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.

In silico strategies for the selection of chelating compounds with potential application in metal-promoted neurodegenerative diseases

NASA Astrophysics Data System (ADS)

Rodríguez-Rodríguez, Cristina; Rimola, Albert; Alí-Torres, Jorge; Sodupe, Mariona; González-Duarte, Pilar

2011-01-01

The development of new strategies to find commercial molecules with promising biochemical features is a main target in the field of biomedicine chemistry. In this work we present an in silico-based protocol that allows identifying commercial compounds with suitable metal coordinating and pharmacokinetic properties to act as metal-ion chelators in metal-promoted neurodegenerative diseases (MpND). Selection of the chelating ligands is done by combining quantum chemical calculations with the search of commercial compounds on different databases via virtual screening. Starting from different designed molecular frameworks, which mainly constitute the binding site, the virtual screening on databases facilitates the identification of different commercial molecules that enclose such scaffolds and, by imposing a set of chemical and pharmacokinetic filters, obey some drug-like requirements mandatory to deal with MpND. The quantum mechanical calculations are useful to gauge the chelating properties of the selected candidate molecules by determining the structure of metal complexes and evaluating their stability constants. With the proposed strategy, commercial compounds containing N and S donor atoms in the binding sites and capable to cross the BBB have been identified and their chelating properties analyzed.

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.

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.

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.

In silico discovery of metal-organic frameworks for precombustion CO2 capture using a genetic algorithm

PubMed Central

Chung, Yongchul G.; Gómez-Gualdrón, Diego A.; Li, Peng; Leperi, Karson T.; Deria, Pravas; Zhang, Hongda; Vermeulen, Nicolaas A.; Stoddart, J. Fraser; You, Fengqi; Hupp, Joseph T.; Farha, Omar K.; Snurr, Randall Q.

2016-01-01

Discovery of new adsorbent materials with a high CO2 working capacity could help reduce CO2 emissions from newly commissioned power plants using precombustion carbon capture. High-throughput computational screening efforts can accelerate the discovery of new adsorbents but sometimes require significant computational resources to explore the large space of possible materials. We report the in silico discovery of high-performing adsorbents for precombustion CO2 capture by applying a genetic algorithm to efficiently search a large database of metal-organic frameworks (MOFs) for top candidates. High-performing MOFs identified from the in silico search were synthesized and activated and show a high CO2 working capacity and a high CO2/H2 selectivity. One of the synthesized MOFs shows a higher CO2 working capacity than any MOF reported in the literature under the operating conditions investigated here. PMID:27757420

In silico pharmacology for drug discovery: applications to targets and beyond

PubMed Central

Ekins, S; Mestres, J; Testa, B

2007-01-01

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

Identification of potential glutaminyl cyclase inhibitors from lead-like libraries by in silico and in vitro fragment-based screening.

PubMed

Szaszkó, Mária; Hajdú, István; Flachner, Beáta; Dobi, Krisztina; Magyar, Csaba; Simon, István; Lőrincz, Zsolt; Kapui, Zoltán; Pázmány, Tamás; Cseh, Sándor; Dormán, György

2017-02-01

A glutaminyl cyclase (QC) fragment library was in silico selected by disconnection of the structure of known QC inhibitors and by lead-like 2D virtual screening of the same set. The resulting fragment library (204 compounds) was acquired from commercial suppliers and pre-screened by differential scanning fluorimetry followed by functional in vitro assays. In this way, 10 fragment hits were identified ([Formula: see text]5 % hit rate, best inhibitory activity: 16 [Formula: see text]). The in vitro hits were then docked to the active site of QC, and the best scoring compounds were analyzed for binding interactions. Two fragments bound to different regions in a complementary manner, and thus, linking those fragments offered a rational strategy to generate novel QC inhibitors. Based on the structure of the virtual linked fragment, a 77-membered QC target focused library was selected from vendor databases and docked to the active site of QC. A PubChem search confirmed that the best scoring analogues are novel, potential QC inhibitors.

Screening the receptorome to discover the molecular targets for plant-derived psychoactive compounds: a novel approach for CNS drug discovery.

PubMed

Roth, Bryan L; Lopez, Estela; Beischel, Scott; Westkaemper, Richard B; Evans, Jon M

2004-05-01

Because psychoactive plants exert profound effects on human perception, emotion, and cognition, discovering the molecular mechanisms responsible for psychoactive plant actions will likely yield insights into the molecular underpinnings of human consciousness. Additionally, it is likely that elucidation of the molecular targets responsible for psychoactive drug actions will yield validated targets for CNS drug discovery. This review article focuses on an unbiased, discovery-based approach aimed at uncovering the molecular targets responsible for psychoactive drug actions wherein the main active ingredients of psychoactive plants are screened at the "receptorome" (that portion of the proteome encoding receptors). An overview of the receptorome is given and various in silico, public-domain resources are described. Newly developed tools for the in silico mining of data derived from the National Institute of Mental Health Psychoactive Drug Screening Program's (NIMH-PDSP) K(i) Database (K(i) DB) are described in detail. Additionally, three case studies aimed at discovering the molecular targets responsible for Hypericum perforatum, Salvia divinorum, and Ephedra sinica actions are presented. Finally, recommendations are made for future studies.

In silico genotoxicity of coumarins: application of the Phenol-Explorer food database to functional food science.

PubMed

Guardado Yordi, E; Matos, M J; Pérez Martínez, A; Tornes, A C; Santana, L; Molina, E; Uriarte, E

2017-08-01

Coumarins are a group of phytochemicals that may be beneficial or harmful to health depending on their type and dosage and the matrix that contains them. Some of these compounds have been proven to display pro-oxidant and clastogenic activities. Therefore, in the current work, we have studied the coumarins that are present in food sources extracted from the Phenol-Explorer database in order to predict their clastogenic activity and identify the structure-activity relationships and genotoxic structural alerts using alternative methods in the field of computational toxicology. It was necessary to compile information on the type and amount of coumarins in different food sources through the analysis of databases of food composition available online. A virtual screening using a clastogenic model and different software, such as MODESLAB, ChemDraw and STATISTIC, was performed. As a result, a table of food composition was prepared and qualitative information from this data was extracted. The virtual screening showed that the esterified substituents inactivate molecules, while the methoxyl and hydroxyl substituents contribute to their activity and constitute, together with the basic structures of the studied subclasses, clastogenic structural alerts. Chemical subclasses of simple coumarins and furocoumarins were classified as active (xanthotoxin, isopimpinellin, esculin, scopoletin, scopolin and bergapten). In silico genotoxicity was mainly predicted for coumarins found in beer, sherry, dried parsley, fresh parsley and raw celery stalks. The results obtained can be interesting for the future design of functional foods and dietary supplements. These studies constitute a reference for the genotoxic chemoinformatic analysis of bioactive compounds present in databases of food composition.

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

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.

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

Developing science gateways for drug discovery in a grid environment.

PubMed

Pérez-Sánchez, Horacio; Rezaei, Vahid; Mezhuyev, Vitaliy; Man, Duhu; Peña-García, Jorge; den-Haan, Helena; Gesing, Sandra

2016-01-01

Methods for in silico screening of large databases of molecules increasingly complement and replace experimental techniques to discover novel compounds to combat diseases. As these techniques become more complex and computationally costly we are faced with an increasing problem to provide the research community of life sciences with a convenient tool for high-throughput virtual screening on distributed computing resources. To this end, we recently integrated the biophysics-based drug-screening program FlexScreen into a service, applicable for large-scale parallel screening and reusable in the context of scientific workflows. Our implementation is based on Pipeline Pilot and Simple Object Access Protocol and provides an easy-to-use graphical user interface to construct complex workflows, which can be executed on distributed computing resources, thus accelerating the throughput by several orders of magnitude.

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

e-Drug3D: 3D structure collections dedicated to drug repurposing and fragment-based drug design.

PubMed

Pihan, Emilie; Colliandre, Lionel; Guichou, Jean-François; Douguet, Dominique

2012-06-01

In the drug discovery field, new uses for old drugs, selective optimization of side activities and fragment-based drug design (FBDD) have proved to be successful alternatives to high-throughput screening. e-Drug3D is a database of 3D chemical structures of drugs that provides several collections of ready-to-screen SD files of drugs and commercial drug fragments. They are natural inputs in studies dedicated to drug repurposing and FBDD. e-Drug3D collections are freely available at http://chemoinfo.ipmc.cnrs.fr/e-drug3d.html either for download or for direct in silico web-based screenings.

Discovery of Dual ETA/ETB Receptor Antagonists from Traditional Chinese Herbs through in Silico and in Vitro Screening

PubMed Central

Wang, Xing; Zhang, Yuxin; Liu, Qing; Ai, Zhixin; Zhang, Yanling; Xiang, Yuhong; Qiao, Yanjiang

2016-01-01

Endothelin-1 receptors (ETAR and ETBR) act as a pivotal regulator in the biological effects of ET-1 and represent a potential drug target for the treatment of multiple cardiovascular diseases. The purpose of the study is to discover dual ETA/ETB receptor antagonists from traditional Chinese herbs. Ligand- and structure-based virtual screening was performed to screen an in-house database of traditional Chinese herbs, followed by a series of in vitro bioassay evaluation. Aristolochic acid A (AAA) was first confirmed to be a dual ETA/ETB receptor antagonist based intracellular calcium influx assay and impedance-based assay. Dose-response curves showed that AAA can block both ETAR and ETBR with IC50 of 7.91 and 7.40 μM, respectively. Target specificity and cytotoxicity bioassay proved that AAA is a selective dual ETA/ETB receptor antagonist and has no significant cytotoxicity on HEK293/ETAR and HEK293/ETBR cells within 24 h. It is a feasible and effective approach to discover bioactive compounds from traditional Chinese herbs using in silico screening combined with in vitro bioassay evaluation. The structural characteristic of AAA for its activity was especially interpreted, which could provide valuable reference for the further structural modification of AAA. PMID:26999111

In silico design of porous polymer networks: high-throughput screening for methane storage materials.

PubMed

Martin, Richard L; Simon, Cory M; Smit, Berend; Haranczyk, Maciej

2014-04-02

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal-organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure-property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane-methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

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.

Reverse screening methods to search for the protein targets of chemopreventive compounds

NASA Astrophysics Data System (ADS)

Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

2018-05-01

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds.

PubMed

Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

2018-01-01

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

PubMed Central

Huang, Hongbin; Zhang, Guigui; Zhou, Yuquan; Lin, Chenru; Chen, Suling; Lin, Yutong; Mai, Shangkang; Huang, Zunnan

2018-01-01

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction. PMID:29868550

In silico screening of small molecule libraries using the dengue virus envelope E protein has identified compounds with antiviral activity against multiple flaviviruses.

PubMed

Kampmann, Thorsten; Yennamalli, Ragothaman; Campbell, Phillipa; Stoermer, Martin J; Fairlie, David P; Kobe, Bostjan; Young, Paul R

2009-12-01

The flaviviruses comprise a large group of related viruses, many of which pose a significant global human health threat, most notably the dengue viruses (DENV), West Nile virus (WNV) and yellow fever virus (YFV). Flaviviruses enter host cells via fusion of the viral and cellular membranes, a process mediated by the major viral envelope protein E as it undergoes a low pH induced conformational change in the endosomal compartment of the host cell. This essential entry stage in the flavivirus life cycle provides an attractive target for the development of antiviral agents. We performed an in silico docking screen of the Maybridge chemical database within a previously described ligand binding pocket in the dengue E protein structure that is thought to play a key role in the conformational transitions that lead to membrane fusion. The biological activity of selected compounds identified from this screen revealed low micromolar antiviral potency against dengue virus for two of the compounds. Our results also provide the first evidence that compounds selected to bind to this ligand binding site on the flavivirus E protein abrogate fusion activity. Interestingly, one of these compounds also has antiviral activity against both WNV (kunjin strain) and YFV.

Discovery of agents that eradicate leukemia stem cells using an in silico screen of public gene expression data

PubMed Central

Hassane, Duane C.; Guzman, Monica L.; Corbett, Cheryl; Li, Xiaojie; Abboud, Ramzi; Young, Fay; Liesveld, Jane L.; Carroll, Martin

2008-01-01

Increasing evidence indicates that malignant stem cells are important for the pathogenesis of acute myelogenous leukemia (AML) and represent a reservoir of cells that drive the development of AML and relapse. Therefore, new treatment regimens are necessary to prevent relapse and improve therapeutic outcomes. Previous studies have shown that the sesquiterpene lactone, parthenolide (PTL), ablates bulk, progenitor, and stem AML cells while causing no appreciable toxicity to normal hematopoietic cells. Thus, PTL must evoke cellular responses capable of mediating AML selective cell death. Given recent advances in chemical genomics such as gene expression-based high-throughput screening (GE-HTS) and the Connectivity Map, we hypothesized that the gene expression signature resulting from treatment of primary AML with PTL could be used to search for similar signatures in publicly available gene expression profiles deposited into the Gene Expression Omnibus (GEO). We therefore devised a broad in silico screen of the GEO database using the PTL gene expression signature as a template and discovered 2 new agents, celastrol and 4-hydroxy-2-nonenal, that effectively eradicate AML at the bulk, progenitor, and stem cell level. These findings suggest the use of multicenter collections of high-throughput data to facilitate discovery of leukemia drugs and drug targets. PMID:18305216

Lattice energy calculation - A quick tool for screening of cocrystals and estimation of relative solubility. Case of flavonoids

NASA Astrophysics Data System (ADS)

Kuleshova, L. N.; Hofmann, D. W. M.; Boese, R.

2013-03-01

Cocrystals (or multicomponent crystals) have physico-chemical properties that are different from crystals of pure components. This is significant in drug development, since the desired properties, e.g. solubility, stability and bioavailability, can be tailored by binding two substances into a single crystal without chemical modification of an active component. Here, the FLEXCRYST program suite, implemented with a data mining force field, was used to estimate the relative stability and, consequently, the relative solubility of cocrystals of flavonoids vs their pure crystals, stored in the Cambridge Structural Database. The considerable potency of this approach for in silico screening of cocrystals, as well as their relative solubility, was demonstrated.

Discovery of novel and cardioselective diltiazem-like calcium channel blockers via virtual screening.

PubMed

Carosati, Emanuele; Budriesi, Roberta; Ioan, Pierfranco; Ugenti, Maria P; Frosini, Maria; Fusi, Fabio; Corda, Gaetano; Cosimelli, Barbara; Spinelli, Domenico; Chiarini, Alberto; Cruciani, Gabriele

2008-09-25

With the effort to discover new chemotypes blocking L-type calcium channels (LTCCs), ligand-based virtual screening was applied with a specific interest toward the diltiazem binding site. Roughly 50000 commercially available compounds served as a database for screening. The filtering through predicted pharmacokinetic properties and structural requirements reduced the initial database to a few compounds for which the similarity was calculated toward two template molecules, diltiazem and 4-chloro-Ncyclopropyl- N-(4-piperidinyl)benzene-sulfonamide, the most interesting hit of a previous screening experiment. For 18 compounds, inotropic and chronotropic activity as well as the vasorelaxant effect on guinea pig were studied "in vitro", and for the most promising, binding studies to the diltiazem site were carried out. The procedure yielded several hits, confirming in silico techniques to be useful for finding new chemotypes. In particular, N-[2-(dimethylamino)ethyl]-3-hydroxy-2-naphthamide, N,Ndimethyl- N'-(2-pyridin-3-ylquinolin-4-yl)ethane-1,2-diamine, 2-[(4-chlorophenyl)(pyridin-2-yl)methoxy]- N,N-dimethylethanamine (carbinoxamine), and 7-[2-(diethylamino)ethoxy]-2H-chromen-2-one revealed interesting activity and binding to the benzothiazepine site.

FilTer BaSe: A web accessible chemical database for small compound libraries.

PubMed

Kolte, Baban S; Londhe, Sanjay R; Solanki, Bhushan R; Gacche, Rajesh N; Meshram, Rohan J

2018-03-01

Finding novel chemical agents for targeting disease associated drug targets often requires screening of large number of new chemical libraries. In silico methods are generally implemented at initial stages for virtual screening. Filtering of such compound libraries on physicochemical and substructure ground is done to ensure elimination of compounds with undesired chemical properties. Filtering procedure, is redundant, time consuming and requires efficient bioinformatics/computer manpower along with high end software involving huge capital investment that forms a major obstacle in drug discovery projects in academic setup. We present an open source resource, FilTer BaSe- a chemoinformatics platform (http://bioinfo.net.in/filterbase/) that host fully filtered, ready to use compound libraries with workable size. The resource also hosts a database that enables efficient searching the chemical space of around 348,000 compounds on the basis of physicochemical and substructure properties. Ready to use compound libraries and database presented here is expected to aid a helping hand for new drug developers and medicinal chemists. Copyright © 2017 Elsevier Inc. All rights reserved.

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, ...

Protocols for the Design of Kinase-focused Compound Libraries.

PubMed

Jacoby, Edgar; Wroblowski, Berthold; Buyck, Christophe; Neefs, Jean-Marc; Meyer, Christophe; Cummings, Maxwell D; van Vlijmen, Herman

2018-05-01

Protocols for the design of kinase-focused compound libraries are presented. Kinase-focused compound libraries can be differentiated based on the design goal. Depending on whether the library should be a discovery library specific for one particular kinase, a general discovery library for multiple distinct kinase projects, or even phenotypic screening, there exists today a variety of in silico methods to design candidate compound libraries. We address the following scenarios: 1) Datamining of SAR databases and kinase focused vendor catalogues; 2) Predictions and virtual screening; 3) Structure-based design of combinatorial kinase inhibitors; 4) Design of covalent kinase inhibitors; 5) Design of macrocyclic kinase inhibitors; and 6) Design of allosteric kinase inhibitors and activators. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Modeling Liver-Related Adverse Effects of Drugs Using kNN QSAR Method

PubMed Central

Rodgers, Amie D.; Zhu, Hao; Fourches, Dennis; Rusyn, Ivan; Tropsha, Alexander

2010-01-01

Adverse effects of drugs (AEDs) continue to be a major cause of drug withdrawals both in development and post-marketing. While liver-related AEDs are a major concern for drug safety, there are few in silico models for predicting human liver toxicity for drug candidates. We have applied the Quantitative Structure Activity Relationship (QSAR) approach to model liver AEDs. In this study, we aimed to construct a QSAR model capable of binary classification (active vs. inactive) of drugs for liver AEDs based on chemical structure. To build QSAR models, we have employed an FDA spontaneous reporting database of human liver AEDs (elevations in activity of serum liver enzymes), which contains data on approximately 500 approved drugs. Approximately 200 compounds with wide clinical data coverage, structural similarity and balanced (40/60) active/inactive ratio were selected for modeling and divided into multiple training/test and external validation sets. QSAR models were developed using the k nearest neighbor method and validated using external datasets. Models with high sensitivity (>73%) and specificity (>94%) for prediction of liver AEDs in external validation sets were developed. To test applicability of the models, three chemical databases (World Drug Index, Prestwick Chemical Library, and Biowisdom Liver Intelligence Module) were screened in silico and the validity of predictions was determined, where possible, by comparing model-based classification with assertions in publicly available literature. Validated QSAR models of liver AEDs based on the data from the FDA spontaneous reporting system can be employed as sensitive and specific predictors of AEDs in pre-clinical screening of drug candidates for potential hepatotoxicity in humans. PMID:20192250

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.

Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns† †The authors declare no competing interests. ‡ ‡Electronic supplementary information (ESI) available: Pipeline Pilot protocols, xls file with the output of the Pipeline Pilot protocols, KNIME workflows, and supplementary figures showing the Pipeline Pilot protocols. See DOI: 10.1039/c6md00065g Click here for additional data file.

PubMed Central

Zdrazil, B.; Neefs, J.-M.; Van Vlijmen, H.; Herhaus, C.; Caracoti, A.; Brea, J.; Roibás, B.; Loza, M. I.; Queralt-Rosinach, N.; Furlong, L. I.; Gaulton, A.; Bartek, L.; Senger, S.; Chichester, C.; Engkvist, O.; Evelo, C. T.; Franklin, N. I.; Marren, D.; Ecker, G. F.

2016-01-01

Phenotypic screening is in a renaissance phase and is expected by many academic and industry leaders to accelerate the discovery of new drugs for new biology. Given that phenotypic screening is per definition target agnostic, the emphasis of in silico and in vitro follow-up work is on the exploration of possible molecular mechanisms and efficacy targets underlying the biological processes interrogated by the phenotypic screening experiments. Herein, we present six exemplar computational protocols for the interpretation of cellular phenotypic screens based on the integration of compound, target, pathway, and disease data established by the IMI Open PHACTS project. The protocols annotate phenotypic hit lists and allow follow-up experiments and mechanistic conclusions. The annotations included are from ChEMBL, ChEBI, GO, WikiPathways and DisGeNET. Also provided are protocols which select from the IUPHAR/BPS Guide to PHARMACOLOGY interaction file selective compounds to probe potential targets and a correlation robot which systematically aims to identify an overlap of active compounds in both the phenotypic as well as any kinase assay. The protocols are applied to a phenotypic pre-lamin A/C splicing assay selected from the ChEMBL database to illustrate the process. The computational protocols make use of the Open PHACTS API and data and are built within the Pipeline Pilot and KNIME workflow tools. PMID:27774140

Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents.

PubMed

Balakumar, Chandrasekaran; Ramesh, Muthusamy; Tham, Chuin Lean; Khathi, Samukelisiwe Pretty; Kozielski, Frank; Srinivasulu, Cherukupalli; Hampannavar, Girish A; Sayyad, Nisar; Soliman, Mahmoud E; Karpoormath, Rajshekhar

2017-11-29

Kinesin spindle protein (KSP) belongs to the kinesin superfamily of microtubule-based motor proteins. KSP is responsible for the establishment of the bipolar mitotic spindle which mediates cell division. Inhibition of KSP expedites the blockade of the normal cell cycle during mitosis through the generation of monoastral MT arrays that finally cause apoptotic cell death. As KSP is highly expressed in proliferating/cancer cells, it has gained considerable attention as a potential drug target for cancer chemotherapy. Therefore, this study envisaged to design novel KSP inhibitors by employing computational techniques/tools such as pharmacophore modelling, virtual database screening, molecular docking and molecular dynamics. Initially, the pharmacophore models were generated from the data-set of highly potent KSP inhibitors and the pharmacophore models were validated against in house test set ligands. The validated pharmacophore model was then taken for database screening (Maybridge and ChemBridge) to yield hits, which were further filtered for their drug-likeliness. The potential hits retrieved from virtual database screening were docked using CDOCKER to identify the ligand binding landscape. The top-ranked hits obtained from molecular docking were progressed to molecular dynamics (AMBER) simulations to deduce the ligand binding affinity. This study identified MB-41570 and CB-10358 as potential hits and evaluated these experimentally using in vitro KSP ATPase inhibition assays.

Accessing biological actions of Ganoderma secondary metabolites by in silico profiling

PubMed Central

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

2016-01-01

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

Shape based virtual screening and molecular docking towards designing novel pancreatic lipase inhibitors

PubMed Central

Veeramachaneni, Ganesh Kumar; Raj, K Kranthi; Chalasani, Leela Madhuri; Annamraju, Sai Krishna; JS, Bondili; Talluri, Venkateswara Rao

2015-01-01

Increase in obesity rates and obesity associated health issues became one of the greatest health concerns in the present world population. With alarming increase in obese percentage there is a need to design new drugs related to the obesity targets. Among the various targets linked to obesity, pancreatic lipase was one of the promising targets for obesity treatment. Using the in silico methods like structure based virtual screening, QikProp, docking studies and binding energy calculations three molecules namely zinc85531017, zinc95919096 and zinc33963788 from the natural database were reported as the potential inhibitors for the pancreatic lipase. Among them zinc95919096 presented all the interactions matching to both standard and crystal ligand and hence it can be further proceeded to drug discovery process. PMID:26770027

In silico study toward the identification of new and safe potential inhibitors of photosynthetic electron transport.

PubMed

Ribeiro, Taisa Pereira Piacentini; Manarin, Flávia Giovana; Borges de Melo, Eduardo

2018-05-30

To address the rising global demand for food, it is necessary to search for new herbicides that can control resistant weeds. We performed a 2D-quantitative structure-activity relationship (QSAR) study to predict compounds with photosynthesis-inhibitory activity. A data set of 44 compounds (quinolines and naphthalenes), which are described as photosynthetic electron transport (PET) inhibitors, was used. The obtained model was approved in internal and external validation tests. 2D Similarity-based virtual screening was performed and 64 compounds were selected from the ZINC database. By using the VEGA QSAR software, 48 compounds were shown to have potential toxic effects (mutagenicity and carcinogenicity). Therefore, the model was also tested using a set of 16 molecules obtained by a similarity search of the ZINC database. Six compounds showed good predicted inhibition of PET. The obtained model shows potential utility in the design of new PET inhibitors, and the hit compounds found by virtual screening are novel bicyclic scaffolds of this class. Copyright © 2018 Elsevier Inc. All rights reserved.

JRC GMO-Matrix: a web application to support Genetically Modified Organisms detection strategies.

PubMed

Angers-Loustau, Alexandre; Petrillo, Mauro; Bonfini, Laura; Gatto, Francesco; Rosa, Sabrina; Patak, Alexandre; Kreysa, Joachim

2014-12-30

The polymerase chain reaction (PCR) is the current state of the art technique for DNA-based detection of Genetically Modified Organisms (GMOs). A typical control strategy starts by analyzing a sample for the presence of target sequences (GM-elements) known to be present in many GMOs. Positive findings from this "screening" are then confirmed with GM (event) specific test methods. A reliable knowledge of which GMOs are detected by combinations of GM-detection methods is thus crucial to minimize the verification efforts. In this article, we describe a novel platform that links the information of two unique databases built and maintained by the European Union Reference Laboratory for Genetically Modified Food and Feed (EU-RL GMFF) at the Joint Research Centre (JRC) of the European Commission, one containing the sequence information of known GM-events and the other validated PCR-based detection and identification methods. The new platform compiles in silico determinations of the detection of a wide range of GMOs by the available detection methods using existing scripts that simulate PCR amplification and, when present, probe binding. The correctness of the information has been verified by comparing the in silico conclusions to experimental results for a subset of forty-nine GM events and six methods. The JRC GMO-Matrix is unique for its reliance on DNA sequence data and its flexibility in integrating novel GMOs and new detection methods. Users can mine the database using a set of web interfaces that thus provide a valuable support to GMO control laboratories in planning and evaluating their GMO screening strategies. The platform is accessible at http://gmo-crl.jrc.ec.europa.eu/jrcgmomatrix/ .

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

In Silico Identification and Pharmacological Evaluation of Novel Endocrine Disrupting Chemicals That Act via the Ligand-Binding Domain of the Estrogen Receptor α

PubMed Central

Kufareva, Irina; Abagyan, Ruben

2014-01-01

Endocrine disrupting chemicals (EDCs) pose a significant threat to human health, society, and the environment. Many EDCs elicit their toxic effects through nuclear hormone receptors, like the estrogen receptor α (ERα). In silico models can be used to prioritize chemicals for toxicological evaluation to reduce the amount of costly pharmacological testing and enable early alerts for newly designed compounds. However, many of the current computational models are overly dependent on the chemistry of known modulators and perform poorly for novel chemical scaffolds. Herein we describe the development of computational, three-dimensional multi-conformational pocket-field docking, and chemical-field docking models for the identification of novel EDCs that act via the ligand-binding domain of ERα. These models were highly accurate in the retrospective task of distinguishing known high-affinity ERα modulators from inactive or decoy molecules, with minimal training. To illustrate the utility of the models in prospective in silico compound screening, we screened a database of over 6000 environmental chemicals and evaluated the 24 top-ranked hits in an ERα transcriptional activation assay and a differential scanning fluorimetry-based ERα binding assay. Promisingly, six chemicals displayed ERα agonist activity (32nM–3.98μM) and two chemicals had moderately stabilizing effects on ERα. Two newly identified active compounds were chemically related β-adrenergic receptor (βAR) agonists, dobutamine, and ractopamine (a feed additive that promotes leanness in cattle and poultry), which are the first βAR agonists identified as activators of ERα-mediated gene transcription. This approach can be applied to other receptors implicated in endocrine disruption. PMID:24928891

In silico identification and pharmacological evaluation of novel endocrine disrupting chemicals that act via the ligand-binding domain of the estrogen receptor α.

PubMed

McRobb, Fiona M; Kufareva, Irina; Abagyan, Ruben

2014-09-01

Endocrine disrupting chemicals (EDCs) pose a significant threat to human health, society, and the environment. Many EDCs elicit their toxic effects through nuclear hormone receptors, like the estrogen receptor α (ERα). In silico models can be used to prioritize chemicals for toxicological evaluation to reduce the amount of costly pharmacological testing and enable early alerts for newly designed compounds. However, many of the current computational models are overly dependent on the chemistry of known modulators and perform poorly for novel chemical scaffolds. Herein we describe the development of computational, three-dimensional multi-conformational pocket-field docking, and chemical-field docking models for the identification of novel EDCs that act via the ligand-binding domain of ERα. These models were highly accurate in the retrospective task of distinguishing known high-affinity ERα modulators from inactive or decoy molecules, with minimal training. To illustrate the utility of the models in prospective in silico compound screening, we screened a database of over 6000 environmental chemicals and evaluated the 24 top-ranked hits in an ERα transcriptional activation assay and a differential scanning fluorimetry-based ERα binding assay. Promisingly, six chemicals displayed ERα agonist activity (32nM-3.98μM) and two chemicals had moderately stabilizing effects on ERα. Two newly identified active compounds were chemically related β-adrenergic receptor (βAR) agonists, dobutamine, and ractopamine (a feed additive that promotes leanness in cattle and poultry), which are the first βAR agonists identified as activators of ERα-mediated gene transcription. This approach can be applied to other receptors implicated in endocrine disruption. © The Author 2014. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

DEKOIS: demanding evaluation kits for objective in silico screening--a versatile tool for benchmarking docking programs and scoring functions.

PubMed

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

2011-10-24

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

Feasibility of Genome-Wide Screening for Biosafety Assessment of Probiotics: A Case Study of Lactobacillus helveticus MTCC 5463.

PubMed

Senan, S; Prajapati, J B; Joshi, C G

2015-12-01

Recent years have witnessed an explosion in genome sequencing of probiotic strains for accurate identification and characterization. Regulatory bodies are emphasizing on the need for performing phase I safety studies for probiotics. The main hypothesis of this study was to explore the feasibility of using genome databases for safety screening of strains. In this study, we attempted to develop a framework for the safety assessment of a potential probiotic strain, Lactobacillus helveticus MTCC 5463 based on genome mining for genes associated with antibiotic resistance, production of harmful metabolites, and virulence. The sequencing of MTCC 5463 was performed using GS-FLX Titanium reagents. Genes coding for antibiotic resistance and virulence were identified using Antibiotic Resistance Genes Database and Virulence Factors Database. Results indicated that MTCC 5463 carried antibiotic resistance genes associated with beta-lactam and fluoroquinolone. There is no threat of transfer of these genes to host gut commensals because the genes are not plasmid encoded. The presence of genes for adhesion, biofilm, surface proteins, and stress-related proteins provides robustness to the strain. The presence of hemolysin gene in the genome revealed a theoretical risk of virulence. The results of in silico analysis complemented the in vitro studies and human clinical trials, confirming the safety of the probiotic strain. We propose that the safety assessment of probiotic strains administered live at high doses using a genome-wide screening could be an effective and time-saving tool for identifying prognostic biomarkers of biosafety.

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.

SuperNatural: a searchable database of available natural compounds

PubMed Central

Dunkel, Mathias; Fullbeck, Melanie; Neumann, Stefanie; Preissner, Robert

2006-01-01

Although tremendous effort has been put into synthetic libraries, most drugs on the market are still natural compounds or derivatives thereof. There are encyclopaedias of natural compounds, but the availability of these compounds is often unclear and catalogues from numerous suppliers have to be checked. To overcome these problems we have compiled a database of ∼50 000 natural compounds from different suppliers. To enable efficient identification of the desired compounds, we have implemented substructure searches with typical templates. Starting points for in silico screenings are about 2500 well-known and classified natural compounds from a compendium that we have added. Possible medical applications can be ascertained via automatic searches for similar drugs in a free conformational drug database containing WHO indications. Furthermore, we have computed about three million conformers, which are deployed to account for the flexibilities of the compounds when the 3D superposition algorithm that we have developed is used. The SuperNatural Database is publicly available at . Viewing requires the free Chime-plugin from MDL (Chime) or Java2 Runtime Environment (MView), which is also necessary for using Marvin application for chemical drawing. PMID:16381957

3D Pharmacophore-Based Virtual Screening and Docking Approaches toward the Discovery of Novel HPPD Inhibitors.

PubMed

Fu, Ying; Sun, Yi-Na; Yi, Ke-Han; Li, Ming-Qiang; Cao, Hai-Feng; Li, Jia-Zhong; Ye, Fei

2017-06-09

p -Hydroxyphenylpyruvate dioxygenase (HPPD) is not only the useful molecular target in treating life-threatening tyrosinemia type I, but also an important target for chemical herbicides. A combined in silico structure-based pharmacophore and molecular docking-based virtual screening were performed to identify novel potential HPPD inhibitors. The complex-based pharmacophore model (CBP) with 0.721 of ROC used for screening compounds showed remarkable ability to retrieve known active ligands from among decoy molecules. The ChemDiv database was screened using CBP-Hypo2 as a 3D query, and the best-fit hits subjected to molecular docking with two methods of LibDock and CDOCKER in Accelrys Discovery Studio 2.5 (DS 2.5) to discern interactions with key residues at the active site of HPPD. Four compounds with top rankings in the HipHop model and well-known binding model were finally chosen as lead compounds with potential inhibitory effects on the active site of target. The results provided powerful insight into the development of novel HPPD inhibitors herbicides using computational techniques.

Informing the Selection of Screening Hit Series with in Silico Absorption, Distribution, Metabolism, Excretion, and Toxicity Profiles.

PubMed

Sanders, John M; Beshore, Douglas C; Culberson, J Christopher; Fells, James I; Imbriglio, Jason E; Gunaydin, Hakan; Haidle, Andrew M; Labroli, Marc; Mattioni, Brian E; Sciammetta, Nunzio; Shipe, William D; Sheridan, Robert P; Suen, Linda M; Verras, Andreas; Walji, Abbas; Joshi, Elizabeth M; Bueters, Tjerk

2017-08-24

High-throughput screening (HTS) has enabled millions of compounds to be assessed for biological activity, but challenges remain in the prioritization of hit series. While biological, absorption, distribution, metabolism, excretion, and toxicity (ADMET), purity, and structural data are routinely used to select chemical matter for further follow-up, the scarcity of historical ADMET data for screening hits limits our understanding of early hit compounds. Herein, we describe a process that utilizes a battery of in-house quantitative structure-activity relationship (QSAR) models to generate in silico ADMET profiles for hit series to enable more complete characterizations of HTS chemical matter. These profiles allow teams to quickly assess hit series for desirable ADMET properties or suspected liabilities that may require significant optimization. Accordingly, these in silico data can direct ADMET experimentation and profoundly impact the progression of hit series. Several prospective examples are presented to substantiate the value of this approach.

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

Improving compound-protein interaction prediction by building up highly credible negative samples.

PubMed

Liu, Hui; Sun, Jianjiang; Guan, Jihong; Zheng, Jie; Zhou, Shuigeng

2015-06-15

Computational prediction of compound-protein interactions (CPIs) is of great importance for drug design and development, as genome-scale experimental validation of CPIs is not only time-consuming but also prohibitively expensive. With the availability of an increasing number of validated interactions, the performance of computational prediction approaches is severely impended by the lack of reliable negative CPI samples. A systematic method of screening reliable negative sample becomes critical to improving the performance of in silico prediction methods. This article aims at building up a set of highly credible negative samples of CPIs via an in silico screening method. As most existing computational models assume that similar compounds are likely to interact with similar target proteins and achieve remarkable performance, it is rational to identify potential negative samples based on the converse negative proposition that the proteins dissimilar to every known/predicted target of a compound are not much likely to be targeted by the compound and vice versa. We integrated various resources, including chemical structures, chemical expression profiles and side effects of compounds, amino acid sequences, protein-protein interaction network and functional annotations of proteins, into a systematic screening framework. We first tested the screened negative samples on six classical classifiers, and all these classifiers achieved remarkably higher performance on our negative samples than on randomly generated negative samples for both human and Caenorhabditis elegans. We then verified the negative samples on three existing prediction models, including bipartite local model, Gaussian kernel profile and Bayesian matrix factorization, and found that the performances of these models are also significantly improved on the screened negative samples. Moreover, we validated the screened negative samples on a drug bioactivity dataset. Finally, we derived two sets of new interactions by training an support vector machine classifier on the positive interactions annotated in DrugBank and our screened negative interactions. The screened negative samples and the predicted interactions provide the research community with a useful resource for identifying new drug targets and a helpful supplement to the current curated compound-protein databases. Supplementary files are available at: http://admis.fudan.edu.cn/negative-cpi/. © The Author 2015. Published by Oxford University Press.

In silico database screening of potential targets and pathways of compounds contained in plants used for psoriasis vulgaris.

PubMed

May, Brian H; Deng, Shiqiang; Zhang, Anthony L; Lu, Chuanjian; Xue, Charlie C L

2015-09-01

Reviews and meta-analyses of clinical trials identified plants used as traditional medicines (TMs) that show promise for psoriasis. These include Rehmannia glutinosa, Camptotheca acuminata, Indigo naturalis and Salvia miltiorrhiza. Compounds contained in these TMs have shown activities of relevance to psoriasis in experimental models. To further investigate the likely mechanisms of action of the multiple compounds in these TMs, we undertook a computer-based in silico investigation of the proteins known to be regulated by these compounds and their associated biological pathways. The proteins reportedly regulated by compounds in these four TMs were identified using the HIT (Herbal Ingredients' Targets) database. The resultant data were entered into the PANTHER (Protein ANnotation THrough Evolutionary Relationship) database to identify the pathways in which the proteins could be involved. The study identified 237 compounds in the TMs and these retrieved 287 proteins from HIT. These proteins identified 59 pathways in PANTHER with most proteins being located in the Apoptosis, Angiogenesis, Inflammation mediated by chemokine and cytokine, Gonadotropin releasing hormone receptor, and/or Interleukin signaling pathways. All four TMs contained compounds that had regulating effects on Apoptosis regulator BAX, Apoptosis regulator Bcl-2, Caspase-3, Tumor necrosis factor (TNF) or Prostaglandin G/H synthase 2 (COX2). The main proteins and pathways are primarily related to inflammation, proliferation and angiogenesis which are all processes involved in psoriasis. Experimental studies have reported that certain compounds from these TMs can regulate the expression of proteins involved in each of these pathways.

In silico study of in vitro GPCR assays by QSAR modeling ...

EPA Pesticide Factsheets

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) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than this set of chemicals. Thus, there is a need to fill data gaps with in silico methods, and quantitative structure-activity relationships (QSARs) are a proven and cost effective approach to predict biological activity. ToxCast in turn provides relatively large datasets that are ideal for training and testing QSAR models. The overall goal of the study described here was to develop QSAR models to fill the data gaps in a larger environmental database of ~32k structures. The specific aim of the current work was to build QSAR models for 18 G-Protein Coupled Receptor (GPCR) assays, part of the aminergic category. Two QSAR modeling strategies were adopted: classification models were developed to separate chemicals into active/non-active classes, and then regression models were built to predict the potency values of the bioassays for the active chemicals. Multiple software programs were used to calculate constitutional, topological and substructural molecular descriptors from two-dimensional (2D) chemical structures. Model-fitting methods included PLSDA (partial least squares d

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.

Self-Organizing Maps for In Silico Screening and Data Visualization.

PubMed

Digles, Daniela; Ecker, Gerhard F

2011-10-01

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

Large-Scale Chemical Similarity Networks for Target Profiling of Compounds Identified in Cell-Based Chemical Screens

PubMed Central

Lo, Yu-Chen; Senese, Silvia; Li, Chien-Ming; Hu, Qiyang; Huang, Yong; Damoiseaux, Robert; Torres, Jorge Z.

2015-01-01

Target identification is one of the most critical steps following cell-based phenotypic chemical screens aimed at identifying compounds with potential uses in cell biology and for developing novel disease therapies. Current in silico target identification methods, including chemical similarity database searches, are limited to single or sequential ligand analysis that have limited capabilities for accurate deconvolution of a large number of compounds with diverse chemical structures. Here, we present CSNAP (Chemical Similarity Network Analysis Pulldown), a new computational target identification method that utilizes chemical similarity networks for large-scale chemotype (consensus chemical pattern) recognition and drug target profiling. Our benchmark study showed that CSNAP can achieve an overall higher accuracy (>80%) of target prediction with respect to representative chemotypes in large (>200) compound sets, in comparison to the SEA approach (60–70%). Additionally, CSNAP is capable of integrating with biological knowledge-based databases (Uniprot, GO) and high-throughput biology platforms (proteomic, genetic, etc) for system-wise drug target validation. To demonstrate the utility of the CSNAP approach, we combined CSNAP's target prediction with experimental ligand evaluation to identify the major mitotic targets of hit compounds from a cell-based chemical screen and we highlight novel compounds targeting microtubules, an important cancer therapeutic target. The CSNAP method is freely available and can be accessed from the CSNAP web server (http://services.mbi.ucla.edu/CSNAP/). PMID:25826798

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.

IMGMD: A platform for the integration and standardisation of In silico Microbial Genome-scale Metabolic Models.

PubMed

Ye, Chao; Xu, Nan; Dong, Chuan; Ye, Yuannong; Zou, Xuan; Chen, Xiulai; Guo, Fengbiao; Liu, Liming

2017-04-07

Genome-scale metabolic models (GSMMs) constitute a platform that combines genome sequences and detailed biochemical information to quantify microbial physiology at the system level. To improve the unity, integrity, correctness, and format of data in published GSMMs, a consensus IMGMD database was built in the LAMP (Linux + Apache + MySQL + PHP) system by integrating and standardizing 328 GSMMs constructed for 139 microorganisms. The IMGMD database can help microbial researchers download manually curated GSMMs, rapidly reconstruct standard GSMMs, design pathways, and identify metabolic targets for strategies on strain improvement. Moreover, the IMGMD database facilitates the integration of wet-lab and in silico data to gain an additional insight into microbial physiology. The IMGMD database is freely available, without any registration requirements, at http://imgmd.jiangnan.edu.cn/database.

Virtual High-Throughput Screening To Identify Novel Activin Antagonists

PubMed Central

Zhu, Jie; Mishra, Rama K.; Schiltz, Gary E.; Makanji, Yogeshwar; Scheidt, Karl A.; Mazar, Andrew P.; Woodruff, Teresa K.

2015-01-01

Activin belongs to the TGFβ superfamily, which is associated with several disease conditions, including cancer-related cachexia, preterm labor with delivery, and osteoporosis. Targeting activin and its related signaling pathways holds promise as a therapeutic approach to these diseases. A small-molecule ligand-binding groove was identified in the interface between the two activin βA subunits and was used for a virtual high-throughput in silico screening of the ZINC database to identify hits. Thirty-nine compounds without significant toxicity were tested in two well-established activin assays: FSHβ transcription and HepG2 cell apoptosis. This screening workflow resulted in two lead compounds: NUCC-474 and NUCC-555. These potential activin antagonists were then shown to inhibit activin A-mediated cell proliferation in ex vivo ovary cultures. In vivo testing showed that our most potent compound (NUCC-555) caused a dose-dependent decrease in FSH levels in ovariectomized mice. The Blitz competition binding assay confirmed target binding of NUCC-555 to the activin A:ActRII that disrupts the activin A:ActRII complex’s binding with ALK4-ECD-Fc in a dose-dependent manner. The NUCC-555 also specifically binds to activin A compared with other TGFβ superfamily member myostatin (GDF8). These data demonstrate a new in silico-based strategy for identifying small-molecule activin antagonists. Our approach is the first to identify a first-in-class small-molecule antagonist of activin binding to ALK4, which opens a completely new approach to inhibiting the activity of TGFβ receptor superfamily members. in addition, the lead compound can serve as a starting point for lead optimization toward the goal of a compound that may be effective in activin-mediated diseases. PMID:26098096

SuperNatural: a searchable database of available natural compounds.

PubMed

Dunkel, Mathias; Fullbeck, Melanie; Neumann, Stefanie; Preissner, Robert

2006-01-01

Although tremendous effort has been put into synthetic libraries, most drugs on the market are still natural compounds or derivatives thereof. There are encyclopaedias of natural compounds, but the availability of these compounds is often unclear and catalogues from numerous suppliers have to be checked. To overcome these problems we have compiled a database of approximately 50,000 natural compounds from different suppliers. To enable efficient identification of the desired compounds, we have implemented substructure searches with typical templates. Starting points for in silico screenings are about 2500 well-known and classified natural compounds from a compendium that we have added. Possible medical applications can be ascertained via automatic searches for similar drugs in a free conformational drug database containing WHO indications. Furthermore, we have computed about three million conformers, which are deployed to account for the flexibilities of the compounds when the 3D superposition algorithm that we have developed is used. The SuperNatural Database is publicly available at http://bioinformatics.charite.de/supernatural. Viewing requires the free Chime-plugin from MDL (Chime) or Java2 Runtime Environment (MView), which is also necessary for using Marvin application for chemical drawing.

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.

Combination of Pharmacophore Matching, 2D Similarity Search, and In Vitro Biological Assays in the Selection of Potential 5-HT6 Antagonists from Large Commercial Repositories.

PubMed

Dobi, Krisztina; Flachner, Beáta; Pukáncsik, Mária; Máthé, Enikő; Bognár, Melinda; Szaszkó, Mária; Magyar, Csaba; Hajdú, István; Lőrincz, Zsolt; Simon, István; Fülöp, Ferenc; Cseh, Sándor; Dormán, György

2015-10-01

Rapid in silico selection of target-focused libraries from commercial repositories is an attractive and cost-effective approach. If structures of active compounds are available, rapid 2D similarity search can be performed on multimillion compound databases, but the generated library requires further focusing. We report here a combination of the 2D approach with pharmacophore matching which was used for selecting 5-HT6 antagonists. In the first screening round, 12 compounds showed >85% antagonist efficacy of the 91 screened. For the second-round (hit validation) screening phase, pharmacophore models were built, applied, and compared with the routine 2D similarity search. Three pharmacophore models were created based on the structure of the reference compounds and the first-round hit compounds. The pharmacophore search resulted in a high hit rate (40%) and led to novel chemotypes, while 2D similarity search had slightly better hit rate (51%), but lacking the novelty. To demonstrate the power of the virtual screening cascade, ligand efficiency indices were also calculated and their steady improvement was confirmed. © 2015 John Wiley & Sons A/S.

[Activity of NTDs Drug-discovery Research Consortium].

PubMed

Namatame, Ichiji

2016-01-01

Neglected tropical diseases (NTDs) are an extremely important issue facing global health care. To improve "access to health" where people are unable to access adequate medical care due to poverty and weak healthcare systems, we have established two consortiums: the NTD drug discovery research consortium, and the pediatric praziquantel consortium. The NTD drug discovery research consortium, which involves six institutions from industry, government, and academia, as well as an international non-profit organization, is committed to developing anti-protozoan active compounds for three NTDs (Leishmaniasis, Chagas disease, and African sleeping sickness). Each participating institute will contribute their efforts to accomplish the following: selection of drug targets based on information technology, and drug discovery by three different approaches (in silico drug discovery, "fragment evolution" which is a unique drug designing method of Astellas Pharma, and phenotypic screening with Astellas' compound library). The consortium has established a brand new database (Integrated Neglected Tropical Disease Database; iNTRODB), and has selected target proteins for the in silico and fragment evolution drug discovery approaches. Thus far, we have identified a number of promising compounds that inhibit the target protein, and we are currently trying to improve the anti-protozoan activity of these compounds. The pediatric praziquantel consortium was founded in July 2012 to develop and register a new praziquantel pediatric formulation for the treatment of schistosomiasis. Astellas Pharma has been a core member in this consortium since its establishment, and has provided expertise and technology in the area of pediatric formulation development and clinical development.

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.

Identification of Natural Compound Inhibitors for Multidrug Efflux Pumps of Escherichia coli and Pseudomonas aeruginosa Using In Silico High-Throughput Virtual Screening and In Vitro Validation

PubMed Central

Aparna, Vasudevan; Dineshkumar, Kesavan; Mohanalakshmi, Narasumani; Velmurugan, Devadasan; Hopper, Waheeta

2014-01-01

Pseudomonas aeruginosa and Escherichia coli are resistant to wide range of antibiotics rendering the treatment of infections very difficult. A main mechanism attributed to the resistance is the function of efflux pumps. MexAB-OprM and AcrAB-TolC are the tripartite efflux pump assemblies, responsible for multidrug resistance in P. aeruginosa and E. coli respectively. Substrates that are more susceptible for efflux are predicted to have a common pharmacophore feature map. In this study, a new criterion of excluding compounds with efflux substrate-like features was used, thereby refining the selection process and enriching the inhibitor identification process. An in-house database of phytochemicals was created and screened using high-throughput virtual screening against AcrB and MexB proteins and filtered by matching with the common pharmacophore models (AADHR, ADHNR, AAHNR, AADHN, AADNR, AAADN, AAADR, AAANR, AAAHN, AAADD and AAADH) generated using known efflux substrates. Phytochemical hits that matched with any one or more of the efflux substrate models were excluded from the study. Hits that do not have features similar to the efflux substrate models were docked using XP docking against the AcrB and MexB proteins. The best hits of the XP docking were validated by checkerboard synergy assay and ethidium bromide accumulation assay for their efflux inhibition potency. Lanatoside C and diadzein were filtered based on the synergistic potential and validated for their efflux inhibition potency using ethidium bromide accumulation study. These compounds exhibited the ability to increase the accumulation of ethidium bromide inside the bacterial cell as evidenced by these increase in fluorescence in the presence of the compounds. With this good correlation between in silico screening and positive efflux inhibitory activity in vitro, the two compounds, lanatoside C and diadzein could be promising efflux pump inhibitors and effective to use in combination therapy against drug resistant strains of P. aeruginosa and E. coli. PMID:25025665

Identification of natural compound inhibitors for multidrug efflux pumps of Escherichia coli and Pseudomonas aeruginosa using in silico high-throughput virtual screening and in vitro validation.

