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J Comput Aided Mol Des. 2019 Jan;33(1):1-18. doi: 10.1007/s10822-018-0180-4. Epub 2019 Jan 10.

D3R Grand Challenge 3: blind prediction of protein-ligand poses and affinity rankings.

Author information

1
Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA.
2
RCSB Protein Data Bank, Institute for Quantitative Biomedicine, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08854, USA.
3
Relay Therapeutics, Cambridge, MA, 20142, USA.
4
GlaxoSmithKline, 1250 South Collegeville Rd, Collegeville, PA, 19426, USA.
5
Janssen Research & Development, San Diego, CA, 92121, USA.
6
Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, 08854, USA.
7
Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA. ramaro@ucsd.edu.
8
Drug Design Data Resource, University of California, San Diego, La Jolla, CA, 92093, USA. mgilson@ucsd.edu.

Abstract

The Drug Design Data Resource aims to test and advance the state of the art in protein-ligand modeling by holding community-wide blinded, prediction challenges. Here, we report on our third major round, Grand Challenge 3 (GC3). Held 2017-2018, GC3 centered on the protein Cathepsin S and the kinases VEGFR2, JAK2, p38-α, TIE2, and ABL1, and included both pose-prediction and affinity-ranking components. GC3 was structured much like the prior challenges GC2015 and GC2. First, Stage 1 tested pose prediction and affinity ranking methods; then all available crystal structures were released, and Stage 2 tested only affinity rankings, now in the context of the available structures. Unique to GC3 was the addition of a Stage 1b self-docking subchallenge, in which the protein coordinates from all of the cocrystal structures used in the cross-docking challenge were released, and participants were asked to predict the pose of CatS ligands using these newly released structures. We provide an overview of the outcomes and discuss insights into trends and best-practices.

KEYWORDS:

Blinded prediction challenge; D3R; Docking; Drug Design Data Resource; Ligand ranking; Scoring

PMID:
30632055
PMCID:
PMC6472484
[Available on 2020-01-10]
DOI:
10.1007/s10822-018-0180-4

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