Abstract
Determining the drug-target residence time (RT) is of major interest in drug discovery given that this kinetic parameter often represents a better indicator of in vivo drug efficacy than binding affinity. However, obtaining drug-target unbinding rates poses significant challenges, both computationally and experimentally. This is particularly palpable for complex systems like G Protein-Coupled Receptors (GPCRs) whose ligand unbinding typically requires very long timescales oftentimes inaccessible by standard molecular dynamics simulations. Enhanced sampling methods offer a useful alternative, and their efficiency can be further improved by using machine learning tools to identify optimal reaction coordinates. Here, we test the combination of two machine learning techniques, automatic mutual information noise omission and reweighted autoencoded variational Bayes for enhanced sampling, with infrequent metadynamics to efficiently study the unbinding kinetics of two classical drugs with different RTs in a prototypic GPCR, the μ-opioid receptor. Dissociation rates derived from these computations are within one order of magnitude from experimental values. We also use the simulation data to uncover the dissociation mechanisms of these drugs, shedding light on the structures of rate-limiting transition states, which, alongside metastable poses, are difficult to obtain experimentally but important to visualize when designing drugs with a desired kinetic profile.
Highlights
A large number of clinically used drugs elicit their biological effects via G protein-coupled receptors (GPCRs).1 Despite decades of drug discovery efforts focused on this important family of membrane proteins, many drug candidates eventually fail in clinical trials because of their lack of in vivo efficacy or safety
We have investigated in this work the efficiency and accuracy of a computational strategy that combines machine learning and scitation.org/journal/jcp infrequent metadynamics to simulate drug dissociation from G Protein-Coupled Receptors (GPCRs), a process that, depending on the ligand, may require minutes or even hours in real life
We investigated the binding of two representative morphinan drugs with different MOPr residence times by using two machine learning algorithms to learn an appropriate drug dissociation reaction coordinate to carry out infrequent metadynamics simulations aimed at providing both kinetic rates and mechanistic information
Summary
A large number of clinically used drugs elicit their biological effects via G protein-coupled receptors (GPCRs). Despite decades of drug discovery efforts focused on this important family of membrane proteins, many drug candidates eventually fail in clinical trials because of their lack of in vivo efficacy or safety. A nudged elastic band (NEB) algorithm applied to an analytical Gaussian Mixture Model (GMM) energy landscape generated from the aforementioned metadynamics simulations provided mechanistic insights into the explored unbinding pathways of morphine and buprenorphine, as well as information about the various metastable states and transition states that may or may not act as kinetic traps along these pathways It does not offer a highthroughput strategy for the determination of drug residence times, the proposed combination of the aforementioned tools with infrequent metadynamics represents a step forward in enhancing existing rational drug design approaches for GPCRs. In particular, the strategy contributes (a) estimates of kinetic rates at a much reduced computational cost ∼7 orders of magnitude speed-up), (b) atomic-resolution structures of metastable states and transition states along the drug unbinding pathway, which are difficult or impossible to determine experimentally, and (c) molecular determinants of drug-receptor binding kinetics whose modulation may guide the design of improved therapeutics
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