and disease attack. Developing robust cacao plant that genetically resistant to pathogen and disease attack is an ideal solution in over taking on this problem. The aim of this study was to identify Theobroma cacao genes on database of cacao genome that homolog to response genes of pathogen and disease attack in other plant, through in silico analysis. Basic information survey and gene identification were performed in GenBank and The Arabidopsis Information Resource database. The In silico analysis contains protein BLAST, homology test of each gene's protein candidates, and identification of homologue gene in Cacao Genome Database using data source "Theobroma cacao cv. Matina 1-6 v1.1" genome. Identification found that Thecc1EG011959t1 (EDS1), Thecc1EG006803t1 (EDS5), Thecc1EG013842t1 (ICS1), and Thecc1EG015614t1 (BG_PPAP) gene of Cacao Genome Database were Theobroma cacao genes that homolog to plant's resistance genes which highly possible to have similar functions of each gene's homologue gene.

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

Computational modeling and in-vitro/in-silico correlation of phospholipid-based prodrugs for targeted drug delivery in inflammatory bowel disease

NASA Astrophysics Data System (ADS)

Dahan, Arik; Markovic, Milica; Keinan, Shahar; Kurnikov, Igor; Aponick, Aaron; Zimmermann, Ellen M.; Ben-Shabat, Shimon

2017-11-01

Targeting drugs to the inflamed intestinal tissue(s) represents a major advancement in the treatment of inflammatory bowel disease (IBD). In this work we present a powerful in-silico modeling approach to guide the molecular design of novel prodrugs targeting the enzyme PLA2, which is overexpressed in the inflamed tissues of IBD patients. The prodrug consists of the drug moiety bound to the sn-2 position of phospholipid (PL) through a carbonic linker, aiming to allow PLA2 to release the free drug. The linker length dictates the affinity of the PL-drug conjugate to PLA2, and the optimal linker will enable maximal PLA2-mediated activation. Thermodynamic integration and Weighted Histogram Analysis Method (WHAM)/Umbrella Sampling method were used to compute the changes in PLA2 transition state binding free energy of the prodrug molecule (ΔΔGtr) associated with decreasing/increasing linker length. The simulations revealed that 6-carbons linker is the optimal one, whereas shorter or longer linkers resulted in decreased PLA2-mediated activation. These in-silico results were shown to be in excellent correlation with experimental in-vitro data. Overall, this modern computational approach enables optimization of the molecular design of novel prodrugs, which may allow targeting the free drug specifically to the diseased intestinal tissue of IBD patients.

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 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.

Altered retinal microRNA expression profiles in early diabetic retinopathy: an in silico analysis.

PubMed

Xiong, Fen; Du, Xinhua; Hu, Jianyan; Li, Tingting; Du, Shanshan; Wu, Qiang

2014-07-01

MicroRNAs (miRNAs) - as negative regulators of target genes - are associated with various human diseases, but their precise role(s) in diabetic retinopathy (DR) remains to be elucidated. The aim of this study was to elucidate the involvement of miRNAs in early DR using in silico analysis to explore their gene expression patterns. We used the streptozotocin (STZ)-induced diabetic rat to investigate the roles of miRNAs in early DR. Retinal miRNA expression profiles from diabetic versus healthy control rats were examined by miRNA array analysis. Based on several bioinformatic systems, specifically, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, we identified signatures of the potential pathological processes, gene functions, and signaling pathways that are influenced by dysregulated miRNAs. We used quantitative real-time polymerase chain reaction (qRT-PCR) to validate six (i.e. those with significant changes in expression levels) of the 17 miRNAs that were detected in the miRNA array. We also describe the significant role of the miRNA-gene network, which is based on the interactions between miRNAs and target genes. GO analysis of the 17 miRNAs detected in the miRNA array analysis revealed the most prevalent miRNAs to be those related to biological processes, olfactory bulb development and axonogenesis. These miRNAs also exert significant influence on additional pathways, including the mitogen-activated protein and calcium signaling pathways. Six of the seventeen miRNAs were chosen for qRT-PCR validation. With the exception of a slight difference in miRNA-350, our results are in close agreement with the differential expressions detected by array analysis. This study, which describes miRNA expression during the early developmental phases of DR, revealed extensive miRNA interactions. Based on both their target genes and signaling pathways, we suggest that miRNAs perform critical regulatory functions during the early stages of DR evolution.

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.

Leucine/Pd-loaded (5,5) single-walled carbon nanotube matrix as a novel nanobiosensors for in silico detection of protein.

PubMed

Yoosefian, Mehdi; Etminan, Nazanin

2018-06-01

We have designed a novel nanobiosensor for in silico detecting proteins based on leucine/Pd-loaded single-walled carbon nanotube matrix. Density functional theory at the B3LYP/6-31G (d) level of theory was realized to analyze the geometrical and electronic structure of the proposed nanobiosensor. The solvent effects were investigated using the Tomasi's polarized continuum model. Atoms-in-molecules theory was used to study the nature of interactions by calculating the electron density ρ(r) and Laplacian at the bond critical points. Natural bond orbital analysis was performed to achieve a deep understanding of the nature of the interactions. The biosensor has potential application for high sensitive and rapid response to protein due to the chemical adsorption of L-leucine amino acid onto Pd-loaded single-walled carbon nanotube and reactive functional groups that can incorporate in hydrogen binding, hydrophobic interactions and van der Waals forces with the protein surface in detection process.

Cloud Infrastructures for In Silico Drug Discovery: Economic and Practical Aspects

PubMed Central

Clematis, Andrea; Quarati, Alfonso; Cesini, Daniele; Milanesi, Luciano; Merelli, Ivan

2013-01-01

Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements. PMID:24106693

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.

In silico investigation of lavandulyl flavonoids for the development of potent fatty acid synthase-inhibitory prototypes.

PubMed

Oh, Joonseok; Liu, Haining; Park, Hyun Bong; Ferreira, Daneel; Jeong, Gil-Saeng; Hamann, Mark T; Doerksen, Robert J; Na, MinKyun

2017-01-01

Inhibition of fatty acid synthase (FAS) is regarded as a sensible therapeutic strategy for the development of optimal anti-cancer agents. Flavonoids exhibit potent anti-neoplastic properties. The MeOH extract of Sophora flavescens was subjected to chromatographic analyses such as VLC and HPLC for the purification of active flavonoids. The DP4 chemical-shift analysis protocol was employed to investigate the elusive chirality of the lavandulyl moiety of the purified polyphenols. Induced Fit docking protocols and per-residue analyses were utilized to scrutinize structural prerequisites for hampering FAS activity. The FAS-inhibitory activity of the purified flavonoids was assessed via the incorporation of [ 3 H] acetyl-CoA into palmitate. Six flavonoids, including lavandulyl flavanones, were purified and evaluated for FAS inhibition. The lavandulyl flavanone sophoraflavanone G (2) exhibited the highest potency (IC 50 of 6.7±0.2μM), which was more potent than the positive controls. Extensive molecular docking studies revealed the structural requirements for blocking FAS. Per-residue interaction analysis demonstrated that the lavandulyl functional group in the active flavonoids (1-3 and 5) significantly contributed to increasing their binding affinity towards the target enzyme. This research suggests a basis for the in silico design of a lavandulyl flavonoid-based architecture showing anti-cancer effects via enhancement of the binding potential to FAS. FAS inhibition by flavonoids and their derivatives may offer significant potential as an approach to lower the risk of various cancer diseases and related fatalities. In silico technologies with available FAS crystal structures may be of significant use in optimizing preliminary leads. Copyright © 2016 Elsevier B.V. All rights reserved.

In silico investigation of lavandulyl flavonoids for the development of potent fatty acid synthase-inhibitory prototypes

PubMed Central

Oh, Joonseok; Liu, Haining; Park, Hyun Bong; Ferreira, Daneel; Jeong, Gil-Saeng; Hamann, Mark T.; Doerksen, Robert J.; Na, MinKyun

2016-01-01

Background Inhibition of fatty acid synthase (FAS) is regarded as a sensible therapeutic strategy for the development of optimal anti-cancer agents. Flavonoids exhibit potent anti-neoplastic properties. Methods The MeOH extract of Sophora flavescens was subjected to chromatographic analyses such as VLC and HPLC for the purification of active flavonoids. The DP4 chemical-shift analysis protocol was employed to investigate the elusive chirality of the lavandulyl moiety of the purified polyphenols. Induced Fit docking protocols and per-residue analyses were utilized to scrutinize structural prerequisites for hampering FAS activity. The FAS-inhibitory activity of the purified flavonoids was assessed via the incorporation of [3H] acetyl-CoA into palmitate. Results Six flavonoids, including lavandulyl flavanones, were purified and evaluated for FAS inhibition. The lavandulyl flavanone sophoraflavanone G (2) exhibited the highest potency (IC50 of 6.7 ± 0.2 μM), which was more potent than the positive controls. Extensive molecular docking studies revealed the structural requirements for blocking FAS. Per-residue interaction analysis demonstrated that the lavandulyl functional group in the active flavonoids (1–3 and 5) significantly contributed to increasing their binding affinity towards the target enzyme. Conclusion This research suggests a basis for the in silico design of a lavandulyl flavonoid-based architecture showing anti-cancer effects via enhancement of the binding potential to FAS. General significance FAS inhibition by flavonoids and their derivatives may offer significant potential as an approach to lower the risk of various cancer diseases and related fatalities. In silico technologies with available FAS crystal structures may be of significant use in optimizing preliminary leads. PMID:27531709

In Vitro vs In Silico Detected SNPs for the Development of a Genotyping Array: What Can We Learn from a Non-Model Species?

PubMed Central

Lepoittevin, Camille; Frigerio, Jean-Marc; Garnier-Géré, Pauline; Salin, Franck; Cervera, María-Teresa; Vornam, Barbara; Harvengt, Luc; Plomion, Christophe

2010-01-01

Background There is considerable interest in the high-throughput discovery and genotyping of single nucleotide polymorphisms (SNPs) to accelerate genetic mapping and enable association studies. This study provides an assessment of EST-derived and resequencing-derived SNP quality in maritime pine (Pinus pinaster Ait.), a conifer characterized by a huge genome size (∼23.8 Gb/C). Methodology/Principal Findings A 384-SNPs GoldenGate genotyping array was built from i/ 184 SNPs originally detected in a set of 40 re-sequenced candidate genes (in vitro SNPs), chosen on the basis of functionality scores, presence of neighboring polymorphisms, minor allele frequencies and linkage disequilibrium and ii/ 200 SNPs screened from ESTs (in silico SNPs) selected based on the number of ESTs used for SNP detection, the SNP minor allele frequency and the quality of SNP flanking sequences. The global success rate of the assay was 66.9%, and a conversion rate (considering only polymorphic SNPs) of 51% was achieved. In vitro SNPs showed significantly higher genotyping-success and conversion rates than in silico SNPs (+11.5% and +18.5%, respectively). The reproducibility was 100%, and the genotyping error rate very low (0.54%, dropping down to 0.06% when removing four SNPs showing elevated error rates). Conclusions/Significance This study demonstrates that ESTs provide a resource for SNP identification in non-model species, which do not require any additional bench work and little bio-informatics analysis. However, the time and cost benefits of in silico SNPs are counterbalanced by a lower conversion rate than in vitro SNPs. This drawback is acceptable for population-based experiments, but could be dramatic in experiments involving samples from narrow genetic backgrounds. In addition, we showed that both the visual inspection of genotyping clusters and the estimation of a per SNP error rate should help identify markers that are not suitable to the GoldenGate technology in species characterized by a large and complex genome. PMID:20543950

Antimicrobial Peptides of Meat Origin - An In silico and In vitro Analysis.

PubMed

Keska, Paulina; Stadnik, Joanna

2017-01-01

The aim of this study was to evaluate the antimicrobial activity of meat protein-derived peptides against selected Gram-positive and Gram-negative bacteria. The in silico and in vitro approach was combined to determine the potency of antimicrobial peptides derived from pig (Sus scrofa) and cow (Bos taurus) proteins. The in silico studies consisted of an analysis of the amino acid composition of peptides obtained from the CAMPR database, their molecular weight and other physicochemical properties (isoelectric point, molar extinction coefficient, instability index, aliphatic index, hydropathy index and net charge). The degree of similarity was estimated between the antimicrobial peptide sequences derived from the slaughtered animals and the main meat proteins. Antimicrobial activity of peptides isolated from dry-cured meat products was analysed (in vitro) against two strains of pathogenic bacteria using the disc diffusion method. There was no evidence of growthinhibitory properties of peptides isolated from dry-cured meat products against Escherichia coli K12 ATCC 10798 and Staphylococcus aureus ATCC 25923. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

In silico profiling for secondary metabolites from Lepidium meyenii (maca) by the pharmacophore and ligand-shape-based joint approach.

PubMed

Yi, Fan; Tan, Xiao-Lei; Yan, Xin; Liu, Hai-Bo

2016-01-01

Lepidium meyenii Walpers (maca) is an herb known as a traditional nutritional supplement and widely used in Peru, North America, and Europe to enhance human fertility and treat osteoporosis. The secondary metabolites of maca, namely, maca alkaloids, macaenes, and macamides, are bioactive compounds, but their targets are undefined. The pharmacophore-based PharmaDB targets database screening joint the ligand shape similarity-based WEGA validation approach is proposed to predict the targets of these unique constituents and was performed using Discovery Studio 4.5 and PharmaDB. A compounds-targets-diseases network was established using Cytoscape 3.2. These suitable targets and their genes were calculated and analyzed using ingenuity pathway analysis and GeneMANIA. Certain targets were identified in osteoporosis (8 targets), prostate cancer (9 targets), and kidney diseases (11 targets). This was the first study to identify the targets of these bioactive compounds in maca for cardiovascular diseases (29 targets). The compound with the most targets (46) was an amide alkaloid (MA-24). In silico target fishing identified maca's traditional effects on treatment and prevention of osteoporosis, prostate cancer, and kidney diseases, and its potential function of treating cardiovascular diseases, as the most important of this herb's possible activities.

In-silico Taxonomic Classification of 373 Genomes Reveals Species Misidentification and New Genospecies within the Genus Pseudomonas.

PubMed

Tran, Phuong N; Savka, Michael A; Gan, Han Ming

2017-01-01

The genus Pseudomonas has one of the largest diversity of species within the Bacteria kingdom. To date, its taxonomy is still being revised and updated. Due to the non-standardized procedure and ambiguous thresholds at species level, largely based on 16S rRNA gene or conventional biochemical assay, species identification of publicly available Pseudomonas genomes remains questionable. In this study, we performed a large-scale analysis of all Pseudomonas genomes with species designation (excluding the well-defined P. aeruginosa ) and re-evaluated their taxonomic assignment via in silico genome-genome hybridization and/or genetic comparison with valid type species. Three-hundred and seventy-three pseudomonad genomes were analyzed and subsequently clustered into 145 distinct genospecies. We detected 207 erroneous labels and corrected 43 to the proper species based on Average Nucleotide Identity Multilocus Sequence Typing (MLST) sequence similarity to the type strain. Surprisingly, more than half of the genomes initially designated as Pseudomonas syringae and Pseudomonas fluorescens should be classified either to a previously described species or to a new genospecies. Notably, high pairwise average nucleotide identity (>95%) indicating species-level similarity was observed between P. synxantha-P. libanensis, P. psychrotolerans - P. oryzihabitans , and P. kilonensis- P. brassicacearum , that were previously differentiated based on conventional biochemical tests and/or genome-genome hybridization techniques.

In Silico and in Vitro Screening for P-Glycoprotein Interaction with Tenofovir, Darunavir, and Dapivirine: An Antiretroviral Drug Combination for Topical Prevention of Colorectal HIV Transmission.

PubMed

Swedrowska, Magda; Jamshidi, Shirin; Kumar, Abhinav; Kelly, Charles; Rahman, Khondaker Miraz; Forbes, Ben

2017-08-07

The aim of the study was to use in silico and in vitro techniques to evaluate whether a triple formulation of antiretroviral drugs (tenofovir, darunavir, and dapivirine) interacted with P-glycoprotein (P-gp) or exhibited any other permeability-altering drug-drug interactions in the colorectal mucosa. Potential drug interactions with P-gp were screened initially using molecular docking, followed by molecular dynamics simulations to analyze the identified drug-transporter interaction more mechanistically. The transport of tenofovir, darunavir, and dapivirine was investigated in the Caco-2 cell models and colorectal tissue, and their apparent permeability coefficient (P app ), efflux ratio (ER), and the effect of transporter inhibitors were evaluated. In silico, dapivirine and darunavir showed strong affinity for P-gp with similar free energy of binding; dapivirine exhibiting a ΔG PB value -38.24 kcal/mol, darunavir a ΔG PB value -36.84 kcal/mol. The rank order of permeability of the compounds in vitro was tenofovir < darunavir < dapivirine. The P app for tenofovir in Caco-2 cell monolayers was 0.10 ± 0.02 × 10 -6 cm/s, ER = 1. For dapivirine, P app was 32.2 ± 3.7 × 10 -6 cm/s, but the ER = 1.3 was lower than anticipated based on the in silico findings. Neither tenofovir nor dapivirine transport was influenced by P-gp inhibitors. The absorptive permeability of darunavir (P app = 6.4 ± 0.9 × 10 -6 cm/s) was concentration dependent with ER = 6.3, which was reduced by verapamil to 1.2. Administration of the drugs in combination did not alter their permeability compared to administration as single agents. In conclusion, in silico modeling, cell culture, and tissue-based assays showed that tenofovir does not interact with P-gp and is poorly permeable, consistent with a paracellular transport mechanism. In silico modeling predicted that darunavir and dapivirine were P-gp substrates, but only darunavir showed P-gp-dependent permeability in the biological models, illustrating that in silico modeling requires experimental validation. When administered in combination, the disposition of the proposed triple-therapy antiretroviral drugs in the colorectal mucosa will depend on their distinctly different permeability, but was not interdependent.

Advancing Peptide-Based Biorecognition Elements for Biosensors Using in-Silico Evolution.

PubMed

Xiao, Xingqing; Kuang, Zhifeng; Slocik, Joseph M; Tadepalli, Sirimuvva; Brothers, Michael; Kim, Steve; Mirau, Peter A; Butkus, Claire; Farmer, Barry L; Singamaneni, Srikanth; Hall, Carol K; Naik, Rajesh R

2018-05-25

Sensors for human health and performance monitoring require biological recognition elements (BREs) at device interfaces for the detection of key molecular biomarkers that are measurable biological state indicators. BREs, including peptides, antibodies, and nucleic acids, bind to biomarkers in the vicinity of the sensor surface to create a signal proportional to the biomarker concentration. The discovery of BREs with the required sensitivity and selectivity to bind biomarkers at low concentrations remains a fundamental challenge. In this study, we describe an in-silico approach to evolve higher sensitivity peptide-based BREs for the detection of cardiac event marker protein troponin I (cTnI) from a previously identified BRE as the parental affinity peptide. The P2 affinity peptide, evolved using our in-silico method, was found to have ∼16-fold higher affinity compared to the parent BRE and ∼10 fM (0.23 pg/mL) limit of detection. The approach described here can be applied towards designing BREs for other biomarkers for human health monitoring.

Evaluating the mutagenic potential of aerosol organic compounds using informatics-based screening

NASA Astrophysics Data System (ADS)

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

2018-02-01

Whilst general policy objectives to reduce airborne particulate matter (PM) health effects are to reduce exposure to PM as a whole, emerging evidence suggests that more detailed metrics associating impacts with different aerosol components might be needed. Since it is impossible to conduct toxicological screening on all possible molecular species expected to occur in aerosol, in this study we perform a proof-of-concept evaluation on the information retrieved from in silico toxicological predictions, in which a subset (N = 104) of secondary organic aerosol (SOA) compounds were screened for their mutagenicity potential. An extensive database search showed that experimental data are available for 13 % of the compounds, while reliable predictions were obtained for 82 %. A multivariate statistical analysis of the compounds based on their physico-chemical, structural, and mechanistic properties showed that 80 % of the compounds predicted as mutagenic were grouped into six clusters, three of which (five-membered lactones from monoterpene oxidation, oxygenated multifunctional compounds from substituted benzene oxidation, and hydroperoxides from several precursors) represent new candidate groups of compounds for future toxicological screenings. These results demonstrate that coupling model-generated compositions to in silico toxicological screening might enable more comprehensive exploration of the mutagenic potential of specific SOA components.

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 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

PEGylated substrates of NSP4 protease: A tool to study protease specificity

NASA Astrophysics Data System (ADS)

Wysocka, Magdalena; Gruba, Natalia; Grzywa, Renata; Giełdoń, Artur; Bąchor, Remigiusz; Brzozowski, Krzysztof; Sieńczyk, Marcin; Dieter, Jenne; Szewczuk, Zbigniew; Rolka, Krzysztof; Lesner, Adam

2016-03-01

Herein we present the synthesis of a novel type of peptidomimetics composed of repeating diaminopropionic acid residues modified with structurally diverse heterobifunctional polyethylene glycol chains (abbreviated as DAPEG). Based on the developed compounds, a library of fluorogenic substrates was synthesized. Further library deconvolution towards human neutrophil serine protease 4 (NSP4) yielded highly sensitive and selective internally quenched peptidomimetic substrates. In silico analysis of the obtained peptidomimetics revealed the presence of an interaction network with distant subsites located on the enzyme surface.

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.

AutoClickChem: click chemistry in silico.

PubMed

Durrant, Jacob D; McCammon, J Andrew

2012-01-01

Academic researchers and many in industry often lack the financial resources available to scientists working in "big pharma." High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu.

AutoClickChem: Click Chemistry in Silico

PubMed Central

Durrant, Jacob D.; McCammon, J. Andrew

2012-01-01

Academic researchers and many in industry often lack the financial resources available to scientists working in “big pharma.” High costs include those associated with high-throughput screening and chemical synthesis. In order to address these challenges, many researchers have in part turned to alternate methodologies. Virtual screening, for example, often substitutes for high-throughput screening, and click chemistry ensures that chemical synthesis is fast, cheap, and comparatively easy. Though both in silico screening and click chemistry seek to make drug discovery more feasible, it is not yet routine to couple these two methodologies. We here present a novel computer algorithm, called AutoClickChem, capable of performing many click-chemistry reactions in silico. AutoClickChem can be used to produce large combinatorial libraries of compound models for use in virtual screens. As the compounds of these libraries are constructed according to the reactions of click chemistry, they can be easily synthesized for subsequent testing in biochemical assays. Additionally, in silico modeling of click-chemistry products may prove useful in rational drug design and drug optimization. AutoClickChem is based on the pymolecule toolbox, a framework that may facilitate the development of future python-based programs that require the manipulation of molecular models. Both the pymolecule toolbox and AutoClickChem are released under the GNU General Public License version 3 and are available for download from http://autoclickchem.ucsd.edu. PMID:22438795

Meta-analysis of genome-wide association studies for personality

PubMed Central

de Moor, Marleen H.M.; Costa, Paul T.; Terracciano, Antonio; Krueger, Robert F.; de Geus, Eco J.C.; Toshiko, Tanaka; Penninx, Brenda W.J.H.; Esko, Tõnu; Madden, Pamela A F; Derringer, Jaime; Amin, Najaf; Willemsen, Gonneke; Hottenga, Jouke-Jan; Distel, Marijn A.; Uda, Manuela; Sanna, Serena; Spinhoven, Philip; Hartman, Catharina A.; Sullivan, Patrick; Realo, Anu; Allik, Jüri; Heath, Andrew C; Pergadia, Michele L; Agrawal, Arpana; Lin, Peng; Grucza, Richard; Nutile, Teresa; Ciullo, Marina; Rujescu, Dan; Giegling, Ina; Konte, Bettina; Widen, Elisabeth; Cousminer, Diana L; Eriksson, Johan G.; Palotie, Aarno; Luciano, Michelle; Tenesa, Albert; Davies, Gail; Lopez, Lorna M.; Hansell, Narelle K.; Medland, Sarah E.; Ferrucci, Luigi; Schlessinger, David; Montgomery, Grant W.; Wright, Margaret J.; Aulchenko, Yurii S.; Janssens, A.Cecile J.W.; Oostra, Ben A.; Metspalu, Andres; Abecasis, Gonçalo R.; Deary, Ian J.; Räikkönen, Katri; Bierut, Laura J.; Martin, Nicholas G.; van Duijn, Cornelia M.; Boomsma, Dorret I.

2013-01-01

Personality can be thought of as a set of characteristics that influence people’s thoughts, feelings, and behaviour across a variety of settings. Variation in personality is predictive of many outcomes in life, including mental health. Here we report on a meta-analysis of genome-wide association (GWA) data for personality in ten discovery samples (17 375 adults) and five in-silico replication samples (3 294 adults). All participants were of European ancestry. Personality scores for Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness were based on the NEO Five-Factor Inventory. Genotype data were available of ~2.4M Single Nucleotide Polymorphisms (SNPs; directly typed and imputed using HAPMAP data). In the discovery samples, classical association analyses were performed under an additive model followed by meta-analysis using the weighted inverse variance method. Results showed genome-wide significance for Openness to Experience near the RASA1 gene on 5q14.3 (rs1477268 and rs2032794, P = 2.8 × 10−8 and 3.1 × 10−8) and for Conscientiousness in the brain-expressed KATNAL2 gene on 18q21.1 (rs2576037, P = 4.9 × 10−8). We further conducted a gene-based test that confirmed the association of KATNAL2 to Conscientiousness. In-silico replication did not, however, show significant associations of the top SNPs with Openness and Conscientiousness, although the direction of effect of the KATNAL2 SNP on Conscientiousness was consistent in all replication samples. Larger scale GWA studies and alternative approaches are required for confirmation of KATNAL2 as a novel gene affecting Conscientiousness. PMID:21173776

A general framework for optimization of probes for gene expression microarray and its application to the fungus Podospora anserina.

PubMed

Bidard, Frédérique; Imbeaud, Sandrine; Reymond, Nancie; Lespinet, Olivier; Silar, Philippe; Clavé, Corinne; Delacroix, Hervé; Berteaux-Lecellier, Véronique; Debuchy, Robert

2010-06-18

The development of new microarray technologies makes custom long oligonucleotide arrays affordable for many experimental applications, notably gene expression analyses. Reliable results depend on probe design quality and selection. Probe design strategy should cope with the limited accuracy of de novo gene prediction programs, and annotation up-dating. We present a novel in silico procedure which addresses these issues and includes experimental screening, as an empirical approach is the best strategy to identify optimal probes in the in silico outcome. We used four criteria for in silico probe selection: cross-hybridization, hairpin stability, probe location relative to coding sequence end and intron position. This latter criterion is critical when exon-intron gene structure predictions for intron-rich genes are inaccurate. For each coding sequence (CDS), we selected a sub-set of four probes. These probes were included in a test microarray, which was used to evaluate the hybridization behavior of each probe. The best probe for each CDS was selected according to three experimental criteria: signal-to-noise ratio, signal reproducibility, and representative signal intensities. This procedure was applied for the development of a gene expression Agilent platform for the filamentous fungus Podospora anserina and the selection of a single 60-mer probe for each of the 10,556 P. anserina CDS. A reliable gene expression microarray version based on the Agilent 44K platform was developed with four spot replicates of each probe to increase statistical significance of analysis.

Flux analysis of the human proximal colon using anaerobic digestion model 1.

PubMed

Motelica-Wagenaar, Anne Marieke; Nauta, Arjen; van den Heuvel, Ellen G H M; Kleerebezem, Robbert

2014-08-01

The colon can be regarded as an anaerobic digestive compartment within the gastro intestinal tract (GIT). An in silico model simulating the fluxes in the human proximal colon was developed on basis of the anaerobic digestion model 1 (ADM1), which is traditionally used to model waste conversion to biogas. Model calibration was conducted using data from in vitro fermentation of the proximal colon (TIM-2), and, amongst others, supplemented with the bio kinetics of prebiotic galactooligosaccharides (GOS) fermentation. The impact of water and solutes absorption by the host was also included. Hydrolysis constants of carbohydrates and proteins were estimated based on total short chain fatty acids (SCFA) and ammonia production in vitro. Model validation was established using an independent dataset of a different in vitro model: an in vitro three-stage continuous culture system. The in silico model was shown to provide quantitative insight in the microbial community structure in terms of functional groups, and the substrate and product fluxes between these groups as well as the host, as a function of the substrate composition, pH and the solids residence time (SRT). The model confirms the experimental observation that methanogens are washed out at low pH or low SRT-values. The in silico model is proposed as useful tool in the design of experimental setups for in vitro experiments by giving insight in fermentation processes in the proximal human colon. Copyright © 2014. Published by Elsevier Ltd.

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 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

Prediction and mechanism elucidation of analyte retention on phospholipid stationary phases (IAM-HPLC) by in silico calculated physico-chemical descriptors.

PubMed

Russo, Giacomo; Grumetto, Lucia; Barbato, Francesco; Vistoli, Giulio; Pedretti, Alessandro

2017-03-01

The present study proposes a method for an in silico calculation of phospholipophilicity. Phospholipophilicity is intended as the measure of analyte affinity for phospholipids; it is currently assessed by HPLC measures of analyte retention on phosphatidylcholine-like stationary phases (IAM - Immobilized Artificial Membrane) resulting in log k W IAM values. Due to the amphipathic and electrically charged nature of phospholipids, retention on these stationary phases results from complex mechanisms, being affected not only by lipophilicity (as measured by n-octanol/aqueous phase partition coefficients, log P) but also by the occurrence of polar and/or electrostatic intermolecular interaction forces. Differently from log P, to date no method has been proposed for in silico calculation of log k W IAM . The study is aimed both at shedding new light into the retention mechanism on IAM stationary phases and at offering a high-throughput method to achieve such values. A wide set of physico-chemical and topological properties were taken into account, yielding a robust final model including four in silico calculated parameters (lipophilicity, hydrophilic/lipophilic balance, molecular size, and molecule flexibility). The here presented model was based on the analysis of 205 experimentally determined values, taken from the literature and measured by a single research group to minimize the interlaboratory variability; such model is able to predict phospholipophilicity values on both the two IAM stationary phases to date marketed, i.e. IAM.PC.MG and IAM.PC.DD2, with a fairly good degree (r 2 =0.85) of accuracy. The present work allowed the development of a free on-line service aimed at calculating log k W IAM values of any molecule included in the PubChem database, which is freely available at http://nova.disfarm.unimi.it/logkwiam.htm. Copyright © 2016 Elsevier B.V. All rights reserved.

Complete mitochondrial genome of Concholepas concholepas inferred by 454 pyrosequencing and mtDNA expression in two mollusc populations.

PubMed

Núñez-Acuña, Gustavo; Aguilar-Espinoza, Andrea; Gallardo-Escárate, Cristian

2013-03-01

Despite the great relevance of mitochondrial genome analysis in evolutionary studies, there is scarce information on how the transcripts associated with the mitogenome are expressed and their role in the genetic structuring of populations. This work reports the complete mitochondrial genome of the marine gastropod Concholepas concholepas, obtained by 454 pryosequencing, and an analysis of mitochondrial transcripts of two populations 1000 km apart along the Chilean coast. The mitochondrion of C. concholepas is 15,495 base pairs (bp) in size and contains the 37 subunits characteristic of metazoans, as well as a non-coding region of 330 bp. In silico analysis of mitochondrial gene variability showed significant differences among populations. In terms of levels of relative abundance of transcripts associated with mitochondrion in the two populations (assessed by qPCR), the genes associated with complexes III and IV of the mitochondrial genome had the highest levels of expression in the northern population while transcripts associated with the ATP synthase complex had the highest levels of expression in the southern population. Moreover, fifteen polymorphic SNPs were identified in silico between the mitogenomes of the two populations. Four of these markers implied different amino acid substitutions (non-synonymous SNPs). This work contributes novel information regarding the mitochondrial genome structure and mRNA expression levels of C. concholepas. Copyright © 2012 Elsevier Inc. All rights reserved.

Computational modeling in melanoma for novel drug discovery.

PubMed

Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

2016-06-01

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

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

In Silico Analysis of Expression Data for Identification of Genes Involved in Spatial Accumulation of Calcium in Developing Seeds of Rice

PubMed Central

Goel, Anshita; Gaur, Vikram S.; Arora, Sandeep; Gupta, Sanjay

2012-01-01

Abstract The calcium (Ca2+) transporters, like Ca2+ channels, Ca2+ ATPases, and Ca2+ exchangers, are instrumental for signaling and transport. However, the mechanism by which they orchestrate the accumulation of Ca2+ in grain filling has not yet been investigated. Hence the present study was designed to identify the potential calcium transporter genes that may be responsible for the spatial accumulation of calcium during grain filling. In silico expression analyses were performed to identify Ca2+ transporters that predominantly express during the different developmental stages of Oryza sativa. A total of 13 unique calcium transporters (7 from massively parallel signature sequencing [MPSS] data analysis, and 9 from microarray analysis) were identified. Analysis of variance (ANOVA) revealed differential expression of the transporters across tissues, and principal component analysis (PCA) exhibited their seed-specific distinctive expression profile. Interestingly, Ca2+ exchanger genes are highly expressed in the initial stages, whereas some Ca2+ ATPase genes are highly expressed throughout seed development. Furthermore, analysis of the cis-elements located in the promoter region of the subset of 13 genes suggested that Dof proteins play essential roles in regulating the expression of Ca2+ transporter genes during rice seed development. Based on these results, we developed a hypothetical model explaining the transport and tissue specific distribution of calcium in developing cereal seeds. The model may be extrapolated to understand the mechanism behind the exceptionally high level of calcium accumulation seen in grains like finger millet. PMID:22734689

Alzheimer's disease biomarker discovery using in silico literature mining and clinical validation

PubMed Central

2012-01-01

Background Alzheimer’s Disease (AD) is the most widespread form of dementia in the elderly but despite progress made in recent years towards a mechanistic understanding, there is still an urgent need for disease modification therapy and for early diagnostic tests. Substantial international efforts are being made to discover and validate biomarkers for AD using candidate analytes and various data-driven 'omics' approaches. Cerebrospinal fluid is in many ways the tissue of choice for biomarkers of brain disease but is limited by patient and clinician acceptability, and increasing attention is being paid to the search for blood-based biomarkers. The aim of this study was to use a novel in silico approach to discover a set of candidate biomarkers for AD. Methods We used an in silico literature mining approach to identify potential biomarkers by creating a summarized set of assertional metadata derived from relevant legacy information. We then assessed the validity of this approach using direct assays of the identified biomarkers in plasma by immunodetection methods. Results Using this in silico approach, we identified 25 biomarker candidates, at least three of which have subsequently been reported to be altered in blood or CSF from AD patients. Two further candidate biomarkers, indicated from the in silico approach, were choline acetyltransferase and urokinase-type plasminogen activator receptor. Using immunodetection, we showed that, in a large sample set, these markers are either altered in disease or correlate with MRI markers of atrophy. Conclusions These data support as a proof of concept the use of data mining and in silico analyses to derive valid biomarker candidates for AD and, by extension, for other disorders. PMID:23113945

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.

Nonlinear optical studies and structure-activity relationship of chalcone derivatives with in silico insights

NASA Astrophysics Data System (ADS)

Kar, Swayamsiddha; Adithya, K. S.; Shankar, Pruthvik; Jagadeesh Babu, N.; Srivastava, Sailesh; Nageswara Rao, G.

2017-07-01

Nine chalcones were prepared via Claisen-Schmidt condensation, and characterized by UV-vis, IR, 1H NMR, 13C NMR and mass spectrometry. One of the representative member 4-NDM-TC has been studied via single crystal XRD and the TGA/DTA technique. SHG efficiency and NLO susceptibilities of the chalcones have been evaluated by the Kurtz and Perry method and Degenerate Four Wave Mixing techniques respectively. 3-Cl-4‧-HC was noted to possess SHG efficiency 1.37 times that of urea while 4-NDM-TC returned the highest third order NLO susceptibilities with respect to CS2. In silico studies help evaluate various physical parameters, in correlating the observed activities. In conclusion, the structure-activity relationship was derived based on the in silico and experimental results for the third order NLO susceptibilities.

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.

Imperfect duplicate insertions type of mutations in plasmepsin V modulates binding properties of PEXEL motifs of export proteins in Indian Plasmodium vivax.

PubMed

Rawat, Manmeet; Vijay, Sonam; Gupta, Yash; Tiwari, Pramod Kumar; Sharma, Arun

2013-01-01

Plasmepsin V (PM-V) have functionally conserved orthologues across the Plasmodium genus who's binding and antigenic processing at the PEXEL motifs for export about 200-300 essential proteins is important for the virulence and viability of the causative Plasmodium species. This study was undertaken to determine P. vivax plasmepsin V Ind (PvPM-V-Ind) PEXEL motif export pathway for pathogenicity-related proteins/antigens export thereby altering plasmodium exportome during erythrocytic stages. We identify and characterize Plasmodium vivax plasmepsin-V-Ind (mutant) gene by cloning, sequence analysis, in silico bioinformatic protocols and structural modeling predictions based on docking studies on binding capacity with PEXEL motifs processing in terms of binding and accessibility of export proteins. Cloning and sequence analysis for genetic diversity demonstrates PvPM-V-Ind (mutant) gene is highly conserved among all isolates from different geographical regions of India. Imperfect duplicate insertion types of mutations (SVSE from 246-249 AA and SLSE from 266-269 AA) were identified among all Indian isolates in comparison to P.vivax Sal-1 (PvPM-V-Sal 1) isolate. In silico bioinformatics interaction studies of PEXEL peptide and active enzyme reveal that PvPM-V-Ind (mutant) is only active in endoplasmic reticulum lumen and membrane embedding is essential for activation of plasmepsin V. Structural modeling predictions based on docking studies with PEXEL motif show significant variation in substrate protein binding of these imperfect mutations with data mined PEXEL sequences. The predicted variation in the docking score and interacting amino acids of PvPM-V-Ind (mutant) proteins with PEXEL and lopinavir suggests a modulation in the activity of PvPM-V in terms of binding and accessibility at these sites. Our functional modeled validation of PvPM-V-Ind (mutant) imperfect duplicate insertions with data mined PEXEL sequences leading to altered binding and substrate accessibility of the enzyme makes it a plausible target to investigate export mechanisms for in silico virtual screening and novel pharmacophore designing.

Imperfect Duplicate Insertions Type of Mutations in Plasmepsin V Modulates Binding Properties of PEXEL Motifs of Export Proteins in Indian Plasmodium vivax

PubMed Central

Rawat, Manmeet; Vijay, Sonam; Gupta, Yash; Tiwari, Pramod Kumar; Sharma, Arun

2013-01-01

Introduction Plasmepsin V (PM-V) have functionally conserved orthologues across the Plasmodium genus who's binding and antigenic processing at the PEXEL motifs for export about 200–300 essential proteins is important for the virulence and viability of the causative Plasmodium species. This study was undertaken to determine P. vivax plasmepsin V Ind (PvPM-V-Ind) PEXEL motif export pathway for pathogenicity-related proteins/antigens export thereby altering plasmodium exportome during erythrocytic stages. Method We identify and characterize Plasmodium vivax plasmepsin-V-Ind (mutant) gene by cloning, sequence analysis, in silico bioinformatic protocols and structural modeling predictions based on docking studies on binding capacity with PEXEL motifs processing in terms of binding and accessibility of export proteins. Results Cloning and sequence analysis for genetic diversity demonstrates PvPM-V-Ind (mutant) gene is highly conserved among all isolates from different geographical regions of India. Imperfect duplicate insertion types of mutations (SVSE from 246–249 AA and SLSE from 266–269 AA) were identified among all Indian isolates in comparison to P.vivax Sal-1 (PvPM-V-Sal 1) isolate. In silico bioinformatics interaction studies of PEXEL peptide and active enzyme reveal that PvPM-V-Ind (mutant) is only active in endoplasmic reticulum lumen and membrane embedding is essential for activation of plasmepsin V. Structural modeling predictions based on docking studies with PEXEL motif show significant variation in substrate protein binding of these imperfect mutations with data mined PEXEL sequences. The predicted variation in the docking score and interacting amino acids of PvPM-V-Ind (mutant) proteins with PEXEL and lopinavir suggests a modulation in the activity of PvPM-V in terms of binding and accessibility at these sites. Conclusion/Significance Our functional modeled validation of PvPM-V-Ind (mutant) imperfect duplicate insertions with data mined PEXEL sequences leading to altered binding and substrate accessibility of the enzyme makes it a plausible target to investigate export mechanisms for in silico virtual screening and novel pharmacophore designing. PMID:23555891

In Silico Neuro-Oncology: Brownian Motion-Based Mathematical Treatment as a Potential Platform for Modeling the Infiltration of Glioma Cells into Normal Brain Tissue.

PubMed

Antonopoulos, Markos; Stamatakos, Georgios

2015-01-01

Intensive glioma tumor infiltration into the surrounding normal brain tissues is one of the most critical causes of glioma treatment failure. To quantitatively understand and mathematically simulate this phenomenon, several diffusion-based mathematical models have appeared in the literature. The majority of them ignore the anisotropic character of diffusion of glioma cells since availability of pertinent truly exploitable tomographic imaging data is limited. Aiming at enriching the anisotropy-enhanced glioma model weaponry so as to increase the potential of exploiting available tomographic imaging data, we propose a Brownian motion-based mathematical analysis that could serve as the basis for a simulation model estimating the infiltration of glioblastoma cells into the surrounding brain tissue. The analysis is based on clinical observations and exploits diffusion tensor imaging (DTI) data. Numerical simulations and suggestions for further elaboration are provided.

In Silico Analysis of Putrefaction Pathways in Bacteria and Its Implication in Colorectal Cancer

PubMed Central

Kaur, Harrisham; Das, Chandrani; Mande, Sharmila S.

2017-01-01

Fermentation of undigested proteins in human gastrointestinal tract (gut) by the resident microbiota, a process called bacterial putrefaction, can sometimes disrupt the gut homeostasis. In this process, essential amino acids (e.g., histidine, tryptophan, etc.) that are required by the host may be utilized by the gut microbes. In addition, some of the products of putrefaction, like ammonia, putrescine, cresol, indole, phenol, etc., have been implicated in the disease pathogenesis of colorectal cancer (CRC). We have investigated bacterial putrefaction pathways that are known to be associated with such metabolites. Results of the comprehensive in silico analysis of the selected putrefaction pathways across bacterial genomes revealed presence of these pathways in limited bacterial groups. Majority of these bacteria are commonly found in human gut. These include Bacillus, Clostridium, Enterobacter, Escherichia, Fusobacterium, Salmonella, etc. Interestingly, while pathogens utilize almost all the analyzed pathways, commensals prefer putrescine and H2S production pathways for metabolizing the undigested proteins. Further, comparison of the putrefaction pathways in the gut microbiomes of healthy, carcinoma and adenoma datasets indicate higher abundances of putrefying bacteria in the carcinoma stage of CRC. The insights obtained from the present study indicate utilization of possible microbiome-based therapies to minimize the adverse effects of gut microbiome in enteric diseases. PMID:29163445

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.

