FINDSITEX: A Structure-Based, Small Molecule Virtual Screening Approach with Application to All Identified Human GPCRs

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• 作者：Hongyi Zhou ; Jeffrey Skolnick
• 刊名：Molecular Pharmaceutics
• 出版年：2012
• 出版时间：June 4, 2012
• 年：2012
• 卷：9
• 期：6
• 页码：1775-1784
• 全文大小：322K
• 年卷期：v.9,no.6(June 4, 2012)
• ISSN：1543-8392

文摘

We have developed FINDSITE^X, an extension of FINDSITE, a protein threading based algorithm for the inference of protein binding sites, biochemical function and virtual ligand screening, that removes the limitation that holo protein structures (those containing bound ligands) of a sufficiently large set of distant evolutionarily related proteins to the target be solved; rather, predicted protein structures and experimental ligand binding information are employed. To provide the predicted protein structures, a fast and accurate version of our recently developed TASSER^VMT, TASSER^VMT-lite, for template-based protein structural modeling applicable up to 1000 residues is developed and tested, with comparable performance to the top CASP9 servers. Then, a hybrid approach that combines structure alignments with an evolutionary similarity score for identifying functional relationships between target and proteins with binding data has been developed. By way of illustration, FINDSITE^X is applied to 998 identified human G-protein coupled receptors (GPCRs). First, TASSER^VMT-lite provides updates of all human GPCR structures previously modeled in our lab. We then use these structures and the new function similarity detection algorithm to screen all human GPCRs against the ZINC8 nonredundant (TC < 0.7) ligand set combined with ligands from the GLIDA database (a total of 88,949 compounds). Testing (excluding GPCRs whose sequence identity > 30% to the target from the binding data library) on a 168 human GPCR set with known binding data, the average enrichment factor in the top 1% of the compound library (EF_0.01) is 22.7, whereas EF_0.01 by FINDSITE is 7.1. For virtual screening when just the target and its native ligands are excluded, the average EF_0.01 reaches 41.4. We also analyze off-target interactions for the 168 protein test set. All predicted structures, virtual screening data and off-target interactions for the 998 human GPCRs are available at http://cssb.biology.gatech.edu/skolnick/webservice/gpcr/index.html.

Keywords:

TASSER^VMT; FINDSITE; GPCR modeling; template-based modeling; virtual screening