PubMed

Aparna, Vasudevan; Dineshkumar, Kesavan; Mohanalakshmi, Narasumani; Velmurugan, Devadasan; Hopper, Waheeta

2014-01-01

Pseudomonas aeruginosa and Escherichia coli are resistant to wide range of antibiotics rendering the treatment of infections very difficult. A main mechanism attributed to the resistance is the function of efflux pumps. MexAB-OprM and AcrAB-TolC are the tripartite efflux pump assemblies, responsible for multidrug resistance in P. aeruginosa and E. coli respectively. Substrates that are more susceptible for efflux are predicted to have a common pharmacophore feature map. In this study, a new criterion of excluding compounds with efflux substrate-like features was used, thereby refining the selection process and enriching the inhibitor identification process. An in-house database of phytochemicals was created and screened using high-throughput virtual screening against AcrB and MexB proteins and filtered by matching with the common pharmacophore models (AADHR, ADHNR, AAHNR, AADHN, AADNR, AAADN, AAADR, AAANR, AAAHN, AAADD and AAADH) generated using known efflux substrates. Phytochemical hits that matched with any one or more of the efflux substrate models were excluded from the study. Hits that do not have features similar to the efflux substrate models were docked using XP docking against the AcrB and MexB proteins. The best hits of the XP docking were validated by checkerboard synergy assay and ethidium bromide accumulation assay for their efflux inhibition potency. Lanatoside C and diadzein were filtered based on the synergistic potential and validated for their efflux inhibition potency using ethidium bromide accumulation study. These compounds exhibited the ability to increase the accumulation of ethidium bromide inside the bacterial cell as evidenced by these increase in fluorescence in the presence of the compounds. With this good correlation between in silico screening and positive efflux inhibitory activity in vitro, the two compounds, lanatoside C and diadzein could be promising efflux pump inhibitors and effective to use in combination therapy against drug resistant strains of P. aeruginosa and E. coli.

Novel Mycosin Protease MycP1 Inhibitors Identified by Virtual Screening and 4D Fingerprints

PubMed Central

2015-01-01

The rise of drug-resistant Mycobacterium tuberculosis lends urgency to the need for new drugs for the treatment of tuberculosis (TB). The identification of a serine protease, mycosin protease-1 (MycP1), as the crucial agent in hydrolyzing the virulence factor, ESX-secretion-associated protein B (EspB), potentially opens the door to new tuberculosis treatment options. Using the crystal structure of mycobacterial MycP1 in the apo form, we performed an iterative ligand- and structure-based virtual screening (VS) strategy to identify novel, nonpeptide, small-molecule inhibitors against MycP1 protease. Screening of ∼485 000 ligands from databases at the Genomics Research Institute (GRI) at the University of Cincinnati and the National Cancer Institute (NCI) using our VS approach, which integrated a pharmacophore model and consensus molecular shape patterns of active ligands (4D fingerprints), identified 81 putative inhibitors, and in vitro testing subsequently confirmed two of them as active inhibitors. Thereafter, the lead structures of each VS round were used to generate a new 4D fingerprint that enabled virtual rescreening of the chemical libraries. Finally, the iterative process identified a number of diverse scaffolds as lead compounds that were tested and found to have micromolar IC50 values against the MycP1 target. This study validated the efficiency of the SABRE 4D fingerprints as a means of identifying novel lead compounds in each screening round of the databases. Together, these results underscored the value of using a combination of in silico iterative ligand- and structure-based virtual screening of chemical libraries with experimental validation for the identification of promising structural scaffolds, such as the MycP1 inhibitors. PMID:24628123

GPURFSCREEN: a GPU based virtual screening tool using random forest classifier.

PubMed

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

2016-01-01

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

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.

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods, and Applications

PubMed Central

Zhao, Mingzhu; Wei, Dong-Qing

2013-01-01

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM. PMID:23818932

In Silico Screening-Level Prioritization of 8468 Chemicals Produced in OECD Countries to Identify Potential Planetary Boundary Threats.

PubMed

Reppas-Chrysovitsinos, Efstathios; Sobek, Anna; MacLeod, Matthew

2018-01-01

Legislation such as the Stockholm Convention and REACH aim to identify and regulate the production and use of chemicals that qualify as persistent organic pollutants (POPs) and very persistent and very bioaccumulative (vPvB) chemicals, respectively. Recently, a series of studies on planetary boundary threats proposed seven chemical hazard profiles that are distinct from the POP and vPvB profiles. We previously defined two exposure-based hazard profiles; airborne persistent contaminants (APCs) and waterborne persistent contaminants (WPCs) that correspond to two profiles of chemicals that are planetary boundary threats. Here, we extend our method to screen a database of chemicals consisting of 8648 substances produced within the OECD countries. We propose a new scoring scheme to disentangle the POP, vPvB, APC and WPC profiles by focusing on the spatial range of exposure potential, discuss the relationship between high exposure hazard and elemental composition of chemicals, and identify chemicals with high exposure hazard potential.

Contributions of computational chemistry and biophysical techniques to fragment-based drug discovery.

PubMed

Gozalbes, Rafael; Carbajo, Rodrigo J; Pineda-Lucena, Antonio

2010-01-01

In the last decade, fragment-based drug discovery (FBDD) has evolved from a novel approach in the search of new hits to a valuable alternative to the high-throughput screening (HTS) campaigns of many pharmaceutical companies. The increasing relevance of FBDD in the drug discovery universe has been concomitant with an implementation of the biophysical techniques used for the detection of weak inhibitors, e.g. NMR, X-ray crystallography or surface plasmon resonance (SPR). At the same time, computational approaches have also been progressively incorporated into the FBDD process and nowadays several computational tools are available. These stretch from the filtering of huge chemical databases in order to build fragment-focused libraries comprising compounds with adequate physicochemical properties, to more evolved models based on different in silico methods such as docking, pharmacophore modelling, QSAR and virtual screening. In this paper we will review the parallel evolution and complementarities of biophysical techniques and computational methods, providing some representative examples of drug discovery success stories by using FBDD.

Designing Second Generation Anti-Alzheimer Compounds as Inhibitors of Human Acetylcholinesterase: Computational Screening of Synthetic Molecules and Dietary Phytochemicals

PubMed Central

Amat-ur-Rasool, Hafsa; Ahmed, Mehboob

2015-01-01

Alzheimer's disease (AD), a big cause of memory loss, is a progressive neurodegenerative disorder. The disease leads to irreversible loss of neurons that result in reduced level of acetylcholine neurotransmitter (ACh). The reduction of ACh level impairs brain functioning. One aspect of AD therapy is to maintain ACh level up to a safe limit, by blocking acetylcholinesterase (AChE), an enzyme that is naturally responsible for its degradation. This research presents an in-silico screening and designing of hAChE inhibitors as potential anti-Alzheimer drugs. Molecular docking results of the database retrieved (synthetic chemicals and dietary phytochemicals) and self-drawn ligands were compared with Food and Drug Administration (FDA) approved drugs against AD as controls. Furthermore, computational ADME studies were performed on the hits to assess their safety. Human AChE was found to be most approptiate target site as compared to commonly used Torpedo AChE. Among the tested dietry phytochemicals, berberastine, berberine, yohimbine, sanguinarine, elemol and naringenin are the worth mentioning phytochemicals as potential anti-Alzheimer drugs The synthetic leads were mostly dual binding site inhibitors with two binding subunits linked by a carbon chain i.e. second generation AD drugs. Fifteen new heterodimers were designed that were computationally more efficient inhibitors than previously reported compounds. Using computational methods, compounds present in online chemical databases can be screened to design more efficient and safer drugs against cognitive symptoms of AD. PMID:26325402

Designing Second Generation Anti-Alzheimer Compounds as Inhibitors of Human Acetylcholinesterase: Computational Screening of Synthetic Molecules and Dietary Phytochemicals.

PubMed

Amat-Ur-Rasool, Hafsa; Ahmed, Mehboob

2015-01-01

Alzheimer's disease (AD), a big cause of memory loss, is a progressive neurodegenerative disorder. The disease leads to irreversible loss of neurons that result in reduced level of acetylcholine neurotransmitter (ACh). The reduction of ACh level impairs brain functioning. One aspect of AD therapy is to maintain ACh level up to a safe limit, by blocking acetylcholinesterase (AChE), an enzyme that is naturally responsible for its degradation. This research presents an in-silico screening and designing of hAChE inhibitors as potential anti-Alzheimer drugs. Molecular docking results of the database retrieved (synthetic chemicals and dietary phytochemicals) and self-drawn ligands were compared with Food and Drug Administration (FDA) approved drugs against AD as controls. Furthermore, computational ADME studies were performed on the hits to assess their safety. Human AChE was found to be most approptiate target site as compared to commonly used Torpedo AChE. Among the tested dietry phytochemicals, berberastine, berberine, yohimbine, sanguinarine, elemol and naringenin are the worth mentioning phytochemicals as potential anti-Alzheimer drugs The synthetic leads were mostly dual binding site inhibitors with two binding subunits linked by a carbon chain i.e. second generation AD drugs. Fifteen new heterodimers were designed that were computationally more efficient inhibitors than previously reported compounds. Using computational methods, compounds present in online chemical databases can be screened to design more efficient and safer drugs against cognitive symptoms of AD.

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.

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

Evaluating the Impact of Uncertainties in Clearance and Exposure When Prioritizing Chemicals Screened in High-Throughput Assays

EPA Science Inventory

The toxicity-testing paradigm has evolved to include high-throughput (HT) methods for addressing the increasing need to screen hundreds to thousands of chemicals rapidly. Approaches that involve in vitro screening assays, in silico predictions of exposure concentrations, and phar...

Merging in-silico and in vitro salivary protein complex partners using the STRING database: A tutorial.

PubMed

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

2018-01-16

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

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 .

In silico serotyping of E. coli from short read data identifies limited novel O-loci but extensive diversity of O:H serotype combinations within and between pathogenic lineages.

PubMed

Ingle, Danielle J; Valcanis, Mary; Kuzevski, Alex; Tauschek, Marija; Inouye, Michael; Stinear, Tim; Levine, Myron M; Robins-Browne, Roy M; Holt, Kathryn E

2016-07-01

The lipopolysaccharide (O) and flagellar (H) surface antigens of Escherichia coli are targets for serotyping that have traditionally been used to identify pathogenic lineages. These surface antigens are important for the survival of E. coli within mammalian hosts. However, traditional serotyping has several limitations, and public health reference laboratories are increasingly moving towards whole genome sequencing (WGS) to characterize bacterial isolates. Here we present a method to rapidly and accurately serotype E. coli isolates from raw, short read WGS data. Our approach bypasses the need for de novo genome assembly by directly screening WGS reads against a curated database of alleles linked to known and novel E. coli O-groups and H-types (the EcOH database) using the software package srst2. We validated the approach by comparing in silico results for 197 enteropathogenic E. coli isolates with those obtained by serological phenotyping in an independent laboratory. We then demonstrated the utility of our method to characterize isolates in public health and clinical settings, and to explore the genetic diversity of >1500 E. coli genomes from multiple sources. Importantly, we showed that transfer of O- and H-antigen loci between E. coli chromosomal backbones is common, with little evidence of constraints by host or pathotype, suggesting that E. coli ' strain space' may be virtually unlimited, even within specific pathotypes. Our findings show that serotyping is most useful when used in combination with strain genotyping to characterize microevolution events within an inferred population structure.

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?

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.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database

NASA Astrophysics Data System (ADS)

Dai, Shao-Xing; Li, Wen-Xing; Han, Fei-Fei; Guo, Yi-Cheng; Zheng, Jun-Juan; Liu, Jia-Qian; Wang, Qian; Gao, Yue-Dong; Li, Gong-Hua; Huang, Jing-Fei

2016-05-01

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database.

PubMed

Dai, Shao-Xing; Li, Wen-Xing; Han, Fei-Fei; Guo, Yi-Cheng; Zheng, Jun-Juan; Liu, Jia-Qian; Wang, Qian; Gao, Yue-Dong; Li, Gong-Hua; Huang, Jing-Fei

2016-05-05

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents.

In silico identification of anti-cancer compounds and plants from traditional Chinese medicine database

PubMed Central

Dai, Shao-Xing; Li, Wen-Xing; Han, Fei-Fei; Guo, Yi-Cheng; Zheng, Jun-Juan; Liu, Jia-Qian; Wang, Qian; Gao, Yue-Dong; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

There is a constant demand to develop new, effective, and affordable anti-cancer drugs. The traditional Chinese medicine (TCM) is a valuable and alternative resource for identifying novel anti-cancer agents. In this study, we aim to identify the anti-cancer compounds and plants from the TCM database by using cheminformatics. We first predicted 5278 anti-cancer compounds from TCM database. The top 346 compounds were highly potent active in the 60 cell lines test. Similarity analysis revealed that 75% of the 5278 compounds are highly similar to the approved anti-cancer drugs. Based on the predicted anti-cancer compounds, we identified 57 anti-cancer plants by activity enrichment. The identified plants are widely distributed in 46 genera and 28 families, which broadens the scope of the anti-cancer drug screening. Finally, we constructed a network of predicted anti-cancer plants and approved drugs based on the above results. The network highlighted the supportive role of the predicted plant in the development of anti-cancer drug and suggested different molecular anti-cancer mechanisms of the plants. Our study suggests that the predicted compounds and plants from TCM database offer an attractive starting point and a broader scope to mine for potential anti-cancer agents. PMID:27145869

Screening of synthetic and natural product databases: Identification of novel androgens and antiandrogens.

PubMed

Bobach, Claudia; Tennstedt, Stephanie; Palberg, Kristin; Denkert, Annika; Brandt, Wolfgang; de Meijere, Armin; Seliger, Barbara; Wessjohann, Ludger A

2015-01-27

The androgen receptor is an important pharmaceutical target for a variety of diseases. This paper presents an in silico/in vitro screening procedure to identify new androgen receptor ligands. The two-step virtual screening procedure uses a three-dimensional pharmacophore model and a docking/scoring routine. About 39,000 filtered compounds were docked with PLANTS and scored by Chemplp. Subsequent to virtual screening, 94 compounds, including 28 steroidal and 66 nonsteroidal compounds, were tested by an androgen receptor fluorescence polarization ligand displacement assay. As a result, 30 compounds were identified that show a relative binding affinity of more than 50% in comparison to 100 nM dihydrotestosterone and were classified as androgen receptor binders. For 11 androgen receptor binders of interest IC50 and Ki values were determined. The compound with the highest affinity exhibits a Ki value of 10.8 nM. Subsequent testing of the 11 compounds in a PC-3 and LNCaP multi readout proliferation assay provides insights into the potential mode of action. Further steroid receptor ligand displacement assays and docking studies on estrogen receptors α and β, glucocorticoid receptor, and progesterone receptor gave information about the specificity of the 11 most active compounds. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Screening for small molecule inhibitors of Toxoplasma gondii.

PubMed

Kortagere, Sandhya

2012-12-01

Toxoplasma gondii, the agent that causes toxoplasmosis, is an opportunistic parasite that infects many mammalian species. It is an obligate intracellular parasite that causes severe congenital neurological and ocular disease mostly in immunocompromised humans. The current regimen of therapy includes only a few medications that often lead to hypersensitivity and toxicity. In addition, there are no vaccines available to prevent the transmission of this agent. Therefore, safer and more effective medicines to treat toxoplasmosis are urgently needed. The author presents in silico and in vitro strategies that are currently used to screen for novel targets and unique chemotypes against T. gondii. Furthermore, this review highlights the screening technologies and characterization of some novel targets and new chemical entities that could be developed into highly efficacious treatments for toxoplasmosis. A number of diverse methods are being used to design inhibitors against T. gondii. These include ligand-based methods, in which drugs that have been shown to be efficacious against other Apicomplexa parasites can be repurposed to identify lead molecules against T. gondii. In addition, structure-based methods use currently available repertoire of structural information in various databases to rationally design small-molecule inhibitors of T. gondii. Whereas the screening methods have their advantages and limitations, a combination of methods is ideally suited to design small-molecule inhibitors of complex parasites such as T. gondii.

Virtual screening for novel Staphylococcus Aureus NorA efflux pump inhibitors from natural products.

PubMed

Thai, Khac-Minh; Ngo, Trieu-Du; Phan, Thien-Vy; Tran, Thanh-Dao; Nguyen, Ngoc-Vinh; Nguyen, Thien-Hai; Le, Minh-Tri

2015-01-01

NorA is a member of the Major Facilitator Superfamily (MFS) drug efflux pumps that have been shown to mediate antibiotic resistance in Staphylococcus aureus (SA). In this study, QSAR analysis, virtual screening and molecular docking were implemented in an effort to discover novel SA NorA efflux pump inhibitors. Originally, a set of 47 structurally diverse compounds compiled from the literature was used to develop linear QSAR models and another set of 15 different compounds were chosen for extra validation. The final model which was estimated by statistical values for the full data set (n = 45, Q(2) = 0.80, RMSE = 0.20) and for the external test set (n = 15, R(2) = 0.60, |res|max = 0.75, |res|min = 0.02) was applied on the collection of 182 flavonoides and the traditional Chinese medicine (TCM) database to screen for novel NorA inhibitors. Finally, 33 lead compounds that met the Lipinski's rules of five/three and had good predicted pIC50 values from in silico screening process were employed to analyze the binding ability by docking studies on NorA homology model in place of its unavailable crystal structures at two active sites, the central channel and the Walker B.

Privacy-preserving search for chemical compound databases.

PubMed

Shimizu, Kana; Nuida, Koji; Arai, Hiromi; Mitsunari, Shigeo; Attrapadung, Nuttapong; Hamada, Michiaki; Tsuda, Koji; Hirokawa, Takatsugu; Sakuma, Jun; Hanaoka, Goichiro; Asai, Kiyoshi

2015-01-01

Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources. In order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation. We proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information.

Privacy-preserving search for chemical compound databases

PubMed Central

2015-01-01

Background Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources. Results In order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation. Conclusion We proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information. PMID:26678650

Metallopeptidases of Toxoplasma gondii: in silico identification and gene expression.

PubMed

Escotte-Binet, Sandie; Huguenin, Antoine; Aubert, Dominique; Martin, Anne-Pascaline; Kaltenbach, Matthieu; Florent, Isabelle; Villena, Isabelle

2018-01-01

Metallopeptidases are a family of proteins with domains that remain highly conserved throughout evolution. These hydrolases require divalent metal cation(s) to activate the water molecule in order to carry out their catalytic action on peptide bonds by nucleophilic attack. Metallopeptidases from parasitic protozoa, including Toxoplasma, are investigated because of their crucial role in parasite biology. In the present study, we screened the T. gondii database using PFAM motifs specific for metallopeptidases in association with the MEROPS peptidase Database (release 10.0). In all, 49 genes encoding proteins with metallopeptidase signatures were identified in the Toxoplasma genome. An Interpro Search enabled us to uncover their domain/motif organization, and orthologs with the highest similarity by BLAST were used for annotation. These 49 Toxoplasma metallopeptidases clustered into 15 families described in the MEROPS database. Experimental expression analysis of their genes in the tachyzoite stage revealed transcription for all genes studied. Further research on the role of these peptidases should increase our knowledge of basic Toxoplasma biology and provide opportunities to identify novel therapeutic targets. This type of study would also open a path towards the comparative biology of apicomplexans. © S. Escotte-Binet et al., published by EDP Sciences, 2018.

Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking.

PubMed

Modi, Palmi; Patel, Shivani; Chhabria, Mahesh T

2018-05-04

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, 1 H-NMR, 13 C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

Computer-aided identification, synthesis, and biological evaluation of novel inhibitors for botulinum neurotoxin serotype A

DOE PAGES

Teng, Y. G.; Berger, W. T.; Nesbitt, N. M.; ...

2015-07-27

Botulinum neurotoxins (BoNTs) are among the most potent biological toxin known to humans, and are classified as Category A bioterrorism agents by the Centers for Disease Control and prevention (CDC). There are seven known BoNT serotypes (A-G) which have been thus far identified in literature. BoNTs have been shown to block neurotransmitter release by cleaving proteins of the soluble NSF attachment protein receptor (SNARE) complex. Disruption of the SNARE complex precludes motor neuron failure which ultimately results in flaccid paralysis in humans and animals. Currently, there are no effective therapeutic treatments against the neurotoxin light chain (LC) after translocation intomore » the cytosols of motor neurons. In this work, high-throughput virtual screening was employed to screen a library of commercially available compounds from ZINC database against BoNT/A-LC. Among the hit compounds from the in-silico screening, two lead compounds were identified and found to have potent inhibitory activity against BoNT/A-LC in vitro, as well as in Neuro-2a cells. A few analogues of the lead compounds were synthesized and their potency examined. One of these analogues showed an enhanced activity than the lead compounds« less

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.

IspE Inhibitors Identified by a Combination of In Silico and In Vitro High-Throughput Screening

PubMed Central

Tidten-Luksch, Naomi; Grimaldi, Raffaella; Torrie, Leah S.; Frearson, Julie A.; Hunter, William N.; Brenk, Ruth

2012-01-01

CDP-ME kinase (IspE) contributes to the non-mevalonate or deoxy-xylulose phosphate (DOXP) pathway for isoprenoid precursor biosynthesis found in many species of bacteria and apicomplexan parasites. IspE has been shown to be essential by genetic methods and since it is absent from humans it constitutes a promising target for antimicrobial drug development. Using in silico screening directed against the substrate binding site and in vitro high-throughput screening directed against both, the substrate and co-factor binding sites, non-substrate-like IspE inhibitors have been discovered and structure-activity relationships were derived. The best inhibitors in each series have high ligand efficiencies and favourable physico-chemical properties rendering them promising starting points for drug discovery. Putative binding modes of the ligands were suggested which are consistent with established structure-activity relationships. The applied screening methods were complementary in discovering hit compounds, and a comparison of both approaches highlights their strengths and weaknesses. It is noteworthy that compounds identified by virtual screening methods provided the controls for the biochemical screens. PMID:22563402

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification

PubMed Central

Ballester, Pedro J.; Mangold, Martina; Howard, Nigel I.; Robinson, Richard L. Marchese; Abell, Chris; Blumberger, Jochen; Mitchell, John B. O.

2012-01-01

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated Ki ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification. PMID:22933186

Hierarchical virtual screening for the discovery of new molecular scaffolds in antibacterial hit identification.

PubMed

Ballester, Pedro J; Mangold, Martina; Howard, Nigel I; Robinson, Richard L Marchese; Abell, Chris; Blumberger, Jochen; Mitchell, John B O

2012-12-07

One of the initial steps of modern drug discovery is the identification of small organic molecules able to inhibit a target macromolecule of therapeutic interest. A small proportion of these hits are further developed into lead compounds, which in turn may ultimately lead to a marketed drug. A commonly used screening protocol used for this task is high-throughput screening (HTS). However, the performance of HTS against antibacterial targets has generally been unsatisfactory, with high costs and low rates of hit identification. Here, we present a novel computational methodology that is able to identify a high proportion of structurally diverse inhibitors by searching unusually large molecular databases in a time-, cost- and resource-efficient manner. This virtual screening methodology was tested prospectively on two versions of an antibacterial target (type II dehydroquinase from Mycobacterium tuberculosis and Streptomyces coelicolor), for which HTS has not provided satisfactory results and consequently practically all known inhibitors are derivatives of the same core scaffold. Overall, our protocols identified 100 new inhibitors, with calculated K(i) ranging from 4 to 250 μM (confirmed hit rates are 60% and 62% against each version of the target). Most importantly, over 50 new active molecular scaffolds were discovered that underscore the benefits that a wide application of prospectively validated in silico screening tools is likely to bring to antibacterial hit identification.

In silico screening for Plasmodium falciparum enoyl-ACP reductase inhibitors

NASA Astrophysics Data System (ADS)

Lindert, Steffen; Tallorin, Lorillee; Nguyen, Quynh G.; Burkart, Michael D.; McCammon, J. Andrew

2015-01-01

The need for novel therapeutics against Plasmodium falciparum is urgent due to recent emergence of multi-drug resistant malaria parasites. Since fatty acids are essential for both the liver and blood stages of the malarial parasite, targeting fatty acid biosynthesis is a promising strategy for combatting P. falciparum. We present a combined computational and experimental study to identify novel inhibitors of enoyl-acyl carrier protein reductase ( PfENR) in the fatty acid biosynthesis pathway. A small-molecule database from ChemBridge was docked into three distinct PfENR crystal structures that provide multiple receptor conformations. Two different docking algorithms were used to generate a consensus score in order to rank possible small molecule hits. Our studies led to the identification of five low-micromolar pyrimidine dione inhibitors of PfENR.

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 Screening of the Human Gut Metaproteome Identifies Th17-Promoting Peptides Encrypted in Proteins of Commensal Bacteria

PubMed Central

Hidalgo-Cantabrana, Claudio; Moro-García, Marco A.; Blanco-Míguez, Aitor; Fdez-Riverola, Florentino; Lourenço, Anália; Alonso-Arias, Rebeca; Sánchez, Borja

2017-01-01

Scientific studies focused on the role of the human microbiome over human health have generated billions of gigabits of genetic information during the last decade. Nowadays integration of all this information in public databases and development of pipelines allowing us to biotechnologically exploit this information are urgently needed. Prediction of the potential bioactivity of the products encoded by the human gut microbiome, or metaproteome, is the first step for identifying proteins responsible for the molecular interaction between microorganisms and the immune system. We have recently published the Mechanism of Action of the Human Microbiome (MAHMI) database (http://www.mahmi.org), conceived as a resource compiling peptide sequences with a potential immunomodulatory activity. Fifteen out of the 300 hundred million peptides contained in the MAHMI database were synthesized. These peptides were identified as being encrypted in proteins produced by gut microbiota members, they do not contain cleavage points for the major intestinal endoproteases and displayed high probability to have immunomodulatory bioactivity. The bacterial peptides FR-16 and LR-17 encrypted in proteins from Bifidobacterium longum DJ010A and Bifidobacterium fragilis YCH46 respectively, showed the higher immune modulation capability over human peripheral blood mononuclear cells. Both peptides modulated the immune response toward increases in the Th17 and decreases in the Th1 cell response, together with an induction of IL-22 production. These results strongly suggest the combined use of bioinformatics and in vitro tools as a first stage in the screening of bioactive peptides encrypted in the human gut metaproteome. PMID:28943872

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

Combinatorial In Silico Strategy towards Identifying Potential Hotspots during Inhibition of Structurally Identical HDAC1 and HDAC2 Enzymes for Effective Chemotherapy against Neurological Disorders

PubMed Central

Ganai, Shabir Ahmad; Abdullah, Ehsaan; Rashid, Romana; Altaf, Mohammad

2017-01-01

Histone deacetylases (HDACs) regulate epigenetic gene expression programs by modulating chromatin architecture and are required for neuronal development. Dysregulation of HDACs and aberrant chromatin acetylation homeostasis have been implicated in various diseases ranging from cancer to neurodegenerative disorders. Histone deacetylase inhibitors (HDACi), the small molecules interfering HDACs have shown enhanced acetylation of the genome and are gaining great attention as potent drugs for treating cancer and neurodegeneration. HDAC2 overexpression has implications in decreasing dendrite spine density, synaptic plasticity and in triggering neurodegenerative signaling. Pharmacological intervention against HDAC2 though promising also targets neuroprotective HDAC1 due to high sequence identity (94%) with former in catalytic domain, culminating in debilitating off-target effects and creating hindrance in the defined intervention. This emphasizes the need of designing HDAC2-selective inhibitors to overcome these vicious effects and for escalating the therapeutic efficacy. Here we report a top-down combinatorial in silico approach for identifying the structural variants that are substantial for interactions against HDAC1 and HDAC2 enzymes. We used extra-precision (XP)-molecular docking, Molecular Mechanics Generalized Born Surface Area (MMGBSA) for predicting affinity of inhibitors against the HDAC1 and HDAC2 enzymes. Importantly, we employed a novel in silico strategy of coupling the state-of-the-art molecular dynamics simulation (MDS) to energetically-optimized structure based pharmacophores (e-Pharmacophores) method via MDS trajectory clustering for hypothesizing the e-Pharmacophore models. Further, we performed e-Pharmacophores based virtual screening against phase database containing millions of compounds. We validated the data by performing the molecular docking and MM-GBSA studies for the selected hits among the retrieved ones. Our studies attributed inhibitor potency to the ability of forming multiple interactions and infirm potency to least interactions. Moreover, our studies delineated that a single HDAC inhibitor portrays differential features against HDAC1 and HDAC2 enzymes. The high affinity and selective HDAC2 inhibitors retrieved through e-Pharmacophores based virtual screening will play a critical role in ameliorating neurodegenerative signaling without hampering the neuroprotective isoform (HDAC1). PMID:29170627

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 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.

Recovering actives in multi-antitarget and target design of analogs of the myosin II inhibitor blebbistatin

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

WISDOM-II: screening against multiple targets implicated in malaria using computational grid infrastructures.

PubMed

Kasam, Vinod; Salzemann, Jean; Botha, Marli; Dacosta, Ana; Degliesposti, Gianluca; Isea, Raul; Kim, Doman; Maass, Astrid; Kenyon, Colin; Rastelli, Giulio; Hofmann-Apitius, Martin; Breton, Vincent

2009-05-01

Despite continuous efforts of the international community to reduce the impact of malaria on developing countries, no significant progress has been made in the recent years and the discovery of new drugs is more than ever needed. Out of the many proteins involved in the metabolic activities of the Plasmodium parasite, some are promising targets to carry out rational drug discovery. Recent years have witnessed the emergence of grids, which are highly distributed computing infrastructures particularly well fitted for embarrassingly parallel computations like docking. In 2005, a first attempt at using grids for large-scale virtual screening focused on plasmepsins and ended up in the identification of previously unknown scaffolds, which were confirmed in vitro to be active plasmepsin inhibitors. Following this success, a second deployment took place in the fall of 2006 focussing on one well known target, dihydrofolate reductase (DHFR), and on a new promising one, glutathione-S-transferase. In silico drug design, especially vHTS is a widely and well-accepted technology in lead identification and lead optimization. This approach, therefore builds, upon the progress made in computational chemistry to achieve more accurate in silico docking and in information technology to design and operate large scale grid infrastructures. On the computational side, a sustained infrastructure has been developed: docking at large scale, using different strategies in result analysis, storing of the results on the fly into MySQL databases and application of molecular dynamics refinement are MM-PBSA and MM-GBSA rescoring. The modeling results obtained are very promising. Based on the modeling results, In vitro results are underway for all the targets against which screening is performed. The current paper describes the rational drug discovery activity at large scale, especially molecular docking using FlexX software on computational grids in finding hits against three different targets (PfGST, PfDHFR, PvDHFR (wild type and mutant forms) implicated in malaria. Grid-enabled virtual screening approach is proposed to produce focus compound libraries for other biological targets relevant to fight the infectious diseases of the developing world.

High throughput ADME screening: practical considerations, impact on the portfolio and enabler of in silico ADME models.

PubMed

Hop, Cornelis E C A; Cole, Mark J; Davidson, Ralph E; Duignan, David B; Federico, James; Janiszewski, John S; Jenkins, Kelly; Krueger, Suzanne; Lebowitz, Rebecca; Liston, Theodore E; Mitchell, Walter; Snyder, Mark; Steyn, Stefan J; Soglia, John R; Taylor, Christine; Troutman, Matt D; Umland, John; West, Michael; Whalen, Kevin M; Zelesky, Veronica; Zhao, Sabrina X

2008-11-01

Evaluation and optimization of drug metabolism and pharmacokinetic data plays an important role in drug discovery and development and several reliable in vitro ADME models are available. Recently higher throughput in vitro ADME screening facilities have been established in order to be able to evaluate an appreciable fraction of synthesized compounds. The ADME screening process can be dissected in five distinct steps: (1) plate management of compounds in need of in vitro ADME data, (2) optimization of the MS/MS method for the compounds, (3) in vitro ADME experiments and sample clean up, (4) collection and reduction of the raw LC-MS/MS data and (5) archival of the processed ADME data. All steps will be described in detail and the value of the data on drug discovery projects will be discussed as well. Finally, in vitro ADME screening can generate large quantities of data obtained under identical conditions to allow building of reliable in silico models.

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.

Generation of ligand-based pharmacophore model and virtual screening for identification of novel tubulin inhibitors with potent anticancer activity.

PubMed

Chiang, Yi-Kun; Kuo, Ching-Chuan; Wu, Yu-Shan; Chen, Chung-Tong; Coumar, Mohane Selvaraj; Wu, Jian-Sung; Hsieh, Hsing-Pang; Chang, Chi-Yen; Jseng, Huan-Yi; Wu, Ming-Hsine; Leou, Jiun-Shyang; Song, Jen-Shin; Chang, Jang-Yang; Lyu, Ping-Chiang; Chao, Yu-Sheng; Wu, Su-Ying

2009-07-23

A pharmacophore model, Hypo1, was built on the basis of 21 training-set indole compounds with varying levels of antiproliferative activity. Hypo1 possessed important chemical features required for the inhibitors and demonstrated good predictive ability for biological activity, with high correlation coefficients of 0.96 and 0.89 for the training-set and test-set compounds, respectively. Further utilization of the Hypo1 pharmacophore model to screen chemical database in silico led to the identification of four compounds with antiproliferative activity. Among these four compounds, 43 showed potent antiproliferative activity against various cancer cell lines with the strongest inhibition on the proliferation of KB cells (IC(50) = 187 nM). Further biological characterization revealed that 43 effectively inhibited tubulin polymerization and significantly induced cell cycle arrest in G(2)-M phase. In addition, 43 also showed the in vivo-like anticancer effects. To our knowledge, 43 is the most potent antiproliferative compound with antitubulin activity discovered by computer-aided drug design. The chemical novelty of 43 and its anticancer activities make this compound worthy of further lead optimization.

Targeting SDF-1/CXCL12 with a ligand that prevents activation of CXCR4 through structure based drug design

PubMed Central

Veldkamp, Christopher T.; Ziarek, Joshua J.; Peterson, Francis C.; Chen, Yu; Volkman, Brian F.

2010-01-01

CXCL12 is an attractive target for clinical therapy because of its involvement in autoimmune diseases, cancer growth, metastasis, and neovascularization. Tyrosine sulfation at three positions in the CXCR4 N-terminus is crucial for specific, high-affinity CXCL12 binding. An NMR structure of the complex between the CXCL12 dimer and a sulfotyrosine-containing CXCR4 fragment enabled high-throughput in silico screening for inhibitors of the chemokine-receptor interface. A total of 1.4 million compounds from the ZINC database were docked into a cleft on the CXCL12 surface normally occupied by sulfotyrosine 21 (sY21), and five were selected for experimental screening. NMR titrations with CXCL12 revealed that four compounds occupy the sY21 site, one of which binds with a Kd of 64 µM. This compound selectively inhibits SDF1-induced CXCR4 signaling in THP1 cells. Our results suggest that sulfotyrosine recognition sites can be targeted for the development of novel chemokine inhibitors. PMID:20459090

In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen

PubMed Central

Plouffe, David; Brinker, Achim; McNamara, Case; Henson, Kerstin; Kato, Nobutaka; Kuhen, Kelli; Nagle, Advait; Adrián, Francisco; Matzen, Jason T.; Anderson, Paul; Nam, Tae-gyu; Gray, Nathanael S.; Chatterjee, Arnab; Janes, Jeff; Yan, S. Frank; Trager, Richard; Caldwell, Jeremy S.; Schultz, Peter G.; Zhou, Yingyao; Winzeler, Elizabeth A.

2008-01-01

The growing resistance to current first-line antimalarial drugs represents a major health challenge. To facilitate the discovery of new antimalarials, we have implemented an efficient and robust high-throughput cell-based screen (1,536-well format) based on proliferation of Plasmodium falciparum (Pf) in erythrocytes. From a screen of ≈1.7 million compounds, we identified a diverse collection of ≈6,000 small molecules comprised of >530 distinct scaffolds, all of which show potent antimalarial activity (<1.25 μM). Most known antimalarials were identified in this screen, thus validating our approach. In addition, we identified many novel chemical scaffolds, which likely act through both known and novel pathways. We further show that in some cases the mechanism of action of these antimalarials can be determined by in silico compound activity profiling. This method uses large datasets from unrelated cellular and biochemical screens and the guilt-by-association principle to predict which cellular pathway and/or protein target is being inhibited by select compounds. In addition, the screening method has the potential to provide the malaria community with many new starting points for the development of biological probes and drugs with novel antiparasitic activities. PMID:18579783

Prediction of anti-cancer drug response by kernelized multi-task learning.

PubMed

Tan, Mehmet

2016-10-01

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

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

Development of Drugs for Epstein - Barr virus using High-Throughput in silico Virtual Screening

PubMed Central

Li, Ning; Thompson, Scott; Jiang, Hualiang; Lieberman, Paul M.; Luo, Cheng

2010-01-01

Importance of the field Epstein-Barr virus (EBV) is a ubiquitious human herpesvirus that is causally associated with endemic forms of Burkitt’s lymphoma (BL), nasopharyngeal carcinoma, and lymphoproliferative disease in immunosuppressed individuals. On a global scale, EBV infects over 90% of the adult population and is responsible for ~1% of all human cancers. To date, there is no efficacious drug or therapy for the treatment of EBV infection and EBV-related diseases. Areas covered in this review In this review, we discuss the existing anti-EBV inhibitors and those under development. We discuss the value of different molecular targets, including EBV lytic DNA replication enzymes, as well as proteins that are expressed exclusively during latent infection, like EBNA1 and LMP1. Since the atomic structure of the EBNA1 DNA binding domain has been described, it is an attractive target for in silico methods of drug design and small molecule screening. We discuss the use of computational methods that can greatly facilitate the development of novel inhibitors and how in silico screening methods can be applied to target proteins with known structures, like EBNA1, to treat EBV infection and disease. What the reader will gain The reader will be familiarized with the problems in targeting of EBV for inhibition by small molecules and how computational methods can greatly facilitate this process. Take home message Despite the impressive efficacy of nucleoside analogues for the treatment of herpesvirus lytic infection, there remain few effective treatments for latent infections. Since EBV-latent infection persists within and contributes to the formation of EBV-associated cancers, targeting EBV latent proteins is an unmet medical need. High throughput in silico screening can accelerate the process of drug discovery for novel and selective agents that inhibit EBV latent infection and associated disease. PMID:22822721

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...

Extrapolating toxicity data across species using U.S. EPA SeqAPASS tool

EPA Science Inventory

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

In silico fragment-based drug design.

PubMed

Konteatis, Zenon D

2010-11-01

In silico fragment-based drug design (FBDD) is a relatively new approach inspired by the success of the biophysical fragment-based drug discovery field. Here, we review the progress made by this approach in the last decade and showcase how it complements and expands the capabilities of biophysical FBDD and structure-based drug design to generate diverse, efficient drug candidates. Advancements in several areas of research that have enabled the development of in silico FBDD and some applications in drug discovery projects are reviewed. The reader is introduced to various computational methods that are used for in silico FBDD, the fragment library composition for this technique, special applications used to identify binding sites on the surface of proteins and how to assess the druggability of these sites. In addition, the reader will gain insight into the proper application of this approach from examples of successful programs. In silico FBDD captures a much larger chemical space than high-throughput screening and biophysical FBDD increasing the probability of developing more diverse, patentable and efficient molecules that can become oral drugs. The application of in silico FBDD holds great promise for historically challenging targets such as protein-protein interactions. Future advances in force fields, scoring functions and automated methods for determining synthetic accessibility will all aid in delivering more successes with in silico FBDD.

ADMET in silico modelling: towards prediction paradise?

PubMed

van de Waterbeemd, Han; Gifford, Eric

2003-03-01

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

GENIUS In Silico Screening Technology for HCV Drug Discovery.

PubMed

Patil, Vaishali M; Masand, Neeraj; Gupta, Satya P

2016-01-01

The various reported in silico screening protocols such as molecular docking are associated with various drawbacks as well as benefits. In molecular docking, on interaction with ligand, the protein or receptor molecule gets activated by adopting conformational changes. These conformational changes cannot be utilized to predict the 3D structure of a protein-ligand complex from unbound protein conformations rigid docking, which necessitates the demand for understanding protein flexibility. Therefore, efficiency and accuracy of docking should be achieved and various available/developed protocols may be adopted. One such protocol is GENIUS induced-fit docking and it is used effectively for the development of anti-HCV NS3-4A serine protease inhibitors. The present review elaborates the GENIUS docking protocol along with its benefits and drawbacks.

PeroxisomeDB: a database for the peroxisomal proteome, functional genomics and disease

PubMed Central

Schlüter, Agatha; Fourcade, Stéphane; Domènech-Estévez, Enric; Gabaldón, Toni; Huerta-Cepas, Jaime; Berthommier, Guillaume; Ripp, Raymond; Wanders, Ronald J. A.; Poch, Olivier; Pujol, Aurora

2007-01-01

Peroxisomes are essential organelles of eukaryotic origin, ubiquitously distributed in cells and organisms, playing key roles in lipid and antioxidant metabolism. Loss or malfunction of peroxisomes causes more than 20 fatal inherited conditions. We have created a peroxisomal database () that includes the complete peroxisomal proteome of Homo sapiens and Saccharomyces cerevisiae, by gathering, updating and integrating the available genetic and functional information on peroxisomal genes. PeroxisomeDB is structured in interrelated sections ‘Genes’, ‘Functions’, ‘Metabolic pathways’ and ‘Diseases’, that include hyperlinks to selected features of NCBI, ENSEMBL and UCSC databases. We have designed graphical depictions of the main peroxisomal metabolic routes and have included updated flow charts for diagnosis. Precomputed BLAST, PSI-BLAST, multiple sequence alignment (MUSCLE) and phylogenetic trees are provided to assist in direct multispecies comparison to study evolutionary conserved functions and pathways. Highlights of the PeroxisomeDB include new tools developed for facilitating (i) identification of novel peroxisomal proteins, by means of identifying proteins carrying peroxisome targeting signal (PTS) motifs, (ii) detection of peroxisomes in silico, particularly useful for screening the deluge of newly sequenced genomes. PeroxisomeDB should contribute to the systematic characterization of the peroxisomal proteome and facilitate system biology approaches on the organelle. PMID:17135190

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

PubMed

Dziuba, Bartłomiej; Dziuba, Marta

2014-08-20

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

New Milk Protein-Derived Peptides with Potential Antimicrobial Activity: An Approach Based on Bioinformatic Studies

PubMed Central

Dziuba, Bartłomiej; Dziuba, Marta

2014-01-01

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

Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

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

Signaling pathway cloud regulation for in silico screening and ranking of the potential geroprotective drugs

PubMed Central

Zhavoronkov, Alex; Buzdin, Anton A.; Garazha, Andrey V.; Borisov, Nikolay M.; Moskalev, Alexey A.

2014-01-01

The major challenges of aging research include absence of the comprehensive set of aging biomarkers, the time it takes to evaluate the effects of various interventions on longevity in humans and the difficulty extrapolating the results from model organisms to humans. To address these challenges we propose the in silico method for screening and ranking the possible geroprotectors followed by the high-throughput in vivo and in vitro validation. The proposed method evaluates the changes in the collection of activated or suppressed signaling pathways involved in aging and longevity, termed signaling pathway cloud, constructed using the gene expression data and epigenetic profiles of young and old patients' tissues. The possible interventions are selected and rated according to their ability to regulate age-related changes and minimize differences in the signaling pathway cloud. While many algorithmic solutions to simulating the induction of the old into young metabolic profiles in silico are possible, this flexible and scalable approach may potentially be used to predict the efficacy of the many drugs that may extend human longevity before conducting pre-clinical work and expensive clinical trials. PMID:24624136

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

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

EPA Science Inventory

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

Cross-species extrapolation of mammalian-based ToxCast Data using Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS)

EPA Science Inventory

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

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.

Screening for Antimicrobial Resistance Genes and Virulence Factors via Genome Sequencing▿†

PubMed Central

Bennedsen, Mads; Stuer-Lauridsen, Birgitte; Danielsen, Morten; Johansen, Eric

2011-01-01

Second-generation genome sequencing and alignment of the resulting reads to in silico genomes containing antimicrobial resistance and virulence factor genes were used to screen for undesirable genes in 28 strains which could be used in human nutrition. No virulence factor genes were detected, while several isolates contained antimicrobial resistance genes. PMID:21335393

Complementing in vitro screening assays with in silico molecular chemistry tools to examine potential in vivo metabolite-mediated effects

EPA Science Inventory

High-throughput in vitro assays offer a rapid, cost-efficient means to screen thousands of chemicals across hundreds of pathway-based toxicity endpoints. However, one main concern involved with the use of in vitro assays is the erroneous omission of chemicals that are inactive un...

Fungal Screening on Olive Oil for Extracellular Triacylglycerol Lipases: Selection of a Trichoderma harzianum Strain and Genome Wide Search for the Genes

PubMed Central

Canseco-Pérez, Miguel Angel; Castillo-Avila, Genny Margarita; Islas-Flores, Ignacio; Apolinar-Hernández, Max M.; Rivera-Muñoz, Gerardo; Gamboa-Angulo, Marcela; Couoh-Uicab, Yeny

2018-01-01

A lipolytic screening with fungal strains isolated from lignocellulosic waste collected in banana plantation dumps was carried out. A Trichoderma harzianum strain (B13-1) showed good extracellular lipolytic activity (205 UmL−1). Subsequently, functional screening of the lipolytic activity on Rhodamine B enriched with olive oil as the only carbon source was performed. The successful growth of the strain allows us to suggest that a true lipase is responsible for the lipolytic activity in the B13-1 strain. In order to identify the gene(s) encoding the protein responsible for the lipolytic activity, in silico identification and characterization of triacylglycerol lipases from T. harzianum is reported for the first time. A survey in the genome of this fungus retrieved 50 lipases; however, bioinformatic analyses and putative functional descriptions in different databases allowed us to choose seven lipases as candidates. Suitability of the bioinformatic screening to select the candidates was confirmed by reverse transcription polymerase chain reaction (RT-PCR). The gene codifying 526309 was expressed when the fungus grew in a medium with olive oil as carbon source. This protein shares homology with commercial lipases, making it a candidate for further applications. The success in identifying a lipase gene inducible with olive oil and the suitability of the functional screening and bioinformatic survey carried out herein, support the premise that the strategy can be used in other microorganisms with sequenced genomes to search for true lipases, or other enzymes belonging to large protein families. PMID:29370083

Common Amino Acid Subsequences in a Universal Proteome—Relevance for Food Science

PubMed Central

Minkiewicz, Piotr; Darewicz, Małgorzata; Iwaniak, Anna; Sokołowska, Jolanta; Starowicz, Piotr; Bucholska, Justyna; Hrynkiewicz, Monika

2015-01-01

A common subsequence is a fragment of the amino acid chain that occurs in more than one protein. Common subsequences may be an object of interest for food scientists as biologically active peptides, epitopes, and/or protein markers that are used in comparative proteomics. An individual bioactive fragment, in particular the shortest fragment containing two or three amino acid residues, may occur in many protein sequences. An individual linear epitope may also be present in multiple sequences of precursor proteins. Although recent recommendations for prediction of allergenicity and cross-reactivity include not only sequence identity, but also similarities in secondary and tertiary structures surrounding the common fragment, local sequence identity may be used to screen protein sequence databases for potential allergens in silico. The main weakness of the screening process is that it overlooks allergens and cross-reactivity cases without identical fragments corresponding to linear epitopes. A single peptide may also serve as a marker of a group of allergens that belong to the same family and, possibly, reveal cross-reactivity. This review article discusses the benefits for food scientists that follow from the common subsequences concept. PMID:26340620

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

PubMed

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

2018-06-18

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

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.

Integrating In Silico Resources to Map a Signaling Network

PubMed Central

Liu, Hanqing; Beck, Tim N.; Golemis, Erica A.; Serebriiskii, Ilya G.

2013-01-01

The abundance of publicly available life science databases offer a wealth of information that can support interpretation of experimentally derived data and greatly enhance hypothesis generation. Protein interaction and functional networks are not simply new renditions of existing data: they provide the opportunity to gain insights into the specific physical and functional role a protein plays as part of the biological system. In this chapter, we describe different in silico tools that can quickly and conveniently retrieve data from existing data repositories and discuss how the available tools are best utilized for different purposes. While emphasizing protein-protein interaction databases (e.g., BioGrid and IntAct), we also introduce metasearch platforms such as STRING and GeneMANIA, pathway databases (e.g., BioCarta and Pathway Commons), text mining approaches (e.g., PubMed and Chilibot), and resources for drug-protein interactions, genetic information for model organisms and gene expression information based on microarray data mining. Furthermore, we provide a simple step-by-step protocol to building customized protein-protein interaction networks in Cytoscape, a powerful network assembly and visualization program, integrating data retrieved from these various databases. As we illustrate, generation of composite interaction networks enables investigators to extract significantly more information about a given biological system than utilization of a single database or sole reliance on primary literature. PMID:24233784

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.

Structural insights into pharmacophore-assisted in silico identification of protein-protein interaction inhibitors for inhibition of human toll-like receptor 4 - myeloid differentiation factor-2 (hTLR4-MD-2) complex.