In-silico Taxonomic Classification of 373 Genomes Reveals Species Misidentification and New Genospecies within the Genus Pseudomonas

PubMed Central

Tran, Phuong N.; Savka, Michael A.; Gan, Han Ming

2017-01-01

The genus Pseudomonas has one of the largest diversity of species within the Bacteria kingdom. To date, its taxonomy is still being revised and updated. Due to the non-standardized procedure and ambiguous thresholds at species level, largely based on 16S rRNA gene or conventional biochemical assay, species identification of publicly available Pseudomonas genomes remains questionable. In this study, we performed a large-scale analysis of all Pseudomonas genomes with species designation (excluding the well-defined P. aeruginosa) and re-evaluated their taxonomic assignment via in silico genome-genome hybridization and/or genetic comparison with valid type species. Three-hundred and seventy-three pseudomonad genomes were analyzed and subsequently clustered into 145 distinct genospecies. We detected 207 erroneous labels and corrected 43 to the proper species based on Average Nucleotide Identity Multilocus Sequence Typing (MLST) sequence similarity to the type strain. Surprisingly, more than half of the genomes initially designated as Pseudomonas syringae and Pseudomonas fluorescens should be classified either to a previously described species or to a new genospecies. Notably, high pairwise average nucleotide identity (>95%) indicating species-level similarity was observed between P. synxantha-P. libanensis, P. psychrotolerans–P. oryzihabitans, and P. kilonensis- P. brassicacearum, that were previously differentiated based on conventional biochemical tests and/or genome-genome hybridization techniques. PMID:28747902

Bridging the provenance gap: opportunities and challenges tracking in and ex silico provenance in sUAS workflows

NASA Astrophysics Data System (ADS)

Thomer, A.

2017-12-01

Data provenance - the record of the varied processes that went into the creation of a dataset, as well as the relationships between resulting data objects - is necessary to support the reusability, reproducibility and reliability of earth science data. In sUAS-based research, capturing provenance can be particularly challenging because of the breadth and distributed nature of the many platforms used to collect, process and analyze data. In any given project, multiple drones, controllers, computers, software systems, sensors, cameras, imaging processing algorithms and data processing workflows are used over sometimes long periods of time. These platforms and processing result in dozens - if not hundreds - of data products in varying stages of readiness-for-analysis and sharing. Provenance tracking mechanisms are needed to make the relationships between these many data products explicit, and therefore more reusable and shareable. In this talk, I discuss opportunities and challenges in tracking provenance in sUAS-based research, and identify gaps in current workflow-capture technologies. I draw on prior work conducted as part of the IMLS-funded Site-Based Data Curation project in which we developed methods of documenting in and ex silico (that is, computational and non-computation) workflows, and demonstrate this approaches applicability to research with sUASes. I conclude with a discussion of ontologies and other semantic technologies that have potential application in sUAS research.

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.

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.

Theoretical investigation on the microstructure of triethylene glycol based deep eutectic solvents: COSMO-RS and TURBOMOLE prediction

NASA Astrophysics Data System (ADS)

Aissaoui, Tayeb; Benguerba, Yacine; AlNashef, Inas M.

2017-08-01

The in-silico combination mechanism of triethylene glycol based DESs has been studied. COSMO-RS and graphical user interface TmoleX software were used to predict the interaction mechanism of hydrogen bond donors (HBDs) with hydrogen bond acceptors (HBA) to form DESs. The predicted IR results were compared with the previously reported experimental FT-IR analysis for the same studied DESs. The sigma profiles for the HBD, HBAs and formed DESs were interpreted to identify qualitatively molecular properties like polarity or hydrogen bonding donor and acceptor abilities. The predicted physicochemical properties reported in this study were in good agreement with experimental ones.

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.

Identification of trans-acting factors regulating SamDC expression in Oryza sativa

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

Basu, Supratim, E-mail: supratim_genetics@yahoo.co.in; Division of Plant Biology, Bose Institute, Kolkata; Roychoudhury, Aryadeep

2014-03-07

Highlights: • Identification of cis elements responsible for SamDC expression by in silico analysis. • qPCR analysis of SamDC expression to abiotic and biotic stress treatments. • Detection of SamDC regulators using identified cis-elements as probe by EMSA. • Southwestern Blot analysis to predict the size of the trans-acting factors. - Abstract: Abiotic stress affects the growth and productivity of crop plants; to cope with the adverse environmental conditions, plants have developed efficient defense machinery comprising of antioxidants like phenolics and flavonoids, and osmolytes like polyamines. SamDC is a key enzyme in the polyamine biosynthesis pathway in plants. In ourmore » present communication we have done in silico analysis of the promoter region of SamDC to look for the presence of different cis-regulatory elements contributing to its expression. Based on the presence of different cis-regulatory elements we completed comparative analysis of SamDC gene expression in rice lamina of IR-29 and Nonabokra by qPCR in response to the abiotic stress treatments of salinity, drought, cold and the biotic stress treatments of ABA and light. Additionally, to explore the role of the cis-regulatory elements in regulating the expression of SamDC gene in plants we comparatively analyzed the binding of rice nuclear proteins prepared from IR-29 and Nonabokra undergoing various stress treatments. The intensity of the complex formed was low and inducible in IR-29 in contrast to Nonabokra. Southwestern blot analysis helped in predicting the size of the trans-acting factors binding to these cis-elements. To our knowledge this is the first report on the comprehensive analysis of SamDC gene expression in rice and identification of the trans-acting factors regulating its expression.« less

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 genomic analyses reveal three distinct lineages of Escherichia coli O157:H7, one of which is associated with hyper-virulence.

PubMed

Laing, Chad R; Buchanan, Cody; Taboada, Eduardo N; Zhang, Yongxiang; Karmali, Mohamed A; Thomas, James E; Gannon, Victor Pj

2009-06-29

Many approaches have been used to study the evolution, population structure and genetic diversity of Escherichia coli O157:H7; however, observations made with different genotyping systems are not easily relatable to each other. Three genetic lineages of E. coli O157:H7 designated I, II and I/II have been identified using octamer-based genome scanning and microarray comparative genomic hybridization (mCGH). Each lineage contains significant phenotypic differences, with lineage I strains being the most commonly associated with human infections. Similarly, a clade of hyper-virulent O157:H7 strains implicated in the 2006 spinach and lettuce outbreaks has been defined using single-nucleotide polymorphism (SNP) typing. In this study an in silico comparison of six different genotyping approaches was performed on 19 E. coli genome sequences from 17 O157:H7 strains and single O145:NM and K12 MG1655 strains to provide an overall picture of diversity of the E. coli O157:H7 population, and to compare genotyping methods for O157:H7 strains. In silico determination of lineage, Shiga-toxin bacteriophage integration site, comparative genomic fingerprint, mCGH profile, novel region distribution profile, SNP type and multi-locus variable number tandem repeat analysis type was performed and a supernetwork based on the combination of these methods was produced. This supernetwork showed three distinct clusters of strains that were O157:H7 lineage-specific, with the SNP-based hyper-virulent clade 8 synonymous with O157:H7 lineage I/II. Lineage I/II/clade 8 strains clustered closest on the supernetwork to E. coli K12 and E. coli O55:H7, O145:NM and sorbitol-fermenting O157 strains. The results of this study highlight the similarities in relationships derived from multi-locus genome sampling methods and suggest a "common genotyping language" may be devised for population genetics and epidemiological studies. Future genotyping methods should provide data that can be stored centrally and accessed locally in an easily transferable, informative and extensible format based on comparative genomic analyses.

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

ACFIS: a web server for fragment-based drug discovery

PubMed Central

Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

2016-01-01

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown ‘chemical space’ to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for ‘chemical space’, which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. PMID:27150808

ACFIS: a web server for fragment-based drug discovery.

PubMed

Hao, Ge-Fei; Jiang, Wen; Ye, Yuan-Nong; Wu, Feng-Xu; Zhu, Xiao-Lei; Guo, Feng-Biao; Yang, Guang-Fu

2016-07-08

In order to foster innovation and improve the effectiveness of drug discovery, there is a considerable interest in exploring unknown 'chemical space' to identify new bioactive compounds with novel and diverse scaffolds. Hence, fragment-based drug discovery (FBDD) was developed rapidly due to its advanced expansive search for 'chemical space', which can lead to a higher hit rate and ligand efficiency (LE). However, computational screening of fragments is always hampered by the promiscuous binding model. In this study, we developed a new web server Auto Core Fragment in silico Screening (ACFIS). It includes three computational modules, PARA_GEN, CORE_GEN and CAND_GEN. ACFIS can generate core fragment structure from the active molecule using fragment deconstruction analysis and perform in silico screening by growing fragments to the junction of core fragment structure. An integrated energy calculation rapidly identifies which fragments fit the binding site of a protein. We constructed a simple interface to enable users to view top-ranking molecules in 2D and the binding mode in 3D for further experimental exploration. This makes the ACFIS a highly valuable tool for drug discovery. The ACFIS web server is free and open to all users at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

In silico analysis of protein toxin and bacteriocins from Lactobacillus paracasei SD1 genome and available online databases

PubMed Central

Surachat, Komwit; Sangket, Unitsa; Deachamag, Panchalika; Chotigeat, Wilaiwan

2017-01-01

Lactobacillus paracasei SD1 is a potential probiotic strain due to its ability to survive several conditions in human dental cavities. To ascertain its safety for human use, we therefore performed a comprehensive bioinformatics analysis and characterization of the bacterial protein toxins produced by this strain. We report the complete genome of Lactobacillus paracasei SD1 and its comparison to other Lactobacillus genomes. Additionally, we identify and analyze its protein toxins and antimicrobial proteins using reliable online database resources and establish its phylogenetic relationship with other bacterial genomes. Our investigation suggests that this strain is safe for human use and contains several bacteriocins that confer health benefits to the host. An in silico analysis of protein-protein interactions between the target bacteriocins and the microbial proteins gtfB and luxS of Streptococcus mutans was performed and is discussed here. PMID:28837656

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

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.

Quantum chemistry-assisted synthesis route development

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

Hori, Kenji; Sumimoto, Michinori; Murafuji, Toshihiro

2015-12-31

We have been investigating “quantum chemistry-assisted synthesis route development” using in silico screenings and applied the method to several targets. Another example was conducted to develop synthesis routes for a urea derivative, namely 1-(4-(trifluoromethyl)-2-oxo-2H-chromen-7-yl)urea. While five synthesis routes were examined, only three routes passed the second in silico screening. Among them, the reaction of 7-amino-4-(trifluoromethyl)-2H-chromen-2-one and O-methyl carbamate with BF{sub 3} as an additive was ranked as the first choice for synthetic work. We were able to experimentally obtain the target compound even though its yield was as low as 21 %. The theoretical result was thus consistent with thatmore » observed. The summary of transition state data base (TSDB) is also provided. TSDB is the key to reducing time of in silico screenings.« less

Single nucleotide polymorphisms from Theobroma cacao expressed sequence tags associated with witches' broom disease in cacao.

PubMed

Lima, L S; Gramacho, K P; Carels, N; Novais, R; Gaiotto, F A; Lopes, U V; Gesteira, A S; Zaidan, H A; Cascardo, J C M; Pires, J L; Micheli, F

2009-07-14

In order to increase the efficiency of cacao tree resistance to witches' broom disease, which is caused by Moniliophthora perniciosa (Tricholomataceae), we looked for molecular markers that could help in the selection of resistant cacao genotypes. Among the different markers useful for developing marker-assisted selection, single nucleotide polymorphisms (SNPs) constitute the most common type of sequence difference between alleles and can be easily detected by in silico analysis from expressed sequence tag libraries. We report the first detection and analysis of SNPs from cacao-M. perniciosa interaction expressed sequence tags, using bioinformatics. Selection based on analysis of these SNPs should be useful for developing cacao varieties resistant to this devastating disease.

Graph Theory-Based Analysis of the Lymph Node Fibroblastic Reticular Cell Network.

PubMed

Novkovic, Mario; Onder, Lucas; Bocharov, Gennady; Ludewig, Burkhard

2017-01-01

Secondary lymphoid organs have developed segregated niches that are able to initiate and maintain effective immune responses. Such global organization requires tight control of diverse cellular components, specifically those that regulate lymphocyte trafficking. Fibroblastic reticular cells (FRCs) form a densely interconnected network in lymph nodes and provide key factors necessary for T cell migration and retention, and foster subsequent interactions between T cells and dendritic cells. Development of integrative systems biology approaches has made it possible to elucidate this multilevel complexity of the immune system. Here, we present a graph theory-based analysis of the FRC network in murine lymph nodes, where generation of the network topology is performed using high-resolution confocal microscopy and 3D reconstruction. This approach facilitates the analysis of physical cell-to-cell connectivity, and estimation of topological robustness and global behavior of the network when it is subjected to perturbation in silico.

A general framework for optimization of probes for gene expression microarray and its application to the fungus Podospora anserina

PubMed Central

2010-01-01

Background The development of new microarray technologies makes custom long oligonucleotide arrays affordable for many experimental applications, notably gene expression analyses. Reliable results depend on probe design quality and selection. Probe design strategy should cope with the limited accuracy of de novo gene prediction programs, and annotation up-dating. We present a novel in silico procedure which addresses these issues and includes experimental screening, as an empirical approach is the best strategy to identify optimal probes in the in silico outcome. Findings We used four criteria for in silico probe selection: cross-hybridization, hairpin stability, probe location relative to coding sequence end and intron position. This latter criterion is critical when exon-intron gene structure predictions for intron-rich genes are inaccurate. For each coding sequence (CDS), we selected a sub-set of four probes. These probes were included in a test microarray, which was used to evaluate the hybridization behavior of each probe. The best probe for each CDS was selected according to three experimental criteria: signal-to-noise ratio, signal reproducibility, and representative signal intensities. This procedure was applied for the development of a gene expression Agilent platform for the filamentous fungus Podospora anserina and the selection of a single 60-mer probe for each of the 10,556 P. anserina CDS. Conclusions A reliable gene expression microarray version based on the Agilent 44K platform was developed with four spot replicates of each probe to increase statistical significance of analysis. PMID:20565839

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).

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.

When Genomics Is Not Enough: Experimental Evidence for a Decrease in LINE-1 Activity During the Evolution of Australian Marsupials

PubMed Central

Gallus, Susanne; Lammers, Fritjof

2016-01-01

The autonomous transposable element LINE-1 is a highly abundant element that makes up between 15% and 20% of therian mammal genomes. Since their origin before the divergence of marsupials and placental mammals, LINE-1 elements have contributed actively to the genome landscape. A previous in silico screen of the Tasmanian devil genome revealed a lack of functional coding LINE-1 sequences. In this study we present the results of an in vitro analysis from a partial LINE-1 reverse transcriptase coding sequence in five marsupial species. Our experimental screen supports the in silico findings of the genome-wide degradation of LINE-1 sequences in the Tasmanian devil, and identifies a high frequency of degraded LINE-1 sequences in other Australian marsupials. The comparison between the experimentally obtained LINE-1 sequences and reference genome assemblies suggests that conclusions from in silico analyses of retrotransposition activity can be influenced by incomplete genome assemblies from short reads. PMID:27389686

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.

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

Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

PubMed Central

Mondal, Shakhinur Islam; Ferdous, Sabiha; Jewel, Nurnabi Azad; Akter, Arzuba; Mahmud, Zabed; Islam, Md Muzahidul; Afrin, Tanzila; Karim, Nurul

2015-01-01

Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bio-informatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E. coli O157:H7 were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets and DNA polymerase III alpha (dnaE) was the top-ranked among these targets. Moreover, druggability of each of the identified drug targets was evaluated by the DrugBank database. In addition, 3D structure of the dnaE was modeled and explored further for in silico docking with ligands having potential druggability. Finally, we confirmed that the compounds N-coeleneterazine and N-(1,4-dihydro-5H-tetrazol-5-ylidene)-9-oxo-9H-xanthene-2-sulfon-amide were the most suitable ligands of dnaE and hence proposed as the potential inhibitors of this target protein. The results of this study could facilitate the discovery and release of new and effective drugs against E. coli O157:H7 and other deadly human bacterial pathogens. PMID:26677339

Ontology-supported research on vaccine efficacy, safety and integrative biological networks.

PubMed

He, Yongqun

2014-07-01

While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

Ontology-supported Research on Vaccine Efficacy, Safety, and Integrative Biological Networks

PubMed Central

He, Yongqun

2016-01-01

Summary While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including the Vaccine Ontology, Ontology of Adverse Events, and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network (“OneNet”) Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms. PMID:24909153

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.

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.

Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

PubMed Central

Jun, Goo; Flickinger, Matthew; Hetrick, Kurt N.; Romm, Jane M.; Doheny, Kimberly F.; Abecasis, Gonçalo R.; Boehnke, Michael; Kang, Hyun Min

2012-01-01

DNA sample contamination is a serious problem in DNA sequencing studies and may result in systematic genotype misclassification and false positive associations. Although methods exist to detect and filter out cross-species contamination, few methods to detect within-species sample contamination are available. In this paper, we describe methods to identify within-species DNA sample contamination based on (1) a combination of sequencing reads and array-based genotype data, (2) sequence reads alone, and (3) array-based genotype data alone. Analysis of sequencing reads allows contamination detection after sequence data is generated but prior to variant calling; analysis of array-based genotype data allows contamination detection prior to generation of costly sequence data. Through a combination of analysis of in silico and experimentally contaminated samples, we show that our methods can reliably detect and estimate levels of contamination as low as 1%. We evaluate the impact of DNA contamination on genotype accuracy and propose effective strategies to screen for and prevent DNA contamination in sequencing studies. PMID:23103226

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

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 © 2016 Elsevier Ireland Ltd. All rights reserved.

Semantics-enabled service discovery framework in the SIMDAT pharma grid.

PubMed

Qu, Cangtao; Zimmermann, Falk; Kumpf, Kai; Kamuzinzi, Richard; Ledent, Valérie; Herzog, Robert

2008-03-01

We present the design and implementation of a semantics-enabled service discovery framework in the data Grids for process and product development using numerical simulation and knowledge discovery (SIMDAT) Pharma Grid, an industry-oriented Grid environment for integrating thousands of Grid-enabled biological data services and analysis services. The framework consists of three major components: the Web ontology language (OWL)-description logic (DL)-based biological domain ontology, OWL Web service ontology (OWL-S)-based service annotation, and semantic matchmaker based on the ontology reasoning. Built upon the framework, workflow technologies are extensively exploited in the SIMDAT to assist biologists in (semi)automatically performing in silico experiments. We present a typical usage scenario through the case study of a biological workflow: IXodus.

Terminal Restriction Fragment Length Polymorphism Analysis Program, a Web-Based Research Tool for Microbial Community Analysis

PubMed Central

Marsh, Terence L.; Saxman, Paul; Cole, James; Tiedje, James

2000-01-01

Rapid analysis of microbial communities has proven to be a difficult task. This is due, in part, to both the tremendous diversity of the microbial world and the high complexity of many microbial communities. Several techniques for community analysis have emerged over the past decade, and most take advantage of the molecular phylogeny derived from 16S rRNA comparative sequence analysis. We describe a web-based research tool located at the Ribosomal Database Project web site (http://www.cme.msu.edu/RDP/html/analyses.html) that facilitates microbial community analysis using terminal restriction fragment length polymorphism of 16S ribosomal DNA. The analysis function (designated TAP T-RFLP) permits the user to perform in silico restriction digestions of the entire 16S sequence database and derive terminal restriction fragment sizes, measured in base pairs, from the 5′ terminus of the user-specified primer to the 3′ terminus of the restriction endonuclease target site. The output can be sorted and viewed either phylogenetically or by size. It is anticipated that the site will guide experimental design as well as provide insight into interpreting results of community analysis with terminal restriction fragment length polymorphisms. PMID:10919828

Elucidating Rice Cell Metabolism under Flooding and Drought Stresses Using Flux-Based Modeling and Analysis1[C][W][OPEN

PubMed Central

Lakshmanan, Meiyappan; Zhang, Zhaoyang; Mohanty, Bijayalaxmi; Kwon, Jun-Young; Choi, Hong-Yeol; Nam, Hyung-Jin; Kim, Dong-Il; Lee, Dong-Yup

2013-01-01

Rice (Oryza sativa) is one of the major food crops in world agriculture, especially in Asia. However, the possibility of subsequent occurrence of flood and drought is a major constraint to its production. Thus, the unique behavior of rice toward flooding and drought stresses has required special attention to understand its metabolic adaptations. However, despite several decades of research investigations, the cellular metabolism of rice remains largely unclear. In this study, in order to elucidate the physiological characteristics in response to such abiotic stresses, we reconstructed what is to our knowledge the first metabolic/regulatory network model of rice, representing two tissue types: germinating seeds and photorespiring leaves. The phenotypic behavior and metabolic states simulated by the model are highly consistent with our suspension culture experiments as well as previous reports. The in silico simulation results of seed-derived rice cells indicated (1) the characteristic metabolic utilization of glycolysis and ethanolic fermentation based on oxygen availability and (2) the efficient sucrose breakdown through sucrose synthase instead of invertase. Similarly, flux analysis on photorespiring leaf cells elucidated the crucial role of plastid-cytosol and mitochondrion-cytosol malate transporters in recycling the ammonia liberated during photorespiration and in exporting the excess redox cofactors, respectively. The model simulations also unraveled the essential role of mitochondrial respiration during drought stress. In the future, the combination of experimental and in silico analyses can serve as a promising approach to understand the complex metabolism of rice and potentially help in identifying engineering targets for improving its productivity as well as enabling stress tolerance. PMID:23753178

MCCE analysis of the pKas of introduced buried acids and bases in staphylococcal nuclease.

PubMed

Gunner, M R; Zhu, Xuyu; Klein, Max C

2011-12-01

The pK(a)s of 96 acids and bases introduced into buried sites in the staphylococcal nuclease protein (SNase) were calculated using the multiconformation continuum electrostatics (MCCE) program and the results compared with experimental values. The pK(a)s are obtained by Monte Carlo sampling of coupled side chain protonation and position as a function of pH. The dependence of the results on the protein dielectric constant (ε(prot)) in the continuum electrostatics analysis and on the Lennard-Jones non-electrostatics parameters was evaluated. The pK(a)s of the introduced residues have a clear dependence on ε(prot,) whereas native ionizable residues do not. The native residues have electrostatic interactions with other residues in the protein favoring ionization, which are larger than the desolvation penalty favoring the neutral state. Increasing ε(prot) scales both terms, which for these residues leads to small changes in pK(a). The introduced residues have a larger desolvation penalty and negligible interactions with residues in the protein. For these residues, changing ε(prot) has a large influence on the calculated pK(a). An ε(prot) of 8-10 and a Lennard-Jones scaling of 0.25 is best here. The X-ray crystal structures of the mutated proteins are found to provide somewhat better results than calculations carried out on mutations made in silico. Initial relaxation of the in silico mutations by Gromacs and extensive side chain rotamer sampling within MCCE can significantly improve the match with experiment. Copyright © 2011 Wiley-Liss, Inc.

Identification and functional analysis of the aspergillic acid gene cluster in Aspergillus flavus

USDA-ARS?s Scientific Manuscript database

Aspergillus flavus can colonize important food staples and produces aflatoxins, toxic and carcinogenic secondary metabolites. In silico analysis of the A. flavus genome revealed 56 gene clusters encoding for secondary metabolites. How these many of these metabolites affect fungal development, surviv...

Spatio-temporal Model of Xenobiotic Distribution and Metabolism in an in Silico Mouse Liver Lobule

NASA Astrophysics Data System (ADS)

Fu, Xiao; Sluka, James; Clendenon, Sherry; Glazier, James; Ryan, Jennifer; Dunn, Kenneth; Wang, Zemin; Klaunig, James

Our study aims to construct a structurally plausible in silico model of a mouse liver lobule to simulate the transport of xenobiotics and the production of their metabolites. We use a physiologically-based model to calculate blood-flow rates in a network of mouse liver sinusoids and simulate transport, uptake and biotransformation of xenobiotics within the in silico lobule. Using our base model, we then explore the effects of variations of compound-specific (diffusion, transport and metabolism) and compound-independent (temporal alteration of blood flow pattern) parameters, and examine their influence on the distribution of xenobiotics and metabolites. Our simulations show that the transport mechanism (diffusive and transporter-mediated) of xenobiotics and blood flow both impact the regional distribution of xenobiotics in a mouse hepatic lobule. Furthermore, differential expression of metabolic enzymes along each sinusoid's portal to central axis, together with differential cellular availability of xenobiotics, induce non-uniform production of metabolites. Thus, the heterogeneity of the biochemical and biophysical properties of xenobiotics, along with the complexity of blood flow, result in different exposures to xenobiotics for hepatocytes at different lobular locations. We acknowledge support from National Institute of Health GM 077138 and GM 111243.

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.

Meal Disturbance Effect on Control of Blood Glucose Level for Critically-ill Patients using In-silico Works

NASA Astrophysics Data System (ADS)

Yusof, N. F. M.; Som, A. M.; Ali, S. A.; Azman, N. H.

2018-05-01

This study was conducted to determine the effect of meal disturbance on blood glucose level of the critically ill patients and to simulate the control algorithm previously developed using in-silico works. The study is significant so as to reduce the mortality rate of critically ill patients who usually encounter hyperglycaemia or/and hypoglycaemia while in treatment. The meal intake is believed to affect the blood glucose regulation and causes the hyperglycaemia to occur. Critically ill patients receive their meal through parenteral and enteral nutrition. Furthermore, by using in-silico works, time consumed and resources needed for clinical evaluation of the patients can be reduced. Hovorka model was employed in which the simulation study was carried out using MATLAB on the virtual patient and it was being compared with actual patient in which the data were provided by Institut Jantung Negara (IJN). Based on the simulation, the disturbance on enteral glucose supplied had affected the blood glucose level of the patient; however, it remained unchanged for the parental glucose. To reduce the occurrence of hypoglycaemia and hyperglycaemia, the patient was injected with 30 g/hr and 10 g/hr of enteral glucose, respectively. In conclusion, the disturbance of meal received can be controlled through in-silico works.

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.

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 vitro and in silico analyses of Vicia faba L. on Peroxisome proliferator-activated receptor gamma.

PubMed

Prabhu, D Sathya; Rajeswari, V Devi

2018-06-20

The agonists of peroxisome proliferator-activated receptor gamma (PPARγ) from natural victual products were used as antidiabetic agents. Faba bean (Vicia faba L.) is a consequential legume that was known to possess potential antidiabetic activity, whose mechanism of action was unknown. The current study was focused to ascertain gene expression of the nuclear receptor PPARγ by Faba bean pod extract in rat cell lines (RINm5F).The real-time polymerase chain reaction analysis demonstrated that Faba bean pod extract in concentrations of 160 µg/mL have shown 4.97-fold stimulation compared with control. The cells treated with 320 µg/mL has shown 5.89-fold upregulation, respectively. Furthermore, in silico docking analysis was carried out against PPARγ, using the bioactive compounds identified from Faba bean pod extracts, which were known reported compounds from the literature. The results suggest that gene expression of PPARγ was inhibited by the constituents in Faba bean. In silico analysis prognosticates, butein has a high binding energy (-8.6 kcal/mol) with an atomic contact energy of -214.10, followed by Apigenin and Quercetin against PPARγ. Similarly, the percentage of interaction was high for butein, followed by Apigenin and Quercetin than other compounds comparatively. Hence, the results conclude inhibition of PPARγ by the bioactive compounds from Faba bean, which may provide insights into developing future therapeutic molecules for diabetes mellitus. © 2018 Wiley Periodicals, Inc.

Inhibition of MAO-A and stimulation of behavioural activities in mice by the inactive prodrug form of the anti-influenza agent oseltamivir.

PubMed

Hiasa, Miki; Isoda, Yumiko; Kishimoto, Yasushi; Saitoh, Kenta; Kimura, Yasuaki; Kanai, Motomu; Shibasaki, Masakatsu; Hatakeyama, Dai; Kirino, Yutaka; Kuzuhara, Takashi

2013-05-01

Oseltamivir is the most widely prescribed anti-influenza medication. However, in rare instances, it has been reported to stimulate behavioural activities in adolescents. The goal of this study was to determine the molecular mechanism responsible for these behavioural activities. We performed an in vitro assay of MAO-A, the enzyme responsible for neurotransmitter degradation, using either the active form - oseltamivir carboxylate (OC) or the inactive prodrug - oseltamivir ethyl ester (OEE). We also analysed the docking of MAO-A with OEE or OC in silico. Mouse behaviours after OEE or OC administration were monitored using automated video and computer analysis. OEE, but not OC, competitively and selectively inhibited human MAO-A. The estimated Ki value was comparable with the Km values of native substrates of MAO-A. Docking simulations in silico based on the tertiary structure of MAO-A suggested that OEE could fit into the inner pocket of the enzyme. Behavioural monitoring using automated video analysis further revealed that OEE, not OC, significantly enhanced spontaneous behavioural activities in mice, such as jumping, rearing, sniffing, turning and walking. Our multilevel analyses suggested OEE to be the cause of the side effects associated with oseltamivir and revealed the molecular mechanism underlying the stimulated behaviours induced by oseltamivir in some circumstances. © 2013 The Authors. British Journal of Pharmacology © 2013 The British Pharmacological Society.

Inhibition of MAO-A and stimulation of behavioural activities in mice by the inactive prodrug form of the anti-influenza agent oseltamivir

PubMed Central

Hiasa, Miki; Isoda, Yumiko; Kishimoto, Yasushi; Saitoh, Kenta; Kimura, Yasuaki; Kanai, Motomu; Shibasaki, Masakatsu; Hatakeyama, Dai; Kirino, Yutaka; Kuzuhara, Takashi

2013-01-01

Background and Purpose Oseltamivir is the most widely prescribed anti-influenza medication. However, in rare instances, it has been reported to stimulate behavioural activities in adolescents. The goal of this study was to determine the molecular mechanism responsible for these behavioural activities. Experimental Approach We performed an in vitro assay of MAO-A, the enzyme responsible for neurotransmitter degradation, using either the active form – oseltamivir carboxylate (OC) or the inactive prodrug – oseltamivir ethyl ester (OEE). We also analysed the docking of MAO-A with OEE or OC in silico. Mouse behaviours after OEE or OC administration were monitored using automated video and computer analysis. Key Results OEE, but not OC, competitively and selectively inhibited human MAO-A. The estimated Ki value was comparable with the Km values of native substrates of MAO-A. Docking simulations in silico based on the tertiary structure of MAO-A suggested that OEE could fit into the inner pocket of the enzyme. Behavioural monitoring using automated video analysis further revealed that OEE, not OC, significantly enhanced spontaneous behavioural activities in mice, such as jumping, rearing, sniffing, turning and walking. Conclusions and Implications Our multilevel analyses suggested OEE to be the cause of the side effects associated with oseltamivir and revealed the molecular mechanism underlying the stimulated behaviours induced by oseltamivir in some circumstances. PMID:23320399

In Silico Identification of Highly Conserved Epitopes of Influenza A H1N1, H2N2, H3N2, and H5N1 with Diagnostic and Vaccination Potential

PubMed Central

Muñoz-Medina, José Esteban; Sánchez-Vallejo, Carlos Javier; Méndez-Tenorio, Alfonso; Monroy-Muñoz, Irma Eloísa; Angeles-Martínez, Javier; Santos Coy-Arechavaleta, Andrea; Santacruz-Tinoco, Clara Esperanza; González-Ibarra, Joaquín; Anguiano-Hernández, Yu-Mei; González-Bonilla, César Raúl; Ramón-Gallegos, Eva; Díaz-Quiñonez, José Alberto

2015-01-01

The unpredictable, evolutionary nature of the influenza A virus (IAV) is the primary problem when generating a vaccine and when designing diagnostic strategies; thus, it is necessary to determine the constant regions in viral proteins. In this study, we completed an in silico analysis of the reported epitopes of the 4 IAV proteins that are antigenically most significant (HA, NA, NP, and M2) in the 3 strains with the greatest world circulation in the last century (H1N1, H2N2, and H3N2) and in one of the main aviary subtypes responsible for zoonosis (H5N1). For this purpose, the HMMER program was used to align 3,016 epitopes reported in the Immune Epitope Database and Analysis Resource (IEDB) and distributed in 34,294 stored sequences in the Pfam database. Eighteen epitopes were identified: 8 in HA, 5 in NA, 3 in NP, and 2 in M2. These epitopes have remained constant since they were first identified (~91 years) and are present in strains that have circulated on 5 continents. These sites could be targets for vaccination design strategies based on epitopes and/or as markers in the implementation of diagnostic techniques. PMID:26346523

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Analysis of the interactome of the Ser/Thr Protein Phosphatase type 1 in Plasmodium falciparum.

PubMed

Hollin, Thomas; De Witte, Caroline; Lenne, Astrid; Pierrot, Christine; Khalife, Jamal

2016-03-17

Protein Phosphatase 1 (PP1) is an enzyme essential to cell viability in the malaria parasite Plasmodium falciparum (Pf). The activity of PP1 is regulated by the binding of regulatory subunits, of which there are up to 200 in humans, but only 3 have been so far reported for the parasite. To better understand the P. falciparum PP1 (PfPP1) regulatory network, we here report the use of three strategies to characterize the PfPP1 interactome: co-affinity purified proteins identified by mass spectrometry, yeast two-hybrid (Y2H) screening and in silico analysis of the P. falciparum predicted proteome. Co-affinity purification followed by MS analysis identified 6 PfPP1 interacting proteins (Pips) of which 3 contained the RVxF consensus binding, 2 with a Fxx[RK]x[RK] motif, also shown to be a PP1 binding motif and one with both binding motifs. The Y2H screens identified 134 proteins of which 30 present the RVxF binding motif and 20 have the Fxx[RK]x[RK] binding motif. The in silico screen of the Pf predicted proteome using a consensus RVxF motif as template revealed the presence of 55 potential Pips. As further demonstration, 35 candidate proteins were validated as PfPP1 interacting proteins in an ELISA-based assay. To the best of our knowledge, this is the first study on PfPP1 interactome. The data reports several conserved PP1 interacting proteins as well as a high number of specific interactors to PfPP1. Their analysis indicates a high diversity of biological functions for PP1 in Plasmodium. Based on the present data and on an earlier study of the Pf interactome, a potential implication of Pips in protein folding/proteolysis, transcription and pathogenicity networks is proposed. The present work provides a starting point for further studies on the structural basis of these interactions and their functions in P. falciparum.

In silico approaches to study mass and energy flows in microbial consortia: a syntrophic case study

PubMed Central

2009-01-01

Background Three methods were developed for the application of stoichiometry-based network analysis approaches including elementary mode analysis to the study of mass and energy flows in microbial communities. Each has distinct advantages and disadvantages suitable for analyzing systems with different degrees of complexity and a priori knowledge. These approaches were tested and compared using data from the thermophilic, phototrophic mat communities from Octopus and Mushroom Springs in Yellowstone National Park (USA). The models were based on three distinct microbial guilds: oxygenic phototrophs, filamentous anoxygenic phototrophs, and sulfate-reducing bacteria. Two phases, day and night, were modeled to account for differences in the sources of mass and energy and the routes available for their exchange. Results The in silico models were used to explore fundamental questions in ecology including the prediction of and explanation for measured relative abundances of primary producers in the mat, theoretical tradeoffs between overall productivity and the generation of toxic by-products, and the relative robustness of various guild interactions. Conclusion The three modeling approaches represent a flexible toolbox for creating cellular metabolic networks to study microbial communities on scales ranging from cells to ecosystems. A comparison of the three methods highlights considerations for selecting the one most appropriate for a given microbial system. For instance, communities represented only by metagenomic data can be modeled using the pooled method which analyzes a community's total metabolic potential without attempting to partition enzymes to different organisms. Systems with extensive a priori information on microbial guilds can be represented using the compartmentalized technique, employing distinct control volumes to separate guild-appropriate enzymes and metabolites. If the complexity of a compartmentalized network creates an unacceptable computational burden, the nested analysis approach permits greater scalability at the cost of more user intervention through multiple rounds of pathway analysis. PMID:20003240

Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP

NASA Astrophysics Data System (ADS)

Jain, Sankalp; Grandits, Melanie; Richter, Lars; Ecker, Gerhard F.

2017-06-01

The bile salt export pump (BSEP) actively transports conjugated monovalent bile acids from the hepatocytes into the bile. This facilitates the formation of micelles and promotes digestion and absorption of dietary fat. Inhibition of BSEP leads to decreased bile flow and accumulation of cytotoxic bile salts in the liver. A number of compounds have been identified to interact with BSEP, which results in drug-induced cholestasis or liver injury. Therefore, in silico approaches for flagging compounds as potential BSEP inhibitors would be of high value in the early stage of the drug discovery pipeline. Up to now, due to the lack of a high-resolution X-ray structure of BSEP, in silico based identification of BSEP inhibitors focused on ligand-based approaches. In this study, we provide a homology model for BSEP, developed using the corrected mouse P-glycoprotein structure (PDB ID: 4M1M). Subsequently, the model was used for docking-based classification of a set of 1212 compounds (405 BSEP inhibitors, 807 non-inhibitors). Using the scoring function ChemScore, a prediction accuracy of 81% on the training set and 73% on two external test sets could be obtained. In addition, the applicability domain of the models was assessed based on Euclidean distance. Further, analysis of the protein-ligand interaction fingerprints revealed certain functional group-amino acid residue interactions that could play a key role for ligand binding. Though ligand-based models, due to their high speed and accuracy, remain the method of choice for classification of BSEP inhibitors, structure-assisted docking models demonstrate reasonably good prediction accuracies while additionally providing information about putative protein-ligand interactions.

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.

Hit identification of novel heparanase inhibitors by structure- and ligand-based approaches.

PubMed

Gozalbes, Rafael; Mosulén, Silvia; Ortí, Leticia; Rodríguez-Díaz, Jesús; Carbajo, Rodrigo J; Melnyk, Patricia; Pineda-Lucena, Antonio

2013-04-01

Heparanase is a key enzyme involved in the dissemination of metastatic cancer cells. In this study a combination of in silico techniques and experimental methods was used to identify new potential inhibitors against this target. A 3D model of heparanase was built from sequence homology and applied to the virtual screening of a library composed of 27 known heparanase inhibitors and a commercial collection of drugs and drug-like compounds. The docking results from this campaign were combined with those obtained from a pharmacophore model recently published based in the same set of chemicals. Compounds were then ranked according to their theoretical binding affinity, and the top-rated commercial drugs were selected for further experimental evaluation. Biophysical methods (NMR and SPR) were applied to assess experimentally the interaction of the selected compounds with heparanase. The binding site was evaluated via competition experiments, using a known inhibitor of heparanase. Three of the selected drugs were found to bind to the active site of the protein and their KD values were determined. Among them, the antimalarial drug amodiaquine presented affinity towards the protein in the low-micromolar range, and was singled out for a SAR study based on its chemical scaffold. A subset of fourteen 4-arylaminoquinolines from a global set of 249 analogues of amodiaquine was selected based on the application of in silico models, a QSAR solubility prediction model and a chemical diversity analysis. Some of these compounds displayed binding affinities in the micromolar range. Copyright © 2013 Elsevier Ltd. All rights reserved.

Rapid communication: Computational simulation and analysis of a candidate for the design of a novel silk-based biopolymer.

PubMed

Golas, Ewa I; Czaplewski, Cezary

2014-09-01

This work theoretically investigates the mechanical properties of a novel silk-derived biopolymer as polymerized in silico from sericin and elastin-like monomers. Molecular Dynamics simulations and Steered Molecular Dynamics were the principal computational methods used, the latter of which applies an external force onto the system and thereby enables an observation of its response to stress. The models explored herein are single-molecule approximations, and primarily serve as tools in a rational design process for the preliminary assessment of properties in a new material candidate. © 2014 Wiley Periodicals, Inc.

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.

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.

In silico assembly and nanomechanical characterization of carbon nanotube buckypaper.

PubMed

Cranford, Steven W; Buehler, Markus J

2010-07-02

Carbon nanotube sheets or films, also known as 'buckypaper', have been proposed for use in actuating, structural and filtration systems, based in part on their unique and robust mechanical properties. Computational modeling of such a fibrous nanostructure is hindered by both the random arrangement of the constituent elements as well as the time- and length-scales accessible to atomistic level molecular dynamics modeling. Here we present a novel in silico assembly procedure based on a coarse-grain model of carbon nanotubes, used to attain a representative mesoscopic buckypaper model that circumvents the need for probabilistic approaches. By variation in assembly parameters, including the initial nanotube density and ratio of nanotube type (single- and double-walled), the porosity of the resulting buckypaper can be varied threefold, from approximately 0.3 to 0.9. Further, through simulation of nanoindentation, the Young's modulus is shown to be tunable through manipulation of nanotube type and density over a range of approximately 0.2-3.1 GPa, in good agreement with experimental findings of the modulus of assembled carbon nanotube films. In addition to carbon nanotubes, the coarse-grain model and assembly process can be adapted for other fibrous nanostructures such as electrospun polymeric composites, high performance nonwoven ballistic materials, or fibrous protein aggregates, facilitating the development and characterization of novel nanomaterials and composites as well as the analysis of biological materials such as protein fiber films and bulk structures.

In silico assembly and nanomechanical characterization of carbon nanotube buckypaper

NASA Astrophysics Data System (ADS)

Cranford, Steven W.; Buehler, Markus J.

2010-07-01

Carbon nanotube sheets or films, also known as 'buckypaper', have been proposed for use in actuating, structural and filtration systems, based in part on their unique and robust mechanical properties. Computational modeling of such a fibrous nanostructure is hindered by both the random arrangement of the constituent elements as well as the time- and length-scales accessible to atomistic level molecular dynamics modeling. Here we present a novel in silico assembly procedure based on a coarse-grain model of carbon nanotubes, used to attain a representative mesoscopic buckypaper model that circumvents the need for probabilistic approaches. By variation in assembly parameters, including the initial nanotube density and ratio of nanotube type (single- and double-walled), the porosity of the resulting buckypaper can be varied threefold, from approximately 0.3 to 0.9. Further, through simulation of nanoindentation, the Young's modulus is shown to be tunable through manipulation of nanotube type and density over a range of approximately 0.2-3.1 GPa, in good agreement with experimental findings of the modulus of assembled carbon nanotube films. In addition to carbon nanotubes, the coarse-grain model and assembly process can be adapted for other fibrous nanostructures such as electrospun polymeric composites, high performance nonwoven ballistic materials, or fibrous protein aggregates, facilitating the development and characterization of novel nanomaterials and composites as well as the analysis of biological materials such as protein fiber films and bulk structures.

Predicting human blood viscosity in silico

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

Fedosov, Dmitry A.; Pan, Wenxiao; Caswell, Bruce

2011-07-05

Cellular suspensions such as blood are a part of living organisms and their rheological and flow characteristics determine and affect majority of vital functions. The rheological and flow properties of cell suspensions are determined by collective dynamics of cells, their structure or arrangement, cell properties and interactions. We study these relations for blood in silico using a mesoscopic particle-based method and two different models (multi-scale/low-dimensional) of red blood cells. The models yield accurate quantitative predictions of the dependence of blood viscosity on shear rate and hematocrit. We explicitly model cell aggregation interactions and demonstrate the formation of reversible rouleaux structuresmore » resulting in a tremendous increase of blood viscosity at low shear rates and yield stress, in agreement with experiments. The non-Newtonian behavior of such cell suspensions (e.g., shear thinning, yield stress) is analyzed and related to the suspension’s microstructure, deformation and dynamics of single cells. We provide the flrst quantitative estimates of normal stress differences and magnitude of aggregation forces in blood. Finally, the flexibility of the cell models allows them to be employed for quantitative analysis of a much wider class of complex fluids including cell, capsule, and vesicle suspensions.« less

HemX is required for production of 2-ketogluconate, the predominant organic anion required for inorganic phosphate solubilization by Burkholderia sp. Ha185.

