QligFEP: an automated workflow for small molecule free energy calculations in Q

Willem Jespers,Mauricio Esguerra,Hugo Gutiérrez-De-Terán,Johan Åqvist

doi:10.1186/s13321-019-0348-5

Willem Jespers, Mauricio Esguerra + Show 2 more

Open Access

https://doi.org/10.1186/s13321-019-0348-5

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Abstract

The process of ligand binding to a biological target can be represented as the equilibrium between the relevant solvated and bound states of the ligand. This which is the basis of structure-based, rigorous methods such as the estimation of relative binding affinities by free energy perturbation (FEP). Despite the growing capacity of computing power and the development of more accurate force fields, a high throughput application of FEP is currently hampered due to the need, in the current schemes, of an expert user definition of the “alchemical” transformations between molecules in the series explored. Here, we present QligFEP, a solution to this problem using an automated workflow for FEP calculations based on a dual topology approach. In this scheme, the starting poses of each of the two ligands, for which the relative affinity is to be calculated, are explicitly present in the MD simulations associated with the (dual topology) FEP transformation, making the perturbation pathway between the two ligands univocal. We show that this generalized method can be applied to accurately estimate solvation free energies for amino acid sidechain mimics, as well as the binding affinity shifts due to the chemical changes typical of lead optimization processes. This is illustrated in a number of protein systems extracted from other FEP studies in the literature: inhibitors of CDK2 kinase and a series of A2A adenosine G protein-coupled receptor antagonists, where the results obtained with QligFEP are in excellent agreement with experimental data. In addition, our protocol allows for scaffold hopping perturbations to identify the binding affinities between different core scaffolds, which we illustrate with a series of Chk1 kinase inhibitors. QligFEP is implemented in the open-source MD package Q, and works with the most common family of force fields: OPLS, CHARMM and AMBER.

Highlights

Calculating physicochemical properties of drug like molecules, such as the solvation free energies or the binding affinities for biological targets, has been a longstanding challenge to computational chemists
If we think of two drug-like molecules for which the binding affinity for a given protein is compared, the free energy perturbation (FEP) simulation will connect them through a series of unphysical intermediates [11]
We describe an automated protocol to setup ligand perturbations utilizing a dual topology approach called QligFEP, which is implemented to interact with the open-source molecular dynamics (MD) package Q

Summary

Introduction

Calculating physicochemical properties of drug like molecules, such as the solvation free energies or the binding affinities for biological targets, has been a longstanding challenge to computational chemists. If we think of two drug-like molecules for which the binding affinity for a given protein is compared, the FEP simulation will connect them through a series of unphysical intermediates [11] If we translate this to the more typical exploration of a congeneric series of ligands around a given lead molecule, Jespers et al J Cheminform (2019) 11:26 this can be seen as a system of nodes interconnected by edges, each of them representing a perturbation between the pair of molecules involved. For drug like molecules the number of nodes considerable increases and the chemical space covered becomes several orders of magnitude larger (in the order of 1033 for all molecules adhering to Lipinski’s rule of five [15, 16]), making it impossible to predesign perturbation libraries for ligands This introduces a bottleneck in the application of FEP simulations in real drug design projects, as the manual setup required is tedious, time consuming and prone to errors. The selection of nodes can a posteriori be refined by a cycle closure analysis, which allows assessing the statistical errors by considering a given node more than once [17]

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