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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Wan S,Bhati AP,Skerratt S,Omoto K,Shanmugasundaram V,Bagal SK,Coveney PVdoi
10.1021/acs.jcim.6b00780subject
Has Abstractpub_date
2017-04-24 00:00:00pages
897-909issue
4eissn
1549-9596issn
1549-960Xjournal_volume
57pub_type
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