Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Its Comparative Analysis
| Autor: | Song Wu, Jie Xia, Xiang Simon Wang, Liangren Zhang, Terry-Elinor Reid |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
General Chemical Engineering Druggability HIV Entry Inhibitors Computational biology Library and Information Sciences Ligands 01 natural sciences Article 03 medical and health sciences Chemokine receptor Drug Discovery Humans Databases Protein Virtual screening Extramural Drug discovery General Chemistry Benchmarking 0104 chemical sciences Computer Science Applications Data set 010404 medicinal & biomolecular chemistry 030104 developmental biology Receptors Chemokine |
| Popis: | Chemokine receptors (CRs) have long been druggable targets for the treatment of inflammatory diseases and HIV 1 infection. As a powerful technique, virtual screening (VS) has been widely applied to identifying small molecule leads for modern drug targets including CRs. For rational selection of a wide variety of VS approaches, ligand enrichment assessment based on a benchmarking data set has become an indispensable practice. However, the lack of versatile benchmarking sets for the whole CRs family that are able to unbiasedly evaluate every single approaches including both structure and ligand based VS, somewhat hinders modern drug discovery efforts. To address this issue, we constructed Maximal Unbiased Benchmarking Data sets for human Chemokine Receptors (MUBD-hCRs) using our recently developed tools of MUBD-DecoyMaker. The MUBD-hCRs encompasses 13 subtypes out of 20 chemokine receptors, composed of 404 ligands and 15756 decoys so far and are readily expandable in the future. It had been thoroughly validated that MUBD-hCRs ligands are chemically diverse while its decoys are maximal unbiased in terms of “artificial enrichment”, “analogue bias”. In addition, we studied the performance of MUBD-hCRs, in particular CXCR4 and CCR5 data sets, in ligand enrichment assessments of both structure and ligand based VS approaches in comparison with other benchmarking data sets available in public domain and demonstrated that MUBD-hCRs is much capable of designating the optimal VS approach. Taken together, MUBD-hCRs is a unique and maximal-unbiased benchmarking set that covers major CRs subtypes so far. |
| Databáze: | OpenAIRE |
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