Abstract
The linear interaction energy (LIE) approach is an end–point method to compute binding affinities. As such it combines explicit conformational sampling (of the protein-bound and unbound-ligand states) with efficiency in calculating values for the protein-ligand binding free energy ΔGbind. This perspective summarizes our recent efforts to use molecular simulation and empirically calibrated LIE models for accurate and efficient calculation of ΔGbind for diverse sets of compounds binding to flexible proteins (e.g., Cytochrome P450s and other proteins of direct pharmaceutical or biochemical interest). Such proteins pose challenges on ΔGbind computation, which we tackle using a previously introduced statistically weighted LIE scheme. Because calibrated LIE models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space. To enable AD assessment of LIE predictions (or other protein-structure and -dynamic based ΔGbind calculations) we recently introduced strategies for AD assignment of LIE models, based on simulation and training data only. These strategies are reviewed here as well, together with available tools to facilitate and/or automate LIE computation (including software for combined statistically-weighted LIE calculations and AD assessment).
Highlights
AIMMS Division of Molecular and Computational Toxicology, Department of Chemistry and Pharmaceutical Sciences, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
Because calibrated linear interaction energy (LIE) models require empirical fitting of scaling parameters, they need to be accompanied with an applicability domain (AD) definition to provide a measure of confidence for predictions for arbitrary query compounds within a reference frame defined by a collective chemical and interaction space
Inspired by a previous applicability domain analysis (ADAN) approach of Pastor and co–workers to define the domain of applicability of ligand–based QSAR models (Carrió et al, 2014), we have proposed an AD analysis strategy in a LIE study on Cytochrome P450 1A2 binding (Capoferri et al, 2015)
Summary
Mutual molecular recognition is the starting point for a wide variety of biological processes (Gohlke and Klebe, 2002). We and others (see e.g., Carlson and Jorgensen, 1995; Wall et al, 1999) have chosen to incorporate β as an effective parameter in LIE binding free energy models and (together with α) train it based on experimentally available affinity data In such cases, separate local models (with different values for α and β) may well be needed to accurately describe binding affinities for complete sets of binders for a given protein of interest, as shown e.g., in van Dijk et al, 2017 for 132 inhibitors of Cytochrome P450 19A1 (CYP19A1). These include our software for automated (statistically–weighted) LIE computation and associated AD assessment, and the availability of these and other tools may well be an important step for applied use of LIE
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