PubMed

Mishra, Vinita; Pathak, Chandramani

2018-05-29

Toll-like receptor 4 (TLR4) is a member of Toll-Like Receptors (TLRs) family that serves as a receptor for bacterial lipopolysaccharide (LPS). TLR4 alone cannot recognize LPS without aid of co-receptor myeloid differentiation factor-2 (MD-2). Binding of LPS with TLR4 forms a LPS-TLR4-MD-2 complex and directs downstream signaling for activation of immune response, inflammation and NF-κB activation. Activation of TLR4 signaling is associated with various pathophysiological consequences. Therefore, targeting protein-protein interaction (PPI) in TLR4-MD-2 complex formation could be an attractive therapeutic approach for targeting inflammatory disorders. The aim of present study was directed to identify small molecule PPI inhibitors (SMPPIIs) using pharmacophore mapping-based approach of computational drug discovery. Here, we had retrieved the information about the hot spot residues and their pharmacophoric features at both primary (TLR4-MD-2) and dimerization (MD-2-TLR4*) protein-protein interaction interfaces in TLR4-MD-2 homo-dimer complex using in silico methods. Promising candidates were identified after virtual screening, which may restrict TLR4-MD-2 protein-protein interaction. In silico off-target profiling over the virtually screened compounds revealed other possible molecular targets. Two of the virtually screened compounds (C11 and C15) were predicted to have an inhibitory concentration in μM range after HYDE assessment. Molecular dynamics simulation study performed for these two compounds in complex with target protein confirms the stability of the complex. After virtual high throughput screening we found selective hTLR4-MD-2 inhibitors, which may have therapeutic potential to target chronic inflammatory diseases.

QSAR Classification of ToxCast and Tox21 Chemicals on the Basis of Estrogen Receptor Assays (FutureToxII)

EPA Science Inventory

The ToxCast and Tox21 programs have tested ~8,200 chemicals in a broad screening panel of in vitro high-throughput screening (HTS) assays for estrogen receptor (ER) agonist and antagonist activity. The present work uses this large in vitro data set to develop in silico QSAR model...

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

PubMed

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

2014-04-01

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

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

NASA Astrophysics Data System (ADS)

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

2014-04-01

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

Pharmacophore Modeling and in Silico/in Vitro Screening for Human Cytochrome P450 11B1 and Cytochrome P450 11B2 Inhibitors.

PubMed

Akram, Muhammad; Waratchareeyakul, Watcharee; Haupenthal, Joerg; Hartmann, Rolf W; Schuster, Daniela

2017-01-01

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing's syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8 - 10 ), one selective CYP11B1 inhibitor (Compound 11 , IC 50 = 2.5 μM), and one selective CYP11B2 inhibitor (compound 12 , IC 50 = 1.1 μM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

Pharmacophore modeling and in silico / in vitro screening for human cytochrome P450 11B1 & cytochrome P450 11B2 inhibitors

NASA Astrophysics Data System (ADS)

Akram, Muhammad; Waratchareeyakul, Watcharee; Haupenthal, Joerg; Hartmann, Rolf W.; Schuster, Daniela

2017-12-01

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing’s syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8-10), one selective CYP11B1 inhibitor (Compound 11, IC50 = 2.5 µM), and one selective CYP11B2 inhibitor (compound 12, IC50 = 1.1 µM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models.

In Silico Approaches and the Role of Ontologies in Aging Research

PubMed Central

Boerries, Melanie; Busch, Hauke; de Grey, Aubrey; Hahn, Udo; Hiller, Thomas; Hoeflich, Andreas; Jansen, Ludger; Janssens, Georges E.; Kaleta, Christoph; Meinema, Anne C.; Schäuble, Sascha; Simm, Andreas; Schofield, Paul N.; Smith, Barry; Sühnel, Juergen; Vera, Julio; Wagner, Wolfgang; Wönne, Eva C.; Wuttke, Daniel

2013-01-01

Abstract The 2013 Rostock Symposium on Systems Biology and Bioinformatics in Aging Research was again dedicated to dissecting the aging process using in silico means. A particular focus was on ontologies, because these are a key technology to systematically integrate heterogeneous information about the aging process. Related topics were databases and data integration. Other talks tackled modeling issues and applications, the latter including talks focused on marker development and cellular stress as well as on diseases, in particular on diseases of kidney and skin. PMID:24188080

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.

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.

A synthetic compound that potentiates bone morphogenetic protein-2-induced transdifferentiation of myoblasts into the osteoblastic phenotype

PubMed Central

Kato, Satoshi; Tomita, Katsuro; Titus, Louisa; Boden, Scott D.

2011-01-01

There is an urgent need to develop methods that lower costs of using recombinant human bone morphogenetic proteins (BMPs) to promote bone induction. In this study, we demonstrate the osteogenic effect of a low-molecular weight compound, SVAK-12, that potentiated the effects of BMP-2 in inducing transdifferentiation of C2C12 myoblasts into the osteoblastic phenotype. Here, we report a specific compound, SVAK-12, which was selected based on in silico screenings of small-molecule databases using the homology modeled interaction motif of Smurf1-WW2 domain. The enhancement of BMP-2 activity by SVAK-12 was characterized by evaluating a BMP-specific reporter activity and by monitoring the BMP-2-induced expression of mRNA for osteocalcin and alkaline phosphatase (ALP), which are widely accepted marker genes of osteoblast differentiation. Finally, we confirmed these results by also measuring the enhancement of BMP-2-induced activity of ALP. Smurf1 is an E3 ligase that targets osteogenic Smads for ubiquitin-mediated proteasomal degradation. Smurf1 is an interesting potential target to enhance bone formation based on the positive effects on bone of proteins that block Smurf1-binding to Smad targets or in Smurf1−/− knockout mice. Since Smads bind Smurf1 via its WW2 domain, we performed in silico screening to identify compounds that might interact with the Smurf1-WW2 domain. We recently reported the activity of a compound, SVAK-3. However, SVAK-3, while exhibiting BMP-potentiating activity, was not stable and thus warranted a new search for a more stable and efficacious compound among a selected group of candidates. In addition to being more stable, SVAK-12 exhibited a dose-dependent activity in inducing osteoblastic differentiation of myoblastic C2C12 cells even when multiple markers of the osteoblastic phenotype were parallelly monitored. PMID:21110071

A synthetic compound that potentiates bone morphogenetic protein-2-induced transdifferentiation of myoblasts into the osteoblastic phenotype.

PubMed

Kato, Satoshi; Sangadala, Sreedhara; Tomita, Katsuro; Titus, Louisa; Boden, Scott D

2011-03-01

There is an urgent need to develop methods that lower costs of using recombinant human bone morphogenetic proteins (BMPs) to promote bone induction. In this study, we demonstrate the osteogenic effect of a low-molecular weight compound, SVAK-12, that potentiated the effects of BMP-2 in inducing transdifferentiation of C2C12 myoblasts into the osteoblastic phenotype. Here, we report a specific compound, SVAK-12, which was selected based on in silico screenings of small-molecule databases using the homology modeled interaction motif of Smurf1-WW2 domain. The enhancement of BMP-2 activity by SVAK-12 was characterized by evaluating a BMP-specific reporter activity and by monitoring the BMP-2-induced expression of mRNA for osteocalcin and alkaline phosphatase (ALP), which are widely accepted marker genes of osteoblast differentiation. Finally, we confirmed these results by also measuring the enhancement of BMP-2-induced activity of ALP. Smurf1 is an E3 ligase that targets osteogenic Smads for ubiquitin-mediated proteasomal degradation. Smurf1 is an interesting potential target to enhance bone formation based on the positive effects on bone of proteins that block Smurf1-binding to Smad targets or in Smurf1-/- knockout mice. Since Smads bind Smurf1 via its WW2 domain, we performed in silico screening to identify compounds that might interact with the Smurf1-WW2 domain. We recently reported the activity of a compound, SVAK-3. However, SVAK-3, while exhibiting BMP-potentiating activity, was not stable and thus warranted a new search for a more stable and efficacious compound among a selected group of candidates. In addition to being more stable, SVAK-12 exhibited a dose-dependent activity in inducing osteoblastic differentiation of myoblastic C2C12 cells even when multiple markers of the osteoblastic phenotype were parallelly monitored.

Virtual screening and optimization of Type II inhibitors of JAK2 from a natural product library.

PubMed

Ma, Dik-Lung; Chan, Daniel Shiu-Hin; Wei, Guo; Zhong, Hai-Jing; Yang, Hui; Leung, Lai To; Gullen, Elizabeth A; Chiu, Pauline; Cheng, Yung-Chi; Leung, Chung-Hang

2014-11-21

Amentoflavone has been identified as a JAK2 inhibitor by structure-based virtual screening of a natural product library. In silico optimization using the DOLPHIN model yielded analogues with enhanced potency against JAK2 activity and HCV activity in cellulo. Molecular modeling and kinetic experiments suggested that the analogues may function as Type II inhibitors of JAK2.

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.

Discovery of novel Trypanosoma brucei phosphodiesterase B1 inhibitors by virtual screening against the unliganded TbrPDEB1 crystal structure

PubMed Central

Jansen, Chimed; Wang, Huanchen; Kooistra, Albert J.; de Graaf, Chris; Orrling, Kristina; Tenor, Hermann; Seebeck, Thomas; Bailey, David; de Esch, Iwan J.P.; Ke, Hengming; Leurs, Rob

2013-01-01

Trypanosoma brucei cyclic nucleotide phosphodiesterase B1 (TbrPDEB1) and TbrPDEB2 have recently been validated as new therapeutic targets for human African Trypanosomiasis by both genetic and pharmacological means. In this study we report the crystal structure of the catalytic domain of the unliganded TbrPDEB1 and its use for the in silico screening for new TbrPDEB1 inhibitors with novel scaffolds. The TbrPDEB1 crystal structure shows the characteristic folds of human PDE enzymes, but also contains the parasite-specific P-pocket found in the structures of Leishmania major PDEB1 and Trypanosoma cruzi PDEC. The unliganded TbrPDEB1 X-ray structure was subjected to a structure-based in silico screening approach that combines molecular docking simulations with a protein-ligand interaction fingerprint (IFP) scoring method. This approach identified, six novel TbrPDEB1 inhibitors with IC50 values of 10–80 μM, which may be further optimized as potential selective TbrPDEB inhibitors. PMID:23409953

Characterization and validation of an in silico toxicology model to predict the mutagenic potential of drug impurities*

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

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

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

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

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

Screening and identification of potential PTP1B allosteric inhibitors using in silico and in vitro approaches.

PubMed

Shinde, Ranajit Nivrutti; Kumar, G Siva; Eqbal, Shahbaz; Sobhia, M Elizabeth

2018-01-01

Protein tyrosine phosphatase 1B (PTP1B) is a validated therapeutic target for Type 2 diabetes due to its specific role as a negative regulator of insulin signaling pathways. Discovery of active site directed PTP1B inhibitors is very challenging due to highly conserved nature of the active site and multiple charge requirements of the ligands, which makes them non-selective and non-permeable. Identification of the PTP1B allosteric site has opened up new avenues for discovering potent and selective ligands for therapeutic intervention. Interactions made by potent allosteric inhibitor in the presence of PTP1B were studied using Molecular Dynamics (MD). Computationally optimized models were used to build separate pharmacophore models of PTP1B and TCPTP, respectively. Based on the nature of interactions the target residues offered, a receptor based pharmacophore was developed. The pharmacophore considering conformational flexibility of the residues was used for the development of pharmacophore hypothesis to identify potentially active inhibitors by screening large compound databases. Two pharmacophore were successively used in the virtual screening protocol to identify potential selective and permeable inhibitors of PTP1B. Allosteric inhibition mechanism of these molecules was established using molecular docking and MD methods. The geometrical criteria values confirmed their ability to stabilize PTP1B in an open conformation. 23 molecules that were identified as potential inhibitors were screened for PTP1B inhibitory activity. After screening, 10 molecules which have good permeability values were identified as potential inhibitors of PTP1B. This study confirms that selective and permeable inhibitors can be identified by targeting allosteric site of PTP1B.

In silico approaches to identify novel myeloid cell leukemia-1 (Mcl-1) inhibitors for treatment of cancer.

PubMed

Ren, Ji-Xia; Li, Cheng-Ping; Zhou, Xiu-Ling; Cao, Xue-Song; Xie, Yong

2017-08-22

Myeloid cell leukemia-1 (Mcl-1) has been a validated and attractive target for cancer therapy. Over-expression of Mcl-1 in many cancers allows cancer cells to evade apoptosis and contributes to the resistance to current chemotherapeutics. Here, we identified new Mcl-1 inhibitors using a multi-step virtual screening approach. First, based on two different ligand-receptor complexes, 20 pharmacophore models were established by simultaneously using 'Receptor-Ligand Pharmacophore Generation' method and manual build feature method, and then carefully validated by a test database. Then, pharmacophore-based virtual screening (PB-VS) could be performed by using the 20 pharmacophore models. In addition, docking study was used to predict the possible binding poses of compounds, and the docking parameters were optimized before performing docking-based virtual screening (DB-VS). Moreover, a 3D QSAR model was established by applying the 55 aligned Mcl-1 inhibitors. The 55 inhibitors sharing the same scaffold were docked into the Mcl-1 active site before alignment, then the inhibitors with possible binding conformations were aligned. For the training set, the 3D QSAR model gave a correlation coefficient r 2 of 0.996; for the test set, the correlation coefficient r 2 was 0.812. Therefore, the developed 3D QSAR model was a good model, which could be applied for carrying out 3D QSAR-based virtual screening (QSARD-VS). After the above three virtual screening methods orderly filtering, 23 potential inhibitors with novel scaffolds were identified. Furthermore, we have discussed in detail the mapping results of two potent compounds onto pharmacophore models, 3D QSAR model, and the interactions between the compounds and active site residues.

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.

Genome mining reveals the genus Xanthomonas to be a promising reservoir for new bioactive non-ribosomally synthesized peptides

PubMed Central

2013-01-01

Background Various bacteria can use non-ribosomal peptide synthesis (NRPS) to produce peptides or other small molecules. Conserved features within the NRPS machinery allow the type, and sometimes even the structure, of the synthesized polypeptide to be predicted. Thus, bacterial genome mining via in silico analyses of NRPS genes offers an attractive opportunity to uncover new bioactive non-ribosomally synthesized peptides. Xanthomonas is a large genus of Gram-negative bacteria that cause disease in hundreds of plant species. To date, the only known small molecule synthesized by NRPS in this genus is albicidin produced by Xanthomonas albilineans. This study aims to estimate the biosynthetic potential of Xanthomonas spp. by in silico analyses of NRPS genes with unknown function recently identified in the sequenced genomes of X. albilineans and related species of Xanthomonas. Results We performed in silico analyses of NRPS genes present in all published genome sequences of Xanthomonas spp., as well as in unpublished draft genome sequences of Xanthomonas oryzae pv. oryzae strain BAI3 and Xanthomonas spp. strain XaS3. These two latter strains, together with X. albilineans strain GPE PC73 and X. oryzae pv. oryzae strains X8-1A and X11-5A, possess novel NRPS gene clusters and share related NRPS-associated genes such as those required for the biosynthesis of non-proteinogenic amino acids or the secretion of peptides. In silico prediction of peptide structures according to NRPS architecture suggests eight different peptides, each specific to its producing strain. Interestingly, these eight peptides cannot be assigned to any known gene cluster or related to known compounds from natural product databases. PCR screening of a collection of 94 plant pathogenic bacteria indicates that these novel NRPS gene clusters are specific to the genus Xanthomonas and are also present in Xanthomonas translucens and X. oryzae pv. oryzicola. Further genome mining revealed other novel NRPS genes specific to X. oryzae pv. oryzicola or Xanthomonas sacchari. Conclusions This study revealed the significant potential of the genus Xanthomonas to produce new non-ribosomally synthesized peptides. Interestingly, this biosynthetic potential seems to be specific to strains of Xanthomonas associated with monocotyledonous plants, suggesting a putative involvement of non-ribosomally synthesized peptides in plant-bacteria interactions. PMID:24069909

In Silico Investigation of Traditional Chinese Medicine Compounds to Inhibit Human Histone Deacetylase 2 for Patients with Alzheimer's Disease

PubMed Central

Hung, Tzu-Chieh; Lee, Wen-Yuan; Chen, Kuen-Bao; Chan, Yueh-Chiu; Lee, Cheng-Chun

2014-01-01

Human histone deacetylase 2 (HDAC2) has been identified as being associated with Alzheimer's disease (AD), a neuropathic degenerative disease. In this study, we screen the world's largest Traditional Chinese Medicine (TCM) database for natural compounds that may be useful as lead compounds in the search for inhibitors of HDAC2 function. The technique of molecular docking was employed to select the ten top TCM candidates. We used three prediction models, multiple linear regression (MLR), support vector machine (SVM), and the Bayes network toolbox (BNT), to predict the bioactivity of the TCM candidates. Molecular dynamics simulation provides the protein-ligand interactions of compounds. The bioactivity predictions of pIC50 values suggest that the TCM candidatesm, (−)-Bontl ferulate, monomethylcurcumin, and ningposides C, have a greater effect on HDAC2 inhibition. The structure variation caused by the hydrogen bonds and hydrophobic interactions between protein-ligand interactions indicates that these compounds have an inhibitory effect on the protein. PMID:25045700

Repositioning Proton Pump Inhibitors as Anticancer Drugs by Targeting the Thioesterase Domain of Human Fatty Acid Synthase

PubMed Central

2015-01-01

Fatty acid synthase (FASN), the enzyme responsible for de novo synthesis of free fatty acids, is up-regulated in many cancers. FASN is essential for cancer cell survival and contributes to drug resistance and poor prognosis. However, it is not expressed in most nonlipogenic normal tissues. Thus, FASN is a desirable target for drug discovery. Although different FASN inhibitors have been identified, none has successfully moved into clinical use. In this study, using in silico screening of an FDA-approved drug database, we identified proton pump inhibitors (PPIs) as effective inhibitors of the thioesterase activity of human FASN. Further investigation showed that PPIs inhibited proliferation and induced apoptosis of cancer cells. Supplementation of palmitate, the end product of FASN catalysis, rescued cancer cells from PPI-induced cell death. These findings provide new evidence for the mechanism by which this FDA-approved class of compounds may be acting on cancer cells. PMID:25513712

Perspectives on pathway perturbation: Focused research to enhance 3R objectives

EPA Science Inventory

In vitro high-throughput screening (HTS) and in silico technologies are emerging as 21st century tools for hazard identification. Computational methods that strategically examine cross-species conservation of protein sequence/structural information for chemical molecular targets ...

Rosette Assay: Highly Customizable Dot-Blot for SH2 Domain Screening.

PubMed

Ng, Khong Y; Machida, Kazuya

2017-01-01

With a growing number of high-throughput studies, structural analyses, and availability of protein-protein interaction databases, it is now possible to apply web-based prediction tools to SH2 domain-interactions. However, in silico prediction is not always reliable and requires experimental validation. Rosette assay is a dot blot-based reverse-phase assay developed for the assessment of binding between SH2 domains and their ligands. It is conveniently customizable, allowing for low- to high-throughput analysis of interactions between various numbers of SH2 domains and their ligands, e.g., short peptides, purified proteins, and cell lysates. The binding assay is performed in a 96-well plate (MBA or MWA apparatus) in which a sample spotted membrane is incubated with up to 96 labeled SH2 domains. Bound domains are detected and quantified using a chemiluminescence or near-infrared fluorescence (IR) imaging system. In this chapter, we describe a practical protocol for rosette assay to assess interactions between synthesized tyrosine phosphorylated peptides and a library of GST-tagged SH2 domains. Since the methodology is not confined to assessment of SH2-pTyr interactions, rosette assay can be broadly utilized for ligand and drug screening using different protein interaction domains or antibodies.

In silico and in vitro screening to identify structurally diverse non-azole CYP51 inhibitors as potent antifungal agent.

PubMed

Singh, Aarti; Paliwal, Sarvesh Kumar; Sharma, Mukta; Mittal, Anupama; Sharma, Swapnil; Sharma, Jai Prakash

2016-01-01

The problem of resistance to azole class of antifungals is a serious cause of concern to the medical fraternity and thus there is an urgent need to identify non-azole scaffolds with high affinity for lanosterol 14α-demethylase (CYP51). In view of this we have attempted to identify novel non-azole CYP51 inhibitors through the application of pharmacophore based virtual screening and in vitro evaluation. A rigorously validated pharmacophore model comprising of 2 hydrogen bond acceptor and 2 hydrophobic features has been developed and used to mine NCI database. Out of 265 retrieved hits, NSC 1215 and 1520 have been chosen on the basis of Lipinski's rule of five, fit and estimated values. Both the hits were docked into the active site of CYP51. In view of high fit value and CDocker score, NSC 1215 and 1520 have been subjected to in vitro microbiological assay. The result reveals that NSC 1215 and 1520 are active against Candida albicans, Candida parapsilosis, Candida tropicalis, and Aspergillus niger. In addition to this the absorption characteristics of both the hits have also been determined using the rat sac technique and permeation in order of NSC 1520>NSC 1215 has been observed. Copyright © 2015 Elsevier Inc. All rights reserved.

In silico investigation of potential mTOR inhibitors from traditional Chinese medicine for treatment of Leigh syndrome.

PubMed

Chen, Kuan-Chung; Lee, Wen-Yuan; Chen, Hsin-Yi; Chen, Calvin Yu-Chian

2014-01-01

A recent research demonstrates that the inhibition of mammalian target of rapamycin (mTOR) improves survival and health for patients with Leigh syndrome. mTOR proteins can be treated as drug target proteins against Leigh syndrome and other mitochondrial disorders. In this study, we aim to identify potent TCM compounds from the TCM Database@Taiwan as lead compounds of mTOR inhibitors. PONDR-Fit protocol was employed to predict the disordered disposition in mTOR protein before virtual screening. After virtual screening, the MD simulation was employed to validate the stability of interactions between each ligand and mTOR protein in the docking poses from docking simulation. The top TCM compounds, picrasidine M and acerosin, have higher binding affinities with target protein in docking simulation than control. There have H-bonds with residues Val2240 and π interactions with common residue Trp2239. After MD simulation, the top TCM compounds maintain similar docking poses under dynamic conditions. The top two TCM compounds, picrasidine M and acerosin, were extracted from Picrasma quassioides (D. Don) Benn. and Vitex negundo L. Hence, we propose the TCM compounds, picrasidine M and acerosin, as potential candidates as lead compounds for further study in drug development process with the mTOR protein against Leigh syndrome and other mitochondrial disorders.

Mitochondrial Targets for Pharmacological Intervention in Human Disease

PubMed Central

2015-01-01

Over the past several years, mitochondrial dysfunction has been linked to an increasing number of human illnesses, making mitochondrial proteins (MPs) an ever more appealing target for therapeutic intervention. With 20% of the mitochondrial proteome (312 of an estimated 1500 MPs) having known interactions with small molecules, MPs appear to be highly targetable. Yet, despite these targeted proteins functioning in a range of biological processes (including induction of apoptosis, calcium homeostasis, and metabolism), very few of the compounds targeting MPs find clinical use. Recent work has greatly expanded the number of proteins known to localize to the mitochondria and has generated a considerable increase in MP 3D structures available in public databases, allowing experimental screening and in silico prediction of mitochondrial drug targets on an unprecedented scale. Here, we summarize the current literature on clinically active drugs that target MPs, with a focus on how existing drug targets are distributed across biochemical pathways and organelle substructures. Also, we examine current strategies for mitochondrial drug discovery, focusing on genetic, proteomic, and chemogenomic assays, and relevant model systems. As cell models and screening techniques improve, MPs appear poised to emerge as relevant targets for a wide range of complex human diseases, an eventuality that can be expedited through systematic analysis of MP function. PMID:25367773

Applications of computer-aided approaches in the development of hepatitis C antiviral agents.

PubMed

Ganesan, Aravindhan; Barakat, Khaled

2017-04-01

Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials. Areas covered: This review discusses the applications of different computational approaches in HCV drug design. Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

Indexing Natural Products for Their Potential Anti-Diabetic Activity: Filtering and Mapping Discriminative Physicochemical Properties.

PubMed

Zeidan, Mouhammad; Rayan, Mahmoud; Zeidan, Nuha; Falah, Mizied; Rayan, Anwar

2017-09-17

Diabetes mellitus (DM) poses a major health problem, for which there is an unmet need to develop novel drugs. The application of in silico techniques and optimization algorithms is instrumental to achieving this goal. A set of 97 approved anti-diabetic drugs, representing the active domain, and a set of 2892 natural products, representing the inactive domain, were used to construct predictive models and to index anti-diabetic bioactivity. Our recently-developed approach of 'iterative stochastic elimination' was utilized. This article describes a highly discriminative and robust model, with an area under the curve above 0.96. Using the indexing model and a mix ratio of 1:1000 (active/inactive), 65% of the anti-diabetic drugs in the sample were captured in the top 1% of the screened compounds, compared to 1% in the random model. Some of the natural products that scored highly as potential anti-diabetic drug candidates are disclosed. One of those natural products is caffeine, which is noted in the scientific literature as having the capability to decrease blood glucose levels. The other nine phytochemicals await evaluation in a wet lab for their anti-diabetic activity. The indexing model proposed herein is useful for the virtual screening of large chemical databases and for the construction of anti-diabetes focused libraries.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability.

PubMed

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-01

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

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.

Pharmacophore Modeling and in Silico/in Vitro Screening for Human Cytochrome P450 11B1 and Cytochrome P450 11B2 Inhibitors

PubMed Central

Akram, Muhammad; Waratchareeyakul, Watcharee; Haupenthal, Joerg; Hartmann, Rolf W.; Schuster, Daniela

2017-01-01

Cortisol synthase (CYP11B1) is the main enzyme for the endogenous synthesis of cortisol and its inhibition is a potential way for the treatment of diseases associated with increased cortisol levels, such as Cushing's syndrome, metabolic diseases, and delayed wound healing. Aldosterone synthase (CYP11B2) is the key enzyme for aldosterone biosynthesis and its inhibition is a promising approach for the treatment of congestive heart failure, cardiac fibrosis, and certain forms of hypertension. Both CYP11B1 and CYP11B2 are structurally very similar and expressed in the adrenal cortex. To facilitate the identification of novel inhibitors of these enzymes, ligand-based pharmacophore models of CYP11B1 and CYP11B2 inhibition were developed. A virtual screening of the SPECS database was performed with our pharmacophore queries. Biological evaluation of the selected hits lead to the discovery of three potent novel inhibitors of both CYP11B1 and CYP11B2 in the submicromolar range (compounds 8–10), one selective CYP11B1 inhibitor (Compound 11, IC50 = 2.5 μM), and one selective CYP11B2 inhibitor (compound 12, IC50 = 1.1 μM), respectively. The overall success rate of this prospective virtual screening experiment is 20.8% indicating good predictive power of the pharmacophore models. PMID:29312923

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

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.

Computational toxicology and in silico modeling of embryogenesis

EPA Science Inventory

High-throughput screening (HTS) is providing a rich source of in vitro data for predictive toxicology. ToxCast™ HTS data presently covers 1060 broad-use chemicals and captures >650 in vitro features for diverse biochemical and receptor binding activities, multiplexed reporter gen...

Putative histidine kinase inhibitors with antibacterial effect against multi-drug resistant clinical isolates identified by in vitro and in silico screens

NASA Astrophysics Data System (ADS)

Velikova, Nadya; Fulle, Simone; Manso, Ana Sousa; Mechkarska, Milena; Finn, Paul; Conlon, J. Michael; Oggioni, Marco Rinaldo; Wells, Jerry M.; Marina, Alberto

2016-05-01

Novel antibacterials are urgently needed to address the growing problem of bacterial resistance to conventional antibiotics. Two-component systems (TCS) are widely used by bacteria to regulate gene expression in response to various environmental stimuli and physiological stress and have been previously proposed as promising antibacterial targets. TCS consist of a sensor histidine kinase (HK) and an effector response regulator. The HK component contains a highly conserved ATP-binding site that is considered to be a promising target for broad-spectrum antibacterial drugs. Here, we describe the identification of putative HK autophosphorylation inhibitors following two independent experimental approaches: in vitro fragment-based screen via differential scanning fluorimetry and in silico structure-based screening, each followed up by the exploration of analogue compounds as identified by ligand-based similarity searches. Nine of the tested compounds showed antibacterial effect against multi-drug resistant clinical isolates of bacterial pathogens and include three novel scaffolds, which have not been explored so far in other antibacterial compounds. Overall, putative HK autophosphorylation inhibitors were found that together provide a promising starting point for further optimization as antibacterials.

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

Mass spectrometry analysis and in silico prediction of allergenicity of peptides in tryptic hydrolysates of the proteins from Ruditapes philippinarum.

PubMed

Yu, Yue; Liu, Hongwei; Tu, Maolin; Qiao, Meiling; Wang, Zhenyu; Du, Ming

2017-12-01

Ruditapes philippinarum is nutrient-rich and widely-distributed, but little attention has been paid to the identification and characterization of the bioactive peptides in the bivalve. In the present study, we evaluated the peptides of the R. philippinarum that were enzymolysised by trypsin using a combination of ultra-performance liquid chromatography separation and electrospray ionization quadrupole time-of-flight tandem mass spectrometry, followed by data processing and sequence-similarity database searching. The potential allergenicity of the peptides was assessed in silico. The enzymolysis was performed under the conditions: E:S 3:100 (w/w), pH 9.0, 45 °C for 4 h. After separation and detection, the Swiss-Prot database and a Ruditapes philippinarum sequence database were used: 966 unique peptides were identified by non-error tolerant database searching; 173 peptides matching 55 precursor proteins comprised highly conserved cytoskeleton proteins. The remaining 793 peptides were identified from the R. philippinarum sequence database. The results showed that 510 peptides were labeled as allergens and 31 peptides were potential allergens; 425 peptides were predicted to be nonallergenic. The abundant peptide information contributes to further investigations of the structure and potential function of R. philippinarum. Additional in vitro studies are required to demonstrate and ensure the correct production of the hydrolysates for use in the food industry with respect to R. philippinarum. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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 vitro screening and in silico validation revealed key microbes for higher production of significant therapeutic enzyme l-asparaginase.

PubMed

Vimal, Archana; Kumar, Awanish

2017-03-01

l-asparaginase is an enzyme of medical prominence and reputable as a chemotherapeutic agent. It also has immense potential to cure autoimmune and infectious diseases. The vast application of this enzyme in healthcare sector increases its market demand. However, presently the huge market demand is not achieved completely. This serves the basis to explore better producer microbial strains to bridge the gap between huge demand and supply of this therapeutic enzyme. The present study deals with the successful screening of potent microorganisms producing l-asparaginase. 47 microorganisms were screened including bacteria, fungi, and yeasts. Among all, Penicillium lilacinum showed the highest enzyme activity i.e., 39.67 IU/ml. Shigella flexneri has 23.21 IU/ml of enzyme activity (highest among all the bacterial strain tested). Further, the 3-D structure of l-asparaginase from higher producer strains was developed and validated in silico for its activity. l-asparagine (substrate for l-asparaginase) was docked inside the binding pocket of P. lilacinum and S. flexneri. Docking score for the most common substrate l-asparagine is -6.188 (P. lilacinum), -5.576 (S. flexneri) which is quite good. Moreover, the chemical property of the binding pocket revealed that amino acid residues Phe 243, Gln 260, Gly 365, Asp 386 in P. lilacinum and residues Asp 181, Thr 318, Asn 320 in S. flexneri have an important role in H-bonding. The in silico results supports and strengthen the wet lab results. The outcome obtained motivates to take the present study result from lab to industry for the economic/massive production of this enzyme for the diverse therapeutic application. Copyright © 2016 Elsevier Inc. All rights reserved.

Development and in silico evaluation of large-scale metabolite identification methods using functional group detection for metabolomics

PubMed Central

Mitchell, Joshua M.; Fan, Teresa W.-M.; Lane, Andrew N.; Moseley, Hunter N. B.

2014-01-01

Large-scale identification of metabolites is key to elucidating and modeling metabolism at the systems level. Advances in metabolomics technologies, particularly ultra-high resolution mass spectrometry (MS) enable comprehensive and rapid analysis of metabolites. However, a significant barrier to meaningful data interpretation is the identification of a wide range of metabolites including unknowns and the determination of their role(s) in various metabolic networks. Chemoselective (CS) probes to tag metabolite functional groups combined with high mass accuracy provide additional structural constraints for metabolite identification and quantification. We have developed a novel algorithm, Chemically Aware Substructure Search (CASS) that efficiently detects functional groups within existing metabolite databases, allowing for combined molecular formula and functional group (from CS tagging) queries to aid in metabolite identification without a priori knowledge. Analysis of the isomeric compounds in both Human Metabolome Database (HMDB) and KEGG Ligand demonstrated a high percentage of isomeric molecular formulae (43 and 28%, respectively), indicating the necessity for techniques such as CS-tagging. Furthermore, these two databases have only moderate overlap in molecular formulae. Thus, it is prudent to use multiple databases in metabolite assignment, since each major metabolite database represents different portions of metabolism within the biosphere. In silico analysis of various CS-tagging strategies under different conditions for adduct formation demonstrate that combined FT-MS derived molecular formulae and CS-tagging can uniquely identify up to 71% of KEGG and 37% of the combined KEGG/HMDB database vs. 41 and 17%, respectively without adduct formation. This difference between database isomer disambiguation highlights the strength of CS-tagging for non-lipid metabolite identification. However, unique identification of complex lipids still needs additional information. PMID:25120557

In silico Screening and Evaluation of the Anticonvulsant Activity of Docosahexaenoic Acid-Like Molecules in Experimental Models of Seizures.

PubMed

Gharibi Loron, Ali; Sardari, Soroush; Narenjkar, Jamshid; Sayyah, Mohammad

2017-01-01

Resistance to antiepileptic drugs and the intolerability in 20-30% of the patients raises demand for developing new drugs with improved efficacy and safety. Acceptable anticonvulsant activity, good tolerability, and inexpensiveness of docosahexaenoic acid (DHA) make it as a good candidate for designing and development of the new anticonvulsant medications. Ten DHA-based molecules were screened based on in silico screening of DHA-like molecules by root-mean-square deviation of atomic positions, the biological activity score of Professional Association for SQL Server, and structural requirements suggested by pharmacophore design. Anticonvulsant activity was tested against clonic seizures induced by pentylenetetrazole (PTZ, 60 mg/kg, i.p.) and tonic seizures induced by maximal electroshock (MES, 50 mA, 50 Hz, 1 ms duration) by intracerebroventricular (i.c.v.) injection of the screened compounds to mice. Among screened compounds, 4-Phenylbutyric acid, 4-Biphenylacetic acid, phenylacetic acid, and 2-Phenylbutyric acid showed significant protective activity in pentylenetetrazole test with ED50 values of 4, 5, 78, and 70 mM, respectively. In MES test, shikimic acid and 4-tert-Butylcyclo-hexanecarboxylic acid showed significant activity with ED50 values 29 and 637 mM, respectively. Effective compounds had no mortality in mice up to the maximum i.c.v. injectable dose of 1 mM. Common electrochemical features and three-dimensional spatial structures of the effective compounds suggest the involvement of the anticonvulsant mechanisms similar to the parent compound DHA.

In silico Screening and Evaluation of the Anticonvulsant Activity of Docosahexaenoic Acid-Like Molecules in Experimental Models of Seizures

PubMed Central

Loron, Ali Gharibi; Sardari, Soroush; Narenjkar, Jamshid; Sayyah, Mohammad

2017-01-01

Background: Resistance to antiepileptic drugs and the intolerability in 20-30% of the patients raises demand for developing new drugs with improved efficacy and safety. Acceptable anticonvulsant activity, good tolerability, and inexpensiveness of docosahexaenoic acid (DHA) make it as a good candidate for designing and development of the new anticonvulsant medications. Methods: Ten DHA-based molecules were screened based on in silico screening of DHA-like molecules by root-mean-square deviation of atomic positions, the biological activity score of Professional Association for SQL Server, and structural requirements suggested by pharmacophore design. Anticonvulsant activity was tested against clonic seizures induced by pentylenetetrazole (PTZ, 60 mg/kg, i.p.) and tonic seizures induced by maximal electroshock (MES, 50 mA, 50 Hz, 1 ms duration) by intracerebroventricular (i.c.v.) injection of the screened compounds to mice. Results: Among screened compounds, 4-Phenylbutyric acid, 4-Biphenylacetic acid, phenylacetic acid, and 2-Phenylbutyric acid showed significant protective activity in pentylenetetrazole test with ED50 values of 4, 5, 78, and 70 mM, respectively. In MES test, shikimic acid and 4-tert-Butylcyclo-hexanecarboxylic acid showed significant activity with ED50 values 29 and 637 mM, respectively. Effective compounds had no mortality in mice up to the maximum i.c.v. injectable dose of 1 mM. Conclusion: Common electrochemical features and three-dimensional spatial structures of the effective compounds suggest the involvement of the anticonvulsant mechanisms similar to the parent compound DHA. PMID:27592363

Drug-Like Protein–Protein Interaction Modulators: Challenges and Opportunities for Drug Discovery and Chemical Biology

PubMed Central

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

2014-01-01

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

Marine steroids as potential anticancer drug candidates: In silico investigation in search of inhibitors of Bcl-2 and CDK-4/Cyclin D1.

PubMed

Saikia, Surovi; Kolita, Bhaskor; Dutta, Partha P; Dutta, Deep J; Neipihoi; Nath, Shyamalendu; Bordoloi, Manobjyoti; Quan, Pham Minh; Thuy, Tran Thu; Phuong, Doan Lan; Long, Pham Quoc

2015-10-01

Star fishes (Asteroidea) are rich in polar steroids with diverse structural characteristics. The structural modifications of star fish steroids occur at 3β, 4β, 5α, 6α (or β), 7α (or β), 8, 15α (or β) and 16β positions of the steroidal nucleus and in the side chain. Widely found polar steroids in starfishes include polyhydroxysteroids, steroidal sulfates, glycosides, steroid oligoglycosides etc. Bioactivity of these steroids is less studied; only a few reports like antibacterial, cytotoxic activity etc. are available. In continuation of our search for bioactive molecules from natural sources, we undertook in silico screening of steroids from star fishes against Bcl-2 and CDK-4/Cyclin D1 - two important targets of progression and proliferation of cancer cells. We have screened 182 natural steroids from star fishes occurring in different parts of the world and their 282 soft-derivatives by in silico methods. Their physico-chemical properties, drug-likeliness, binding potential with the selected targets, ADMET (absorption, distribution, metabolism, toxicity) were predicted. Further, the results were compared with those of existing steroidal and non steroidal drugs and inhibitors of Bcl-2 and CDK-4/Cyclin D1. The results are promising and unveil that some of these steroids can be potent leads for cancer treatments. Copyright © 2015 Elsevier Inc. All rights reserved.

Rational design of novel TLR4 ligands by in silico screening and their functional and structural characterization in vitro.

PubMed

Honegr, Jan; Malinak, David; Dolezal, Rafael; Soukup, Ondrej; Benkova, Marketa; Hroch, Lukas; Benek, Ondrej; Janockova, Jana; Kuca, Kamil; Prymula, Roman

2018-02-25

The purpose of this study was to identify new small molecules that possess activity on human toll-like receptor 4 associated with the myeloid differentiation protein 2 (hTLR4/MD2). Following current rational drug design principles, we firstly performed a ligand and structure based virtual screening of more than 130 000 compounds to discover until now unknown class of hTLR4/MD2 modulators that could be used as novel type of immunologic adjuvants. The core of the in silico study was molecular docking of flexible ligands in a partially flexible hTLR4/MD2 receptor model using a peta-flops-scale supercomputer. The most promising substances resulting from this study, related to anthracene-succimide hybrids, were synthesized and tested. The best prepared candidate exhibited 80% of Monophosphoryl Lipid A in vitro agonistic activity in cell lines expressing hTLR4/MD2. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Identification of a small molecule inhibitor of Ebola virus genome replication and transcription using in silico screening.

PubMed

Easton, Victoria; McPhillie, Martin; Garcia-Dorival, Isabel; Barr, John N; Edwards, Thomas A; Foster, Richard; Fishwick, Colin; Harris, Mark

2018-06-02

Ebola virus (EBOV) causes a severe haemorrhagic fever in humans and has a mortality rate over 50%. With no licensed drug treatments available, EBOV poses a significant threat. Investigations into possible therapeutics have been severely hampered by the classification of EBOV as a BSL4 pathogen. Here, we describe a drug discovery pathway combining in silico screening of compounds predicted to bind to a hydrophobic pocket on the nucleoprotein (NP); with a robust and rapid EBOV minigenome assay for inhibitor validation at BSL2. One compound (MCCB4) was efficacious (EC 50 4.8 μM), exhibited low cytotoxicity (CC 50  > 100 μM) and was specific, with no effect on either a T7 RNA polymerase driven firefly luciferase or a Bunyamwera virus minigenome. Further investigations revealed that this small molecule inhibitor was able to outcompete established replication complexes, an essential aspect for a potential EBOV treatment. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

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

2018-05-01

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

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.

Small molecule correctors of F508del-CFTR discovered by structure-based virtual screening

NASA Astrophysics Data System (ADS)

Kalid, Ori; Mense, Martin; Fischman, Sharon; Shitrit, Alina; Bihler, Hermann; Ben-Zeev, Efrat; Schutz, Nili; Pedemonte, Nicoletta; Thomas, Philip J.; Bridges, Robert J.; Wetmore, Diana R.; Marantz, Yael; Senderowitz, Hanoch

2010-12-01

Folding correctors of F508del-CFTR were discovered by in silico structure-based screening utilizing homology models of CFTR. The intracellular segment of CFTR was modeled and three cavities were identified at inter-domain interfaces: (1) Interface between the two Nucleotide Binding Domains (NBDs); (2) Interface between NBD1 and Intracellular Loop (ICL) 4, in the region of the F508 deletion; (3) multi-domain interface between NBD1:2:ICL1:2:4. We hypothesized that compounds binding at these interfaces may improve the stability of the protein, potentially affecting the folding yield or surface stability. In silico structure-based screening was performed at the putative binding-sites and a total of 496 candidate compounds from all three sites were tested in functional assays. A total of 15 compounds, representing diverse chemotypes, were identified as F508del folding correctors. This corresponds to a 3% hit rate, tenfold higher than hit rates obtained in corresponding high-throughput screening campaigns. The same binding sites also yielded potentiators and, most notably, compounds with a dual corrector-potentiator activity (dual-acting). Compounds harboring both activity types may prove to be better leads for the development of CF therapeutics than either pure correctors or pure potentiators. To the best of our knowledge this is the first report of structure-based discovery of CFTR modulators.

SeqAPASS: Sequence alignment to predict across-species susceptibility

EPA Science Inventory

Efforts to shift the toxicity testing paradigm from whole organism studies to those focused on the initiation of toxicity and relevant pathways have led to increased utilization of in vitro and in silico methods. Hence the emergence of high through-put screening (HTS) programs, s...

Variability within Systemic In Vivo Toxicity Studies (ASCCT)

EPA Science Inventory

In vivo studies have long been considered the gold standard for toxicology screening. Often time models developed in silico and/or using in vitro data to estimate points of departures (POD) are compared to the in vivo data to benchmark and evaluate quality and goodness of fit. ...

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 Screening for Biothreat Countermeasures

DTIC Science & Technology

2006-02-03

drug candidates to each kinase structure using the well-known docking algorithm LibDock . This population of 1200 ligands includes ~400 ligands with...mentioned previously, each of the known p38 inhibitors in the population was docked to its target using the LibDock application. This method resulted

Bioinformatics: A History of Evolution "In Silico"

ERIC Educational Resources Information Center

Ondrej, Vladan; Dvorak, Petr

2012-01-01

Bioinformatics, biological databases, and the worldwide use of computers have accelerated biological research in many fields, such as evolutionary biology. Here, we describe a primer of nucleotide sequence management and the construction of a phylogenetic tree with two examples; the two selected are from completely different groups of organisms:…

Discovery of secondary metabolites from Bacillus spp. biocontrol strains using genome mining and mass spectroscopy

USDA-ARS?s Scientific Manuscript database

Genome sequencing, data mining and mass spectrometry were used to identify secondary metabolites produced by several Bacillus spp. biocontrol strains. These biocontrol strains have shown promise in managing Fusarium head blight in wheat. Draft genomes were produced and screened in silico using genom...

A fluorescence high throughput screening method for the detection of reactive electrophiles as potential skin sensitizers

USDA-ARS?s Scientific Manuscript database

Skin sensitization is an important toxicological end-point in the risk assessment of chemical allergens. Because of the complexity of the biological mechanisms associated with skin sensitization integrated approaches combining different chemical, biological and in silico methods are recommended to r...

Differentially Expressed Plasma MicroRNAs and the Potential Regulatory Function of Let-7b in Chronic Thromboembolic Pulmonary Hypertension

PubMed Central

Guo, Lijuan; Yang, Yuanhua; Liu, Jie; Wang, Lei; Li, Jifeng; Wang, Ying; Liu, Yan; Gu, Song; Gan, Huili; Cai, Jun; Yuan, Jason X.-J.; Wang, Jun; Wang, Chen

2014-01-01

Chronic thromboembolic pulmonary hypertension (CTEPH) is a progressive disease characterized by misguided thrombolysis and remodeling of pulmonary arteries. MicroRNAs are small non-coding RNAs involved in multiple cell processes and functions. During CTEPH, circulating microRNA profile endued with characteristics of diseased cells could be identified as a biomarker, and might help in recognition of pathogenesis. Thus, in this study, we compared the differentially expressed microRNAs in plasma of CTEPH patients and healthy controls and investigated their potential functions. Microarray was used to identify microRNA expression profile and qRT-PCR for validation. The targets of differentially expressed microRNAs were identified in silico, and the Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes pathway database were used for functional investigation of target gene profile. Targets of let-7b were validated by fluorescence reporter assay. Protein expression of target genes was determined by ELISA or western blotting. Cell migration was evaluated by wound healing assay. The results showed that 1) thirty five microRNAs were differentially expressed in CTEPH patients, among which, a signature of 17 microRNAs, which was shown to be related to the disease pathogenesis by in silico analysis, gave diagnostic efficacy of both sensitivity and specificity >0.9. 2) Let-7b, one of the down-regulated anti-oncogenic microRNAs in the signature, was validated to decrease to about 0.25 fold in CTEPH patients. 3) ET-1 and TGFBR1 were direct targets of let-7b. Altering let-7b level influenced ET-1 and TGFBR1 expression in pulmonary arterial endothelial cells (PAECs) as well as the migration of PAECs and pulmonary arterial smooth muscle cells (PASMCs). These results suggested that CTEPH patients had aberrant microRNA signature which might provide some clue for pathogenesis study and biomarker screening. Reduced let-7b might be involved in the pathogenesis of CTEPH by affecting ET-1 expression and the function of PAECs and PASMCs. PMID:24978044

Osteosarcoma enters a post genomic era with in silico opportunities: Generation of the High Dimensional Database for facilitating sarcoma biology research: A report from the Children's Oncology Group and the QuadW Foundation.

PubMed

Glover, Jason; Man, Tsz-Kwong; Barkauskas, Donald A; Hall, David; Tello, Tanya; Sullivan, Mary Beth; Gorlick, Richard; Janeway, Katherine; Grier, Holcombe; Lau, Ching; Toretsky, Jeffrey A; Borinstein, Scott C; Khanna, Chand; Fan, Timothy M

2017-01-01

The prospective banking of osteosarcoma tissue samples to promote research endeavors has been realized through the establishment of a nationally centralized biospecimen repository, the Children's Oncology Group (COG) biospecimen bank located at the Biopathology Center (BPC)/Nationwide Children's Hospital in Columbus, Ohio. Although the physical inventory of osteosarcoma biospecimens is substantive (>15,000 sample specimens), the nature of these resources remains exhaustible. Despite judicious allocation of these high-value biospecimens for conducting sarcoma-related research, a deeper understanding of osteosarcoma biology, in particular metastases, remains unrealized. In addition the identification and development of novel diagnostics and effective therapeutics remain elusive. The QuadW-COG Childhood Sarcoma Biostatistics and Annotation Office (CSBAO) has developed the High Dimensional Data (HDD) platform to complement the existing physical inventory and to promote in silico hypothesis testing in sarcoma biology. The HDD is a relational biologic database derived from matched osteosarcoma biospecimens in which diverse experimental readouts have been generated and digitally deposited. As proof-of-concept, we demonstrate that the HDD platform can be utilized to address previously unrealized biologic questions though the systematic juxtaposition of diverse datasets derived from shared biospecimens. The continued population of the HDD platform with high-value, high-throughput and mineable datasets allows a shared and reusable resource for researchers, both experimentalists and bioinformatics investigators, to propose and answer questions in silico that advance our understanding of osteosarcoma biology.

MINEs: Open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

DOE PAGES

Jeffryes, James G.; Colastani, Ricardo L.; Elbadawi-Sidhu, Mona; ...

2015-08-28

Metabolomics have proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography–mass spectrometry (LC–MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likelymore » to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC–MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures.« 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

Predictive Models for Carcinogenicity and Mutagenicity ...

EPA Pesticide Factsheets

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

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 Repositioning-Chemogenomics Strategy Identifies New Drugs with Potential Activity against Multiple Life Stages of Schistosoma mansoni

PubMed Central

Neves, Bruno J.; Braga, Rodolpho C.; Bezerra, José C. B.; Cravo, Pedro V. L.; Andrade, Carolina H.

2015-01-01

Morbidity and mortality caused by schistosomiasis are serious public health problems in developing countries. Because praziquantel is the only drug in therapeutic use, the risk of drug resistance is a concern. In the search for new schistosomicidal drugs, we performed a target-based chemogenomics screen of a dataset of 2,114 proteins to identify drugs that are approved for clinical use in humans that may be active against multiple life stages of Schistosoma mansoni. Each of these proteins was treated as a potential drug target, and its amino acid sequence was used to interrogate three databases: Therapeutic Target Database (TTD), DrugBank and STITCH. Predicted drug-target interactions were refined using a combination of approaches, including pairwise alignment, conservation state of functional regions and chemical space analysis. To validate our strategy, several drugs previously shown to be active against Schistosoma species were correctly predicted, such as clonazepam, auranofin, nifedipine, and artesunate. We were also able to identify 115 drugs that have not yet been experimentally tested against schistosomes and that require further assessment. Some examples are aprindine, gentamicin, clotrimazole, tetrabenazine, griseofulvin, and cinnarizine. In conclusion, we have developed a systematic and focused computer-aided approach to propose approved drugs that may warrant testing and/or serve as lead compounds for the design of new drugs against schistosomes. PMID:25569258

A Methodology for Cancer Therapeutics by Systems Pharmacology-Based Analysis: A Case Study on Breast Cancer-Related Traditional Chinese Medicines.