PubMed

Hsu, Pei-Chun Lisa; Condron, Leo; O'Callaghan, Maureen; Hurst, Mark R H

2015-12-01

The bacterium Burkholderia sp. Ha185 readily solubilizes inorganic phosphate by releasing the low molecular weight organic anion, 2-ketogluconate. Using random transposon mutagenesis and in silico analysis, a mutation that caused almost complete abolition of phosphate solubilization was located within hemX, which is part of the hem operon. Burkholderia sp. Ha185 HemX is a multidomain protein, predicted to encode a bifunctional uroporphyrinogen-III synthetase/uroporphyrin-III C-methyltransferase, which has not previously been implicated in phosphate solubilization. Complementation of hemX restored the ability of the mutant to solubilize phosphate in both plate and liquid cultures. Based on a combination of organic-anion profiling, quantitative polymerase chain reaction and in silico analyses, hemX was confirmed to be solely responsible for hydroxyapatite solubilization in Burkholderia sp. Ha185. It is proposed that the biosynthesis of a yet to be determined redox cofactor by HemX is the main pathway for generating 2-ketogluconate via a haem-dependent gluconate 2-dehydrogenase in Burkholderia sp. Ha185. © 2015 Society for Applied Microbiology and John Wiley & Sons Ltd.

A practical guidance for Cramer class determination.

PubMed

Roberts, David W; Aptula, Aynur; Schultz, Terry W; Shen, Jie; Api, Anne Marie; Bhatia, Sneha; Kromidas, Lambros

2015-12-01

Expanded use of the Threshold of Toxicological Concern (TTC) methodology has brought into discussion the intent of the original questions used in the Cramer scheme or Cramer decision tree. We have analysed, both manually and by Toxtree software, a large dataset of fragrance ingredients and identified several issues with the original Cramer questions. Some relate to definitions and wording of questions; others relate to in silico interpretation of the questions. We have endeavoured to address all of these inconsistencies and misinterpretations without changing the basic structure and principles of the original decision tree. Based on the analysis of a large data set of over 2500 fragrance ingredients, we found that most of the 33 questions in the original Cramer scheme are straightforward. Through repeated examination each of the 33 questions, we found 14 where the logic underlying the development of the rule is unclear. These questions are well served by minor wording changes and/or further explanation designed to capture what we perceive to be the intent of the original decision tree. The findings reported here could be used as a guidance for conducting Cramer classification and provide advices for the improvement of the in silico tools. Copyright © 2015 Elsevier Inc. All rights reserved.

Schiff Bases of Benzothiazol-2-ylamine and Thiazolo[5,4-b] pyridin-2-ylamine as Anticonvulsants: Synthesis, Characterization and Toxicity Profiling.

PubMed

Shukla, Rashmi; Singh, Ajeet P; Sonar, Pankaj K; Mishra, Mudita; Saraf, Shailendra K

2016-01-01

Schiff bases have a broad spectrum of biological activities like antiinflammatory, analgesic, antimicrobial, anticonvulsant, antitubercular, anticancer, antioxidant, anthelmintic and so forth. Thus, after a thorough perusal of literature, it was decided to conjugate benzothiazol-2-ylamine/thiazolo [5, 4-b] pyridin-2-ylamine with aromatic and heteroaromatic aldehydes to get a series of Schiff bases. Synthesis, characterization, in-silico toxicity profiling and anticonvulsant activity of the Schiff bases of Benzothiazol-2-ylamine and Thiazolo [5, 4-b] pyridin-2-ylamine. Aniline/4-aminopyridine was converted to the corresponding thiourea derivatives, which were cyclized to obtain benzothiazol-2-ylamine/thiazolo [5, 4-b] pyridin-2-ylamine. Finally, these were condensed with various aromatic and heteroaromatic aldehydes to obtain Schiff bases of benzothiazol-2-ylamine and thiazolo [5, 4-b] pyridin-2-ylamine. The synthesized compounds were characterized and screened for their anticonvulsant activity using maximal electroshock (MES) test and isoniazid (INH) induced convulsions test. In-silico toxicity profiling of all the synthesized compounds was done through "Lazar" and "Osiris" properties explorer. Majority of the compounds were more potent against MES induced convulsions than INH induced convulsions. Schiff bases of benzothiazol-2-ylamine were more effective than thiazolo [5, 4-b] pyridin-2-ylamine against MES induced convulsions. The compound benzothiazol-2-yl-(1H-indol-2-ylmethylene)-amine (VI) was the most potent member of the series against both types of convulsions. Compound VI exhibited the most significant activity profile in both the models. The compounds did not exhibit any carcinogenicity or acute toxicity in the in-silico studies. Thus, it may be concluded that the Schiff bases of benzothiazol-2-ylamine exhibit the potential to be promising and non-toxic anticonvulsant agents.

Linking disease-associated genes to regulatory networks via promoter organization

PubMed Central

Döhr, S.; Klingenhoff, A.; Maier, H.; de Angelis, M. Hrabé; Werner, T.; Schneider, R.

2005-01-01

Pathway- or disease-associated genes may participate in more than one transcriptional co-regulation network. Such gene groups can be readily obtained by literature analysis or by high-throughput techniques such as microarrays or protein-interaction mapping. We developed a strategy that defines regulatory networks by in silico promoter analysis, finding potentially co-regulated subgroups without a priori knowledge. Pairs of transcription factor binding sites conserved in orthologous genes (vertically) as well as in promoter sequences of co-regulated genes (horizontally) were used as seeds for the development of promoter models representing potential co-regulation. This approach was applied to a Maturity Onset Diabetes of the Young (MODY)-associated gene list, which yielded two models connecting functionally interacting genes within MODY-related insulin/glucose signaling pathways. Additional genes functionally connected to our initial gene list were identified by database searches with these promoter models. Thus, data-driven in silico promoter analysis allowed integrating molecular mechanisms with biological functions of the cell. PMID:15701758

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

Lack of Detectable Allergenicity in Genetically Modified Maize Containing “Cry” Proteins as Compared to Native Maize Based on In Silico & In Vitro Analysis

PubMed Central

Mathur, Chandni; Kathuria, Pooran C.; Dahiya, Pushpa; Singh, Anand B.

2015-01-01

Background Genetically modified, (GM) crops with potential allergens must be evaluated for safety and endogenous IgE binding pattern compared to native variety, prior to market release. Objective To compare endogenous IgE binding proteins of three GM maize seeds containing Cry 1Ab,1Ac,1C transgenic proteins with non GM maize. Methods An integrated approach of in silico & in vitro methods was employed. Cry proteins were tested for presence of allergen sequence by FASTA in allergen databases. Biochemical assays for maize extracts were performed. Specific IgE (sIgE) and Immunoblot using food sensitized patients sera (n = 39) to non GM and GM maize antigens was performed. Results In silico approaches, confirmed for non sequence similarity of stated transgenic proteins in allergen databases. An insignificant (p> 0.05) variation in protein content between GM and non GM maize was observed. Simulated Gastric Fluid (SGF) revealed reduced number of stable protein fractions in GM then non GM maize which might be due to shift of constituent protein expression. Specific IgE values from patients showed insignificant difference in non GM and GM maize extracts. Five maize sensitized cases, recognized same 7 protein fractions of 88-28 kD as IgE bindng in both GM and non-GM maize, signifying absence of variation. Four of the reported IgE binding proteins were also found to be stable by SGF. Conclusion Cry proteins did not indicate any significant similarity of >35% in allergen databases. Immunoassays also did not identify appreciable differences in endogenous IgE binding in GM and non GM maize. PMID:25706412

Strength of bond with Comspan Opaque to three silicoated alloys and titanium.

PubMed

Hansson, O

1990-06-01

In Sweden high-gold alloys or cobalt-chromium alloys are used for resin-bonded prostheses. The bond strength between a resin cement and different sandblasted or silicoated metals were measured before and after thermocycling; in connection with this some rapid thermocycling methods were studied. The effect of different storage times and different protection coatings on bond strength were tested. Finally, the influence of rubbing and contamination with saliva on bond strength were investigated. Silicoating increased the bond strength significantly. The highest bond strengths were these of silicoated Wirobond and titanium, unsusceptible to thermal stress; the bond strengths of the sandblasted metals were the weakest, and sensitive to thermocycling as well. The influence on bond strength for silicoated gold alloys, protected with an unpolymerized composite resin coating, stored in sealed plastic bags up to 7 days, was negligible. Rubbing and contamination with saliva did not influence bond strength. Preferably, silicoated Wirobond and titanium should be used for resin-bonded prostheses, but gold alloys may still be adequate for clinical use. The experimental method described for storing, sealing, and cleaning the silicoated metal surfaces in this article can be recommended for laboratory and clinical use.

Discovery of Anti-Hypertensive Oligopeptides from Adlay Based on In Silico Proteolysis and Virtual Screening

PubMed Central

Qiao, Liansheng; Li, Bin; Chen, Yankun; Li, Lingling; Chen, Xi; Wang, Lingzhi; Lu, Fang; Luo, Ganggang; Li, Gongyu; Zhang, Yanling

2016-01-01

Adlay (Coix larchryma-jobi L.) was the commonly used Traditional Chinese Medicine (TCM) with high content of seed storage protein. The hydrolyzed bioactive oligopeptides of adlay have been proven to be anti-hypertensive effective components. However, the structures and anti-hypertensive mechanism of bioactive oligopeptides from adlay were not clear. To discover the definite anti-hypertensive oligopeptides from adlay, in silico proteolysis and virtual screening were implemented to obtain potential oligopeptides, which were further identified by biochemistry assay and molecular dynamics simulation. In this paper, ten sequences of adlay prolamins were collected and in silico hydrolyzed to construct the oligopeptide library with 134 oligopeptides. This library was reverse screened by anti-hypertensive pharmacophore database, which was constructed by our research team and contained ten anti-hypertensive targets. Angiotensin-I converting enzyme (ACE) was identified as the main potential target for the anti-hypertensive activity of adlay oligopeptides. Three crystal structures of ACE were utilized for docking studies and 19 oligopeptides were finally identified with potential ACE inhibitory activity. According to mapping features and evaluation indexes of pharmacophore and docking, three oligopeptides were selected for biochemistry assay. An oligopeptide sequence, NPATY (IC50 = 61.88 ± 2.77 µM), was identified as the ACE inhibitor by reverse-phase high performance liquid chromatography (RP-HPLC) assay. Molecular dynamics simulation of NPATY was further utilized to analyze interactive bonds and key residues. ALA354 was identified as a key residue of ACE inhibitors. Hydrophobic effect of VAL518 and electrostatic effects of HIS383, HIS387, HIS513 and Zn2+ were also regarded as playing a key role in inhibiting ACE activities. This study provides a research strategy to explore the pharmacological mechanism of Traditional Chinese Medicine (TCM) proteins based on in silico proteolysis and virtual screening, which could be beneficial to reveal the pharmacological action of TCM proteins and provide new lead compounds for peptides-based drug design. PMID:27983650

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 experiment system for testing hypothesis on gene functions using three condition specific biological networks.

PubMed

Lee, Chai-Jin; Kang, Dongwon; Lee, Sangseon; Lee, Sunwon; Kang, Jaewoo; Kim, Sun

2018-05-25

Determining functions of a gene requires time consuming, expensive biological experiments. Scientists can speed up this experimental process if the literature information and biological networks can be adequately provided. In this paper, we present a web-based information system that can perform in silico experiments of computationally testing hypothesis on the function of a gene. A hypothesis that is specified in English by the user is converted to genes using a literature and knowledge mining system called BEST. Condition-specific TF, miRNA and PPI (protein-protein interaction) networks are automatically generated by projecting gene and miRNA expression data to template networks. Then, an in silico experiment is to test how well the target genes are connected from the knockout gene through the condition-specific networks. The test result visualizes path from the knockout gene to the target genes in the three networks. Statistical and information-theoretic scores are provided on the resulting web page to help scientists either accept or reject the hypothesis being tested. Our web-based system was extensively tested using three data sets, such as E2f1, Lrrk2, and Dicer1 knockout data sets. We were able to re-produce gene functions reported in the original research papers. In addition, we comprehensively tested with all disease names in MalaCards as hypothesis to show the effectiveness of our system. Our in silico experiment system can be very useful in suggesting biological mechanisms which can be further tested in vivo or in vitro. http://biohealth.snu.ac.kr/software/insilico/. Copyright © 2018 Elsevier Inc. All rights reserved.

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

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.

Bond efficacy of recycled orthodontic brackets: A comparative in vitro evaluation of two methods.

PubMed

Shetty, Vikram; Shekatkar, Yash; Kumbhat, Neesu; Gautam, G; Karbelkar, Shalan; Vandekar, Meghna

2015-01-01

Recycling of orthodontic brackets in developing orthodontic economies is an extremely common procedure. Bonding protocols and reliability of these brackets is, however, questionable, and still the subject of research. The aim was to evaluate and compare the shear bond strength of brackets recycled with sandblasting and silicoating. Ninety extracted human premolars were bonded with 0.022" SS brackets (American Orthodontics, Sheboygan USA) and then debonded. The debonded brackets were divided into three groups of 30 each. Group I: Sandblasting with 50-μm aluminum oxide (control group) Group II: Sandblasting with 50-μm aluminum oxide followed by metal primer application Group III: Silicoating with 30-μm Cojet sand followed by silane application and rebonded with Transbond XT. The sandblasted brackets and silicoated brackets were viewed under the scanning electron microscope, immediately after surface conditioning before rebonding. The shear bond strength with each group was tested. One-way analysis of variance, post-hoc Scheffe multiple comparison tests. The results showed that sandblasting created more irregularities and deeper erosions while silica coating created superficial irregularities and shallow erosions.

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

In-silico studies in Chinese herbal medicines' research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date.

PubMed

Barlow, D J; Buriani, A; Ehrman, T; Bosisio, E; Eberini, I; Hylands, P J

2012-04-10

The available databases that catalogue information on traditional Chinese medicines are reviewed in terms of their content and utility for in-silico research on Chinese herbal medicines, as too are the various protein database resources, and the software available for use in such studies. The software available for bioinformatics and 'omics studies of Chinese herbal medicines are summarised, and a critical evaluation given of the various in-silico methods applied in screening Chinese herbal medicines, including classification trees, neural networks, support vector machines, docking and inverse docking algorithms. Recommendations are made regarding any future in-silico studies of Chinese herbal medicines. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Biological Evaluation in Vitro and in Silico of Azetidin-2-one Derivatives as Potential Anticancer Agents.

PubMed

Olazaran, Fabián E; Rivera, Gildardo; Pérez-Vázquez, Alondra M; Morales-Reyes, Cynthia M; Segura-Cabrera, Aldo; Balderas-Rentería, Isaías

2017-01-12

Potential anticancer activity of 16 azetidin-2-one derivatives was evaluated showing that compound 6 [ N -( p -methoxy-phenyl)-2-( p -methyl-phenyl)-3-phenoxy-azetidin-2-one] presented cytotoxic activity in SiHa cells and B16F10 cells. The caspase-3 assay in B16F10 cells displayed that azetidin-2-one derivatives induce apoptosis. Microarray and molecular analysis showed that compound 6 was involved on specific gene overexpression of cytoskeleton regulation and apoptosis due to the inhibition of some cell cycle genes. From the 16 derivatives, compound 6 showed the highest selectivity to neoplastic cells, it was an inducer of apoptosis, and according to an in silico analysis of chemical interactions with colchicine binding site of human α/β-tubulin, the mechanism of action could be a molecular interaction involving the amino acids outlining such binding site.

Biological Evaluation in Vitro and in Silico of Azetidin-2-one Derivatives as Potential Anticancer Agents

PubMed Central

2016-01-01

Potential anticancer activity of 16 azetidin-2-one derivatives was evaluated showing that compound 6 [N-(p-methoxy-phenyl)-2-(p-methyl-phenyl)-3-phenoxy-azetidin-2-one] presented cytotoxic activity in SiHa cells and B16F10 cells. The caspase-3 assay in B16F10 cells displayed that azetidin-2-one derivatives induce apoptosis. Microarray and molecular analysis showed that compound 6 was involved on specific gene overexpression of cytoskeleton regulation and apoptosis due to the inhibition of some cell cycle genes. From the 16 derivatives, compound 6 showed the highest selectivity to neoplastic cells, it was an inducer of apoptosis, and according to an in silico analysis of chemical interactions with colchicine binding site of human α/β-tubulin, the mechanism of action could be a molecular interaction involving the amino acids outlining such binding site. PMID:28105271

Management of Gene Variants of Unknown Significance: Analysis Method and Risk Assessment of the VHL Mutation p.P81S (c.241C>T).

PubMed

Alosi, Daniela; Bisgaard, Marie Luise; Hemmingsen, Sophie Nowak; Krogh, Lotte Nylandsted; Mikkelsen, Hanne Birte; Binderup, Marie Louise Mølgaard

2017-02-01

Evaluation of the pathogenicity of a gene variant of unknown significance (VUS) is crucial for molecular diagnosis and genetic counseling, but can be challenging. This is especially so in phenotypically variable diseases, such as von Hippel-Lindau disease (vHL). vHL is caused by germline mutations in the VHL gene, which predispose to the development of multiple tumors such as central nervous system hemangioblastomas and renal cell carcinoma (RCC). We propose a method for the evaluation of VUS pathogenicity through our experience with the VHL missense mutation c.241C>T (p.P81S). 1) Clinical evaluation of known variant carriers: We evaluated a family of five VHL p.P81S carriers, as well as the clinical characteristics of all the p.P81S carriers reported in the literature; 2) Evaluation of tumor tissue via genetic analysis, histology, and immunohistochemistry (IHC); 3) Assessment of the variant's impact on protein structure and function, using multiple databases, in silico algorithms, and reports of functional studies. Only one family member had clinical signs of vHL with early-onset RCC. IHC analysis showed no VHL protein expressed in the tumor, consistent with biallelic VHL inactivation. The majority of in silico algorithms reported p.P81S as possibly pathogenic in relation to vHL or RCC, but there were discrepancies. Functional studies suggest that p.P81S impairs the VHL protein's function. The VHL p.P81S mutation is most likely a low-penetrant pathogenic variant predisposing to RCC development. We suggest the above-mentioned method for VUS evaluation with use of different methods, especially a variety of in silico methods and tumor tissue analysis.

Challenges and recommendations for obtaining chemical structures of industry-provided repurposing candidates.

PubMed

Southan, Christopher; Williams, Antony J; Ekins, Sean

2013-01-01

There is an expanding amount of interest directed at the repurposing and repositioning of drugs, as well as how in silico methods can assist these endeavors. Recent repurposing project tendering calls by the National Center for Advancing Translational Sciences (USA) and the Medical Research Council (UK) have included compound information and pharmacological data. However, none of the internal company development code names were assigned to chemical structures in the official documentation. This not only abrogates in silico analysis to support repurposing but consequently necessitates data gathering and curation to assign structures. Here, we describe the approaches, results and major challenges associated with this. Copyright © 2012 Elsevier Ltd. All rights reserved.

Integrative, multimodal analysis of glioblastoma using TCGA molecular data, pathology images, and clinical outcomes.

PubMed

Kong, Jun; Cooper, Lee A D; Wang, Fusheng; Gutman, David A; Gao, Jingjing; Chisolm, Candace; Sharma, Ashish; Pan, Tony; Van Meir, Erwin G; Kurc, Tahsin M; Moreno, Carlos S; Saltz, Joel H; Brat, Daniel J

2011-12-01

Multimodal, multiscale data synthesis is becoming increasingly critical for successful translational biomedical research. In this letter, we present a large-scale investigative initiative on glioblastoma, a high-grade brain tumor, with complementary data types using in silico approaches. We integrate and analyze data from The Cancer Genome Atlas Project on glioblastoma that includes novel nuclear phenotypic data derived from microscopic slides, genotypic signatures described by transcriptional class and genetic alterations, and clinical outcomes defined by response to therapy and patient survival. Our preliminary results demonstrate numerous clinically and biologically significant correlations across multiple data types, revealing the power of in silico multimodal data integration for cancer research.

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.

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...

An attempt to calculate in silico disintegration time of tablets containing mefenamic acid, a low water-soluble drug.

PubMed

Kimura, Go; Puchkov, Maxim; Leuenberger, Hans

2013-07-01

Based on a Quality by Design (QbD) approach, it is important to follow International Conference on Harmonization (ICH) guidance Q8 (R2) recommendations to explore the design space. The application of an experimental design is, however, not sufficient because of the fact that it is necessary to take into account the effects of percolation theory. For this purpose, an adequate software needs to be applied, capable of detecting percolation thresholds as a function of the distribution of the functional powder particles. Formulation-computer aided design (F-CAD), originally designed to calculate in silico the drug dissolution profiles of a tablet formulation is, for example, a suitable software for this purpose. The study shows that F-CAD can calculate a good estimate of the disintegration time of a tablet formulation consisting of mefenamic acid. More important, F-CAD is capable of replacing expensive laboratory work by performing in silico experiments for the exploration of the formulation design space according to ICH guidance Q8 (R2). As a consequence, a similar workflow existing as best practice in the automotive and aircraft industry can be adopted by the pharmaceutical industry: The drug delivery vehicle can be first fully designed and tested in silico, which will improve the quality of the marketed formulation and save time and money. Copyright © 2013 Wiley Periodicals, Inc.

Extensive in silico analysis of Mimivirus coded Rab GTPase homolog suggests a possible role in virion membrane biogenesis.

PubMed

Zade, Amrutraj; Sengupta, Malavi; Kondabagil, Kiran

2015-01-01

Rab GTPases are the key regulators of intracellular membrane trafficking in eukaryotes. Many viruses and intracellular bacterial pathogens have evolved to hijack the host Rab GTPase functions, mainly through activators and effector proteins, for their benefit. Acanthamoeba polyphaga mimivirus (APMV) is one of the largest viruses and belongs to the monophyletic clade of nucleo-cytoplasmic large DNA viruses (NCLDV). The inner membrane lining is integral to the APMV virion structure. APMV assembly involves extensive host membrane modifications, like vesicle budding and fusion, leading to the formation of a membrane sheet that is incorporated into the virion. Intriguingly, APMV and all group I members of the Mimiviridae family code for a putative Rab GTPase protein. APMV is the first reported virus to code for a Rab GTPase (encoded by R214 gene). Our thorough in silico analysis of the subfamily specific (SF) region of Mimiviridae Rab GTPase sequences suggests that they are related to Rab5, a member of the group II Rab GTPases, of lower eukaryotes. Because of their high divergence from the existing three isoforms, A, B, and C of the Rab5-family, we suggest that Mimiviridae Rabs constitute a new isoform, Rab5D. Phylogenetic analysis indicated probable horizontal acquisition from a lower eukaryotic ancestor followed by selection and divergence. Furthermore, interaction network analysis suggests that vps34 (a Class III PI3K homolog, coded by APMV L615), Atg-8 and dynamin (host proteins) are recruited by APMV Rab GTPase during capsid assembly. Based on these observations, we hypothesize that APMV Rab plays a role in the acquisition of inner membrane during virion assembly.

Implementation of an ADME enabling selection and visualization tool for drug discovery.

PubMed

Stoner, Chad L; Gifford, Eric; Stankovic, Charles; Lepsy, Christopher S; Brodfuehrer, Joanne; Prasad, J V N Vara; Surendran, Narayanan

2004-05-01

The pharmaceutical industry has large investments in compound library enrichment, high throughput biological screening, and biopharmaceutical (ADME) screening. As the number of compounds submitted for in vitro ADME screens increases, data analysis, interpretation, and reporting will become rate limiting in providing ADME-structure-activity relationship information to guide the synthetic strategy for chemical series. To meet these challenges, a software tool was developed and implemented that enables scientists to explore in vitro and in silico ADME and chemistry data in a multidimensional framework. The present work integrates physicochemical and ADME data, encompassing results for Caco-2 permeability, human liver microsomal half-life, rat liver microsomal half-life, kinetic solubility, measured log P, rule of 5 descriptors (molecular weight, hydrogen bond acceptors, hydrogen bond donors, calculated log P), polar surface area, chemical stability, and CYP450 3A4 inhibition. To facilitate interpretation of this data, a semicustomized software solution using Spotfire was designed that allows for multidimensional data analysis and visualization. The solution also enables simultaneous viewing and export of chemical structures with the corresponding ADME properties, enabling a more facile analysis of ADME-structure-activity relationship. In vitro and in silico ADME data were generated for 358 compounds from a series of human immunodeficiency virus protease inhibitors, resulting in a data set of 5370 experimental values which were subsequently analyzed and visualized using the customized Spotfire application. Implementation of this analysis and visualization tool has accelerated the selection of molecules for further development based on optimum ADME characteristics, and provided medicinal chemistry with specific, data driven structural recommendations for improvements in the ADME profile. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association J Pharm Sci 93: 1131-1141, 2004

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes.

PubMed

Kireeva, Natalia V; Ovchinnikova, Svetlana I; Kuznetsov, Sergey L; Kazennov, Andrey M; Tsivadze, Aslan Yu

2014-02-01

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

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

The Development of CK2 Inhibitors: From Traditional Pharmacology to in Silico Rational Drug Design

PubMed Central

Cozza, Giorgio

2017-01-01

Casein kinase II (CK2) is an ubiquitous and pleiotropic serine/threonine protein kinase able to phosphorylate hundreds of substrates. Being implicated in several human diseases, from neurodegeneration to cancer, the biological roles of CK2 have been intensively studied. Upregulation of CK2 has been shown to be critical to tumor progression, making this kinase an attractive target for cancer therapy. Several CK2 inhibitors have been developed so far, the first being discovered by “trial and error testing”. In the last decade, the development of in silico rational drug design has prompted the discovery, de novo design and optimization of several CK2 inhibitors, active in the low nanomolar range. The screening of big chemical libraries and the optimization of hit compounds by Structure Based Drug Design (SBDD) provide telling examples of a fruitful application of rational drug design to the development of CK2 inhibitors. Ligand Based Drug Design (LBDD) models have been also applied to CK2 drug discovery, however they were mainly focused on methodology improvements rather than being critical for de novo design and optimization. This manuscript provides detailed description of in silico methodologies whose applications to the design and development of CK2 inhibitors proved successful and promising. PMID:28230762

Impact of distance-based metric learning on classification and visualization model performance and structure-activity landscapes

NASA Astrophysics Data System (ADS)

Kireeva, Natalia V.; Ovchinnikova, Svetlana I.; Kuznetsov, Sergey L.; Kazennov, Andrey M.; Tsivadze, Aslan Yu.

2014-02-01

This study concerns large margin nearest neighbors classifier and its multi-metric extension as the efficient approaches for metric learning which aimed to learn an appropriate distance/similarity function for considered case studies. In recent years, many studies in data mining and pattern recognition have demonstrated that a learned metric can significantly improve the performance in classification, clustering and retrieval tasks. The paper describes application of the metric learning approach to in silico assessment of chemical liabilities. Chemical liabilities, such as adverse effects and toxicity, play a significant role in drug discovery process, in silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Here, to our knowledge for the first time, a distance-based metric learning procedures have been applied for in silico assessment of chemical liabilities, the impact of metric learning on structure-activity landscapes and predictive performance of developed models has been analyzed, the learned metric was used in support vector machines. The metric learning results have been illustrated using linear and non-linear data visualization techniques in order to indicate how the change of metrics affected nearest neighbors relations and descriptor space.

In silico identification of anthropogenic chemicals as ligands of zebrafish sex hormone binding globulin

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

Thorsteinson, Nels; Ban, Fuqiang; Santos-Filho, Osvaldo

2009-01-01

Anthropogenic compounds with the capacity to interact with the steroid-binding site of sex hormone binding globulin (SHBG) pose health risks to humans and other vertebrates including fish. Building on studies of human SHBG, we have applied in silico drug discovery methods to identify potential binders for SHBG in zebrafish (Danio rerio) as a model aquatic organism. Computational methods, including; homology modeling, molecular dynamics simulations, virtual screening, and 3D QSAR analysis, successfully identified 6 non-steroidal substances from the ZINC chemical database that bind to zebrafish SHBG (zfSHBG) with low-micromolar to nanomolar affinities, as determined by a competitive ligand-binding assay. We alsomore » screened 80,000 commercial substances listed by the European Chemicals Bureau and Environment Canada, and 6 non-steroidal hits from this in silico screen were tested experimentally for zfSHBG binding. All 6 of these compounds displaced the [{sup 3}H]5{alpha}-dihydrotestosterone used as labeled ligand in the zfSHBG screening assay when tested at a 33 {mu}M concentration, and 3 of them (hexestrol, 4-tert-octylcatechol, and dihydrobenzo(a)pyren-7(8H)-one) bind to zfSHBG in the micromolar range. The study demonstrates the feasibility of large-scale in silico screening of anthropogenic compounds that may disrupt or highjack functionally important protein:ligand interactions. Such studies could increase the awareness of hazards posed by existing commercial chemicals at relatively low cost.« less

Exploring Wound-Healing Genomic Machinery with a Network-Based Approach

PubMed Central

Vitali, Francesca; Marini, Simone; Balli, Martina; Grosemans, Hanne; Sampaolesi, Maurilio; Lussier, Yves A.; Cusella De Angelis, Maria Gabriella; Bellazzi, Riccardo

2017-01-01

The molecular mechanisms underlying tissue regeneration and wound healing are still poorly understood despite their importance. In this paper we develop a bioinformatics approach, combining biology and network theory to drive experiments for better understanding the genetic underpinnings of wound healing mechanisms and for selecting potential drug targets. We start by selecting literature-relevant genes in murine wound healing, and inferring from them a Protein-Protein Interaction (PPI) network. Then, we analyze the network to rank wound healing-related genes according to their topological properties. Lastly, we perform a procedure for in-silico simulation of a treatment action in a biological pathway. The findings obtained by applying the developed pipeline, including gene expression analysis, confirms how a network-based bioinformatics method is able to prioritize candidate genes for in vitro analysis, thus speeding up the understanding of molecular mechanisms and supporting the discovery of potential drug targets. PMID:28635674

Transcriptome mining and in silico structural and functional analysis of ascorbic acid and tartaric acid biosynthesis pathway enzymes in rose-scanted geranium.

PubMed

Narnoliya, Lokesh K; Sangwan, Rajender S; Singh, Sudhir P

2018-06-01

Rose-scented geranium (Pelargonium sp.) is widely known as aromatic and medicinal herb, accumulating specialized metabolites of high economic importance, such as essential oils, ascorbic acid, and tartaric acid. Ascorbic acid and tartaric acid are multifunctional metabolites of human value to be used as vital antioxidants and flavor enhancing agents in food products. No information is available related to the structural and functional properties of the enzymes involved in ascorbic acid and tartaric acid biosynthesis in rose-scented geranium. In the present study, transcriptome mining was done to identify full-length genes, followed by their bioinformatic and molecular modeling investigations and understanding of in silico structural and functional properties of these enzymes. Evolutionary conserved domains were identified in the pathway enzymes. In silico physicochemical characterization of the catalytic enzymes revealed isoelectric point (pI), instability index, aliphatic index, and grand average hydropathy (GRAVY) values of the enzymes. Secondary structural prediction revealed abundant proportion of alpha helix and random coil confirmations in the pathway enzymes. Three-dimensional homology models were developed for these enzymes. The predicted structures showed significant structural similarity with their respective templates in root mean square deviation analysis. Ramachandran plot analysis of the modeled enzymes revealed that more than 84% of the amino acid residues were within the favored regions. Further, functionally important residues were identified corresponding to catalytic sites located in the enzymes. To, our best knowledge, this is the first report which provides a foundation on functional annotation and structural determination of ascorbic acid and tartaric acid pathway enzymes in rose-scanted geranium.

Modeling Dynamic Contrast-Enhanced MRI Data with a Constrained Local AIF.

PubMed

Duan, Chong; Kallehauge, Jesper F; Pérez-Torres, Carlos J; Bretthorst, G Larry; Beeman, Scott C; Tanderup, Kari; Ackerman, Joseph J H; Garbow, Joel R

2018-02-01

This study aims to develop a constrained local arterial input function (cL-AIF) to improve quantitative analysis of dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) data by accounting for the contrast-agent bolus amplitude error in the voxel-specific AIF. Bayesian probability theory-based parameter estimation and model selection were used to compare tracer kinetic modeling employing either the measured remote-AIF (R-AIF, i.e., the traditional approach) or an inferred cL-AIF against both in silico DCE-MRI data and clinical, cervical cancer DCE-MRI data. When the data model included the cL-AIF, tracer kinetic parameters were correctly estimated from in silico data under contrast-to-noise conditions typical of clinical DCE-MRI experiments. Considering the clinical cervical cancer data, Bayesian model selection was performed for all tumor voxels of the 16 patients (35,602 voxels in total). Among those voxels, a tracer kinetic model that employed the voxel-specific cL-AIF was preferred (i.e., had a higher posterior probability) in 80 % of the voxels compared to the direct use of a single R-AIF. Maps of spatial variation in voxel-specific AIF bolus amplitude and arrival time for heterogeneous tissues, such as cervical cancer, are accessible with the cL-AIF approach. The cL-AIF method, which estimates unique local-AIF amplitude and arrival time for each voxel within the tissue of interest, provides better modeling of DCE-MRI data than the use of a single, measured R-AIF. The Bayesian-based data analysis described herein affords estimates of uncertainties for each model parameter, via posterior probability density functions, and voxel-wise comparison across methods/models, via model selection in data modeling.

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.

Decoding Crucial LncRNAs Implicated in Neurogenesis and Neurological Disorders.

PubMed

Ayana, R; Singh, Shailja; Pati, Soumya

2017-04-15

Unraveling transcriptional heterogeneity and the labyrinthine nature of neurodevelopment can probe insights into neuropsychiatric disorders. It is noteworthy that adult neurogenesis is restricted to the subventricular and subgranular zones of the brain. Recent studies suggest long non-coding RNAs (lncRNAs) as an avant-garde class of regulators implicated in neurodevelopment. But, paucity exists in the knowledge regarding lncRNAs in neurogenesis and their associations with neurodevelopmental defects. To address this, we extensively reviewed the existing literature databases as well as performed relevant in-silico analysis. We utilized Allen Brain Atlas (ABA) differential search module and generated a catalogue of ∼30,000 transcripts specific to the neurogenic zones, including coding and non-coding transcripts. To explore the existing lncRNAs reported in neurogenesis, we performed extensive literature mining and identified 392 lncRNAs. These degenerate lncRNAs were mapped onto the ABA transcript list leading to detection of 20 lncRNAs specific to neurogenic zones (Dentate gyrus/Lateral ventricle), among which 10 showed associations to several neurodevelopmental disorders following in-silico mapping onto brain disease databases like Simons Foundation Autism Research Initiative, AutDB, and lncRNADisease. Notably, using ABA correlation module, we could establish lncRNA-to-mRNA coexpression networks for the above 10 candidate lncRNAs. Finally, pathway prediction revealed physical, biochemical, or regulatory interactions for nine lncRNAs. In addition, ABA differential search also revealed 54 novel significant lncRNAs from the null set (∼30,000). Conclusively, this review represents an updated catalogue of lncRNAs in neurogenesis and neurological diseases, and overviews the field of OMICs-based data analysis for understanding lncRNome-based regulation in neurodevelopment.

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...

The Use of Multidimensional Image-Based Analysis to Accurately Monitor Cell Growth in 3D Bioreactor Culture

PubMed Central

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells. PMID:22028809

The use of multidimensional image-based analysis to accurately monitor cell growth in 3D bioreactor culture.

PubMed

Baradez, Marc-Olivier; Marshall, Damian

2011-01-01

The transition from traditional culture methods towards bioreactor based bioprocessing to produce cells in commercially viable quantities for cell therapy applications requires the development of robust methods to ensure the quality of the cells produced. Standard methods for measuring cell quality parameters such as viability provide only limited information making process monitoring and optimisation difficult. Here we describe a 3D image-based approach to develop cell distribution maps which can be used to simultaneously measure the number, confluency and morphology of cells attached to microcarriers in a stirred tank bioreactor. The accuracy of the cell distribution measurements is validated using in silico modelling of synthetic image datasets and is shown to have an accuracy >90%. Using the cell distribution mapping process and principal component analysis we show how cell growth can be quantitatively monitored over a 13 day bioreactor culture period and how changes to manufacture processes such as initial cell seeding density can significantly influence cell morphology and the rate at which cells are produced. Taken together, these results demonstrate how image-based analysis can be incorporated in cell quality control processes facilitating the transition towards bioreactor based manufacture for clinical grade cells.

NAC transcription factor genes: genome-wide identification, phylogenetic, motif and cis-regulatory element analysis in pigeonpea (Cajanus cajan (L.) Millsp.).

PubMed

Satheesh, Viswanathan; Jagannadham, P Tej Kumar; Chidambaranathan, Parameswaran; Jain, P K; Srinivasan, R

2014-12-01

The NAC (NAM, ATAF and CUC) proteins are plant-specific transcription factors implicated in development and stress responses. In the present study 88 pigeonpea NAC genes were identified from the recently published draft genome of pigeonpea by using homology based and de novo prediction programmes. These sequences were further subjected to phylogenetic, motif and promoter analyses. In motif analysis, highly conserved motifs were identified in the NAC domain and also in the C-terminal region of the NAC proteins. A phylogenetic reconstruction using pigeonpea, Arabidopsis and soybean NAC genes revealed 33 putative stress-responsive pigeonpea NAC genes. Several stress-responsive cis-elements were identified through in silico analysis of the promoters of these putative stress-responsive genes. This analysis is the first report of NAC gene family in pigeonpea and will be useful for the identification and selection of candidate genes associated with stress tolerance.

Integral equation and discontinuous Galerkin methods for the analysis of light-matter interaction

NASA Astrophysics Data System (ADS)

Baczewski, Andrew David

Light-matter interaction is among the most enduring interests of the physical sciences. The understanding and control of this physics is of paramount importance to the design of myriad technologies ranging from stained glass, to molecular sensing and characterization techniques, to quantum computers. The development of complex engineered systems that exploit this physics is predicated at least partially upon in silico design and optimization that properly capture the light-matter coupling. In this thesis, the details of computational frameworks that enable this type of analysis, based upon both Integral Equation and Discontinuous Galerkin formulations will be explored. There will be a primary focus on the development of efficient and accurate software, with results corroborating both. The secondary focus will be on the use of these tools in the analysis of a number of exemplary systems.

Sensitivity analysis of Repast computational ecology models with R/Repast.

PubMed

Prestes García, Antonio; Rodríguez-Patón, Alfonso

2016-12-01

Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual-based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom-up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in-silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.

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.

Investigating the metabolic capabilities of Mycobacterium tuberculosis H37Rv using the in silico strain iNJ661 and proposing alternative drug targets

PubMed Central

Jamshidi, Neema; Palsson, Bernhard Ø

2007-01-01

Background: Mycobacterium tuberculosis continues to be a major pathogen in the third world, killing almost 2 million people a year by the most recent estimates. Even in industrialized countries, the emergence of multi-drug resistant (MDR) strains of tuberculosis hails the need to develop additional medications for treatment. Many of the drugs used for treatment of tuberculosis target metabolic enzymes. Genome-scale models can be used for analysis, discovery, and as hypothesis generating tools, which will hopefully assist the rational drug development process. These models need to be able to assimilate data from large datasets and analyze them. Results: We completed a bottom up reconstruction of the metabolic network of Mycobacterium tuberculosis H37Rv. This functional in silico bacterium, iNJ661, contains 661 genes and 939 reactions and can produce many of the complex compounds characteristic to tuberculosis, such as mycolic acids and mycocerosates. We grew this bacterium in silico on various media, analyzed the model in the context of multiple high-throughput data sets, and finally we analyzed the network in an 'unbiased' manner by calculating the Hard Coupled Reaction (HCR) sets, groups of reactions that are forced to operate in unison due to mass conservation and connectivity constraints. Conclusion: Although we observed growth rates comparable to experimental observations (doubling times ranging from about 12 to 24 hours) in different media, comparisons of gene essentiality with experimental data were less encouraging (generally about 55%). The reasons for the often conflicting results were multi-fold, including gene expression variability under different conditions and lack of complete biological knowledge. Some of the inconsistencies between in vitro and in silico or in vivo and in silico results highlight specific loci that are worth further experimental investigations. Finally, by considering the HCR sets in the context of known drug targets for tuberculosis treatment we proposed new alternative, but equivalent drug targets. PMID:17555602

Combining Basal–Bolus Insulin Infusion for Tight Postprandial Glucose Control: An in Silico Evaluation in Adults, Children, and Adolescents

PubMed Central

Revert, Ana; Rossetti, Paolo; Calm, Remei; Vehí, Josep; Bondia, Jorge

2010-01-01

Background Achieving good postprandial glycemic control, without triggering hypoglycemia events, is a challenge of treatment strategies for type 1 diabetes subjects. Continuous subcutaneous insulin infusion, the gold standard of therapy, is based on heuristic adjustments of both basal and prandial insulin. Some tools, such as bolus calculators, are available to aid patients in selecting a meal-related insulin dose. However, they are still based on empiric parameters such as the insulin-to-carbohydrate ratio and on the physicians’ and patients’ ability to fit bolus mode to meal composition. Method In this article, a nonheuristic method for assessment of prandial insulin administration is presented and evaluated. An algorithm based on set inversion via interval analysis is used to coordinate basal and bolus insulin infusions to deal with postprandial glucose excursions. The evaluation is carried out through an in silico study using the 30 virtual patients available in the educational version of the Food and Drug Administration-accepted University of Virginia simulator. Results obtained using the standard bolus strategy and different coordinated basal–bolus solutions provided by the algorithm are compared. Results Coordinated basal–bolus solutions improve postprandial glucose performance in most cases, mainly in terms of reducing hypoglycemia risk, but also increasing the percentage of time in normoglycemia. Moreover, glycemic variability is reduced considerably by using these innovative solutions. Conclusions The algorithm presented here is a robust nonheuristic alternative to deal with postprandial glycemic control. It is shown as a powerful tool that could be integrated in future smart insulin pumps. PMID:21129338

Towards Coleoptera-specific high-throughput screening systems for compounds with ecdysone activity: development of EcR reporter assays using weevil (Anthonomus grandis)-derived cell lines and in silico analysis of ligand binding to A. grandis EcR ligand-binding pocket.

PubMed

Soin, Thomas; Iga, Masatoshi; Swevers, Luc; Rougé, Pierre; Janssen, Colin R; Smagghe, Guy

2009-08-01

Molting in insects is regulated by ecdysteroids and juvenile hormones. Several synthetic non-steroidal ecdysone agonists are on the market as insecticides. These ecdysone agonists are dibenzoylhydrazine (DBH) analogue compounds that manifest their toxicity via interaction with the ecdysone receptor (EcR). Of the four commercial available ecdysone agonists, three (tebufenozide, methoxyfenozide and chromafenozide) are highly lepidopteran specific, one (halofenozide) is used to control coleopteran and lepidopteran insects in turf and ornamentals. However, compared to the very high binding affinity of these DBH analogues to lepidopteran EcRs, halofenozide has a low binding affinity for coleopteran EcRs. For the discovery of ecdysone agonists that target non-lepidopteran insect groups, efficient screening systems that are based on the activation of the EcR are needed. We report here the development and evaluation of two coleopteran-specific reporter-based screening systems to discover and evaluate ecdysone agonists. The screening systems are based on the cell lines BRL-AG-3A and BRL-AG-3C that are derived from the weevil Anthonomus grandis, which can be efficiently transduced with an EcR reporter cassette for evaluation of induction of reporter activity by ecdysone agonists. We also cloned the almost full length coding sequence of EcR expressed in the cell line BRL-AG-3C and used it to make an initial in silico 3D-model of its ligand-binding pocket docked with ponasterone A and tebufenozide.

