| Autor: |
Parui S; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States., Robertson JC; Janssen Research and Development, Spring House, Pennsylvania 19477, United States., Somani S; Janssen Research and Development, Spring House, Pennsylvania 19477, United States., Tresadern G; Janssen Research and Development, Turnhoutseweg 30, Beerse B-2340, Belgium., Liu C; Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States., Dill KA; Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, United States.; Department of Chemistry, Stony Brook University, Stony Brook, New York 11794, United States.; Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, United States. |
| Abstrakt: |
Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns. |