PubMed

Li, Yan; Wang, Jinghui; Lin, Feng; Yang, Yinfeng; Chen, Su-Shing

2017-01-01

Breast cancer is the most common carcinoma in women. Comprehensive therapy on breast cancer including surgical operation, chemotherapy, radiotherapy, endocrinotherapy, etc. could help, but still has serious side effect and resistance against anticancer drugs. Complementary and alternative medicine (CAM) may avoid these problems, in which traditional Chinese medicine (TCM) has been highlighted. In this section, to analyze the mechanism through which TCM act on breast cancer, we have built a virtual model consisting of the construction of database, oral bioavailability prediction, drug-likeness evaluation, target prediction, network construction. The 20 commonly employed herbs for the treatment of breast cancer were used as a database to carry out research. As a result, 150 ingredient compounds were screened out as active molecules for the herbs, with 33 target proteins predicted. Our analysis indicates that these herbs 1) takes a 'Jun-Chen-Zuo-Shi" as rule of prescription, 2) which function mainly through perturbing three pathways involving the epidermal growth factor receptor, estrogen receptor, and inflammatory pathways, to 3) display the breast cancer-related anti-estrogen, anti-inflammatory, regulation of cell metabolism and proliferation activities. To sum it up, by providing a novel in silico strategy for investigation of the botanical drugs, this work may be of some help for understanding the action mechanisms of herbal medicines and for discovery of new drugs from plants.

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor

PubMed Central

Hanif, Rumeza; Jabeen, Ishrat; Mansoor, Qaisar; Ismail, Muhammad

2018-01-01

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R. PMID:29787591

The controversial nature of the Weissella genus: technological and functional aspects versus whole genome analysis-based pathogenic potential for their application in food and health

PubMed Central

Abriouel, Hikmate; Lerma, Leyre Lavilla; Casado Muñoz, María del Carmen; Montoro, Beatriz Pérez; Kabisch, Jan; Pichner, Rohtraud; Cho, Gyu-Sung; Neve, Horst; Fusco, Vincenzina; Franz, Charles M. A. P.; Gálvez, Antonio; Benomar, Nabil

2015-01-01

Despite the use of several Weissella (W.) strains for biotechnological and probiotic purposes, certain species of this genus were found to act as opportunistic pathogens, while strains of W. ceti were recognized to be pathogenic for farmed rainbow trout. Herein, we investigated the pathogenic potential of weissellas based on in silico analyses of the 13 whole genome sequences available to date in the NCBI database. Our screening allowed us to find several virulence determinants such as collagen adhesins, aggregation substances, mucus-binding proteins, and hemolysins in some species. Moreover, we detected several antibiotic resistance-encoding genes, whose presence could increase the potential pathogenicity of some strains, but should not be regarded as an excluding trait for beneficial weissellas, as long as these genes are not present on mobile genetic elements. Thus, selection of weissellas intended to be used as starters or for biotechnological or probiotic purposes should be investigated regarding their safety aspects on a strain to strain basis, preferably also by genome sequencing, since nucleotide sequence heterogeneity in virulence and antibiotic resistance genes makes PCR-based screening unreliable for safety assessments. In this sense, the application of W. confusa and W. cibaria strains as starter cultures or as probiotics should be approached with caution, by carefully selecting strains that lack pathogenic potential. PMID:26579103

Prevention of IcaA regulated poly N-acetyl glucosamine formation in Staphylococcus aureus biofilm through new-drug like inhibitors: In silico approach and MD simulation study.

PubMed

Gupta, Ayushi; Mishra, Swechha; Singh, Sangeeta; Mishra, Sonali

2017-09-01

The effectiveness of various ligands against the protein structure of IcaA of the IcaABCD gene locus of Staphylococcus aureus were examined using the approach of structure based drug designing in reference with the protein's efficiency to form biofilms. Four compounds CID42738592, CID90468752, CID24277882, and CID6435208 were secluded from a database of 31,242 inhibitory ligands on the justification of the evaluated values falling under the four - tier structure based virtual screening. Under this principle value of least binding energy, human oral absorption and ADME properties were taken into consideration. Using the Glide module of Schrödinger, the above mentioned ligands showed an effective action against the protein IcaA which showed reduced activity as a glucosaminyl transferase. The complex of protein and ligand with best docking score was chosen for simulation studies. Structure based drug designing for the protein IcaA has given us potential leads as anti - biofilm agents. These screened out ligands might enable the development of new therapeutic strategies aimed at disrupting Staphylococcus aureus biofilms. The complex was showing stability towards the end of time for which it has been put for simulation. Thus molecule could be considered for making of biofilms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Pharmacophore modeling for identification of anti-IGF-1R drugs and in-vitro validation of fulvestrant as a potential inhibitor.

PubMed

Khalid, Samra; Hanif, Rumeza; Jabeen, Ishrat; Mansoor, Qaisar; Ismail, Muhammad

2018-01-01

Insulin-like growth factor 1 receptor (IGF-1R) is an important therapeutic target for breast cancer treatment. The alteration in the IGF-1R associated signaling network due to various genetic and environmental factors leads the system towards metastasis. The pharmacophore modeling and logical approaches have been applied to analyze the behaviour of complex regulatory network involved in breast cancer. A total of 23 inhibitors were selected to generate ligand based pharmacophore using the tool, Molecular Operating Environment (MOE). The best model consisted of three pharmacophore features: aromatic hydrophobic (HyD/Aro), hydrophobic (HyD) and hydrogen bond acceptor (HBA). This model was validated against World drug bank (WDB) database screening to identify 189 hits with the required pharmacophore features and was further screened by using Lipinski positive compounds. Finally, the most effective drug, fulvestrant, was selected. Fulvestrant is a selective estrogen receptor down regulator (SERD). This inhibitor was further studied by using both in-silico and in-vitro approaches that showed the targeted effect of fulvestrant in ER+ MCF-7 cells. Results suggested that fulvestrant has selective cytotoxic effect and a dose dependent response on IRS-1, IGF-1R, PDZK1 and ER-α in MCF-7 cells. PDZK1 can be an important inhibitory target using fulvestrant because it directly regulates IGF-1R.

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

PubMed

Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

2016-11-29

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity.

PubMed

Vyas, V K; Qureshi, G; Ghate, M; Patel, H; Dalai, S

2016-06-01

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC50 values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere-Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a QFIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.

In silico-based identification of human α-enolase inhibitors to block cancer cell growth metabolically

PubMed Central

Lung, Jrhau; Chen, Kuan-Liang; Hung, Chien-Hui; Chen, Chih-Cheng; Hung, Ming-Szu; Lin, Yu-Ching; Wu, Ching-Yuan; Lee, Kuan-Der; Shih, Neng-Yao; Tsai, Ying Huang

2017-01-01

Unlimited growth of cancer cells requires an extensive nutrient supply. To meet this demand, cancer cells drastically upregulate glucose uptake and metabolism compared to normal cells. This difference has made the blocking of glycolysis a fascinating strategy to treat this malignant disease. α-enolase is not only one of the most upregulated glycolytic enzymes in cancer cells, but also associates with many cellular processes or conditions important to cancer cell survival, such as cell migration, invasion, and hypoxia. Targeting α-enolase could simultaneously disturb cancer cells in multiple ways and, therefore, is a good target for anticancer drug development. In the current study, more than 22 million chemical structures meeting the criteria of Lipinski’s rule of five from the ZINC database were docked to α-enolase by virtual screening. Twenty-four chemical structures with docking scores better than that of the enolase substrate, 2-phosphoglycerate, were further screened by the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties prediction. Four of them were classified as non-mutagenic, non-carcinogenic, and capable of oral administration where they showed steady interactions to α-enolase that were comparable, even superior, to the currently available inhibitors in molecular dynamics (MD) simulation. These compounds may be considered promising leads for further development of the α-enolase inhibitors and could help fight cancer metabolically. PMID:29180852

Identification of novel Ebola virus (EBOV) VP24 inhibitor from Indonesian natural products through in silico drug design approach

NASA Astrophysics Data System (ADS)

Tambunan, U. S. F.; Nasution, M. A. F.

2017-07-01

Ebola remains as one of the deadliest diseases in the world, with almost 29,000 cases were reported and kill 11,000 of them, and yet neither treatment nor vaccine that can combat this disease effectively. This disease is caused by ebolavirus (EBOV), a primary member of Filoviridae family. The life cycle of this virus has been operated by several key proteins, one of them is VP24 protein, which has been known for its crucial role in the transcription and replication of EBOV. Therefore, targeting VP24 protein can be a solution for treating this pathogenic disease. In this study, virtual screening of Indonesian natural products as EBOV VP24 inhibitor was performed. About 2,020 ligands from many sources, including HerbalDB database, were obtained and screened by using DataWarrior software to measure its molecular and pharmacological properties, resulting 301 ligands in the process. Then, the molecular docking simulation was performed to check the ligand's binding interaction and affinity with EBOV VP24 protein; this simulation was done by using MOE 2014.09 software. This study resulted that cycloartocarpin was the best ligand to inhibit the EBOV VP24 protein. Therefore, this ligand should be checked its stability through molecular dynamics simulation and performed in vitro test to verify its bioactivity against the EBOV VP24 protein.

Structural insight into exosite binding and discovery of novel exosite inhibitors of botulinum neurotoxin serotype A through in silico screening

NASA Astrophysics Data System (ADS)

Hu, Xin; Legler, Patricia M.; Southall, Noel; Maloney, David J.; Simeonov, Anton; Jadhav, Ajit

2014-07-01

Botulinum neurotoxin serotype A (BoNT/A) is the most lethal toxin among the Tier 1 Select Agents. Development of potent and selective small molecule inhibitors against BoNT/A zinc metalloprotease remains a challenging problem due to its exceptionally large substrate binding surface and conformational plasticity. The exosites of the catalytic domain of BoNT/A are intriguing alternative sites for small molecule intervention, but their suitability for inhibitor design remains largely unexplored. In this study, we employed two recently identified exosite inhibitors, D-chicoric acid and lomofungin, to probe the structural features of the exosites and molecular mechanisms of synergistic inhibition. The results showed that D-chicoric acid favors binding at the α-exosite, whereas lomofungin preferentially binds at the β-exosite by mimicking the substrate β-sheet binding interaction. Molecular dynamics simulations and binding interaction analysis of the exosite inhibitors with BoNT/A revealed key elements and hotspots that likely contribute to the inhibitor binding and synergistic inhibition. Finally, we performed database virtual screening for novel inhibitors of BoNT/A targeting the exosites. Hits C1 and C2 showed non-competitive inhibition and likely target the α- and β-exosites, respectively. The identified exosite inhibitors may provide novel candidates for structure-based development of therapeutics against BoNT/A intoxication.

Structural insight into exosite binding and discovery of novel exosite inhibitors of botulinum neurotoxin serotype A through in silico screening.

PubMed

Hu, Xin; Legler, Patricia M; Southall, Noel; Maloney, David J; Simeonov, Anton; Jadhav, Ajit

2014-07-01

Botulinum neurotoxin serotype A (BoNT/A) is the most lethal toxin among the Tier 1 Select Agents. Development of potent and selective small molecule inhibitors against BoNT/A zinc metalloprotease remains a challenging problem due to its exceptionally large substrate binding surface and conformational plasticity. The exosites of the catalytic domain of BoNT/A are intriguing alternative sites for small molecule intervention, but their suitability for inhibitor design remains largely unexplored. In this study, we employed two recently identified exosite inhibitors, D-chicoric acid and lomofungin, to probe the structural features of the exosites and molecular mechanisms of synergistic inhibition. The results showed that D-chicoric acid favors binding at the α-exosite, whereas lomofungin preferentially binds at the β-exosite by mimicking the substrate β-sheet binding interaction. Molecular dynamics simulations and binding interaction analysis of the exosite inhibitors with BoNT/A revealed key elements and hotspots that likely contribute to the inhibitor binding and synergistic inhibition. Finally, we performed database virtual screening for novel inhibitors of BoNT/A targeting the exosites. Hits C1 and C2 showed non-competitive inhibition and likely target the α- and β-exosites, respectively. The identified exosite inhibitors may provide novel candidates for structure-based development of therapeutics against BoNT/A intoxication.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics.

PubMed

Jeffryes, James G; Colastani, Ricardo L; Elbadawi-Sidhu, Mona; Kind, Tobias; Niehaus, Thomas D; Broadbelt, Linda J; Hanson, Andrew D; Fiehn, Oliver; Tyo, Keith E J; Henry, Christopher S

2015-01-01

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases. Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted. MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

In silico environmental chemical science: properties and processes from statistical and computational modelling

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

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

2017-01-01

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

in silico 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.

Grid heterogeneity in in-silico experiments: an exploration of drug screening using DOCK on cloud environments.

PubMed

Yim, Wen-Wai; Chien, Shu; Kusumoto, Yasuyuki; Date, Susumu; Haga, Jason

2010-01-01

Large-scale in-silico screening is a necessary part of drug discovery and Grid computing is one answer to this demand. A disadvantage of using Grid computing is the heterogeneous computational environments characteristic of a Grid. In our study, we have found that for the molecular docking simulation program DOCK, different clusters within a Grid organization can yield inconsistent results. Because DOCK in-silico virtual screening (VS) is currently used to help select chemical compounds to test with in-vitro experiments, such differences have little effect on the validity of using virtual screening before subsequent steps in the drug discovery process. However, it is difficult to predict whether the accumulation of these discrepancies over sequentially repeated VS experiments will significantly alter the results if VS is used as the primary means for identifying potential drugs. Moreover, such discrepancies may be unacceptable for other applications requiring more stringent thresholds. This highlights the need for establishing a more complete solution to provide the best scientific accuracy when executing an application across Grids. One possible solution to platform heterogeneity in DOCK performance explored in our study involved the use of virtual machines as a layer of abstraction. This study investigated the feasibility and practicality of using virtual machine and recent cloud computing technologies in a biological research application. We examined the differences and variations of DOCK VS variables, across a Grid environment composed of different clusters, with and without virtualization. The uniform computer environment provided by virtual machines eliminated inconsistent DOCK VS results caused by heterogeneous clusters, however, the execution time for the DOCK VS increased. In our particular experiments, overhead costs were found to be an average of 41% and 2% in execution time for two different clusters, while the actual magnitudes of the execution time costs were minimal. Despite the increase in overhead, virtual clusters are an ideal solution for Grid heterogeneity. With greater development of virtual cluster technology in Grid environments, the problem of platform heterogeneity may be eliminated through virtualization, allowing greater usage of VS, and will benefit all Grid applications in general.

Safety assessment of mushrooms in dietary supplements by combining analytical data with in silico toxicology evaluation.

PubMed

VanderMolen, Karen M; Little, Jason G; Sica, Vincent P; El-Elimat, Tamam; Raja, Huzefa A; Oberlies, Nicholas H; Baker, Timothy R; Mahony, Catherine

2017-05-01

Despite growing popularity in dietary supplements, many medicinal mushrooms have not been evaluated for their safe human consumption using modern techniques. The multifaceted approach described here relies on five key principles to evaluate the safety of non-culinary fungi for human use: (1) identification by sequencing the nuclear ribosomal internal transcribed spacer (ITS) region (commonly referred to as ITS barcoding), (2) screening an extract of each fungal raw material against a database of known fungal metabolites, (3) comparison of these extracts to those prepared from grocery store-bought culinary mushrooms using UHPLCPDA-ELS-HRMS, (4) review of the toxicological and chemical literature for each fungus, and (5) evaluation of data establishing presence in-market. This weight-of-evidence approach was used to evaluate seven fungal raw materials and determine safe human use for each. Such an approach may provide an effective alternative to conventional toxicological animal studies (or more efficiently identifies when studies are necessary) for the safety assessment of fungal dietary ingredients. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Machine learning-, rule- and pharmacophore-based classification on the inhibition of P-glycoprotein and NorA.

PubMed

Ngo, T-D; Tran, T-D; Le, M-T; Thai, K-M

2016-09-01

The efflux pumps P-glycoprotein (P-gp) in humans and NorA in Staphylococcus aureus are of great interest for medicinal chemists because of their important roles in multidrug resistance (MDR). The high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of these transmembrane proteins lead us to combining ligand-based approaches, which in the case of this study were machine learning, perceptual mapping and pharmacophore modelling. For P-gp inhibitory activity, individual models were developed using different machine learning algorithms and subsequently combined into an ensemble model which showed a good discrimination between inhibitors and noninhibitors (acctrain-diverse = 84%; accinternal-test = 92% and accexternal-test = 100%). For ligand promiscuity between P-gp and NorA, perceptual maps and pharmacophore models were generated for the detection of rules and features. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening in an attempt to restore drug sensitivity in cancer cells and bacteria.

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

EPA Science Inventory

There are little available toxicity data on the vast majority of chemicals in commerce. High-throughput screening (HTS) studies, such as those being carried out by the U.S. Environmental Protection Agency (EPA) ToxCast program in partnership with the federal Tox21 research progra...

Evaluating the Value of Augmenting In Vitro Hazard Assessment with Exposure and Pharmacokinetics Considerations for Chemical Prioritization

EPA Science Inventory

Over time, toxicity-testing paradigms have progressed from low-throughput in vivo animal studies for limited numbers of chemicals to high-throughput (HT) in vitro screening assays for thousands of chemicals. Such HT in vitro methods, along with HT in silico predictions of popula...

Possible strategies for EDC testing in the future: exploring roles of pathway-based in silico, in vitro and in vivo methods

EPA Science Inventory

Current methods for screening, testing and monitoring endocrine-disrupting chemicals (EDCs) rely relatively substantially upon moderate- to long-term assays that can, in some instances, require significant numbers of animals. Recent developments in the areas of in vitro testing...

1-[(2-arylthiazol-4-yl)methyl]azoles as a new class of anticonvulsants: design, synthesis, in vivo screening, and in silico drug-like properties.

PubMed

Ahangar, Nematollah; Ayati, Adile; Alipour, Eskandar; Pashapour, Arsalan; Foroumadi, Alireza; Emami, Saeed

2011-11-01

A series of novel thiazole incorporated (arylalkyl)azoles were synthesized and screened for their anticonvulsant properties using maximal electroshock and pentylenetetrazole models in mice. Among target compounds, 1-[(2-(4-chlorophenyl)thiazol-4-yl)methyl]-1H-imidazole (compound 4b), 1-[(2-phenylthiazol-4-yl)methyl]-1H-1,2,4-tria-zole (8a), and its 4-chlorophenyl analog (compound 8b) were able to display noticeable anticonvulsant activity in both pentylenetetrazole and maximal electroshock tests with percentage protection range of 33-100%. A computational study was carried out for prediction of pharmacokinetics properties and drug-likeness. The structure-activity relationship and in silico drug relevant properties (molecular weight, topological polar surface area, clog P, hydrogen bond donors, hydrogen bond acceptors, and log BB) confirmed that the compounds were within the range set by Lipinski's rule-of-five, and possessing favorable physicochemical properties for acting as CNS-drugs, making them potentially promising agents for epilepsy therapy. © 2011 John Wiley & Sons A/S.

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 probing and biological evaluation of SETDB1/ESET-targeted novel compounds that reduce tri-methylated histone H3K9 (H3K9me3) level

NASA Astrophysics Data System (ADS)

Park, Insun; Hwang, Yu Jin; Kim, TaeHun; Viswanath, Ambily Nath Indu; Londhe, Ashwini M.; Jung, Seo Yun; Sim, Kyoung Mi; Min, Sun-Joon; Lee, Ji Eun; Seong, Jihye; Kim, Yun Kyung; No, Kyoung Tai; Ryu, Hoon; Pae, Ae Nim

2017-10-01

ERG-associated protein with the SET domain (ESET/SET domain bifurcated 1/SETDB1/KMT1E) is a histone lysine methyltransferase (HKMT) and it preferentially tri-methylates lysine 9 of histone H3 (H3K9me3). SETDB1/ESET leads to heterochromatin condensation and epigenetic gene silencing. These functional changes are reported to correlate with Huntington's disease (HD) progression and mood-related disorders which make SETDB1/ESET a viable drug target. In this context, the present investigation was performed to identify novel peptide-competitive small molecule inhibitors of the SETDB1/ESET by a combined in silico-in vitro approach. A ligand-based pharmacophore model was built and employed for the virtual screening of ChemDiv and Asinex database. Also, a human SETDB1/ESET homology model was constructed to supplement the data further. Biological evaluation of the selected 21 candidates singled out 5 compounds exhibiting a notable reduction of the H3K9me3 level via inhibitory potential of SETDB1/ESET activity in SETDB1/ESET-inducible cell line and HD striatal cells. Later on, we identified two compounds as final hits that appear to have neuronal effects without cytotoxicity based on the result from MTT assay. These compounds hold the calibre to become the future lead compounds and can provide structural insights into more SETDB1/ESET-focused drug discovery research. Moreover, these SETDB1/ESET inhibitors may be applicable for the preclinical study to ameliorate neurodegenerative disorders via epigenetic regulation.

In silico probing and biological evaluation of SETDB1/ESET-targeted novel compounds that reduce tri-methylated histone H3K9 (H3K9me3) level.

PubMed

Park, Insun; Hwang, Yu Jin; Kim, TaeHun; Viswanath, Ambily Nath Indu; Londhe, Ashwini M; Jung, Seo Yun; Sim, Kyoung Mi; Min, Sun-Joon; Lee, Ji Eun; Seong, Jihye; Kim, Yun Kyung; No, Kyoung Tai; Ryu, Hoon; Pae, Ae Nim

2017-10-01

ERG-associated protein with the SET domain (ESET/SET domain bifurcated 1/SETDB1/KMT1E) is a histone lysine methyltransferase (HKMT) and it preferentially tri-methylates lysine 9 of histone H3 (H3K9me3). SETDB1/ESET leads to heterochromatin condensation and epigenetic gene silencing. These functional changes are reported to correlate with Huntington's disease (HD) progression and mood-related disorders which make SETDB1/ESET a viable drug target. In this context, the present investigation was performed to identify novel peptide-competitive small molecule inhibitors of the SETDB1/ESET by a combined in silico-in vitro approach. A ligand-based pharmacophore model was built and employed for the virtual screening of ChemDiv and Asinex database. Also, a human SETDB1/ESET homology model was constructed to supplement the data further. Biological evaluation of the selected 21 candidates singled out 5 compounds exhibiting a notable reduction of the H3K9me3 level via inhibitory potential of SETDB1/ESET activity in SETDB1/ESET-inducible cell line and HD striatal cells. Later on, we identified two compounds as final hits that appear to have neuronal effects without cytotoxicity based on the result from MTT assay. These compounds hold the calibre to become the future lead compounds and can provide structural insights into more SETDB1/ESET-focused drug discovery research. Moreover, these SETDB1/ESET inhibitors may be applicable for the preclinical study to ameliorate neurodegenerative disorders via epigenetic regulation.

In silico mapping of quantitative trait loci in maize.

PubMed

Parisseaux, B; Bernardo, R

2004-08-01

Quantitative trait loci (QTL) are most often detected through designed mapping experiments. An alternative approach is in silico mapping, whereby genes are detected using existing phenotypic and genomic databases. We explored the usefulness of in silico mapping via a mixed-model approach in maize (Zea mays L.). Specifically, our objective was to determine if the procedure gave results that were repeatable across populations. Multilocation data were obtained from the 1995-2002 hybrid testing program of Limagrain Genetics in Europe. Nine heterotic patterns comprised 22,774 single crosses. These single crosses were made from 1,266 inbreds that had data for 96 simple sequence repeat (SSR) markers. By a mixed-model approach, we estimated the general combining ability effects associated with marker alleles in each heterotic pattern. The numbers of marker loci with significant effects--37 for plant height, 24 for smut [Ustilago maydis (DC.) Cda.] resistance, and 44 for grain moisture--were consistent with previous results from designed mapping experiments. Each trait had many loci with small effects and few loci with large effects. For smut resistance, a marker in bin 8.05 on chromosome 8 had a significant effect in seven (out of a maximum of 18) instances. For this major QTL, the maximum effect of an allele substitution ranged from 5.4% to 41.9%, with an average of 22.0%. We conclude that in silico mapping via a mixed-model approach can detect associations that are repeatable across different populations. We speculate that in silico mapping will be more useful for gene discovery than for selection in plant breeding programs. Copyright 2004 Springer-Verlag

Osteosarcoma enters a post genomic era with in silico opportunities: Generation of the High Dimensional Database for facilitating sarcoma biology research: A report from the Children's Oncology Group and the QuadW Foundation

PubMed Central

Glover, Jason; Man, Tsz-Kwong; Barkauskas, Donald A.; Hall, David; Tello, Tanya; Sullivan, Mary Beth; Gorlick, Richard; Janeway, Katherine; Grier, Holcombe; Lau, Ching; Toretsky, Jeffrey A.; Borinstein, Scott C.; Khanna, Chand

2017-01-01

The prospective banking of osteosarcoma tissue samples to promote research endeavors has been realized through the establishment of a nationally centralized biospecimen repository, the Children’s Oncology Group (COG) biospecimen bank located at the Biopathology Center (BPC)/Nationwide Children’s Hospital in Columbus, Ohio. Although the physical inventory of osteosarcoma biospecimens is substantive (>15,000 sample specimens), the nature of these resources remains exhaustible. Despite judicious allocation of these high-value biospecimens for conducting sarcoma-related research, a deeper understanding of osteosarcoma biology, in particular metastases, remains unrealized. In addition the identification and development of novel diagnostics and effective therapeutics remain elusive. The QuadW-COG Childhood Sarcoma Biostatistics and Annotation Office (CSBAO) has developed the High Dimensional Data (HDD) platform to complement the existing physical inventory and to promote in silico hypothesis testing in sarcoma biology. The HDD is a relational biologic database derived from matched osteosarcoma biospecimens in which diverse experimental readouts have been generated and digitally deposited. As proof-of-concept, we demonstrate that the HDD platform can be utilized to address previously unrealized biologic questions though the systematic juxtaposition of diverse datasets derived from shared biospecimens. The continued population of the HDD platform with high-value, high-throughput and mineable datasets allows a shared and reusable resource for researchers, both experimentalists and bioinformatics investigators, to propose and answer questions in silico that advance our understanding of osteosarcoma biology. PMID:28732082

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.

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.

Development of an In Silico Metabolic Simulator and Searchable Metabolism Database for Chemical Risk Assessments

EPA Science Inventory

The US EPA is faced with long lists of chemicals that need to be assessed for hazard, and a gap in evaluating chemical risk is accounting for metabolic activation resulting in increased toxicity. The goals of this project are to develop a capability to predict metabolic maps of x...

In silico, in vitro and in vivo analyses of dipeptidyl peptidase IV inhibitory activity and the antidiabetic effect of sodium caseinate hydrolysate.

PubMed

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

2016-02-01

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

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.

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.

Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.

PubMed

Carpenter, Kristy A; Huang, Xudong

2018-06-07

Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity. The study aims to review ML-based methods used for VS and applications to Alzheimer's disease (AD) drug discovery. To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN). All techniques have found success in VS, but the future of VS is likely to lean more heavily toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics of for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation. Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

In Silico Identification of Mimicking Molecules as Defense Inducers Triggering Jasmonic Acid Mediated Immunity against Alternaria Blight Disease in Brassica Species

PubMed Central

Pathak, Rajesh K.; Baunthiyal, Mamta; Shukla, Rohit; Pandey, Dinesh; Taj, Gohar; Kumar, Anil

2017-01-01

Alternaria brassicae and Alternaria brassicicola are two major phytopathogenic fungi which cause Alternaria blight, a recalcitrant disease on Brassica crops throughout the world, which is highly destructive and responsible for significant yield losses. Since no resistant source is available against Alternaria blight, therefore, efforts have been made in the present study to identify defense inducer molecules which can induce jasmonic acid (JA) mediated defense against the disease. It is believed that JA triggered defense response will prevent necrotrophic mode of colonization of Alternaria brassicae fungus. The JA receptor, COI1 is one of the potential targets for triggering JA mediated immunity through interaction with JA signal. In the present study, few mimicking compounds more efficient than naturally occurring JA in terms of interaction with COI1 were identified through virtual screening and molecular dynamics simulation studies. A high quality structural model of COI1 was developed using the protein sequence of Brassica rapa. This was followed by virtual screening of 767 analogs of JA from ZINC database for interaction with COI1. Two analogs viz. ZINC27640214 and ZINC43772052 showed more binding affinity with COI1 as compared to naturally occurring JA. Molecular dynamics simulation of COI1 and COI1-JA complex, as well as best screened interacting structural analogs of JA with COI1 was done for 50 ns to validate the stability of system. It was found that ZINC27640214 possesses efficient, stable, and good cell permeability properties. Based on the obtained results and its physicochemical properties, it is capable of mimicking JA signaling and may be used as defense inducers for triggering JA mediated resistance against Alternaria blight, only after further validation through field trials. PMID:28487711

Comprehensive molecular diagnosis of a large cohort of Japanese retinitis pigmentosa and Usher syndrome patients by next-generation sequencing.

PubMed

Oishi, Maho; Oishi, Akio; Gotoh, Norimoto; Ogino, Ken; Higasa, Koichiro; Iida, Kei; Makiyama, Yukiko; Morooka, Satoshi; Matsuda, Fumihiko; Yoshimura, Nagahisa

2014-10-16

Retinitis pigmentosa (RP), a major cause of blindness in developed countries, has multiple causative genes; its prevalence differs by ethnicity. Usher syndrome is the most common form of syndromic RP and is accompanied by hearing impairment. Although molecular diagnosis is challenging, recent technological advances such as targeted high-throughput resequencing are efficient screening tools. We performed comprehensive molecular testing in 329 Japanese RP and Usher syndrome patients by using a custom capture panel that covered the coding exons and exon/intron boundaries of all 193 known inherited eye disease genes combined with Illumina HiSequation 2500. Candidate variants were screened using systematic data analyses, and their potential pathogenicity was assessed according to the frequency of the variants in normal populations, in silico prediction tools, and compatibility with known phenotypes or inheritance patterns. Molecular diagnoses were made in 115/317 RP patients (36.3%) and 6/12 Usher syndrome patients (50%). We identified 104 distinct mutations, including 66 novel mutations. EYS, USH2A, and RHO were common causative genes. In particular, mutations in EYS accounted for 15.0% of the autosomal recessive/simplex RP patients or 10.7% of the entire RP cohort. Among the 189 previously reported mutations detected in the current study, 55 (29.1%) were found commonly in Japanese or other public databases and were excluded from molecular diagnoses. By screening a large cohort of patients, this study catalogued the genetic variations involved in RP and Usher syndrome in a Japanese population and highlighted the different distribution of causative genes among populations. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

Mass spectrometric screening and identification of acidic metabolites in fulvic acid fractions of contaminated groundwater.

PubMed

Jobelius, Carsten; Frimmel, Fritz H; Zwiener, Christian

2014-05-01

The anaerobic microbial degradation of aromatic and heterocyclic compounds is a prevalent process in contaminated groundwater systems. The introduction of functional groups into the contaminant molecules often results in aromatic and heterocyclic and succinic acids. These metabolites can be used as indicators for prevailing degradation processes. Therefore, there is a strong interest in developing analytical methods for screening and identification of these metabolites. In this study, neutral loss scans (NLS) by liquid chromatography-electrospray ionization/tandem mass spectrometry with losses of CO2 (NL ∆m/z = 44) and C2H4(CO2)2 (NL ∆m/z = 116) were applied for the first time successfully to screen selectively for acidic and succinic metabolites of aromatic and heterocyclic contaminants in two fulvic acid fractions from a contaminated site and a downstream region of a tar oil-polluted groundwater. Identification of these preselected signals was performed by high-resolution mass spectrometry with a liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry instrument. High-resolution mass and mass fragmentation data were then compared with a list of known metabolites from a literature search or matched with chemical databases supported with in silico fragmentation. Based on authentic analytical standards, several compounds from NLS were identified (e.g., 4-hydroxy-3-methylbenzoic acid, benzylsuccinic acid, naphthyl-2-methylsuccinic acid, 2-carboxyindane, and 2-carboxybenzothiophene) and tentatively identified (e.g., benzofuranmethylsuccinic acid and dihydrocarboxybenzothiophene) as aromatic, phenolic, heterocyclic, and succinic acids. The acidic metabolites were found exclusively in the contaminated region of the aquifer which indicates active biodegradation processes and no relevant occurrence of acidic metabolites in the downstream region.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach.

PubMed

Ambure, Pravin; Bhat, Jyotsna; Puzyn, Tomasz; Roy, Kunal

2018-04-23

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

Novel chemical scaffolds of the tumor marker AKR1B10 inhibitors discovered by 3D QSAR pharmacophore modeling

PubMed Central

Kumar, Raj; Son, Minky; Bavi, Rohit; Lee, Yuno; Park, Chanin; Arulalapperumal, Venkatesh; Cao, Guang Ping; Kim, Hyong-ha; Suh, Jung-keun; Kim, Yong-seong; Kwon, Yong Jung; Lee, Keun Woo

2015-01-01

Aim: Recent evidence suggests that aldo-keto reductase family 1 B10 (AKR1B10) may be a potential diagnostic or prognostic marker of human tumors, and that AKR1B10 inhibitors offer a promising choice for treatment of many types of human cancers. The aim of this study was to identify novel chemical scaffolds of AKR1B10 inhibitors using in silico approaches. Methods: The 3D QSAR pharmacophore models were generated using HypoGen. A validated pharmacophore model was selected for virtual screening of 4 chemical databases. The best mapped compounds were assessed for their drug-like properties. The binding orientations of the resulting compounds were predicted by molecular docking. Density functional theory calculations were carried out using B3LYP. The stability of the protein-ligand complexes and the final binding modes of the hit compounds were analyzed using 10 ns molecular dynamics (MD) simulations. Results: The best pharmacophore model (Hypo 1) showed the highest correlation coefficient (0.979), lowest total cost (102.89) and least RMSD value (0.59). Hypo 1 consisted of one hydrogen-bond acceptor, one hydrogen-bond donor, one ring aromatic and one hydrophobic feature. This model was validated by Fischer's randomization and 40 test set compounds. Virtual screening of chemical databases and the docking studies resulted in 30 representative compounds. Frontier orbital analysis confirmed that only 3 compounds had sufficiently low energy band gaps. MD simulations revealed the binding modes of the 3 hit compounds: all of them showed a large number of hydrogen bonds and hydrophobic interactions with the active site and specificity pocket residues of AKR1B10. Conclusion: Three compounds with new structural scaffolds have been identified, which have stronger binding affinities for AKR1B10 than known inhibitors. PMID:26051108

Modeling the Dynamics of High-Grade Serous Ovarian Cancer Progression for Transvaginal Ultrasound-Based Screening and Early Detection

PubMed Central

Lee, Jung-Min; Levy, Doron

2016-01-01

High-grade serous ovarian cancer (HGSOC) represents the majority of ovarian cancers and accounts for the largest proportion of deaths from the disease. A timely detection of low volume HGSOC should be the goal of any screening studies. However, numerous transvaginal ultrasound (TVU) detection-based population studies aimed at detecting low-volume disease have not yielded reduced mortality rates. A quantitative invalidation of TVU as an effective HGSOC screening strategy is a necessary next step. Herein, we propose a mathematical model for a quantitative explanation on the reported failure of TVU-based screening to improve HGSOC low-volume detectability and overall survival.We develop a novel in silico mathematical assessment of the efficacy of a unimodal TVU monitoring regimen as a strategy aimed at detecting low-volume HGSOC in cancer-positive cases, defined as cases for which the inception of the first malignant cell has already occurred. Our findings show that the median window of opportunity interval length for TVU monitoring and HGSOC detection is approximately 1.76 years. This does not translate into reduced mortality levels or improved detection accuracy in an in silico cohort across multiple TVU monitoring frequencies or detection sensitivities. We demonstrate that even a semiannual, unimodal TVU monitoring protocol is expected to miss detectable HGSOC. Lastly, we find that circa 50% of the simulated HGSOC growth curves never reach the baseline detectability threshold, and that on average, 5–7 infrequent, rate-limiting stochastic changes in the growth parameters are associated with reaching HGSOC detectability and mortality thresholds respectively. Focusing on a malignancy poorly studied in the mathematical oncology community, our model captures the dynamic, temporal evolution of HGSOC progression. Our mathematical model is consistent with recent case reports and prospective TVU screening population studies, and provides support to the empirical recommendation against frequent HGSOC screening. PMID:27257824

Solutions for data integration in functional genomics: a critical assessment and case study.

PubMed

Smedley, Damian; Swertz, Morris A; Wolstencroft, Katy; Proctor, Glenn; Zouberakis, Michael; Bard, Jonathan; Hancock, John M; Schofield, Paul

2008-11-01

The torrent of data emerging from the application of new technologies to functional genomics and systems biology can no longer be contained within the traditional modes of data sharing and publication with the consequence that data is being deposited in, distributed across and disseminated through an increasing number of databases. The resulting fragmentation poses serious problems for the model organism community which increasingly rely on data mining and computational approaches that require gathering of data from a range of sources. In the light of these problems, the European Commission has funded a coordination action, CASIMIR (coordination and sustainability of international mouse informatics resources), with a remit to assess the technical and social aspects of database interoperability that currently prevent the full realization of the potential of data integration in mouse functional genomics. In this article, we assess the current problems with interoperability, with particular reference to mouse functional genomics, and critically review the technologies that can be deployed to overcome them. We describe a typical use-case where an investigator wishes to gather data on variation, genomic context and metabolic pathway involvement for genes discovered in a genome-wide screen. We go on to develop an automated approach involving an in silico experimental workflow tool, Taverna, using web services, BioMart and MOLGENIS technologies for data retrieval. Finally, we focus on the current impediments to adopting such an approach in a wider context, and strategies to overcome them.

QSAR of phytochemicals for the design of better drugs.

PubMed

Kar, Supratik; Roy, Kunal

2012-10-01

Phytochemicals have been the single most prolific source of leads for the development of new drug entities from the dawn of the drug discovery. They cover a wide range of therapeutic indications with a great diversity of chemical structures. The research fraternity still believes in exploring the phytochemicals for new drug discovery. Application of molecular biological techniques has increased the availability of novel compounds that can be conveniently isolated from natural sources. Combinatorial chemistry approaches are being applied based on phytochemical scaffolds to create screening libraries that closely resemble drug-like compounds. In silico techniques like quantitative structure-activity relationships (QSAR), pharmacophore and virtual screening are playing crucial and rate accelerating steps for the better drug design in modern era. QSAR models of different classes of phytochemicals covering different therapeutic areas are thoroughly discussed in the review. Further, the authors have enlisted all the available phytochemical databases for the convenience of researchers working in the area. This review justifies the need to develop more QSAR models for the design of better drugs from phytochemicals. Technical drawbacks associated with phytochemical research have been lessened, and there are better opportunities to explore the biological activity of previously inaccessible sources of phytochemicals although there is still the need to reduce the time and cost involvement in such exercise. The future possibilities for the integration of ethnopharmacology with QSAR, place us at an exciting stage that will allow us to explore plant sources worldwide and design better drugs.

Combination of 2D/3D ligand-based similarity search in rapid virtual screening from multimillion compound repositories. Selection and biological evaluation of potential PDE4 and PDE5 inhibitors.

PubMed

Dobi, Krisztina; Hajdú, István; Flachner, Beáta; Fabó, Gabriella; Szaszkó, Mária; Bognár, Melinda; Magyar, Csaba; Simon, István; Szisz, Dániel; Lőrincz, Zsolt; Cseh, Sándor; Dormán, György

2014-05-28

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost effective approach. If structures of active compounds are available rapid 2D similarity search can be performed on multimillion compound databases but the generated library requires further focusing by various 2D/3D chemoinformatics tools. We report here a combination of the 2D approach with a ligand-based 3D method (Screen3D) which applies flexible matching to align reference and target compounds in a dynamic manner and thus to assess their structural and conformational similarity. In the first case study we compared the 2D and 3D similarity scores on an existing dataset derived from the biological evaluation of a PDE5 focused library. Based on the obtained similarity metrices a fusion score was proposed. The fusion score was applied to refine the 2D similarity search in a second case study where we aimed at selecting and evaluating a PDE4B focused library. The application of this fused 2D/3D similarity measure led to an increase of the hit rate from 8.5% (1st round, 47% inhibition at 10 µM) to 28.5% (2nd round at 50% inhibition at 10 µM) and the best two hits had 53 nM inhibitory activities.

Comparison of a homology model and the crystallographic structure of human 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) in a structure-based identification of inhibitors

NASA Astrophysics Data System (ADS)

Miguet, Laurence; Zhang, Ziding; Barbier, Maryse; Grigorov, Martin G.

2006-02-01

Human 11β-hydroxysteroid dehydrogenase type 1 (11βHSD1) catalyzes the interconversion of cortisone into active cortisol. 11βHSD1 inhibition is a tempting target for the treatment of a host of human disorders that might benefit from blockade of glucocorticoid action, such as obesity, metabolic syndrome, and diabetes type 2. Here, we report an in silico screening study aimed at identifying new selective inhibitors of human 11βHSD1 enzyme. In the first step, homology modeling was employed to build the 3D structure of 11βHSD1. Further, molecular docking was used to validate the predicted model by showing that it was able to discriminate between known 11βHSD1 inhibitors or substrates and non-inhibitors. The homology model was found to reproduce closely the crystal structure that became publicly available in the final stages of this work. Finally, we carried out structure-based virtual screening experiments on both the homology model and the crystallographic structure with a database of 114'000 natural molecules. Among these, 15 molecules were consistently selected as inhibitors based on both the model and crystal structures of the enzyme, implying a good quality for the homology model. Among these putative 11βHSD1 inhibitors, two were flavonone derivatives that have already been shown to be potent inhibitors of the enzyme.

76 FR 39315 - Privacy Act of 1974: Implementation of Exemptions; Department of Homeland Security/ALL-030 Use of...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-06

... Terrorist Screening Database System of Records AGENCY: Privacy Office, DHS. ACTION: Notice of proposed... Use of the Terrorist Screening Database System of Records'' and this proposed rulemaking. In this... Use of the Terrorist Screening Database (TSDB) System of Records.'' DHS is maintaining a mirror copy...

Version 1.00 programmer`s tools used in constructing the INEL RML/analytical radiochemistry sample tracking database and its user interface

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

Femec, D.A.

This report describes two code-generating tools used to speed design and implementation of relational databases and user interfaces: CREATE-SCHEMA and BUILD-SCREEN. CREATE-SCHEMA produces the SQL commands that actually create and define the database. BUILD-SCREEN takes templates for data entry screens and generates the screen management system routine calls to display the desired screen. Both tools also generate the related FORTRAN declaration statements and precompiled SQL calls. Included with this report is the source code for a number of FORTRAN routines and functions used by the user interface. This code is broadly applicable to a number of different databases.

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 ...

Using diverse U.S. beef cattle genomes to identify missense mutations in EPAS1, a gene associated with pulmonary hypertension

USDA-ARS?s Scientific Manuscript database

The availability of whole genome sequence (WGS) data has made it possible to discover protein variants in silico. However, existing bovine WGS databases do not show data in a form conducive to protein variant analysis, and tend to under represent the breadth of genetic diversity in U.S. beef cattle...

Using diverse U.S. beef cattle genomes to identify missense mutations in EPAS1, a gene associated with high-altitude pulmonary hypertension

USDA-ARS?s Scientific Manuscript database

The availability of whole genome sequence (WGS) data has made it possible to discover protein variants in silico. However, bovine WGS databases comprised of related influential sires from relatively few breeds tend to under represent the breadth of genetic diversity in U.S. beef cattle. Thus, our ...

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.

In silico modelling of thiazolidine derivatives with antioxidant potency: Models quantify the degree of contribution of molecular fragments towards the free radical scavenging ability

NASA Astrophysics Data System (ADS)

De, Biplab; Adhikari, Indrani; Nandy, Ashis; Saha, Achintya; Goswami, Binoy Behari

2017-06-01

Design and development of antioxidant supplements constitute an essential aspect of research in order to derive molecules that would help to combat the free radical invasion to the human body and curb oxidative stress related diseases. The present work deals with the development of in silico models for a series of thiazolidine derivatives having antioxidant potential. The objective of the work is to obtain models that would help to design new thazolidine derivatives based on substituent modification and thereby predict their activity profile. The QSAR model thus developed helps in quantification of the extent of contribution of the various molecular fragments towards the activity of the molecules, while the 3D pharmacophore model provides a brief idea of the essential molecular features that help the molecules to interact with the neighbouring free radicals. Both the models have been extensively validated which ensures their predictive ability as well the potential to search molecular databases for selection of thiazolidine derivatives with potent antioxidant activity. The models can thus be utilised effectively for database searching with the aim to isolate active antioxidants belonging to the thiazolidine group.

Understanding the toxicological potential of aerosol organic compounds using informatics based screening

NASA Astrophysics Data System (ADS)

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

2016-04-01

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

Pharmacophore Identification, Molecular Docking, Virtual Screening, and In Silico ADME Studies of Non-Nucleoside Reverse Transcriptase Inhibitors.

PubMed

Pirhadi, Somayeh; Ghasemi, Jahan B

2012-12-01

Non-nucleoside reverse transcriptase inhibitors (NNRTIs) have gained a definitive place due to their unique antiviral potency, high specificity and low toxicity in antiretroviral combination therapies used to treat HIV. In this study, chemical feature based pharmacophore models of different classes of NNRT inhibitors of HIV-1 have been developed. The best HypoRefine pharmacophore model, Hypo 1, which has the best correlation coefficient (0.95) and the lowest RMS (0.97), contains two hydrogen bond acceptors, one hydrophobic and one ring aromatic feature, as well as four excluded volumes. Hypo 1 was further validated by test set and Fischer validation method. The best pharmacophore model was then utilized as a 3D search query to perform a virtual screening to retrieve potential inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five and docking studies by Libdock and Gold methods to refine the retrieved hits. Finally, 7 top ranked compounds based on Gold score fitness function were subjected to in silico ADME studies to investigate for compliance with the standard ranges. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Discovering new mTOR inhibitors for cancer treatment through virtual screening methods and in vitro assays

NASA Astrophysics Data System (ADS)

Wang, Ling; Chen, Lei; Yu, Miao; Xu, Li-Hui; Cheng, Bao; Lin, Yong-Sheng; Gu, Qiong; He, Xian-Hui; Xu, Jun

2016-01-01

Mammalian target of rapamycin (mTOR) is an attractive target for new anticancer drug development. We recently developed in silico models to distinguish mTOR inhibitors and non-inhibitors. In this study, we developed an integrated strategy for identifying new mTOR inhibitors using cascaded in silico screening models. With this strategy, fifteen new mTOR kinase inhibitors including four compounds with IC50 values below 10 μM were discovered. In particular, compound 17 exhibited potent anticancer activities against four tumor cell lines, including MCF-7, HeLa, MGC-803, and C6, with IC50 values of 1.90, 2.74, 3.50 and 11.05 μM. Furthermore, cellular studies and western blot analyses revealed that 17 induces cell death via apoptosis by targeting both mTORC1 and mTORC2 within cells and arrests the cell cycle of HeLa at the G1/G0-phase. Finally, multi-nanosecond explicit solvent simulations and MM/GBSA analyses were carried out to study the inhibitory mechanisms of 13, 17, and 40 for mTOR. The potent compounds presented here are worthy of further investigation.

Value of shared preclinical safety studies - The eTOX database.

PubMed

Briggs, Katharine; Barber, Chris; Cases, Montserrat; Marc, Philippe; Steger-Hartmann, Thomas

2015-01-01

A first analysis of a database of shared preclinical safety data for 1214 small molecule drugs and drug candidates extracted from 3970 reports donated by thirteen pharmaceutical companies for the eTOX project (www.etoxproject.eu) is presented. Species, duration of exposure and administration route data were analysed to assess if large enough subsets of homogenous data are available for building in silico predictive models. Prevalence of treatment related effects for the different types of findings recorded were analysed. The eTOX ontology was used to determine the most common treatment-related clinical chemistry and histopathology findings reported in the database. The data were then mined to evaluate sensitivity of established in vivo biomarkers for liver toxicity risk assessment. The value of the database to inform other drug development projects during early drug development is illustrated by a case study.

Screening of pharmacokinetic properties of fifty dihydropyrimidin(thi)one derivatives using a combo of in vitro and in silico assays.

PubMed

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

2017-11-15

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

Performance evaluation of structure based and ligand based virtual screening methods on ten selected anti-cancer targets.

PubMed

Ramasamy, Thilagavathi; Selvam, Chelliah

2015-10-15

Virtual screening has become an important tool in drug discovery process. Structure based and ligand based approaches are generally used in virtual screening process. To date, several benchmark sets for evaluating the performance of the virtual screening tool are available. In this study, our aim is to compare the performance of both structure based and ligand based virtual screening methods. Ten anti-cancer targets and their corresponding benchmark sets from 'Demanding Evaluation Kits for Objective In silico Screening' (DEKOIS) library were selected. X-ray crystal structures of protein-ligand complexes were selected based on their resolution. Openeye tools such as FRED, vROCS were used and the results were carefully analyzed. At EF1%, vROCS produced better results but at EF5% and EF10%, both FRED and ROCS produced almost similar results. It was noticed that the enrichment factor values were decreased while going from EF1% to EF5% and EF10% in many cases. Published by Elsevier Ltd.

Temperature Effects on Kinetics of KV11.1 Drug Block Have Important Consequences for In Silico Proarrhythmic Risk Prediction.