In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future

NASA Astrophysics Data System (ADS)

Kar, Supratik; Roy, Juganta K.; Leszczynski, Jerzy

2017-06-01

Advances in solar cell technology require designing of new organic dye sensitizers for dye-sensitized solar cells with high power conversion efficiency to circumvent the disadvantages of silicon-based solar cells. In silico studies including quantitative structure-property relationship analysis combined with quantum chemical analysis were employed to understand the primary electron transfer mechanism and photo-physical properties of 273 arylamine organic dyes from 11 diverse chemical families explicit to iodine electrolyte. The direct quantitative structure-property relationship models enable identification of the essential electronic and structural attributes necessary for quantifying the molecular prerequisites of 11 classes of arylamine organic dyes, responsible for high power conversion efficiency of dye-sensitized solar cells. Tetrahydroquinoline, N,N'-dialkylaniline and indoline have been least explored classes under arylamine organic dyes for dye-sensitized solar cells. Therefore, the identified properties from the corresponding quantitative structure-property relationship models of the mentioned classes were employed in designing of "lead dyes". Followed by, a series of electrochemical and photo-physical parameters were computed for designed dyes to check the required variables for electron flow of dye-sensitized solar cells. The combined computational techniques yielded seven promising lead dyes each for all three chemical classes considered. Significant (130, 183, and 46%) increment in predicted %power conversion efficiency was observed comparing with the existing dye with highest experimental %power conversion efficiency value for tetrahydroquinoline, N,N'-dialkylaniline and indoline, respectively maintaining required electrochemical parameters.

Interplay between CedA, rpoB and double stranded DNA: A step towards understanding CedA mediated cell division in E. coli.

PubMed

Sharma, Pankaj; Tomar, Anil Kumar; Kundu, Bishwajit

2018-02-01

Cell division is compromised in DnaAcos mutant E. coli cells due to chromosome over-replication. In these cells, CedA acts as a regulatory protein and initiates cell division by a hitherto unknown mechanism. CedA, a double stranded DNA binding protein, interacts with various subunits of RNA polymerase complex, including rpoB. To reveal how this concert between CedA, rpoB and DNA brings about cell division in E. coli, we performed biophysical and in silico analysis and obtained mechanistic insights. Interaction between CedA and rpoB was shown by circular dichroism spectrometry and in silico docking experiments. Further, CedA and rpoB were allowed to interact individually to a selected DNA and their binding was monitored by fluorescence spectroscopy. The binding constants of these interactions as determined by BioLayer Interferometry clearly show that rpoB binds to DNA with higher affinity (K D2 =<1.0E-12M) as compared to CedA (K D2 =9.58E-09M). These findings were supported by docking analysis where 12 intermolecular H-bonds were formed in rpoB-DNA complex as compared to 4 in CedA-DNA complex. Based on our data we propose that in E. coli cells chromosome over-replication signals CedA to recruit rpoB to specific DNA site(s), which initiates transcription of cell division regulatory elements. Copyright © 2017 Elsevier B.V. All rights reserved.

Isolation and characterization of full-length putative alcohol dehydrogenase genes from polygonum minus

NASA Astrophysics Data System (ADS)

Hamid, Nur Athirah Abd; Ismail, Ismanizan

2013-11-01

Polygonum minus, locally named as Kesum is an aromatic herb which is high in secondary metabolite content. Alcohol dehydrogenase is an important enzyme that catalyzes the reversible oxidation of alcohol and aldehyde with the presence of NAD(P)(H) as co-factor. The main focus of this research is to identify the gene of ADH. The total RNA was extracted from leaves of P. minus which was treated with 150 μM Jasmonic acid. Full-length cDNA sequence of ADH was isolated via rapid amplification cDNA end (RACE). Subsequently, in silico analysis was conducted on the full-length cDNA sequence and PCR was done on genomic DNA to determine the exon and intron organization. Two sequences of ADH, designated as PmADH1 and PmADH2 were successfully isolated. Both sequences have ORF of 801 bp which encode 266 aa residues. Nucleotide sequence comparison of PmADH1 and PmADH2 indicated that both sequences are highly similar at the ORF region but divergent in the 3' untranslated regions (UTR). The amino acid is differ at the 107 residue; PmADH1 contains Gly (G) residue while PmADH2 contains Cys (C) residue. The intron-exon organization pattern of both sequences are also same, with 3 introns and 4 exons. Based on in silico analysis, both sequences contain "classical" short chain alcohol dehydrogenases/reductases ((c) SDRs) conserved domain. The results suggest that both sequences are the members of short chain alcohol dehydrogenase family.

Extended Field Laser Confocal Microscopy (EFLCM): Combining automated Gigapixel image capture with in silico virtual microscopy

PubMed Central

Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

2008-01-01

Background Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Methods Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM). Results We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. Conclusion The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA) instrument for automated screening processes. PMID:18627634

Evolutionary distance from human homologs reflects allergenicity of animal food proteins.

PubMed

Jenkins, John A; Breiteneder, Heimo; Mills, E N Clare

2007-12-01

In silico analysis of allergens can identify putative relationships among protein sequence, structure, and allergenic properties. Such systematic analysis reveals that most plant food allergens belong to a restricted number of protein superfamilies, with pollen allergens behaving similarly. We have investigated the structural relationships of animal food allergens and their evolutionary relatedness to human homologs to define how closely a protein must resemble a human counterpart to lose its allergenic potential. Profile-based sequence homology methods were used to classify animal food allergens into Pfam families, and in silico analyses of their evolutionary and structural relationships were performed. Animal food allergens could be classified into 3 main families--tropomyosins, EF-hand proteins, and caseins--along with 14 minor families each composed of 1 to 3 allergens. The evolutionary relationships of each of these allergen superfamilies showed that in general, proteins with a sequence identity to a human homolog above approximately 62% were rarely allergenic. Single substitutions in otherwise highly conserved regions containing IgE epitopes in EF-hand parvalbumins may modulate allergenicity. These data support the premise that certain protein structures are more allergenic than others. Contrasting with plant food allergens, animal allergens, such as the highly conserved tropomyosins, challenge the capability of the human immune system to discriminate between foreign and self-proteins. Such immune responses run close to becoming autoimmune responses. Exploiting the closeness between animal allergens and their human homologs in the development of recombinant allergens for immunotherapy will need to consider the potential for developing unanticipated autoimmune responses.

Extended Field Laser Confocal Microscopy (EFLCM): combining automated Gigapixel image capture with in silico virtual microscopy.

PubMed

Flaberg, Emilie; Sabelström, Per; Strandh, Christer; Szekely, Laszlo

2008-07-16

Confocal laser scanning microscopy has revolutionized cell biology. However, the technique has major limitations in speed and sensitivity due to the fact that a single laser beam scans the sample, allowing only a few microseconds signal collection for each pixel. This limitation has been overcome by the introduction of parallel beam illumination techniques in combination with cold CCD camera based image capture. Using the combination of microlens enhanced Nipkow spinning disc confocal illumination together with fully automated image capture and large scale in silico image processing we have developed a system allowing the acquisition, presentation and analysis of maximum resolution confocal panorama images of several Gigapixel size. We call the method Extended Field Laser Confocal Microscopy (EFLCM). We show using the EFLCM technique that it is possible to create a continuous confocal multi-colour mosaic from thousands of individually captured images. EFLCM can digitize and analyze histological slides, sections of entire rodent organ and full size embryos. It can also record hundreds of thousands cultured cells at multiple wavelength in single event or time-lapse fashion on fixed slides, in live cell imaging chambers or microtiter plates. The observer independent image capture of EFLCM allows quantitative measurements of fluorescence intensities and morphological parameters on a large number of cells. EFLCM therefore bridges the gap between the mainly illustrative fluorescence microscopy and purely quantitative flow cytometry. EFLCM can also be used as high content analysis (HCA) instrument for automated screening processes.

Floating gastroretentive drug delivery systems: Comparison of experimental and simulated dissolution profiles and floatation behavior.

PubMed

Eberle, Veronika A; Schoelkopf, Joachim; Gane, Patrick A C; Alles, Rainer; Huwyler, Jörg; Puchkov, Maxim

2014-07-16

Gastroretentive drug delivery systems (GRDDS) play an important role in the delivery of drug substances to the upper part of the gastrointestinal tract; they offer a possibility to overcome the limited gastric residence time of conventional dosage forms. The aim of the study was to understand drug-release and floatation mechanisms of a floating GRDDS based on functionalized calcium carbonate (FCC). The inherently low apparent density of the excipient (approx. 0.6 g/cm(3)) enabled a mechanism of floatation. The higher specific surface of FCC (approx. 70 m(2)) allowed sufficient hardness of resulting compacts. The floating mechanism of GRDDS was simulated in silico under simulated acidic and neutral conditions, and the results were compared to those obtained in vitro. United States Pharmacopeia (USP) dissolution methods are of limited usefulness for evaluating floating behavior and drug release of floating dosage forms. Therefore, we developed a custom-built stomach model to simultaneously analyze floating characteristics and drug release. In silico dissolution and floatation profiles of the FCC-based tablet were simulated using a three-dimensional cellular automata-based model. In simulated gastric fluid, the FCC-based tablets showed instant floatation. The compacts stayed afloat during the measurement in 0.1 N HCl and eroded completely while releasing the model drug substance. When water was used as dissolution medium, the tablets had no floating lag time and sank down during the measurement, resulting in a change of release kinetics. Floating dosage forms based on FCC appear promising. It was possible to manufacture floating tablets featuring a density of less than unity and sufficient hardness for further processing. In silico dissolution simulation offered a possibility to understand floating behavior and drug-release mechanism. Copyright © 2014 Elsevier B.V. All rights reserved.

Medium-sized tandem repeats represent an abundant component of the Drosophila virilis genome.

PubMed

Abdurashitov, Murat A; Gonchar, Danila A; Chernukhin, Valery A; Tomilov, Victor N; Tomilova, Julia E; Schostak, Natalia G; Zatsepina, Olga G; Zelentsova, Elena S; Evgen'ev, Michael B; Degtyarev, Sergey K H

2013-11-09

Previously, we developed a simple method for carrying out a restriction enzyme analysis of eukaryotic DNA in silico, based on the known DNA sequences of the genomes. This method allows the user to calculate lengths of all DNA fragments that are formed after a whole genome is digested at the theoretical recognition sites of a given restriction enzyme. A comparison of the observed peaks in distribution diagrams with the results from DNA cleavage using several restriction enzymes performed in vitro have shown good correspondence between the theoretical and experimental data in several cases. Here, we applied this approach to the annotated genome of Drosophila virilis which is extremely rich in various repeats. Here we explored the combined approach to perform the restriction analysis of D. virilis DNA. This approach enabled to reveal three abundant medium-sized tandem repeats within the D. virilis genome. While the 225 bp repeats were revealed previously in intergenic non-transcribed spacers between ribosomal genes of D. virilis, two other families comprised of 154 bp and 172 bp repeats were not described. Tandem Repeats Finder search demonstrated that 154 bp and 172 bp units are organized in multiple clusters in the genome of D. virilis. Characteristically, only 154 bp repeats derived from Helitron transposon are transcribed. Using in silico digestion in combination with conventional restriction analysis and sequencing of repeated DNA fragments enabled us to isolate and characterize three highly abundant families of medium-sized repeats present in the D. virilis genome. These repeats comprise a significant portion of the genome and may have important roles in genome function and structural integrity. Therefore, we demonstrated an approach which makes possible to investigate in detail the gross arrangement and expression of medium-sized repeats basing on sequencing data even in the case of incompletely assembled and/or annotated genomes.

In silico study of protein to protein interaction analysis of AMP-activated protein kinase and mitochondrial activity in three different farm animal species

NASA Astrophysics Data System (ADS)

Prastowo, S.; Widyas, N.

2018-03-01

AMP-activated protein kinase (AMPK) is cellular energy censor which works based on ATP and AMP concentration. This protein interacts with mitochondria in determine its activity to generate energy for cell metabolism purposes. For that, this paper aims to compare the protein to protein interaction of AMPK and mitochondrial activity genes in the metabolism of known animal farm (domesticated) that are cattle (Bos taurus), pig (Sus scrofa) and chicken (Gallus gallus). In silico study was done using STRING V.10 as prominent protein interaction database, followed with biological function comparison in KEGG PATHWAY database. Set of genes (12 in total) were used as input analysis that are PRKAA1, PRKAA2, PRKAB1, PRKAB2, PRKAG1, PRKAG2, PRKAG3, PPARGC1, ACC, CPT1B, NRF2 and SOD. The first 7 genes belong to gene in AMPK family, while the last 5 belong to mitochondrial activity genes. The protein interaction result shows 11, 8 and 5 metabolism pathways in Bos taurus, Sus scrofa and Gallus gallus, respectively. The top pathway in Bos taurus is AMPK signaling pathway (10 genes), Sus scrofa is Adipocytokine signaling pathway (8 genes) and Gallus gallus is FoxO signaling pathway (5 genes). Moreover, the common pathways found in those 3 species are Adipocytokine signaling pathway, Insulin signaling pathway and FoxO signaling pathway. Genes clustered in Adipocytokine and Insulin signaling pathway are PRKAA2, PPARGC1A, PRKAB1 and PRKAG2. While, in FoxO signaling pathway are PRKAA2, PRKAB1, PRKAG2. According to that, we found PRKAA2, PRKAB1 and PRKAG2 are the common genes. Based on the bioinformatics analysis, we can demonstrate that protein to protein interaction shows distinct different of metabolism in different species. However, further validation is needed to give a clear explanation.

In Silico Analysis of the Structural and Biochemical Features of the NMD Factor UPF1 in Ustilago maydis.

PubMed

Martínez-Montiel, Nancy; Morales-Lara, Laura; Hernández-Pérez, Julio M; Martínez-Contreras, Rebeca D

2016-01-01

The molecular mechanisms regulating the accuracy of gene expression are still not fully understood. Among these mechanisms, Nonsense-mediated Decay (NMD) is a quality control process that detects post-transcriptionally abnormal transcripts and leads them to degradation. The UPF1 protein lays at the heart of NMD as shown by several structural and functional features reported for this factor mainly for Homo sapiens and Saccharomyces cerevisiae. This process is highly conserved in eukaryotes but functional diversity can be observed in various species. Ustilago maydis is a basidiomycete and the best-known smut, which has become a model to study molecular and cellular eukaryotic mechanisms. In this study, we performed in silico analysis to investigate the structural and biochemical properties of the putative UPF1 homolog in Ustilago maydis. The putative homolog for UPF1 was recognized in the annotated genome for the basidiomycete, exhibiting 66% identity with its human counterpart at the protein level. The known structural and functional domains characteristic of UPF1 homologs were also found. Based on the crystal structures available for UPF1, we constructed different three-dimensional models for umUPF1 in order to analyze the secondary and tertiary structural features of this factor. Using these models, we studied the spatial arrangement of umUPF1 and its capability to interact with UPF2. Moreover, we identified the critical amino acids that mediate the interaction of umUPF1 with UPF2, ATP, RNA and with UPF1 itself. Mutating these amino acids in silico showed an important effect over the native structure. Finally, we performed molecular dynamic simulations for UPF1 proteins from H. sapiens and U. maydis and the results obtained show a similar behavior and physicochemical properties for the protein in both organisms. Overall, our results indicate that the putative UPF1 identified in U. maydis shows a very similar sequence, structural organization, mechanical stability, physicochemical properties and spatial organization in comparison to the NMD factor depicted for Homo sapiens. These observations strongly support the notion that human and fungal UPF1 could perform equivalent biological activities.

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.

Issues on machine learning for prediction of classes among molecular sequences of plants and animals

NASA Astrophysics Data System (ADS)

Stehlik, Milan; Pant, Bhasker; Pant, Kumud; Pardasani, K. R.

2012-09-01

Nowadays major laboratories of the world are turning towards in-silico experimentation due to their ease, reproducibility and accuracy. The ethical issues concerning wet lab experimentations are also minimal in in-silico experimentations. But before we turn fully towards dry lab simulations it is necessary to understand the discrepancies and bottle necks involved with dry lab experimentations. It is necessary before reporting any result using dry lab simulations to perform in-depth statistical analysis of the data. Keeping same in mind here we are presenting a collaborative effort to correlate findings and results of various machine learning algorithms and checking underlying regressions and mutual dependencies so as to develop an optimal classifier and predictors.

In Silico and Fluorescence In Situ Hybridization Mapping Reveals Collinearity between the Pennisetum squamulatum Apomixis Carrier-Chromosome and Chromosome 2 of Sorghum and Foxtail Millet.

PubMed

Sapkota, Sirjan; Conner, Joann A; Hanna, Wayne W; Simon, Bindu; Fengler, Kevin; Deschamps, Stéphane; Cigan, Mark; Ozias-Akins, Peggy

2016-01-01

Apomixis, or clonal propagation through seed, is a trait identified within multiple species of the grass family (Poaceae). The genetic locus controlling apomixis in Pennisetum squamulatum (syn Cenchrus squamulatus) and Cenchrus ciliaris (syn Pennisetum ciliare, buffelgrass) is the apospory-specific genomic region (ASGR). Previously, the ASGR was shown to be highly conserved but inverted in marker order between P. squamulatum and C. ciliaris based on fluorescence in situ hybridization (FISH) and varied in both karyotype and position of the ASGR on the ASGR-carrier chromosome among other apomictic Cenchrus/Pennisetum species. Using in silico transcript mapping and verification of physical positions of some of the transcripts via FISH, we discovered that the ASGR-carrier chromosome from P. squamulatum is collinear with chromosome 2 of foxtail millet and sorghum outside of the ASGR. The in silico ordering of the ASGR-carrier chromosome markers, previously unmapped in P. squamulatum, allowed for the identification of a backcross line with structural changes to the P. squamulatum ASGR-carrier chromosome derived from gamma irradiated pollen.

In Silico and Fluorescence In Situ Hybridization Mapping Reveals Collinearity between the Pennisetum squamulatum Apomixis Carrier-Chromosome and Chromosome 2 of Sorghum and Foxtail Millet

PubMed Central

Sapkota, Sirjan; Conner, Joann A.; Hanna, Wayne W.; Simon, Bindu; Fengler, Kevin; Deschamps, Stéphane; Cigan, Mark; Ozias-Akins, Peggy

2016-01-01

Apomixis, or clonal propagation through seed, is a trait identified within multiple species of the grass family (Poaceae). The genetic locus controlling apomixis in Pennisetum squamulatum (syn Cenchrus squamulatus) and Cenchrus ciliaris (syn Pennisetum ciliare, buffelgrass) is the apospory-specific genomic region (ASGR). Previously, the ASGR was shown to be highly conserved but inverted in marker order between P. squamulatum and C. ciliaris based on fluorescence in situ hybridization (FISH) and varied in both karyotype and position of the ASGR on the ASGR-carrier chromosome among other apomictic Cenchrus/Pennisetum species. Using in silico transcript mapping and verification of physical positions of some of the transcripts via FISH, we discovered that the ASGR-carrier chromosome from P. squamulatum is collinear with chromosome 2 of foxtail millet and sorghum outside of the ASGR. The in silico ordering of the ASGR-carrier chromosome markers, previously unmapped in P. squamulatum, allowed for the identification of a backcross line with structural changes to the P. squamulatum ASGR-carrier chromosome derived from gamma irradiated pollen. PMID:27031857

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 analysis of Schmidtea mediterranea TIR domain-containing proteins.

PubMed

Tsoumtsa, Landry Laure; Sougoufora, Seynabou; Torre, Cedric; Lemichez, Emmanuel; Pontarotti, Pierre; Ghigo, Eric

2018-09-01

While genetic evidence points towards an absence of Toll-Like Receptors (TLRs) in Platyhelminthes, the Toll/IL-1 Receptor (TIR)-domains that drive the assembly of signalling complexes downstream TLR are present in these organisms. Here, we undertook the characterisation of the repertoire of TIR-domain containing proteins in Schmidtea mediterranea in order to gain valuable information on TLR evolution in metazoan. We report the presence of twenty proteins containing between one and two TIR domains. In addition, our phylogenetic-based reconstruction approach identified Smed-SARM and Smed-MyD88 as conserved TLR adaptors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Assessing the risk of pH-dependent absorption for new molecular entities: a novel in vitro dissolution test, physicochemical analysis, and risk assessment strategy.

PubMed

Mathias, Neil R; Xu, Yan; Patel, Dhaval; Grass, Michael; Caldwell, Brett; Jager, Casey; Mullin, Jim; Hansen, Luke; Crison, John; Saari, Amy; Gesenberg, Christoph; Morrison, John; Vig, Balvinder; Raghavan, Krishnaswamy

2013-11-04

Weak base therapeutic agents can show reduced absorption or large pharmacokinetic variability when coadministered with pH-modifying agents, or in achlorhydria disease states, due to reduced dissolution rate and/or solubility at high gastric pH. This is often referred to as pH-effect. The goal of this study was to understand why some drugs exhibit a stronger pH-effect than others. To study this, an API-sparing, two-stage, in vitro microdissolution test was developed to generate drug dissolution, supersaturation, and precipitation kinetic data under conditions that mimic the dynamic pH changes in the gastrointestinal tract. In vitro dissolution was assessed for a chemically diverse set of compounds under high pH and low pH, analogous to elevated and normal gastric pH conditions observed in pH-modifier cotreated and untreated subjects, respectively. Represented as a ratio between the conditions, the in vitro pH-effect correlated linearly with clinical pH-effect based on the Cmax ratio and in a non-linear relationship based on AUC ratio. Additionally, several in silico approaches that use the in vitro dissolution data were found to be reasonably predictive of the clinical pH-effect. To explore the hypothesis that physicochemical properties are predictors of clinical pH-effect, statistical correlation analyses were conducted using linear sequential feature selection and partial least-squares regression. Physicochemical parameters did not show statistically significant linear correlations to clinical pH-effect for this data set, which highlights the complexity and poorly understood nature of the interplay between parameters. Finally, a strategy is proposed for implementation early in clinical development, to systematically assess the risk of clinical pH-effect for new molecular entities that integrates physicochemical analysis and in vitro, in vivo and in silico methods.

Scheffersomyces stipitis: a comparative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae

PubMed Central

2012-01-01

Background Scheffersomyces stipitis is a Crabtree negative yeast, commonly known for its capacity to ferment pentose sugars. Differently from Crabtree positive yeasts such as Saccharomyces cerevisiae, the onset of fermentation in S. stipitis is not dependent on the sugar concentration, but is regulated by a decrease in oxygen levels. Even though S. stipitis has been extensively studied due to its potential application in pentoses fermentation, a limited amount of information is available about its metabolism during aerobic growth on glucose. Here, we provide a systems biology based comparison between the two yeasts, uncovering the metabolism of S. stipitis during aerobic growth on glucose under batch and chemostat cultivations. Results Starting from the analysis of physiological data, we confirmed through 13C-based flux analysis the fully respiratory metabolism of S. stipitis when growing both under glucose limited or glucose excess conditions. The patterns observed showed similarity to the fully respiratory metabolism observed for S. cerevisiae under chemostat cultivations however, intracellular metabolome analysis uncovered the presence of several differences in metabolite patterns. To describe gene expression levels under the two conditions, we performed RNA sequencing and the results were used to quantify transcript abundances of genes from the central carbon metabolism and compared with those obtained with S. cerevisiae. Interestingly, genes involved in central pathways showed different patterns of expression, suggesting different regulatory networks between the two yeasts. Efforts were focused on identifying shared and unique families of transcription factors between the two yeasts through in silico transcription factors analysis, suggesting a different regulation of glycolytic and glucoenogenic pathways. Conclusions The work presented addresses the impact of high-throughput methods in describing and comparing the physiology of Crabtree positive and Crabtree negative yeasts. Based on physiological data and flux analysis we identified the presence of one metabolic condition for S. stipitis under aerobic batch and chemostat cultivations, which shows similarities to the oxidative metabolism observed for S. cerevisiae under chemostat cultivations. Through metabolome analysis and genome-wide transcriptomic analysis several differences were identified. Interestingly, in silico analysis of transciption factors was useful to address a different regulation of mRNAs of genes involved in the central carbon metabolism. To our knowledge, this is the first time that the metabolism of S. stiptis is investigated in details and is compared to S. cerevisiae. Our study provides useful results and allows for the possibility to incorporate these data into recently developed genome-scaled metabolic, thus contributing to improve future industrial applications of S. stipitis as cell factory. PMID:23043429

Targeting Dengue Virus NS-3 Helicase by Ligand based Pharmacophore Modeling and Structure based Virtual Screening

NASA Astrophysics Data System (ADS)

Halim, Sobia A.; Khan, Shanza; Khan, Ajmal; Wadood, Abdul; Mabood, Fazal; Hussain, Javid; Al-Harrasi, Ahmed

2017-10-01

Dengue fever is an emerging public health concern, with several million viral infections occur annually, for which no effective therapy currently exist. Non-structural protein 3 (NS-3) Helicase encoded by the dengue virus (DENV) is considered as a potential drug target to design new and effective drugs against dengue. Helicase is involved in unwinding of dengue RNA. This study was conducted to design new NS-3 Helicase inhibitor by in silico ligand- and structure based approaches. Initially ligand-based pharmacophore model was generated that was used to screen a set of 1201474 compounds collected from ZINC Database. The compounds matched with the pharmacophore model were docked into the active site of NS-3 helicase. Based on docking scores and binding interactions, twenty five compounds are suggested to be potential inhibitors of NS3 Helicase. The pharmacokinetic properties of these hits were predicted. The selected hits revealed acceptable ADMET properties. This study identified potential inhibitors of NS-3 Helicase in silico, and can be helpful in the treatment of Dengue.

Integrated in silico and in vivo approaches to investigate effects of BDE-99 mediated by the nuclear receptors on developing zebrafish.

PubMed

Zhang, Li; Jin, Yaru; Han, Zhihua; Liu, Hongling; Shi, Laihao; Hua, Xiaoxue; Doering, Jon A; Tang, Song; Giesy, John P; Yu, Hongxia

2018-03-01

One of the most abundant polybrominated diphenyl ethers (PBDEs) is 2,2',4,4',5-pentabromodiphenyl ether (BDE-99), which persists and potentially bioaccumulates in aquatic wildlife. Previous studies in mammals have shown that BDE-99 affects development and disrupts certain endocrine functions through signaling pathways mediated by nuclear receptors. However, fewer studies have investigated the potential of BDE-99 to interact with nuclear receptors in aquatic vertebrates such as fish. In the present study, interactions between BDE-99 and nuclear receptors were investigated by in silico and in vivo approaches. This PBDE was able to dock into the ligand-binding domain of zebrafish aryl hydrocarbon receptor 2 (AhR2) and pregnane X receptor (PXR). It had a significant effect on the transcriptional profiles of genes associated with AhR or PXR. Based on the developed cytoscape of all zebrafish genes, it was also inferred that AhR and PXR could interact via cross-talk. In addition, both the in silico and in vivo approaches found that BDE-99 affected peroxisome proliferator-activated receptor alpha (PPARα), glucocorticoid receptor, and thyroid receptor. Collectively, our results demonstrate for the first time detailed in silico evidence that BDE-99 can bind to and interact with zebrafish AhR and PXR. These findings can be used to elaborate the molecular mechanism of BDE-99 and guide more objective environmental risk assessments. Environ Toxicol Chem 2018;37:780-787. © 2017 SETAC. © 2017 SETAC.

Myocardial electrical conduction blockade time dominated by irradiance on photodynamic reaction: in vitro and in silico study

NASA Astrophysics Data System (ADS)

Ogawa, Emiyu; Arai, Tsunenori

2018-02-01

The time for electrical conduction blockade induced by a photodynamic reaction was studied on a myocardial cell wire in vitro and an in silico simulation model was constructed to understand the necessary time for electrical conduction blockade for the wire. Vulnerable state of the cells on a laser interaction would be an unstable and undesirable state since the cells might progress to completely damaged or repaired to change significantly therapeutic effect. So that in silico model, which can calculate the vulnerable cell state, is needed. Understanding an immediate electrical conduction blockade is needed for our proposed new methodology for tachyarrhythmia catheter ablation applying a photodynamic reaction. We studied the electrical conduction blockade occurrence on the electrical conduction wire made of cultured myocardial cells in a line shape and constructed in silico model based on this experimental data. The intracellular Ca2+ ion concentrations were obtained using Fluo-4 AM dye under a confocal laser microscope. A cross-correlation function was used for the electrical conduction blockade judgment. The photodynamic reaction was performed under the confocal microscopy with 3-120 mW/cm2 in irradiance by the diode laser with 663 nm in wavelength. We obtained that the time for the electrical conduction blockade decreased with the irradiance increasing. We constructed a simulation model composed of three states; living cells, vulnerable cells, and blocked cells, using the obtained experimental data and we found the rate constant by an optimization using a conjugate gradient method.

In silico design of fragment-based drug targeting host processing α-glucosidase i for dengue fever

NASA Astrophysics Data System (ADS)

Toepak, E. P.; Tambunan, U. S. F.

2017-02-01

Dengue is a major health problem in the tropical and sub-tropical regions. The development of antiviral that targeting dengue’s host enzyme can be more effective and efficient treatment than the viral enzyme. Host enzyme processing α-glucosidase I has an important role in the maturation process of dengue virus envelope glycoprotein. The inhibition of processing α-glucosidase I can become a promising target for dengue fever treatment. The antiviral approach using in silico fragment-based drug design can generate drug candidates with high binding affinity. In this research, 198.621 compounds were obtained from ZINC15 Biogenic Database. These compounds were screened to find the favorable fragments according to Rules of Three and pharmacological properties. The screening fragments were docked into the active site of processing α-glucosidase I. The potential fragment candidates from the molecular docking simulation were linked with castanospermine (CAST) to generate ligands with a better binding affinity. The Analysis of ligand - enzyme interaction showed ligands with code LRS 22, 28, and 47 have the better binding free energy than the standard ligand. Ligand LRS 28 (N-2-4-methyl-5-((1S,3S,6S,7R,8R,8aR)-1,6,7,8-tetrahydroxyoctahydroindolizin-3-yl) pentyl) indolin-1-yl) propionamide) itself among the other ligands has the lowest binding free energy. Pharmacological properties prediction also showed the ligands LRS 22, 28, and 47 can be promising as the dengue fever drug candidates.

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

Experimental and in silico analysis of cordycepin and its derivatives as endometrial cancer treatment.

PubMed

Fong, Pedro; Ao, Cheng N; Tou, Kai I; Huang, Ka M; Cheong, Chi C; Meng, Li R

2018-04-19

The aim of this study was to investigate the inhibition effects of cordycepin and its derivatives on endometrial cancercell growth. Cytotoxicity MTT assays, clonogenic assays and flow cytometry were used to observe the effects on apoptosis and regulation of the cell cycle of Ishikawa cells under various concentrations of cordycepin, cisplatin and combinations of the two. Validated in silico docking simulations were performed on 31 cordycepin derivatives against adenosine deaminase (ADA) to predict their binding affinities and hence their potential tendency to be metabolized by ADA. Cordycepin has a significant dose-dependent inhibitory effect on cell proliferation. The combination of cordycepin and cisplatin produced greater inhibition effects than did cordycepin alone. Apoptosis investigations confirmed the ability of cordycepin to induce the apoptosis of Ishikawa cells. The in silico results indicate that compound MRS5698 is least metabolized by ADA and has acceptable drug-likeness and safety profiles. This is the first study to confirm the cytotoxic effects of cordycepin on endometrial cancer cells. This study also identified cordycepin derivatives with promising pharmacological and pharmacokinetic properties for further investigation in the development of new treatments for endometrial cancer.

A comprehensive characterization of rare mitochondrial DNA variants in neuroblastoma.

PubMed

Calabrese, Francesco Maria; Clima, Rosanna; Pignataro, Piero; Lasorsa, Vito Alessandro; Hogarty, Michael D; Castellano, Aurora; Conte, Massimo; Tonini, Gian Paolo; Iolascon, Achille; Gasparre, Giuseppe; Capasso, Mario

2016-08-02

Neuroblastoma, a tumor of the developing sympathetic nervous system, is a common childhood neoplasm that is often lethal. Mitochondrial DNA (mtDNA) mutations have been found in most tumors including neuroblastoma. We extracted mtDNA data from a cohort of neuroblastoma samples that had undergone Whole Exome Sequencing (WES) and also used snap-frozen samples in which mtDNA was entirely sequenced by Sanger technology. We next undertook the challenge of determining those mutations that are relevant to, or arisen during tumor development. The bioinformatics pipeline used to extract mitochondrial variants from matched tumor/blood samples was enriched by a set of filters inclusive of heteroplasmic fraction, nucleotide variability, and in silico prediction of pathogenicity. Our in silico multistep workflow applied both on WES and Sanger-sequenced neuroblastoma samples, allowed us to identify a limited burden of somatic and germline mitochondrial mutations with a potential pathogenic impact. The few singleton germline and somatic mitochondrial mutations emerged, according to our in silico analysis, do not appear to impact on the development of neuroblastoma. Our findings are consistent with the hypothesis that most mitochondrial somatic mutations can be considered as 'passengers' and consequently have no discernible effect in this type of cancer.

In silico peptide prediction for antibody generation to recognize 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) in genetically modified organisms.

PubMed

Marani, Mariela M; Costa, Joana; Mafra, Isabel; Oliveira, Maria Beatriz P P; Camperi, Silvia A; Leite, José Roberto de Souza Almeida

2015-03-01

For the prospective immunorecognition of 5-enolpyruvylshikimate-3-phosphate synthase (CP4-EPSPS) as a biomarker protein expressed by transgenic soybean, an extensive in silico evaluation of the referred protein was performed. The main objective of this study was the selection of a set of peptides that could function as potential immunogens for the production of novel antibodies against CP4-EPSPS protein. For this purpose, the protein was in silico cleaved with trypsin/chymotrypsin and the resultant peptides were extensively analyzed for further selection of the best candidates for antibody production. The analysis enabled the successful proposal of four peptides with potential immunogenicity for their future use as screening biomarkers of genetically modified organisms. To our knowledge, this is the first attempt to select and define potential linear epitopes for the immunization of animals and, subsequently, to generate adequate antibodies for CP4-EPSPS recognition. The present work will be followed by the synthesis of the candidate peptides to be incubated in animals for antibody generation and potential applicability for the development of an immunosensor for CP4-EPSPS detection. © 2015 Wiley Periodicals, Inc.

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.

Evaluation of a focused virtual library of heterobifunctional ligands for Clostridium difficile toxins.

PubMed

Sanhueza, Carlos A; Cartmell, Jonathan; El-Hawiet, Amr; Szpacenko, Adam; Kitova, Elena N; Daneshfar, Rambod; Klassen, John S; Lang, Dean E; Eugenio, Luiz; Ng, Kenneth K-S; Kitov, Pavel I; Bundle, David R

2015-01-07

A focused library of virtual heterobifunctional ligands was generated in silico and a set of ligands with recombined fragments was synthesized and evaluated for binding to Clostridium difficile toxins. The position of the trisaccharide fragment was used as a reference for filtering docked poses during virtual screening to match the trisaccharide ligand in a crystal structure. The peptoid, a diversity fragment probing the protein surface area adjacent to a known binding site, was generated by a multi-component Ugi reaction. Our approach combines modular fragment-based design with in silico screening of synthetically feasible compounds and lays the groundwork for future efforts in development of composite bifunctional ligands for large clostridial toxins.

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.

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.

AlgaGEM – a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome

PubMed Central

2011-01-01

Background Microalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data. Results We have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and identified new targets to further improve H2 yield. Conclusions AlgaGEM is a viable and comprehensive framework for in silico functional analysis and can be used to derive new, non-trivial hypotheses for exploring this metabolically versatile organism. Flux balance analysis can be used to identify bottlenecks and new targets to metabolically engineer microalgae for production of biofuels. PMID:22369158

Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

PubMed Central

Misra, Ashish; Conway, Matthew F.; Johnnie, Joseph; Qureshi, Tabish M.; Lige, Bao; Derrick, Anne M.; Agbo, Eddy C.; Sriram, Ganesh

2013-01-01

Synthetic biology enables metabolic engineering of industrial microbes to synthesize value-added molecules. In this, a major challenge is the efficient redirection of carbon to the desired metabolic pathways. Pinpointing strategies toward this goal requires an in-depth investigation of the metabolic landscape of the organism, particularly primary metabolism, to identify precursor and cofactor availability for the target compound. The potent antimalarial therapeutic artemisinin and its precursors are promising candidate molecules for production in microbial hosts. Recent advances have demonstrated the production of artemisinin precursors in engineered yeast strains as an alternative to extraction from plants. We report the application of in silico and in vivo metabolic pathway analyses to identify metabolic engineering targets to improve the yield of the direct artemisinin precursor dihydroartemisinic acid (DHA) in yeast. First, in silico extreme pathway (ExPa) analysis identified NADPH-malic enzyme and the oxidative pentose phosphate pathway (PPP) as mechanisms to meet NADPH demand for DHA synthesis. Next, we compared key DHA-synthesizing ExPas to the metabolic flux distributions obtained from in vivo 13C metabolic flux analysis of a DHA-synthesizing strain. This comparison revealed that knocking out ethanol synthesis and overexpressing glucose-6-phosphate dehydrogenase in the oxidative PPP (gene YNL241C) or the NADPH-malic enzyme ME2 (YKL029C) are vital steps toward overproducing DHA. Finally, we employed in silico flux balance analysis and minimization of metabolic adjustment on a yeast genome-scale model to identify gene knockouts for improving DHA yields. The best strategy involved knockout of an oxaloacetate transporter (YKL120W) and an aspartate aminotransferase (YKL106W), and was predicted to improve DHA yields by 70-fold. Collectively, our work elucidates multiple non-trivial metabolic engineering strategies for improving DHA yield in yeast. PMID:23898325

Isolation and in silico analysis of Fe-superoxide dismutase in the cyanobacterium Nostoc commune.

PubMed

Kesheri, Minu; Kanchan, Swarna; Richa; Sinha, Rajeshwar P

2014-12-15

Cyanobacteria are known to endure various stress conditions due to the inbuilt potential for oxidative stress alleviation owing to the presence of an array of antioxidants. The present study shows that Antarctic cyanobacterium Nostoc commune possesses two antioxidative enzymes viz., superoxide dismutase (SOD) and catalase that jointly cope with environmental stresses prevailing at its natural habitat. Native-PAGE analysis illustrates the presence of a single prominent isoform recognized as Fe-SOD and three distinct isoforms of catalase. The protein sequence of Fe-SOD in N. commune retrieved from NCBI protein sequence database was used for in silico analysis. 3D structure of N. commune was predicted by comparative modeling using MODELLER 9v11. Further, this model was validated for its quality by Ramachandran plot, ERRAT, Verify 3D and ProSA-web which revealed good structure quality of the model. Multiple sequence alignment showed high conservation in N and C-terminal domain regions along with all metal binding positions in Fe-SOD which were also found to be highly conserved in all 28 cyanobacterial species under study, including N. commune. In silico prediction of isoelectric point and molecular weight of Fe-SOD was found to be 5.48 and 22,342.98Da respectively. The phylogenetic tree revealed that among 28 cyanobacterial species, Fe-SOD in N. commune was the closest evolutionary homolog of Fe-SOD in Nostoc punctiforme as evident by strong bootstrap value. Thus, N. commune may serve as a good biological model for studies related to survival of life under extreme conditions prevailing at the Antarctic region. Moreover cyanobacteria may be exploited for biochemical and biotechnological applications of enzymatic antioxidants. Copyright © 2014 Elsevier B.V. All rights reserved.

In silico analysis of high affinity potassium transporter (HKT) isoforms in different plants

PubMed Central

2014-01-01

Background High affinity potassium transporters (HKTs) are located in the plasma membrane of the vessels and have significant influence on salt tolerance in some plants. They exclude Na+ from the parenchyma cells to reduce Na+ concentration. Despite many studies, the underlying regulatory mechanisms and the exact functions of HKTs within different genomic backgrounds are relatively unknown. In this study, various bioinformatics techniques, including promoter analysis, identification of HKT-surrounding genes, and construction of gene networks, were applied to investigate the HKT regulatory mechanism. Results Promoter analysis showed that rice HKTs carry ABA response elements. Additionally, jasmonic acid response elements were detected on promoter region of TmHKT1;5. In silico synteny highlighted several unknown and new loci near rice, Arabidopsis thaliana and Physcomitrella patent HKTs, which may play a significant role in salt stress tolerance in concert with HKTs. Gene network prediction unravelled that crosstalk between jasmonate and ethylene reduces AtHKT1;1 expression. Furthermore, antiporter and transferase proteins were found in AtHKT1;1 gene network. Interestingly, regulatory elements on the promoter region of HKT in wild genotype (TmHKT1;5) were more frequent and variable than the ones in cultivated wheat (TaHKT1;5) which provides the possibility of rapid response and better understanding of environmental conditions for wild genotype. Conclusion Detecting ABA and jasmonic acid response elements on promoter regions of HKTs provide valuable clues on underlying regulatory mechanisms of HKTs. In silico synteny and pathway discovery indicated several candidates which act in concert with HKTs in stress condition. We highlighted different arrangement of regulatory elements on promoter region of wild wheat (TmHKT1;5) compared to bread wheat (TaHKT1;5) in this study. PMID:25279141

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs.

PubMed

Chakraborty, Chiranjib; Mallick, Bidyut; Sharma, Ashish Ranjan; Sharma, Garima; Jagga, Supriya; Doss, C George Priya; Nam, Ju-Suk; Lee, Sang-Soo

2017-01-01

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin). During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (E VHD ) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server. Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively. Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Structural Diversity in the Dandelion (Taraxacum officinale) Polyphenol Oxidase Family Results in Different Responses to Model Substrates

PubMed Central

Dirks-Hofmeister, Mareike E.; Singh, Ratna; Leufken, Christine M.; Inlow, Jennifer K.; Moerschbacher, Bruno M.

2014-01-01

Polyphenol oxidases (PPOs) are ubiquitous type-3 copper enzymes that catalyze the oxygen-dependent conversion of o-diphenols to the corresponding quinones. In most plants, PPOs are present as multiple isoenzymes that probably serve distinct functions, although the precise relationship between sequence, structure and function has not been addressed in detail. We therefore compared the characteristics and activities of recombinant dandelion PPOs to gain insight into the structure–function relationships within the plant PPO family. Phylogenetic analysis resolved the 11 isoenzymes of dandelion into two evolutionary groups. More detailed in silico and in vitro analyses of four representative PPOs covering both phylogenetic groups were performed. Molecular modeling and docking predicted differences in enzyme-substrate interactions, providing a structure-based explanation for grouping. One amino acid side chain positioned at the entrance to the active site (position HB2+1) potentially acts as a “selector” for substrate binding. In vitro activity measurements with the recombinant, purified enzymes also revealed group-specific differences in kinetic parameters when the selected PPOs were presented with five model substrates. The combination of our enzyme kinetic measurements and the in silico docking studies therefore indicate that the physiological functions of individual PPOs might be defined by their specific interactions with different natural substrates. PMID:24918587

In Silico Characterization of miRNA and Long Non-Coding RNA Interplay in Multiple Myeloma

PubMed Central

Ronchetti, Domenica; Manzoni, Martina; Todoerti, Katia; Neri, Antonino; Agnelli, Luca

2016-01-01

The identification of deregulated microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In addition, the cross-regulation between lncRNAs and miRNAs has begun to emerge, and theoretical and experimental studies have demonstrated the competing endogenous RNA (ceRNA) activity of lncRNAs as natural miRNA decoys in pathophysiological conditions, including cancer. Currently, information concerning lncRNA and miRNA interplay in MM is virtually absent. Herein, we investigated in silico the lncRNA and miRNA relationship in a representative datasets encompassing 95 MM and 30 plasma cell leukemia patients at diagnosis and in four normal controls, whose expression profiles were generated by a custom annotation pipeline to detect specific lncRNAs. We applied target prediction analysis based on miRanda and RNA22 algorithms to 235 lncRNAs and 459 miRNAs selected with a potential pivotal role in the pathology of MM. Among pairs that showed a significant correlation between lncRNA and miRNA expression levels, we identified 11 lncRNA–miRNA relationships suggestive of a novel ceRNA network with relevance in MM. PMID:27916857

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.

