Abstract
We report the performance of HADDOCK in the 2018 iteration of the Grand Challenge organised by the D3R consortium. Building on the findings of our participation in last year’s challenge, we significantly improved our pose prediction protocol which resulted in a mean RMSD for the top scoring pose of 3.04 and 2.67 Å for the cross-docking and self-docking experiments respectively, which corresponds to an overall success rate of 63% and 71% when considering the top1 and top5 models respectively. This performance ranks HADDOCK as the 6th and 3rd best performing group (excluding multiple submissions from a same group) out of a total of 44 and 47 submissions respectively. Our ligand-based binding affinity predictor is the 3rd best predictor overall, behind only the two leading structure-based implementations, and the best ligand-based one with a Kendall’s Tau correlation of 0.36 for the Cathepsin challenge. It also performed well in the classification part of the Kinase challenges, with Matthews Correlation Coefficients of 0.49 (ranked 1st), 0.39 (ranked 4th) and 0.21 (ranked 4th) for the JAK2, vEGFR2 and p38a targets respectively. Through our participation in last year’s competition we came to the conclusion that template selection is of critical importance for the successful outcome of the docking. This year we have made improvements in two additional areas of importance: ligand conformer selection and initial positioning, which have been key to our excellent pose prediction performance this year.
Highlights
The Drug Design Data Resource (D3R) Grand Challenge (GC) of 2018 is the third iteration of the major docking competition organised by the D3R consortium [1, 2] and to previous years, it has two goals
In addition to the Cathepsin S-centric assessment, which includes a binding affinity prediction component, binding affinity prediction approaches are evaluated in four subchallenges that focus on kinases
Using BindingDB we identified 7049, 4582 and 4563 compounds with IC50 binding affinity measurements for the vEGFR2, JAK2-SC2 and p38a kinases respectively
Summary
The Drug Design Data Resource (D3R) Grand Challenge (GC) of 2018 is the third iteration of the major docking competition organised by the D3R consortium [1, 2] and to previous years, it has two goals. The first, is the assessment of the ability of docking algorithms to accurately predict the binding poses of a protein against a diverse set of small molecules, and the second, the evaluation of the performance of binding affinity prediction algorithms. In addition to the Cathepsin S-centric assessment, which includes a binding affinity prediction component, binding affinity prediction approaches are evaluated in four subchallenges that focus on kinases. Kinases catalyse the process of phosphorylation through which a phosphate group is covalently bound to a protein substrate. Their role in cell signalling has been well understood for decades and
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