PubMed

Windley, Monique J; Mann, Stefan A; Vandenberg, Jamie I; Hill, Adam P

2016-07-01

Drug block of voltage-gated potassium channel subtype 11.1 human ether-a-go-go related gene (Kv11.1) (hERG) channels, encoded by the KCNH2 gene, is associated with reduced repolarization of the cardiac action potential and is the predominant cause of acquired long QT syndrome that can lead to fatal cardiac arrhythmias. Current safety guidelines require that potency of KV11.1 block is assessed in the preclinical phase of drug development. However, not all drugs that block KV11.1 are proarrhythmic, meaning that screening on the basis of equilibrium measures of block can result in high attrition of potentially low-risk drugs. The basis of the next generation of drug-screening approaches is set to be in silico risk prediction, informed by in vitro mechanistic descriptions of drug binding, including measures of the kinetics of block. A critical issue in this regard is characterizing the temperature dependence of drug binding. Specifically, it is important to address whether kinetics relevant to physiologic temperatures can be inferred or extrapolated from in vitro data gathered at room temperature in high-throughout systems. Here we present the first complete study of the temperature-dependent kinetics of block and unblock of a proarrhythmic drug, cisapride, to KV11.1. Our data highlight a complexity to binding that manifests at higher temperatures and can be explained by accumulation of an intermediate, non-blocking encounter-complex. These results suggest that for cisapride, physiologically relevant kinetic parameters cannot be simply extrapolated from those measured at lower temperatures; rather, data gathered at physiologic temperatures should be used to constrain in silico models that may be used for proarrhythmic risk prediction. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

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.

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

Target-Specific Assay for Rapid and Quantitative Detection of Mycobacterium chimaera DNA

PubMed Central

Zozaya-Valdés, Enrique; Porter, Jessica L.; Coventry, John; Fyfe, Janet A. M.; Carter, Glen P.; Gonçalves da Silva, Anders; Schultz, Mark B.; Seemann, Torsten; Johnson, Paul D. R.; Stewardson, Andrew J.; Bastian, Ivan; Roberts, Sally A.; Howden, Benjamin P.; Williamson, Deborah A.

2017-01-01

ABSTRACT Mycobacterium chimaera is an opportunistic environmental mycobacterium belonging to the Mycobacterium avium-M. intracellulare complex. Although most commonly associated with pulmonary disease, there has been growing awareness of invasive M. chimaera infections following cardiac surgery. Investigations suggest worldwide spread of a specific M. chimaera clone, associated with contaminated hospital heater-cooler units used during the surgery. Given the global dissemination of this clone, its potential to cause invasive disease, and the laboriousness of current culture-based diagnostic methods, there is a pressing need to develop rapid and accurate diagnostic assays specific for M. chimaera. Here, we assessed 354 mycobacterial genome sequences and confirmed that M. chimaera is a phylogenetically coherent group. In silico comparisons indicated six DNA regions present only in M. chimaera. We targeted one of these regions and developed a TaqMan quantitative PCR (qPCR) assay for M. chimaera with a detection limit of 100 CFU/ml in whole blood spiked with bacteria. In vitro screening against DNA extracted from 40 other mycobacterial species and 22 bacterial species from 21 diverse genera confirmed the in silico-predicted specificity for M. chimaera. Screening 33 water samples from heater-cooler units with this assay highlighted the increased sensitivity of PCR compared to culture, with 15 of 23 culture-negative samples positive by M. chimaera qPCR. We have thus developed a robust molecular assay that can be readily and rapidly deployed to screen clinical and environmental specimens for M. chimaera. PMID:28381604

Target-Specific Assay for Rapid and Quantitative Detection of Mycobacterium chimaera DNA.

PubMed

Zozaya-Valdés, Enrique; Porter, Jessica L; Coventry, John; Fyfe, Janet A M; Carter, Glen P; Gonçalves da Silva, Anders; Schultz, Mark B; Seemann, Torsten; Johnson, Paul D R; Stewardson, Andrew J; Bastian, Ivan; Roberts, Sally A; Howden, Benjamin P; Williamson, Deborah A; Stinear, Timothy P

2017-06-01

Mycobacterium chimaera is an opportunistic environmental mycobacterium belonging to the Mycobacterium avium - M. intracellulare complex. Although most commonly associated with pulmonary disease, there has been growing awareness of invasive M. chimaera infections following cardiac surgery. Investigations suggest worldwide spread of a specific M. chimaera clone, associated with contaminated hospital heater-cooler units used during the surgery. Given the global dissemination of this clone, its potential to cause invasive disease, and the laboriousness of current culture-based diagnostic methods, there is a pressing need to develop rapid and accurate diagnostic assays specific for M. chimaera Here, we assessed 354 mycobacterial genome sequences and confirmed that M. chimaera is a phylogenetically coherent group. In silico comparisons indicated six DNA regions present only in M. chimaera We targeted one of these regions and developed a TaqMan quantitative PCR (qPCR) assay for M. chimaera with a detection limit of 100 CFU/ml in whole blood spiked with bacteria. In vitro screening against DNA extracted from 40 other mycobacterial species and 22 bacterial species from 21 diverse genera confirmed the in silico -predicted specificity for M. chimaera Screening 33 water samples from heater-cooler units with this assay highlighted the increased sensitivity of PCR compared to culture, with 15 of 23 culture-negative samples positive by M. chimaera qPCR. We have thus developed a robust molecular assay that can be readily and rapidly deployed to screen clinical and environmental specimens for M. chimaera . Copyright © 2017 American Society for Microbiology.

Combinatorial support vector machines approach for virtual screening of selective multi-target serotonin reuptake inhibitors from large compound libraries.

PubMed

Shi, Z; Ma, X H; Qin, C; Jia, J; Jiang, Y Y; Tan, C Y; Chen, Y Z

2012-02-01

Selective multi-target serotonin reuptake inhibitors enhance antidepressant efficacy. Their discovery can be facilitated by multiple methods, including in silico ones. In this study, we developed and tested an in silico method, combinatorial support vector machines (COMBI-SVMs), for virtual screening (VS) multi-target serotonin reuptake inhibitors of seven target pairs (serotonin transporter paired with noradrenaline transporter, H(3) receptor, 5-HT(1A) receptor, 5-HT(1B) receptor, 5-HT(2C) receptor, melanocortin 4 receptor and neurokinin 1 receptor respectively) from large compound libraries. COMBI-SVMs trained with 917-1951 individual target inhibitors correctly identified 22-83.3% (majority >31.1%) of the 6-216 dual inhibitors collected from literature as independent testing sets. COMBI-SVMs showed moderate to good target selectivity in misclassifying as dual inhibitors 2.2-29.8% (majority <15.4%) of the individual target inhibitors of the same target pair and 0.58-7.1% of the other 6 targets outside the target pair. COMBI-SVMs showed low dual inhibitor false hit rates (0.006-0.056%, 0.042-0.21%, 0.2-4%) in screening 17 million PubChem compounds, 168,000 MDDR compounds, and 7-8181 MDDR compounds similar to the dual inhibitors. Compared with similarity searching, k-NN and PNN methods, COMBI-SVM produced comparable dual inhibitor yields, similar target selectivity, and lower false hit rate in screening 168,000 MDDR compounds. The annotated classes of many COMBI-SVMs identified MDDR virtual hits correlate with the reported effects of their predicted targets. COMBI-SVM is potentially useful for searching selective multi-target agents without explicit knowledge of these agents. Copyright © 2011 Elsevier Inc. All rights reserved.

Histone deacetylase inhibitors induce growth arrest and differentiation in uveal melanoma

PubMed Central

Landreville, Solange; Agapova, Olga A.; Matatall, Katie A.; Kneass, Zachary T.; Onken, Michael D.; Lee, Ryan S.; Bowcock, Anne M.; Harbour, J. William

2011-01-01

Purpose Metastasis is responsible for the death of most cancer patients, yet few therapeutic agents are available which specifically target the molecular events that lead to metastasis. We recently showed that inactivating mutations in the tumor suppressor gene BAP1 are closely associated with loss of melanocytic differentiation in uveal melanoma and metastasis (UM). The purpose of this study was to identify therapeutic agents that reverse the phenotypic effects of BAP1 loss in UM. Experimental Design In silico screens were performed to identify therapeutic compounds predicted to differentiate UM cells using Gene Set Enrichment Analysis and Connectivity Map databases. Valproic acid, trichostatin A, LBH-589 and suberoylanilide hydroxamic acid were evaluated for their effects on UM cells using morphologic evaluation, MTS viability assays, BrdU incorporation, flow cytometry, clonogenic assays, gene expression profiling, histone acetylation and ubiquitination assays, and a murine xenograft tumorigenicity model. Results HDAC inhibitors induced morphologic differentiation, cell cycle exit, and a shift to a differentiated, melanocytic gene expression profile in cultured UM cells. Valproic acid inhibited the growth of UM tumors in vivo. Conclusions These findings suggest that HDAC inhibitors may have therapeutic potential for inducing differentiation and prolonged dormancy of micrometastatic disease in UM. PMID:22038994

In silico analysis of β-mannanases and β-mannosidase from Aspergillus flavus and Trichoderma virens UKM1

NASA Astrophysics Data System (ADS)

Yee, Chai Sin; Murad, Abdul Munir Abdul; Bakar, Farah Diba Abu

2013-11-01

A gene encoding an endo-β-1,4-mannanase from Trichoderma virens UKM1 (manTV) and Aspergillus flavus UKM1 (manAF) was analysed with bioinformatic tools. In addition, A. flavus NRRL 3357 genome database was screened for a β-mannosidase gene and analysed (mndA-AF). These three genes were analysed to understand their gene properties. manTV and manAF both consists of 1,332-bp and 1,386-bp nucleotides encoding 443 and 461 amino acid residues, respectively. Both the endo-β-1,4-mannanases belong to the glycosyl hydrolase family 5 and contain a carbohydrate-binding module family 1 (CBM1). On the other hand, mndA-AF which is a 2,745-bp gene encodes a protein sequence of 914 amino acid residues. This β-mannosidase belongs to the glycosyl hydrolase family 2. Predicted molecular weight of manTV, manAF and mndA-AF are 47.74 kDa, 49.71 kDa and 103 kDa, respectively. All three predicted protein sequences possessed signal peptide sequence and are highly conserved among other fungal β-mannanases and β-mannosidases.

Microsatellite analysis in the genome of Acanthaceae: An in silico approach.

PubMed

Kaliswamy, Priyadharsini; Vellingiri, Srividhya; Nathan, Bharathi; Selvaraj, Saravanakumar

2015-01-01

Acanthaceae is one of the advanced and specialized families with conventionally used medicinal plants. Simple sequence repeats (SSRs) play a major role as molecular markers for genome analysis and plant breeding. The microsatellites existing in the complete genome sequences would help to attain a direct role in the genome organization, recombination, gene regulation, quantitative genetic variation, and evolution of genes. The current study reports the frequency of microsatellites and appropriate markers for the Acanthaceae family genome sequences. The whole nucleotide sequences of Acanthaceae species were obtained from National Center for Biotechnology Information database and screened for the presence of SSRs. SSR Locator tool was used to predict the microsatellites and inbuilt Primer3 module was used for primer designing. Totally 110 repeats from 108 sequences of Acanthaceae family plant genomes were identified, and the occurrence of dinucleotide repeats was found to be abundant in the genome sequences. The essential amino acid isoleucine was found rich in all the sequences. We also designed the SSR-based primers/markers for 59 sequences of this family that contains microsatellite repeats in their genome. The identified microsatellites and primers might be useful for breeding and genetic studies of plants that belong to Acanthaceae family in the future.

Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters.

PubMed

Stork, Conrad; Wagner, Johannes; Friedrich, Nils-Ole; de Bruyn Kops, Christina; Šícho, Martin; Kirchmair, Johannes

2018-03-20

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

FireProt: Energy- and Evolution-Based Computational Design of Thermostable Multiple-Point Mutants.

PubMed

Bednar, David; Beerens, Koen; Sebestova, Eva; Bendl, Jaroslav; Khare, Sagar; Chaloupkova, Radka; Prokop, Zbynek; Brezovsky, Jan; Baker, David; Damborsky, Jiri

2015-11-01

There is great interest in increasing proteins' stability to enhance their utility as biocatalysts, therapeutics, diagnostics and nanomaterials. Directed evolution is a powerful, but experimentally strenuous approach. Computational methods offer attractive alternatives. However, due to the limited reliability of predictions and potentially antagonistic effects of substitutions, only single-point mutations are usually predicted in silico, experimentally verified and then recombined in multiple-point mutants. Thus, substantial screening is still required. Here we present FireProt, a robust computational strategy for predicting highly stable multiple-point mutants that combines energy- and evolution-based approaches with smart filtering to identify additive stabilizing mutations. FireProt's reliability and applicability was demonstrated by validating its predictions against 656 mutations from the ProTherm database. We demonstrate that thermostability of the model enzymes haloalkane dehalogenase DhaA and γ-hexachlorocyclohexane dehydrochlorinase LinA can be substantially increased (ΔTm = 24°C and 21°C) by constructing and characterizing only a handful of multiple-point mutants. FireProt can be applied to any protein for which a tertiary structure and homologous sequences are available, and will facilitate the rapid development of robust proteins for biomedical and biotechnological applications.

Computational design of hepatitis C vaccines using empirical fitness landscapes and population dynamics

NASA Astrophysics Data System (ADS)

Hart, Gregory; Ferguson, Andrew

Hepatitis C virus (HCV) afflicts 170 million people and kills 350,000 annually. Vaccination offers the most realistic and cost effective hope of controlling this epidemic. Despite 25 years of research, no vaccine is available. A major obstacle is the virus' extreme genetic variability and rapid mutational escape from immune pressure. Improvements in the vaccine design process are urgently needed. Coupling data mining and maximum entropy inference, we have developed a computational approach to translate sequence databases into empirical fitness landscapes. These landscapes explicitly connect viral genotype to phenotypic fitness and reveal vulnerable targets that can be exploited to rationally design vaccines. These landscapes represent the mutational ''playing field'' over which the virus evolves. We have integrated them with agent-based models of the viral mutational and host immune response, establishing a data-driven multi-scale immune simulator. We have used this simulator to perform in silico screening of HCV immunogens to rationally design vaccines to both cripple viral fitness and block escape. By systematically identifying a small number of promising vaccine candidates, these models can accelerate the search for a vaccine by massively reducing the experimental search space.

Computational design of hepatitis C vaccines using maximum entropy models and population dynamics

NASA Astrophysics Data System (ADS)

Hart, Gregory; Ferguson, Andrew

Hepatitis C virus (HCV) afflicts 170 million people and kills 350,000 annually. Vaccination offers the most realistic and cost effective hope of controlling this epidemic. Despite 20 years of research, no vaccine is available. A major obstacle is the virus' extreme genetic variability and rapid mutational escape from immune pressure. Improvements in the vaccine design process are urgently needed. Coupling data mining with spin glass models and maximum entropy inference, we have developed a computational approach to translate sequence databases into empirical fitness landscapes. These landscapes explicitly connect viral genotype to phenotypic fitness and reveal vulnerable targets that can be exploited to rationally design immunogens. Viewing these landscapes as the mutational ''playing field'' over which the virus is constrained to evolve, we have integrated them with agent-based models of the viral mutational and host immune response dynamics, establishing a data-driven immune simulator of HCV infection. We have employed this simulator to perform in silico screening of HCV immunogens. By systematically identifying a small number of promising vaccine candidates, these models can accelerate the search for a vaccine by massively reducing the experimental search space.

Identification and the molecular mechanism of a novel myosin-derived ACE inhibitory peptide.

PubMed

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

2018-01-24

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

Incorporation of in silico biodegradability screening in early drug development--a feasible approach?

PubMed

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

2011-05-01

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

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

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

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

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

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

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

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.

Design, synthesis, anticancer screening, docking studies and in silico ADME prediction of some β-carboline derivatives.

PubMed

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

2018-05-22

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

SH2 Ligand Prediction-Guidance for In-Silico Screening.

PubMed

Li, Shawn S C; Li, Lei

2017-01-01

Systematic identification of binding partners for SH2 domains is important for understanding the biological function of the corresponding SH2 domain-containing proteins. Here, we describe two different web-accessible computer programs, SMALI and DomPep, for predicting binding ligands for SH2 domains. The former was developed using a Scoring Matrix method and the latter based on the Support Vector Machine model.

In silico screening for candidate chassis strains of free fatty acid-producing cyanobacteria.

PubMed

Motwalli, Olaa; Essack, Magbubah; Jankovic, Boris R; Ji, Boyang; Liu, Xinyao; Ansari, Hifzur Rahman; Hoehndorf, Robert; Gao, Xin; Arold, Stefan T; Mineta, Katsuhiko; Archer, John A C; Gojobori, Takashi; Mijakovic, Ivan; Bajic, Vladimir B

2017-01-05

Finding a source from which high-energy-density biofuels can be derived at an industrial scale has become an urgent challenge for renewable energy production. Some microorganisms can produce free fatty acids (FFA) as precursors towards such high-energy-density biofuels. In particular, photosynthetic cyanobacteria are capable of directly converting carbon dioxide into FFA. However, current engineered strains need several rounds of engineering to reach the level of production of FFA to be commercially viable; thus new chassis strains that require less engineering are needed. Although more than 120 cyanobacterial genomes are sequenced, the natural potential of these strains for FFA production and excretion has not been systematically estimated. Here we present the FFA SC (FFASC), an in silico screening method that evaluates the potential for FFA production and excretion of cyanobacterial strains based on their proteomes. A literature search allowed for the compilation of 64 proteins, most of which influence FFA production and a few of which affect FFA excretion. The proteins are classified into 49 orthologous groups (OGs) that helped create rules used in the scoring/ranking of algorithms developed to estimate the potential for FFA production and excretion of an organism. Among 125 cyanobacterial strains, FFASC identified 20 candidate chassis strains that rank in their FFA producing and excreting potential above the specifically engineered reference strain, Synechococcus sp. PCC 7002. We further show that the top ranked cyanobacterial strains are unicellular and primarily include Prochlorococcus (order Prochlorales) and marine Synechococcus (order Chroococcales) that cluster phylogenetically. Moreover, two principal categories of enzymes were shown to influence FFA production the most: those ensuring precursor availability for the biosynthesis of lipids, and those involved in handling the oxidative stress associated to FFA synthesis. To our knowledge FFASC is the first in silico method to screen cyanobacteria proteomes for their potential to produce and excrete FFA, as well as the first attempt to parameterize the criteria derived from genetic characteristics that are favorable/non-favorable for this purpose. Thus, FFASC helps focus experimental evaluation only on the most promising cyanobacteria.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

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

Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh

2013-10-01

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure–activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα bindingmore » affinity (MTL R{sup 2} = 0.71, STL R{sup 2} = 0.73). For ERβ binding affinity, MTL models were significantly more predictive (R{sup 2} = 0.53, p < 0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation. - Highlights: • This is the largest curated dataset inclusive of ERα and β (the latter is unique). • New methodology that for the first time affords acceptable ERβ models. • A combination of QSAR and docking enables prediction of affinity and function. • The results have potential applications to green chemistry. • Models are publicly available for virtual screening via a web portal.« less

A PATO-compliant zebrafish screening database (MODB): management of morpholino knockdown screen information.

PubMed

Knowlton, Michelle N; Li, Tongbin; Ren, Yongliang; Bill, Brent R; Ellis, Lynda Bm; Ekker, Stephen C

2008-01-07

The zebrafish is a powerful model vertebrate amenable to high throughput in vivo genetic analyses. Examples include reverse genetic screens using morpholino knockdown, expression-based screening using enhancer trapping and forward genetic screening using transposon insertional mutagenesis. We have created a database to facilitate web-based distribution of data from such genetic studies. The MOrpholino DataBase is a MySQL relational database with an online, PHP interface. Multiple quality control levels allow differential access to data in raw and finished formats. MODBv1 includes sequence information relating to almost 800 morpholinos and their targets and phenotypic data regarding the dose effect of each morpholino (mortality, toxicity and defects). To improve the searchability of this database, we have incorporated a fixed-vocabulary defect ontology that allows for the organization of morpholino affects based on anatomical structure affected and defect produced. This also allows comparison between species utilizing Phenotypic Attribute Trait Ontology (PATO) designated terminology. MODB is also cross-linked with ZFIN, allowing full searches between the two databases. MODB offers users the ability to retrieve morpholino data by sequence of morpholino or target, name of target, anatomical structure affected and defect produced. MODB data can be used for functional genomic analysis of morpholino design to maximize efficacy and minimize toxicity. MODB also serves as a template for future sequence-based functional genetic screen databases, and it is currently being used as a model for the creation of a mutagenic insertional transposon database.

BioQ: tracing experimental origins in public genomic databases using a novel data provenance model.

PubMed

Saccone, Scott F; Quan, Jiaxi; Jones, Peter L

2012-04-15

Public genomic databases, which are often used to guide genetic studies of human disease, are now being applied to genomic medicine through in silico integrative genomics. These databases, however, often lack tools for systematically determining the experimental origins of the data. We introduce a new data provenance model that we have implemented in a public web application, BioQ, for assessing the reliability of the data by systematically tracing its experimental origins to the original subjects and biologics. BioQ allows investigators to both visualize data provenance as well as explore individual elements of experimental process flow using precise tools for detailed data exploration and documentation. It includes a number of human genetic variation databases such as the HapMap and 1000 Genomes projects. BioQ is freely available to the public at http://bioq.saclab.net.

Discovery of the glycogen phosphorylase-modulating activity of a resveratrol glucoside by using a virtual screening protocol optimized for solvation effects.

PubMed

Mavrokefalos, Nikolaos; Myrianthopoulos, Vassilios; Chajistamatiou, Aikaterini S; Chrysina, Evangelia D; Mikros, Emmanuel

2015-04-01

The identification of natural products that can modulate blood glucose levels is of great interest as it can possibly facilitate the utilization of mild interventions such as herbal medicine or functional foods in the treatment of chronic diseases like diabetes. One of the established drug targets for antihyperglycemic therapy is glycogen phosphorylase. To evaluate the glycogen phosphorylase inhibitory properties of an in-house compound collection consisting to a large extent of natural products, a stepwise virtual and experimental screening protocol was devised and implemented. The fact that the active site of glycogen phosphorylase is highly hydrated emphasized that a methodological aspect needed to be efficiently addressed prior to an in silico evaluation of the compound collection. The effect of water molecules on docking calculations was regarded as a key parameter in terms of virtual screening protocol optimization. Statistical analysis of 125 structures of glycogen phosphorylase and solvent mapping focusing on the active site hydration motif in combination with a retrospective screening revealed the importance of a set of 29 crystallographic water molecules for achieving high enrichment as to the discrimination between active compounds and inactive decoys. The scaling of Van der Waals radii of system atoms had an additional effect on screening performance. Having optimized the in silico protocol, a prospective evaluation of the in-house compound collection derived a set of 18 top-ranked natural products that were subsequently evaluated in vitro for their activity as glycogen phosphorylase inhibitors. Two phenolic glucosides with glycogen phosphorylase-modulating activity were identified, whereas the most potent compound affording mid-micromolar inhibition was a glucosidic derivative of resveratrol, a stilbene well-known for its wide range of biological activities. Results show the possible phytotherapeutic and nutraceutical potential of products common in the Mediterranean countries, such as red wine and Vitis products in general or green raw salads and herbal preparations, where such compounds are abundant. Georg Thieme Verlag KG Stuttgart · New York.

Tempest: Accelerated MS/MS database search software for heterogeneous computing platforms

PubMed Central

Adamo, Mark E.; Gerber, Scott A.

2017-01-01

MS/MS database search algorithms derive a set of candidate peptide sequences from in-silico digest of a protein sequence database, and compute theoretical fragmentation patterns to match these candidates against observed MS/MS spectra. The original Tempest publication described these operations mapped to a CPU-GPU model, in which the CPU generates peptide candidates that are asynchronously sent to a discrete GPU to be scored against experimental spectra in parallel (Milloy et al., 2012). The current version of Tempest expands this model, incorporating OpenCL to offer seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors. Three protocols describe how to configure and run a Tempest search, including discussion of how to leverage Tempest's unique feature set to produce optimal results. PMID:27603022

VerSeDa: vertebrate secretome database

PubMed Central

Cortazar, Ana R.; Oguiza, José A.

2017-01-01

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

siRNA screen identifies QPCT as a druggable target for Huntington's disease.

PubMed

Jimenez-Sanchez, Maria; Lam, Wun; Hannus, Michael; Sönnichsen, Birte; Imarisio, Sara; Fleming, Angeleen; Tarditi, Alessia; Menzies, Fiona; Dami, Teresa Ed; Xu, Catherine; Gonzalez-Couto, Eduardo; Lazzeroni, Giulia; Heitz, Freddy; Diamanti, Daniela; Massai, Luisa; Satagopam, Venkata P; Marconi, Guido; Caramelli, Chiara; Nencini, Arianna; Andreini, Matteo; Sardone, Gian Luca; Caradonna, Nicola P; Porcari, Valentina; Scali, Carla; Schneider, Reinhard; Pollio, Giuseppe; O'Kane, Cahir J; Caricasole, Andrea; Rubinsztein, David C

2015-05-01

Huntington's disease (HD) is a currently incurable neurodegenerative condition caused by an abnormally expanded polyglutamine tract in huntingtin (HTT). We identified new modifiers of mutant HTT toxicity by performing a large-scale 'druggable genome' siRNA screen in human cultured cells, followed by hit validation in Drosophila. We focused on glutaminyl cyclase (QPCT), which had one of the strongest effects on mutant HTT-induced toxicity and aggregation in the cell-based siRNA screen and also rescued these phenotypes in Drosophila. We found that QPCT inhibition induced the levels of the molecular chaperone αB-crystallin and reduced the aggregation of diverse proteins. We generated new QPCT inhibitors using in silico methods followed by in vitro screening, which rescued the HD-related phenotypes in cell, Drosophila and zebrafish HD models. Our data reveal a new HD druggable target affecting mutant HTT aggregation and provide proof of principle for a discovery pipeline from druggable genome screen to drug development.

An Automatic Quality Control Pipeline for High-Throughput Screening Hit Identification.

PubMed

Zhai, Yufeng; Chen, Kaisheng; Zhong, Yang; Zhou, Bin; Ainscow, Edward; Wu, Ying-Ta; Zhou, Yingyao

2016-09-01

The correction or removal of signal errors in high-throughput screening (HTS) data is critical to the identification of high-quality lead candidates. Although a number of strategies have been previously developed to correct systematic errors and to remove screening artifacts, they are not universally effective and still require fair amount of human intervention. We introduce a fully automated quality control (QC) pipeline that can correct generic interplate systematic errors and remove intraplate random artifacts. The new pipeline was first applied to ~100 large-scale historical HTS assays; in silico analysis showed auto-QC led to a noticeably stronger structure-activity relationship. The method was further tested in several independent HTS runs, where QC results were sampled for experimental validation. Significantly increased hit confirmation rates were obtained after the QC steps, confirming that the proposed method was effective in enriching true-positive hits. An implementation of the algorithm is available to the screening community. © 2016 Society for Laboratory Automation and Screening.

In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB

PubMed Central

2013-01-01

Background Though India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and “finishing” expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence. Description By in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly. Conclusion Being first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity. PMID:23336431

In silico mining of putative microsatellite markers from whole genome sequence of water buffalo (Bubalus bubalis) and development of first BuffSatDB.

PubMed

Sarika; Arora, Vasu; Iquebal, Mir Asif; Rai, Anil; Kumar, Dinesh

2013-01-19

Though India has sequenced water buffalo genome but its draft assembly is based on cattle genome BTau 4.0, thus de novo chromosome wise assembly is a major pending issue for global community. The existing radiation hybrid of buffalo and these reported STR can be used further in final gap plugging and "finishing" expected in de novo genome assembly. QTL and gene mapping needs mining of putative STR from buffalo genome at equal interval on each and every chromosome. Such markers have potential role in improvement of desirable characteristics, such as high milk yields, resistance to diseases, high growth rate. The STR mining from whole genome and development of user friendly database is yet to be done to reap the benefit of whole genome sequence. By in silico microsatellite mining of whole genome, we have developed first STR database of water buffalo, BuffSatDb (Buffalo MicroSatellite Database (http://cabindb.iasri.res.in/buffsatdb/) which is a web based relational database of 910529 microsatellite markers, developed using PHP and MySQL database. Microsatellite markers have been generated using MIcroSAtellite tool. It is simple and systematic web based search for customised retrieval of chromosome wise and genome-wide microsatellites. Search has been enabled based on chromosomes, motif type (mono-hexa), repeat motif and repeat kind (simple and composite). The search may be customised by limiting location of STR on chromosome as well as number of markers in that range. This is a novel approach and not been implemented in any of the existing marker database. This database has been further appended with Primer3 for primer designing of the selected markers enabling researcher to select markers of choice at desired interval over the chromosome. The unique add-on of degenerate bases further helps in resolving presence of degenerate bases in current buffalo assembly. Being first buffalo STR database in the world , this would not only pave the way in resolving current assembly problem but shall be of immense use for global community in QTL/gene mapping critically required to increase knowledge in the endeavour to increase buffalo productivity, especially for third world country where rural economy is significantly dependent on buffalo productivity.

Proteasix: a tool for automated and large-scale prediction of proteases involved in naturally occurring peptide generation.

PubMed

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

2013-04-01

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

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.

In Silico Mining for Antimalarial Structure-Activity Knowledge and Discovery of Novel Antimalarial Curcuminoids.

PubMed

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

2016-06-29

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

The in silico screening and X-ray structure analysis of the inhibitor complex of Plasmodium falciparum orotidine 5'-monophosphate decarboxylase.

PubMed

Takashima, Yasuhide; Mizohata, Eiichi; Krungkrai, Sudaratana R; Fukunishi, Yoshifumi; Kinoshita, Takayoshi; Sakata, Tsuneaki; Matsumura, Hiroyoshi; Krungkrai, Jerapan; Horii, Toshihiro; Inoue, Tsuyoshi

2012-08-01

Orotidine 5'-monophosphate decarboxylase from Plasmodium falciparum (PfOMPDC) catalyses the final step in the de novo synthesis of uridine 5'-monophosphate (UMP) from orotidine 5'-monophosphate (OMP). A defective PfOMPDC enzyme is lethal to the parasite. Novel in silico screening methods were performed to select 14 inhibitors against PfOMPDC, with a high hit rate of 9%. X-ray structure analysis of PfOMPDC in complex with one of the inhibitors, 4-(2-hydroxy-4-methoxyphenyl)-4-oxobutanoic acid, was carried out to at 2.1 Å resolution. The crystal structure revealed that the inhibitor molecule occupied a part of the active site that overlaps with the phosphate-binding region in the OMP- or UMP-bound complexes. Space occupied by the pyrimidine and ribose rings of OMP or UMP was not occupied by this inhibitor. The carboxyl group of the inhibitor caused a dramatic movement of the L1 and L2 loops that play a role in the recognition of the substrate and product molecules. Combining part of the inhibitor molecule with moieties of the pyrimidine and ribose rings of OMP and UMP represents a suitable avenue for further development of anti-malarial drugs.

Complementing in vitro screening assays with in silico ...

EPA Pesticide Factsheets

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

Virtual screening for development of new effective compounds against Staphylococcus aureus.

PubMed

Diniz, Roseane Costa; Soares, Lucas Weba; da Silva, Luis Claudio Nascimento

2018-03-26

Staphylococcus aureus is a notorious pathogenic bacterium causing a wide range of diseases from soft-tissue contamination, to more serious and deep-seated infections. This species is highlighted by its ability to express several kinds of virulence factors and to acquire genes related to drug resistance. Target this number of factors to design any drug is not an easy task. In this review we discuss the importance of computational methods to impulse the development of new drugs against S. aureus. The application of docking methods to screen large library of natural or synthetic compounds and to provide insights into action mechanisms is demonstrated. Particularly, highlighted the studies that validated in silico results with biochemical and microbiological assays. We also comment the computer-aided design of new molecules using some known inhibitors. The confirmation of in silico results with biochemical and microbiological assays allowed the identification of lead molecules that could be used for drug design such as rhodomyrtone, quinuclidine, berberine (and their derivative compounds). The fast development in the computational methods is essential to improve our ability to discovery new drugs, as well as to expand understanding about drug-target interactions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Predicting human liver microsomal stability with machine learning techniques.

PubMed

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

2008-02-01

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

In Silico Discovery of a Substituted 6-Methoxy-quinalidine with Leishmanicidal Activity in Leishmania infantum.

PubMed

Stevanović, Strahinja; Perdih, Andrej; Senćanski, Milan; Glišić, Sanja; Duarte, Margarida; Tomás, Ana M; Sena, Filipa V; Sousa, Filipe M; Pereira, Manuela M; Solmajer, Tom

2018-03-27

There is an urgent need for the discovery of new antileishmanial drugs with a new mechanism of action. Type 2 NADH dehydrogenase from Leishmania infantum ( Li NDH2) is an enzyme of the parasite's respiratory system, which catalyzes the electron transfer from NADH to ubiquinone without coupled proton pumping. In previous studies of the related NADH: ubiquinone oxidoreductase crystal structure from Saccharomyces cerevisiae , two ubiquinone-binding sites (UQ I and UQ II ) were identified and shown to play an important role in the NDH-2-catalyzed oxidoreduction reaction. Based on the available structural data, we developed a three-dimensional structural model of Li NDH2 using homology detection methods and performed an in silico virtual screening campaign to search for potential inhibitors targeting the Li NDH2 ubiquinone-binding site 1-UQ I . Selected compounds displaying favorable properties in the computational screening experiments were assayed for inhibitory activity in the structurally similar recombinant NDH-2 from S. aureus and leishmanicidal activity was determined in the wild-type axenic amastigotes and promastigotes of L. infantum . The identified compound, a substituted 6-methoxy-quinalidine, showed promising nanomolar leishmanicidal activity on wild-type axenic promastigotes and amastigotes of L. infantum and the potential for further development.

In silico-designed novel non-peptidic ABAD LD hot spot mimetics reverse Aβ-induced mitochondrial impairments in vitro.

PubMed

Viswanath, Ambily Nath Indu; Kim, TaeHun; Jung, Seo Yun; Lim, Sang Min; Pae, Ae Nim

2017-12-01

Present work aimed to introduce non-peptidic ABAD loop D (L D ) hot spot mimetics as ABAD-Aβ inhibitors. A full-length atomistic model of ABAD-Aβ complex was built as a scaffold to launch the lead design and its topology later verified by cross-checking the computational mutagenesis results with that of in vitro data. Thereafter, the interactions of prime Aβ-binding L D residues-Tyr101, Thr108, and Thr110-were translated into specific pharmacophore features and this hypothesis subsequently used as a virtual screen query. ELISA-based screening of 20 hits identified two promising lead candidates, VC15 and VC19 with an IC 50 of 4.4 ± 0.3 and 9.6 ± 0.1 μm, respectively. They productively reversed Aβ-induced mitochondrial dysfunctions such as mitochondrial membrane potential loss (JC-1 assay), toxicity (MTT assay), and ATP reduction (ATP assay) in addition to increased cell viabilities. This is the first reporting of L D hot spot-centric in silico scheme to discover novel compounds with promising ABAD-Aβ inhibitory potential. These chemotypes are proposed for further structural optimization to derive novel Alzheimer's disease (AD) therapeutics. © 2017 John Wiley & Sons A/S.

An Approach for Identification of Novel Drug Targets in Streptococcus pyogenes SF370 Through Pathway Analysis.

PubMed

Singh, Satendra; Singh, Dev Bukhsh; Singh, Anamika; Gautam, Budhayash; Ram, Gurudayal; Dwivedi, Seema; Ramteke, Pramod W

2016-12-01

Streptococcus pyogenes is one of the most important pathogens as it is involved in various infections affecting upper respiratory tract and skin. Due to the emergence of multidrug resistance and cross-resistance, S. Pyogenes is becoming more pathogenic and dangerous. In the present study, an in silico comparative analysis of total 65 metabolic pathways of the host (Homo sapiens) and the pathogen was performed. Initially, 486 paralogous enzymes were identified so that they can be removed from possible drug target list. The 105 enzymes of the biochemical pathways of S. pyogenes from the KEGG metabolic pathway database were compared with the proteins from the Homo sapiens by performing a BLASTP search against the non-redundant database restricted to the Homo sapiens subset. Out of these, 83 enzymes were identified as non-human homologous while 30 enzymes of inadequate amino acid length were removed for further processing. Essential enzymes were finally mined from remaining 53 enzymes. Finally, 28 essential enzymes were identified in S. pyogenes SF370 (serotype M1). In subcellular localization study, 18 enzymes were predicted with cytoplasmic localization and ten enzymes with the membrane localization. These ten enzymes with putative membrane localization should be of particular interest. Acyl-carrier-protein S-malonyltransferase, DNA polymerase III subunit beta and dihydropteroate synthase are novel drug targets and thus can be used to design potential inhibitors against S. pyogenes infection. 3D structure of dihydropteroate synthase was modeled and validated that can be used for virtual screening and interaction study of potential inhibitors with the target enzyme.

Exploring the selectivity of auto-inducer complex with LuxR using molecular docking, mutational studies and molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Rajamanikandan, Sundaraj; Srinivasan, Pappu

2017-03-01

Bacteria communicate with one another using extracellular signaling molecules called auto-inducers (AHLs), a process termed as quorum sensing. The quorum sensing process allows bacteria to regulate various physiological activities. In this regard, quorum sensing master regulator LuxR from Vibrio harveyi represents an attractive therapeutic target for the development of novel anti-quorum sensing agents. Eventhough the binding of AHL complex with LuxR is evidenced in earlier reports, but their mode of binding is not clearly determined. Therefore, in the present work, molecular docking, in silico mutational studies, molecular dynamics simulations and free energy calculations were performed to understand the selectivity of AHL into the binding site of LuxR. The results revealed that Asn133 and Gln137 residues play a crucial role in recognizing AHL more effectively into the binding site of LuxR with good binding free energy. In addition to that, the carbonyl group presents in the lactone ring and amide group of AHL plays a vital role in the formation of hydrogen bond interactions with the protein. Further, structure based virtual screening was performed using ChemBridge database to screen potent lead molecules against LuxR. 4-benzyl-2-pyrrolidinone and N-[2(1-cyclohexen-1-yl) enthyl]-N'(2-ethoxyphenyl) were selected based on dock score, binding affinity and mode of interactions with the receptor. Furthermore, binding free energy, density functional theory and ADME prediction were performed to rank the lead molecules. Thus, the identified lead molecules can be used for the development of anti-quorum sensing drugs.

Bitterness prediction in-silico: A step towards better drugs.

PubMed

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

2018-02-05

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

Non-clinical studies required for new drug development - Part I: early in silico and in vitro studies, new target discovery and validation, proof of principles and robustness of animal studies.

PubMed

Andrade, E L; Bento, A F; Cavalli, J; Oliveira, S K; Freitas, C S; Marcon, R; Schwanke, R C; Siqueira, J M; Calixto, J B

2016-10-24

This review presents a historical overview of drug discovery and the non-clinical stages of the drug development process, from initial target identification and validation, through in silico assays and high throughput screening (HTS), identification of leader molecules and their optimization, the selection of a candidate substance for clinical development, and the use of animal models during the early studies of proof-of-concept (or principle). This report also discusses the relevance of validated and predictive animal models selection, as well as the correct use of animal tests concerning the experimental design, execution and interpretation, which affect the reproducibility, quality and reliability of non-clinical studies necessary to translate to and support clinical studies. Collectively, improving these aspects will certainly contribute to the robustness of both scientific publications and the translation of new substances to clinical development.

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.

A Web-based Alternative Non-animal Method Database for Safety Cosmetic Evaluations

PubMed Central

Kim, Seung Won; Kim, Bae-Hwan

2016-01-01

Animal testing was used traditionally in the cosmetics industry to confirm product safety, but has begun to be banned; alternative methods to replace animal experiments are either in development, or are being validated, worldwide. Research data related to test substances are critical for developing novel alternative tests. Moreover, safety information on cosmetic materials has neither been collected in a database nor shared among researchers. Therefore, it is imperative to build and share a database of safety information on toxicological mechanisms and pathways collected through in vivo, in vitro, and in silico methods. We developed the CAMSEC database (named after the research team; the Consortium of Alternative Methods for Safety Evaluation of Cosmetics) to fulfill this purpose. On the same website, our aim is to provide updates on current alternative research methods in Korea. The database will not be used directly to conduct safety evaluations, but researchers or regulatory individuals can use it to facilitate their work in formulating safety evaluations for cosmetic materials. We hope this database will help establish new alternative research methods to conduct efficient safety evaluations of cosmetic materials. PMID:27437094

A Web-based Alternative Non-animal Method Database for Safety Cosmetic Evaluations.

PubMed

Kim, Seung Won; Kim, Bae-Hwan

2016-07-01

Animal testing was used traditionally in the cosmetics industry to confirm product safety, but has begun to be banned; alternative methods to replace animal experiments are either in development, or are being validated, worldwide. Research data related to test substances are critical for developing novel alternative tests. Moreover, safety information on cosmetic materials has neither been collected in a database nor shared among researchers. Therefore, it is imperative to build and share a database of safety information on toxicological mechanisms and pathways collected through in vivo, in vitro, and in silico methods. We developed the CAMSEC database (named after the research team; the Consortium of Alternative Methods for Safety Evaluation of Cosmetics) to fulfill this purpose. On the same website, our aim is to provide updates on current alternative research methods in Korea. The database will not be used directly to conduct safety evaluations, but researchers or regulatory individuals can use it to facilitate their work in formulating safety evaluations for cosmetic materials. We hope this database will help establish new alternative research methods to conduct efficient safety evaluations of cosmetic materials.

Screening of Osteogenic-Enhancing Short Peptides from BMPs for Biomimetic Material Applications

PubMed Central

Kanie, Kei; Kurimoto, Rio; Tian, Jing; Ebisawa, Katsumi; Narita, Yuji; Honda, Hiroyuki; Kato, Ryuji

2016-01-01

Bone regeneration is an important issue in many situations, such as bone fracture and surgery. Umbilical cord mesenchymal stem cells (UC-MSCs) are promising cell sources for bone regeneration. Bone morphogenetic proteins and their bioactive peptides are biomolecules known to enhance the osteogenic differentiation of MSCs. However, fibrosis can arise during the development of implantable biomaterials. Therefore, it is important to control cell organization by enhancing osteogenic proliferation and differentiation and inhibiting fibroblast proliferation. Thus, we focused on the screening of such osteogenic-enhancing peptides. In the present study, we developed new peptide array screening platforms to evaluate cell proliferation and alkaline phosphatase activity in osteoblasts, UC-MSCs and fibroblasts. The conditions for the screening platform were first defined using UC-MSCs and an osteogenic differentiation peptide known as W9. Next, in silico screening to define the candidate peptides was carried out to evaluate the homology of 19 bone morphogenetic proteins. Twenty-five candidate 9-mer peptides were selected for screening. Finally, the screening of osteogenic-enhancing (osteogenic cell-selective proliferation and osteogenic differentiation) short peptide was carried out using the peptide array method, and three osteogenic-enhancing peptides were identified, confirming the validity of this screening. PMID:28773850

In silico gene expression analysis – an overview

PubMed Central

Murray, David; Doran, Peter; MacMathuna, Padraic; Moss, Alan C

2007-01-01

Efforts aimed at deciphering the molecular basis of complex disease are underpinned by the availability of high throughput strategies for the identification of biomolecules that drive the disease process. The completion of the human genome-sequencing project, coupled to major technological developments, has afforded investigators myriad opportunities for multidimensional analysis of biological systems. Nowhere has this research explosion been more evident than in the field of transcriptomics. Affordable access and availability to the technology that supports such investigations has led to a significant increase in the amount of data generated. As most biological distinctions are now observed at a genomic level, a large amount of expression information is now openly available via public databases. Furthermore, numerous computational based methods have been developed to harness the power of these data. In this review we provide a brief overview of in silico methodologies for the analysis of differential gene expression such as Serial Analysis of Gene Expression and Digital Differential Display. The performance of these strategies, at both an operational and result/output level is assessed and compared. The key considerations that must be made when completing an in silico expression analysis are also presented as a roadmap to facilitate biologists. Furthermore, to highlight the importance of these in silico methodologies in contemporary biomedical research, examples of current studies using these approaches are discussed. The overriding goal of this review is to present the scientific community with a critical overview of these strategies, so that they can be effectively added to the tool box of biomedical researchers focused on identifying the molecular mechanisms of disease. PMID:17683638

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.

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.

The CSB Incident Screening Database: description, summary statistics and uses.

PubMed

Gomez, Manuel R; Casper, Susan; Smith, E Allen

2008-11-15

This paper briefly describes the Chemical Incident Screening Database currently used by the CSB to identify and evaluate chemical incidents for possible investigations, and summarizes descriptive statistics from this database that can potentially help to estimate the number, character, and consequences of chemical incidents in the US. The report compares some of the information in the CSB database to roughly similar information available from databases operated by EPA and the Agency for Toxic Substances and Disease Registry (ATSDR), and explores the possible implications of these comparisons with regard to the dimension of the chemical incident problem. Finally, the report explores in a preliminary way whether a system modeled after the existing CSB screening database could be developed to serve as a national surveillance tool for chemical incidents.

CrossCheck: an open-source web tool for high-throughput screen data analysis.

PubMed

Najafov, Jamil; Najafov, Ayaz

2017-07-19

Modern high-throughput screening methods allow researchers to generate large datasets that potentially contain important biological information. However, oftentimes, picking relevant hits from such screens and generating testable hypotheses requires training in bioinformatics and the skills to efficiently perform database mining. There are currently no tools available to general public that allow users to cross-reference their screen datasets with published screen datasets. To this end, we developed CrossCheck, an online platform for high-throughput screen data analysis. CrossCheck is a centralized database that allows effortless comparison of the user-entered list of gene symbols with 16,231 published datasets. These datasets include published data from genome-wide RNAi and CRISPR screens, interactome proteomics and phosphoproteomics screens, cancer mutation databases, low-throughput studies of major cell signaling mediators, such as kinases, E3 ubiquitin ligases and phosphatases, and gene ontological information. Moreover, CrossCheck includes a novel database of predicted protein kinase substrates, which was developed using proteome-wide consensus motif searches. CrossCheck dramatically simplifies high-throughput screen data analysis and enables researchers to dig deep into the published literature and streamline data-driven hypothesis generation. CrossCheck is freely accessible as a web-based application at http://proteinguru.com/crosscheck.

In Silico and In Vitro Anticancer Activity of Isolated Novel Marker Compound from Chemically Modified Bioactive Fraction from Curcuma longa (NCCL).

PubMed

Naqvi, Arshi; Malasoni, Richa; Gupta, Swati; Srivastava, Akansha; Pandey, Rishi R; Dwivedi, Anil Kumar

2017-10-01

Turmeric ( Curcuma longa ) is reported to possess wide array of biological activities. Herbal Medicament (HM) is a standardized hexane-soluble fraction of C. longa and is well known for its neuroprotective effect. In this study, we attempted to synthesize a novel chemically modified bioactive fraction from HM (NCCL) along with isolation and characterization of a novel marker compound (I). NCCL was prepared from HM. The chemical structure of the marker compound isolated from NCCL was determined from 1D/2D nuclear magnetic resonance, mass spectroscopy, and Fourier transform infrared. The compound so isolated was subjected to in silico and in vitro screenings to test its inhibitory effect on estrogen receptors. Molecular docking studies revealed that the binding poses of the compound I was energetically favorable. Among NCCL and compound I taken for in vitro studies, NCCL had exhibited good anti-cancer activity over compound I against MCF-7, MDA-MB-231, DU-145, and PC-3 cells. This is the first study about the synthesis of a chemically modified bioactive fraction which used a standardized extract since the preparation of the HM. It may be concluded that NCCL fraction having residual components induce more cell death than compound I alone. Thus, NCCL may be used as a potent therapeutic drug. In the present paper, a standardized hexane soluble fraction of Curcuma longa (HM) was chemically modified to give a novel bioactive fraction (NCCL). A novel marker compound was isolated from NCCL and was characerized using various spectral techniques. The compound so isolated was investigated for in-silico screenings. NCCL and isolated compound was subjected to in-vitro anti-cancer screenings against MCF 7, MDA MB 231 (breast adenocarcinoma) and DU 145 and PC 3 cell lines (androgen independent human prostate cancer cells). The virtual screenings reveals that isolated compound has shown favourable drug like properties. NCCL fraction having residual components induces more cell death in these four cancer cell lines than isolated compound alone. Abbreviations used: HM: Herbal Medicament; NCCL: Chemically modified HM; FT-IR: Fourier transform-infrared spectroscopy; NMR: Nuclear magnetic resonance spectroscopy; MS: Mass spectroscopy; HPLC: High-performance liquid chromatography; ER: Estrogen receptor; MTT: 3-(4,5 dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide; MIC: Minimum inhibitory concentration; TAM: Tamoxifen KBr: Potassium bromide; DMSO: Dimethyl sulfoxide; ACN: Acetonitrile; PDB: Protein Data Bank; PDA: Photodiode array detector.

The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies.

PubMed

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

2016-01-01

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

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

Integrating in Silico and in Vitro Approaches To Predict Drug Accessibility to the Central Nervous System.

PubMed

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

2016-05-02

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

In Silico Models for Ecotoxicity of Pharmaceuticals.

PubMed

Roy, Kunal; Kar, Supratik

2016-01-01

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

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.

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

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.

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

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

JBEI Registry

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

Ham, Timothy

2008-12-01

The JBEI Registry is a software to store and manage to a database of biological parts. It is intended to be used as a web service that is accessed via a web browser. It is also capable of running as a desktop program for a single user. The registry software stores, indexes, categories, and allows users to enter, search, retrieve, and contruct biological constructs in silico. It is also able to communicate with other Registries for data sharing and exchange.

Immunoinformatics: an integrated scenario

PubMed Central

Tomar, Namrata; De, Rajat K

2010-01-01

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

A Public-Use, Full-Screen Interface for SPIRES Databases.

ERIC Educational Resources Information Center

Kriz, Harry M.

