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J Chem Inf Model. 2016 Sep 26;56(9):1676-91. doi: 10.1021/acs.jcim.6b00163. Epub 2016 Aug 19.

PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method That Is Tolerant to Target and Ligand Structure Variation.

Author information

1
Department of Biological Science, Purdue University , 249 South Martin Jischke Street, West Lafayette, Indiana 47907, United States.
2
Department of Computer Science, Purdue University , 305 North University Street, West Lafayette, Indiana 47907, United States.
3
Discovery Chemistry Research and Technologies, Eli Lilly and Company , 893 South Delaware Street, Indianapolis, Indiana 46225, United States.

Abstract

Virtual screening has become an indispensable procedure in drug discovery. Virtual screening methods can be classified into two categories: ligand-based and structure-based. While the former have advantages, including being quick to compute, in general they are relatively weak at discovering novel active compounds because they use known actives as references. On the other hand, structure-based methods have higher potential to find novel compounds because they directly predict the binding affinity of a ligand in a target binding pocket, albeit with substantially lower speed than ligand-based methods. Here we report a novel structure-based virtual screening method, PL-PatchSurfer2. In PL-PatchSurfer2, protein and ligand surfaces are represented by a set of overlapping local patches, each of which is represented by three-dimensional Zernike descriptors (3DZDs). By means of 3DZDs, the shapes and physicochemical complementarities of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared with the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by the addition of two more features, atom-based hydrophobicity and hydrogen-bond acceptors and donors. Benchmark studies showed that PL-PatchSurfer2 performed better than or comparable to popular existing methods. Particularly, PL-PatchSurfer2 significantly outperformed existing methods when apo-form or template-based protein models were used for queries. The computational time of PL-PatchSurfer2 is about 20 times shorter than those of conventional structure-based methods. The PL-PatchSurfer2 program is available at http://www.kiharalab.org/plps2/ .

PMID:
27500657
PMCID:
PMC5037053
DOI:
10.1021/acs.jcim.6b00163
[Indexed for MEDLINE]
Free PMC Article

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