Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions

C Ruben Vosmeer,Luigi Capoferri,Nico P E Vermeulen,Derk P Kooi,Margreet M Terpstra,Daan P Geerke

doi:10.1007/s00894-015-2883-y

Abstract

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.Electronic supplementary materialThe online version of this article (doi:10.1007/s00894-015-2883-y) contains supplementary material, which is available to authorized users.

Highlights

We explored an iterative linear interaction energy (LIE) method to efficiently predict binding affinities of novel compounds to highly flexible proteins [1, 2]
Using heavy-atom coordinate based principal component analysis (PCA) and k-means clustering [13], up to eight different ligand poses were selected per protein conformation to start molecular dynamics (MD) simulations using GROMACS 4.5.7 [14] in order to calculate Vlig−surr i values in Eq 3
Starting from the two Cytochrome P450s (CYPs) 2D6 structures of Hritz et al [10] used previously [2], MD simulations and LIE calculations were set up and performed according to the settings described in the Methods section

Summary

Introduction

We explored an iterative linear interaction energy (LIE) method to efficiently predict binding affinities of novel compounds to highly flexible proteins [1, 2]. The approach relies on the LIE method [4], which has been chosen for its merits to be fast thanks to a scoring component and to be able to include protein flexibility through the underlying MD simulations. The latter is crucial when dealing with flexible and promiscuous proteins such as Cytochrome P450 (CYPs), which can oxidize a broad range of (apolar) compounds [5]. Many CYPs are able to bind ligands in different binding poses as demonstrated by the possibility of several

Methods

Results

Conclusion