This paper describes the techniques for implementing a full-screen, custom SPIRES interface for a public-use library database. The database-independent protocol that controls the system is described in detail. Source code for an entire working application using this interface is included. The protocol, with less than 170 lines of procedural code,…

Identification by Virtual Screening and In Vitro Testing of Human DOPA Decarboxylase Inhibitors

PubMed Central

Cellini, Barbara; Macchiarulo, Antonio; Giardina, Giorgio; Bossa, Francesco; Borri Voltattorni, Carla

2012-01-01

Dopa decarboxylase (DDC), a pyridoxal 5′-phosphate (PLP) enzyme responsible for the biosynthesis of dopamine and serotonin, is involved in Parkinson's disease (PD). PD is a neurodegenerative disease mainly due to a progressive loss of dopamine-producing cells in the midbrain. Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD. Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects. Therefore, the main goals of this study were (a) to use virtual screening to identify potential human DDC inhibitors and (b) to evaluate the reliability of our virtual-screening (VS) protocol by experimentally testing the “in vitro” activity of selected molecules. Starting from the crystal structure of the DDC-carbidopa complex, a new VS protocol, integrating pharmacophore searches and molecular docking, was developed. Analysis of 15 selected compounds, obtained by filtering the public ZINC database, yielded two molecules that bind to the active site of human DDC and behave as competitive inhibitors with Ki values ≥10 µM. By performing in silico similarity search on the latter compounds followed by a substructure search using the core of the most active compound we identified several competitive inhibitors of human DDC with Ki values in the low micromolar range, unable to bind free PLP, and predicted to not cross the blood-brain barrier. The most potent inhibitor with a Ki value of 500 nM represents a new lead compound, targeting human DDC, that may be the basis for lead optimization in the development of new DDC inhibitors. To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery. PMID:22384042

Identification by virtual screening and in vitro testing of human DOPA decarboxylase inhibitors.

PubMed

Daidone, Frederick; Montioli, Riccardo; Paiardini, Alessandro; Cellini, Barbara; Macchiarulo, Antonio; Giardina, Giorgio; Bossa, Francesco; Borri Voltattorni, Carla

2012-01-01

Dopa decarboxylase (DDC), a pyridoxal 5'-phosphate (PLP) enzyme responsible for the biosynthesis of dopamine and serotonin, is involved in Parkinson's disease (PD). PD is a neurodegenerative disease mainly due to a progressive loss of dopamine-producing cells in the midbrain. Co-administration of L-Dopa with peripheral DDC inhibitors (carbidopa or benserazide) is the most effective symptomatic treatment for PD. Although carbidopa and trihydroxybenzylhydrazine (the in vivo hydrolysis product of benserazide) are both powerful irreversible DDC inhibitors, they are not selective because they irreversibly bind to free PLP and PLP-enzymes, thus inducing diverse side effects. Therefore, the main goals of this study were (a) to use virtual screening to identify potential human DDC inhibitors and (b) to evaluate the reliability of our virtual-screening (VS) protocol by experimentally testing the "in vitro" activity of selected molecules. Starting from the crystal structure of the DDC-carbidopa complex, a new VS protocol, integrating pharmacophore searches and molecular docking, was developed. Analysis of 15 selected compounds, obtained by filtering the public ZINC database, yielded two molecules that bind to the active site of human DDC and behave as competitive inhibitors with K(i) values ≥10 µM. By performing in silico similarity search on the latter compounds followed by a substructure search using the core of the most active compound we identified several competitive inhibitors of human DDC with K(i) values in the low micromolar range, unable to bind free PLP, and predicted to not cross the blood-brain barrier. The most potent inhibitor with a K(i) value of 500 nM represents a new lead compound, targeting human DDC, that may be the basis for lead optimization in the development of new DDC inhibitors. To our knowledge, a similar approach has not been reported yet in the field of DDC inhibitors discovery.

The Druggable Pocketome of Corynebacterium diphtheriae: A New Approach for in silico Putative Druggable Targets

PubMed Central

Hassan, Syed S.; Jamal, Syed B.; Radusky, Leandro G.; Tiwari, Sandeep; Ullah, Asad; Ali, Javed; Behramand; de Carvalho, Paulo V. S. D.; Shams, Rida; Khan, Sabir; Figueiredo, Henrique C. P.; Barh, Debmalya; Ghosh, Preetam; Silva, Artur; Baumbach, Jan; Röttger, Richard; Turjanski, Adrián G.; Azevedo, Vasco A. C.

2018-01-01

Diphtheria is an acute and highly infectious disease, previously regarded as endemic in nature but vaccine-preventable, is caused by Corynebacterium diphtheriae (Cd). In this work, we used an in silico approach along the 13 complete genome sequences of C. diphtheriae followed by a computational assessment of structural information of the binding sites to characterize the “pocketome druggability.” To this end, we first computed the “modelome” (3D structures of a complete genome) of a randomly selected reference strain Cd NCTC13129; that had 13,763 open reading frames (ORFs) and resulted in 1,253 (∼9%) structure models. The amino acid sequences of these modeled structures were compared with the remaining 12 genomes and consequently, 438 conserved protein sequences were obtained. The RCSB-PDB database was consulted to check the template structures for these conserved proteins and as a result, 401 adequate 3D models were obtained. We subsequently predicted the protein pockets for the obtained set of models and kept only the conserved pockets that had highly druggable (HD) values (137 across all strains). Later, an off-target host homology analyses was performed considering the human proteome using NCBI database. Furthermore, the gene essentiality analysis was carried out that gave a final set of 10-conserved targets possessing highly druggable protein pockets. To check the target identification robustness of the pipeline used in this work, we crosschecked the final target list with another in-house target identification approach for C. diphtheriae thereby obtaining three common targets, these were; hisE-phosphoribosyl-ATP pyrophosphatase, glpX-fructose 1,6-bisphosphatase II, and rpsH-30S ribosomal protein S8. Our predicted results suggest that the in silico approach used could potentially aid in experimental polypharmacological target determination in C. diphtheriae and other pathogens, thereby, might complement the existing and new drug-discovery pipelines. PMID:29487617

Isolation, characterization and in silico docking studies of synergistic estrogen receptor a anticancer polyphenols from Syzygium alternifolium (Wt.) Walp.

PubMed Central

Yugandhar, Pulicherla; Kumar, Konidala Kranthi; Neeraja, Pabbaraju; Savithramma, Nataru

2017-01-01

Aim: This study aims to isolate, characterize, and in silico evaluate of anticancer polyphenols from different parts of Syzygium alternifolium. Materials and Methods: The polyphenols were isolated by standard protocol and characterized using Fourier-transform infrared (FT-IR), High performance liquid chromatography - Photodiode array detector coupled with Electrospray ionization - mass spectrometry (MS/MS). The compounds were elucidated based on retention time and molecular ions (m/z) either by [M+H]+/[M-H]− with the comparison of standard phenols as well as ReSpect software tool. Furthermore, absorption, distribution, metabolism, and excretion (ADME)/toxicity properties of selected phenolic scaffolds were screened using OSIRIS and SwissADME programs, which incorporate toxicity risk assessments, pharmacokinetics, and rule of five principles. Molecular docking studies were carried out for selected toxicity filtered compounds against breast cancer estrogen receptor a (ERa) structure (protein data bank-ID: 1A52) through AutoDock scoring functions by PyRx virtual screening program. Results: The obtained results showed two intensive peaks in each polyphenol fraction analyzed with FT-IR, confirms O-H/C-O stretch of the phenolic functional group. A total of 40 compounds were obtained, which categorized as 9 different classes. Among them, flavonol group represents more number of polyphenols. In silico studies suggest seven compounds have the possibility to use as future nontoxic inhibitors. Molecular docking studies with ERa revealed the lead molecules unequivocally interact with Leu346, Glu353, Leu391, Arg394, Gly521, Leu525 residues, and Phe404 formed atomic π-stacking with dihydrochromen-4-one ring of ligands as like estrodial, which stabilizes the receptor structure and complicated to generate a single mutation for drug resistance. Conclusion: Overall, these results significantly proposed that isolated phenolics could be served as potential ER mitigators for breast cancer therapy. PMID:28894629

SCRIPDB: a portal for easy access to syntheses, chemicals and reactions in patents

PubMed Central

Heifets, Abraham; Jurisica, Igor

2012-01-01

The patent literature is a rich catalog of biologically relevant chemicals; many public and commercial molecular databases contain the structures disclosed in patent claims. However, patents are an equally rich source of metadata about bioactive molecules, including mechanism of action, disease class, homologous experimental series, structural alternatives, or the synthetic pathways used to produce molecules of interest. Unfortunately, this metadata is discarded when chemical structures are deposited separately in databases. SCRIPDB is a chemical structure database designed to make this metadata accessible. SCRIPDB provides the full original patent text, reactions and relationships described within any individual patent, in addition to the molecular files common to structural databases. We discuss how such information is valuable in medical text mining, chemical image analysis, reaction extraction and in silico pharmaceutical lead optimization. SCRIPDB may be searched by exact chemical structure, substructure or molecular similarity and the results may be restricted to patents describing synthetic routes. SCRIPDB is available at http://dcv.uhnres.utoronto.ca/SCRIPDB. PMID:22067445

BioQ: tracing experimental origins in public genomic databases using a novel data provenance model

PubMed Central

Saccone, Scott F.; Quan, Jiaxi; Jones, Peter L.

2012-01-01

Motivation: Public genomic databases, which are often used to guide genetic studies of human disease, are now being applied to genomic medicine through in silico integrative genomics. These databases, however, often lack tools for systematically determining the experimental origins of the data. Results: We introduce a new data provenance model that we have implemented in a public web application, BioQ, for assessing the reliability of the data by systematically tracing its experimental origins to the original subjects and biologics. BioQ allows investigators to both visualize data provenance as well as explore individual elements of experimental process flow using precise tools for detailed data exploration and documentation. It includes a number of human genetic variation databases such as the HapMap and 1000 Genomes projects. Availability and implementation: BioQ is freely available to the public at http://bioq.saclab.net Contact: ssaccone@wustl.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22426342

VerSeDa: vertebrate secretome database.

PubMed

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

2017-01-01

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

DrugBank: a knowledgebase for drugs, drug actions and drug targets

PubMed Central

Wishart, David S.; Knox, Craig; Guo, An Chi; Cheng, Dean; Shrivastava, Savita; Tzur, Dan; Gautam, Bijaya; Hassanali, Murtaza

2008-01-01

DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With ∼4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food–drug interactions, drug–drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca PMID:18048412

The National Microbial Pathogen Database Resource (NMPDR): a genomics platform based on subsystem annotation.

PubMed

McNeil, Leslie Klis; Reich, Claudia; Aziz, Ramy K; Bartels, Daniela; Cohoon, Matthew; Disz, Terry; Edwards, Robert A; Gerdes, Svetlana; Hwang, Kaitlyn; Kubal, Michael; Margaryan, Gohar Rem; Meyer, Folker; Mihalo, William; Olsen, Gary J; Olson, Robert; Osterman, Andrei; Paarmann, Daniel; Paczian, Tobias; Parrello, Bruce; Pusch, Gordon D; Rodionov, Dmitry A; Shi, Xinghua; Vassieva, Olga; Vonstein, Veronika; Zagnitko, Olga; Xia, Fangfang; Zinner, Jenifer; Overbeek, Ross; Stevens, Rick

2007-01-01

The National Microbial Pathogen Data Resource (NMPDR) (http://www.nmpdr.org) is a National Institute of Allergy and Infections Disease (NIAID)-funded Bioinformatics Resource Center that supports research in selected Category B pathogens. NMPDR contains the complete genomes of approximately 50 strains of pathogenic bacteria that are the focus of our curators, as well as >400 other genomes that provide a broad context for comparative analysis across the three phylogenetic Domains. NMPDR integrates complete, public genomes with expertly curated biological subsystems to provide the most consistent genome annotations. Subsystems are sets of functional roles related by a biologically meaningful organizing principle, which are built over large collections of genomes; they provide researchers with consistent functional assignments in a biologically structured context. Investigators can browse subsystems and reactions to develop accurate reconstructions of the metabolic networks of any sequenced organism. NMPDR provides a comprehensive bioinformatics platform, with tools and viewers for genome analysis. Results of precomputed gene clustering analyses can be retrieved in tabular or graphic format with one-click tools. NMPDR tools include Signature Genes, which finds the set of genes in common or that differentiates two groups of organisms. Essentiality data collated from genome-wide studies have been curated. Drug target identification and high-throughput, in silico, compound screening are in development.

Improved group-specific primers based on the full SILVA 16S rRNA gene reference database.

PubMed

Pfeiffer, Stefan; Pastar, Milica; Mitter, Birgit; Lippert, Kathrin; Hackl, Evelyn; Lojan, Paul; Oswald, Andreas; Sessitsch, Angela

2014-08-01

Quantitative PCR (qPCR) and community fingerprinting methods, such as the Terminal Restriction Fragment Length Polymorphism (T-RFLP) analysis,are well-suited techniques for the examination of microbial community structures. The use of phylum and class-specific primers can provide enhanced sensitivity and phylogenetic resolution as compared with domain-specific primers. To date, several phylum- and class-specific primers targeting the 16S ribosomal RNA gene have been published. However, many of these primers exhibit low discriminatory power against non-target bacteria in PCR. In this study, we evaluated the precision of certain published primers in silico and via specific PCR. We designed new qPCR and T-RFLP primer pairs (for the classes Alphaproteobacteria and Betaproteobacteria, and the phyla Bacteroidetes, Firmicutes and Actinobacteria) by combining the sequence information from a public dataset (SILVA SSU Ref 102 NR) with manual primer design. We evaluated the primer pairs via PCR using isolates of the above-mentioned groups and via screening of clone libraries from environmental soil samples and human faecal samples. As observed through theoretical and practical evaluation, the primers developed in this study showed a higher level of precision than previously published primers, thus allowing a deeper insight into microbial community dynamics.

Designing a Quantitative Structure-Activity Relationship for the ...

EPA Pesticide Factsheets

Toxicokinetic models serve a vital role in risk assessment by bridging the gap between chemical exposure and potentially toxic endpoints. While intrinsic metabolic clearance rates have a strong impact on toxicokinetics, limited data is available for environmentally relevant chemicals including nearly 8000 chemicals tested for in vitro bioactivity in the Tox21 program. To address this gap, a quantitative structure-activity relationship (QSAR) for intrinsic metabolic clearance rate was developed to offer reliable in silico predictions for a diverse array of chemicals. Models were constructed with curated in vitro assay data for both pharmaceutical-like chemicals (ChEMBL database) and environmentally relevant chemicals (ToxCast screening) from human liver microsomes (2176 from ChEMBL) and human hepatocytes (757 from ChEMBL and 332 from ToxCast). Due to variability in the experimental data, a binned approach was utilized to classify metabolic rates. Machine learning algorithms, such as random forest and k-nearest neighbor, were coupled with open source molecular descriptors and fingerprints to provide reasonable estimates of intrinsic metabolic clearance rates. Applicability domains defined the optimal chemical space for predictions, which covered environmental chemicals well. A reduced set of informative descriptors (including relative charge and lipophilicity) and a mixed training set of pharmaceuticals and environmentally relevant chemicals provided the best intr

Microsatellite analysis in the genome of Acanthaceae: An in silico approach

PubMed Central

Kaliswamy, Priyadharsini; Vellingiri, Srividhya; Nathan, Bharathi; Selvaraj, Saravanakumar

2015-01-01

Background: Acanthaceae is one of the advanced and specialized families with conventionally used medicinal plants. Simple sequence repeats (SSRs) play a major role as molecular markers for genome analysis and plant breeding. The microsatellites existing in the complete genome sequences would help to attain a direct role in the genome organization, recombination, gene regulation, quantitative genetic variation, and evolution of genes. Objective: The current study reports the frequency of microsatellites and appropriate markers for the Acanthaceae family genome sequences. Materials and Methods: The whole nucleotide sequences of Acanthaceae species were obtained from National Center for Biotechnology Information database and screened for the presence of SSRs. SSR Locator tool was used to predict the microsatellites and inbuilt Primer3 module was used for primer designing. Results: Totally 110 repeats from 108 sequences of Acanthaceae family plant genomes were identified, and the occurrence of dinucleotide repeats was found to be abundant in the genome sequences. The essential amino acid isoleucine was found rich in all the sequences. We also designed the SSR-based primers/markers for 59 sequences of this family that contains microsatellite repeats in their genome. Conclusion: The identified microsatellites and primers might be useful for breeding and genetic studies of plants that belong to Acanthaceae family in the future. PMID:25709226

Aspergillus Section Fumigati Typing by PCR-Restriction Fragment Polymorphism▿

PubMed Central

Staab, Janet F.; Balajee, S. Arunmozhi; Marr, Kieren A.

2009-01-01

Recent studies have shown that there are multiple clinically important members of the Aspergillus section Fumigati that are difficult to distinguish on the basis of morphological features (e.g., Aspergillus fumigatus, A. lentulus, and Neosartorya udagawae). Identification of these organisms may be clinically important, as some species vary in their susceptibilities to antifungal agents. In a prior study, we utilized multilocus sequence typing to describe A. lentulus as a species distinct from A. fumigatus. The sequence data show that the gene encoding β-tubulin, benA, has high interspecies variability at intronic regions but is conserved among isolates of the same species. These data were used to develop a PCR-restriction fragment length polymorphism (PCR-RFLP) method that rapidly and accurately distinguishes A. fumigatus, A. lentulus, and N. udagawae, three major species within the section Fumigati that have previously been implicated in disease. Digestion of the benA amplicon with BccI generated unique banding patterns; the results were validated by screening a collection of clinical strains and by in silico analysis of the benA sequences of Aspergillus spp. deposited in the GenBank database. PCR-RFLP of benA is a simple method for the identification of clinically important, similar morphotypes of Aspergillus spp. within the section Fumigati. PMID:19403766

Aspergillus section Fumigati typing by PCR-restriction fragment polymorphism.

PubMed

Staab, Janet F; Balajee, S Arunmozhi; Marr, Kieren A

2009-07-01

Recent studies have shown that there are multiple clinically important members of the Aspergillus section Fumigati that are difficult to distinguish on the basis of morphological features (e.g., Aspergillus fumigatus, A. lentulus, and Neosartorya udagawae). Identification of these organisms may be clinically important, as some species vary in their susceptibilities to antifungal agents. In a prior study, we utilized multilocus sequence typing to describe A. lentulus as a species distinct from A. fumigatus. The sequence data show that the gene encoding beta-tubulin, benA, has high interspecies variability at intronic regions but is conserved among isolates of the same species. These data were used to develop a PCR-restriction fragment length polymorphism (PCR-RFLP) method that rapidly and accurately distinguishes A. fumigatus, A. lentulus, and N. udagawae, three major species within the section Fumigati that have previously been implicated in disease. Digestion of the benA amplicon with BccI generated unique banding patterns; the results were validated by screening a collection of clinical strains and by in silico analysis of the benA sequences of Aspergillus spp. deposited in the GenBank database. PCR-RFLP of benA is a simple method for the identification of clinically important, similar morphotypes of Aspergillus spp. within the section Fumigati.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

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

2016-11-01

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

Computer-aided design of liposomal drugs: In silico prediction and experimental validation of drug candidates for liposomal remote loading.

PubMed

Cern, Ahuva; Barenholz, Yechezkel; Tropsha, Alexander; Goldblum, Amiram

2014-01-10

Previously we have developed and statistically validated Quantitative Structure Property Relationship (QSPR) models that correlate drugs' structural, physical and chemical properties as well as experimental conditions with the relative efficiency of remote loading of drugs into liposomes (Cern et al., J. Control. Release 160 (2012) 147-157). Herein, these models have been used to virtually screen a large drug database to identify novel candidate molecules for liposomal drug delivery. Computational hits were considered for experimental validation based on their predicted remote loading efficiency as well as additional considerations such as availability, recommended dose and relevance to the disease. Three compounds were selected for experimental testing which were confirmed to be correctly classified by our previously reported QSPR models developed with Iterative Stochastic Elimination (ISE) and k-Nearest Neighbors (kNN) approaches. In addition, 10 new molecules with known liposome remote loading efficiency that were not used by us in QSPR model development were identified in the published literature and employed as an additional model validation set. The external accuracy of the models was found to be as high as 82% or 92%, depending on the model. This study presents the first successful application of QSPR models for the computer-model-driven design of liposomal drugs. © 2013.

Computer-aided design of liposomal drugs: in silico prediction and experimental validation of drug candidates for liposomal remote loading

PubMed Central

Cern, Ahuva; Barenholz, Yechezkel; Tropsha, Alexander; Goldblum, Amiram

2014-01-01

Previously we have developed and statistically validated Quantitative Structure Property Relationship (QSPR) models that correlate drugs’ structural, physical and chemical properties as well as experimental conditions with the relative efficiency of remote loading of drugs into liposomes (Cern et al, Journal of Controlled Release, 160(2012) 14–157). Herein, these models have been used to virtually screen a large drug database to identify novel candidate molecules for liposomal drug delivery. Computational hits were considered for experimental validation based on their predicted remote loading efficiency as well as additional considerations such as availability, recommended dose and relevance to the disease. Three compounds were selected for experimental testing which were confirmed to be correctly classified by our previously reported QSPR models developed with Iterative Stochastic Elimination (ISE) and k-nearest neighbors (kNN) approaches. In addition, 10 new molecules with known liposome remote loading efficiency that were not used in QSPR model development were identified in the published literature and employed as an additional model validation set. The external accuracy of the models was found to be as high as 82% or 92%, depending on the model. This study presents the first successful application of QSPR models for the computer-model-driven design of liposomal drugs. PMID:24184343

Exome analysis of a family with Wolff-Parkinson-White syndrome identifies a novel disease locus.

PubMed

Bowles, Neil E; Jou, Chuanchau J; Arrington, Cammon B; Kennedy, Brett J; Earl, Aubree; Matsunami, Norisada; Meyers, Lindsay L; Etheridge, Susan P; Saarel, Elizabeth V; Bleyl, Steven B; Yost, H Joseph; Yandell, Mark; Leppert, Mark F; Tristani-Firouzi, Martin; Gruber, Peter J

2015-12-01

Wolff-Parkinson-White (WPW) syndrome is a common cause of supraventricular tachycardia that carries a risk of sudden cardiac death. To date, mutations in only one gene, PRKAG2, which encodes the 5'-AMP-activated protein kinase subunit γ-2, have been identified as causative for WPW. DNA samples from five members of a family with WPW were analyzed by exome sequencing. We applied recently designed prioritization strategies (VAAST/pedigree VAAST) coupled with an ontology-based algorithm (Phevor) that reduced the number of potentially damaging variants to 10: a variant in KCNE2 previously associated with Long QT syndrome was also identified. Of these 11 variants, only MYH6 p.E1885K segregated with the WPW phenotype in all affected individuals and was absent in 10 unaffected family members. This variant was predicted to be damaging by in silico methods and is not present in the 1,000 genome and NHLBI exome sequencing project databases. Screening of a replication cohort of 47 unrelated WPW patients did not identify other likely causative variants in PRKAG2 or MYH6. MYH6 variants have been identified in patients with atrial septal defects, cardiomyopathies, and sick sinus syndrome. Our data highlight the pleiotropic nature of phenotypes associated with defects in this gene. © 2015 Wiley Periodicals, Inc.

Exome Analysis of a Family with Wolff–Parkinson–White Syndrome Identifies a Novel Disease Locus

PubMed Central

Bowles, Neil E.; Jou, Chuanchau J.; Arrington, Cammon B.; Kennedy, Brett J.; Earl, Aubree; Matsunami, Norisada; Meyers, Lindsay L.; Etheridge, Susan P.; Saarel, Elizabeth V.; Bleyl, Steven B.; Yost, H. Joseph; Yandell, Mark; Leppert, Mark F.; Tristani-Firouzi, Martin; Gruber, Peter J.

2016-01-01

Wolff–Parkinson–White (WPW) syndrome is a common cause of supraventricular tachycardia that carries a risk of sudden cardiac death. To date, mutations in only one gene, PRKAG2, which encodes the 5’ -AMP-activated protein kinase subunit γ-2, have been identified as causative for WPW. DNA samples from five members of a family with WPW were analyzed by exome sequencing. We applied recently designed prioritization strategies (VAAST/pedigree VAAST) coupled with an ontology-based algorithm (Phevor) that reduced the number of potentially damaging variants to 10: a variant in KCNE2 previously associated with Long QT syndrome was also identified. Of these 11 variants, only MYH6 p.E1885K segregated with the WPW phenotype in all affected individuals and was absent in 10 unaffected family members. This variant was predicted to be damaging by in silico methods and is not present in the 1,000 genome and NHLBI exome sequencing project databases. Screening of a replication cohort of 47 unrelated WPW patients did not identify other likely causative variants in PRKAG2 or MYH6. MYH6 variants have been identified in patients with atrial septal defects, cardiomyopathies, and sick sinus syndrome. Our data highlight the pleiotropic nature of phenotypes associated with defects in this gene. PMID:26284702

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

Gragg, Evan James; Middleton, Richard Stephen

This report describes the benefits of the BECCUS screening tools. The goals of this project are to utilize NATCARB database for site screening; enhance NATCARB database; run CO 2-EOR simulations and economic models using updated reservoir data sets (SCO 2T-EOR).

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.

Update of the Diatom EST Database: a new tool for digital transcriptomics

PubMed Central

Maheswari, Uma; Mock, Thomas; Armbrust, E. Virginia; Bowler, Chris

2009-01-01

The Diatom Expressed Sequence Tag (EST) Database was constructed to provide integral access to ESTs from these ecologically and evolutionarily interesting microalgae. It has now been updated with 130 000 Phaeodactylum tricornutum ESTs from 16 cDNA libraries and 77 000 Thalassiosira pseudonana ESTs from seven libraries, derived from cells grown in different nutrient and stress regimes. The updated relational database incorporates results from statistical analyses such as log-likelihood ratios and hierarchical clustering, which help to identify differentially expressed genes under different conditions, and allow similarities in gene expression in different libraries to be investigated in a functional context. The database also incorporates links to the recently sequenced genomes of P. tricornutum and T. pseudonana, enabling an easy cross-talk between the expression pattern of diatom orthologs and the genome browsers. These improvements will facilitate exploration of diatom responses to conditions of ecological relevance and will aid gene function identification of diatom-specific genes and in silico gene prediction in this largely unexplored class of eukaryotes. The updated Diatom EST Database is available at http://www.biologie.ens.fr/diatomics/EST3. PMID:19029140

Integrated in silico strategy for PBT assessment and prioritization under REACH.

PubMed

Pizzo, Fabiola; Lombardo, Anna; Manganaro, Alberto; Cappelli, Claudia I; Petoumenou, Maria I; Albanese, Federica; Roncaglioni, Alessandra; Brandt, Marc; Benfenati, Emilio

2016-11-01

Chemicals may persist in the environment, bioaccumulate and be toxic for humans and wildlife, posing great concern. These three properties, persistence (P), bioaccumulation (B), and toxicity (T) are the key targets of the PBT-hazard assessment. The European regulation for the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) requires assessment of PBT-properties for all chemicals that are produced or imported in Europe in amounts exceeding 10 tonnes per year, checking whether the criteria set out in REACH Annex XIII are met, so the substance should therefore be considered to have properties of very high concern. Considering how many substances can fall under the REACH regulation, there is a pressing need for new strategies to identify and screen large numbers fast and inexpensively. An efficient non-testing screening approach to identify PBT candidates is necessary, as a valuable alternative to money- and time-consuming laboratory tests and a good start for prioritization since few tools exist (e.g. the PBT profiler developed by US EPA). The aim of this work was to offer a conceptual scheme for identifying and prioritizing chemicals for further assessment and if appropriate further testing, based on their PBT-potential, using a non-testing screening approach. We integrated in silico models (using existing and developing new ones) in a final algorithm for screening and ranking PBT-potential, which uses experimental and predicted values as well as associated uncertainties. The Multi-Criteria Decision-Making (MCDM) theory was used to integrate the different values. Then we compiled a new set of data containing known PBT and non-PBT substances, in order to check how well our approach clearly differentiated compounds labeled as PBT from those labeled as non-PBT. This indicated that the integrated model distinguished between PBT from non-PBT compounds. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Development of a small-molecule screening method for inhibitors of cellular response to myostatin and activin A.

PubMed

Cash, Jennifer N; Angerman, Elizabeth B; Kirby, R Jason; Merck, Lisa; Seibel, William L; Wortman, Matthew D; Papoian, Ruben; Nelson, Sandra; Thompson, Thomas B

2013-08-01

Myostatin, a member of the transforming growth factor (TGF)-β family of secreted ligands, is a strong negative regulator of muscle growth. As such, therapeutic inhibitors of myostatin are actively being investigated for their potential in the treatment of muscle-wasting diseases such as muscular dystrophy and sarcopenia. Here, we sought to develop a high-throughput screening (HTS) method for small-molecule inhibitors that target myostatin. We created a HEK293 stable cell line that expresses the (CAGA)12-luciferase reporter construct and robustly responds to signaling of certain classes of TGF-β family ligands. After optimization and miniaturization of the assay to a 384-well format, we successfully screened a library of compounds for inhibition of myostatin and the closely related activin A. Selection of some of the tested compounds was directed by in silico screening against myostatin, which led to an enrichment of target hits as compared with random selection. Altogether, we present an HTS method that will be useful for screening potential inhibitors of not only myostatin but also many other ligands of the TGF-β family.

Targeting the Prometastatic Microenvironment of the Involuting Mammary Gland

DTIC Science & Technology

2014-09-01

analyses. To assess expression of Ltbp1 in breast cancer we began by mining in silico data using database available online specifically Kaplan-Meier... Dermatology , New York University School of Medicine, 550 First Ave, New York, NY 10016, USA Full list of author information is available at the end of the...1Department of Cell Biology, New York University School of Medicine, New York, NY, USA. 2The Ronald O Perelman Department of Dermatology , New York

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 vitro, in silico and in vivo studies of ursolic acid as an anti-filarial agent.

PubMed

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

2014-01-01

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

In Silico Identification of 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 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.

Mining for Nonribosomal Peptide Synthetase and Polyketide Synthase Genes Revealed a High Level of Diversity in the Sphagnum Bog Metagenome

PubMed Central

Müller, Christina A.; Oberauner-Wappis, Lisa; Peyman, Armin; Amos, Gregory C. A.; Wellington, Elizabeth M. H.

2015-01-01

Sphagnum bog ecosystems are among the oldest vegetation forms harboring a specific microbial community and are known to produce an exceptionally wide variety of bioactive substances. Although the Sphagnum metagenome shows a rich secondary metabolism, the genes have not yet been explored. To analyze nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs), the diversity of NRPS and PKS genes in Sphagnum-associated metagenomes was investigated by in silico data mining and sequence-based screening (PCR amplification of 9,500 fosmid clones). The in silico Illumina-based metagenomic approach resulted in the identification of 279 NRPSs and 346 PKSs, as well as 40 PKS-NRPS hybrid gene sequences. The occurrence of NRPS sequences was strongly dominated by the members of the Protebacteria phylum, especially by species of the Burkholderia genus, while PKS sequences were mainly affiliated with Actinobacteria. Thirteen novel NRPS-related sequences were identified by PCR amplification screening, displaying amino acid identities of 48% to 91% to annotated sequences of members of the phyla Proteobacteria, Actinobacteria, and Cyanobacteria. Some of the identified metagenomic clones showed the closest similarity to peptide synthases from Burkholderia or Lysobacter, which are emerging bacterial sources of as-yet-undescribed bioactive metabolites. This report highlights the role of the extreme natural ecosystems as a promising source for detection of secondary compounds and enzymes, serving as a source for biotechnological applications. PMID:26002894

An in silico high-throughput screen identifies potential selective inhibitors for the non-receptor tyrosine kinase Pyk2

PubMed Central

Meirson, Tomer; Samson, Abraham O; Gil-Henn, Hava

2017-01-01

The non-receptor tyrosine kinase proline-rich tyrosine kinase 2 (Pyk2) is a critical mediator of signaling from cell surface growth factor and adhesion receptors to cell migration, proliferation, and survival. Emerging evidence indicates that signaling by Pyk2 regulates hematopoietic cell response, bone density, neuronal degeneration, angiogenesis, and cancer. These physiological and pathological roles of Pyk2 warrant it as a valuable therapeutic target for invasive cancers, osteoporosis, Alzheimer’s disease, and inflammatory cellular response. Despite its potential as a therapeutic target, no potent and selective inhibitor of Pyk2 is available at present. As a first step toward discovering specific potential inhibitors of Pyk2, we used an in silico high-throughput screening approach. A virtual library of six million lead-like compounds was docked against four different high-resolution Pyk2 kinase domain crystal structures and further selected for predicted potency and ligand efficiency. Ligand selectivity for Pyk2 over focal adhesion kinase (FAK) was evaluated by comparative docking of ligands and measurement of binding free energy so as to obtain 40 potential candidates. Finally, the structural flexibility of a subset of the docking complexes was evaluated by molecular dynamics simulation, followed by intermolecular interaction analysis. These compounds may be considered as promising leads for further development of highly selective Pyk2 inhibitors. PMID:28572720

Tempest: Accelerated MS/MS Database Search Software for Heterogeneous Computing Platforms.

PubMed

Adamo, Mark E; Gerber, Scott A

2016-09-07

MS/MS database search algorithms derive a set of candidate peptide sequences from in silico digest of a protein sequence database, and compute theoretical fragmentation patterns to match these candidates against observed MS/MS spectra. The original Tempest publication described these operations mapped to a CPU-GPU model, in which the CPU (central processing unit) generates peptide candidates that are asynchronously sent to a discrete GPU (graphics processing unit) to be scored against experimental spectra in parallel. The current version of Tempest expands this model, incorporating OpenCL to offer seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors. Three protocols describe how to configure and run a Tempest search, including discussion of how to leverage Tempest's unique feature set to produce optimal results. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

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.

Cross-species extrapolation of toxicity information using the ...

EPA Pesticide Factsheets

In the United States, the Endocrine Disruptor Screening Program (EDSP) was established to identify chemicals that may lead to adverse effects via perturbation of the endocrine system (i.e., estrogen, androgen, and thyroid hormone systems). In the mid-1990s the EDSP adopted a two tiered approach for screening chemicals that applied standardized in vitro and in vivo toxicity tests. The Tier 1 screening assays were designed to identify substances that have the potential of interacting with the endocrine system and Tier 2 testing was developed to identify adverse effects caused by the chemical, with documentation of dose-response relationships. While this tiered approach was effective in identifying possible endocrine disrupting chemicals, the cost and time to screen a single chemical was significant. Therefore, in 2012 the EDSP proposed a transition to make greater use of computational approaches (in silico) and high-throughput screening (HTS; in vitro) assays to more rapidly and cost-efficiently screen chemicals for endocrine activity. This transition from resource intensive, primarily in vivo, screening methods to more pathway-based approaches aligns with the simultaneously occurring transformation in toxicity testing termed “Toxicity Testing in the 21st Century” which shifts the focus to the disturbance of the biological pathway predictive of the observable toxic effects. An example of such screening tools include the US Environmental Protection Agency’s

Assessment of circulating copy number variant detection for cancer screening.

PubMed

Molparia, Bhuvan; Nichani, Eshaan; Torkamani, Ali

2017-01-01

Current high-sensitivity cancer screening methods, largely utilizing correlative biomarkers, suffer from false positive rates that lead to unnecessary medical procedures and debatable public health benefit overall. Detection of circulating tumor DNA (ctDNA), a causal biomarker, has the potential to revolutionize cancer screening. Thus far, the majority of ctDNA studies have focused on detection of tumor-specific point mutations after cancer diagnosis for the purpose of post-treatment surveillance. However, ctDNA point mutation detection methods developed to date likely lack either the scope or analytical sensitivity necessary to be useful for cancer screening, due to the low (<1%) ctDNA fraction derived from early stage tumors. On the other hand, tumor-derived copy number variant (CNV) detection is hypothetically a superior means of ctDNA-based cancer screening for many tumor types, given that, relative to point mutations, each individual tumor CNV contributes a much larger number of ctDNA fragments to the overall pool of circulating free DNA (cfDNA). A small number of studies have demonstrated the potential of ctDNA CNV-based screening in select cancer types. Here we perform an in silico assessment of the potential for ctDNA CNV-based cancer screening across many common cancers, and suggest ctDNA CNV detection shows promise as a broad cancer screening methodology.

Compound prioritization methods increase rates of chemical probe discovery in model organisms

PubMed Central

Wallace, Iain M; Urbanus, Malene L; Luciani, Genna M; Burns, Andrew R; Han, Mitchell KL; Wang, Hao; Arora, Kriti; Heisler, Lawrence E; Proctor, Michael; St. Onge, Robert P; Roemer, Terry; Roy, Peter J; Cummins, Carolyn L; Bader, Gary D; Nislow, Corey; Giaever, Guri

2011-01-01

SUMMARY Pre-selection of compounds that are more likely to induce a phenotype can increase the efficiency and reduce the costs for model organism screening. To identify such molecules, we screened ~81,000 compounds in S. cerevisiae and identified ~7,500 that inhibit cell growth. Screening these growth-inhibitory molecules across a diverse panel of model organisms resulted in an increased phenotypic hit-rate. This data was used to build a model to predict compounds that inhibit yeast growth. Empirical and in silico application of the model enriched the discovery of bioactive compounds in diverse model organisms. To demonstrate the potential of these molecules as lead chemical probes we used chemogenomic profiling in yeast and identified specific inhibitors of lanosterol synthase and of stearoyl-CoA 9-desaturase. As community resources, the ~7,500 growth-inhibitory molecules has been made commercially available and the computational model and filter used are provided. PMID:22035796

In silico identification of novel ligands for G-quadruplex in the c- MYC promoter

NASA Astrophysics Data System (ADS)

Kang, Hyun-Jin; Park, Hyun-Ju

2015-04-01

G-quadruplex DNA formed in NHEIII1 region of oncogene promoter inhibits transcription of the genes. In this study, virtual screening combining pharmacophore-based search and structure-based docking screening was conducted to discover ligands binding to G-quadruplex in promoter region of c- MYC. Several hit ligands showed the selective PCR-arresting effects for oligonucleotide containing c- MYC G-quadruplex forming sequence. Among them, three hits selectively inhibited cell proliferation and decreased c- MYC mRNA level in Ramos cells, where NHEIII1 is included in translocated c- MYC gene for overexpression. Promoter assay using two kinds of constructs with wild-type and mutant sequences showed that interaction of these ligands with the G-quadruplex resulted in turning-off of the reporter gene. In conclusion, combined virtual screening methods were successfully used for discovery of selective c- MYC promoter G-quadruplex binders with anticancer activity.

Rationally reduced libraries for combinatorial pathway optimization minimizing experimental effort.

PubMed

Jeschek, Markus; Gerngross, Daniel; Panke, Sven

2016-03-31

Rational flux design in metabolic engineering approaches remains difficult since important pathway information is frequently not available. Therefore empirical methods are applied that randomly change absolute and relative pathway enzyme levels and subsequently screen for variants with improved performance. However, screening is often limited on the analytical side, generating a strong incentive to construct small but smart libraries. Here we introduce RedLibs (Reduced Libraries), an algorithm that allows for the rational design of smart combinatorial libraries for pathway optimization thereby minimizing the use of experimental resources. We demonstrate the utility of RedLibs for the design of ribosome-binding site libraries by in silico and in vivo screening with fluorescent proteins and perform a simple two-step optimization of the product selectivity in the branched multistep pathway for violacein biosynthesis, indicating a general applicability for the algorithm and the proposed heuristics. We expect that RedLibs will substantially simplify the refactoring of synthetic metabolic pathways.

Identification of STAT1 and STAT3 Specific Inhibitors Using Comparative Virtual Screening and Docking Validation

PubMed Central

Szelag, Malgorzata; Czerwoniec, Anna; Wesoly, Joanna; Bluyssen, Hans A. R.

2015-01-01

Signal transducers and activators of transcription (STATs) facilitate action of cytokines, growth factors and pathogens. STAT activation is mediated by a highly conserved SH2 domain, which interacts with phosphotyrosine motifs for specific STAT-receptor contacts and STAT dimerization. The active dimers induce gene transcription in the nucleus by binding to a specific DNA-response element in the promoter of target genes. Abnormal activation of STAT signaling pathways is implicated in many human diseases, like cancer, inflammation and auto-immunity. Searches for STAT-targeting compounds, exploring the phosphotyrosine (pTyr)-SH2 interaction site, yielded many small molecules for STAT3 but sparsely for other STATs. However, many of these inhibitors seem not STAT3-specific, thereby questioning the present modeling and selection strategies of SH2 domain-based STAT inhibitors. We generated new 3D structure models for all human (h)STATs and developed a comparative in silico docking strategy to obtain further insight into STAT-SH2 cross-binding specificity of a selection of previously identified STAT3 inhibitors. Indeed, by primarily targeting the highly conserved pTyr-SH2 binding pocket the majority of these compounds exhibited similar binding affinity and tendency scores for all STATs. By comparative screening of a natural product library we provided initial proof for the possibility to identify STAT1 as well as STAT3-specific inhibitors, introducing the ‘STAT-comparative binding affinity value’ and ‘ligand binding pose variation’ as selection criteria. In silico screening of a multi-million clean leads (CL) compound library for binding of all STATs, likewise identified potential specific inhibitors for STAT1 and STAT3 after docking validation. Based on comparative virtual screening and docking validation, we developed a novel STAT inhibitor screening tool that allows identification of specific STAT1 and STAT3 inhibitory compounds. This could increase our understanding of the functional role of these STATs in different diseases and benefit the clinical need for more drugable STAT inhibitors with high specificity, potency and excellent bioavailability. PMID:25710482

Mutations in the LHX2 gene are not a frequent cause of micro/anophthalmia

PubMed Central

Desmaison, Annaïck; Vigouroux, Adeline; Rieubland, Claudine; Peres, Christine; Calvas, Patrick

2010-01-01

Purpose Microphthalmia and anophthalmia are at the severe end of the spectrum of abnormalities in ocular development. A few genes (orthodenticle homeobox 2 [OTX2], retina and anterior neural fold homeobox [RAX], SRY-box 2 [SOX2], CEH10 homeodomain-containing homolog [CHX10], and growth differentiation factor 6 [GDF6]) have been implicated mainly in isolated micro/anophthalmia but causative mutations of these genes explain less than a quarter of these developmental defects. The essential role of the LIM homeobox 2 (LHX2) transcription factor in early eye development has recently been documented. We postulated that mutations in this gene could lead to micro/anophthalmia, and thus performed molecular screening of its sequence in patients having micro/anophthalmia. Methods Seventy patients having non-syndromic forms of colobomatous microphthalmia (n=25), isolated microphthalmia (n=18), or anophthalmia (n=17), and syndromic forms of micro/anophthalmia (n=10) were included in this study after negative molecular screening for OTX2, RAX, SOX2, and CHX10 mutations. Mutation screening of LHX2 was performed by direct sequencing of the coding sequences and intron/exon boundaries. Results Two heterozygous variants of unknown significance (c.128C>G [p.Pro43Arg]; c.776C>A [p.Pro259Gln]) were identified in LHX2 among the 70 patients. These variations were not identified in a panel of 100 control patients of mixed origins. The variation c.776C>A (p.Pro259Gln) was considered as non pathogenic by in silico analysis, while the variation c.128C>G (p.Pro43Arg) considered as deleterious by in silico analysis and was inherited from the asymptomatic father. Conclusions Mutations in LHX2 do not represent a frequent cause of micro/anophthalmia. PMID:21203406

Mutations in the LHX2 gene are not a frequent cause of micro/anophthalmia.

PubMed

Desmaison, Annaïck; Vigouroux, Adeline; Rieubland, Claudine; Peres, Christine; Calvas, Patrick; Chassaing, Nicolas

2010-12-18

Microphthalmia and anophthalmia are at the severe end of the spectrum of abnormalities in ocular development. A few genes (orthodenticle homeobox 2 [OTX2], retina and anterior neural fold homeobox [RAX], SRY-box 2 [SOX2], CEH10 homeodomain-containing homolog [CHX10], and growth differentiation factor 6 [GDF6]) have been implicated mainly in isolated micro/anophthalmia but causative mutations of these genes explain less than a quarter of these developmental defects. The essential role of the LIM homeobox 2 (LHX2) transcription factor in early eye development has recently been documented. We postulated that mutations in this gene could lead to micro/anophthalmia, and thus performed molecular screening of its sequence in patients having micro/anophthalmia. Seventy patients having non-syndromic forms of colobomatous microphthalmia (n=25), isolated microphthalmia (n=18), or anophthalmia (n=17), and syndromic forms of micro/anophthalmia (n=10) were included in this study after negative molecular screening for OTX2, RAX, SOX2, and CHX10 mutations. Mutation screening of LHX2 was performed by direct sequencing of the coding sequences and intron/exon boundaries. Two heterozygous variants of unknown significance (c.128C>G [p.Pro43Arg]; c.776C>A [p.Pro259Gln]) were identified in LHX2 among the 70 patients. These variations were not identified in a panel of 100 control patients of mixed origins. The variation c.776C>A (p.Pro259Gln) was considered as non pathogenic by in silico analysis, while the variation c.128C>G (p.Pro43Arg) considered as deleterious by in silico analysis and was inherited from the asymptomatic father. Mutations in LHX2 do not represent a frequent cause of micro/anophthalmia.

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

Rational Drug Discovery of HCV Helicase Inhibitor: Improved Docking Accuracy with Multiple Seeding in AutoDock Vina and In Situ Minimization.

PubMed

Lim, See K; Othman, Rozana; Yusof, Rohana; Heh, Choon H

2017-01-01

Hepatitis C is a significant cause for end-stage liver diseases and liver transplantation which affects approximately 3% of the global populations. Despite the current several direct antiviral agents in the treatment of Hepatitis C, the standard treatment for HCV infection is accompanied by several drawbacks, such as adverse side effects, high pricing of medications and the rapid emerging rate of resistant HCV variants. To discover potential inhibitors for HCV helicase through an optimized in silico approach. In this study, a homology model (HCV Genotype 3 helicase) was used as the target and screened through a benzopyran-based virtual library. Multiple-seedings of AutoDock Vina and in situ minimization were to account for the non-deterministic nature of AutoDock Vina search algorithm and binding site flexibility, respectively. ADME/T and interaction analyses were also done on the top hits via FAFDRUG3 web server and Discovery Studio 4.5. This study involved the development of an improved flow for virtual screening via implemention of multiple-seeding screening approach and in situ minimization. With the new docking protocol, the redocked standards have shown better RMSD value in reference to their native conformations. Ten benzopyran-like compounds with satisfactory physicochemical properties were discovered to be potential inhibitors of HCV helicase. ZINC38649350 was identified as the most potential inhibitor. Ten potential HCV helicase inhibitors were discovered via a new docking optimization protocol with better docking accuracy. These findings could contribute to the discovery of novel HCV antivirals and serve as an alternative approach of in silico rational drug discovery. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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.

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

Extra-virgin olive oil contains a metabolo-epigenetic inhibitor of cancer stem cells

PubMed Central

Corominas-Faja, Bruna; Cuyàs, Elisabet; Lozano-Sánchez, Jesús; Cufí, Sílvia; Verdura, Sara; Fernández-Arroyo, Salvador; Borrás-Linares, Isabel; Martin-Castillo, Begoña; Martin, Ángel G; Lupu, Ruth; Nonell-Canals, Alfons; Micol, Vicente; Joven, Jorge; Segura-Carretero, Antonio; Menendez, Javier A

2018-01-01

Abstract Targeting tumor-initiating, drug-resistant populations of cancer stem cells (CSC) with phytochemicals is a novel paradigm for cancer prevention and treatment. We herein employed a phenotypic drug discovery approach coupled to mechanism-of-action profiling and target deconvolution to identify phenolic components of extra virgin olive oil (EVOO) capable of suppressing the functional traits of CSC in breast cancer (BC). In vitro screening revealed that the secoiridoid decarboxymethyl oleuropein aglycone (DOA) could selectively target subpopulations of epithelial-like, aldehyde dehydrogenase (ALDH)-positive and mesenchymal-like, CD44+CD24−/low CSC. DOA could potently block the formation of multicellular tumorspheres generated from single-founder stem-like cells in a panel of genetically diverse BC models. Pretreatment of BC populations with noncytotoxic doses of DOA dramatically reduced subsequent tumor-forming capacity in vivo. Mice orthotopically injected with CSC-enriched BC-cell populations pretreated with DOA remained tumor-free for several months. Phenotype microarray-based screening pointed to a synergistic interaction of DOA with the mTOR inhibitor rapamycin and the DNA methyltransferase (DNMT) inhibitor 5-azacytidine. In silico computational studies indicated that DOA binds and inhibits the ATP-binding kinase domain site of mTOR and the S-adenosyl-l-methionine (SAM) cofactor-binding pocket of DNMTs. FRET-based Z-LYTE™ and AlphaScreen-based in vitro assays confirmed the ability of DOA to function as an ATP-competitive mTOR inhibitor and to block the SAM-dependent methylation activity of DNMTs. Our systematic in vitro, in vivo and in silico approaches establish the phenol-conjugated oleoside DOA as a dual mTOR/DNMT inhibitor naturally occurring in EVOO that functionally suppresses CSC-like states responsible for maintaining tumor-initiating cell properties within BC populations. PMID:29452350

Data integration and warehousing: coordination between newborn screening and related public health programs.

PubMed

Therrell, Bradford L

2003-01-01

At birth, patient demographic and health information begin to accumulate in varied databases. There are often multiple sources of the same or similar data. New public health programs are often created without considering data linkages. Recently, newborn hearing screening (NHS) programs and immunization programs have virtually ignored the existence of newborn dried blood spot (DBS) newborn screening databases containing similar demographic data, creating data duplication in their 'new' systems. Some progressive public health departments are developing data warehouses of basic, recurrent patient information, and linking these databases to other health program databases where programs and services can benefit from such linkages. Demographic data warehousing saves time (and money) by eliminating duplicative data entry and reducing the chances of data errors. While newborn screening data are usually the first data available, they should not be the only data source considered for early data linkage or for populating a data warehouse. Birth certificate information should also be considered along with other data sources for infants that may not have received newborn screening or who may have been born outside of the jurisdiction and not have birth certificate information locally available. This newborn screening serial number provides a convenient identification number for use in the DBS program and for linking with other systems. As a minimum, data linkages should exist between newborn dried blood spot screening, newborn hearing screening, immunizations, birth certificates and birth defect registries.

