RT Journal Article
SR Electronic
T1 A multiscale approach for computing gated ligand binding from molecular dynamics and Brownian dynamics simulations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.22.449380
DO 10.1101/2021.06.22.449380
A1 Kashif Sadiq, S.
A1 Chicharro, Abraham Muñiz
A1 Friedrich, Patrick
A1 Wade, Rebecca C.
YR 2021
UL http://biorxiv.org/content/early/2021/06/23/2021.06.22.449380.abstract
AB We develop an approach to characterise the effects of gating by a multi-conformation protein consisting of macrostate conformations that are either accessible or inaccessible to ligand binding. We first construct a Markov state model of the apo-protein from atomistic molecular dynamics simulations from which we identify macrostates and their conformations, compute their relative macrostate populations and interchange kinetics, and structurally characterise them in terms of ligand accessibility. We insert the calculated first-order rate constants for conformational transitions into a multi-state gating theory from which we derive a gating factor γ that quantifies the degree of conformational gating. Applied to HIV-1 protease, our approach yields a kinetic network of three accessible (semi-open, open and wide-open) and two inaccessible (closed and a newly identified, ‘parted’) macrostate conformations. The ‘parted’ conformation sterically partitions the active site, suggesting a possible role in product release. We find that the binding kinetics of drugs and drug-like inhibitors to HIV-1 protease falls in the slow gating regime. However, because γ=0.75, conformational gating only modestly slows ligand binding. Brownian dynamics simulations of the diffusional association of eight inhibitors to the protease - that have a wide range of experimental association constants (~104 - 1010 M−1s−1) - yields gated rate constants in the range ~0.5-5.7 × 108 M−1s−1. This indicates that, whereas the association rate of some inhibitors is described by the model, for most inhibitors the subsequent induced fit step leads to slower association rates. For systems known to be modulated by conformational gating, the approach could be scaled computationally efficiently to screen association kinetics for a large number of ligands.Competing Interest StatementThe authors have declared no competing interest.