ArticleJournal of computer-aided molecular design2019
Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.
Article in Journal of computer-aided molecular design, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
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- Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors.Journal of computer-aided molecular design · 2023Article
- Discovery of Potential Noncovalent Inhibitors of Dehydroquinate Dehydratase from Methicillin-ResistantPharmaceuticals (Basel, Switzerland) · 2023Article
- Putting the Puzzle Together To Get the Whole Picture: Molecular Basis of the Affinity of Two Steroid Derivatives to Acetylcholinesterase.ACS omega · 2023Article
- Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking.Journal of chemical information and modeling · 2023Article
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Authors and funding
10 authors.
Funding
Abstract
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 predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of 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 [Formula: see text]-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 affinity predictions.
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