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  • 标题:Experimental validation of FINDSITE comb virtual ligand screening results for eight proteins yields novel nanomolar and micromolar binders
  • 本地全文:下载
  • 作者:Bharath Srinivasan ; Hongyi Zhou ; Julia Kubanek
  • 期刊名称:Journal of Cheminformatics
  • 印刷版ISSN:1758-2946
  • 电子版ISSN:1758-2946
  • 出版年度:2014
  • 卷号:6
  • 期号:1
  • 页码:16
  • DOI:10.1186/1758-2946-6-16
  • 语种:English
  • 出版社:BioMed Central
  • 摘要:Identification of ligand-protein binding interactions is a critical step in drug discovery. Experimental screening of large chemical libraries, in spite of their specific role and importance in drug discovery, suffer from the disadvantages of being random, time-consuming and expensive. To accelerate the process, traditional structure- or ligand-based VLS approaches are combined with experimental high-throughput screening, HTS. Often a single protein or, at most, a protein family is considered. Large scale VLS benchmarking across diverse protein families is rarely done, and the reported success rate is very low. Here, we demonstrate the experimental HTS validation of a novel VLS approach, FINDSITEcomb, across a diverse set of medically-relevant proteins. For eight different proteins belonging to different fold-classes and from diverse organisms, the top 1% of FINDSITEcomb’s VLS predictions were tested, and depending on the protein target, 4%-47% of the predicted ligands were shown to bind with μM or better affinities. In total, 47 small molecule binders were identified. Low nanomolar (nM) binders for dihydrofolate reductase and protein tyrosine phosphatases (PTPs) and micromolar binders for the other proteins were identified. Six novel molecules had cytotoxic activity (<10 μg/ml) against the HCT-116 colon carcinoma cell line and one novel molecule had potent antibacterial activity. We show that FINDSITEcomb is a promising new VLS approach that can assist drug discovery.
  • 关键词:Drug discovery ; Virtual ligand screening (VLS) ; High-throughput screening (HTS) ; Differential scanning fluorimetry (DSF) ; Ligand homology modeling
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