Evaluation and Characterization of Trk Kinase Inhibitors for the Treatment of Pain: Reliable Binding Affinity Predictions from Theory and Computation.

Shunzhou Wan,Agastya P Bhati,Sharan K Bagal,Veerabahu Shanmugasundaram,Peter V Coveney,Sarah Skerratt,Kiyoyuki Omoto

doi:10.1021/acs.jcim.6b00780

Abstract

Optimization of ligand binding affinity to the target protein of interest is a primary objective in small-molecule drug discovery. Until now, the prediction of binding affinities by computational methods has not been widely applied in the drug discovery process, mainly because of its lack of accuracy and reproducibility as well as the long turnaround times required to obtain results. Herein we report on a collaborative study that compares tropomyosin receptor kinase A (TrkA) binding affinity predictions using two recently formulated fast computational approaches, namely, Enhanced Sampling of Molecular dynamics with Approximation of Continuum Solvent (ESMACS) and Thermodynamic Integration with Enhanced Sampling (TIES), to experimentally derived TrkA binding affinities for a set of Pfizer pan-Trk compounds. ESMACS gives precise and reproducible results and is applicable to highly diverse sets of compounds. It also provides detailed chemical insight into the nature of ligand-protein binding. TIES can predict and thus optimize more subtle changes in binding affinities between compounds of similar structure. Individual binding affinities were calculated in a few hours, exhibiting good correlations with the experimental data of 0.79 and 0.88 from the ESMACS and TIES approaches, respectively. The speed, level of accuracy, and precision of the calculations are such that the affinity predictions can be used to rapidly explain the effects of compound modifications on TrkA binding affinity. The methods could therefore be used as tools to guide lead optimization efforts across multiple prospective structurally enabled programs in the drug discovery setting for a wide range of compounds and targets.

Highlights

The availability of computational methods that can reliably, rapidly, and accurately predict the binding affinities of ligands to a target protein of interest would greatly facilitate drug discovery programs by enabling project teams to more effectively triage design ideas and synthesize only those compounds with a high probability of being pharmacologically active
These compounds were picked as representatives of the full series previously studied experimentally,[2] covering the dynamic range of the tropomyosin receptor kinase A (TrkA) pharmacology assay and containing the structural features deemed as key determinants of TrkA activity
Of the TrkA cocrystal structures provided by Pfizer,[2] we elected to utilize the cocrystal structure of TrkA and 1 (Figure 1, PDB code 5JFV) as it contained the highest number of crystallographically defined TrkA residues

Summary

Introduction

The availability of computational methods that can reliably, rapidly, and accurately predict the binding affinities of ligands to a target protein of interest would greatly facilitate drug discovery programs by enabling project teams to more effectively triage design ideas and synthesize only those compounds with a high probability of being pharmacologically active. We report here a prospective computational study on a -ongoing project at Pfizer[2] to assess the effectiveness of these methods based on the use of a Binding Affinity Calculator (BAC) software tool and associated services.[3] The approach makes use of an automated workflow[4] running in a high-performance computing environment that builds models, runs large numbers of calculations, and analyzes the output data in order to place reliable error bounds on predicted ligand binding affinities. For a Received: December 21, 2016 Published: March 20, 2017

Methods

Results

Discussion

Conclusion