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Journal of Computer-Aided Molecular Design

, Volume 33, Issue 12, pp 1083–1094 | Cite as

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

  • Woong-Hee Shin
  • Daisuke KiharaEmail author
Article

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

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

Notes

Acknowledgements

We thank Charles Christoffer for proofreading the manuscript. This work was partly supported by the National Institutes of Health (R01GM123055), the National Science Foundation (DMS1614777, CMMI1825941), and the Purdue Institute of Drug Discovery.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Biological SciencePurdue UniversityWest LafayetteUSA
  2. 2.Department of Chemistry EducationSunchon National UniversitySuncheonRepublic of Korea
  3. 3.Department of Computer SciencePurdue UniversityWest LafayetteUSA
  4. 4.Purdue University Center for Cancer ResearchPurdue UniversityWest LafayetteUSA
  5. 5.Department of PediatricsUniversity of CincinnatiCincinnatiUSA

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