A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury.

PubMed

Ziraldo, Cordelia; Solovyev, Alexey; Allegretti, Ana; Krishnan, Shilpa; Henzel, M Kristi; Sowa, Gwendolyn A; Brienza, David; An, Gary; Mi, Qi; Vodovotz, Yoram

2015-06-01

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to "better" vs. "worse" outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU.

A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury

PubMed Central

Ziraldo, Cordelia; Solovyev, Alexey; Allegretti, Ana; Krishnan, Shilpa; Henzel, M. Kristi; Sowa, Gwendolyn A.; Brienza, David; An, Gary; Mi, Qi; Vodovotz, Yoram

2015-01-01

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to “better” vs. “worse” outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU. PMID:26111346

One Hundred False-Positive Amphetamine Specimens Characterized by Liquid Chromatography Time-of-Flight Mass Spectrometry.

PubMed

Marin, Stephanie J; Doyle, Kelly; Chang, Annie; Concheiro-Guisan, Marta; Huestis, Marilyn A; Johnson-Davis, Kamisha L

2016-01-01

Some amphetamine (AMP) and ecstacy (MDMA) urine immunoassay (IA) kits are prone to false-positive results due to poor specificity of the antibody. We employed two techniques, high-resolution mass spectrometry (HRMS) and an in silico structure search, to identify compounds likely to cause false-positive results. Hundred false-positive IA specimens for AMP and/or MDMA were analyzed by an Agilent 6230 time-of-flight (TOF) mass spectrometer. Separately, SciFinder (Chemical Abstracts) was used as an in silico structure search to generate a library of compounds that are known to cross-react with AMP/MDMA IAs. Chemical formulas and exact masses of 145 structures were then compared against masses identified by TOF. Compounds known to have cross-reactivity with the IAs were identified in the structure-based search. The chemical formulas and exact masses of 145 structures (of 20 chemical formulas) were compared against masses identified by TOF. Urine analysis by HRMS correlates accurate mass with chemical formulae, but provides little information regarding compound structure. Structural data of targeted antigens can be utilized to correlate HRMS-derived chemical formulas with structural analogs. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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

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.

Structural diversity in the dandelion (Taraxacum officinale) polyphenol oxidase family results in different responses to model substrates.

PubMed

Dirks-Hofmeister, Mareike E; Singh, Ratna; Leufken, Christine M; Inlow, Jennifer K; Moerschbacher, Bruno M

2014-01-01

Polyphenol oxidases (PPOs) are ubiquitous type-3 copper enzymes that catalyze the oxygen-dependent conversion of o-diphenols to the corresponding quinones. In most plants, PPOs are present as multiple isoenzymes that probably serve distinct functions, although the precise relationship between sequence, structure and function has not been addressed in detail. We therefore compared the characteristics and activities of recombinant dandelion PPOs to gain insight into the structure-function relationships within the plant PPO family. Phylogenetic analysis resolved the 11 isoenzymes of dandelion into two evolutionary groups. More detailed in silico and in vitro analyses of four representative PPOs covering both phylogenetic groups were performed. Molecular modeling and docking predicted differences in enzyme-substrate interactions, providing a structure-based explanation for grouping. One amino acid side chain positioned at the entrance to the active site (position HB2+1) potentially acts as a "selector" for substrate binding. In vitro activity measurements with the recombinant, purified enzymes also revealed group-specific differences in kinetic parameters when the selected PPOs were presented with five model substrates. The combination of our enzyme kinetic measurements and the in silico docking studies therefore indicate that the physiological functions of individual PPOs might be defined by their specific interactions with different natural substrates.

Degradation of Aflatoxins by Means of Laccases from Trametes versicolor: An In Silico Insight.

PubMed

Dellafiora, Luca; Galaverna, Gianni; Reverberi, Massimo; Dall'Asta, Chiara

2017-01-01

Mycotoxins are secondary metabolites of fungi that contaminate food and feed, and are involved in a series of foodborne illnesses and disorders in humans and animals. The mitigation of mycotoxin content via enzymatic degradation is a strategy to ensure safer food and feed, and to address the forthcoming issues in view of the global trade and sustainability. Nevertheless, the search for active enzymes is still challenging and time-consuming. The in silico analysis may strongly support the research by providing the evidence-based hierarchization of enzymes for a rational design of more effective experimental trials. The present work dealt with the degradation of aflatoxin B₁ and M₁ by laccase enzymes from Trametes versicolor . The enzymes-substrate interaction for various enzyme isoforms was investigated through 3D molecular modeling techniques. Structural differences among the isoforms have been pinpointed, which may cause different patterns of interaction between aflatoxin B₁ and M₁. The possible formation of different products of degradation can be argued accordingly. Moreover, the laccase gamma isoform was identified as the most suitable for protein engineering aimed at ameliorating the substrate specificity. Overall, 3D modeling proved to be an effective analytical tool to assess the enzyme-substrate interaction and provided a solid foothold for supporting the search of degrading enzyme at the early stage.

In Silico, Experimental, Mechanistic Model for Extended-Release Felodipine Disposition Exhibiting Complex Absorption and a Highly Variable Food Interaction

PubMed Central

Kim, Sean H. J.; Jackson, Andre J.; Hunt, C. Anthony

2014-01-01

The objective of this study was to develop and explore new, in silico experimental methods for deciphering complex, highly variable absorption and food interaction pharmacokinetics observed for a modified-release drug product. Toward that aim, we constructed an executable software analog of study participants to whom product was administered orally. The analog is an object- and agent-oriented, discrete event system, which consists of grid spaces and event mechanisms that map abstractly to different physiological features and processes. Analog mechanisms were made sufficiently complicated to achieve prespecified similarity criteria. An equation-based gastrointestinal transit model with nonlinear mixed effects analysis provided a standard for comparison. Subject-specific parameterizations enabled each executed analog’s plasma profile to mimic features of the corresponding six individual pairs of subject plasma profiles. All achieved prespecified, quantitative similarity criteria, and outperformed the gastrointestinal transit model estimations. We observed important subject-specific interactions within the simulation and mechanistic differences between the two models. We hypothesize that mechanisms, events, and their causes occurring during simulations had counterparts within the food interaction study: they are working, evolvable, concrete theories of dynamic interactions occurring within individual subjects. The approach presented provides new, experimental strategies for unraveling the mechanistic basis of complex pharmacological interactions and observed variability. PMID:25268237

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.

Pathway-based predictive approaches for non-animal assessment of acute inhalation toxicity.

PubMed

Clippinger, Amy J; Allen, David; Behrsing, Holger; BéruBé, Kelly A; Bolger, Michael B; Casey, Warren; DeLorme, Michael; Gaça, Marianna; Gehen, Sean C; Glover, Kyle; Hayden, Patrick; Hinderliter, Paul; Hotchkiss, Jon A; Iskandar, Anita; Keyser, Brian; Luettich, Karsta; Ma-Hock, Lan; Maione, Anna G; Makena, Patrudu; Melbourne, Jodie; Milchak, Lawrence; Ng, Sheung P; Paini, Alicia; Page, Kathryn; Patlewicz, Grace; Prieto, Pilar; Raabe, Hans; Reinke, Emily N; Roper, Clive; Rose, Jane; Sharma, Monita; Spoo, Wayne; Thorne, Peter S; Wilson, Daniel M; Jarabek, Annie M

2018-06-20

New approaches are needed to assess the effects of inhaled substances on human health. These approaches will be based on mechanisms of toxicity, an understanding of dosimetry, and the use of in silico modeling and in vitro test methods. In order to accelerate wider implementation of such approaches, development of adverse outcome pathways (AOPs) can help identify and address gaps in our understanding of relevant parameters for model input and mechanisms, and optimize non-animal approaches that can be used to investigate key events of toxicity. This paper describes the AOPs and the toolbox of in vitro and in silico models that can be used to assess the key events leading to toxicity following inhalation exposure. Because the optimal testing strategy will vary depending on the substance of interest, here we present a decision tree approach to identify an appropriate non-animal integrated testing strategy that incorporates consideration of a substance's physicochemical properties, relevant mechanisms of toxicity, and available in silico models and in vitro test methods. This decision tree can facilitate standardization of the testing approaches. Case study examples are presented to provide a basis for proof-of-concept testing to illustrate the utility of non-animal approaches to inform hazard identification and risk assessment of humans exposed to inhaled substances. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

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

Combined in Vitro Cell-Based/in Silico Screening of Naturally Occurring Flavonoids and Phenolic Compounds as Potential Anti-Alzheimer Drugs.

PubMed

Espargaró, Alba; Ginex, Tiziana; Vadell, Maria Del Mar; Busquets, Maria A; Estelrich, Joan; Muñoz-Torrero, Diego; Luque, F Javier; Sabate, Raimon

2017-02-24

Alzheimer's disease (AD) is the main cause of dementia in people over 65 years. One of the major culprits in AD is the self-aggregation of amyloid-β peptide (Aβ), which has stimulated the search for small molecules able to inhibit Aβ aggregation. In this context, we recently reported a simple, but effective in vitro cell-based assay to evaluate the potential antiaggregation activity of putative Aβ aggregation inhibitors. In this work this assay was used together with docking and molecular dynamics simulations to analyze the anti-Aβ aggregation activity of several naturally occurring flavonoids and phenolic compounds. The results showed that rosmarinic acid, melatonin, and o-vanillin displayed zero or low inhibitory capacity, curcumin was found to have an intermediate inhibitory potency, and apigenin and quercetin showed potent antiaggregation activity. Finally, the suitability of the combined in vitro cell-based/in silico approach to distinguish between active and inactive compounds was further assessed for an additional set of flavonols and dihydroflavonols.

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.

Physically-based in silico light sheet microscopy for visualizing fluorescent brain models

PubMed Central

2015-01-01

Background We present a physically-based computational model of the light sheet fluorescence microscope (LSFM). Based on Monte Carlo ray tracing and geometric optics, our method simulates the operational aspects and image formation process of the LSFM. This simulated, in silico LSFM creates synthetic images of digital fluorescent specimens that can resemble those generated by a real LSFM, as opposed to established visualization methods producing visually-plausible images. We also propose an accurate fluorescence rendering model which takes into account the intrinsic characteristics of fluorescent dyes to simulate the light interaction with fluorescent biological specimen. Results We demonstrate first results of our visualization pipeline to a simplified brain tissue model reconstructed from the somatosensory cortex of a young rat. The modeling aspects of the LSFM units are qualitatively analysed, and the results of the fluorescence model were quantitatively validated against the fluorescence brightness equation and characteristic emission spectra of different fluorescent dyes. AMS subject classification Modelling and simulation PMID:26329404

The cytochrome P450 genes of channel catfish: their involvement in disease defense responses as revealed by meta-analysis of RNA-Seq datasets

USDA-ARS?s Scientific Manuscript database

Cytochrome P450s (CYPs) encode one of the most diverse enzyme superfamily in nature. They catalyze oxidative reactions of endogenous molecules and exogenous chemicals. Methods: We identifiedCYPs genes through in silico analysis using EST, RNA-Seq and genome databases of channel catfish.Phylogenetic ...

Genome-Wide Analyses of the Soybean F-Box Gene Family in Response to Salt Stress

PubMed Central

Jia, Qi; Xiao, Zhi-Xia; Wong, Fuk-Ling; Sun, Song; Liang, Kang-Jing; Lam, Hon-Ming

2017-01-01

The F-box family is one of the largest gene families in plants that regulate diverse life processes, including salt responses. However, the knowledge of the soybean F-box genes and their roles in salt tolerance remains limited. Here, we conducted a genome-wide survey of the soybean F-box family, and their expression analysis in response to salinity via in silico analysis of online RNA-sequencing (RNA-seq) data and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) to predict their potential functions. A total of 725 potential F-box proteins encoded by 509 genes were identified and classified into 9 subfamilies. The gene structures, conserved domains and chromosomal distributions were characterized. There are 76 pairs of duplicate genes identified, including genome-wide segmental and tandem duplication events, which lead to the expansion of the number of F-box genes. The in silico expression analysis showed that these genes would be involved in diverse developmental functions and play an important role in salt response. Our qRT-PCR analysis confirmed 12 salt-responding F-box genes. Overall, our results provide useful information on soybean F-box genes, especially their potential roles in salt tolerance. PMID:28417911

Genome-Wide Analyses of the Soybean F-Box Gene Family in Response to Salt Stress.

PubMed

Jia, Qi; Xiao, Zhi-Xia; Wong, Fuk-Ling; Sun, Song; Liang, Kang-Jing; Lam, Hon-Ming

2017-04-12

The F-box family is one of the largest gene families in plants that regulate diverse life processes, including salt responses. However, the knowledge of the soybean F-box genes and their roles in salt tolerance remains limited. Here, we conducted a genome-wide survey of the soybean F-box family, and their expression analysis in response to salinity via in silico analysis of online RNA-sequencing (RNA-seq) data and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) to predict their potential functions. A total of 725 potential F-box proteins encoded by 509 genes were identified and classified into 9 subfamilies. The gene structures, conserved domains and chromosomal distributions were characterized. There are 76 pairs of duplicate genes identified, including genome-wide segmental and tandem duplication events, which lead to the expansion of the number of F-box genes. The in silico expression analysis showed that these genes would be involved in diverse developmental functions and play an important role in salt response. Our qRT-PCR analysis confirmed 12 salt-responding F-box genes. Overall, our results provide useful information on soybean F-box genes, especially their potential roles in salt tolerance.

Purification, developmental expression, and in silico characterization of α-amylase inhibitor from Echinochloa frumentacea.

PubMed

Panwar, Priyankar; Verma, A K; Dubey, Ashutosh

2018-05-01

Barnyard ( Echinochloa frumentacea ) and finger ( Eleusine coracana ) millet growing at northwestern Himalaya were explored for the α-amylase inhibitor (α-AI). The mature seeds of barnyard millet variety PRJ1 had maximum α-AI activity which increases in different developmental stage. α-AI was purified up to 22.25-fold from barnyard millet variety PRJ1. Semi-quantitative PCR of different developmental stages of barnyard millet seeds showed increased levels of the transcript from 7 to 28 days. Sequence analysis revealed that it contained 315 bp nucleotide which encodes 104 amino acid sequence with molecular weight 10.72 kDa. The predicted 3D structure of α-AI was 86.73% similar to a bifunctional inhibitor of ragi. In silico analysis of 71 α-AI protein sequences were carried out for biochemical features, homology search, multiple sequence alignment, phylogenetic tree construction, motif, and superfamily distribution of protein sequences. Analysis of multiple sequence alignment revealed the existence of conserved regions NPLP[S/G]CRWYVV[S/Q][Q/R]TCG[V/I] throughout sequences. Superfam analysis revealed that α-AI protein sequences were distributed among seven different superfamilies.

Discovering Novel Alternaria solani Succinate Dehydrogenase Inhibitors by In Silico Modeling and Virtual Screening Strategies to Combat Early Blight

NASA Astrophysics Data System (ADS)

Iftikhar, Sehrish; Shahid, Ahmad A.; Halim, Sobia A.; Wolters, Pieter J.; Vleeshouwers, Vivianne G. A. A.; Khan, Ajmal; Al-Harrasi, Ahmed; Ahmad, Shahbaz

2017-11-01

Alternaria blight is an important foliage disease caused by Alternaria solani. The enzyme Succinate dehydrogenase (SDH) is a potential drug target because of its role in tricarboxylic acid cycle. Hence targeting Alternaria solani SDH enzyme could be efficient tool to design novel fungicides against A. solani. We employed computational methodologies to design new SDH inhibitors using homology modeling; pharmacophore modeling and structure based virtual screening protocol. The three dimensional SDH model showed good stereo-chemical and structural properties. Based on virtual screening results twelve commercially available compounds were purchased and tested in vitro and in vivo. The compounds were found to inhibit mycelial growth of A. solani. Moreover in vitro trials showed that inhibitory effects were enhanced with increase in concentrations. Similarly increased disease control was observed in pre-treated potato tubers. Hence the applied in silico strategy led us to identify new and novel fungicides.

Discovering Novel Alternaria solani Succinate Dehydrogenase Inhibitors by in Silico Modeling and Virtual Screening Strategies to Combat Early Blight

PubMed Central

Iftikhar, Sehrish; Shahid, Ahmad A.; Halim, Sobia A.; Wolters, Pieter J.; Vleeshouwers, Vivianne G. A. A.; Khan, Ajmal; Al-Harrasi, Ahmed; Ahmad, Shahbaz

2017-01-01

Alternaria blight is an important foliage disease caused by Alternaria solani. The enzyme Succinate dehydrogenase (SDH) is a potential drug target because of its role in tricarboxylic acid cycle. Hence targeting Alternaria solani SDH enzyme could be efficient tool to design novel fungicides against A. solani. We employed computational methodologies to design new SDH inhibitors using homology modeling; pharmacophore modeling and structure based virtual screening. The three dimensional SDH model showed good stereo-chemical and structural properties. Based on virtual screening results twelve commercially available compounds were purchased and tested in vitro and in vivo. The compounds were found to inhibit mycelial growth of A. solani. Moreover in vitro trials showed that inhibitory effects were enhanced with increase in concentrations. Similarly increased disease control was observed in pre-treated potato tubers. Hence the applied in silico strategy led us to identify novel fungicides. PMID:29204422

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.

In-silico mining, type and frequency analysis of genic microsatellites of finger millet (Eleusine coracana (L.) Gaertn.): a comparative genomic analysis of NBS-LRR regions of finger millet with rice.

PubMed

Kalyana Babu, B; Pandey, Dinesh; Agrawal, P K; Sood, Salej; Kumar, Anil

2014-05-01

In recent years, the increased availability of the DNA sequences has given the possibility to develop and explore the expressed sequence tags (ESTs) derived SSR markers. In the present study, a total of 1956 ESTs of finger millet were used to find the microsatellite type, distribution, frequency and developed a total of 545 primer pairs from the ESTs of finger millet. Thirty-two EST sequences had more than two microsatellites and 1357 sequences did not have any SSR repeats. The most frequent type of repeats was trimeric motif, however the second place was occupied by dimeric motif followed by tetra-, hexa- and penta repeat motifs. The most common dimer repeat motif was GA and in case of trimeric SSRs, it was CGG. The EST sequences of NBS-LRR region of finger millet and rice showed higher synteny and were found on nearly same positions on the rice chromosome map. A total of eight, out of 15 EST based SSR primers were polymorphic among the selected resistant and susceptible finger millet genotypes. The primer FMBLEST5 could able to differentiate them into resistant and susceptible genotypes. The alleles specific to the resistant and susceptible genotypes were sequenced using the ABI 3130XL genetic analyzer and found similarity to NBS-LRR regions of rice and finger millet and contained the characteristic kinase-2 and kinase 3a motifs of plant R-genes belonged to NBS-LRR region. The In-silico and comparative analysis showed that the genes responsible for blast resistance can be identified, mapped and further introgressed through molecular breeding approaches for enhancing the blast resistance in finger millet.

Better, Cheaper Biofuels through Computational Analysis - Continuum

Science.gov Websites

than 30 years, NREL researchers have made significant experimental advances in understanding the polymers to fermentable sugars. But while experimental studies are critical, this research approach can increasingly use computational (or "in silico") studies to complement their experimental work

In Silico Synthesis of Synthetic Receptors: A Polymerization Algorithm.

PubMed

Cowen, Todd; Busato, Mirko; Karim, Kal; Piletsky, Sergey A

2016-12-01

Molecularly imprinted polymer (MIP) synthetic receptors have proposed and applied applications in chemical extraction, sensors, assays, catalysis, targeted drug delivery, and direct inhibition of harmful chemicals and pathogens. However, they rely heavily on effective design for success. An algorithm has been written which mimics radical polymerization atomistically, accounting for chemical and spatial discrimination, hybridization, and geometric optimization. Synthetic ephedrine receptors were synthesized in silico to demonstrate the accuracy of the algorithm in reproducing polymers structures at the atomic level. Comparative analysis in the design of a synthetic ephedrine receptor demonstrates that the new method can effectively identify affinity trends and binding site selectivities where commonly used alternative methods cannot. This new method is believed to generate the most realistic models of MIPs thus produced. This suggests that the algorithm could be a powerful new tool in the design and analysis of various polymers, including MIPs, with significant implications in areas of biotechnology, biomimetics, and the materials sciences more generally. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Comparative Analysis and Modeling of the Severity of Steatohepatitis in DDC-Treated Mouse Strains

PubMed Central

Pandey, Vikash; Sultan, Marc; Kashofer, Karl; Ralser, Meryem; Amstislavskiy, Vyacheslav; Starmann, Julia; Osprian, Ingrid; Grimm, Christina; Hache, Hendrik; Yaspo, Marie-Laure; Sültmann, Holger; Trauner, Michael; Denk, Helmut; Zatloukal, Kurt; Lehrach, Hans; Wierling, Christoph

2014-01-01

Background Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. Methodology and Results In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. Conclusions We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other. PMID:25347188

Comparative analysis and modeling of the severity of steatohepatitis in DDC-treated mouse strains.

PubMed

Pandey, Vikash; Sultan, Marc; Kashofer, Karl; Ralser, Meryem; Amstislavskiy, Vyacheslav; Starmann, Julia; Osprian, Ingrid; Grimm, Christina; Hache, Hendrik; Yaspo, Marie-Laure; Sültmann, Holger; Trauner, Michael; Denk, Helmut; Zatloukal, Kurt; Lehrach, Hans; Wierling, Christoph

2014-01-01

Non-alcoholic fatty liver disease (NAFLD) has a broad spectrum of disease states ranging from mild steatosis characterized by an abnormal retention of lipids within liver cells to steatohepatitis (NASH) showing fat accumulation, inflammation, ballooning and degradation of hepatocytes, and fibrosis. Ultimately, steatohepatitis can result in liver cirrhosis and hepatocellular carcinoma. In this study we have analyzed three different mouse strains, A/J, C57BL/6J, and PWD/PhJ, that show different degrees of steatohepatitis when administered a 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) containing diet. RNA-Seq gene expression analysis, protein analysis and metabolic profiling were applied to identify differentially expressed genes/proteins and perturbed metabolite levels of mouse liver samples upon DDC-treatment. Pathway analysis revealed alteration of arachidonic acid (AA) and S-adenosylmethionine (SAMe) metabolism upon other pathways. To understand metabolic changes of arachidonic acid metabolism in the light of disease expression profiles a kinetic model of this pathway was developed and optimized according to metabolite levels. Subsequently, the model was used to study in silico effects of potential drug targets for steatohepatitis. We identified AA/eicosanoid metabolism as highly perturbed in DDC-induced mice using a combination of an experimental and in silico approach. Our analysis of the AA/eicosanoid metabolic pathway suggests that 5-hydroxyeicosatetraenoic acid (5-HETE), 15-hydroxyeicosatetraenoic acid (15-HETE) and prostaglandin D2 (PGD2) are perturbed in DDC mice. We further demonstrate that a dynamic model can be used for qualitative prediction of metabolic changes based on transcriptomics data in a disease-related context. Furthermore, SAMe metabolism was identified as being perturbed due to DDC treatment. Several genes as well as some metabolites of this module show differences between A/J and C57BL/6J on the one hand and PWD/PhJ on the other.

Rapid in silico cloning of genes using expressed sequence tags (ESTs).

PubMed

Gill, R W; Sanseau, P

2000-01-01

Expressed sequence tags (ESTs) are short single-pass DNA sequences obtained from either end of cDNA clones. These ESTs are derived from a vast number of cDNA libraries obtained from different species. Human ESTs are the bulk of the data and have been widely used to identify new members of gene families, as markers on the human chromosomes, to discover polymorphism sites and to compare expression patterns in different tissues or pathologies states. Information strategies have been devised to query EST databases. Since most of the analysis is performed with a computer, the term "in silico" strategy has been coined. In this chapter we will review the current status of EST databases, the pros and cons of EST-type data and describe possible strategies to retrieve meaningful information.

Knowledge base for v-Embryo: Information Infrastructure for in silico modeling

EPA Science Inventory

Computers, imaging technologies, and the worldwide web have assumed an important role in augmenting traditional learning. Resources to disseminate multimedia information across platforms, and the emergence of communal knowledge environments, facilitate the visualization of diffi...

Cytochrome C oxydase deficiency: SURF1 gene investigation in patients with Leigh syndrome.

PubMed

Maalej, Marwa; Kammoun, Thouraya; Alila-Fersi, Olfa; Kharrat, Marwa; Ammar, Marwa; Felhi, Rahma; Mkaouar-Rebai, Emna; Keskes, Leila; Hachicha, Mongia; Fakhfakh, Faiza

2018-03-18

Leigh syndrome (LS) is a rare progressive neurodegenerative disorder occurring in infancy. The most common clinical signs reported in LS are growth retardation, optic atrophy, ataxia, psychomotor retardation, dystonia, hypotonia, seizures and respiratory disorders. The paper reported a manifestation of 3 Tunisian patients presented with LS syndrome. The aim of this study is the MT[HYPHEN]ATP6 and SURF1 gene screening in Tunisian patients affected with classical Leigh syndrome and the computational investigation of the effect of detected mutations on its structure and functions by clinical and bioinformatics analyses. After clinical investigations, three Tunisian patients were tested for mutations in both MT-ATP6 and SURF1 genes by direct sequencing followed by in silico analyses to predict the effects of sequence variation. The result of mutational analysis revealed the absence of mitochondrial mutations in MT-ATP6 gene and the presence of a known homozygous splice site mutation c.516-517delAG in sibling patients added to the presence of a novel double het mutations in LS patient (c.752-18 A > C/c. c.751 + 16G > A). In silico analyses of theses intronic variations showed that it could alters splicing processes as well as SURF1 protein translation. Leigh syndrome (LS) is a rare progressive neurodegenerative disorder occurring in infancy. The most common clinical signs reported in LS are growth retardation, optic atrophy, ataxia, psychomotor retardation, dystonia, hypotonia, seizures and respiratory disorders. The paper reported a manifestation of 3 Tunisian patients presented with LS syndrome. The aim of this study is MT-ATP6 and SURF1 genes screening in Tunisian patients affected with classical Leigh syndrome and the computational investigation of the effect of detected mutations on its structure and functions. After clinical investigations, three Tunisian patients were tested for mutations in both MT-ATP6 and SURF1 genes by direct sequencing followed by in silico analysis to predict the effects of sequence variation. The result of mutational analysis revealed the absence of mitochondrial mutations in MT-ATP6 gene and the presence of a known homozygous splice site mutation c.516-517delAG in sibling patients added to the presence of a novel double het mutations in LS patient (c.752-18 A>C/ c.751+16G>A). In silico analysis of theses intronic vaiations showed that it could alters splicing processes as well as SURF1 protein translation. Copyright © 2018 Elsevier Inc. All rights reserved.

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.

Fed-batch culture of Escherichia coli for L-valine production based on in silico flux response analysis.

PubMed

Park, Jin Hwan; Kim, Tae Yong; Lee, Kwang Ho; Lee, Sang Yup

2011-04-01

We have previously reported the development of a 100% genetically defined engineered Escherichia coli strain capable of producing L-valine from glucose with a high yield of 0.38 g L-valine per gram glucose (0.58 mol L-valine per mol glucose) by batch culture. Here we report a systems biological strategy of employing flux response analysis in bioprocess development using L-valine production by fed-batch culture as an example. Through the systems-level analysis, the source of ATP was found to be important for efficient L-valine production. There existed a trade-off between L-valine production and biomass formation, which was optimized for the most efficient L-valine production. Furthermore, acetic acid feeding strategy was optimized based on flux response analysis. The final fed-batch cultivation strategy allowed production of 32.3 g/L L-valine, the highest concentration reported for E. coli. This approach of employing systems-level analysis of metabolic fluxes in developing fed-batch cultivation strategy would also be applicable in developing strategies for the efficient production of other bioproducts. Copyright © 2010 Wiley Periodicals, Inc.

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.

Rational drug design for anti-cancer chemotherapy: multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents.

PubMed

Speck-Planche, Alejandro; Kleandrova, Valeria V; Luan, Feng; Cordeiro, M Natália D S

2012-08-01

The discovery of new and more potent anti-cancer agents constitutes one of the most active fields of research in chemotherapy. Colorectal cancer (CRC) is one of the most studied cancers because of its high prevalence and number of deaths. In the current pharmaceutical design of more efficient anti-CRC drugs, the use of methodologies based on Chemoinformatics has played a decisive role, including Quantitative-Structure-Activity Relationship (QSAR) techniques. However, until now, there is no methodology able to predict anti-CRC activity of compounds against more than one CRC cell line, which should constitute the principal goal. In an attempt to overcome this problem we develop here the first multi-target (mt) approach for the virtual screening and rational in silico discovery of anti-CRC agents against ten cell lines. Here, two mt-QSAR classification models were constructed using a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors while the second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted from the molecules and their contributions to anti-CRC activity were calculated using mt-QSAR-LDA model. Several fragments were identified as potential substructural features responsible for the anti-CRC activity and new molecules designed from those fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-CRC agents. Copyright © 2012 Elsevier Ltd. All rights reserved.

In silico Analysis of HIV-1 Env-gp120 Reveals Structural Bases for Viral Adaptation in Growth-Restrictive Cells

PubMed Central

Yokoyama, Masaru; Nomaguchi, Masako; Doi, Naoya; Kanda, Tadahito; Adachi, Akio; Sato, Hironori

2016-01-01

Variable V1/V2 and V3 loops on human immunodeficiency virus type 1 (HIV-1) envelope-gp120 core play key roles in modulating viral competence to recognize two infection receptors, CD4 and chemokine-receptors. However, molecular bases for the modulation largely remain unclear. To address these issues, we constructed structural models for a full-length gp120 in CD4-free and -bound states. The models showed topologies of gp120 surface loop that agree with those in reported structural data. Molecular dynamics simulation showed that in the unliganded state, V1/V2 loop settled into a thermodynamically stable arrangement near V3 loop for conformational masking of V3 tip, a potent neutralization epitope. In the CD4-bound state, however, V1/V2 loop was rearranged near the bound CD4 to support CD4 binding. In parallel, cell-based adaptation in the absence of anti-viral antibody pressures led to the identification of amino acid substitutions that individually enhance viral entry and growth efficiencies in association with reduced sensitivity to CCR5 antagonist TAK-779. Notably, all these substitutions were positioned on the receptors binding surfaces in V1/V2 or V3 loop. In silico structural studies predicted some physical changes of gp120 by substitutions with alterations in viral replication phenotypes. These data suggest that V1/V2 loop is critical for creating a gp120 structure that masks co-receptor binding site compatible with maintenance of viral infectivity, and for tuning a functional balance of gp120 between immune escape ability and infectivity to optimize HIV-1 replication fitness. PMID:26903989

Development and validation of an rDNA operon based primer walking strategy applicable to de novo bacterial genome finishing

PubMed Central

Eastman, Alexander W.; Yuan, Ze-Chun

2015-01-01

Advances in sequencing technology have drastically increased the depth and feasibility of bacterial genome sequencing. However, little information is available that details the specific techniques and procedures employed during genome sequencing despite the large numbers of published genomes. Shotgun approaches employed by second-generation sequencing platforms has necessitated the development of robust bioinformatics tools for in silico assembly, and complete assembly is limited by the presence of repetitive DNA sequences and multi-copy operons. Typically, re-sequencing with multiple platforms and laborious, targeted Sanger sequencing are employed to finish a draft bacterial genome. Here we describe a novel strategy based on the identification and targeted sequencing of repetitive rDNA operons to expedite bacterial genome assembly and finishing. Our strategy was validated by finishing the genome of Paenibacillus polymyxa strain CR1, a bacterium with potential in sustainable agriculture and bio-based processes. An analysis of the 38 contigs contained in the P. polymyxa strain CR1 draft genome revealed 12 repetitive rDNA operons with varied intragenic and flanking regions of variable length, unanimously located at contig boundaries and within contig gaps. These highly similar but not identical rDNA operons were experimentally verified and sequenced simultaneously with multiple, specially designed primer sets. This approach also identified and corrected significant sequence rearrangement generated during the initial in silico assembly of sequencing reads. Our approach reduces the required effort associated with blind primer walking for contig assembly, increasing both the speed and feasibility of genome finishing. Our study further reinforces the notion that repetitive DNA elements are major limiting factors for genome finishing. Moreover, we provided a step-by-step workflow for genome finishing, which may guide future bacterial genome finishing projects. PMID:25653642

Integrated In Silico-In Vitro Identification and Characterization of the SH3-Mediated Interaction between Human PTTG and its Cognate Partners in Medulloblastoma.

PubMed

Liu, Jiangang; Wang, Dapeng; Li, Yanyan; Yao, Hui; Zhang, Nan; Zhang, Xuewen; Zhong, Fangping; Huang, Yulun

2018-06-01

The human pituitary tumor-transforming gene is an oncogenic protein which serves as a central hub in the cellular signaling network of medulloblastoma. The protein contains two vicinal PxxP motifs at its C terminus that are potential binding sites of peptide-recognition SH3 domains. Here, a synthetic protocol that integrated in silico analysis and in vitro assay was described to identify the SH3-binding partners of pituitary tumor-transforming gene in the gene expression profile of medulloblastoma. In the procedure, a variety of structurally diverse, non-redundant SH3 domains with high gene expression in medulloblastoma were compiled, and their three-dimensional structures were either manually retrieved from the protein data bank database or computationally modeled through bioinformatics technique. The binding capability of these domains towards the two PxxP-containing peptides m1p: 161 LGPPSPVK 168 and m2p: 168 KMPSPPWE 175 of pituitary tumor-transforming gene were ranked by structure-based scoring and fluorescence-based assay. Consequently, a number of SH3 domains, including MAP3K and PI3K, were found to have moderate or high affinity for m1p and/or m2p. Interestingly, the two overlapping peptides exhibits a distinct binding profile to these identified domain partners, suggesting that the binding selectivity of m1p and m2p is optimized across the medulloblastoma expression spectrum by competing for domain candidates. In addition, two redesigned versions of m1p peptide ware obtained via a structure-based rational mutation approach, which exhibited an increased affinity for the domain as compared to native peptide.

Efficacy of Silicosec, a diatomaceous earth formulation against Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae).

PubMed

Ziaee, Masumeh; Safaralizadeh, Mohammad H; Shayesteh, Nouraddin

2007-11-01

Laboratory bioassays were carried out to evaluate the insecticidal efficacy of SilicoSec against 7- 14-days-old adults of Tribolium castaneum; old and young larvae with the mean weight of 3.4 +/- 0.1 and 0.6 +/- 0.1 mg, respectively at 27 degrees C and 55 +/- 5% r.h in the dark. Wheat treated with four dose rates of SilicoSec with three replications. Adult's mortality was measured after 2, 7 and 14 days of exposure. After 14 days mortality count, all adults were removed and samples retained under the same conditions for a further 60 days to assess progeny production. In the case of larvae, mortality was counted after 1, 2 and 7 days. After 2 days of exposure no concentration achieved 11% mortality for adults, however; adult's mortality exceeds 89.65% when exposed for 7 days to SilicoSec. Mortality of old and young larvae at 0.6 g kg(-1) after 2 days were 28.88 and 22.22%, respectively and exceed to 60.71 and 69.04% at longer exposure of 7 days. Results indicated that mortality of T. castaneum was influenced by interval exposed to wheat treated with SilicoSec and over this exposure; the increases in application rate of SilicoSec had significant effect on the mortality. Young larvae of red flour beetle were more sensitive to SilicoSec than old larvae and adults were more tolerant. Reproductive potential of adults in the treated wheat was suppressed when compared with untreated wheat. The high retention level of SilicoSec (78.62%) was noted in wheat kernels.

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.

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.

In silico analysis of the fucosylation-associated genome of the human blood fluke Schistosoma mansoni: cloning and characterization of the fucosyltransferase multigene family.

PubMed

Peterson, Nathan A; Anderson, Tavis K; Yoshino, Timothy P

2013-01-01

Fucosylated glycans of the parasitic flatworm Schistosoma mansoni play key roles in its development and immunobiology. In the present study we used a genome-wide homology-based bioinformatics approach to search for genes that contribute to fucosylated glycan expression in S. mansoni, specifically the α2-, α3-, and α6-fucosyltransferases (FucTs), which transfer L-fucose from a GDP-L-fucose donor to an oligosaccharide acceptor. We identified and in silico characterized several novel schistosome FucT homologs, including six α3-FucTs and six α6-FucTs, as well as two protein O-FucTs that catalyze the unrelated transfer of L-fucose to serine and threonine residues of epidermal growth factor- and thrombospondin-type repeats. No α2-FucTs were observed. Primary sequence analyses identified key conserved FucT motifs as well as characteristic transmembrane domains, consistent with their putative roles as fucosyltransferases. Most genes exhibit alternative splicing, with multiple transcript variants generated. A phylogenetic analysis demonstrated that schistosome α3- and α6-FucTs form monophyletic clades within their respective gene families, suggesting multiple gene duplications following the separation of the schistosome lineage from the main evolutionary tree. Quantitative decreases in steady-state transcript levels of some FucTs during early larval development suggest a possible mechanism for differential expression of fucosylated glycans in schistosomes. This study systematically identifies the complete repertoire of FucT homologs in S. mansoni and provides fundamental information regarding their genomic organization, genetic variation, developmental expression, and evolutionary history.

In Silico and In Vitro Investigations of the Mutability of Disease-Causing Missense Mutation Sites in Spermine Synthase

PubMed Central

Zhang, Zhe; Norris, Joy; Schwartz, Charles; Alexov, Emil

2011-01-01

Background Spermine synthase (SMS) is a key enzyme controlling the concentration of spermidine and spermine in the cell. The importance of SMS is manifested by the fact that single missense mutations were found to cause Snyder-Robinson Syndrome (SRS). At the same time, currently there are no non-synonymous single nucleoside polymorphisms, nsSNPs (harmless mutations), found in SMS, which may imply that the SMS does not tolerate amino acid substitutions, i.e. is not mutable. Methodology/Principal Findings To investigate the mutability of the SMS, we carried out in silico analysis and in vitro experiments of the effects of amino acid substitutions at the missense mutation sites (G56, V132 and I150) that have been shown to cause SRS. Our investigation showed that the mutation sites have different degree of mutability depending on their structural micro-environment and involvement in the function and structural integrity of the SMS. It was found that the I150 site does not tolerate any mutation, while V132, despite its key position at the interface of SMS dimer, is quite mutable. The G56 site is in the middle of the spectra, but still quite sensitive to charge residue replacement. Conclusions/Significance The performed analysis showed that mutability depends on the detail of the structural and functional factors and cannot be predicted based on conservation of wild type properties alone. Also, harmless nsSNPs can be expected to occur even at sites at which missense mutations were found to cause diseases. PMID:21647366

Gene signature associated with benign neurofibroma transformation to malignant peripheral nerve sheath tumors

PubMed Central

Sorzano, Carlos O. S.; Pascual-Montano, Alberto; Carazo, Jose M.

2017-01-01

Benign neurofibromas, the main phenotypic manifestations of the rare neurological disorder neurofibromatosis type 1, degenerate to malignant tumors associated to poor prognosis in about 10% of patients. Despite efforts in the field of (epi)genomics, the lack of prognostic biomarkers with which to predict disease evolution frustrates the adoption of appropriate early therapeutic measures. To identify potential biomarkers of malignant neurofibroma transformation, we integrated four human experimental studies and one for mouse, using a gene score-based meta-analysis method, from which we obtained a score-ranked signature of 579 genes. Genes with the highest absolute scores were classified as promising disease biomarkers. By grouping genes with similar neurofibromatosis-related profiles, we derived panels of potential biomarkers. The addition of promoter methylation data to gene profiles indicated a panel of genes probably silenced by hypermethylation. To identify possible therapeutic treatments, we used the gene signature to query drug expression databases. Trichostatin A and other histone deacetylase inhibitors, as well as cantharidin and tamoxifen, were retrieved as putative therapeutic means to reverse the aberrant regulation that drives to malignant cell proliferation and metastasis. This in silico prediction corroborated reported experimental results that suggested the inclusion of these compounds in clinical trials. This experimental validation supported the suitability of the meta-analysis method used to integrate several sources of public genomic information, and the reliability of the gene signature associated to the malignant evolution of neurofibromas to generate working hypotheses for prognostic and drug-responsive biomarkers or therapeutic measures, thus showing the potential of this in silico approach for biomarker discovery. PMID:28542306

Characterization of free nitrogen fixing bacteria of the genus Azotobacter in organic vegetable-grown Colombian soils

PubMed Central

Jiménez, Diego Javier; Montaña, José Salvador; Martínez, María Mercedes

2011-01-01

With the purpose of isolating and characterizing free nitrogen fixing bacteria (FNFB) of the genus Azotobacter, soil samples were collected randomly from different vegetable organic cultures with neutral pH in different zones of Boyacá-Colombia. Isolations were done in selective free nitrogen Ashby-Sucrose agar obtaining a recovery of 40%. Twenty four isolates were evaluated for colony and cellular morphology, pigment production and metabolic activities. Molecular characterization was carried out using amplified ribosomal DNA restriction analysis (ARDRA). After digestion of 16S rDNA Y1-Y3 PCR products (1487pb) with AluI, HpaII and RsaI endonucleases, a polymorphism of 16% was obtained. Cluster analysis showed three main groups based on DNA fingerprints. Comparison between ribotypes generated by isolates and in silico restriction of 16S rDNA partial sequences with same restriction enzymes was done with Gen Workbench v.2.2.4 software. Nevertheless, Y1-Y2 PCR products were analysed using BLASTn. Isolate C5T from tomato (Lycopersicon esculentum) grown soils presented the same in silico restriction patterns with A. chroococcum (AY353708) and 99% of similarity with the same sequence. Isolate C5CO from cauliflower (Brassica oleracea var. botrytis) grown soils showed black pigmentation in Ashby-Benzoate agar and high similarity (91%) with A. nigricans (AB175651) sequence. In this work we demonstrated the utility of molecular techniques and bioinformatics tools as a support to conventional techniques in characterization of the genus Azotobacter from vegetable-grown soils. PMID:24031700

Physical and in silico approaches identify DNA-PK in a Tax DNA-damage response interactome

PubMed Central

Ramadan, Emad; Ward, Michael; Guo, Xin; Durkin, Sarah S; Sawyer, Adam; Vilela, Marcelo; Osgood, Christopher; Pothen, Alex; Semmes, Oliver J

2008-01-01

Background We have initiated an effort to exhaustively map interactions between HTLV-1 Tax and host cellular proteins. The resulting Tax interactome will have significant utility toward defining new and understanding known activities of this important viral protein. In addition, the completion of a full Tax interactome will also help shed light upon the functional consequences of these myriad Tax activities. The physical mapping process involved the affinity isolation of Tax complexes followed by sequence identification using tandem mass spectrometry. To date we have mapped 250 cellular components within this interactome. Here we present our approach to prioritizing these interactions via an in silico culling process. Results We first constructed an in silico Tax interactome comprised of 46 literature-confirmed protein-protein interactions. This number was then reduced to four Tax-interactions suspected to play a role in DNA damage response (Rad51, TOP1, Chk2, 53BP1). The first-neighbor and second-neighbor interactions of these four proteins were assembled from available human protein interaction databases. Through an analysis of betweenness and closeness centrality measures, and numbers of interactions, we ranked proteins in the first neighborhood. When this rank list was compared to the list of physical Tax-binding proteins, DNA-PK was the highest ranked protein common to both lists. An overlapping clustering of the Tax-specific second-neighborhood protein network showed DNA-PK to be one of three bridge proteins that link multiple clusters in the DNA damage response network. Conclusion The interaction of Tax with DNA-PK represents an important biological paradigm as suggested via consensus findings in vivo and in silico. We present this methodology as an approach to discovery and as a means of validating components of a consensus Tax interactome. PMID:18922151

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

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.