REDIdb: the RNA editing database.

PubMed

Picardi, Ernesto; Regina, Teresa Maria Rosaria; Brennicke, Axel; Quagliariello, Carla

2007-01-01

The RNA Editing Database (REDIdb) is an interactive, web-based database created and designed with the aim to allocate RNA editing events such as substitutions, insertions and deletions occurring in a wide range of organisms. The database contains both fully and partially sequenced DNA molecules for which editing information is available either by experimental inspection (in vitro) or by computational detection (in silico). Each record of REDIdb is organized in a specific flat-file containing a description of the main characteristics of the entry, a feature table with the editing events and related details and a sequence zone with both the genomic sequence and the corresponding edited transcript. REDIdb is a relational database in which the browsing and identification of editing sites has been simplified by means of two facilities to either graphically display genomic or cDNA sequences or to show the corresponding alignment. In both cases, all editing sites are highlighted in colour and their relative positions are detailed by mousing over. New editing positions can be directly submitted to REDIdb after a user-specific registration to obtain authorized secure access. This first version of REDIdb database stores 9964 editing events and can be freely queried at http://biologia.unical.it/py_script/search.html.

Access to digital library databases in higher education: design problems and infrastructural gaps.

PubMed

Oswal, Sushil K

2014-01-01

After defining accessibility and usability, the author offers a broad survey of the research studies on digital content databases which have thus far primarily depended on data drawn from studies conducted by sighted researchers with non-disabled users employing screen readers and low vision devices. This article aims at producing a detailed description of the difficulties confronted by blind screen reader users with online library databases which now hold most of the academic, peer-reviewed journal and periodical content essential for research and teaching in higher education. The approach taken here is borrowed from descriptive ethnography which allows the author to create a complete picture of the accessibility and usability problems faced by an experienced academic user of digital library databases and screen readers. The author provides a detailed analysis of the different aspects of accessibility issues in digital databases under several headers with a special focus on full-text PDF files. The author emphasizes that long-term studies with actual, blind screen reader users employing both qualitative and computerized research tools can yield meaningful data for the designers and developers to improve these databases to a level that they begin to provide an equal access to the blind.

Creating and virtually screening databases of fluorescently-labelled compounds for the discovery of target-specific molecular probes

NASA Astrophysics Data System (ADS)

Kamstra, Rhiannon L.; Dadgar, Saedeh; Wigg, John; Chowdhury, Morshed A.; Phenix, Christopher P.; Floriano, Wely B.

2014-11-01

Our group has recently demonstrated that virtual screening is a useful technique for the identification of target-specific molecular probes. In this paper, we discuss some of our proof-of-concept results involving two biologically relevant target proteins, and report the development of a computational script to generate large databases of fluorescence-labelled compounds for computer-assisted molecular design. The virtual screening of a small library of 1,153 fluorescently-labelled compounds against two targets, and the experimental testing of selected hits reveal that this approach is efficient at identifying molecular probes, and that the screening of a labelled library is preferred over the screening of base compounds followed by conjugation of confirmed hits. The automated script for library generation explores the known reactivity of commercially available dyes, such as NHS-esters, to create large virtual databases of fluorescence-tagged small molecules that can be easily synthesized in a laboratory. A database of 14,862 compounds, each tagged with the ATTO680 fluorophore was generated with the automated script reported here. This library is available for downloading and it is suitable for virtual ligand screening aiming at the identification of target-specific fluorescent molecular probes.

Comparing the outcomes of two strategies for colorectal tumor detection: policy-promoted screening program versus health promotion service.

PubMed

Wu, Ping-Hsiu; Lin, Yu-Min; Liao, Chao-Sheng; Chang, Hung-Chuen; Chen, Yu-Hung; Yang, Kuo-Ching; Shih, Chia-Hui

2013-06-01

The Taiwanese government has proposed a population-based colorectal tumor detection program for the average-risk population. This study's objectives were to understand the outcomes of these screening policies and to evaluate the effectiveness of the program. We compared two databases compiled in one medical center. The "policy-promoted cancer screening" (PPS) database was built on the basis of the policy of the Taiwan Bureau of National Health Insurance for cancer screening. The "health promotion service" (HPS) database was built to provide health check-ups for self-paid volunteers. Both the PPS and HPS databases employ the immunochemical fecal occult blood test (iFOBT) and colonoscopy for colorectal tumor screening using different strategies. A comparison of outcomes between the PPS and HPS included: (1) quality indicators-compliance rate, cecum reaching rate, and tumor detection rate; and (2) validity indicators-sensitivity, specificity, positive, and negative predictive values for detecting colorectal neoplasms. A total of 10,563 and 1481 individuals were enrolled in PPS and HPS, respectively. Among quality indicators, there was no statistically significant difference in the cecum reaching rate between PPS and HPS. The compliance rates were 56.1% for PPS and 91.8% for HPS (p < 0.001). The advanced adenoma detection rates of PPS and HPS were 1.0% and 3.6%, respectively (p < 0.01). The carcinoma detection rates were 0.3% and 0.4%, respectively (p = 0.59). For validity indicators, PPS provides only a positive predictive value for colorectal tumor detection. HPS provides additional validity indicators, including sensitivity, specificity, positive predictive value, and negative predictive value, for colorectal tumor screening. In comparison with the outcomes of the HPS database, the screening efficacy of the PPS database is even for detecting colorectal carcinoma but is limited in detecting advanced adenoma. HPS may provide comprehensive validity indicators and will be helpful in adjusting current policies for improving screening performance. Copyright © 2013. Published by Elsevier B.V.

PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach

PubMed Central

Liu, Xiaofeng; Ouyang, Sisheng; Yu, Biao; Liu, Yabo; Huang, Kai; Gong, Jiayu; Zheng, Siyuan; Li, Zhihua; Li, Honglin; Jiang, Hualiang

2010-01-01

In silico drug target identification, which includes many distinct algorithms for finding disease genes and proteins, is the first step in the drug discovery pipeline. When the 3D structures of the targets are available, the problem of target identification is usually converted to finding the best interaction mode between the potential target candidates and small molecule probes. Pharmacophore, which is the spatial arrangement of features essential for a molecule to interact with a specific target receptor, is an alternative method for achieving this goal apart from molecular docking method. PharmMapper server is a freely accessed web server designed to identify potential target candidates for the given small molecules (drugs, natural products or other newly discovered compounds with unidentified binding targets) using pharmacophore mapping approach. PharmMapper hosts a large, in-house repertoire of pharmacophore database (namely PharmTargetDB) annotated from all the targets information in TargetBank, BindingDB, DrugBank and potential drug target database, including over 7000 receptor-based pharmacophore models (covering over 1500 drug targets information). PharmMapper automatically finds the best mapping poses of the query molecule against all the pharmacophore models in PharmTargetDB and lists the top N best-fitted hits with appropriate target annotations, as well as respective molecule’s aligned poses are presented. Benefited from the highly efficient and robust triangle hashing mapping method, PharmMapper bears high throughput ability and only costs 1 h averagely to screen the whole PharmTargetDB. The protocol was successful in finding the proper targets among the top 300 pharmacophore candidates in the retrospective benchmarking test of tamoxifen. PharmMapper is available at http://59.78.96.61/pharmmapper. PMID:20430828

Empirical fitness models for hepatitis C virus immunogen design

NASA Astrophysics Data System (ADS)

Hart, Gregory R.; Ferguson, Andrew L.

2015-12-01

Hepatitis C virus (HCV) afflicts 170 million people worldwide, 2%-3% of the global population, and kills 350 000 each year. Prophylactic vaccination offers the most realistic and cost effective hope of controlling this epidemic in the developing world where expensive drug therapies are not available. Despite 20 years of research, the high mutability of the virus and lack of knowledge of what constitutes effective immune responses have impeded development of an effective vaccine. Coupling data mining of sequence databases with spin glass models from statistical physics, we have developed a computational approach to translate clinical sequence databases into empirical fitness landscapes quantifying the replicative capacity of the virus as a function of its amino acid sequence. These landscapes explicitly connect viral genotype to phenotypic fitness, and reveal vulnerable immunological targets within the viral proteome that can be exploited to rationally design vaccine immunogens. We have recovered the empirical fitness landscape for the HCV RNA-dependent RNA polymerase (protein NS5B) responsible for viral genome replication, and validated the predictions of our model by demonstrating excellent accord with experimental measurements and clinical observations. We have used our landscapes to perform exhaustive in silico screening of 16.8 million T-cell immunogen candidates to identify 86 optimal formulations. By reducing the search space of immunogen candidates by over five orders of magnitude, our approach can offer valuable savings in time, expense, and labor for experimental vaccine development and accelerate the search for a HCV vaccine. Abbreviations: HCV—hepatitis C virus, HLA—human leukocyte antigen, CTL—cytotoxic T lymphocyte, NS5B—nonstructural protein 5B, MSA—multiple sequence alignment, PEG-IFN—pegylated interferon.

The MycoBrowser portal: a comprehensive and manually annotated resource for mycobacterial genomes.

PubMed

Kapopoulou, Adamandia; Lew, Jocelyne M; Cole, Stewart T

2011-01-01

In this paper, we present the MycoBrowser portal (http://mycobrowser.epfl.ch/), a resource that provides both in silico generated and manually reviewed information within databases dedicated to the complete genomes of Mycobacterium tuberculosis, Mycobacterium leprae, Mycobacterium marinum and Mycobacterium smegmatis. A central component of MycoBrowser is TubercuList (http://tuberculist.epfl.ch), which has recently benefited from a new data management system and web interface. These improvements were extended to all MycoBrowser databases. We provide an overview of the functionalities available and the different ways of interrogating the data then discuss how both the new information and the latest features are helping the mycobacterial research communities. Copyright © 2010 Elsevier Ltd. All rights reserved.

Critical evaluation of a simple retention time predictor based on LogKow as a complementary tool in the identification of emerging contaminants in water.

PubMed

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

2015-07-01

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

Whole Genome Sequencing and Multiplex qPCR Methods to Identify Campylobacter jejuni Encoding cst-II or cst-III Sialyltransferase

PubMed Central

Neal-McKinney, Jason M.; Liu, Kun C.; Jinneman, Karen C.; Wu, Wen-Hsin; Rice, Daniel H.

2018-01-01

Campylobacter jejuni causes more than 2 million cases of gastroenteritis annually in the United States, and is also linked to the autoimmune sequelae Guillan–Barre syndrome (GBS). GBS often results in flaccid paralysis, as the myelin sheaths of nerve cells are degraded by the adaptive immune response. Certain strains of C. jejuni modify their lipooligosaccharide (LOS) with the addition of neuraminic acid, resulting in LOS moieties that are structurally similar to gangliosides present on nerve cells. This can trigger GBS in a susceptible host, as antibodies generated against C. jejuni can cross-react with gangliosides, leading to demyelination of nerves and a loss of signal transduction. The goal of this study was to develop a quantitative PCR (qPCR) method and use whole genome sequencing data to detect the Campylobacter sialyltransferase (cst) genes responsible for the addition of neuraminic acid to LOS. The qPCR method was used to screen a library of 89 C. jejuni field samples collected by the Food and Drug Administration Pacific Northwest Lab (PNL) as well as clinical isolates transferred to PNL. In silico analysis was used to screen 827 C. jejuni genomes in the FDA GenomeTrakr SRA database. The results indicate that a majority of C. jejuni strains could produce LOS with ganglioside mimicry, as 43.8% of PNL isolates and 46.9% of the GenomeTrakr isolates lacked the cst genes. The methods described in this study can be used by public health laboratories to rapidly determine whether a C. jejuni isolate has the potential to induce GBS. Based on these results, a majority of C. jejuni in the PNL collection and submitted to GenomeTrakr have the potential to produce LOS that mimics human gangliosides. PMID:29615986

Whole Genome Sequencing and Multiplex qPCR Methods to Identify Campylobacter jejuni Encoding cst-II or cst-III Sialyltransferase.

PubMed

Neal-McKinney, Jason M; Liu, Kun C; Jinneman, Karen C; Wu, Wen-Hsin; Rice, Daniel H

2018-01-01

Campylobacter jejuni causes more than 2 million cases of gastroenteritis annually in the United States, and is also linked to the autoimmune sequelae Guillan-Barre syndrome (GBS). GBS often results in flaccid paralysis, as the myelin sheaths of nerve cells are degraded by the adaptive immune response. Certain strains of C. jejuni modify their lipooligosaccharide (LOS) with the addition of neuraminic acid, resulting in LOS moieties that are structurally similar to gangliosides present on nerve cells. This can trigger GBS in a susceptible host, as antibodies generated against C. jejuni can cross-react with gangliosides, leading to demyelination of nerves and a loss of signal transduction. The goal of this study was to develop a quantitative PCR (qPCR) method and use whole genome sequencing data to detect the Campylobacter sialyltransferase ( cst ) genes responsible for the addition of neuraminic acid to LOS. The qPCR method was used to screen a library of 89 C. jejuni field samples collected by the Food and Drug Administration Pacific Northwest Lab (PNL) as well as clinical isolates transferred to PNL. In silico analysis was used to screen 827 C. jejuni genomes in the FDA GenomeTrakr SRA database. The results indicate that a majority of C. jejuni strains could produce LOS with ganglioside mimicry, as 43.8% of PNL isolates and 46.9% of the GenomeTrakr isolates lacked the cst genes. The methods described in this study can be used by public health laboratories to rapidly determine whether a C. jejuni isolate has the potential to induce GBS. Based on these results, a majority of C. jejuni in the PNL collection and submitted to GenomeTrakr have the potential to produce LOS that mimics human gangliosides.

Using co-expression analysis and stress-based screens to uncover Arabidopsis peroxisomal proteins involved in drought response

DOE PAGES

Li, Jiying; Hu, Jianping; Bassham, Diane

2015-09-14

Peroxisomes are essential organelles that house a wide array of metabolic reactions important for plant growth and development. However, our knowledge regarding the role of peroxisomal proteins in various biological processes, including plant stress response, is still incomplete. Recent proteomic studies of plant peroxisomes significantly increased the number of known peroxisomal proteins and greatly facilitated the study of peroxisomes at the systems level. The objectives of this study were to determine whether genes that encode peroxisomal proteins with related functions are co-expressed in Arabidopsis and identify peroxisomal proteins involved in stress response using in silico analysis and mutant screens. Usingmore » microarray data from online databases, we performed hierarchical clustering analysis to generate a comprehensive view of transcript level changes for Arabidopsis peroxisomal genes during development and under abiotic and biotic stress conditions. Many genes involved in the same metabolic pathways exhibited co-expression, some genes known to be involved in stress response are regulated by the corresponding stress conditions, and function of some peroxisomal proteins could be predicted based on their coexpression pattern. Since drought caused expression changes to the highest number of genes that encode peroxisomal proteins, we subjected a subset of Arabidopsis peroxisomal mutants to a drought stress assay. Mutants of the LON2 protease and the photorespiratory enzyme hydroxypyruvate reductase 1 (HPR1) showed enhanced susceptibility to drought, suggesting the involvement of peroxisomal quality control and photorespiration in drought resistance. Lastly, our study provided a global view of how genes that encode peroxisomal proteins respond to developmental and environmental cues and began to reveal additional peroxisomal proteins involved in stress response, thus opening up new avenues to investigate the role of peroxisomes in plant adaptation to environmental stresses.« less

Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models.

PubMed

AbdulHameed, Mohamed Diwan M; Ippolito, Danielle L; Wallqvist, Anders

2016-10-17

The pregnane X receptor (PXR) is a ligand-activated transcription factor that acts as a master regulator of metabolizing enzymes and transporters. To avoid adverse drug-drug interactions and diseases such as steatosis and cancers associated with PXR activation, identifying drugs and chemicals that activate PXR is of crucial importance. In this work, we developed ligand-based predictive computational models for both rat and human PXR activation, which allowed us to identify potentially harmful chemicals and evaluate species-specific effects of a given compound. We utilized a large publicly available data set of nearly 2000 compounds screened in cell-based reporter gene assays to develop Bayesian quantitative structure-activity relationship models using physicochemical properties and structural descriptors. Our analysis showed that PXR activators tend to be hydrophobic and significantly different from nonactivators in terms of their physicochemical properties such as molecular weight, logP, number of rings, and solubility. Our Bayesian models, evaluated by using 5-fold cross-validation, displayed a sensitivity of 75% (76%), specificity of 76% (75%), and accuracy of 89% (89%) for human (rat) PXR activation. We identified structural features shared by rat and human PXR activators as well as those unique to each species. We compared rat in vitro PXR activation data to in vivo data by using DrugMatrix, a large toxicogenomics database with gene expression data obtained from rats after exposure to diverse chemicals. Although in vivo gene expression data pointed to cross-talk between nuclear receptor activators that is captured only by in vivo assays, overall we found broad agreement between in vitro and in vivo PXR activation. Thus, the models developed here serve primarily as efficient initial high-throughput in silico screens of in vitro activity.

"Watching the Detectives" report of the general assembly of the EU project DETECTIVE Brussels, 24-25 November 2015.

PubMed

Fernando, Ruani N; Chaudhari, Umesh; Escher, Sylvia E; Hengstler, Jan G; Hescheler, Jürgen; Jennings, Paul; Keun, Hector C; Kleinjans, Jos C S; Kolde, Raivo; Kollipara, Laxmikanth; Kopp-Schneider, Annette; Limonciel, Alice; Nemade, Harshal; Nguemo, Filomain; Peterson, Hedi; Prieto, Pilar; Rodrigues, Robim M; Sachinidis, Agapios; Schäfer, Christoph; Sickmann, Albert; Spitkovsky, Dimitry; Stöber, Regina; van Breda, Simone G J; van de Water, Bob; Vivier, Manon; Zahedi, René P; Vinken, Mathieu; Rogiers, Vera

2016-06-01

SEURAT-1 is a joint research initiative between the European Commission and Cosmetics Europe aiming to develop in vitro- and in silico-based methods to replace the in vivo repeated dose systemic toxicity test used for the assessment of human safety. As one of the building blocks of SEURAT-1, the DETECTIVE project focused on a key element on which in vitro toxicity testing relies: the development of robust and reliable, sensitive and specific in vitro biomarkers and surrogate endpoints that can be used for safety assessments of chronically acting toxicants, relevant for humans. The work conducted by the DETECTIVE consortium partners has established a screening pipeline of functional and "-omics" technologies, including high-content and high-throughput screening platforms, to develop and investigate human biomarkers for repeated dose toxicity in cellular in vitro models. Identification and statistical selection of highly predictive biomarkers in a pathway- and evidence-based approach constitute a major step in an integrated approach towards the replacement of animal testing in human safety assessment. To discuss the final outcomes and achievements of the consortium, a meeting was organized in Brussels. This meeting brought together data-producing and supporting consortium partners. The presentations focused on the current state of ongoing and concluding projects and the strategies employed to identify new relevant biomarkers of toxicity. The outcomes and deliverables, including the dissemination of results in data-rich "-omics" databases, were discussed as were the future perspectives of the work completed under the DETECTIVE project. Although some projects were still in progress and required continued data analysis, this report summarizes the presentations, discussions and the outcomes of the project.

“Watching the Detectives” Report of the general assembly of the EU project DETECTIVE Brussels, 24-25 November, 2015

PubMed Central

Fernando, Ruani N.; Chaudhari, Umesh; Escher, Sylvia E.; Hengstler, Jan G.; Hescheler, Jürgen; Jennings, Paul; Keun, Hector C.; Kleinjans, Jos C. S.; Kolde, Raivo; Kollipara, Laxmikanth; Kopp-Schneider, Annette; Limonciel, Alice; Nemade, Harshal; Nguemo, Filomain; Peterson, Hedi; Prieto, Pilar; Rodrigues, Robim M.; Sachinidis, Agapios; Schäfer, Christoph; Sickmann, Albert; Spitkovsky, Dimitry; Stöber, Regina; van Breda, Simone G.J.; van de Water, Bob; Vivier, Manon; Zahedi, René P.

2017-01-01

SEURAT-1 is a joint research initiative between the European Commission and Cosmetics Europe aiming to develop in vitro and in silico based methods to replace the in vivo repeated dose systemic toxicity test used for the assessment of human safety. As one of the building blocks of SEURAT-1, the DETECTIVE project focused on a key element on which in vitro toxicity testing relies: the development of robust and reliable, sensitive and specific in vitro biomarkers and surrogate endpoints that can be used for safety assessments of chronically acting toxicants, relevant for humans. The work conducted by the DETECTIVE consortium partners has established a screening pipeline of functional and “-omics” technologies, including high-content and high-throughput screening platforms, to develop and investigate human biomarkers for repeated dose toxicity in cellular in vitro models. Identification and statistical selection of highly predictive biomarkers in a pathway- and evidence-based approach constitutes a major step in an integrated approach towards the replacement of animal testing in human safety assessment. To discuss the final outcomes and achievements of the consortium, a meeting was organized in Brussels. This meeting brought together data-producing and supporting consortium partners. The presentations focused on the current state of ongoing and concluding projects and the strategies employed to identify new relevant biomarkers of toxicity. The outcomes and deliverables, including the dissemination of results in data-rich “-omics” databases, were discussed as were the future perspectives of the work completed under the DETECTIVE project. Although some projects were still in progress and required continued data analysis, this report summarizes the presentations, discussions and the outcomes of the project. PMID:27129694

Blocking the interaction between HIV-1 integrase and human LEDGF/p75: mutational studies, virtual screening and molecular dynamics simulations.

PubMed

Reddy, Karnati Konda; Singh, Poonam; Singh, Sanjeev Kumar

2014-03-04

HIV-1 integrase (IN) mediates integration of viral cDNA into the host cell genome, an essential step in the retroviral life cycle. The human lens epithelium-derived growth factor (LEDGF/p75) is a co-factor of HIV-1 IN that plays a crucial role in viral integration. Because of its crucial role in early steps of HIV replication, the IN-LEDGF/p75 interaction represents an attractive target for anti-HIV drug discovery. In this study, the IN-LEDGF/p75 interaction was studied by in silico mutational studies and molecular dynamics simulations. The results showed that all of the key residues in the LEDGF/p75 binding pocket of IN protein are important for stabilization of the complex. Structure-based virtual screening against HIV-1 IN using the ChemBridge database was performed through three different protocols of docking simulations with varying precisions and computational intensities. Six compounds based on the docking score, binding affinity and pharmacokinetic parameters were selected and an analysis of the interactions with key amino acid residues of IN was carried out. Subsequently, molecular dynamics simulations of these compounds in the LEDGF/p75 binding site of IN were carried out in order to study the stability of complexes and their hydrogen bonding interactions. IN residues Glu170, His171, and Thr174 in chain A as well as Gln95 and Thr125 in chain B were discovered to play important roles in the binding of compounds. These findings could be helpful for blocking IN-LEDGF/p75 interaction, and provide a method for avoiding viral resistance and cross-resistance.

iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan

NASA Astrophysics Data System (ADS)

Tsai, Tsung-Ying; Chang, Kai-Wei; Chen, Calvin Yu-Chian

2011-06-01

The rapidly advancing researches on traditional Chinese medicine (TCM) have greatly intrigued pharmaceutical industries worldwide. To take initiative in the next generation of drug development, we constructed a cloud-computing system for TCM intelligent screening system (iScreen) based on TCM Database@Taiwan. iScreen is compacted web server for TCM docking and followed by customized de novo drug design. We further implemented a protein preparation tool that both extract protein of interest from a raw input file and estimate the size of ligand bind site. In addition, iScreen is designed in user-friendly graphic interface for users who have less experience with the command line systems. For customized docking, multiple docking services, including standard, in-water, pH environment, and flexible docking modes are implemented. Users can download first 200 TCM compounds of best docking results. For TCM de novo drug design, iScreen provides multiple molecular descriptors for a user's interest. iScreen is the world's first web server that employs world's largest TCM database for virtual screening and de novo drug design. We believe our web server can lead TCM research to a new era of drug development. The TCM docking and screening server is available at http://iScreen.cmu.edu.tw/.

iScreen: world's first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan.

PubMed

Tsai, Tsung-Ying; Chang, Kai-Wei; Chen, Calvin Yu-Chian

2011-06-01

The rapidly advancing researches on traditional Chinese medicine (TCM) have greatly intrigued pharmaceutical industries worldwide. To take initiative in the next generation of drug development, we constructed a cloud-computing system for TCM intelligent screening system (iScreen) based on TCM Database@Taiwan. iScreen is compacted web server for TCM docking and followed by customized de novo drug design. We further implemented a protein preparation tool that both extract protein of interest from a raw input file and estimate the size of ligand bind site. In addition, iScreen is designed in user-friendly graphic interface for users who have less experience with the command line systems. For customized docking, multiple docking services, including standard, in-water, pH environment, and flexible docking modes are implemented. Users can download first 200 TCM compounds of best docking results. For TCM de novo drug design, iScreen provides multiple molecular descriptors for a user's interest. iScreen is the world's first web server that employs world's largest TCM database for virtual screening and de novo drug design. We believe our web server can lead TCM research to a new era of drug development. The TCM docking and screening server is available at http://iScreen.cmu.edu.tw/.

Automated Protocol for Large-Scale Modeling of Gene Expression Data.

PubMed

Hall, Michelle Lynn; Calkins, David; Sherman, Woody

2016-11-28

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

Identification of novel antitubulin agents by using a virtual screening approach based on a 7-point pharmacophore model of the tubulin colchi-site.

PubMed

Massarotti, Alberto; Theeramunkong, Sewan; Mesenzani, Ornella; Caldarelli, Antonio; Genazzani, Armando A; Tron, Gian Cesare

2011-12-01

Tubulin inhibition represents an established target in the field of anticancer research, and over the last 20 years, an intensive search for new antimicrotubule agents has occurred. Indeed, in silico models have been presented that might aid the discovery of novel agents. Among these, a 7-point pharmacophore model has been recently proposed. As a formal proof of this model, we carried out a ligand-based virtual screening on the colchicine-binding site. In vitro testing demonstrated that two compounds displayed a cytotoxic profile on neuroblastoma cancer cells (SH-SY5H) and one had an antitubulinic profile. © 2011 John Wiley & Sons A/S.

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.

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

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

PubMed Central

Tabchy, Adel; Eltonsy, Nevine; Housman, David E.; Mills, Gordon B.

2013-01-01

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance. PMID:23577104

Systematic identification of combinatorial drivers and targets in cancer cell lines.

PubMed

Tabchy, Adel; Eltonsy, Nevine; Housman, David E; Mills, Gordon B

2013-01-01

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance.

[In silico, in vitro, in omic experimental models and drug safety evaluation].

PubMed

Claude, Nancy; Goldfain-Blanc, Françoise; Guillouzo, André

2009-01-01

Over the last few decades, toxicology has benefited from scientific, technical, and bioinformatic developments relating to patient safety assessment during clinical and drug marketing studies. Based on this knowledge, new in silico, in vitro, and "omic" experimental models are emerging. Although these models cannot currently replace classic safety evaluations performed on laboratory animals, they allow compounds with unacceptable toxicity to be rejected in the early stages of drug development, thereby reducing the number of laboratory animals needed. In addition, because these models are particularly adapted to mechanistic studies, they can help to improve the relevance of the data obtained, thus enabling better prevention and screening of the adverse effects that may occur in humans. Much progress remains to be done, especially in the field of validation. Nevertheless, current efforts by industrial, academic laboratories, and regulatory agencies should, in coming years, significantly improve preclinical drug safety evaluation thanks to the integration of these new methods into the drug research and development process.

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.

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.

In silico design of novel proton-pump inhibitors with reduced adverse effects.

PubMed

Li, Xiaoyi; Kang, Hong; Liu, Wensheng; Singhal, Sarita; Jiao, Na; Wang, Yong; Zhu, Lixin; Zhu, Ruixin

2018-05-30

The development of new proton-pump inhibitors (PPIs) with less adverse effects by lowering the pKa values of nitrogen atoms in pyrimidine rings has been previously suggested by our group. In this work, we proposed that new PPIs should have the following features: (1) number of ring II = number of ring I + 1; (2) preferably five, six, or seven-membered heteroatomic ring for stability; and (3) 1 < pKa1 < 4. Six molecular scaffolds based on the aforementioned criteria were constructed, and R groups were extracted from compounds in extensive data sources. A virtual molecule dataset was established, and the pKa values of specific atoms on the molecules in the dataset were calculated to select the molecules with required pKa values. Drug-likeness screening was further conducted to obtain the candidates that significantly reduced the adverse effects of long-term PPI use. This study provided insights and tools for designing targeted molecules in silico that are suitable for practical applications.

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.

Pharmacokinetic properties and in silico ADME modeling in drug discovery.

PubMed

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

2013-03-01

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

Lead discovery and in silico 3D structure modeling of tumorigenic FAM72A (p17).

PubMed

Pramanik, Subrata; Kutzner, Arne; Heese, Klaus

2015-01-01

FAM72A (p17) is a novel neuronal protein that has been linked to tumorigenic effects in non-neuronal tissue. Using state of the art in silico physicochemical analyses (e.g., I-TASSER, RaptorX, and Modeller), we determined the three-dimensional (3D) protein structure of FAM72A and further identified potential ligand-protein interactions. Our data indicate a Zn(2+)/Fe(3+)-containing 3D protein structure, based on a 3GA3_A model template, which potentially interacts with the organic molecule RSM ((2s)-2-(acetylamino)-N-methyl-4-[(R)-methylsulfinyl] butanamide). The discovery of RSM may serve as potential lead for further anti-FAM72A drug screening tests in the pharmaceutical industry because interference with FAM72A's activities via RSM-related molecules might be a novel option to influence the tumor suppressor protein p53 signaling pathways for the treatment of various types of cancers.

The "GeneTrustee": a universal identification system that ensures privacy and confidentiality for human genetic databases.

PubMed

Burnett, Leslie; Barlow-Stewart, Kris; Proos, Anné L; Aizenberg, Harry

2003-05-01

This article describes a generic model for access to samples and information in human genetic databases. The model utilises a "GeneTrustee", a third-party intermediary independent of the subjects and of the investigators or database custodians. The GeneTrustee model has been implemented successfully in various community genetics screening programs and has facilitated research access to genetic databases while protecting the privacy and confidentiality of research subjects. The GeneTrustee model could also be applied to various types of non-conventional genetic databases, including neonatal screening Guthrie card collections, and to forensic DNA samples.

Knowledge representation in metabolic pathway databases.

PubMed

Stobbe, Miranda D; Jansen, Gerbert A; Moerland, Perry D; van Kampen, Antoine H C

2014-05-01

The accurate representation of all aspects of a metabolic network in a structured format, such that it can be used for a wide variety of computational analyses, is a challenge faced by a growing number of researchers. Analysis of five major metabolic pathway databases reveals that each database has made widely different choices to address this challenge, including how to deal with knowledge that is uncertain or missing. In concise overviews, we show how concepts such as compartments, enzymatic complexes and the direction of reactions are represented in each database. Importantly, also concepts which a database does not represent are described. Which aspects of the metabolic network need to be available in a structured format and to what detail differs per application. For example, for in silico phenotype prediction, a detailed representation of gene-protein-reaction relations and the compartmentalization of the network is essential. Our analysis also shows that current databases are still limited in capturing all details of the biology of the metabolic network, further illustrated with a detailed analysis of three metabolic processes. Finally, we conclude that the conceptual differences between the databases, which make knowledge exchange and integration a challenge, have not been resolved, so far, by the exchange formats in which knowledge representation is standardized.

Novel Hybrid Virtual Screening Protocol Based on Molecular Docking and Structure-Based Pharmacophore for Discovery of Methionyl-tRNA Synthetase Inhibitors as Antibacterial Agents

PubMed Central

Liu, Chi; He, Gu; Jiang, Qinglin; Han, Bo; Peng, Cheng

2013-01-01

Methione tRNA synthetase (MetRS) is an essential enzyme involved in protein biosynthesis in all living organisms and is a potential antibacterial target. In the current study, the structure-based pharmacophore (SBP)-guided method has been suggested to generate a comprehensive pharmacophore of MetRS based on fourteen crystal structures of MetRS-inhibitor complexes. In this investigation, a hybrid protocol of a virtual screening method, comprised of pharmacophore model-based virtual screening (PBVS), rigid and flexible docking-based virtual screenings (DBVS), is used for retrieving new MetRS inhibitors from commercially available chemical databases. This hybrid virtual screening approach was then applied to screen the Specs (202,408 compounds) database, a structurally diverse chemical database. Fifteen hit compounds were selected from the final hits and shifted to experimental studies. These results may provide important information for further research of novel MetRS inhibitors as antibacterial agents. PMID:23839093

In silico free energy predictions for ionic liquid-assisted exfoliation of a graphene bilayer into individual graphene nanosheets.

PubMed

Kamath, Ganesh; Baker, Gary A

2012-06-14

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

Virtual medicinal chemistry: in silico pre-docking functional group transformation for discovery of novel inhibitors of botulinum toxin serotype A light chain.

PubMed

O'Malley, Sean; Sareth, Sina; Jiao, Guan-Sheng; Kim, Seongjin; Thai, April; Cregar-Hernandez, Lynne; McKasson, Linda; Margosiak, Stephen A; Johnson, Alan T

2013-05-01

A novel method for applying high-throughput docking to challenging metalloenzyme targets is described. The method utilizes information-based virtual transformation of library carboxylates to hydroxamic acids prior to docking, followed by compound acquisition, one-pot (two steps) chemical synthesis and in vitro screening. In two experiments targeting the botulinum neurotoxin serotype A metalloprotease light chain, hit rates of 32% and 18% were observed. Copyright © 2013 Elsevier Ltd. All rights reserved.

Fragment virtual screening based on Bayesian categorization for discovering novel VEGFR-2 scaffolds.

PubMed

Zhang, Yanmin; Jiao, Yu; Xiong, Xiao; Liu, Haichun; Ran, Ting; Xu, Jinxing; Lu, Shuai; Xu, Anyang; Pan, Jing; Qiao, Xin; Shi, Zhihao; Lu, Tao; Chen, Yadong

2015-11-01

The discovery of novel scaffolds against a specific target has long been one of the most significant but challengeable goals in discovering lead compounds. A scaffold that binds in important regions of the active pocket is more favorable as a starting point because scaffolds generally possess greater optimization possibilities. However, due to the lack of sufficient chemical space diversity of the databases and the ineffectiveness of the screening methods, it still remains a great challenge to discover novel active scaffolds. Since the strengths and weaknesses of both fragment-based drug design and traditional virtual screening (VS), we proposed a fragment VS concept based on Bayesian categorization for the discovery of novel scaffolds. This work investigated the proposal through an application on VEGFR-2 target. Firstly, scaffold and structural diversity of chemical space for 10 compound databases were explicitly evaluated. Simultaneously, a robust Bayesian classification model was constructed for screening not only compound databases but also their corresponding fragment databases. Although analysis of the scaffold diversity demonstrated a very unevenly distribution of scaffolds over molecules, results showed that our Bayesian model behaved better in screening fragments than molecules. Through a literature retrospective research, several generated fragments with relatively high Bayesian scores indeed exhibit VEGFR-2 biological activity, which strongly proved the effectiveness of fragment VS based on Bayesian categorization models. This investigation of Bayesian-based fragment VS can further emphasize the necessity for enrichment of compound databases employed in lead discovery by amplifying the diversity of databases with novel structures.

Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers.

PubMed

Monge, Aurélien; Arrault, Alban; Marot, Christophe; Morin-Allory, Luc

2006-08-01

The data for 3.8 million compounds from structural databases of 32 providers were gathered and stored in a single chemical database. Duplicates are removed using the IUPAC International Chemical Identifier. After this, 2.6 million compounds remain. Each database and the final one were studied in term of uniqueness, diversity, frameworks, 'drug-like' and 'lead-like' properties. This study also shows that there are more than 87 000 frameworks in the database. It contains 2.1 million 'drug-like' molecules among which, more than one million are 'lead-like'. This study has been carried out using 'ScreeningAssistant', a software dedicated to chemical databases management and screening sets generation. Compounds are stored in a MySQL database and all the operations on this database are carried out by Java code. The druglikeness and leadlikeness are estimated with 'in-house' scores using functions to estimate convenience to properties; unicity using the InChI code and diversity using molecular frameworks and fingerprints. The software has been conceived in order to facilitate the update of the database. 'ScreeningAssistant' is freely available under the GPL license.

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.

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

PubMed

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

2018-05-10

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

DoD Identity Matching Engine for Security and Analysis (IMESA) Access to Criminal Justice Information (CJI) and Terrorist Screening Databases (TSDB)

DTIC Science & Technology

2016-05-04

IMESA) Access to Criminal Justice Information (CJI) and Terrorist Screening Databases (TSDB) References: See Enclosure 1 1. PURPOSE. In...CJI database mirror image files. (3) Memorandums of understanding with the FBI CJIS as the data broker for DoD organizations that need access ...not for access determinations. (3) Legal restrictions established by the Sex Offender Registration and Notification Act (SORNA) jurisdictions on

Fragment-based lead generation: identification of seed fragments by a highly efficient fragment screening technology

NASA Astrophysics Data System (ADS)

Neumann, Lars; Ritscher, Allegra; Müller, Gerhard; Hafenbradl, Doris

2009-08-01

For the detection of the precise and unambiguous binding of fragments to a specific binding site on the target protein, we have developed a novel reporter displacement binding assay technology. The application of this technology for the fragment screening as well as the fragment evolution process with a specific modelling based design strategy is demonstrated for inhibitors of the protein kinase p38alpha. In a fragment screening approach seed fragments were identified which were then used to build compounds from the deep-pocket towards the hinge binding area of the protein kinase p38alpha based on a modelling approach. BIRB796 was used as a blueprint for the alignment of the fragments. The fragment evolution of these deep-pocket binding fragments towards the fully optimized inhibitor BIRB796 included the modulation of the residence time as well as the affinity. The goal of our study was to evaluate the robustness and efficiency of our novel fragment screening technology at high fragment concentrations, compare the screening data with biochemical activity data and to demonstrate the evolution of the hit fragments with fast kinetics, into slow kinetic inhibitors in an in silico approach.

Binding-Site Assessment by Virtual Fragment Screening

PubMed Central

Huang, Niu; Jacobson, Matthew P.

2010-01-01

The accurate prediction of protein druggability (propensity to bind high-affinity drug-like small molecules) would greatly benefit the fields of chemical genomics and drug discovery. We have developed a novel approach to quantitatively assess protein druggability by computationally screening a fragment-like compound library. In analogy to NMR-based fragment screening, we dock ∼11000 fragments against a given binding site and compute a computational hit rate based on the fraction of molecules that exceed an empirically chosen score cutoff. We perform a large-scale evaluation of the approach on four datasets, totaling 152 binding sites. We demonstrate that computed hit rates correlate with hit rates measured experimentally in a previously published NMR-based screening method. Secondly, we show that the in silico fragment screening method can be used to distinguish known druggable and non-druggable targets, including both enzymes and protein-protein interaction sites. Finally, we explore the sensitivity of the results to different receptor conformations, including flexible protein-protein interaction sites. Besides its original aim to assess druggability of different protein targets, this method could be used to identifying druggable conformations of flexible binding site for lead discovery, and suggesting strategies for growing or joining initial fragment hits to obtain more potent inhibitors. PMID:20404926

Virtual screening and pharmacophore design for a novel theoretical inhibitor of macrophage stimulating factor as a metastatic agent.

PubMed

Torktaz, Ibrahim; Mohamadhashem, Faezeh; Esmaeili, Abolghasem; Behjati, Mohaddeseh; Sharifzadeh, Sara

2013-01-01

Metastasis is a crucial aspect of cancer. Macrophage stimulating protein (MSP) is a single chain protein and can be cleaved by serum proteases. MSP has several roles in metastasis. In this in silico study, MSP as a metastatic agent was considered as a drug target. Crystallographic structure of MSP was retrieved from protein data bank. To find a chemical inhibitor of MSP, a library of KEGG compounds was screened and 1000 shape complemented ligands were retrieved with FindSite algorithm. Molegro Virtual Docker (MVD) software was used for docking simulation of shape complemented ligands against MSP. Moldock score was used as scoring function for virtual screening and potential inhibitors with more negative binding energy were obtained. PLANS scoring function was used for revaluation of virtual screening data. The top found chemical had binding affinity of -183.55 based on MolDock score and equal to -66.733 PLANTs score to MSP structure. Based on pharmacophore model of potential inhibitor, this study suggests that the chemical which was found in this research and its derivate can be used for subsequent laboratory studies.

An assessment of biodegradability of quaternary carbon-containing fragrance compounds: comparison of experimental OECD screening test results and in silico prediction data.

PubMed

Seyfried, Markus; Boschung, Alain

2014-05-01

An assessment of biodegradability was carried out for fragrance substances containing quaternary carbons by using data obtained from Organisation for Economic Co-operation and Development (OECD) 301F screening tests for ready biodegradation and from Biowin and Catalogic prediction models. Despite an expected challenging profile, a relatively high percentage of common-use fragrance substances showed significant biodegradation under the stringent conditions applied in the OECD 301F test. Among 27 test compounds, 37% met the pass level criteria after 28 d, while another 26% indicated partial breakdown (≥20% biodegradation). For several compounds for which structural analogs were available, the authors found that structures that were rendered less water soluble by either the presence of an acetate ester or the absence of oxygen tended to degrade to a lesser extent compared to the primary alcohols or oxygenated counterparts under the test conditions applied. Difficulties were encountered when attempting to correlate experimental with in silico data. Whereas the Biowin model combinations currently recommended by regulatory agencies did not allow for a reliable discrimination between readily and nonbiodegradable compounds, only a comparably small proportion of the chemicals studied (30% and 63% depending on the model) fell within the applicability domain of Catalogic, a factor that critically reduced its predictive power. According to these results, currently neither Biowin nor Catalogic accurately reflects the potential for biodegradation of fragrance compounds containing quaternary carbons. © 2014 SETAC.

Mining for Nonribosomal Peptide Synthetase and Polyketide Synthase Genes Revealed a High Level of Diversity in the Sphagnum Bog Metagenome.

PubMed

Müller, Christina A; Oberauner-Wappis, Lisa; Peyman, Armin; Amos, Gregory C A; Wellington, Elizabeth M H; Berg, Gabriele

2015-08-01

Sphagnum bog ecosystems are among the oldest vegetation forms harboring a specific microbial community and are known to produce an exceptionally wide variety of bioactive substances. Although the Sphagnum metagenome shows a rich secondary metabolism, the genes have not yet been explored. To analyze nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs), the diversity of NRPS and PKS genes in Sphagnum-associated metagenomes was investigated by in silico data mining and sequence-based screening (PCR amplification of 9,500 fosmid clones). The in silico Illumina-based metagenomic approach resulted in the identification of 279 NRPSs and 346 PKSs, as well as 40 PKS-NRPS hybrid gene sequences. The occurrence of NRPS sequences was strongly dominated by the members of the Protebacteria phylum, especially by species of the Burkholderia genus, while PKS sequences were mainly affiliated with Actinobacteria. Thirteen novel NRPS-related sequences were identified by PCR amplification screening, displaying amino acid identities of 48% to 91% to annotated sequences of members of the phyla Proteobacteria, Actinobacteria, and Cyanobacteria. Some of the identified metagenomic clones showed the closest similarity to peptide synthases from Burkholderia or Lysobacter, which are emerging bacterial sources of as-yet-undescribed bioactive metabolites. This report highlights the role of the extreme natural ecosystems as a promising source for detection of secondary compounds and enzymes, serving as a source for biotechnological applications. Copyright © 2015, American Society for Microbiology. All Rights Reserved.

Small molecule inhibitors of ERCC1-XPF protein-protein interaction synergize alkylating agents in cancer cells.

PubMed

Jordheim, Lars Petter; Barakat, Khaled H; Heinrich-Balard, Laurence; Matera, Eva-Laure; Cros-Perrial, Emeline; Bouledrak, Karima; El Sabeh, Rana; Perez-Pineiro, Rolando; Wishart, David S; Cohen, Richard; Tuszynski, Jack; Dumontet, Charles

2013-07-01

The benefit of cancer chemotherapy based on alkylating agents is limited because of the action of DNA repair enzymes, which mitigate the damage induced by these agents. The interaction between the proteins ERCC1 and XPF involves two major components of the nucleotide excision repair pathway. Here, novel inhibitors of this interaction were identified by virtual screening based on available structures with use of the National Cancer Institute diversity set and a panel of DrugBank small molecules. Subsequently, experimental validation of the in silico screening was undertaken. Top hits were evaluated on A549 and HCT116 cancer cells. In particular, the compound labeled NSC 130813 [4-[(6-chloro-2-methoxy-9-acridinyl)amino]-2-[(4-methyl-1-piperazinyl)methyl

Magnetic Resonance Imaging as an Adjunct to Mammography for Breast Cancer Screening in Women at Less Than High Risk for Breast Cancer: A Health Technology Assessment

PubMed Central

Nikitovic-Jokic, Milica; Holubowich, Corinne

2016-01-01

Background Screening with mammography can detect breast cancer early, before clinical symptoms appear. Some cancers, however, are not captured with mammography screening alone. Among women at high risk for breast cancer, magnetic resonance imaging (MRI) has been suggested as a safe adjunct (supplemental) screening tool that can detect breast cancers missed on screening mammography, potentially reducing the number of deaths associated with the disease. However, the use of adjunct screening tests may also increase the number of false-positive test results, which may lead to unnecessary follow-up testing, as well as patient stress and anxiety. We investigated the benefits and harms of MRI as an adjunct to mammography compared with mammography alone for screening women at less than high risk (average or higher than average risk) for breast cancer. Methods We searched Ovid MEDLINE, Ovid Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects (DARE), Centre for Reviews and Dissemination (CRD) Health Technology Assessment Database, and National Health Service (NHS) Economic Evaluation Database, from January 2002 to January 2016, for evidence of effectiveness, harms, and diagnostic accuracy. Only studies evaluating the use of screening breast MRI as an adjunct to mammography in the specified populations were included. Results No studies in women at less than high risk for breast cancer met our inclusion criteria. Conclusions It remains uncertain if the use of adjunct screening breast MRI in women at less than high risk (average or higher than average risk) for breast cancer will reduce breast cancer–related mortality without significant increases in unnecessary follow-up testing and treatment. PMID:27990198

Development and Validation of a Qualitative Method for Target Screening of 448 Pesticide Residues in Fruits and Vegetables Using UHPLC/ESI Q-Orbitrap Based on Data-Independent Acquisition and Compound Database.

PubMed

Wang, Jian; Chow, Willis; Chang, James; Wong, Jon W

2017-01-18

A semiautomated qualitative method for target screening of 448 pesticide residues in fruits and vegetables was developed and validated using ultrahigh-performance liquid chromatography coupled with electrospray ionization quadrupole Orbitrap high-resolution mass spectrometry (UHPLC/ESI Q-Orbitrap). The Q-Orbitrap Full MS/dd-MS 2 (data dependent acquisition) was used to acquire product-ion spectra of individual pesticides to build a compound database or an MS library, while its Full MS/DIA (data independent acquisition) was utilized for sample data acquisition from fruit and vegetable matrices fortified with pesticides at 10 and 100 μg/kg for target screening purpose. Accurate mass, retention time and response threshold were three key parameters in a compound database that were used to detect incurred pesticide residues in samples. The concepts and practical aspects of in-spectrum mass correction or solvent background lock-mass correction, retention time alignment and response threshold adjustment are discussed while building a functional and working compound database for target screening. The validated target screening method is capable of screening at least 94% and 99% of 448 pesticides at 10 and 100 μg/kg, respectively, in fruits and vegetables without having to evaluate every compound manually during data processing, which significantly reduced the workload in routine practice.

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.

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 methods in the discovery of endocrine disrupting chemicals.

PubMed

Vuorinen, Anna; Odermatt, Alex; Schuster, Daniela

2013-09-01

The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'. Copyright © 2013 Elsevier Ltd. All rights reserved.

Reprint of "In silico methods in the discovery of endocrine disrupting chemicals".

PubMed

Vuorinen, Anna; Odermatt, Alex; Schuster, Daniela

2015-09-01

The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'. Copyright © 2015 Elsevier Ltd. All rights reserved.

In Silico and In Vitro Analysis of Bacoside A Aglycones and Its Derivatives as the Constituents Responsible for the Cognitive Effects of Bacopa monnieri

PubMed Central

Ramasamy, Seetha; Chin, Sek Peng; Sukumaran, Sri Devi; Buckle, Michael James Christopher; Kiew, Lik Voon; Chung, Lip Yong

2015-01-01

Bacopa monnieri has been used in Ayurvedic medicine to improve memory and cognition. The active constituent responsible for its pharmacological effects is bacoside A, a mixture of dammarane-type triterpenoid saponins containing sugar chains linked to a steroid aglycone skeleton. Triterpenoid saponins have been reported to be transformed in vivo to metabolites that give better biological activity and pharmacokinetic characteristics. Thus, the activities of the parent compounds (bacosides), aglycones (jujubogenin and pseudojujubogenin) and their derivatives (ebelin lactone and bacogenin A1) were compared using a combination of in silico and in vitro screening methods. The compounds were docked into 5-HT1A, 5-HT2A, D1, D2, M1 receptors and acetylcholinesterase (AChE) using AutoDock and their central nervous system (CNS) drug-like properties were determined using Discovery Studio molecular properties and ADMET descriptors. The compounds were screened in vitro using radioligand receptor binding and AChE inhibition assays. In silico studies showed that the parent bacosides were not able to dock into the chosen CNS targets and had poor molecular properties as a CNS drug. In contrast, the aglycones and their derivatives showed better binding affinity and good CNS drug-like properties, were well absorbed through the intestines and had good blood brain barrier (BBB) penetration. Among the compounds tested in vitro, ebelin lactone showed binding affinity towards M1 (Ki = 0.45 μM) and 5-HT2A (4.21 μM) receptors. Bacoside A and bacopaside X (9.06 μM) showed binding affinity towards the D1 receptor. None of the compounds showed any inhibitory activity against AChE. Since the stimulation of M1 and 5-HT2A receptors has been implicated in memory and cognition and ebelin lactone was shown to have the strongest binding energy, highest BBB penetration and binding affinity towards M1 and 5-HT2A receptors, we suggest that B. monnieri constituents may be transformed in vivo to the active form before exerting their pharmacological activity. PMID:25965066

In Silico and In Vitro Analysis of Bacoside A Aglycones and Its Derivatives as the Constituents Responsible for the Cognitive Effects of Bacopa monnieri.

