Comparison of Ligand Affinity Ranking Using AutoDock-GPU and MM-GBSA Scores in the D3R Grand Challenge 4

24 June 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can more accurately predict binding affinities. In this perspective, we decided to take part in Stage 2 in the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, Autodock4, to that of MM-GBSA in predicting the binding affinities of a set of Beta-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding pose and affinity predictions.


Scoring functions

Supplementary weblinks


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