Discovery of DNA dyes Hoechst 34580 and 33342 as good candidates for inhibiting amyloid beta formation: in silico and in vitro study

NASA Astrophysics Data System (ADS)

Thai, Nguyen Quoc; Tseng, Ning-Hsuan; Vu, Mui Thi; Nguyen, Tin Trung; Linh, Huynh Quang; Hu, Chin-Kun; Chen, Yun-Ru; Li, Mai Suan

2016-08-01

Combining Lipinski's rule with the docking and steered molecular dynamics simulations and using the PubChem data base of about 1.4 million compounds, we have obtained DNA dyes Hoechst 34580 and Hoechst 33342 as top-leads for the Alzheimer's disease. The binding properties of these ligands to amyloid beta (Aβ) fibril were thoroughly studied by in silico and in vitro experiments. Hoechst 34580 and Hoechst 33342 prefer to locate near hydrophobic regions with binding affinity mainly governed by the van der Waals interaction. By the Thioflavin T assay, it was found that the inhibition constant IC50 ≈ 0.86 and 0.68 μM for Hoechst 34580 and Hoechst 33342, respectively. This result qualitatively agrees with the binding free energy estimated using the molecular mechanic-Poisson Boltzmann surface area method and all-atom simulations with the AMBER-f99SB-ILDN force field and water model TIP3P. In addition, DNA dyes have the high capability to cross the blood brain barrier. Thus, both in silico and in vitro experiments have shown that Hoechst 34580 and 33342 are good candidates for treating the Alzheimer's disease by inhibiting Aβ formation.

20170312 - Computer Simulation of Developmental ...

EPA Pesticide Factsheets

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of

Computer Simulation of Developmental Processes and ...

EPA Pesticide Factsheets

Rationale: Recent progress in systems toxicology and synthetic biology have paved the way to new thinking about in vitro/in silico modeling of developmental processes and toxicities, both for embryological and reproductive impacts. Novel in vitro platforms such as 3D organotypic culture models, engineered microscale tissues and complex microphysiological systems (MPS), together with computational models and computer simulation of tissue dynamics, lend themselves to a integrated testing strategies for predictive toxicology. As these emergent methodologies continue to evolve, they must be integrally tied to maternal/fetal physiology and toxicity of the developing individual across early lifestage transitions, from fertilization to birth, through puberty and beyond. Scope: This symposium will focus on how the novel technology platforms can help now and in the future, with in vitro/in silico modeling of complex biological systems for developmental and reproductive toxicity issues, and translating systems models into integrative testing strategies. The symposium is based on three main organizing principles: (1) that novel in vitro platforms with human cells configured in nascent tissue architectures with a native microphysiological environments yield mechanistic understanding of developmental and reproductive impacts of drug/chemical exposures; (2) that novel in silico platforms with high-throughput screening (HTS) data, biologically-inspired computational models of

Novel Polyurethane Matrix Systems Reveal a Particular Sustained Release Behavior Studied by Imaging and Computational Modeling.

PubMed

Campiñez, María Dolores; Caraballo, Isidoro; Puchkov, Maxim; Kuentz, Martin

2017-07-01

The aim of the present work was to better understand the drug-release mechanism from sustained release matrices prepared with two new polyurethanes, using a novel in silico formulation tool based on 3-dimensional cellular automata. For this purpose, two polymers and theophylline as model drug were used to prepare binary matrix tablets. Each formulation was simulated in silico, and its release behavior was compared to the experimental drug release profiles. Furthermore, the polymer distributions in the tablets were imaged by scanning electron microscopy (SEM) and the changes produced by the tortuosity were quantified and verified using experimental data. The obtained results showed that the polymers exhibited a surprisingly high ability for controlling drug release at low excipient concentrations (only 10% w/w of excipient controlled the release of drug during almost 8 h). The mesoscopic in silico model helped to reveal how the novel biopolymers were controlling drug release. The mechanism was found to be a special geometrical arrangement of the excipient particles, creating an almost continuous barrier surrounding the drug in a very effective way, comparable to lipid or waxy excipients but with the advantages of a much higher compactability, stability, and absence of excipient polymorphism.

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.

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.

High throughput screening: an in silico solubility parameter approach for lipids and solvents in SLN preparations.

PubMed

Shah, Malay; Agrawal, Yadvendra

2013-01-01

The present paper describes an in silico solubility behavior of drug and lipids, an essential screening study in preparation of solid lipid nanoparticles (SLN). Ciprofloxacin HCl was selected as a model drug along with 11 lipids and 5 organic solvents. In silico miscibility study of drug/lipid/solvent was performed using Hansen solubility parameter approach calculated by group contribution method of Van Krevelen and Hoftyzer. Predicted solubility was validated by determining solubility of lipids in various solvent at different temperature range, while miscibility of drug in lipids was determined by apparent solubility study and partition experiment. The presence of oxygen and OH functionality increases the polarity and hydrogen bonding possibilities of the compound which has reflected the highest solubility parameter values for Geleol and Capmul MCM C8. Ethyl acetate, Geleol and Capmul MCM C8 was identified as suitable organic solvent, solid lipid and liquid lipid respectively based on a solubility parameter approach which was in agreement with the result of an apparent solubility study and partition coefficient. These works demonstrate the validity of solubility parameter approach and provide a feasible predictor to the rational selection of excipients in designing SLN formulation.

Microbial genomic taxonomy

PubMed Central

2013-01-01

A need for a genomic species definition is emerging from several independent studies worldwide. In this commentary paper, we discuss recent studies on the genomic taxonomy of diverse microbial groups and a unified species definition based on genomics. Accordingly, strains from the same microbial species share >95% Average Amino Acid Identity (AAI) and Average Nucleotide Identity (ANI), >95% identity based on multiple alignment genes, <10 in Karlin genomic signature, and > 70% in silico Genome-to-Genome Hybridization similarity (GGDH). Species of the same genus will form monophyletic groups on the basis of 16S rRNA gene sequences, Multilocus Sequence Analysis (MLSA) and supertree analysis. In addition to the established requirements for species descriptions, we propose that new taxa descriptions should also include at least a draft genome sequence of the type strain in order to obtain a clear outlook on the genomic landscape of the novel microbe. The application of the new genomic species definition put forward here will allow researchers to use genome sequences to define simultaneously coherent phenotypic and genomic groups. PMID:24365132

Metabolic engineering of Synechocystis sp. PCC 6803 for enhanced ethanol production based on flux balance analysis.

PubMed

Yoshikawa, Katsunori; Toya, Yoshihiro; Shimizu, Hiroshi

2017-05-01

Synechocystis sp. PCC 6803 is an attractive host for bio-ethanol production due to its ability to directly convert atmospheric carbon dioxide into ethanol using photosystems. To enhance ethanol production in Synechocystis sp. PCC 6803, metabolic engineering was performed based on in silico simulations, using the genome-scale metabolic model. Comprehensive reaction knockout simulations by flux balance analysis predicted that the knockout of NAD(P)H dehydrogenase enhanced ethanol production under photoautotrophic conditions, where ammonium is the nitrogen source. This deletion inhibits the re-oxidation of NAD(P)H, which is generated by ferredoxin-NADP + reductase and imposes re-oxidation in the ethanol synthesis pathway. The effect of deleting the ndhF1 gene, which encodes NADH dehydrogenase subunit 5, on ethanol production was experimentally evaluated using ethanol-producing strains of Synechocystis sp. PCC 6803. The ethanol titer of the ethanol-producing ∆ndhF1 strain increased by 145%, compared with that of the control strain.

The Prediction of Botulinum Toxin Structure Based on in Silico and in Vitro Analysis

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

Many of biological system mediated through protein-protein interactions. Knowledge of protein-protein complex structure is required for understanding the function. The determination of huge size and flexible protein-protein complex structure by experimental studies remains difficult, costly and five-consuming, therefore computational prediction of protein structures by homolog modeling and docking studies is valuable method. In addition, MD simulation is also one of the most powerful methods allowing to see the real dynamics of proteins. Here, we predict protein-protein complex structure of botulinum toxin to analyze its property. These bioinformatics methods are useful to report the relation between the flexibility of backbone structure and the activity.

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.

In silico analysis of fragile histidine triad involved in regression of carcinoma.

PubMed

Rasheed, Muhammad Asif; Tariq, Fatima; Afzal, Sara; Mannanv, Shazia

2017-04-01

Hepatocellular carcinoma (HCCa) is a primary malignancy of the liver. Many different proteins are involved in HCCa including insulin growth factor (IGF) II , signal transducers and activators of transcription (STAT) 3, STAT4, mothers against decapentaplegic homolog 4 (SMAD 4), fragile histidine triad (FHIT) and selective internal radiation therapy (SIRT) etc. The present study is based on the bioinformatics analysis of FHIT protein in order to understand the proteomics aspect and improvement of the diagnosis of the disease based on the protein. Different information related to protein were gathered from different databases, including National Centre for Biotechnology Information (NCBI) Gene, Protein and Online Mendelian Inheritance in Man (OMIM) databases, Uniprot database, String database and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Moreover, the structure of the protein and evaluation of the quality of the structure were included from Easy modeler programme. Hence, this analysis not only helped to gather information related to the protein at one place, but also analysed the structure and quality of the protein to conclude that the protein has a role in carcinoma.

Virtual Tissues and Developmental Systems Biology (book chapter)

EPA Science Inventory

Virtual tissue (VT) models provide an in silico environment to simulate cross-scale properties in specific tissues or organs based on knowledge of the underlying biological networks. These integrative models capture the fundamental interactions in a biological system and enable ...

Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes.

PubMed

Qiu, Yunping; Moir, Robyn D; Willis, Ian M; Seethapathy, Suresh; Biniakewitz, Robert C; Kurland, Irwin J

2018-01-18

Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different 13 C-enriched carbon sources (randomized 95% 12 C and 95% 13 C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks.

In Silico Analysis of Epitope-Based Vaccine Candidates against Hepatitis B Virus Polymerase Protein

PubMed Central

Zheng, Juzeng; Lin, Xianfan; Wang, Xiuyan; Zheng, Liyu; Lan, Songsong; Jin, Sisi; Ou, Zhanfan; Wu, Jinming

2017-01-01

Hepatitis B virus (HBV) infection has persisted as a major public health problem due to the lack of an effective treatment for those chronically infected. Therapeutic vaccination holds promise, and targeting HBV polymerase is pivotal for viral eradication. In this research, a computational approach was employed to predict suitable HBV polymerase targeting multi-peptides for vaccine candidate selection. We then performed in-depth computational analysis to evaluate the predicted epitopes’ immunogenicity, conservation, population coverage, and toxicity. Lastly, molecular docking and MHC-peptide complex stabilization assay were utilized to determine the binding energy and affinity of epitopes to the HLA-A0201 molecule. Criteria-based analysis provided four predicted epitopes, RVTGGVFLV, VSIPWTHKV, YMDDVVLGA and HLYSHPIIL. Assay results indicated the lowest binding energy and high affinity to the HLA-A0201 molecule for epitopes VSIPWTHKV and YMDDVVLGA and epitopes RVTGGVFLV and VSIPWTHKV, respectively. Regions 307 to 320 and 377 to 387 were considered to have the highest probability to be involved in B cell epitopes. The T cell and B cell epitopes identified in this study are promising targets for an epitope-focused, peptide-based HBV vaccine, and provide insight into HBV-induced immune response. PMID:28509875

In silico analysis of a novel MKRN3 missense mutation in familial central precocious puberty.

PubMed

Neocleous, Vassos; Shammas, Christos; Phelan, Marie M; Nicolaou, Stella; Phylactou, Leonidas A; Skordis, Nicos

2016-01-01

The onset of puberty is influenced by the interplay of stimulating and restraining factors, many of which have a genetic origin. Premature activation of the GnRH secretion in central precocious puberty (CPP) may arise either from gain-of-function mutations of the KISS1 and KISS1R genes or from loss-of-function manner mutations of the MKRN3 gene leading to MKRN3 deficiency. To explore the genetic causes responsible for CPP and the potential role of the RING finger protein 3 (MKRN3) gene. We investigated potential sequence variations in the intronless MKRN3 gene by Sanger sequencing of the entire 507 amino acid coding region of exon 1 in a family with two affected girls presented with CPP at the age of 6 and 5·7 years, respectively. A novel heterozygous g.Gly312Asp missense mutation in the MKRN3 gene was identified in these siblings. The imprinted MKRN3 missense mutation was also identified as expected in the unaffected father and followed as expected an imprinted mode of inheritance. In silico analysis of the altered missense variant using the computational algorithms Polyphen2, SIFT and Mutation Taster predicted a damage and pathogenic alteration causing CPP. The pathogenicity of the alteration at the protein level via an in silico structural model is also explored. A novel mutation in the MKRN3 gene in two sisters with CPP was identified, supporting the fundamental role of this gene in the suppression of the hypothalamic GnRH neurons. © 2015 John Wiley & Sons Ltd.

Robust Phagocyte Recruitment Controls the Opportunistic Fungal Pathogen Mucor circinelloides in Innate Granulomas In Vivo

PubMed Central

2018-01-01

ABSTRACT Mucormycosis is an emerging fungal infection with extremely high mortality rates in patients with defects in their innate immune response, specifically in functions mediated through phagocytes. However, we currently have a limited understanding of the molecular and cellular interactions between these innate immune effectors and mucormycete spores during the early immune response. Here, the early events of innate immune recruitment in response to infection by Mucor circinelloides spores are modeled by a combined in silico modeling approach and real-time in vivo microscopy. Phagocytes are rapidly recruited to the site of infection in a zebrafish larval model of mucormycosis. This robust early recruitment protects from disease onset in vivo. In silico analysis identified that protection is dependent on the number of phagocytes at the infection site, but not the speed of recruitment. The mathematical model highlights the role of proinflammatory signals for phagocyte recruitment and the importance of inhibition of spore germination for protection from active fungal disease. These in silico data are supported by an in vivo lack of fungal spore killing and lack of reactive oxygen burst, which together result in latent fungal infection. During this latent stage of infection, spores are controlled in innate granulomas in vivo. Disease can be reactivated by immunosuppression. Together, these data represent the first in vivo real-time analysis of innate granuloma formation during the early stages of a fungal infection. The results highlight a potential latent stage during mucormycosis that should urgently be considered for clinical management of patients. PMID:29588406

DESIGN AND PERFORMANCE OF A XENOBIOTIC METABOLISM DATABASE MANAGER FOR METABOLIC SIMULATOR ENHANCEMENT AND CHEMICAL RISK ANALYSIS

EPA Science Inventory

A major uncertainty that has long been recognized in evaluating chemical toxicity is accounting for metabolic activation of chemicals resulting in increased toxicity. In silico approaches to predict chemical metabolism and to subsequently screen and prioritize chemicals for risk ...

Sybil--efficient constraint-based modelling in R.

PubMed

Gelius-Dietrich, Gabriel; Desouki, Abdelmoneim Amer; Fritzemeier, Claus Jonathan; Lercher, Martin J

2013-11-13

Constraint-based analyses of metabolic networks are widely used to simulate the properties of genome-scale metabolic networks. Publicly available implementations tend to be slow, impeding large scale analyses such as the genome-wide computation of pairwise gene knock-outs, or the automated search for model improvements. Furthermore, available implementations cannot easily be extended or adapted by users. Here, we present sybil, an open source software library for constraint-based analyses in R; R is a free, platform-independent environment for statistical computing and graphics that is widely used in bioinformatics. Among other functions, sybil currently provides efficient methods for flux-balance analysis (FBA), MOMA, and ROOM that are about ten times faster than previous implementations when calculating the effect of whole-genome single gene deletions in silico on a complete E. coli metabolic model. Due to the object-oriented architecture of sybil, users can easily build analysis pipelines in R or even implement their own constraint-based algorithms. Based on its highly efficient communication with different mathematical optimisation programs, sybil facilitates the exploration of high-dimensional optimisation problems on small time scales. Sybil and all its dependencies are open source. Sybil and its documentation are available for download from the comprehensive R archive network (CRAN).

Identification of hepta-histidine as a candidate drug for Huntington’s disease by in silico-in vitro- in vivo-integrated screens of chemical libraries

NASA Astrophysics Data System (ADS)

Imamura, Tomomi; Fujita, Kyota; Tagawa, Kazuhiko; Ikura, Teikichi; Chen, Xigui; Homma, Hidenori; Tamura, Takuya; Mao, Ying; Taniguchi, Juliana Bosso; Motoki, Kazumi; Nakabayashi, Makoto; Ito, Nobutoshi; Yamada, Kazunori; Tomii, Kentaro; Okano, Hideyuki; Kaye, Julia; Finkbeiner, Steven; Okazawa, Hitoshi

2016-09-01

We identified drug seeds for treating Huntington’s disease (HD) by combining in vitro single molecule fluorescence spectroscopy, in silico molecular docking simulations, and in vivo fly and mouse HD models to screen for inhibitors of abnormal interactions between mutant Htt and physiological Ku70, an essential DNA damage repair protein in neurons whose function is known to be impaired by mutant Htt. From 19,468 and 3,010,321 chemicals in actual and virtual libraries, fifty-six chemicals were selected from combined in vitro-in silico screens; six of these were further confirmed to have an in vivo effect on lifespan in a fly HD model, and two chemicals exerted an in vivo effect on the lifespan, body weight and motor function in a mouse HD model. Two oligopeptides, hepta-histidine (7H) and Angiotensin III, rescued the morphological abnormalities of primary neurons differentiated from iPS cells of human HD patients. For these selected drug seeds, we proposed a possible common structure. Unexpectedly, the selected chemicals enhanced rather than inhibited Htt aggregation, as indicated by dynamic light scattering analysis. Taken together, these integrated screens revealed a new pathway for the molecular targeted therapy of HD.

In Silico Functional Networks Identified in Fish Nucleated Red Blood Cells by Means of Transcriptomic and Proteomic Profiling.

PubMed

Puente-Marin, Sara; Nombela, Iván; Ciordia, Sergio; Mena, María Carmen; Chico, Verónica; Coll, Julio; Ortega-Villaizan, María Del Mar

2018-04-09

Nucleated red blood cells (RBCs) of fish have, in the last decade, been implicated in several immune-related functions, such as antiviral response, phagocytosis or cytokine-mediated signaling. RNA-sequencing (RNA-seq) and label-free shotgun proteomic analyses were carried out for in silico functional pathway profiling of rainbow trout RBCs. For RNA-seq, a de novo assembly was conducted, in order to create a transcriptome database for RBCs. For proteome profiling, we developed a proteomic method that combined: (a) fractionation into cytosolic and membrane fractions, (b) hemoglobin removal of the cytosolic fraction, (c) protein digestion, and (d) a novel step with pH reversed-phase peptide fractionation and final Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC ESI-MS/MS) analysis of each fraction. Combined transcriptome- and proteome- sequencing data identified, in silico, novel and striking immune functional networks for rainbow trout nucleated RBCs, which are mainly linked to innate and adaptive immunity. Functional pathways related to regulation of hematopoietic cell differentiation, antigen presentation via major histocompatibility complex class II (MHCII), leukocyte differentiation and regulation of leukocyte activation were identified. These preliminary findings further implicate nucleated RBCs in immune function, such as antigen presentation and leukocyte activation.

In Silico Functional Networks Identified in Fish Nucleated Red Blood Cells by Means of Transcriptomic and Proteomic Profiling

PubMed Central

Puente-Marin, Sara; Ciordia, Sergio; Mena, María Carmen; Chico, Verónica; Coll, Julio

2018-01-01

Nucleated red blood cells (RBCs) of fish have, in the last decade, been implicated in several immune-related functions, such as antiviral response, phagocytosis or cytokine-mediated signaling. RNA-sequencing (RNA-seq) and label-free shotgun proteomic analyses were carried out for in silico functional pathway profiling of rainbow trout RBCs. For RNA-seq, a de novo assembly was conducted, in order to create a transcriptome database for RBCs. For proteome profiling, we developed a proteomic method that combined: (a) fractionation into cytosolic and membrane fractions, (b) hemoglobin removal of the cytosolic fraction, (c) protein digestion, and (d) a novel step with pH reversed-phase peptide fractionation and final Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometric (LC ESI-MS/MS) analysis of each fraction. Combined transcriptome- and proteome- sequencing data identified, in silico, novel and striking immune functional networks for rainbow trout nucleated RBCs, which are mainly linked to innate and adaptive immunity. Functional pathways related to regulation of hematopoietic cell differentiation, antigen presentation via major histocompatibility complex class II (MHCII), leukocyte differentiation and regulation of leukocyte activation were identified. These preliminary findings further implicate nucleated RBCs in immune function, such as antigen presentation and leukocyte activation. PMID:29642539

A comprehensive characterization of rare mitochondrial DNA variants in neuroblastoma

PubMed Central

Pignataro, Piero; Lasorsa, Vito Alessandro; Hogarty, Michael D.; Castellano, Aurora; Conte, Massimo; Tonini, Gian Paolo; Iolascon, Achille; Gasparre, Giuseppe; Capasso, Mario

2016-01-01

Background Neuroblastoma, a tumor of the developing sympathetic nervous system, is a common childhood neoplasm that is often lethal. Mitochondrial DNA (mtDNA) mutations have been found in most tumors including neuroblastoma. We extracted mtDNA data from a cohort of neuroblastoma samples that had undergone Whole Exome Sequencing (WES) and also used snap-frozen samples in which mtDNA was entirely sequenced by Sanger technology. We next undertook the challenge of determining those mutations that are relevant to, or arisen during tumor development. The bioinformatics pipeline used to extract mitochondrial variants from matched tumor/blood samples was enriched by a set of filters inclusive of heteroplasmic fraction, nucleotide variability, and in silico prediction of pathogenicity. Results Our in silico multistep workflow applied both on WES and Sanger-sequenced neuroblastoma samples, allowed us to identify a limited burden of somatic and germline mitochondrial mutations with a potential pathogenic impact. Conclusions The few singleton germline and somatic mitochondrial mutations emerged, according to our in silico analysis, do not appear to impact on the development of neuroblastoma. Our findings are consistent with the hypothesis that most mitochondrial somatic mutations can be considered as ‘passengers’ and consequently have no discernible effect in this type of cancer. PMID:27351283

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

Identification of hepta-histidine as a candidate drug for Huntington’s disease by in silico-in vitro- in vivo-integrated screens of chemical libraries

PubMed Central

Imamura, Tomomi; Fujita, Kyota; Tagawa, Kazuhiko; Ikura, Teikichi; Chen, Xigui; Homma, Hidenori; Tamura, Takuya; Mao, Ying; Taniguchi, Juliana Bosso; Motoki, Kazumi; Nakabayashi, Makoto; Ito, Nobutoshi; Yamada, Kazunori; Tomii, Kentaro; Okano, Hideyuki; Kaye, Julia; Finkbeiner, Steven; Okazawa, Hitoshi

2016-01-01

We identified drug seeds for treating Huntington’s disease (HD) by combining in vitro single molecule fluorescence spectroscopy, in silico molecular docking simulations, and in vivo fly and mouse HD models to screen for inhibitors of abnormal interactions between mutant Htt and physiological Ku70, an essential DNA damage repair protein in neurons whose function is known to be impaired by mutant Htt. From 19,468 and 3,010,321 chemicals in actual and virtual libraries, fifty-six chemicals were selected from combined in vitro-in silico screens; six of these were further confirmed to have an in vivo effect on lifespan in a fly HD model, and two chemicals exerted an in vivo effect on the lifespan, body weight and motor function in a mouse HD model. Two oligopeptides, hepta-histidine (7H) and Angiotensin III, rescued the morphological abnormalities of primary neurons differentiated from iPS cells of human HD patients. For these selected drug seeds, we proposed a possible common structure. Unexpectedly, the selected chemicals enhanced rather than inhibited Htt aggregation, as indicated by dynamic light scattering analysis. Taken together, these integrated screens revealed a new pathway for the molecular targeted therapy of HD. PMID:27653664

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 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.

PNA-COMBO-FISH: From combinatorial probe design in silico to vitality compatible, specific labelling of gene targets in cell nuclei

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

Müller, Patrick; Rößler, Jens; Schwarz-Finsterle, Jutta

Recently, advantages concerning targeting specificity of PCR constructed oligonucleotide FISH probes in contrast to established FISH probes, e.g. BAC clones, have been demonstrated. These techniques, however, are still using labelling protocols with DNA denaturing steps applying harsh heat treatment with or without further denaturing chemical agents. COMBO-FISH (COMBinatorial Oligonucleotide FISH) allows the design of specific oligonucleotide probe combinations in silico. Thus, being independent from primer libraries or PCR laboratory conditions, the probe sequences extracted by computer sequence data base search can also be synthesized as single stranded PNA-probes (Peptide Nucleic Acid probes). Gene targets can be specifically labelled with atmore » least about 20 PNA-probes obtaining visibly background free specimens. By using appropriately designed triplex forming oligonucleotides, the denaturing procedures can completely be omitted. These results reveal a significant step towards oligonucleotide-FISH maintaining the 3D-nanostructure and even the viability of the cell target. The method is demonstrated with the detection of Her2/neu and GRB7 genes, which are indicators in breast cancer diagnosis and therapy. - Highlights: • Denaturation free protocols preserve 3D architecture of chromosomes and nuclei. • Labelling sets are determined in silico for duplex and triplex binding. • Probes are produced chemically with freely chosen backbones and base variants. • Peptide nucleic acid backbones reduce hindering charge interactions. • Intercalating side chains stabilize binding of short oligonucleotides.« less

In Silico Oncology: Quantification of the In Vivo Antitumor Efficacy of Cisplatin-Based Doublet Therapy in Non-Small Cell Lung Cancer (NSCLC) through a Multiscale Mechanistic Model

PubMed Central

Kolokotroni, Eleni; Dionysiou, Dimitra; Veith, Christian; Kim, Yoo-Jin; Franz, Astrid; Grgic, Aleksandar; Bohle, Rainer M.; Stamatakos, Georgios

2016-01-01

The 5-year survival of non-small cell lung cancer patients can be as low as 1% in advanced stages. For patients with resectable disease, the successful choice of preoperative chemotherapy is critical to eliminate micrometastasis and improve operability. In silico experimentations can suggest the optimal treatment protocol for each patient based on their own multiscale data. A determinant for reliable predictions is the a priori estimation of the drugs’ cytotoxic efficacy on cancer cells for a given treatment. In the present work a mechanistic model of cancer response to treatment is applied for the estimation of a plausible value range of the cell killing efficacy of various cisplatin-based doublet regimens. Among others, the model incorporates the cancer related mechanism of uncontrolled proliferation, population heterogeneity, hypoxia and treatment resistance. The methodology is based on the provision of tumor volumetric data at two time points, before and after or during treatment. It takes into account the effect of tumor microenvironment and cell repopulation on treatment outcome. A thorough sensitivity analysis based on one-factor-at-a-time and latin hypercube sampling/partial rank correlation coefficient approaches has established the volume growth rate and the growth fraction at diagnosis as key features for more accurate estimates. The methodology is applied on the retrospective data of thirteen patients with non-small cell lung cancer who received cisplatin in combination with gemcitabine, vinorelbine or docetaxel in the neoadjuvant context. The selection of model input values has been guided by a comprehensive literature survey on cancer-specific proliferation kinetics. The latin hypercube sampling has been recruited to compensate for patient-specific uncertainties. Concluding, the present work provides a quantitative framework for the estimation of the in-vivo cell-killing ability of various chemotherapies. Correlation studies of such estimates with the molecular profile of patients could serve as a basis for reliable personalized predictions. PMID:27657742

Astrocyte - neuron lactate shuttle may boost more ATP supply to the neuron under hypoxic conditions - in silico study supported by in vitro expression data

PubMed Central

2011-01-01

Background Neuro-glial interactions are important for normal functioning of the brain as well as brain energy metabolism. There are two major working models - in the classical view, both neurons and astrocytes can utilize glucose as the energy source through oxidative metabolism, whereas in the astrocyte-neuron lactate shuttle hypothesis (ANLSH) it is the astrocyte which can consume glucose through anaerobic glycolysis to pyruvate and then to lactate, and this lactate is secreted to the extracellular space to be taken up by the neuron for further oxidative degradation. Results In this computational study, we have included hypoxia-induced genetic regulation of these enzymes and transporters, and analyzed whether the ANLSH model can provide an advantage to either cell type in terms of supplying the energy demand. We have based this module on our own experimental analysis of hypoxia-dependent regulation of transcription of key metabolic enzymes. Using this experimentation-supported in silico modeling, we show that under both normoxic and hypoxic conditions in a given time period ANLSH model does indeed provide the neuron with more ATP than in the classical view. Conclusions Although the ANLSH is energetically more favorable for the neuron, it is not the case for the astrocyte in the long term. Considering the fact that astrocytes are more resilient to hypoxia, we would propose that there is likely a switch between the two models, based on the energy demand of the neuron, so as to maintain the survival of the neuron under hypoxic or glucose-and-oxygen-deprived conditions. PMID:21995951

Trivaric acid, a new inhibitor of PTP1b with potent beneficial effect on diabetes.

PubMed

Sun, Wenlong; Zhang, Bowei; Zheng, Haizhou; Zhuang, Chunlin; Li, Xia; Lu, Xinhua; Quan, Chunshan; Dong, Yuesheng; Zheng, Zhihui; Xiu, Zhilong

2017-01-15

To screen a potential PTP1b inhibitor from the microbial origin-based compound library and to investigate the potential anti-diabetic effects of the inhibitor in vivo and determine its primary anti-diabetic mechanism in vitro and in silico. PTP1b inhibitory activity was measured using recombination protein as the enzyme and p-NPP as the substrate. The binding of the inhibitor to PTP1b was analysed by docking in silico and confirmed by ITC experiments. The intracellular signalling pathway was detected by Western blot analysis in HepG2 cells. The anti-diabetic effects were evaluated using a diabetic mice model in vivo. Among 545 microbial origin-based pure compounds tested, trivaric acid, a tridepside, was selected as a PTP1B inhibitor exhibiting strong inhibitory activity with an IC 50 of 173nM. Docking and ITC studies showed that trivaric acid was able to spontaneously bind to PTP1b and may inhibit PTP1b by blocking the catalytic domain of the phosphatase. Trivaric acid also enhanced the ability of insulin to stimulate the IR/IRS/Akt/GLUT2 pathway and increase the glucose consumption in HepG2 cells. In diabetic mice, trivaric acid that had been encapsulated into Eudrgit L100-5.5 showed significant anti-diabetic effects, improving insulin resistance, leptin resistance and lipid profile and weight control at doses of 5mg/kg and 50mg/kg. Trivaric acid is a potential lead compound in the search for anti-diabetic agents targeting PTP1b. Copyright © 2016 Elsevier Inc. All rights reserved.

Astrocyte-neuron lactate shuttle may boost more ATP supply to the neuron under hypoxic conditions--in silico study supported by in vitro expression data.

PubMed

Genc, Seda; Kurnaz, Isil A; Ozilgen, Mustafa

2011-10-13

Neuro-glial interactions are important for normal functioning of the brain as well as brain energy metabolism. There are two major working models--in the classical view, both neurons and astrocytes can utilize glucose as the energy source through oxidative metabolism, whereas in the astrocyte-neuron lactate shuttle hypothesis (ANLSH) it is the astrocyte which can consume glucose through anaerobic glycolysis to pyruvate and then to lactate, and this lactate is secreted to the extracellular space to be taken up by the neuron for further oxidative degradation. In this computational study, we have included hypoxia-induced genetic regulation of these enzymes and transporters, and analyzed whether the ANLSH model can provide an advantage to either cell type in terms of supplying the energy demand. We have based this module on our own experimental analysis of hypoxia-dependent regulation of transcription of key metabolic enzymes. Using this experimentation-supported in silico modeling, we show that under both normoxic and hypoxic conditions in a given time period ANLSH model does indeed provide the neuron with more ATP than in the classical view. Although the ANLSH is energetically more favorable for the neuron, it is not the case for the astrocyte in the long term. Considering the fact that astrocytes are more resilient to hypoxia, we would propose that there is likely a switch between the two models, based on the energy demand of the neuron, so as to maintain the survival of the neuron under hypoxic or glucose-and-oxygen-deprived conditions.

Should DNA sequence be incorporated with other taxonomical data for routine identifying of plant species?

PubMed

Suesatpanit, Tanakorn; Osathanunkul, Kitisak; Madesis, Panagiotis; Osathanunkul, Maslin

2017-08-31

A variety of plants in Acanthaceae have long been used in traditional Thai ailment and commercialised with significant economic value. Nowadays medicinal plants are sold in processed forms and thus morphological authentication is almost impossible. Full identification requires comparison of the specimen with some authoritative sources, such as a full and accurate description and verification of the species deposited in herbarium. Intake of wrong herbals can cause adverse effects. Identification of both raw materials and end products is therefore needed. Here, the potential of a DNA-based identification method, called Bar-HRM (DNA barcoding coupled with High Resolution Melting analysis), in raw material species identification is investigated. DNA barcode sequences from five regions (matK, rbcL, trnH-psbA spacer region, trnL and ITS2) of Acanthaceae species were retrieved for in silico analysis. Then the specific primer pairs were used in HRM assay to generate unique melting profiles for each plants species. The method allows identification of samples lacking necessary morphological parts. In silico analyses of all five selected regions suggested that ITS2 is the most suitable marker for Bar-HRM in this study. The HRM analysis on dried samples of 16 Acanthaceae medicinal species was then performed using primer pair derived from ITS2 region. 100% discrimination of the tested samples at both genus and species level was observed. However, two samples documented as Clinacanthus nutans and Clinacanthus siamensis were recognised as the same species from the HRM analysis. Further investigation reveals that C. siamensis is now accepted as C. nutans. The results here proved that Bar-HRM is a promising technique in species identification of the studied medicinal plants in Acanthaceae. In addition, molecular biological data is currently used in plant taxonomy and increasingly popular in recent years. Here, DNA barcode sequence data should be incorporated with morphological characters in the species identification.

De novo transcriptome analysis of rose-scented geranium provides insights into the metabolic specificity of terpene and tartaric acid biosynthesis.

PubMed

Narnoliya, Lokesh K; Kaushal, Girija; Singh, Sudhir P; Sangwan, Rajender S

2017-01-13

Rose-scented geranium (Pelargonium sp.) is a perennial herb that produces a high value essential oil of fragrant significance due to the characteristic compositional blend of rose-oxide and acyclic monoterpenoids in foliage. Recently, the plant has also been shown to produce tartaric acid in leaf tissues. Rose-scented geranium represents top-tier cash crop in terms of economic returns and significance of the plant and plant products. However, there has hardly been any study on its metabolism and functional genomics, nor any genomic expression dataset resource is available in public domain. Therefore, to begin the gains in molecular understanding of specialized metabolic pathways of the plant, de novo sequencing of rose-scented geranium leaf transcriptome, transcript assembly, annotation, expression profiling as well as their validation were carried out. De novo transcriptome analysis resulted a total of 78,943 unique contigs (average length: 623 bp, and N50 length: 752 bp) from 15.44 million high quality raw reads. In silico functional annotation led to the identification of several putative genes representing terpene, ascorbic acid and tartaric acid biosynthetic pathways, hormone metabolism, and transcription factors. Additionally, a total of 6,040 simple sequence repeat (SSR) motifs were identified in 6.8% of the expressed transcripts. The highest frequency of SSR was of tri-nucleotides (50%). Further, transcriptome assembly was validated for randomly selected putative genes by standard PCR-based approach. In silico expression profile of assembled contigs were validated by real-time PCR analysis of selected transcripts. Being the first report on transcriptome analysis of rose-scented geranium the data sets and the leads and directions reflected in this investigation will serve as a foundation for pursuing and understanding molecular aspects of its biology, and specialized metabolic pathways, metabolic engineering, genetic diversity as well as molecular breeding.

Role of Alternative Polyadenylation during Adipogenic Differentiation: An In Silico Approach

PubMed Central

Spangenberg, Lucía; Correa, Alejandro; Dallagiovanna, Bruno; Naya, Hugo

2013-01-01

Post-transcriptional regulation of stem cell differentiation is far from being completely understood. Changes in protein levels are not fully correlated with corresponding changes in mRNAs; the observed differences might be partially explained by post-transcriptional regulation mechanisms, such as alternative polyadenylation. This would involve changes in protein binding, transcript usage, miRNAs and other non-coding RNAs. In the present work we analyzed the distribution of alternative transcripts during adipogenic differentiation and the potential role of miRNAs in post-transcriptional regulation. Our in silico analysis suggests a modest, consistent, bias in 3′UTR lengths during differentiation enabling a fine-tuned transcript regulation via small non-coding RNAs. Including these effects in the analyses partially accounts for the observed discrepancies in relative abundance of protein and mRNA. PMID:24143171

Analysis of ZP1 gene reveals differences in zona pellucida composition in carnivores.

PubMed

Moros-Nicolás, C; Leza, A; Chevret, P; Guillén-Martínez, A; González-Brusi, L; Boué, F; Lopez-Bejar, M; Ballesta, J; Avilés, M; Izquierdo-Rico, M J

2018-01-01

The zona pellucida (ZP) is an extracellular envelope that surrounds mammalian oocytes. This coat participates in the interaction between gametes, induction of the acrosome reaction, block of polyspermy and protection of the oviductal embryo. Previous studies suggested that carnivore ZP was formed by three glycoproteins (ZP2, ZP3 and ZP4), with ZP1 being a pseudogene. However, a recent study in the cat found that all four proteins were expressed. In the present study, in silico and molecular analyses were performed in several carnivores to clarify the ZP composition in this order of mammals. The in silico analysis demonstrated the presence of the ZP1 gene in five carnivores: cheetah, panda, polar bear, tiger and walrus, whereas in the Antarctic fur seal and the Weddell seal there was evidence of pseudogenisation. Molecular analysis showed the presence of four ZP transcripts in ferret ovaries (ZP1, ZP2, ZP3 and ZP4) and three in fox ovaries (ZP2, ZP3 and ZP4). Analysis of the fox ZP1 gene showed the presence of a stop codon. The results strongly suggest that all four ZP genes are expressed in most carnivores, whereas ZP1 pseudogenisation seems to have independently affected three families (Canidae, Otariidae and Phocidae) of the carnivore tree.

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.

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.

Enhanced hexose fermentation by Saccharomyces cerevisiae through integration of stoichiometric modeling and genetic screening.

PubMed

Quarterman, Josh; Kim, Soo Rin; Kim, Pan-Jun; Jin, Yong-Su

2015-01-20

In order to determine beneficial gene deletions for ethanol production by the yeast Saccharomyces cerevisiae, we performed an in silico gene deletion experiment based on a genome-scale metabolic model. Genes coding for two oxidative phosphorylation reactions (cytochrome c oxidase and ubiquinol cytochrome c reductase) were identified by the model-based simulation as potential deletion targets for enhancing ethanol production and maintaining acceptable overall growth rate in oxygen-limited conditions. Since the two target enzymes are composed of multiple subunits, we conducted a genetic screening study to evaluate the in silico results and compare the effect of deleting various portions of the respiratory enzyme complexes. Over two-thirds of the knockout mutants identified by the in silico study did exhibit experimental behavior in qualitative agreement with model predictions, but the exceptions illustrate the limitation of using a purely stoichiometric model-based approach. Furthermore, there was a substantial quantitative variation in phenotype among the various respiration-deficient mutants that were screened in this study, and three genes encoding respiratory enzyme subunits were identified as the best knockout targets for improving hexose fermentation in microaerobic conditions. Specifically, deletion of either COX9 or QCR9 resulted in higher ethanol production rates than the parental strain by 37% and 27%, respectively, with slight growth disadvantages. Also, deletion of QCR6 led to improved ethanol production rate by 24% with no growth disadvantage. The beneficial effects of these gene deletions were consistently demonstrated in different strain backgrounds and with four common hexoses. The combination of stoichiometric modeling and genetic screening using a systematic knockout collection was useful for narrowing a large set of gene targets and identifying targets of interest. Copyright © 2014 Elsevier B.V. All rights reserved.

Using argument notation to engineer biological simulations with increased confidence

PubMed Central

Alden, Kieran; Andrews, Paul S.; Polack, Fiona A. C.; Veiga-Fernandes, Henrique; Coles, Mark C.; Timmis, Jon

2015-01-01

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions. PMID:25589574

Using argument notation to engineer biological simulations with increased confidence.

PubMed

Alden, Kieran; Andrews, Paul S; Polack, Fiona A C; Veiga-Fernandes, Henrique; Coles, Mark C; Timmis, Jon

2015-03-06

The application of computational and mathematical modelling to explore the mechanics of biological systems is becoming prevalent. To significantly impact biological research, notably in developing novel therapeutics, it is critical that the model adequately represents the captured system. Confidence in adopting in silico approaches can be improved by applying a structured argumentation approach, alongside model development and results analysis. We propose an approach based on argumentation from safety-critical systems engineering, where a system is subjected to a stringent analysis of compliance against identified criteria. We show its use in examining the biological information upon which a model is based, identifying model strengths, highlighting areas requiring additional biological experimentation and providing documentation to support model publication. We demonstrate our use of structured argumentation in the development of a model of lymphoid tissue formation, specifically Peyer's Patches. The argumentation structure is captured using Artoo (www.york.ac.uk/ycil/software/artoo), our Web-based tool for constructing fitness-for-purpose arguments, using a notation based on the safety-critical goal structuring notation. We show how argumentation helps in making the design and structured analysis of a model transparent, capturing the reasoning behind the inclusion or exclusion of each biological feature and recording assumptions, as well as pointing to evidence supporting model-derived conclusions.

Topological robustness analysis of protein interaction networks reveals key targets for overcoming chemotherapy resistance in glioma

NASA Astrophysics Data System (ADS)

Azevedo, Hátylas; Moreira-Filho, Carlos Alberto

2015-11-01

Biological networks display high robustness against random failures but are vulnerable to targeted attacks on central nodes. Thus, network topology analysis represents a powerful tool for investigating network susceptibility against targeted node removal. Here, we built protein interaction networks associated with chemoresistance to temozolomide, an alkylating agent used in glioma therapy, and analyzed their modular structure and robustness against intentional attack. These networks showed functional modules related to DNA repair, immunity, apoptosis, cell stress, proliferation and migration. Subsequently, network vulnerability was assessed by means of centrality-based attacks based on the removal of node fractions in descending orders of degree, betweenness, or the product of degree and betweenness. This analysis revealed that removing nodes with high degree and high betweenness was more effective in altering networks’ robustness parameters, suggesting that their corresponding proteins may be particularly relevant to target temozolomide resistance. In silico data was used for validation and confirmed that central nodes are more relevant for altering proliferation rates in temozolomide-resistant glioma cell lines and for predicting survival in glioma patients. Altogether, these results demonstrate how the analysis of network vulnerability to topological attack facilitates target prioritization for overcoming cancer chemoresistance.

Company Profile: Selventa, Inc.

PubMed

Fryburg, David A; Latino, Louis J; Tagliamonte, John; Kenney, Renee D; Song, Diane H; Levine, Arnold J; de Graaf, David

2012-08-01

Selventa, Inc. (MA, USA) is a biomarker discovery company that enables personalized healthcare. Originally founded as Genstruct, Inc., Selventa has undergone significant evolution from a technology-based service provider to an active partner in the development of diagnostic tests, functioning as a molecular dashboard of disease activity using a unique platform. As part of that evolution, approximately 2 years ago the company was rebranded as Selventa to reflect its new identity and mission. The contributions to biomedical research by Selventa are based on in silico, reverse-engineering methods to determine biological causality. That is, given a set of in vitro or in vivo biological observations, which biological mechanisms can explain the measured results? Facilitated by a large and carefully curated knowledge base, these in silico methods generated new insights into the mechanisms driving a disease. As Selventa's methods would enable biomarker discovery and be directly applicable to generating novel diagnostics, the scientists at Selventa have focused on the development of predictive biomarkers of response in autoimmune and oncologic diseases. Selventa is presently building a portfolio of independent, as well as partnered, biomarker projects with the intention to create diagnostic tests that predict response to therapy.

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

Genome-Wide Identification and Transferability of Microsatellite Markers between Palmae Species

PubMed Central

Xiao, Yong; Xia, Wei; Ma, Jianwei; Mason, Annaliese S.; Fan, Haikuo; Shi, Peng; Lei, Xintao; Ma, Zilong; Peng, Ming

2016-01-01

The Palmae family contains 202 genera and approximately 2800 species. Except for Elaeis guineensis and Phoenix dactylifera, almost no genetic and genomic information is available for Palmae species. Therefore, this is an obstacle to the conservation and genetic assessment of Palmae species, especially those that are currently endangered. The study was performed to develop a large number of microsatellite markers which can be used for genetic analysis in different Palmae species. Based on the assembled genome of E. guineensis and P. dactylifera, a total of 814 383 and 371 629 microsatellites were identified. Among these microsatellites identified in E. guineensis, 734 509 primer pairs could be designed from the flanking sequences of these microsatellites. The majority (618 762) of these designed primer pairs had in silico products in the genome of E. guineensis. These 618 762 primer pairs were subsequently used to in silico amplify the genome of P. dactylifera. A total of 7 265 conserved microsatellites were identified between E. guineensis and P. dactylifera. One hundred and thirty-five primer pairs flanking the conserved SSRs were stochastically selected and validated to have high cross-genera transferability, varying from 16.7 to 93.3% with an average of 73.7%. These genome-wide conserved microsatellite markers will provide a useful tool for genetic assessment and conservation of different Palmae species in the future. PMID:27826307

New insights into plant glycoside hydrolase family 32 in Agave species

PubMed Central

Avila de Dios, Emmanuel; Gomez Vargas, Alan D.; Damián Santos, Maura L.; Simpson, June

2015-01-01

In order to optimize the use of agaves for commercial applications, an understanding of fructan metabolism in these species at the molecular and genetic level is essential. Based on transcriptome data, this report describes the identification and molecular characterization of cDNAs and deduced amino acid sequences for genes encoding fructosyltransferases, invertases and fructan exohydrolases (FEH) (enzymes belonging to plant glycoside hydrolase family 32) from four different agave species (A. tequilana, A. deserti, A. victoriae-reginae, and A. striata). Conserved amino acid sequences and a hypervariable domain allowed classification of distinct isoforms for each enzyme type. Notably however neither 1-FFT nor 6-SFT encoding cDNAs were identified. In silico analysis revealed that distinct isoforms for certain enzymes found in a single species, showed different levels and tissue specific patterns of expression whereas in other cases expression patterns were conserved both within the species and between different species. Relatively high levels of in silico expression for specific isoforms of both invertases and fructosyltransferases were observed in floral tissues in comparison to vegetative tissues such as leaves and stems and this pattern was confirmed by Quantitative Real Time PCR using RNA obtained from floral and leaf tissue of A. tequilana. Thin layer chromatography confirmed the presence of fructans with degree of polymerization (DP) greater than DP three in both immature buds and fully opened flowers also obtained from A. tequilana. PMID:26300895

New insights into plant glycoside hydrolase family 32 in Agave species.

PubMed

Avila de Dios, Emmanuel; Gomez Vargas, Alan D; Damián Santos, Maura L; Simpson, June

2015-01-01

In order to optimize the use of agaves for commercial applications, an understanding of fructan metabolism in these species at the molecular and genetic level is essential. Based on transcriptome data, this report describes the identification and molecular characterization of cDNAs and deduced amino acid sequences for genes encoding fructosyltransferases, invertases and fructan exohydrolases (FEH) (enzymes belonging to plant glycoside hydrolase family 32) from four different agave species (A. tequilana, A. deserti, A. victoriae-reginae, and A. striata). Conserved amino acid sequences and a hypervariable domain allowed classification of distinct isoforms for each enzyme type. Notably however neither 1-FFT nor 6-SFT encoding cDNAs were identified. In silico analysis revealed that distinct isoforms for certain enzymes found in a single species, showed different levels and tissue specific patterns of expression whereas in other cases expression patterns were conserved both within the species and between different species. Relatively high levels of in silico expression for specific isoforms of both invertases and fructosyltransferases were observed in floral tissues in comparison to vegetative tissues such as leaves and stems and this pattern was confirmed by Quantitative Real Time PCR using RNA obtained from floral and leaf tissue of A. tequilana. Thin layer chromatography confirmed the presence of fructans with degree of polymerization (DP) greater than DP three in both immature buds and fully opened flowers also obtained from A. tequilana.

Efficient HIV-1 inhibition by a 16 nt-long RNA aptamer designed by combining in vitro selection and in silico optimisation strategies

PubMed Central

Sánchez-Luque, Francisco J.; Stich, Michael; Manrubia, Susanna; Briones, Carlos; Berzal-Herranz, Alfredo

2014-01-01

The human immunodeficiency virus type-1 (HIV-1) genome contains multiple, highly conserved structural RNA domains that play key roles in essential viral processes. Interference with the function of these RNA domains either by disrupting their structures or by blocking their interaction with viral or cellular factors may seriously compromise HIV-1 viability. RNA aptamers are amongst the most promising synthetic molecules able to interact with structural domains of viral genomes. However, aptamer shortening up to their minimal active domain is usually necessary for scaling up production, what requires very time-consuming, trial-and-error approaches. Here we report on the in vitro selection of 64 nt-long specific aptamers against the complete 5′-untranslated region of HIV-1 genome, which inhibit more than 75% of HIV-1 production in a human cell line. The analysis of the selected sequences and structures allowed for the identification of a highly conserved 16 nt-long stem-loop motif containing a common 8 nt-long apical loop. Based on this result, an in silico designed 16 nt-long RNA aptamer, termed RNApt16, was synthesized, with sequence 5′-CCCCGGCAAGGAGGGG-3′. The HIV-1 inhibition efficiency of such an aptamer was close to 85%, thus constituting the shortest RNA molecule so far described that efficiently interferes with HIV-1 replication. PMID:25175101

In silico study of carvone derivatives as potential neuraminidase inhibitors.

PubMed

Jusoh, Noorakmar; Zainal, Hasanuddin; Abdul Hamid, Azzmer Azzar; Bunnori, Noraslinda M; Abd Halim, Khairul Bariyyah; Abd Hamid, Shafida

2018-03-15

Recent outbreaks of highly pathogenic influenza strains have highlighted the need to develop new anti-influenza drugs. Here, we report an in silico study of carvone derivatives to analyze their binding modes with neuraminidase (NA) active sites. Two proposed carvone analogues, CV(A) and CV(B), with 36 designed ligands were predicted to inhibit NA (PDB ID: 3TI6) using molecular docking. The design is based on structural resemblance with the commercial inhibitor, oseltamivir (OTV), ligand polarity, and amino acid residues in the NA active sites. Docking simulations revealed that ligand A18 has the lowest energy binding (∆G bind ) value of -8.30 kcal mol -1 , comparable to OTV with ∆G bind of -8.72 kcal mol -1 . A18 formed seven hydrogen bonds (H-bonds) at residues Arg292, Arg371, Asp151, Trp178, Glu227, and Tyr406, while eight H-bonds were formed by OTV with amino acids Arg118, Arg292, Arg371, Glu119, Asp151, and Arg152. Molecular dynamics (MD) simulation was conducted to compare the stability between ligand A18 and OTV with NA. Our simulation study showed that the A18-NA complex is as stable as the OTV-NA complex during the MD simulation of 50 ns through the analysis of RMSD, RMSF, total energy, hydrogen bonding, and MM/PBSA free energy calculations.

Nimbolide targets BCL2 and induces apoptosis in preclinical models of Waldenströms macroglobulinemia

PubMed Central

Chitta, K; Paulus, A; Caulfield, T R; Akhtar, S; Blake, M-KK; Ailawadhi, S; Knight, J; Heckman, M G; Pinkerton, A; Chanan-Khan, A

2014-01-01

Neem leaf extract (NLE) has medicinal properties, which have been attributed to its limonoid content. We identified the NLE tetranorterpenoid, nimbolide, as being the key limonoid responsible for the cytotoxicity of NLE in various preclinical models of human B-lymphocyte cancer. Of the models tested, Waldenströms macroglobulinemia (WM) cells were most sensitive to nimbolide, undergoing significant mitochondrial mediated apoptosis. Notably, nimbolide toxicity was also observed in drug-resistant (bortezomib or ibrutinib) WM cells. To identify putative targets of nimbolide, relevant in WM, we used chemoinformatics-based approaches comprised of virtual in silico screening, molecular modeling and target–ligand reverse docking. In silico analysis revealed the antiapoptotic protein BCL2 was the preferential binding partner of nimbolide. The significance of this finding was further tested in vitro in RS4;11 (BCL2-dependent) tumor cells, in which nimbolide induced significantly more apoptosis compared with BCL2 mutated (Jurkat BCL2Ser70-Ala) cells. Lastly, intraperitoneal administration of nimbolide in WM tumor xenografted mice, significantly reduced tumor growth and IgM secretion in vivo, while modulating the expression of several proteins as seen on immunohistochemistry. Overall, our data demonstrate that nimbolide is highly active in WM cells, as well as other B-cell cancers, and engages BCL2 to exert its cytotoxic activity. PMID:25382610

Degradation of Aflatoxins by Means of Laccases from Trametes versicolor: An In Silico Insight

PubMed Central

Dellafiora, Luca; Galaverna, Gianni; Reverberi, Massimo; Dall’Asta, Chiara

2017-01-01

Mycotoxins are secondary metabolites of fungi that contaminate food and feed, and are involved in a series of foodborne illnesses and disorders in humans and animals. The mitigation of mycotoxin content via enzymatic degradation is a strategy to ensure safer food and feed, and to address the forthcoming issues in view of the global trade and sustainability. Nevertheless, the search for active enzymes is still challenging and time-consuming. The in silico analysis may strongly support the research by providing the evidence-based hierarchization of enzymes for a rational design of more effective experimental trials. The present work dealt with the degradation of aflatoxin B1 and M1 by laccase enzymes from Trametes versicolor. The enzymes–substrate interaction for various enzyme isoforms was investigated through 3D molecular modeling techniques. Structural differences among the isoforms have been pinpointed, which may cause different patterns of interaction between aflatoxin B1 and M1. The possible formation of different products of degradation can be argued accordingly. Moreover, the laccase gamma isoform was identified as the most suitable for protein engineering aimed at ameliorating the substrate specificity. Overall, 3D modeling proved to be an effective analytical tool to assess the enzyme–substrate interaction and provided a solid foothold for supporting the search of degrading enzyme at the early stage. PMID:28045427

CAPRRESI: Chimera Assembly by Plasmid Recovery and Restriction Enzyme Site Insertion.

PubMed

Santillán, Orlando; Ramírez-Romero, Miguel A; Dávila, Guillermo

2017-06-25

Here, we present chimera assembly by plasmid recovery and restriction enzyme site insertion (CAPRRESI). CAPRRESI benefits from many strengths of the original plasmid recovery method and introduces restriction enzyme digestion to ease DNA ligation reactions (required for chimera assembly). For this protocol, users clone wildtype genes into the same plasmid (pUC18 or pUC19). After the in silico selection of amino acid sequence regions where chimeras should be assembled, users obtain all the synonym DNA sequences that encode them. Ad hoc Perl scripts enable users to determine all synonym DNA sequences. After this step, another Perl script searches for restriction enzyme sites on all synonym DNA sequences. This in silico analysis is also performed using the ampicillin resistance gene (ampR) found on pUC18/19 plasmids. Users design oligonucleotides inside synonym regions to disrupt wildtype and ampR genes by PCR. After obtaining and purifying complementary DNA fragments, restriction enzyme digestion is accomplished. Chimera assembly is achieved by ligating appropriate complementary DNA fragments. pUC18/19 vectors are selected for CAPRRESI because they offer technical advantages, such as small size (2,686 base pairs), high copy number, advantageous sequencing reaction features, and commercial availability. The usage of restriction enzymes for chimera assembly eliminates the need for DNA polymerases yielding blunt-ended products. CAPRRESI is a fast and low-cost method for fusing protein-coding genes.

Lactobacillus acidophilus binds to MUC3 component of cultured intestinal epithelial cells with highest affinity.

PubMed

Das, Jugal Kishore; Mahapatra, Rajani Kanta; Patro, Shubhransu; Goswami, Chandan; Suar, Mrutyunjay

2016-04-01

Lactobacillus strains have been shown to adhere to the mucosal components of intestinal epithelial cells. However, established in vitro adhesion assays have several drawbacks in assessing the adhesion of new Lactobacillus strains. The present study aimed to compare the adhesion of four different Lactobacillus strains and select the most adherent microbe, based on in silico approach supported by in vitro results. The mucus-binding proteins in Lactobacillus acidophilus, L. plantarum, L. brevis and L. fermentum were identified and their capacities to interact with intestinal mucin were compared by molecular docking analysis. Lactobacillus acidophilus had the maximal affinity of binding to mucin with predicted free energy of -6.066 kcal mol(-1) Further, in vitro experimental assay of adhesion was performed to validate the in silico results. The adhesion of L. acidophilus to mucous secreting colon epithelial HT-29 MTX cells was highest at 12%, and it formed biofilm with maximum depth (Z = 84 μm). Lactobacillus acidophilus was determined to be the most adherent strain in the study. All the Lactobacillus strains tested in this study, displayed maximum affinity of binding to MUC3 component of mucus as compared to other gastrointestinal mucins. These findings may have importance in the design of probiotics and health care management. © FEMS 2016. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

High throughput virtual screening and in silico ADMET analysis for rapid and efficient identification of potential PAP248-286 aggregation inhibitors as anti-HIV agents

NASA Astrophysics Data System (ADS)

Malik, Ruchi; Bunkar, Devendra; Choudhary, Bhanwar Singh; Srivastava, Shubham; Mehta, Pakhuri; Sharma, Manish

2016-10-01

Human semen is principal vehicle for transmission of HIV-1 and other enveloped viruses. Several endogenous peptides present in semen, including a 39-amino acid fragments of prostatic acid phosphatase (PAP248-286) assemble into amyloid fibrils named as semen-derived enhancer of viral infection (SEVI) that promote virion attachment to target cells which dramatically enhance HIV virus infection by up to 105-fold. Epigallocatechin-3-gallate (EGCG), a polyphenolic compound, is the major catechin found in green tea which disaggregates existing SEVI fibers, and inhibits the formation of SEVI fibers. The aim of this study was to screen a number of relevant polyphenols to develop a rational approach for designing PAP248-286 aggregation inhibitors as potential anti-HIV agents. The molecular docking based virtual screening results showed that polyphenolic compounds 2-6 possessed good docking score and interacted well with the active site residues of PAP248-286. Amino acid residues of binding site namely; Lys255, Ser256, Leu258 and Asn265 are involved in binding of these compounds. In silico ADMET prediction studies on these hits were also found to be promising. Polyphenolic compounds 2-6 identified as hits may act as novel leads for inhibiting aggregation of PAP248-286 into SEVI.

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

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa

PubMed Central

Ridder, Lars; van der Hooft, Justin J. J.; Verhoeven, Stefan

2014-01-01

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed. PMID:26819876

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

PubMed

Ridder, Lars; van der Hooft, Justin J J; Verhoeven, Stefan

2014-01-01

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking. In 6 of the 12 submitted solutions in category 2, the correct chemical structure obtained the best score, whereas 3 molecules were ranked outside the top 5. All top ranked molecular formulas submitted in category 1 were correct. In addition, we present MAGMa results generated retrospectively for the remaining challenges. Successful application of the MAGMa algorithm required inclusion of the relevant candidate molecules, application of the appropriate mass tolerance and a sufficient degree of in silico fragmentation of the candidate molecules. Furthermore, the effect of the exhaustiveness of the candidate lists and limitations of substructure based scoring are discussed.

In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation.

PubMed

An, Gary; Bartels, John; Vodovotz, Yoram

2011-03-01

The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.

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

Non-invasive pressure difference estimation from PC-MRI using the work-energy equation

PubMed Central

Donati, Fabrizio; Figueroa, C. Alberto; Smith, Nicolas P.; Lamata, Pablo; Nordsletten, David A.

2015-01-01

Pressure difference is an accepted clinical biomarker for cardiovascular disease conditions such as aortic coarctation. Currently, measurements of pressure differences in the clinic rely on invasive techniques (catheterization), prompting development of non-invasive estimates based on blood flow. In this work, we propose a non-invasive estimation procedure deriving pressure difference from the work-energy equation for a Newtonian fluid. Spatial and temporal convergence is demonstrated on in silico Phase Contrast Magnetic Resonance Image (PC-MRI) phantoms with steady and transient flow fields. The method is also tested on an image dataset generated in silico from a 3D patient-specific Computational Fluid Dynamics (CFD) simulation and finally evaluated on a cohort of 9 subjects. The performance is compared to existing approaches based on steady and unsteady Bernoulli formulations as well as the pressure Poisson equation. The new technique shows good accuracy, robustness to noise, and robustness to the image segmentation process, illustrating the potential of this approach for non-invasive pressure difference estimation. PMID:26409245

EVALLER: a web server for in silico assessment of potential protein allergenicity

PubMed Central

Barrio, Alvaro Martinez; Soeria-Atmadja, Daniel; Nistér, Anders; Gustafsson, Mats G.; Hammerling, Ulf; Bongcam-Rudloff, Erik

2007-01-01

Bioinformatics testing approaches for protein allergenicity, involving amino acid sequence comparisons, have evolved appreciably over the last several years to increased sophistication and performance. EVALLER, the web server presented in this article is based on our recently published ‘Detection based on Filtered Length-adjusted Allergen Peptides’ (DFLAP) algorithm, which affords in silico determination of potential protein allergenicity of high sensitivity and excellent specificity. To strengthen bioinformatics risk assessment in allergology EVALLER provides a comprehensive outline of its judgment on a query protein's potential allergenicity. Each such textual output incorporates a scoring figure, a confidence numeral of the assignment and information on high- or low-scoring matches to identified allergen-related motifs, including their respective location in accordingly derived allergens. The interface, built on a modified Perl Open Source package, enables dynamic and color-coded graphic representation of key parts of the output. Moreover, pertinent details can be examined in great detail through zoomed views. The server can be accessed at http://bioinformatics.bmc.uu.se/evaller.html. PMID:17537818

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.

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…

In silico cloning, expression of Rieske-like apoprotein gene and protein subcellular localization in the Pacific oyster, Crassostrea gigas.

PubMed

He, Xiaocui; Zhang, Yang; Yu, Ziniu

2010-10-01

Rieske protein gene in the Pacific oyster Crassostrea gigas was obtained by in silico cloning for the first time, and its expression profiles and subcellular localization were determined, respectively. The full-length cDNA of Cgisp is 985 bp in length and contains a 5'- and 3'-untranslated regions of 35 and 161 bp, respectively, with an open reading frame of 786 bp encoding a protein of 262 amino acids. The predicted molecular weight of 30 kDa of Cgisp protein was verified by prokaryotic expression. Conserved Rieske [2Fe-2S] cluster binding sites and highly matched-pair tertiary structure with 3CWB_E (Gallus gallus) were revealed by homologous analysis and molecular modeling. Eleven putative SNP sites and two conserved hexapeptide sequences, box I (THLGC) and II (PCHGS), were detected by multiple alignments. Real-time PCR analysis showed that Cgisp is expressed in a wide range of tissues, with adductor muscle exhibiting the top expression level, suggesting its biological function of energy transduction. The GFP tagging Cgisp indicated a mitochondrial localization, further confirming its physiological function.

Laboratory diagnosis of gestational diabetes: An in silico investigation into the effects of pre-analytical processing on the diagnostic sensitivity and specificity of the oral glucose tolerance test.

PubMed

Mansell, Erin; Lunt, Helen; Docherty, Paul

2017-06-01

Delayed separation of red cells from plasma causes pre analytical glucose loss, which in turn results in an under-diagnosis of GDM (gestational diabetes) based on the OGTT (oral glucose tolerance test). In silico investigations may help laboratory decision making, when exploring pragmatic improvements to sample processing. Late pregnancy 0, 1 and 2h 75g OGTT values were obtained from two distinct populations of pregnant women: 1. Values derived from the HAPO (Hyperglycemia and Adverse Pregnancy Outcome) Study and 2. New Zealand women identified as at higher risk of GDM by their caregivers, undergoing OGTT during routine antenatal care. In both populations studied, in silico modelling focussed on the effects of pre-analytical delays in plasma separation, when using fluoride collection tubes. Using a model that 'batched' samples from the three OGTT collection times, diagnostic sensitivity was estimated as follows: 66.1% for HAPO research population and 48.4% for the 1305 women receiving routine antenatal care. If samples were not batched, but processed shortly after each blood sample was collected, then sensitivity increased to 81%. Exploration of a range of clinical and laboratory scenarios using in silico modelling, showed that delaying the processing of pregnancy OGTT samples, using batched sample collection into fluoride tubes, causes unacceptable loss of GDM diagnostic sensitivity across two distinct population groups. This modelling approach will hopefully provide information that helps with final decision making around improved laboratory processing techniques. Copyright © 2017 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

In Silico and in Vitro Modeling of Hepatocyte Drug Transport Processes: Importance of ABCC2 Expression Levels in the Disposition of CarboxydichloroflurosceinS⃞

PubMed Central

Howe, Katharine; Gibson, G. Gordon; Coleman, Tanya; Plant, Nick

2009-01-01

The impact of transport proteins in the disposition of chemicals is becoming increasingly evident. Alteration in disposition can cause altered pharmacokinetic and pharmacodynamic parameters, potentially leading to reduced efficacy or overt toxicity. We have developed a quantitative in silico model, based upon literature and experimentally derived data, to model the disposition of carboxydichlorofluroscein (CDF), a substrate for the SLCO1A/B and ABCC subfamilies of transporters. Kinetic parameters generated by the in silico model closely match both literature and experimentally derived kinetic values, allowing this model to be used for the examination of transporter action in primary rat hepatocytes. In particular, we show that the in silico model is suited to the rapid, accurate determination of Ki values, using 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK571) as a prototypical pan-ABCC inhibitor. In vitro-derived data are often used to predict in vivo response, and we have examined how differences in protein expression levels between these systems may affect chemical disposition. We show that ABCC2 and ABCC3 are overexpressed in sandwich culture hepatocytes by 3.5- and 2.3-fold, respectively, at the protein level. Correction for this in markedly different disposition of CDF, with the area under the concentration versus time curve and Cmax of intracellular CDF increasing by 365 and 160%, respectively. Finally, using kinetic simulations we show that ABCC2 represents a fragile node within this pathway, with alterations in ABCC2 having the most prominent effects on both the Km and Vmax through the pathway. This is the first demonstration of the utility of modeling approaches to estimate the impact of drug transport processes on chemical disposition. PMID:19022944

BchY-based degenerate primers target all types of anoxygenic photosynthetic bacteria in a single PCR.

PubMed

Yutin, Natalya; Suzuki, Marcelino T; Rosenberg, Mira; Rotem, Denisse; Madigan, Michael T; Süling, Jörg; Imhoff, Johannes F; Béjà, Oded

2009-12-01

To detect anoxygenic bacteria containing either type 1 or type 2 photosynthetic reaction centers in a single PCR, we designed a degenerate primer set based on the bchY gene. The new primers were validated in silico using the GenBank nucleotide database as well as by PCR on pure strains and environmental DNA.

Addressing Early Life Sensitivity Using Physiologically Based Pharmacokinetic Modeling and In Vitro to In Vivo Extrapolation

PubMed Central

Yoon, Miyoung; Clewell, Harvey J.

2016-01-01

Physiologically based pharmacokinetic (PBPK) modeling can provide an effective way to utilize in vitro and in silico based information in modern risk assessment for children and other potentially sensitive populations. In this review, we describe the process of in vitro to in vivo extrapolation (IVIVE) to develop PBPK models for a chemical in different ages in order to predict the target tissue exposure at the age of concern in humans. We present our on-going studies on pyrethroids as a proof of concept to guide the readers through the IVIVE steps using the metabolism data collected either from age-specific liver donors or expressed enzymes in conjunction with enzyme ontogeny information to provide age-appropriate metabolism parameters in the PBPK model in the rat and human, respectively. The approach we present here is readily applicable to not just to other pyrethroids, but also to other environmental chemicals and drugs. Establishment of an in vitro and in silico-based evaluation strategy in conjunction with relevant exposure information in humans is of great importance in risk assessment for potentially vulnerable populations like early ages where the necessary information for decision making is limited. PMID:26977255

Addressing Early Life Sensitivity Using Physiologically Based Pharmacokinetic Modeling and In Vitro to In Vivo Extrapolation.

PubMed

Yoon, Miyoung; Clewell, Harvey J

2016-01-01

Physiologically based pharmacokinetic (PBPK) modeling can provide an effective way to utilize in vitro and in silico based information in modern risk assessment for children and other potentially sensitive populations. In this review, we describe the process of in vitro to in vivo extrapolation (IVIVE) to develop PBPK models for a chemical in different ages in order to predict the target tissue exposure at the age of concern in humans. We present our on-going studies on pyrethroids as a proof of concept to guide the readers through the IVIVE steps using the metabolism data collected either from age-specific liver donors or expressed enzymes in conjunction with enzyme ontogeny information to provide age-appropriate metabolism parameters in the PBPK model in the rat and human, respectively. The approach we present here is readily applicable to not just to other pyrethroids, but also to other environmental chemicals and drugs. Establishment of an in vitro and in silico-based evaluation strategy in conjunction with relevant exposure information in humans is of great importance in risk assessment for potentially vulnerable populations like early ages where the necessary information for decision making is limited.

In Silico Analysis of Single Nucleotide Polymorphism (SNPs) in Human β-Globin Gene

PubMed Central

Alanazi, Mohammed; Abduljaleel, Zainularifeen; Khan, Wajahatullah; Warsy, Arjumand S.; Elrobh, Mohamed; Khan, Zahid; Amri, Abdullah Al; Bazzi, Mohammad D.

2011-01-01

Single amino acid substitutions in the globin chain are the most common forms of genetic variations that produce hemoglobinopathies- the most widespread inherited disorders worldwide. Several hemoglobinopathies result from homozygosity or compound heterozygosity to beta-globin (HBB) gene mutations, such as that producing sickle cell hemoglobin (HbS), HbC, HbD and HbE. Several of these mutations are deleterious and result in moderate to severe hemolytic anemia, with associated complications, requiring lifelong care and management. Even though many hemoglobinopathies result from single amino acid changes producing similar structural abnormalities, there are functional differences in the generated variants. Using in silico methods, we examined the genetic variations that can alter the expression and function of the HBB gene. Using a sequence homology-based Sorting Intolerant from Tolerant (SIFT) server we have searched for the SNPs, which showed that 200 (80%) non-synonymous polymorphism were found to be deleterious. The structure-based method via PolyPhen server indicated that 135 (40%) non-synonymous polymorphism may modify protein function and structure. The Pupa Suite software showed that the SNPs will have a phenotypic consequence on the structure and function of the altered protein. Structure analysis was performed on the key mutations that occur in the native protein coded by the HBB gene that causes hemoglobinopathies such as: HbC (E→K), HbD (E→Q), HbE (E→K) and HbS (E→V). Atomic Non-Local Environment Assessment (ANOLEA), Yet Another Scientific Artificial Reality Application (YASARA), CHARMM-GUI webserver for macromolecular dynamics and mechanics, and Normal Mode Analysis, Deformation and Refinement (NOMAD-Ref) of Gromacs server were used to perform molecular dynamics simulations and energy minimization calculations on β-Chain residue of the HBB gene before and after mutation. Furthermore, in the native and altered protein models, amino acid residues were determined and secondary structures were observed for solvent accessibility to confirm the protein stability. The functional study in this investigation may be a good model for additional future studies. PMID:22028795

LipidMatch: an automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data.

PubMed

Koelmel, Jeremy P; Kroeger, Nicholas M; Ulmer, Candice Z; Bowden, John A; Patterson, Rainey E; Cochran, Jason A; Beecher, Christopher W W; Garrett, Timothy J; Yost, Richard A

2017-07-10

Lipids are ubiquitous and serve numerous biological functions; thus lipids have been shown to have great potential as candidates for elucidating biomarkers and pathway perturbations associated with disease. Methods expanding coverage of the lipidome increase the likelihood of biomarker discovery and could lead to more comprehensive understanding of disease etiology. We introduce LipidMatch, an R-based tool for lipid identification for liquid chromatography tandem mass spectrometry workflows. LipidMatch currently has over 250,000 lipid species spanning 56 lipid types contained in in silico fragmentation libraries. Unique fragmentation libraries, compared to other open source software, include oxidized lipids, bile acids, sphingosines, and previously uncharacterized adducts, including ammoniated cardiolipins. LipidMatch uses rule-based identification. For each lipid type, the user can select which fragments must be observed for identification. Rule-based identification allows for correct annotation of lipids based on the fragments observed, unlike typical identification based solely on spectral similarity scores, where over-reporting structural details that are not conferred by fragmentation data is common. Another unique feature of LipidMatch is ranking lipid identifications for a given feature by the sum of fragment intensities. For each lipid candidate, the intensities of experimental fragments with exact mass matches to expected in silico fragments are summed. The lipid identifications with the greatest summed intensity using this ranking algorithm were comparable to other lipid identification software annotations, MS-DIAL and Greazy. For example, for features with identifications from all 3 software, 92% of LipidMatch identifications by fatty acyl constituents were corroborated by at least one other software in positive mode and 98% in negative ion mode. LipidMatch allows users to annotate lipids across a wide range of high resolution tandem mass spectrometry experiments, including imaging experiments, direct infusion experiments, and experiments employing liquid chromatography. LipidMatch leverages the most extensive in silico fragmentation libraries of freely available software. When integrated into a larger lipidomics workflow, LipidMatch may increase the probability of finding lipid-based biomarkers and determining etiology of disease by covering a greater portion of the lipidome and using annotation which does not over-report biologically relevant structural details of identified lipid molecules.

Differential Expression of In Vivo and In Vitro Protein Profile of Outer Membrane of Acidovorax avenae Subsp. avenae

PubMed Central

Qiu, Hui; Li, Bin; Jabeen, Amara; Li, Liping; Liu, He; Kube, Michael; Xie, Guanlin; Wang, Yanli; Sun, Guochang

2012-01-01

Outer membrane (OM) proteins play a significant role in bacterial pathogenesis. In this work, we examined and compared the expression of the OM proteins of the rice pathogen Acidovorax avenae subsp. avenae strain RS-1, a Gram-negative bacterium, both in an in vitro culture medium and in vivo rice plants. Global proteomic profiling of A. avenae subsp. avenae strain RS-1 comparing in vivo and in vitro conditions revealed the differential expression of proteins affecting the survival and pathogenicity of the rice pathogen in host plants. The shotgun proteomics analysis of OM proteins resulted in the identification of 97 proteins in vitro and 62 proteins in vivo by mass spectrometry. Among these OM proteins, there is a high number of porins, TonB-dependent receptors, lipoproteins of the NodT family, ABC transporters, flagellins, and proteins of unknown function expressed under both conditions. However, the major proteins such as phospholipase and OmpA domain containing proteins were expressed in vitro, while the proteins such as the surface anchored protein F, ATP-dependent Clp protease, OmpA and MotB domain containing proteins were expressed in vivo. This may indicate that these in vivo OM proteins have roles in the pathogenicity of A. avenae subsp. avenae strain RS-1. In addition, the LC-MS/MS identification of OmpA and MotB validated the in silico prediction of the existance of Type VI secretion system core components. To the best of our knowledge, this is the first study to reveal the in vitro and in vivo protein profiles, in combination with LC-MS/MS mass spectra, in silico OM proteome and in silico genome wide analysis, of pathogenicity or plant host required proteins of a plant pathogenic bacterium. PMID:23166741

Identification of IDUA and WNT16 Phosphorylation-Related Non-Synonymous Polymorphisms for Bone Mineral Density in Meta-Analyses of Genome-Wide Association Studies

PubMed Central

Niu, Tianhua; Liu, Ning; Yu, Xun; Zhao, Ming; Choi, Hyung Jin; Leo, Paul J.; Brown, Matthew A.; Zhang, Lei; Pei, Yu-Fang; Shen, Hui; He, Hao; Fu, Xiaoying; Lu, Shan; Chen, Xiang-Ding; Tan, Li-Jun; Yang, Tie-Lin; Guo, Yan; Cho, Nam H.; Shen, Jie; Guo, Yan-Fang; Nicholson, Geoffrey C.; Prince, Richard L.; Eisman, John A.; Jones, Graeme; Sambrook, Philip N.; Tian, Qing; Zhu, Xue-Zhen; Papasian, Christopher J.; Duncan, Emma L.; Uitterlinden, André G.; Shin, Chan Soo; Xiang, Shuanglin; Deng, Hong-Wen

2016-01-01

Protein phosphorylation regulates a wide variety of cellular processes. Thus, we hypothesize that single nucleotide polymorphisms (SNPs) that may modulate protein phosphorylation could affect osteoporosis risk. Based on a previous conventional genome-wide association (GWA) study, we conducted a three-stage meta-analysis targeting phosphorylation-related SNPs (phosSNPs) for femoral neck (FN)-, total hip (HIP)-, and Lumbar Spine (LS)-BMD phenotypes. In stage 1, 9,593 phosSNPs were meta-analyzed in 11,140 individuals of various ancestries. Genome-wide significance (GWS) and suggestive significance were defined by α = 5.21×10−6 (0.05/9,593) and 1.00×10−4, respectively. In stage 2, 9 stage 1-discovered phosSNPs (based on α = 1.00×10−4) were in silico meta-analyzed in Dutch, Korean, and Australian cohorts. In stage 3, four phosSNPs that replicated in stage 2 (based on α = 5.56×10−3, 0.05/9) were de novo genotyped in two independent cohorts. IDUA rs3755955 and rs6831280, and WNT16 rs2707466 were associated with BMD phenotypes in each respective stage, and in 3 stages combined, achieving GWS for both FN-BMD (P-value = 8.36×10−10, 5.26×10−10, and 3.01×10−10, respectively) and HIP-BMD (P-value = 3.26×10−6, 1.97×10−6, and 1.63×10−12, respectively). Although in vitro studies demonstrated no differences in expressions of wild-type and mutant forms of IDUA and WNT16B proteins, in silico analysis predicts that WNT16 rs2707466 directly abolishes a phosphorylation site, which could cause a deleterious effect on WNT16 protein, and that IDUA phosSNPs rs3755955 and rs6831280 could exert indirect effects on nearby phosphorylation sites. Further studies will be required to determine the detailed and specific molecular effects of these BMD-associated non-synonymous variants. PMID:26256109

UNCLES: method for the identification of genes differentially consistently co-expressed in a specific subset of datasets.

PubMed

Abu-Jamous, Basel; Fa, Rui; Roberts, David J; Nandi, Asoke K

2015-06-04

Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.

Large scale rigidity-based flexibility analysis of biomolecules

PubMed Central

Streinu, Ileana

2016-01-01

KINematics And RIgidity (KINARI) is an on-going project for in silico flexibility analysis of proteins. The new version of the software, Kinari-2, extends the functionality of our free web server KinariWeb, incorporates advanced web technologies, emphasizes the reproducibility of its experiments, and makes substantially improved tools available to the user. It is designed specifically for large scale experiments, in particular, for (a) very large molecules, including bioassemblies with high degree of symmetry such as viruses and crystals, (b) large collections of related biomolecules, such as those obtained through simulated dilutions, mutations, or conformational changes from various types of dynamics simulations, and (c) is intended to work as seemlessly as possible on the large, idiosyncratic, publicly available repository of biomolecules, the Protein Data Bank. We describe the system design, along with the main data processing, computational, mathematical, and validation challenges underlying this phase of the KINARI project. PMID:26958583

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).

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides.

PubMed

Agyei, Dominic; Tsopmo, Apollinaire; Udenigwe, Chibuike C

2018-06-01

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.

Modeling Emergence in Neuroprotective Regulatory Networks

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

Sanfilippo, Antonio P.; Haack, Jereme N.; McDermott, Jason E.

2013-01-05

The use of predictive modeling in the analysis of gene expression data can greatly accelerate the pace of scientific discovery in biomedical research by enabling in silico experimentation to test disease triggers and potential drug therapies. Techniques that focus on modeling emergence, such as agent-based modeling and multi-agent simulations, are of particular interest as they support the discovery of pathways that may have never been observed in the past. Thus far, these techniques have been primarily applied at the multi-cellular level, or have focused on signaling and metabolic networks. We present an approach where emergence modeling is extended to regulatorymore » networks and demonstrate its application to the discovery of neuroprotective pathways. An initial evaluation of the approach indicates that emergence modeling provides novel insights for the analysis of regulatory networks that can advance the discovery of acute treatments for stroke and other diseases.« less

Sweetness prediction of natural compounds.

PubMed

Chéron, Jean-Baptiste; Casciuc, Iuri; Golebiowski, Jérôme; Antonczak, Serge; Fiorucci, Sébastien

2017-04-15

Based on the most exhaustive database of sweeteners with known sweetness values, a new quantitative structure-activity relationship model for sweetness prediction has been set up. Analysis of the physico-chemical properties of sweeteners in the database indicates that the structure of most potent sweeteners combines a hydrophobic scaffold functionalized by a limited number of hydrogen bond sites (less than 4 hydrogen bond donors and 10 acceptors), with a moderate molecular weight ranging from 350 to 450g·mol -1 . Prediction of sweetness, bitterness and toxicity properties of the largest database of natural compounds have been performed. In silico screening reveals that the majority of the predicted natural intense sweeteners comprise saponin or stevioside scaffolds. The model highlights that their sweetness potency is comparable to known natural sweeteners. The identified compounds provide a rational basis to initiate the design and chemosensory analysis of new low-calorie sweeteners. Copyright © 2016 Elsevier Ltd. All rights reserved.

Computational analysis and functional expression of ancestral copepod luciferase.

PubMed

Takenaka, Yasuhiro; Noda-Ogura, Akiko; Imanishi, Tadashi; Yamaguchi, Atsushi; Gojobori, Takashi; Shigeri, Yasushi

2013-10-10

We recently reported the cDNA sequences of 11 copepod luciferases from the superfamily Augaptiloidea in the order Calanoida. They were classified into two groups, Metridinidae and Heterorhabdidae/Lucicutiidae families, by phylogenetic analyses. To elucidate the evolutionary processes, we have now further isolated 12 copepod luciferases from Augaptiloidea species (Metridia asymmetrica, Metridia curticauda, Pleuromamma scutullata, Pleuromamma xiphias, Lucicutia ovaliformis and Heterorhabdus tanneri). Codon-based synonymous/nonsynonymous tests of positive selection for 25 identified copepod luciferases suggested that positive Darwinian selection operated in the evolution of Heterorhabdidae luciferases, whereas two types of Metridinidae luciferases had diversified via neutral mechanism. By in silico analysis of the decoded amino acid sequences of 25 copepod luciferases, we inferred two protein sequences as ancestral copepod luciferases. They were expressed in HEK293 cells where they exhibited notable luciferase activity both in intracellular lysates and cultured media, indicating that the luciferase activity was established before evolutionary diversification of these copepod species. © 2013.

Human health and the environment: Predicting plasma protein binding and metabolic clearance rates of environmentally relevant chemicals.

EPA Science Inventory

In silico methods provide a rapid, inexpensive means of screening a wide array of environmentally relevant pollutants, pesticides, fungicides and consumer products for further toxicity testing. Physiologically based pharmacokinetic (PBPK) models bridge the gap between in vitro as...

In Silico Prediction of Toxicokinetic Parameters for Environmentally Relevant Chemicals for Risk-Based Prioritization

EPA Science Inventory

Toxicokinetic (TK) models can address an important component of chemical risk assessments by helping bridge the gap between chemical exposure and measured toxicity endpoints. The metabolic clearance rate (CLint) and fraction of a chemical unbound by plasma proteins (Fub) are crit...

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.

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.

Unravelling the effects of mechanical physiological conditioning on cardiac adipose tissue-derived progenitor cells in vitro and in silico.

PubMed

Llucià-Valldeperas, Aida; Bragós, Ramon; Soler-Botija, Carolina; Roura, Santiago; Gálvez-Montón, Carolina; Prat-Vidal, Cristina; Perea-Gil, Isaac; Bayes-Genis, Antoni

2018-01-11

Mechanical conditioning is incompletely characterized for stimulating therapeutic cells within the physiological range. We sought to unravel the mechanism of action underlying mechanical conditioning of adipose tissue-derived progenitor cells (ATDPCs), both in vitro and in silico. Cardiac ATDPCs, grown on 3 different patterned surfaces, were mechanically stretched for 7 days at 1 Hz. A custom-designed, magnet-based, mechanical stimulator device was developed to apply ~10% mechanical stretching to monolayer cell cultures. Gene and protein analyses were performed for each cell type and condition. Cell supernatants were also collected to analyze secreted proteins and construct an artificial neural network. Gene and protein modulations were different for each surface pattern. After mechanostimulation, cardiac ATDPCs increased the expression of structural genes and there was a rising trend on cardiac transcription factors. Finally, secretome analyses revealed upregulation of proteins associated with both myocardial infarction and cardiac regeneration, such as regulators of the immune response, angiogenesis or cell adhesion. To conclude, mechanical conditioning of cardiac ATDPCs enhanced the expression of early and late cardiac genes in vitro. Additionally, in silico analyses of secreted proteins showed that mechanical stimulation of cardiac ATDPCs was highly associated with myocardial infarction and repair.

Animal and in silico models for the study of sarcomeric cardiomyopathies

PubMed Central

Duncker, Dirk J.; Bakkers, Jeroen; Brundel, Bianca J.; Robbins, Jeff; Tardiff, Jil C.; Carrier, Lucie

2015-01-01

Over the past decade, our understanding of cardiomyopathies has improved dramatically, due to improvements in screening and detection of gene defects in the human genome as well as a variety of novel animal models (mouse, zebrafish, and drosophila) and in silico computational models. These novel experimental tools have created a platform that is highly complementary to the naturally occurring cardiomyopathies in cats and dogs that had been available for some time. A fully integrative approach, which incorporates all these modalities, is likely required for significant steps forward in understanding the molecular underpinnings and pathogenesis of cardiomyopathies. Finally, novel technologies, including CRISPR/Cas9, which have already been proved to work in zebrafish, are currently being employed to engineer sarcomeric cardiomyopathy in larger animals, including pigs and non-human primates. In the mouse, the increased speed with which these techniques can be employed to engineer precise ‘knock-in’ models that previously took years to make via multiple rounds of homologous recombination-based gene targeting promises multiple and precise models of human cardiac disease for future study. Such novel genetically engineered animal models recapitulating human sarcomeric protein defects will help bridging the gap to translate therapeutic targets from small animal and in silico models to the human patient with sarcomeric cardiomyopathy. PMID:25600962

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

Understanding the antiangiogenic effect of metronomic chemotherapy through a simple mathematical model

NASA Astrophysics Data System (ADS)

Rodrigues, Diego S.; Mancera, Paulo F. A.; Pinho, Suani T. R.