PubMed

Ramasamy, Seetha; Chin, Sek Peng; Sukumaran, Sri Devi; Buckle, Michael James Christopher; Kiew, Lik Voon; Chung, Lip Yong

2015-01-01

Bacopa monnieri has been used in Ayurvedic medicine to improve memory and cognition. The active constituent responsible for its pharmacological effects is bacoside A, a mixture of dammarane-type triterpenoid saponins containing sugar chains linked to a steroid aglycone skeleton. Triterpenoid saponins have been reported to be transformed in vivo to metabolites that give better biological activity and pharmacokinetic characteristics. Thus, the activities of the parent compounds (bacosides), aglycones (jujubogenin and pseudojujubogenin) and their derivatives (ebelin lactone and bacogenin A1) were compared using a combination of in silico and in vitro screening methods. The compounds were docked into 5-HT1A, 5-HT2A, D1, D2, M1 receptors and acetylcholinesterase (AChE) using AutoDock and their central nervous system (CNS) drug-like properties were determined using Discovery Studio molecular properties and ADMET descriptors. The compounds were screened in vitro using radioligand receptor binding and AChE inhibition assays. In silico studies showed that the parent bacosides were not able to dock into the chosen CNS targets and had poor molecular properties as a CNS drug. In contrast, the aglycones and their derivatives showed better binding affinity and good CNS drug-like properties, were well absorbed through the intestines and had good blood brain barrier (BBB) penetration. Among the compounds tested in vitro, ebelin lactone showed binding affinity towards M1 (Ki = 0.45 μM) and 5-HT2A (4.21 μM) receptors. Bacoside A and bacopaside X (9.06 μM) showed binding affinity towards the D1 receptor. None of the compounds showed any inhibitory activity against AChE. Since the stimulation of M1 and 5-HT2A receptors has been implicated in memory and cognition and ebelin lactone was shown to have the strongest binding energy, highest BBB penetration and binding affinity towards M1 and 5-HT2A receptors, we suggest that B. monnieri constituents may be transformed in vivo to the active form before exerting their pharmacological activity.

In Silico Analysis of the Association Relationship between Neuroprotection and Flavors of Traditional Chinese Medicine Based on the mGluRs

PubMed Central

Qiao, Liansheng; Chen, Yankun; Zhao, Bowen; Gu, Yu; Huo, Xiaoqian; Zhang, Yanling; Li, Gongyu

2018-01-01

The metabotropic glutamate receptors (mGluRs) are known as both synaptic receptors and taste receptors. This feature is highly similar to the Property and Flavor theory of Traditional Chinese medicine (TCM), which has the pharmacological effect and flavor. In this study, six ligand based pharmacophore (LBP) models, seven homology modeling models, and fourteen molecular docking models of mGluRs were built based on orthosteric and allosteric sites to screening potential compounds from Traditional Chinese Medicine Database (TCMD). Based on the Pharmacopoeia of the People’s Republic of China, TCMs of compounds and their flavors were traced and listed. According to the tracing result, we found that the TCMs of the compounds which bound to orthosteric sites of mGluRs are highly correlated to a sweet flavor, while the allosteric site corresponds to a bitter flavor. Meanwhile, the pharmacological effects of TCMs with highly frequent flavors were further analyzed. We found that those TCMs play a neuroprotective role through the efficiencies of detumescence, promoting blood circulation, analgesic effect, and so on. This study provides a guide for developing new neuroprotective drugs from TCMs which target mGluRs. Moreover, it is the first study to present a novel approach to discuss the association relationship between flavor and the neuroprotective mechanism of TCM based on mGluRs. PMID:29320397

Deciphering the Structural Requirements of Nucleoside Bisubstrate Analogues for Inhibition of MbtA in Mycobacterium tuberculosis: A FB-QSAR Study and Combinatorial Library Generation for Identifying Potential Hits.

PubMed

Maganti, Lakshmi; Das, Sanjit Kumar; Mascarenhas, Nahren Manuel; Ghoshal, Nanda

2011-10-01

The re-emergence of tuberculosis infections, which are resistant to conventional drug therapy, has steadily risen in the last decade. Inhibitors of aryl acid adenylating enzyme known as MbtA, involved in siderophore biosynthesis in Mycobacterium tuberculosis, are being explored as potential antitubercular agents. The ability to identify fragments that interact with a biological target is a key step in fragment based drug design (FBDD). To expand the boundaries of quantitative structure activity relationship (QSAR) paradigm, we have proposed a Fragment Based QSAR methodology, referred here in as FB-QSAR, for deciphering the structural requirements of a series of nucleoside bisubstrate analogs for inhibition of MbtA, a key enzyme involved in siderophore biosynthetic pathway. For the development of FB-QSAR models, statistical techniques such as stepwise multiple linear regression (SMLR), genetic function approximation (GFA) and GFAspline were used. The predictive ability of the generated models was validated using different statistical metrics, and similarity-based coverage estimation was carried out to define applicability boundaries. To aid the creation of novel antituberculosis compounds, a bioisosteric database was enumerated using the combichem approach endorsed mining in a lead-like chemical space. The generated library was screened using an integrated in-silico approach and potential hits identified. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Rational discovery of dengue type 2 non-competitive inhibitors.

PubMed

Heh, Choon H; Othman, Rozana; Buckle, Michael J C; Sharifuddin, Yusrizam; Yusof, Rohana; Rahman, Noorsaadah A

2013-07-01

Various works have been carried out in developing therapeutics against dengue. However, to date, no effective vaccine or anti-dengue agent has yet been discovered. The development of protease inhibitors is considered as a promising option, but most previous works have involved competitive inhibition. In this study, we focused on rational discovery of potential anti-dengue agents based on non-competitive inhibition of DEN-2 NS2B/NS3 protease. A homology model of the DEN-2 NS2B/NS3 protease (using West Nile Virus NS2B/NS3 protease complex, 2FP7, as the template) was used as the target, and pinostrobin, a flavanone, was used as the standard ligand. Virtual screening was performed involving a total of 13 341 small compounds, with the backbone structures of chalcone, flavanone, and flavone, available in the ZINC database. Ranking of the resulting compounds yielded compounds with higher binding affinities compared with the standard ligand. Inhibition assay of the selected top-ranking compounds against DEN-2 NS2B/NS3 proteolytic activity resulted in significantly better inhibition compared with the standard and correlated well with in silico results. In conclusion, via this rational discovery technique, better inhibitors were identified. This method can be used in further work to discover lead compounds for anti-dengue agents. © 2013 John Wiley & Sons A/S.

IFPTarget: A Customized Virtual Target Identification Method Based on Protein-Ligand Interaction Fingerprinting Analyses.

PubMed

Li, Guo-Bo; Yu, Zhu-Jun; Liu, Sha; Huang, Lu-Yi; Yang, Ling-Ling; Lohans, Christopher T; Yang, Sheng-Yong

2017-07-24

Small-molecule target identification is an important and challenging task for chemical biology and drug discovery. Structure-based virtual target identification has been widely used, which infers and prioritizes potential protein targets for the molecule of interest (MOI) principally via a scoring function. However, current "universal" scoring functions may not always accurately identify targets to which the MOI binds from the retrieved target database, in part due to a lack of consideration of the important binding features for an individual target. Here, we present IFPTarget, a customized virtual target identification method, which uses an interaction fingerprinting (IFP) method for target-specific interaction analyses and a comprehensive index (Cvalue) for target ranking. Evaluation results indicate that the IFP method enables substantially improved binding pose prediction, and Cvalue has an excellent performance in target ranking for the test set. When applied to screen against our established target library that contains 11,863 protein structures covering 2842 unique targets, IFPTarget could retrieve known targets within the top-ranked list and identified new potential targets for chemically diverse drugs. IFPTarget prediction led to the identification of the metallo-β-lactamase VIM-2 as a target for quercetin as validated by enzymatic inhibition assays. This study provides a new in silico target identification tool and will aid future efforts to develop new target-customized methods for target identification.

Structure⁻Activity Relationship and Molecular Docking of Natural Product Library Reveal Chrysin as a Novel Dipeptidyl Peptidase-4 (DPP-4) Inhibitor: An Integrated In Silico and In Vitro Study.

PubMed

Kalhotra, Poonam; Chittepu, Veera C S R; Osorio-Revilla, Guillermo; Gallardo-Velázquez, Tzayhri

2018-06-06

Numerous studies indicate that diets with a variety of fruits and vegetables decrease the incidence of severe diseases, like diabetes, obesity, and cancer. Diets contain a variety of bioactive compounds, and their features, like diverge scaffolds, and structural complexity make them the most successful source of potential leads or hits in the process of drug discovery and drug development. Recently, novel serine protease dipeptidyl peptidase-4 (DPP-4) inhibitors played a role in the management of diabetes, obesity, and cancer. This study describes the development of field template, field-based qualitative structure⁻activity relationship (SAR) model demonstrating DPP-4 inhibitors of natural origin, and the same model is used to screen virtually focused food database composed of polyphenols as potential DPP-4 inhibitors. Compounds’ similarity to field template, and novelty score “high and very high”, were used as primary criteria to identify novel DPP-4 inhibitors. Molecular docking simulations were performed on the resulting natural compounds using FlexX algorithm. Finally, one natural compound, chrysin, was chosen to be evaluated experimentally to demonstrate the applicability of constructed SAR model. This study provides the molecular insights necessary in the discovery of new leads as DPP-4 inhibitors, to improve the potency of existing DPP-4 natural inhibitors.

A web-based platform for virtual screening.

PubMed

Watson, Paul; Verdonk, Marcel; Hartshorn, Michael J

2003-09-01

A fully integrated, web-based, virtual screening platform has been developed to allow rapid virtual screening of large numbers of compounds. ORACLE is used to store information at all stages of the process. The system includes a large database of historical compounds from high throughput screenings (HTS) chemical suppliers, ATLAS, containing over 3.1 million unique compounds with their associated physiochemical properties (ClogP, MW, etc.). The database can be screened using a web-based interface to produce compound subsets for virtual screening or virtual library (VL) enumeration. In order to carry out the latter task within ORACLE a reaction data cartridge has been developed. Virtual libraries can be enumerated rapidly using the web-based interface to the cartridge. The compound subsets can be seamlessly submitted for virtual screening experiments, and the results can be viewed via another web-based interface allowing ad hoc querying of the virtual screening data stored in ORACLE.

In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

NASA Astrophysics Data System (ADS)

Menichetti, Roberto; Kanekal, Kiran H.; Kremer, Kurt; Bereau, Tristan

2017-09-01

The partitioning of small molecules in cell membranes—a key parameter for pharmaceutical applications—typically relies on experimentally available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics-based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400 000 compounds. The potential of mean force hereby becomes an easily accessible quantity—already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules.

Heme and menaquinone induced electron transport in lactic acid bacteria.

PubMed

Brooijmans, Rob; Smit, Bart; Santos, Filipe; van Riel, Jan; de Vos, Willem M; Hugenholtz, Jeroen

2009-05-29

For some lactic acid bacteria higher biomass production as a result of aerobic respiration has been reported upon supplementation with heme and menaquinone. In this report, we have studied a large number of species among lactic acid bacteria for the existence of this trait. Heme- (and menaquinone) stimulated aerobic growth was observed for several species and genera of lactic acid bacteria. These include Lactobacillus plantarum, Lactobacillus rhamnosus, Lactobacilllus brevis, Lactobacillus paralimentarius, Streptococcus entericus and Lactococcus garviae. The increased biomass production without further acidification, which are respiration associated traits, are suitable for high-throughput screening as demonstrated by the screening of 8000 Lactococcus lactis insertion mutants. Respiration-negative insertion-mutants were found with noxA, bd-type cytochrome and menaquinol biosynthesis gene-disruptions. Phenotypic screening and in silico genome analysis suggest that respiration can be considered characteristic for certain species. We propose that the cyd-genes were present in the common ancestor of lactic acid bacteria, and that multiple gene-loss events best explains the observed distribution of these genes among the species.

Educational websites--Bioinformatics Tools II.

PubMed

Lomberk, Gwen

2009-01-01

In this issue, the highlighted websites are a continuation of a series of educational websites; this one in particular from a couple of years ago, Bioinformatics Tools [Pancreatology 2005;5:314-315]. These include sites that are valuable resources for many research needs in genomics and proteomics. Bioinformatics has become a laboratory tool to map sequences to databases, develop models of molecular interactions, evaluate structural compatibilities, describe differences between normal and disease-associated DNA, identify conserved motifs within proteins, and chart extensive signaling networks, all in silico. Copyright 2008 S. Karger AG, Basel and IAP.

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.

Report from the EPAA workshop: in vitro ADME in safety testing used by EPAA industry sectors.

PubMed

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

2011-04-01

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

Prediction of pH dependent absorption using in vitro, in silico, and in vivo rat models: Early liability assessment during lead optimization.

PubMed

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

2015-08-30

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

FlyRNAi.org—the database of the Drosophila RNAi screening center and transgenic RNAi project: 2017 update

PubMed Central

Hu, Yanhui; Comjean, Aram; Roesel, Charles; Vinayagam, Arunachalam; Flockhart, Ian; Zirin, Jonathan; Perkins, Lizabeth; Perrimon, Norbert; Mohr, Stephanie E.

2017-01-01

The FlyRNAi database of the Drosophila RNAi Screening Center (DRSC) and Transgenic RNAi Project (TRiP) at Harvard Medical School and associated DRSC/TRiP Functional Genomics Resources website (http://fgr.hms.harvard.edu) serve as a reagent production tracking system, screen data repository, and portal to the community. Through this portal, we make available protocols, online tools, and other resources useful to researchers at all stages of high-throughput functional genomics screening, from assay design and reagent identification to data analysis and interpretation. In this update, we describe recent changes and additions to our website, database and suite of online tools. Recent changes reflect a shift in our focus from a single technology (RNAi) and model species (Drosophila) to the application of additional technologies (e.g. CRISPR) and support of integrated, cross-species approaches to uncovering gene function using functional genomics and other approaches. PMID:27924039

Mammography usage with relevant factors among women with mental disabilities in Taiwan: a nationwide population-based study.

PubMed

Yen, Suh-May; Kung, Pei-Tseng; Tsai, Wen-Chen

2015-02-01

Women with mental illness are at increased risk of developing and dying from breast cancer and are thus in urgent need of breast cancer preventive care. This study examined the use of screening mammography by Taiwanese women with mental disabilities and analyzed factors affecting this use. 17,243 Taiwanese women with mental disabilities aged 50-69 years were retrospectively included as study subjects. Linked patient data were obtained from three national databases in Taiwan (the 2008 database of physically and mentally disabled persons, the Health Promotion Administration's 2007-2008 mammography screening data, and claims data from the National Health Insurance Research Database). Besides descriptive statistics and bivariate analysis, logistic regression analysis was also performed to examine factors affecting screening mammography use. The 2007-2008 mammography screening rate for Taiwanese women with mental disabilities was 8.79% (n=1515). Variables that significantly influenced screening use were income, education, presence of catastrophic illness/injury, severity of mental disability, and usage of other preventive care services. Screening was positively correlated with income and education. Those with catastrophic illness/injury were more likely to be screened (odds ratio [OR], 1.40; 95% CI=1.15-1.72). Severity of disability was negatively correlated with screening, with very severe, severe, and moderate disability being associated with 0.34-0.69 times the odds of screening as mild disability. In Taiwan, women with mental disabilities receive far less mammography screening than women in general. Copyright © 2014 Elsevier Ltd. All rights reserved.

An integrated computational approach can classify VHL missense mutations according to risk of clear cell renal carcinoma

PubMed Central

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

2014-01-01

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

[In silico identification of molecular mimicry between T-cell epitopes of Neisseria meningitidis B and the human proteome].

PubMed

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

2013-07-01

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

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

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.

In silico and experimental methods revealed highly diverse bacteria with quorum sensing and aromatics biodegradation systems--a potential broad application on bioremediation.

PubMed

Huang, Yili; Zeng, Yanhua; Yu, Zhiliang; Zhang, Jing; Feng, Hao; Lin, Xiuchun

2013-11-01

Phylogenetic overlaps between aromatics-degrading bacteria and acyl-homoserine-lactone (AHL) or autoinducer (AI) based quorum-sensing (QS) bacteria were evident in literatures; however, the diversity of bacteria with both activities had never been finely described. In-silico searching in NCBI genome database revealed that more than 11% of investigated population harbored both aromatic ring-hydroxylating-dioxygenase (RHD) gene and AHL/AI-synthetase gene. These bacteria were distributed in 10 orders, 15 families, 42 genus and 78 species. Horizontal transfers of both genes were common among them. Using enrichment and culture dependent method, 6 Sphingomonadales and 4 Rhizobiales with phenanthrene- or pyrene-degrading ability and AHL-production were isolated from marine, wetland and soil samples. Thin-layer-chromatography and gas-chromatography-mass-spectrum revealed that these Sphingomonads produced various AHL molecules. This is the first report of highly diverse bacteria that harbored both aromatics-degrading and QS systems. QS regulation may have broad impacts on aromatics biodegradation, and would be a new angle for developing bioremediation technology. Copyright © 2013 Elsevier Ltd. All rights reserved.

In silico serine β-lactamases analysis reveals a huge potential resistome in environmental and pathogenic species.

PubMed

Brandt, Christian; Braun, Sascha D; Stein, Claudia; Slickers, Peter; Ehricht, Ralf; Pletz, Mathias W; Makarewicz, Oliwia

2017-02-24

The secretion of antimicrobial compounds is an ancient mechanism with clear survival benefits for microbes competing with other microorganisms. Consequently, mechanisms that confer resistance are also ancient and may represent an underestimated reservoir in environmental bacteria. In this context, β-lactamases (BLs) are of great interest due to their long-term presence and diversification in the hospital environment, leading to the emergence of Gram-negative pathogens that are resistant to cephalosporins (extended spectrum BLs = ESBLs) and carbapenems (carbapenemases). In the current study, protein sequence databases were used to analyze BLs, and the results revealed a substantial number of unknown and functionally uncharacterized BLs in a multitude of environmental and pathogenic species. Together, these BLs represent an uncharacterized reservoir of potentially transferable resistance genes. Considering all available data, in silico approaches appear to more adequately reflect a given resistome than analyses of limited datasets. This approach leads to a more precise definition of BL clades and conserved motifs. Moreover, it may support the prediction of new resistance determinants and improve the tailored development of robust molecular diagnostics.

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

PubMed Central

An, Gary; Bartels, John; Vodovotz, Yoram

2011-01-01

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. PMID:21552346

An in-silico investigation of anti-Chagas phytochemicals.

PubMed

McCulley, Stephanie F; Setzer, William N

2014-01-01

Over 18 million people in tropical and subtropical America are afflicted by American trypanosomiasis or Chagas disease. In humans, symptoms of the disease include fever, swelling, and heart and brain damage, usually leading to death. There is currently no effective treatment for this disease. Plant products continue to be rich sources of clinically useful drugs, and the biodiversity of the Neotropics suggests great phytomedicinal potential. Screening programs have revealed numerous plant species and phytochemical agents that have shown in-vitro or in-vivo antitrypanosomal activity, but the biochemical targets of these phytochemicals are not known. In this work, we present a molecular docking analysis of Neotropical phytochemicals, which have already demonstrated antiparasitic activity against Trypanosoma cruzi, with potential druggable protein targets of the parasite. Several protein targets showed in-silico selectivity for trypanocidal phytochemicals, including trypanothione reductase, pteridine reductase 2, lipoamide dehydrogenase, glucokinase, dihydroorotate dehydrogenase, cruzain, dihydrofolate-reductase/thymidylate-synthase, and farnesyl diphosphate synthase. Some of the phytochemical ligands showed notable docking preference for trypanothione reductase, including flavonoids, fatty-acid-derived oxygenated hydrocarbons, geranylgeraniol and the lignans ganschisandrine and eupomatenoid-6.

Natural Compounds for the Treatment of Psoriatic Arthritis: A Proposal Based on Multi-Targeted Osteoclastic Regulation and on a Preclinical Study.

PubMed

Deng, Shiqiang; Cheng, Jianwen; Zhao, Jinmin; Yao, Felix; Xu, Jiake

2017-07-11

Psoriatic arthritis (PsA) is a chronic inflammatory arthritis affecting approximately 2% to 3% of the population globally, and is characterized by both peripheral articular manifestations and axial skeletal involvement. Conventional therapies for PsA have not been fully satisfactory, though natural products (NPs) have been shown to be highly effective and represent important treatment options for psoriasis. PsA is a multigenic autoimmune disease with both environmental and genetic factors contributing to its pathogenesis. Accordingly, it is likely that the use of natural compounds with a multi-targeted approach will enable us to develop better therapies for PsA and related disorders. PsA, either on joint damage or on bone erosion, has been shown to respond to anti-psoriatic pharmacotherapy (APP), APP-like NPs, and their natural compounds. This study aims to uncover specific natural compounds for improved PsA remedies. Specifically, by targeting bone erosion caused by increased osteoclastic bone resorption, we aim to predict the key signaling pathways affected by natural compounds. Further, the study will explore their anti-arthritis effects using an in silico, in vitro, and in vivo approach. Following the signaling pathway prediction, a preclinical efficacy study on animal models will be undertaken. Collectively, this work will discover lead compounds with improved therapeutic effects on PsA. We hypothesize that 9 potential APP-like NPs will have therapeutic effects on arthritis via the modulation of osteoclast bone resorption and signaling pathways. For in silico identification, the Latin name of each NP will be identified using the Encyclopedia of Traditional Chinese Medicine (Encyclopedia of TCM). The biological targets of NPs will be predicted or screened using the Herbal Ingredients' Targets (HIT) database. With the designed search terms, DrugBank will be used to further filter the above biological targets. Protein ANnotation THrough Evolutionary Relationship (PANTHER) will be used to predict the pathways of the natural compound sources. Subsequently, an in vitro sample preparation including extraction, fractionation, isolation, purification, and bioassays with high-speed counter-current chromatography-high-performance liquid chromatography-diode array detection (HSCCC-HPLC-DAD) will be carried out for each identified natural source. In vitro investigations into the effect of NPs on osteoclast signaling pathways will be performed. The experimental methods include cell viability assays, osteoclastogenesis and resorption pit assays, quantitative reverse transcription polymerase chain reaction (RT-PCR), western blot, and luciferase reporter gene assays. Finally, an in vivo preclinical efficacy on a collagen-induced arthritis rat model will be carried out using a treatment group (n=10), a control group (n=10), and a non-arthritis group (n=10). Main outcome measure assessments during intervention include daily macroscopic scores and a digital calipers measurement. Post-treatment tissue measurements will be analyzed by serological testing, radiographic imaging, and histopathological assessment. Studies are currently underway to evaluate the in silico data and the in vitro effects of compounds on osteoclastogenesis and bone resorption. The preclinical study is expected to start a year following completion of the in silico analysis. The in silico rapid approach is proposed as a more general method for adding value to the results of a systematic review of NPs. More importantly, the proposed study builds on a multi-targeted approach for the identification of natural compounds for future drug discovery. This innovative approach is likely to be more precise, efficient, and compatible to identify the novel natural compounds for effective treatment of PsA. ©Shiqiang Deng, Jianwen Cheng, Jinmin Zhao, Felix Yao, Jiake Xu. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 11.07.2017.

Design and exploration of semiconductors from first principles: A review of recent advances

NASA Astrophysics Data System (ADS)

Oba, Fumiyasu; Kumagai, Yu

2018-06-01

Recent first-principles approaches to semiconductors are reviewed, with an emphasis on theoretical insight into emerging materials and in silico exploration of as-yet-unreported materials. As relevant theory and methodologies have developed, along with computer performance, it is now feasible to predict a variety of material properties ab initio at the practical level of accuracy required for detailed understanding and elaborate design of semiconductors; these material properties include (i) fundamental bulk properties such as band gaps, effective masses, dielectric constants, and optical absorption coefficients; (ii) the properties of point defects, including native defects, residual impurities, and dopants, such as donor, acceptor, and deep-trap levels, and formation energies, which determine the carrier type and density; and (iii) absolute and relative band positions, including ionization potentials and electron affinities at semiconductor surfaces, band offsets at heterointerfaces between dissimilar semiconductors, and Schottky barrier heights at metal–semiconductor interfaces, which are often discussed systematically using band alignment or lineup diagrams. These predictions from first principles have made it possible to elucidate the characteristics of semiconductors used in industry, including group III–V compounds such as GaN, GaP, and GaAs and their alloys with related Al and In compounds; amorphous oxides, represented by In–Ga–Zn–O transparent conductive oxides (TCOs), represented by In2O3, SnO2, and ZnO; and photovoltaic absorber and buffer layer materials such as CdTe and CdS among group II–VI compounds and chalcopyrite CuInSe2, CuGaSe2, and CuIn1‑ x Ga x Se2 (CIGS) alloys, in addition to the prototypical elemental semiconductors Si and Ge. Semiconductors attracting renewed or emerging interest have also been investigated, for instance, divalent tin compounds, including SnO and SnS; wurtzite-derived ternary compounds such as ZnSnN2 and CuGaO2; perovskite oxides such as SrTiO3 and BaSnO3; and organic–inorganic hybrid perovskites, represented by CH3NH3PbI3. Moreover, the deployment of first-principles calculations allows us to predict the crystal structure, stability, and properties of as-yet-unreported materials. Promising materials have been explored via high-throughput screening within either publicly available computational databases or unexplored composition and structure space. Reported examples include the identification of nitride semiconductors, TCOs, solar cell photoabsorber materials, and photocatalysts, some of which have been experimentally verified. Machine learning in combination with first-principles calculations has emerged recently as a technique to accelerate and enhance in silico screening. A blend of computation and experimentation with data science toward the development of materials is often referred to as materials informatics and is currently attracting growing interest.

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.

A novel in silico approach to drug discovery via computational intelligence.

PubMed

Hecht, David; Fogel, Gary B

2009-04-01

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

In silico screening of carbon-capture materials

NASA Astrophysics Data System (ADS)

Lin, Li-Chiang; Berger, Adam H.; Martin, Richard L.; Kim, Jihan; Swisher, Joseph A.; Jariwala, Kuldeep; Rycroft, Chris H.; Bhown, Abhoyjit S.; Deem, Michael W.; Haranczyk, Maciej; Smit, Berend

2012-07-01

One of the main bottlenecks to deploying large-scale carbon dioxide capture and storage (CCS) in power plants is the energy required to separate the CO2 from flue gas. For example, near-term CCS technology applied to coal-fired power plants is projected to reduce the net output of the plant by some 30% and to increase the cost of electricity by 60-80%. Developing capture materials and processes that reduce the parasitic energy imposed by CCS is therefore an important area of research. We have developed a computational approach to rank adsorbents for their performance in CCS. Using this analysis, we have screened hundreds of thousands of zeolite and zeolitic imidazolate framework structures and identified many different structures that have the potential to reduce the parasitic energy of CCS by 30-40% compared with near-term technologies.

Database for High Throughput Screening Hits (dHITS): a simple tool to retrieve gene specific phenotypes from systematic screens done in yeast.

PubMed

Chuartzman, Silvia G; Schuldiner, Maya

2018-03-25

In the last decade several collections of Saccharomyces cerevisiae yeast strains have been created. In these collections every gene is modified in a similar manner such as by a deletion or the addition of a protein tag. Such libraries have enabled a diversity of systematic screens, giving rise to large amounts of information regarding gene functions. However, often papers describing such screens focus on a single gene or a small set of genes and all other loci affecting the phenotype of choice ('hits') are only mentioned in tables that are provided as supplementary material and are often hard to retrieve or search. To help unify and make such data accessible, we have created a Database of High Throughput Screening Hits (dHITS). The dHITS database enables information to be obtained about screens in which genes of interest were found as well as the other genes that came up in that screen - all in a readily accessible and downloadable format. The ability to query large lists of genes at the same time provides a platform to easily analyse hits obtained from transcriptional analyses or other screens. We hope that this platform will serve as a tool to facilitate investigation of protein functions to the yeast community. © 2018 The Authors Yeast Published by John Wiley & Sons Ltd.

An integrated molecular docking and rescoring method for predicting the sensitivity spectrum of various serine hydrolases to organophosphorus pesticides.

PubMed

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

2016-04-01

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

Discovery of DPP IV inhibitors by pharmacophore modeling and QSAR analysis followed by in silico screening.

PubMed

Al-Masri, Ihab M; Mohammad, Mohammad K; Taha, Mutasem O

2008-11-01

Dipeptidyl peptidase IV (DPP IV) deactivates the natural hypoglycemic incretin hormones. Inhibition of this enzyme should restore glucose homeostasis in diabetic patients making it an attractive target for the development of new antidiabetic drugs. With this in mind, the pharmacophoric space of DPP IV was explored using a set of 358 known inhibitors. Thereafter, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that yield selfconsistent and predictive quantitative structure-activity relationships (QSAR) (r(2) (287)=0.74, F-statistic=44.5, r(2) (BS)=0.74, r(2) (LOO)=0.69, r(2) (PRESS) against 71 external testing inhibitors=0.51). Two orthogonal pharmacophores (of cross-correlation r(2)=0.23) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within the DPP IV binding pocket. Docking experiments supported the binding modes suggested by QSAR/pharmacophore analyses. The validity of the QSAR equation and the associated pharmacophore models were established by the identification of new low-micromolar anti-DPP IV leads retrieved by in silico screening. One of our interesting potent anti-DPP IV hits is the fluoroquinolone gemifloxacin (IC(50)=1.12 muM). The fact that gemifloxacin was recently reported to potently inhibit the prodiabetic target glycogen synthase kinase 3beta (GSK-3beta) suggests that gemifloxacin is an excellent lead for the development of novel dual antidiabetic inhibitors against DPP IV and GSK-3beta.

DOVIS: an implementation for high-throughput virtual screening using AutoDock.

PubMed

Zhang, Shuxing; Kumar, Kamal; Jiang, Xiaohui; Wallqvist, Anders; Reifman, Jaques

2008-02-27

Molecular-docking-based virtual screening is an important tool in drug discovery that is used to significantly reduce the number of possible chemical compounds to be investigated. In addition to the selection of a sound docking strategy with appropriate scoring functions, another technical challenge is to in silico screen millions of compounds in a reasonable time. To meet this challenge, it is necessary to use high performance computing (HPC) platforms and techniques. However, the development of an integrated HPC system that makes efficient use of its elements is not trivial. We have developed an application termed DOVIS that uses AutoDock (version 3) as the docking engine and runs in parallel on a Linux cluster. DOVIS can efficiently dock large numbers (millions) of small molecules (ligands) to a receptor, screening 500 to 1,000 compounds per processor per day. Furthermore, in DOVIS, the docking session is fully integrated and automated in that the inputs are specified via a graphical user interface, the calculations are fully integrated with a Linux cluster queuing system for parallel processing, and the results can be visualized and queried. DOVIS removes most of the complexities and organizational problems associated with large-scale high-throughput virtual screening, and provides a convenient and efficient solution for AutoDock users to use this software in a Linux cluster platform.

probeBase—an online resource for rRNA-targeted oligonucleotide probes and primers: new features 2016

PubMed Central

Greuter, Daniel; Loy, Alexander; Horn, Matthias; Rattei, Thomas

2016-01-01

probeBase http://www.probebase.net is a manually maintained and curated database of rRNA-targeted oligonucleotide probes and primers. Contextual information and multiple options for evaluating in silico hybridization performance against the most recent rRNA sequence databases are provided for each oligonucleotide entry, which makes probeBase an important and frequently used resource for microbiology research and diagnostics. Here we present a major update of probeBase, which was last featured in the NAR Database Issue 2007. This update describes a complete remodeling of the database architecture and environment to accommodate computationally efficient access. Improved search functions, sequence match tools and data output now extend the opportunities for finding suitable hierarchical probe sets that target an organism or taxon at different taxonomic levels. To facilitate the identification of complementary probe sets for organisms represented by short rRNA sequence reads generated by amplicon sequencing or metagenomic analysis with next generation sequencing technologies such as Illumina and IonTorrent, we introduce a novel tool that recovers surrogate near full-length rRNA sequences for short query sequences and finds matching oligonucleotides in probeBase. PMID:26586809

MouseNet database: digital management of a large-scale mutagenesis project.

PubMed

Pargent, W; Heffner, S; Schäble, K F; Soewarto, D; Fuchs, H; Hrabé de Angelis, M

2000-07-01

The Munich ENU Mouse Mutagenesis Screen is a large-scale mutant production, phenotyping, and mapping project. It encompasses two animal breeding facilities and a number of screening groups located in the general area of Munich. A central database is required to manage and process the immense amount of data generated by the mutagenesis project. This database, which we named MouseNet(c), runs on a Sybase platform and will finally store and process all data from the entire project. In addition, the system comprises a portfolio of functions needed to support the workflow management of the core facility and the screening groups. MouseNet(c) will make all of the data available to the participating screening groups, and later to the international scientific community. MouseNet(c) will consist of three major software components:* Animal Management System (AMS)* Sample Tracking System (STS)* Result Documentation System (RDS)MouseNet(c) provides the following major advantages:* being accessible from different client platforms via the Internet* being a full-featured multi-user system (including access restriction and data locking mechanisms)* relying on a professional RDBMS (relational database management system) which runs on a UNIX server platform* supplying workflow functions and a variety of plausibility checks.

Large-scale virtual screening on public cloud resources with Apache Spark.

PubMed

Capuccini, Marco; Ahmed, Laeeq; Schaal, Wesley; Laure, Erwin; Spjuth, Ola

2017-01-01

Structure-based virtual screening is an in-silico method to screen a target receptor against a virtual molecular library. Applying docking-based screening to large molecular libraries can be computationally expensive, however it constitutes a trivially parallelizable task. Most of the available parallel implementations are based on message passing interface, relying on low failure rate hardware and fast network connection. Google's MapReduce revolutionized large-scale analysis, enabling the processing of massive datasets on commodity hardware and cloud resources, providing transparent scalability and fault tolerance at the software level. Open source implementations of MapReduce include Apache Hadoop and the more recent Apache Spark. We developed a method to run existing docking-based screening software on distributed cloud resources, utilizing the MapReduce approach. We benchmarked our method, which is implemented in Apache Spark, docking a publicly available target receptor against [Formula: see text]2.2 M compounds. The performance experiments show a good parallel efficiency (87%) when running in a public cloud environment. Our method enables parallel Structure-based virtual screening on public cloud resources or commodity computer clusters. The degree of scalability that we achieve allows for trying out our method on relatively small libraries first and then to scale to larger libraries. Our implementation is named Spark-VS and it is freely available as open source from GitHub (https://github.com/mcapuccini/spark-vs).Graphical abstract.

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors.

PubMed

Martinez-Rosell, Gerard; Harvey, Matt J; De Fabritiis, Gianni

2018-03-26

Fragment-based drug discovery (FBDD) has become a mainstream approach in drug design because it allows the reduction of the chemical space and screening libraries while identifying fragments with high protein-ligand efficiency interactions that can later be grown into drug-like leads. In this work, we leverage high-throughput molecular dynamics (MD) simulations to screen a library of 129 fragments for a total of 5.85 ms against the CXCL12 monomer, a chemokine involved in inflammation and diseases such as cancer. Our in silico binding assay was able to recover binding poses, affinities, and kinetics for the selected library and was able to predict 8 mM-affinity fragments with ligand efficiencies higher than 0.3. All of the fragment hits present a similar chemical structure, with a hydrophobic core and a positively charged group, and bind to either sY7 or H1S68 pockets, where they share pharmacophoric properties with experimentally resolved natural binders. This work presents a large-scale screening assay using an exclusive combination of thousands of short MD adaptive simulations analyzed with a Markov state model (MSM) framework.

An in vitro screening cascade to identify neuroprotective antioxidants in ALS

PubMed Central

Barber, Siân C.; Higginbottom, Adrian; Mead, Richard J.; Barber, Stuart; Shaw, Pamela J.

2009-01-01

Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease, characterized by progressive dysfunction and death of motor neurons. Although evidence for oxidative stress in ALS pathogenesis is well described, antioxidants have generally shown poor efficacy in animal models and human clinical trials. We have developed an in vitro screening cascade to identify antioxidant molecules capable of rescuing NSC34 motor neuron cells expressing an ALS-associated mutation of superoxide dismutase 1. We have tested known antioxidants and screened a library of 2000 small molecules. The library screen identified 164 antioxidant molecules, which were refined to the 9 most promising molecules in subsequent experiments. Analysis of the in silico properties of hit compounds and a review of published literature on their in vivo effectiveness have enabled us to systematically identify molecules with antioxidant activity combined with chemical properties necessary to penetrate the central nervous system. The top-performing molecules identified include caffeic acid phenethyl ester, esculetin, and resveratrol. These compounds were tested for their ability to rescue primary motor neuron cultures after trophic factor withdrawal, and the mechanisms of action of their antioxidant effects were investigated. Subsequent in vivo studies can be targeted using molecules with the greatest probability of success. PMID:19439221

Screening and prioritization of micropollutants in wastewaters from on-site sewage treatment facilities.

PubMed

Gros, Meritxell; Blum, Kristin M; Jernstedt, Henrik; Renman, Gunno; Rodríguez-Mozaz, Sara; Haglund, Peter; Andersson, Patrik L; Wiberg, Karin; Ahrens, Lutz

2017-04-15

A comprehensive screening of micropollutants was performed in wastewaters from on-site sewage treatment facilities (OSSFs) and urban wastewater treatment plants (WWTPs) in Sweden. A suspect screening approach, using high resolution mass spectrometry, was developed and used in combination with target analysis. With this strategy, a total number of 79 micropollutants were successfully identified, which belong to the groups of per- and polyfluoroalkyl substances (PFASs), pesticides, phosphorus-containing flame retardants (PFRs) and pharmaceuticals and personal care products (PPCPs). Results from this screening indicate that concentrations of micropollutants are similar in influents and effluents of OSSFs and WWTPs, respectively. Removal efficiencies of micropollutants were assessed in the OSSFs and compared with those observed in WWTPs. In general, removal of PFASs and PFRs was higher in package treatment OSSFs, which are based on biological treatments, while removal of PPCPs was more efficient in soil bed OSSFs. A novel comprehensive prioritization strategy was then developed to identify OSSF specific chemicals of environmental relevance. The strategy was based on the compound concentrations in the wastewater, removal efficiency, frequency of detection in OSSFs and on in silico based data for toxicity, persistency and bioaccumulation potential. Copyright © 2016 Elsevier B.V. All rights reserved.

Ligand screening systems for human glucose transporters as tools in drug discovery

NASA Astrophysics Data System (ADS)

Schmidl, Sina; Iancu, Cristina V.; Choe, Jun-yong; Oreb, Mislav

2018-05-01

Hexoses are the major source of energy and carbon skeletons for biosynthetic processes in all kingdoms of life. Their cellular uptake is mediated by specialized transporters, including glucose transporters (GLUT, SLC2 gene family). Malfunction or altered expression pattern of GLUTs in humans is associated with several widespread diseases including cancer, diabetes and severe metabolic disorders. Their high relevance in the medical area makes these transporters valuable drug targets and potential biomarkers. Nevertheless, the lack of a suitable high-throughput screening system has impeded the determination of compounds that would enable specific manipulation of GLUTs so far. Availability of structural data on several GLUTs enabled in silico ligand screening, though limited by the fact that only two major conformations of the transporters can be tested. Recently, convenient high-throughput microbial and cell-free screening systems have been developed. These remarkable achievements set the foundation for further and detailed elucidation of the molecular mechanisms of glucose transport and will also lead to great progress in the discovery of GLUT effectors as therapeutic agents. In this mini-review, we focus on recent efforts to identify potential GLUT-targeting drugs, based on a combination of structural biology and different assay systems.

Virtual Screening and Pharmacophore Design for a Novel Theoretical Inhibitor of Macrophage Stimulating Factor as a Metastatic Agent

PubMed Central

Torktaz, Ibrahim; Mohamadhashem, Faezeh; Esmaeili, Abolghasem; Behjati, Mohaddeseh; Sharifzadeh, Sara

2013-01-01

Introduction: Metastasis is a crucial aspect of cancer. Macrophage stimulating protein (MSP) is a single chain protein and can be cleaved by serum proteases. MSP has several roles in metastasis. In this in silico study, MSP as a metastatic agent was considered as a drug target. Methods: Crystallographic structure of MSP was retrieved from protein data bank. To find a chemical inhibitor of MSP, a library of KEGG compounds was screened and 1000 shape complemented ligands were retrieved with FindSite algorithm. Molegro Virtual Docker (MVD) software was used for docking simulation of shape complemented ligands against MSP. Moldock score was used as scoring function for virtual screening and potential inhibitors with more negative binding energy were obtained. PLANS scoring function was used for revaluation of virtual screening data. Results: The top found chemical had binding affinity of -183.55 based on MolDock score and equal to -66.733 PLANTs score to MSP structure. Conclusion: Based on pharmacophore model of potential inhibitor, this study suggests that the chemical which was found in this research and its derivate can be used for subsequent laboratory studies. PMID:24163807

Computational discovery of putative quorum sensing inhibitors against LasR and RhlR receptor proteins of Pseudomonas aeruginosa

NASA Astrophysics Data System (ADS)

Annapoorani, Angusamy; Umamageswaran, Venugopal; Parameswari, Radhakrishnan; Pandian, Shunmugiah Karutha; Ravi, Arumugam Veera

2012-09-01

Drugs have been discovered in the past mainly either by identification of active components from traditional remedies or by unpredicted discovery. A key motivation for the study of structure based virtual screening is the exploitation of such information to design targeted drugs. In this study, structure based virtual screening was used in search for putative quorum sensing inhibitors (QSI) of Pseudomonas aeruginosa. The virtual screening programme Glide version 5.5 was applied to screen 1,920 natural compounds/drugs against LasR and RhlR receptor proteins of P. aeruginosa. Based on the results of in silico docking analysis, five top ranking compounds namely rosmarinic acid, naringin, chlorogenic acid, morin and mangiferin were subjected to in vitro bioassays against laboratory strain PAO1 and two more antibiotic resistant clinical isolates, P. aeruginosa AS1 (GU447237) and P. aeruginosa AS2 (GU447238). Among the five compounds studied, except mangiferin other four compounds showed significant inhibition in the production of protease, elastase and hemolysin. Further, all the five compounds potentially inhibited the biofilm related behaviours. This interaction study provided promising ligands to inhibit the quorum sensing (QS) mediated virulence factors production in P. aeruginosa.

Vitual screening and binding mode elucidation of curcumin analogues on Cyclooxygenase-2 using AYO_COX2_V1.1 protocol

NASA Astrophysics Data System (ADS)

Mulatsari, E.; Mumpuni, E.; Herfian, A.

2017-05-01

Curcumin is yellow colored phenolic compounds contained in Curcuma longa. Curcumin is known to have biological activities as anti-inflammatory, antiviral, antioxidant, and anti-infective agent [1]. Synthesis of curcumin analogue compounds has been done and some of them had biological activity like curcumin. In this research, the virtual screening of curcumin analogue compounds has been conducted. The purpose of this research was to determine the activity of these compounds as selective Cyclooxygenase-2inhibitors in in-silico. Binding mode elucidation was made by active and inactive representative compounds to see the interaction of the amino acids in the binding site of the compounds. This research used AYO_COX2_V.1.1, a structure-based virtual screening protocol (SBVS) that has been validated by Mumpuni E et al, 2014 [2]. AYO_COX2_V.1.1 protocol using a variety of integrated applications such as SPORES, PLANTS, BKchem, OpenBabel and PyMOL. The results of virtual screening conducted on 49 curcumin analogue compounds obtained 8 compounds with 4 active amino acid residues (GLY340, ILE503, PHE343, and PHE367) that were considered active as COX-2 inhibitor.

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

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.

Database Dictionary for Ethiopian National Ground-Water DAtabase (ENGDA) Data Fields

USGS Publications Warehouse

Kuniansky, Eve L.; Litke, David W.; Tucci, Patrick

2007-01-01

Introduction This document describes the data fields that are used for both field forms and the Ethiopian National Ground-water Database (ENGDA) tables associated with information stored about production wells, springs, test holes, test wells, and water level or water-quality observation wells. Several different words are used in this database dictionary and in the ENGDA database to describe a narrow shaft constructed in the ground. The most general term is borehole, which is applicable to any type of hole. A well is a borehole specifically constructed to extract water from the ground; however, for this data dictionary and for the ENGDA database, the words well and borehole are used interchangeably. A production well is defined as any well used for water supply and includes hand-dug wells, small-diameter bored wells equipped with hand pumps, or large-diameter bored wells equipped with large-capacity motorized pumps. Test holes are borings made to collect information about the subsurface with continuous core or non-continuous core and/or where geophysical logs are collected. Test holes are not converted into wells. A test well is a well constructed for hydraulic testing of an aquifer in order to plan a larger ground-water production system. A water-level or water-quality observation well is a well that is used to collect information about an aquifer and not used for water supply. A spring is any naturally flowing, local, ground-water discharge site. The database dictionary is designed to help define all fields on both field data collection forms (provided in attachment 2 of this report) and for the ENGDA software screen entry forms (described in Litke, 2007). The data entered into each screen entry field are stored in relational database tables within the computer database. The organization of the database dictionary is designed based on field data collection and the field forms, because this is what the majority of people will use. After each field, however, the ENGDA database field name and relational database table is designated; along with the ENGDA screen entry form(s) and the ENGDA field form (attachment 2). The database dictionary is separated into sections. The first section, Basic Site Data Fields, describes the basic site information that is similar for all of the different types of sites. The remaining sections may be applicable for only one type of site; for example, the Well Drilling and Construction Data Fields and Lithologic Description Data Fields are applicable to boreholes and not to springs. Attachment 1 contains a table for conversion from English to metric units. Attachment 2 contains selected field forms used in conjunction with ENGDA. A separate document, 'Users Reference Manual for the Ethiopian National Ground-Water DAtabase (ENGDA),' by David W. Litke was developed as a users guide for the computer database and screen entry. This database dictionary serves as a reference for both the field forms and the computer database. Every effort has been made to have identical field names between the field forms and the screen entry forms in order to avoid confusion.

DR HAGIS-a fundus image database for the automatic extraction of retinal surface vessels from diabetic patients.

PubMed

Holm, Sven; Russell, Greg; Nourrit, Vincent; McLoughlin, Niall

2017-01-01

A database of retinal fundus images, the DR HAGIS database, is presented. This database consists of 39 high-resolution color fundus images obtained from a diabetic retinopathy screening program in the UK. The NHS screening program uses service providers that employ different fundus and digital cameras. This results in a range of different image sizes and resolutions. Furthermore, patients enrolled in such programs often display other comorbidities in addition to diabetes. Therefore, in an effort to replicate the normal range of images examined by grading experts during screening, the DR HAGIS database consists of images of varying image sizes and resolutions and four comorbidity subgroups: collectively defined as the diabetic retinopathy, hypertension, age-related macular degeneration, and Glaucoma image set (DR HAGIS). For each image, the vasculature has been manually segmented to provide a realistic set of images on which to test automatic vessel extraction algorithms. Modified versions of two previously published vessel extraction algorithms were applied to this database to provide some baseline measurements. A method based purely on the intensity of images pixels resulted in a mean segmentation accuracy of 95.83% ([Formula: see text]), whereas an algorithm based on Gabor filters generated an accuracy of 95.71% ([Formula: see text]).

Molecular scaffold analysis of natural products databases in the public domain.

PubMed

Yongye, Austin B; Waddell, Jacob; Medina-Franco, José L

2012-11-01

Natural products represent important sources of bioactive compounds in drug discovery efforts. In this work, we compiled five natural products databases available in the public domain and performed a comprehensive chemoinformatic analysis focused on the content and diversity of the scaffolds with an overview of the diversity based on molecular fingerprints. The natural products databases were compared with each other and with a set of molecules obtained from in-house combinatorial libraries, and with a general screening commercial library. It was found that publicly available natural products databases have different scaffold diversity. In contrast to the common concept that larger libraries have the largest scaffold diversity, the largest natural products collection analyzed in this work was not the most diverse. The general screening library showed, overall, the highest scaffold diversity. However, considering the most frequent scaffolds, the general reference library was the least diverse. In general, natural products databases in the public domain showed low molecule overlap. In addition to benzene and acyclic compounds, flavones, coumarins, and flavanones were identified as the most frequent molecular scaffolds across the different natural products collections. The results of this work have direct implications in the computational and experimental screening of natural product databases for drug discovery. © 2012 John Wiley & Sons A/S.

Deorphaning the Macromolecular Targets of the Natural Anticancer Compound Doliculide.

PubMed

Schneider, Gisbert; Reker, Daniel; Chen, Tao; Hauenstein, Kurt; Schneider, Petra; Altmann, Karl-Heinz

2016-09-26

The cyclodepsipeptide doliculide is a marine natural product with strong actin-polymerizing and anticancer activities. Evidence for doliculide acting as a potent and subtype-selective antagonist of prostanoid E receptor 3 (EP3) is presented. Computational target prediction suggested that this membrane receptor is a likely macromolecular target and enabled immediate in vitro validation. This proof-of-concept study demonstrates the in silico deorphanization of phenotypic screening hits as a viable concept for future natural-product-inspired chemical biology and drug discovery efforts. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.