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

Wan, S; Bhati, AP; Skerratt, S; Omoto, K; Shanmugasundaram, V; Bagal, SK; Coveney, PV; (2017) Evaluation and Characterization of Trk Kinase Inhibitors for the Treatment of Pain: Reliable Binding Affinity Predictions from Theory and Computation. J. Chem. Inf. Model , 57 (4) pp. 897-909. 10.1021/acs.jcim.6b00780. Green open access

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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.

Type: Article
Title: Evaluation and Characterization of Trk Kinase Inhibitors for the Treatment of Pain: Reliable Binding Affinity Predictions from Theory and Computation
Open access status: An open access version is available from UCL Discovery
DOI: 10.1021/acs.jcim.6b00780
Publisher version: http://dx.doi.org/10.1021/acs.jcim.6b00780
Additional information: ACS AuthorChoice - This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Keywords: Science & Technology, Life Sciences & Biomedicine, Physical Sciences, Technology, Chemistry, Medicinal, Chemistry, Multidisciplinary, Computer Science, Information Systems, Computer Science, Interdisciplinary Applications, Pharmacology & Pharmacy, Chemistry, Computer Science, Free-Energy Calculation, Molecular-Dynamics, Drug Discovery, Force-Field, Antagonism, Accurate, Precise, NGF
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry
URI: https://discovery.ucl.ac.uk/id/eprint/1555356
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