2016-12-01

Despite the current and increasingly successful fight against cancer, there are some important questions concerning the efficiency of its treatment - in particular, the design of oncology chemotherapy protocols. Seeking efficiency, schedules based on more frequent, low-doses of drugs, known as metronomic chemotherapy, have been proposed as an alternative to the classical standard protocol of chemotherapy administration. The in silico approach may be very useful for providing a comparative analysis of these two kinds of protocols. In so doing, we found that metronomic schedules are more effective in eliminating tumour cells mainly due to their chemotherapeutic action on endothelial cells and that more frequent, low drug doses also entail outcomes in which the survival time of patient is increased.

Reconstruction of metabolic networks from high-throughput metabolite profiling data: in silico analysis of red blood cell metabolism.

PubMed

Nemenman, Ilya; Escola, G Sean; Hlavacek, William S; Unkefer, Pat J; Unkefer, Clifford J; Wall, Michael E

2007-12-01

We investigate the ability of algorithms developed for reverse engineering of transcriptional regulatory networks to reconstruct metabolic networks from high-throughput metabolite profiling data. For benchmarking purposes, we generate synthetic metabolic profiles based on a well-established model for red blood cell metabolism. A variety of data sets are generated, accounting for different properties of real metabolic networks, such as experimental noise, metabolite correlations, and temporal dynamics. These data sets are made available online. We use ARACNE, a mainstream algorithm for reverse engineering of transcriptional regulatory networks from gene expression data, to predict metabolic interactions from these data sets. We find that the performance of ARACNE on metabolic data is comparable to that on gene expression data.

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

Pitfalls in genetic analysis of pheochromocytomas/paragangliomas-case report.

PubMed

Canu, Letizia; Rapizzi, Elena; Zampetti, Benedetta; Fucci, Rossella; Nesi, Gabriella; Richter, Susan; Qin, Nan; Giachè, Valentino; Bergamini, Carlo; Parenti, Gabriele; Valeri, Andrea; Ercolino, Tonino; Eisenhofer, Graeme; Mannelli, Massimo

2014-07-01

About 35% of patients with pheochromocytoma/paraganglioma carry a germline mutation in one of the 10 main susceptibility genes. The recent introduction of next-generation sequencing will allow the analysis of all these genes in one run. When positive, the analysis is generally unequivocal due to the association between a germline mutation and a concordant clinical presentation or positive family history. When genetic analysis reveals a novel mutation with no clinical correlates, particularly in the presence of a missense variant, the question arises whether the mutation is pathogenic or a rare polymorphism. We report the case of a 35-year-old patient operated for a pheochromocytoma who turned out to be a carrier of a novel SDHD (succinate dehydrogenase subunit D) missense mutation. With no positive family history or clinical correlates, we decided to perform additional analyses to test the clinical significance of the mutation. We performed in silico analysis, tissue loss of heterozygosity analysis, immunohistochemistry, Western blot analysis, SDH enzymatic assay, and measurement of the succinate/fumarate concentration ratio in the tumor tissue by tandem mass spectrometry. Although the in silico analysis gave contradictory results according to the different methods, all the other tests demonstrated that the SDH complex was conserved and normally active. We therefore came to the conclusion that the variant was a nonpathogenic polymorphism. Advancements in technology facilitate genetic analysis of patients with pheochromocytoma but also offer new challenges to the clinician who, in some cases, needs clinical correlates and/or functional tests to give significance to the results of the genetic assay.

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

Genetic Variants from Lipid-Related Pathways and Risk for Incident Myocardial Infarction

PubMed Central

Song, Ci; Pedersen, Nancy L.; Reynolds, Chandra A.; Sabater-Lleal, Maria; Kanoni, Stavroula; Willenborg, Christina; Syvänen, Ann-Christine; Watkins, Hugh; Hamsten, Anders; Prince, Jonathan A.; Ingelsson, Erik

2013-01-01

Background Circulating lipids levels, as well as several familial lipid metabolism disorders, are strongly associated with initiation and progression of atherosclerosis and incidence of myocardial infarction (MI). Objectives We hypothesized that genetic variants associated with circulating lipid levels would also be associated with MI incidence, and have tested this in three independent samples. Setting and Subjects Using age- and sex-adjusted additive genetic models, we analyzed 554 single nucleotide polymorphisms (SNPs) in 41 candidate gene regions proposed to be involved in lipid-related pathways potentially predisposing to incidence of MI in 2,602 participants of the Swedish Twin Register (STR; 57% women). All associations with nominal P<0.01 were further investigated in the Uppsala Longitudinal Study of Adult Men (ULSAM; N = 1,142). Results In the present study, we report associations of lipid-related SNPs with incident MI in two community-based longitudinal studies with in silico replication in a meta-analysis of genome-wide association studies. Overall, there were 9 SNPs in STR with nominal P-value <0.01 that were successfully genotyped in ULSAM. rs4149313 located in ABCA1 was associated with MI incidence in both longitudinal study samples with nominal significance (hazard ratio, 1.36 and 1.40; P-value, 0.004 and 0.015 in STR and ULSAM, respectively). In silico replication supported the association of rs4149313 with coronary artery disease in an independent meta-analysis including 173,975 individuals of European descent from the CARDIoGRAMplusC4D consortium (odds ratio, 1.03; P-value, 0.048). Conclusions rs4149313 is one of the few amino acid changing variants in ABCA1 known to associate with reduced cholesterol efflux. Our results are suggestive of a weak association between this variant and the development of atherosclerosis and MI. PMID:23555974

Novel in silico multivariate mapping of intrinsic and anticorrelated connectivity to neurocognitive functional maps supports the maturational hypothesis of ADHD.

PubMed

de Lacy, Nina; Kodish, Ian; Rachakonda, Srinivas; Calhoun, Vince D

2018-04-22

From childhood to adolescence, strengthened coupling in frontal, striatal and parieto-temporal regions associated with cognitive control, and increased anticorrelation between task-positive and task-negative circuits, subserve the reshaping of behavior. ADHD is a common condition peaking in adolescence and regressing in adulthood, with a wide variety of cognitive control deficits. Alternate hypotheses of ADHD emphasize lagging circuitry refinement versus categorical differences in network function. However, quantifying the individual circuit contributions to behavioral findings, and relative roles of maturational versus categorical effects, is challenging in vivo or in meta-analyses using task-based paradigms within the same pipeline, given the multiplicity of neurobehavioral functions implicated. To address this, we analyzed 46 positively-correlated and anticorrelated circuits in a multivariate model in resting-state data from 504 age- and gender-matched youth, and created a novel in silico method to map individual quantified effects to reverse inference maps of 8 neurocognitive functions consistently implicated in ADHD, as well as dopamine and hyperactivity. We identified only age- and gender-related effects in intrinsic connectivity, and found that maturational refinement of circuits in youth with ADHD occupied 3-10x more brain locations than in typical development, with the footprint, effect size and contribution of individual circuits varying substantially. Our analysis supports the maturational hypothesis of ADHD, suggesting lagging connectivity reorganization within specific subnetworks of fronto-parietal control, ventral attention, cingulo-opercular, temporo-limbic and cerebellar sub-networks contribute across neurocognitive findings present in this complex condition. We present the first analysis of anti-correlated connectivity in ADHD and suggest new directions for exploring residual and non-responsive symptoms. © 2018 Wiley Periodicals, Inc.

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

JRC GMO-Amplicons: a collection of nucleic acid sequences related to genetically modified organisms

PubMed Central

Petrillo, Mauro; Angers-Loustau, Alexandre; Henriksson, Peter; Bonfini, Laura; Patak, Alex; Kreysa, Joachim

2015-01-01

The DNA target sequence is the key element in designing detection methods for genetically modified organisms (GMOs). Unfortunately this information is frequently lacking, especially for unauthorized GMOs. In addition, patent sequences are generally poorly annotated, buried in complex and extensive documentation and hard to link to the corresponding GM event. Here, we present the JRC GMO-Amplicons, a database of amplicons collected by screening public nucleotide sequence databanks by in silico determination of PCR amplification with reference methods for GMO analysis. The European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) provides these methods in the GMOMETHODS database to support enforcement of EU legislation and GM food/feed control. The JRC GMO-Amplicons database is composed of more than 240 000 amplicons, which can be easily accessed and screened through a web interface. To our knowledge, this is the first attempt at pooling and collecting publicly available sequences related to GMOs in food and feed. The JRC GMO-Amplicons supports control laboratories in the design and assessment of GMO methods, providing inter-alia in silico prediction of primers specificity and GM targets coverage. The new tool can assist the laboratories in the analysis of complex issues, such as the detection and identification of unauthorized GMOs. Notably, the JRC GMO-Amplicons database allows the retrieval and characterization of GMO-related sequences included in patents documentation. Finally, it can help annotating poorly described GM sequences and identifying new relevant GMO-related sequences in public databases. The JRC GMO-Amplicons is freely accessible through a web-based portal that is hosted on the EU-RL GMFF website. Database URL: http://gmo-crl.jrc.ec.europa.eu/jrcgmoamplicons/ PMID:26424080

JRC GMO-Amplicons: a collection of nucleic acid sequences related to genetically modified organisms.

PubMed

Petrillo, Mauro; Angers-Loustau, Alexandre; Henriksson, Peter; Bonfini, Laura; Patak, Alex; Kreysa, Joachim

2015-01-01

The DNA target sequence is the key element in designing detection methods for genetically modified organisms (GMOs). Unfortunately this information is frequently lacking, especially for unauthorized GMOs. In addition, patent sequences are generally poorly annotated, buried in complex and extensive documentation and hard to link to the corresponding GM event. Here, we present the JRC GMO-Amplicons, a database of amplicons collected by screening public nucleotide sequence databanks by in silico determination of PCR amplification with reference methods for GMO analysis. The European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) provides these methods in the GMOMETHODS database to support enforcement of EU legislation and GM food/feed control. The JRC GMO-Amplicons database is composed of more than 240 000 amplicons, which can be easily accessed and screened through a web interface. To our knowledge, this is the first attempt at pooling and collecting publicly available sequences related to GMOs in food and feed. The JRC GMO-Amplicons supports control laboratories in the design and assessment of GMO methods, providing inter-alia in silico prediction of primers specificity and GM targets coverage. The new tool can assist the laboratories in the analysis of complex issues, such as the detection and identification of unauthorized GMOs. Notably, the JRC GMO-Amplicons database allows the retrieval and characterization of GMO-related sequences included in patents documentation. Finally, it can help annotating poorly described GM sequences and identifying new relevant GMO-related sequences in public databases. The JRC GMO-Amplicons is freely accessible through a web-based portal that is hosted on the EU-RL GMFF website. Database URL: http://gmo-crl.jrc.ec.europa.eu/jrcgmoamplicons/. © The Author(s) 2015. Published by Oxford University Press.

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.

UBioLab: a web-LABoratory for Ubiquitous in-silico experiments.

PubMed

Bartocci, E; Di Berardini, M R; Merelli, E; Vito, L

2012-03-01

The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction. The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) "type" of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.

Binding affinity toward human prion protein of some anti-prion compounds - Assessment based on QSAR modeling, molecular docking and non-parametric ranking.

PubMed

Kovačević, Strahinja; Karadžić, Milica; Podunavac-Kuzmanović, Sanja; Jevrić, Lidija

2018-01-01

The present study is based on the quantitative structure-activity relationship (QSAR) analysis of binding affinity toward human prion protein (huPrP C ) of quinacrine, pyridine dicarbonitrile, diphenylthiazole and diphenyloxazole analogs applying different linear and non-linear chemometric regression techniques, including univariate linear regression, multiple linear regression, partial least squares regression and artificial neural networks. The QSAR analysis distinguished molecular lipophilicity as an important factor that contributes to the binding affinity. Principal component analysis was used in order to reveal similarities or dissimilarities among the studied compounds. The analysis of in silico absorption, distribution, metabolism, excretion and toxicity (ADMET) parameters was conducted. The ranking of the studied analogs on the basis of their ADMET parameters was done applying the sum of ranking differences, as a relatively new chemometric method. The main aim of the study was to reveal the most important molecular features whose changes lead to the changes in the binding affinities of the studied compounds. Another point of view on the binding affinity of the most promising analogs was established by application of molecular docking analysis. The results of the molecular docking were proven to be in agreement with the experimental outcome. Copyright © 2017 Elsevier B.V. All rights reserved.

Molecular identification of aiiA homologous gene from endophytic Enterobacter species and in silico analysis of putative tertiary structure of AHL-lactonase.

PubMed

Rajesh, P S; Rai, V Ravishankar

2014-01-03

The aiiA homologous gene known to encode AHL- lactonase enzyme which hydrolyze the N-acylhomoserine lactone (AHL) quorum sensing signaling molecules produced by Gram negative bacteria. In this study, the degradation of AHL molecules was determined by cell-free lysate of endophytic Enterobacter species. The percentage of quorum quenching was confirmed and quantified by HPLC method (p<0.0001). Amplification and sequence BLAST analysis showed the presence of aiiA homologous gene in endophytic Enterobacter asburiae VT65, Enterobacter aerogenes VT66 and Enterobacter ludwigii VT70 strains. Sequence alignment analysis revealed the presence of two zinc binding sites, "HXHXDH" motif as well as tyrosine residue at the position 194. Based on known template available at Swiss-Model, putative tertiary structure of AHL-lactonase was constructed. The result showed that novel endophytic strains of Enterobacter genera encode the novel aiiA homologous gene and its structural importance for future study. Copyright © 2013 Elsevier Inc. All rights reserved.

Proteomic analysis of sweet algerian apricot kernels (Prunus armeniaca L.) by combinatorial peptide ligand libraries and LC-MS/MS.

PubMed

Ghorab, Hamida; Lammi, Carmen; Arnoldi, Anna; Kabouche, Zahia; Aiello, Gilda

2018-01-15

An investigation on the proteome of the sweet kernel of apricot, based on equalisation with combinatorial peptide ligand libraries (CPLLs), SDS-PAGE, nLC-ESI-MS/MS, and database search, permitted identifying 175 proteins. Gene ontology analysis indicated that their main molecular functions are in nucleotide binding (20.9%), hydrolase activities (10.6%), kinase activities (7%), and catalytic activity (5.6%). A protein-protein association network analysis using STRING software permitted to build an interactomic map of all detected proteins, characterised by 34 interactions. In order to forecast the potential health benefits deriving from the consumption of these proteins, the two most abundant, i.e. Prunin 1 and 2, were enzymatically digested in silico predicting 10 and 14 peptides, respectively. Searching their sequences in the database BIOPEP, it was possible to suggest a variety of bioactivities, including dipeptidyl peptidase-IV (DPP-IV) and angiotensin converting enzyme I (ACE) inhibition, glucose uptake stimulation and antioxidant properties. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

Molecular rationale delineating the role of lycopene as a potent HMG-CoA reductase inhibitor: in vitro and in silico study.

PubMed

Alvi, Sahir Sultan; Iqbal, Danish; Ahmad, Saheem; Khan, M Salman

2016-09-01

This study initially aimed to depict the molecular rationale evolving the role of lycopene in inhibiting the enzymatic activity of β-hydroxy-β-methylglutaryl-CoA (HMG-CoA) reductase via in vitro and in silico analysis. Our results illustrated that lycopene exhibited strong HMG-CoA reductase inhibitory activity (IC50 value of 36 ng/ml) quite better than pravastatin (IC50 = 42 ng/ml) and strong DPPH free radical scavenging activity (IC50 value = 4.57 ± 0.23 μg/ml) as compared to ascorbic acid (IC50 value = 9.82 ± 0.42 μg/ml). Moreover, the Ki value of lycopene (36 ng/ml) depicted via Dixon plot was well concurred with an IC50 value of 36 ± 1.8 ng/ml. Moreover, molecular informatics study showed that lycopene exhibited binding energy of -5.62 kcal/mol indicating high affinity for HMG-CoA reductase than HMG-CoA (ΔG: -5.34 kcal/mol). Thus, in silico data clearly demonstrate and support the in vitro results that lycopene competitively inhibit HMG-CoA reductase activity by binding at the hydrophobic portion of HMG-CoA reductase.

Model-Based Phenotypic Signatures Governing the Dynamics of the Stem and Semi-differentiated Cell Populations in Dysplastic Colonic Crypts.

PubMed

Nikolov, Svetoslav; Santos, Guido; Wolkenhauer, Olaf; Vera, Julio

2018-02-01

Mathematical modeling of cell differentiated in colonic crypts can contribute to a better understanding of basic mechanisms underlying colonic tissue organization, but also its deregulation during carcinogenesis and tumor progression. Here, we combined bifurcation analysis to assess the effect that time delay has in the complex interplay of stem cells and semi-differentiated cells at the niche of colonic crypts, and systematic model perturbation and simulation to find model-based phenotypes linked to cancer progression. The models suggest that stem cell and semi-differentiated cell population dynamics in colonic crypts can display chaotic behavior. In addition, we found that clinical profiling of colorectal cancer correlates with the in silico phenotypes proposed by the mathematical model. Further, potential therapeutic targets for chemotherapy resistant phenotypes are proposed, which in any case will require experimental validation.

Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

PubMed Central

Bacik, Karol A.; Schaub, Michael T.; Billeh, Yazan N.; Barahona, Mauricio

2016-01-01

We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios. PMID:27494178

ADME-Space: a new tool for medicinal chemists to explore ADME properties.

PubMed

Bocci, Giovanni; Carosati, Emanuele; Vayer, Philippe; Arrault, Alban; Lozano, Sylvain; Cruciani, Gabriele

2017-07-25

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

The Virtual Liver Project: Modeling Tissue Response To Chemicals Through Multiscale Simulation

EPA Science Inventory

The US EPA Virtual Liver Project is aimed at simulating the risk of toxic effects from environmental chemicals in silico. The computational systems model of organ injury due to chronic chemical exposure is based on: (i) the dynamics of perturbed molecular pathways, (ii) their lin...

In silico study of in vitro GPCR assays by QSAR modeling

EPA Science Inventory

The U.S. EPA is screening thousands of chemicals of environmental interest in hundreds of in vitro high-throughput screening (HTS) assays (the ToxCast program). One goal is to prioritize chemicals for more detailed analyses based on activity in molecular initiating events (MIE) o...

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...

EDC testing in the future: Exploring roles of pathway-based in silico, in vitro and in vivo methods

EPA Science Inventory

Many thoroughly validated, robust tests with both mammalian and non-mammalian models have been developed to identify chemicals with the potential to impact endocrine pathways associated with the hypothalamic-pituitary-gonadal (HPG) and thyroidal axes. In the US, for example, the...

In Silico Prediction of Toxicokinetic Parameters for Environmentally Relevant Chemicals with Application to Risk-Based Prioritization

EPA Science Inventory

Toxicokinetic (TK) models can help bridge the gap between chemical exposure and measured toxicity endpoints, thereby addressing an important component of chemical risk assessments. The fraction of a chemical unbound by plasma proteins (Fub) and metabolic clearance rate (CLint) ar...

A Survey on Faculty Perspectives on the Transition to a Biochemistry Course-Based Undergraduate Research Experience Laboratory

ERIC Educational Resources Information Center

Craig, Paul A.

2017-01-01

It will always remain a goal of an undergraduate biochemistry laboratory course to engage students hands-on in a wide range of biochemistry laboratory experiences. In 2006, our research group initiated a project for "in silico" prediction of enzyme function based only on the 3D coordinates of the more than 3800 proteins "of unknown…

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

Designing Isoform-selective Inhibitors Against Classical HDACs for Effective Anticancer Therapy: Insight and Perspectives from In Silico.

PubMed

Ganai, Shabir Ahmad

2018-01-01

Histone deacetylase inhibitors, the small molecules modulating the biological activity of histone deacetylases are emerging as potent chemotherapeutic agents. Despite their considerable therapeutic benefits in disease models, the lack of isoform specificity culminates in debilitating off target effects, raising serious concerns regarding their applicability. This emphasizes the pressing and unmet medical need of designing isoform selective inhibitors for safe and effective anticancer therapy. Keeping these grim facts in view, the current article sheds light on structural basis of off-targeting. Furthermore, the article discusses extensively the role of in silico strategies such as Molecular Docking, Molecular Dynamics Simulation and Energetically-optimized structure based pharmacophore approach in designing on-target inhibitors against classical HDACs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

In silico genome-wide identification and characterization of the glutathione S-transferase gene family in Vigna radiata.

PubMed

Vaish, Swati; Awasthi, Praveen; Tiwari, Siddharth; Tiwari, Shailesh Kumar; Gupta, Divya; Basantani, Mahesh Kumar

2018-05-01

Plant glutathione S-transferases (GSTs) are integral to normal plant metabolism and biotic and abiotic stress tolerance. The GST gene family has been characterized in diverse plant species using molecular biology and bioinformatics approaches. In the current study, in silico analysis identified 44 GSTs in Vigna radiata. Of the total 44 GSTs identified, chromosomal locations of 31 GSTs were confirmed. The pI value of GST proteins ranged from 5.10 to 9.40. The predicted molecular weights ranged from 13.12 to 50 kDa. Subcellular localization analysis revealed that all GSTs were predominantly localized in the cytoplasm. The active site amino acids were confirmed to be serine in tau, phi, theta, zeta, and TCHQD; cysteine in lambda, DHAR, and omega; and tyrosine in EF1G. The gene architecture conformed to the two-exon/one-intron and three-exon/two-intron organization in the case of tau and phi classes, respectively. MEME analysis identified 10 significantly conserved motifs with the width of 8-50 amino acids. The motifs identified were either specific to a specific GST class or were shared by multiple GST classes. The results of the current study will be of potential importance in the characterization of the GST gene family in V. radiata, an economically important leguminous crop.

Protein cleavage strategies for an improved analysis of the membrane proteome

PubMed Central

Fischer, Frank; Poetsch, Ansgar

2006-01-01

Background Membrane proteins still remain elusive in proteomic studies. This is in part due to the distribution of the amino acids lysine and arginine, which are less frequent in integral membrane proteins and almost absent in transmembrane helices. As these amino acids are cleavage targets for the commonly used protease trypsin, alternative cleavage conditions, which should improve membrane protein analysis, were tested by in silico digestion for the three organisms Saccharomyces cerevisiae, Halobacterium sp. NRC-1, and Corynebacterium glutamicum as hallmarks for eukaryotes, archea and eubacteria. Results For the membrane proteomes from all three analyzed organisms, we identified cleavage conditions that achieve better sequence and proteome coverage than trypsin. Greater improvement was obtained for bacteria than for yeast, which was attributed to differences in protein size and GRAVY. It was demonstrated for bacteriorhodopsin that the in silico predictions agree well with the experimental observations. Conclusion For all three examined organisms, it was found that a combination of chymotrypsin and staphylococcal peptidase I gave significantly better results than trypsin. As some of the improved cleavage conditions are not more elaborate than trypsin digestion and have been proven useful in practice, we suppose that the cleavage at both hydrophilic and hydrophobic amino acids should facilitate in general the analysis of membrane proteins for all organisms. PMID:16512920

Rational design of small-molecule stabilizers of spermine synthase dimer by virtual screening and free energy-based approach.

PubMed

Zhang, Zhe; Martiny, Virginie; Lagorce, David; Ikeguchi, Yoshihiko; Alexov, Emil; Miteva, Maria A

2014-01-01

Snyder-Robinson Syndrome (SRS) is a rare mental retardation disorder which is caused by the malfunctioning of an enzyme, the spermine synthase (SMS), which functions as a homo-dimer. The malfunctioning of SMS in SRS patients is associated with several identified missense mutations that occur away from the active site. This investigation deals with a particular SRS-causing mutation, the G56S mutation, which was shown computationally and experimentally to destabilize the SMS homo-dimer and thus to abolish SMS enzymatic activity. As a proof-of-concept, we explore the possibility to restore the enzymatic activity of the malfunctioning SMS mutant G56S by stabilizing the dimer through small molecule binding at the mutant homo-dimer interface. For this purpose, we designed an in silico protocol that couples virtual screening and a free binding energy-based approach to identify potential small-molecule binders on the destabilized G56S dimer, with the goal to stabilize it and thus to increase SMS G56S mutant activity. The protocol resulted in extensive list of plausible stabilizers, among which we selected and tested 51 compounds experimentally for their capability to increase SMS G56S mutant enzymatic activity. In silico analysis of the experimentally identified stabilizers suggested five distinctive chemical scaffolds. This investigation suggests that druggable pockets exist in the vicinity of the mutation sites at protein-protein interfaces which can be used to alter the disease-causing effects by small molecule binding. The identified chemical scaffolds are drug-like and can serve as original starting points for development of lead molecules to further rescue the disease-causing effects of the Snyder-Robinson syndrome for which no efficient treatment exists up to now.

Novel Non-Peptide Inhibitors against SmCL1 of Schistosoma mansoni: In Silico Elucidation, Implications and Evaluation via Knowledge Based Drug Discovery

PubMed Central

Zafar, Atif; Ahmad, Sabahuddin; Rizvi, Asim; Ahmad, Masood

2015-01-01

Schistosomiasis is a major endemic disease known for excessive mortality and morbidity in developing countries. Because praziquantel is the only drug available for its treatment, the risk of drug resistance emphasizes the need to discover new drugs for this disease. Cathepsin SmCL1 is the critical target for drug design due to its essential role in the digestion of host proteins for growth and development of Schistosoma mansoni. Inhibiting the function of SmCL1 could control the wide spread of infections caused by S. mansoni in humans. With this objective, a homology modeling approach was used to obtain theoretical three-dimensional (3D) structure of SmCL1. In order to find the potential inhibitors of SmCL1, a plethora of in silico techniques were employed to screen non-peptide inhibitors against SmCL1 via structure-based drug discovery protocol. Receiver operating characteristic (ROC) curve analysis and molecular dynamics (MD) simulation were performed on the results of docked protein-ligand complexes to identify top ranking molecules against the modelled 3D structure of SmCL1. MD simulation results suggest the phytochemical Simalikalactone-D as a potential lead against SmCL1, whose pharmacophore model may be useful for future screening of potential drug molecules. To conclude, this is the first report to discuss the virtual screening of non-peptide inhibitors against SmCL1 of S. mansoni, with significant therapeutic potential. Results presented herein provide a valuable contribution to identify the significant leads and further derivatize them to suitable drug candidates for antischistosomal therapy. PMID:25933436

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

In silico peptide-binding predictions of passerine MHC class I reveal similarities across distantly related species, suggesting convergence on the level of protein function.

PubMed

Follin, Elna; Karlsson, Maria; Lundegaard, Claus; Nielsen, Morten; Wallin, Stefan; Paulsson, Kajsa; Westerdahl, Helena

2013-04-01

The major histocompatibility complex (MHC) genes are the most polymorphic genes found in the vertebrate genome, and they encode proteins that play an essential role in the adaptive immune response. Many songbirds (passerines) have been shown to have a large number of transcribed MHC class I genes compared to most mammals. To elucidate the reason for this large number of genes, we compared 14 MHC class I alleles (α1-α3 domains), from great reed warbler, house sparrow and tree sparrow, via phylogenetic analysis, homology modelling and in silico peptide-binding predictions to investigate their functional and genetic relationships. We found more pronounced clustering of the MHC class I allomorphs (allele specific proteins) in regards to their function (peptide-binding specificities) compared to their genetic relationships (amino acid sequences), indicating that the high number of alleles is of functional significance. The MHC class I allomorphs from house sparrow and tree sparrow, species that diverged 10 million years ago (MYA), had overlapping peptide-binding specificities, and these similarities across species were also confirmed in phylogenetic analyses based on amino acid sequences. Notably, there were also overlapping peptide-binding specificities in the allomorphs from house sparrow and great reed warbler, although these species diverged 30 MYA. This overlap was not found in a tree based on amino acid sequences. Our interpretation is that convergent evolution on the level of the protein function, possibly driven by selection from shared pathogens, has resulted in allomorphs with similar peptide-binding repertoires, although trans-species evolution in combination with gene conversion cannot be ruled out.

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.

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.

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 analysis and experimental validation of azelastine hydrochloride (N4) targeting sodium taurocholate co-transporting polypeptide (NTCP) in HBV therapy.

PubMed

Fu, L-L; Liu, J; Chen, Y; Wang, F-T; Wen, X; Liu, H-Q; Wang, M-Y; Ouyang, L; Huang, J; Bao, J-K; Wei, Y-Q

2014-08-01

The aim of this study was to explore sodium taurocholate co-transporting polypeptide (NTCP) exerting its function with hepatitis B virus (HBV) and its targeted candidate compounds, in HBV therapy. Identification of NTCP as a novel HBV target for screening candidate small molecules, was used by phylogenetic analysis, network construction, molecular modelling, molecular docking and molecular dynamics (MD) simulation. In vitro virological examination, q-PCR, western blotting and cytotoxicity studies were used for validating efficacy of the candidate compound. We used the phylogenetic analysis of NTCP and constructed its protein-protein network. Also, we screened compounds from Drugbank and ZINC, among which five were validated for their authentication in HepG 2.2.15 cells. Then, we selected compound N4 (azelastine hydrochloride) as the most potent of them. This showed good inhibitory activity against HBsAg (IC50 = 7.5 μm) and HBeAg (IC50 = 3.7 μm), as well as high SI value (SI = 4.68). Further MD simulation results supported good interaction between compound N4 and NTCP. In silico analysis and experimental validation together demonstrated that compound N4 can target NTCP in HepG2.2.15 cells, which may shed light on exploring it as a potential anti-HBV drug. © 2014 John Wiley & Sons Ltd.

Molecular genetic analysis of macular corneal dystrophy patients from North India.

PubMed

Paliwal, Preeti; Sharma, Arundhati; Tandon, Radhika; Sharma, Namrata; Titiyal, Jeevan S; Sen, Seema; Vajpayee, Rasik B

2012-01-01

To identify underlying genetic defects in the carbohydrate sulfotransferase-6 (CHST6) gene in North Indian patients with macular corneal dystrophy (MCD). 30 clinically diagnosed MCD patients from 21 families and 50 healthy normal controls were recruited in the study. Detailed clinical evaluation in the patients was undertaken followed by histopathology and ultrastructural studies in corneal tissues. DNA from blood samples was amplified for the CHST6 coding and upstream region followed by direct sequencing and in silico analysis. We identified pathogenic mutations in 17 patients from 11 families. Of these 4 were novel (p.Ser54Tyr, p.Gln58Arg, p.Leu59His and p.Leu293Phe), 2 were previously reported (Arg93His and Glu274Lys) homozygous, 1 heterozygous stop codon (p.Trp123X) and 2 compound heterozygous (p.Arg93His + p.Arg97Pro; p.Leu22Arg + p.Gln58X) mutations. A missense single-nucleotide polymorphism was also identified in 11 patients. The novel mutations were conserved as shown by in silico analysis. Thirteen patients did not show any pathogenic CHST6 changes. This is the first report on molecular analysis of MCD in North Indian patients. All cases could not be explained by mutations in CHST6, suggesting that MCD may result from other changes in the regulatory elements of CHST6 or from genetic heterogeneity. Copyright © 2012 S. Karger AG, Basel.

Specific Transcriptome Changes Associated with Blood Pressure Reduction in Hypertensive Patients After Relaxation Response Training.

PubMed

Bhasin, Manoj K; Denninger, John W; Huffman, Jeff C; Joseph, Marie G; Niles, Halsey; Chad-Friedman, Emma; Goldman, Roberta; Buczynski-Kelley, Beverly; Mahoney, Barbara A; Fricchione, Gregory L; Dusek, Jeffery A; Benson, Herbert; Zusman, Randall M; Libermann, Towia A

2018-05-01

Mind-body practices that elicit the relaxation response (RR) have been demonstrated to reduce blood pressure (BP) in essential hypertension (HTN) and may be an adjunct to antihypertensive drug therapy. However, the molecular mechanisms by which the RR reduces BP remain undefined. Genomic determinants associated with responsiveness to an 8-week RR-based mind-body intervention for lowering HTN in 13 stage 1 hypertensive patients classified as BP responders and 11 as nonresponders were identified. Transcriptome analysis in peripheral blood mononuclear cells identified 1771 genes regulated by the RR in responders. Biological process- and pathway-based analysis of transcriptome data demonstrated enrichment in the following gene categories: immune regulatory pathways and metabolism (among downregulated genes); glucose metabolism, cardiovascular system development, and circadian rhythm (among upregulated genes). Further in silico estimation of cell abundance from the microarray data showed enrichment of the anti-inflammatory M2 subtype of macrophages in BP responders. Nuclear factor-κB, vascular endothelial growth factor, and insulin were critical molecules emerging from interactive network analysis. These findings provide the first insights into the molecular mechanisms that are associated with the beneficial effects of the RR on HTN.

Specific Transcriptome Changes Associated with Blood Pressure Reduction in Hypertensive Patients After Relaxation Response Training

PubMed Central

Bhasin, Manoj K.; Denninger, John W.; Huffman, Jeff C.; Joseph, Marie G.; Niles, Halsey; Chad-Friedman, Emma; Goldman, Roberta; Buczynski-Kelley, Beverly; Mahoney, Barbara A.; Fricchione, Gregory L.; Dusek, Jeffery A.; Benson, Herbert; Zusman, Randall M.

2018-01-01

Abstract Objective: Mind–body practices that elicit the relaxation response (RR) have been demonstrated to reduce blood pressure (BP) in essential hypertension (HTN) and may be an adjunct to antihypertensive drug therapy. However, the molecular mechanisms by which the RR reduces BP remain undefined. Design: Genomic determinants associated with responsiveness to an 8-week RR-based mind–body intervention for lowering HTN in 13 stage 1 hypertensive patients classified as BP responders and 11 as nonresponders were identified. Results: Transcriptome analysis in peripheral blood mononuclear cells identified 1771 genes regulated by the RR in responders. Biological process- and pathway-based analysis of transcriptome data demonstrated enrichment in the following gene categories: immune regulatory pathways and metabolism (among downregulated genes); glucose metabolism, cardiovascular system development, and circadian rhythm (among upregulated genes). Further in silico estimation of cell abundance from the microarray data showed enrichment of the anti-inflammatory M2 subtype of macrophages in BP responders. Nuclear factor-κB, vascular endothelial growth factor, and insulin were critical molecules emerging from interactive network analysis. Conclusions: These findings provide the first insights into the molecular mechanisms that are associated with the beneficial effects of the RR on HTN. PMID:29616846

Whole-genome comparative analysis of three phytopathogenic Xylella fastidiosa strains.

PubMed

Bhattacharyya, Anamitra; Stilwagen, Stephanie; Ivanova, Natalia; D'Souza, Mark; Bernal, Axel; Lykidis, Athanasios; Kapatral, Vinayak; Anderson, Iain; Larsen, Niels; Los, Tamara; Reznik, Gary; Selkov, Eugene; Walunas, Theresa L; Feil, Helene; Feil, William S; Purcell, Alexander; Lassez, Jean-Louis; Hawkins, Trevor L; Haselkorn, Robert; Overbeek, Ross; Predki, Paul F; Kyrpides, Nikos C

2002-09-17

Xylella fastidiosa (Xf) causes wilt disease in plants and is responsible for major economic and crop losses globally. Owing to the public importance of this phytopathogen we embarked on a comparative analysis of the complete genome of Xf pv citrus and the partial genomes of two recently sequenced strains of this species: Xf pv almond and Xf pv oleander, which cause leaf scorch in almond and oleander plants, respectively. We report a reanalysis of the previously sequenced Xf 9a5c (CVC, citrus) strain and the two "gapped" Xf genomes revealing ORFs encoding critical functions in pathogenicity and conjugative transfer. Second, a detailed whole-genome functional comparison was based on the three sequenced Xf strains, identifying the unique genes present in each strain, in addition to those shared between strains. Third, an "in silico" cellular reconstruction of these organisms was made, based on a comparison of their core functional subsystems that led to a characterization of their conjugative transfer machinery, identification of potential differences in their adhesion mechanisms, and highlighting of the absence of a classical quorum-sensing mechanism. This study demonstrates the effectiveness of comparative analysis strategies in the interpretation of genomes that are closely related.

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...

In-Silico Screening of Ligand Based Pharmacophore, Database Mining and Molecular Docking on 2, 5-Diaminopyrimidines Azapurines as Potential Inhibitors of Glycogen Synthase Kinase-3β.

PubMed

Mishra, Pooja; Kesar, Seema; Paliwal, Sarvesh K; Chauhan, Monika; Madan, Kirtika

2018-05-29

Glycogen synthase kinase-3β plays a significant role in the regulation of various pathological pathways relating to central nervous system (CNS). Dysregulation of Glycogen synthase kinase 3 (GSK-3) activity gives a rise to numerous neuroinflammation and neurodegenerative related disorders that affect the whole central nervous system. By the sequential application of in-silico tools, efforts have been attempted to design the novel GSK-3β inhibitors. Owing to the potential role of GSK-3β in nervous disorders, we have attempted to develop the quantitative four featured pharmacophore model comprising two hydrogen bond acceptors (HBA), one ring aromatic (RA), and one hydrophobe (HY), which were further affirmed by cost-function analysis, rm2 matrices, internal and external test set validation and Güner-Henry (GH) scoring analysis. Validated pharmacophoric model was used for virtual screening and out of 345 compounds, two potential virtual hits were finalized that were on the basis of fit value, estimated activity and Lipinski's violation. The chosen compounds were subjected to dock within the active site of GSK-3β Result: Four essential features, i.e., two hydrogen bond acceptors(HBA), one ring aromatic(RA), and one hydrophobe(HY), were subjected to build the pharmacophoric model and showed good correlation coefficient, RMSD and cost difference values of 0.91, 0.94 and 42.9 respectively and further model was validated employing cost-function analysis, rm2-matrices, internal and external test set prediction with r2 value of 0.77 and 0.84. Docked conformations showed potential interactions in between the features of the identified hits (NCI 4296, NCI 3034) and the amino acids present in the active site. In line with the overhead discussion, and through our stepwise computational approaches, we have identified novel, structurally diverse glycogen synthase kinase inhibitors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

NASA Astrophysics Data System (ADS)

Pandit, Amit; Sengupta, Sagnik; Krishnan, Mena Asha; Reddy, Ramesh B.; Sharma, Rajesh; Venkatesh, Chelvam

2018-05-01

Prostate Specific Membrane Antigen (PSMA) or Glutamate carboxypeptidase II (GCPII) has been identified as an important target in diagnosis and therapy of prostate cancer. Among several types of inhibitors, urea based inhibitors are the most common and widely employed in preclinical and clinical studies. Computational studies have been carried out to uncover active sites and interaction of PSMA inhibitors with the protein by modifying the core structure of the ligand. Analysis of the literature, however, show lack of 3-D quantitative structure activity relationship (QSAR) and molecular dynamics based molecular docking study to identify structural modifications responsible for better GCPII inhibitory activity. The present study aims to fulfil this gap by analysing well known PSMA inhibitors reported in the literature with known experimental PSMA inhibition constants. Also in order to validate the in silico study, a new GCPII inhibitor 7 was designed, synthesized and experimental PSMA enzyme inhibition was evaluated by using freshly isolated PSMA protein from human cancer cell line derived from lymph node, LNCaP. 3D-QSAR CoMFA models on 58 urea based GCPII inhibitors were generated, and the best correlation was obtained in Gast-Huck charge assigning method with q2, r2 and predictive r2 values as 0.592, 0.995 and 0.842 respectively. Moreover, steric, electrostatic, and hydrogen bond donor field contribution analysis provided best statistical values from CoMSIA model (q2, r2 and predictive r2 as 0.527, 0.981 and 0.713 respectively). Contour maps study revealed that electrostatic field contribution is the major factor for discovering better binding affinity ligands. Further molecular dynamic assisted molecular docking was also performed on GCPII receptor (PDB ID 4NGM) and most active GCPII inhibitor, DCIBzL. 4NGM co-crystallised ligand, JB7 was used to validate the docking procedure and the amino acid interactions present in JB7 are compared with DCIBzL. The results suggest that Arg210, Asn257, Gly518, Tyr552, Lys699, and Tyr700 amino acid residues may play a crucial role in GCPII inhibition. Molecular Dynamics Simulation provides information about docked pose stability of DCIBzL. By combination of CoMFA-CoMSIA field analysis and docking interaction analysis studies, conclusive SAR was generated for urea based derivatives based on which GCPII inhibitor 7 was designed and chemically synthesized in our laboratory. Evaluation of GCPII inhibitory activity of 7 by performing NAALADase assay provided IC50 value of 113 nM which is in close agreement with in silico predicted value (119 nM). Thus we have successfully validated our 3D-QSAR and molecular docking based designing of GCPII inhibitors methodology through biological experiments. This conclusive SAR would be helpful to generate novel and more potent GCPII inhibitors for drug delivery applications.

Discovery of novel DAPY-IAS hybrid derivatives as potential HIV-1 inhibitors using molecular hybridization based on crystallographic overlays.

PubMed

Huang, Boshi; Wang, Xueshun; Liu, Xinhao; Chen, Zihui; Li, Wanzhuo; Sun, Songkai; Liu, Huiqing; Daelemans, Dirk; De Clercq, Erik; Pannecouque, Christophe; Zhan, Peng; Liu, Xinyong

2017-08-15

Crystallographic overlap studies and pharmacophoric analysis indicated that diarylpyrimidine (DAPY)-based HIV-1 NNRTIs showed a similar binding mode and pharmacophoric features as indolylarylsulfones (IASs), another class of potent NNRTIs. Thus, a novel series of DAPY-IAS hybrid derivatives were identified as newer NNRTIs using structure-based molecular hybridization. Some target compounds exhibited moderate activities against HIV-1 IIIB strain, among which the two most potent inhibitors possessed EC 50 values of 1.48μM and 1.61μM, respectively. They were much potent than the reference drug ddI (EC 50 =76.0μM) and comparable to 3TC (EC 50 =2.54μM). Compound 7a also exhibited the favorable selectivity index (SI=80). Preliminary structure-activity relationships (SARs), structure-cytotoxicity relationships, molecular modeling studies, and in silico calculation of physicochemical properties of these new inhibitors were also discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Developing an in silico model of the modulation of base excision repair using methoxyamine for more targeted cancer therapeutics.

PubMed

Gurkan-Cavusoglu, Evren; Avadhani, Sriya; Liu, Lili; Kinsella, Timothy J; Loparo, Kenneth A

2013-04-01

Base excision repair (BER) is a major DNA repair pathway involved in the processing of exogenous non-bulky base damages from certain classes of cancer chemotherapy drugs as well as ionising radiation (IR). Methoxyamine (MX) is a small molecule chemical inhibitor of BER that is shown to enhance chemotherapy and/or IR cytotoxicity in human cancers. In this study, the authors have analysed the inhibitory effect of MX on the BER pathway kinetics using a computational model of the repair pathway. The inhibitory effect of MX depends on the BER efficiency. The authors have generated variable efficiency groups using different sets of protein concentrations generated by Latin hypercube sampling, and they have clustered simulation results into high, medium and low efficiency repair groups. From analysis of the inhibitory effect of MX on each of the three groups, it is found that the inhibition is most effective for high efficiency BER, and least effective for low efficiency repair.