Dataset Information

Virtual target screening: validation using kinase inhibitors.

ABSTRACT: Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the MOI docked to any individual protein can be problematic. In our method, which we term "Virtual Target Screening (VTS)", a set of small drug-like molecules are docked against each structure in the protein library to produce benchmark statistics. This calibration provides a reference for each protein so that hits can be identified for an MOI. VTS can then be used as tool for: drug repositioning (repurposing), specificity and toxicity testing, identifying potential metabolites, probing protein structures for allosteric sites, and testing focused libraries (collection of MOIs with similar chemotypes) for selectivity. To validate our VTS method, twenty kinase inhibitors were docked to a collection of calibrated protein structures. Here, we report our results where VTS predicted protein kinases as hits in preference to other proteins in our database. Concurrently, a graphical interface for VTS was developed.

SUBMITTER: Santiago DN

PROVIDER: S-EPMC3488111 | biostudies-literature | 2012 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Virtual target screening: validation using kinase inhibitors.

Santiago Daniel N DN Pevzner Yuri Y Durand Ashley A AA Tran MinhPhuong M Scheerer Rachel R RR Daniel Kenyon K Sung Shen-Shu SS Woodcock H Lee HL Guida Wayne C WC Brooks Wesley H WH

Journal of chemical information and modeling 20120723 8

Computational methods involving virtual screening could potentially be employed to discover new biomolecular targets for an individual molecule of interest (MOI). However, existing scoring functions may not accurately differentiate proteins to which the MOI binds from a larger set of macromolecules in a protein structural database. An MOI will most likely have varying degrees of predicted binding affinities to many protein targets. However, correctly interpreting a docking score as a hit for the ...[more]

PMID: 22747098

Similar Datasets

Project description: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.

Project description:Discovery of new pharmaceutical substances is currently boosted by the possibility of utilization of the Synthetically Accessible Virtual Inventory (SAVI) library, which includes about 283 million molecules, each annotated with a proposed synthetic one-step route from commercially available starting materials. The SAVI database is well-suited for ligand-based methods of virtual screening to select molecules for experimental testing. In this study, we compare the performance of three approaches for the analysis of structure-activity relationships that differ in their criteria for selecting of "active" and "inactive" compounds included in the training sets. PASS (Prediction of Activity Spectra for Substances), which is based on a modified Naïve Bayes algorithm, was applied since it had been shown to be robust and to provide good predictions of many biological activities based on just the structural formula of a compound even if the information in the training set is incomplete. We used different subsets of kinase inhibitors for this case study because many data are currently available on this important class of drug-like molecules. Based on the subsets of kinase inhibitors extracted from the ChEMBL 20 database we performed the PASS training, and then applied the model to ChEMBL 23 compounds not yet present in ChEMBL 20 to identify novel kinase inhibitors. As one may expect, the best prediction accuracy was obtained if only the experimentally confirmed active and inactive compounds for distinct kinases in the training procedure were used. However, for some kinases, reasonable results were obtained even if we used merged training sets, in which we designated as inactives the compounds not tested against the particular kinase. Thus, depending on the availability of data for a particular biological activity, one may choose the first or the second approach for creating ligand-based computational tools to achieve the best possible results in virtual screening.

Dataset Information

Virtual target screening: validation using kinase inhibitors.

Publications

Virtual target screening: validation using kinase inhibitors.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets