Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes Using Nonequilibrium Candidate Monte Carlo

Abstract

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragmentlike ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation timescales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over two orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step towards applying this methodology to pharmaceutically-relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding Modes of Ligands using Enhanced Sampling (BLUES) package which is freely available on GitHub.

Best Practices for Alchemical Free Energy Calculations [Article v1.0]

“…In some cases, it is tempting to simply use docking techniques to generate initial bound structures for starting molecular dynamics simulations. However, timescales for binding mode interconversion are usually slow compared to MD/free energy timescales, meaning that simulations started from different potential binding modes are likely to yield disparate computed binding free energies [47,85,149,202]. Moreover, docking techniques are good at identifying sterically reasonable potential binding modes, but still perform relatively poorly at identifying a single dominant binding mode a priori.…”

“…Routes to handle multiple potential binding modes are different depending on whether absolute or relative calculations are selected, unless a method is available to estimate the relative populations of different stable binding modes in advance (e.g. such as the BLUES approach currently in development [47]), in which case this approach could be applied to assist both types of calculations.…”

“…Sampling all potential ligand binding modes within a single free energy calculation is usually impractical without some form of enhanced sampling or at least Hamiltonian replica exchange [153] because barriers for binding mode interconversion result in kinetics which are too slow compared to simulation timescales [47,85,149,202]. Hamiltonian exchange, coupled with appropriate restraints, can allow the ligand to relatively rapidly exchange between potential binding modes when non-interacting, accelerating sampling of binding modes [153].…”

“…This approach necessitates a separate binding free energy calculation for each potential binding mode, however, so it can be computationally quite costly. If relative populations of different stable binding modes were available from some other technique, it could make this separation of states approach considerably more efficient [47,85].…”

“…• covalent inhibition [46] • free energies of mutation of protein side chains [7,10] • nonspecific binding or multiple binding sites [47] • approximate and often less accurate endpoint free energy methods such as MM-PBSA [48] and LIE [49] • Free energy methods that extract the ligand using geometric order parameters and potential of mean force methods [50] • forcefield dependence for protein, ligand, ions, cosolvents, and co-factors. A number of different studies have looked at the influence of force fields and it is assumed the user has made an appropriate choice for the system under study [51][52][53].…”