Protein flexibility and dissociation pathway differentiation can explain onset of resistance mutations in kinases

Abstract

Understanding how point mutations can render a ligand or a drug ineffective against a given biological target is a problem of immense fundamental and practical relevance. Often the efficacy of such resistance mutations can be explained purely on a thermodynamic basis wherein the mutated system displays a reduced binding affinity for the ligand. However, the more perplexing and harder to explain situation is when two protein sequences have the same binding affinity for a drug. In this work, we demonstrate how all-atom molecular dynamics simulations, specifically using recent developments grounded in statistical mechanics and information theory, can provide a detailed mechanistic rationale for such variances. We establish the dissociation mechanism for the popular anti-cancer drug Imatinib (Gleevec) against wild-type and N387S mutant of Abl kinase. We show how this single point mutation triggers a non-local response in the protein's flexibility and eventually leads to pathway differentiation during dissociation. This pathway differentiation explains why Gleevec has a long residence time in the wild-type Abl, but for the mutant, by opening up a backdoor pathway for ligand exit, an order of magnitude shorter residence time is obtained. We thus believe that this work marks an efficient and scalable approach to pinpoint the molecular determinants of resistance mutations in biomolecular receptors of pharmacological relevance that are hard to explain using a simple structural perspective and require mechanistic and kinetic insights.

Accelerating All-Atom Simulations and Gaining Mechanistic Understanding of Biophysical Systems through State Predictive Information Bottleneck

“…Ideally, the length of the unbiased simulation used to learn a system RC should be long enough so that the rare event of interest has been seen at least once. However, for truly rare event systems such as ligand dissociation, , this might be impossible, and in this case, the protocol might need more rounds to converge to an optimized RC, as shown in Figure . The metadynamics trajectory, appropriately reweighted, can be used to learn an improved RC and corresponding metastable states.…”

Selectivity and ranking of tight-binding JAK-STAT inhibitors using Markovian milestoning with Voronoi tessellations

“…Simulations often get stuck in metastable regions. Enhanced sampling methods, including and not limited to, metadynamics, [58][59][60][61][62] adaptive biasing force (ABF), [63][64][65] and umbrella sampling 66,67 are employed to overcome such limitations where the applied bias potential steers the system to overcome deep energy wells. The bias potential for these methods is a function of collective variables (CVs), which are predefined and often require an in-depth understanding of the biological systems of interest.…”

Practical Protocols for Efficient Sampling of Kinase-Inhibitor Binding Pathways Using Two-Dimensional Replica-Exchange Molecular Dynamics

“…As the size and flexibility of the ligand increases in the aforementioned order, binding simulations are expected to be more challenging. Kinase-inhibitor binding processes have been subjected to both long-time conventional MD (Shan et al, 2011;Morando et al, 2016;Paul et al, 2020;Sohraby et al, 2020) and enhanced sampling MD simulations (Yang et al, 2009;Lin et al, 2013;Tiwary et al, 2017;Gobbo et al, 2019;Koneru et al, 2019;Narayan et al, 2020;Spitaleri et al, 2020;Narayan et al, 2021;Shekhar et al, 2021). However, to gain more atomistic insight on protein-ligand binding processes, better computational algorithms and practical protocols are necessary.…”