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J Comput Aided Mol Des. 2019 Sep 10. doi: 10.1007/s10822-019-00222-y. [Epub ahead of print]

Predicting binding poses and affinity ranking in D3R Grand Challenge using PL-PatchSurfer2.0.

Shin WH1,2, Kihara D3,4,5,6.

Author information

1
Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA.
2
Department of Chemistry Education, Sunchon National University, Suncheon, 57922, Republic of Korea.
3
Department of Biological Science, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.
4
Department of Computer Science, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.
5
Purdue University Center for Cancer Research, Purdue University, West Lafayette, IN, 47907, USA. dkihara@purdue.edu.
6
Department of Pediatrics, University of Cincinnati, Cincinnati, OH, 45229, USA. dkihara@purdue.edu.

Abstract

Computational prediction of protein-ligand interactions is a useful approach that aids the drug discovery process. Two major tasks of computational approaches are to predict the docking pose of a compound in a known binding pocket and to rank compounds in a library according to their predicted binding affinities. There are many computational tools developed in the past decades both in academia and industry. To objectively assess the performance of existing tools, the community has held a blind assessment of computational predictions, the Drug Design Data Resource Grand Challenge. This round, Grand Challenge 4 (GC4), focused on two targets, protein beta-secretase 1 (BACE-1) and cathepsin S (CatS). We participated in GC4 in both BACE-1 and CatS challenges using our molecular surface-based virtual screening method, PL-PatchSurfer2.0. A unique feature of PL-PatchSurfer2.0 is that it uses the three-dimensional Zernike descriptor, a mathematical moment-based shape descriptor, to quantify local shape complementarity between a ligand and a receptor, which properly incorporates molecular flexibility and provides stable affinity assessment for a bound ligand-receptor complex. Since PL-PatchSurfer2.0 does not explicitly build a bound pose of a ligand, we used an external docking program, such as AutoDock Vina, to provide an ensemble of poses, which were then evaluated by PL-PatchSurfer2.0. Here, we provide an overview of our method and report the performance in GC4.

KEYWORDS:

BACE-1; CatS; D3R Grand Challenge; PL-PatchSurfer; Protein–ligand interaction; Virtual screening

PMID:
31506789
DOI:
10.1007/s10822-019-00222-y

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