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      PL-PatchSurfer2: Improved Local Surface Matching-Based Virtual Screening Method that is Tolerant to Target and Ligand Structure Variation

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          Abstract

          Virtual screening has become an indispensable procedure in the drug discovery. Virtual screening methods can be classified into two categories, ligand-based and structure-based methods. 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 binding affinity of a ligand in a target binding pocket, albeit with substantially slower 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 are represented by 3D Zernike descriptors (3DZDs). By using 3DZDs, shape and physicochemical complementarity of local surface regions of a pocket surface and a ligand molecule can be concisely and effectively computed. Compared to the previous version of the program, the performance of PL-PatchSurfer2 is substantially improved by adding 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 faster than conventional structure-based methods. The PL-PatchSurfer2 program are available at kiharalab.org/plps2/.

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          Author and article information

          Journal
          101230060
          32252
          J Chem Inf Model
          J Chem Inf Model
          Journal of chemical information and modeling
          1549-9596
          1549-960X
          25 August 2016
          19 August 2016
          26 September 2016
          26 September 2017
          : 56
          : 9
          : 1676-1691
          Affiliations
          [1 ]Department of Biological Science, Purdue University, 249 S. Martin Jischke Street, West Lafayette, IN, USA
          [2 ]Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, USA
          [3 ]Discovery Chemistry Research and Technologies, Eli Lilly and Company, 893 S. Delaware Street, Indianapolis, IN, USA
          Author notes
          Corresponding Author: Daisuke Kihara, dkihara@ 123456purdue.edu , +1-765-496-2284
          Article
          PMC5037053 PMC5037053 5037053 nihpa812592
          10.1021/acs.jcim.6b00163
          5037053
          27500657
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