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Estimation of relative free energies of binding using pre‐computed ensembles based on the single‐step free energy perturbation and the site‐identification by Ligand competitive saturation approaches
Author(s) -
Raman E. Prabhu,
Lakkaraju Sirish Kaushik,
Denny Rajiah Aldrin,
MacKerell Alexander D.
Publication year - 2017
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.24522
Subject(s) - free energy perturbation , ligand (biochemistry) , affinities , computer science , biological system , chemistry , context (archaeology) , computational chemistry , algorithm , molecular dynamics , stereochemistry , biology , paleontology , biochemistry , receptor
Accurate and rapid estimation of relative binding affinities of ligand‐protein complexes is a requirement of computational methods for their effective use in rational ligand design. Of the approaches commonly used, free energy perturbation (FEP) methods are considered one of the most accurate, although they require significant computational resources. Accordingly, it is desirable to have alternative methods of similar accuracy but greater computational efficiency to facilitate ligand design. In the present study relative free energies of binding are estimated for one or two non‐hydrogen atom changes in compounds targeting the proteins ACK1 and p38 MAP kinase using three methods. The methods include standard FEP, single‐step free energy perturbation (SSFEP) and the site‐identification by ligand competitive saturation (SILCS) ligand grid free energy (LGFE) approach. Results show the SSFEP and SILCS LGFE methods to be competitive with or better than the FEP results for the studied systems, with SILCS LGFE giving the best agreement with experimental results. This is supported by additional comparisons with published FEP data on p38 MAP kinase inhibitors. While both the SSFEP and SILCS LGFE approaches require a significant upfront computational investment, they offer a 1000‐fold computational savings over FEP for calculating the relative affinities of ligand modifications once those pre‐computations are complete. An illustrative example of the potential application of these methods in the context of screening large numbers of transformations is presented. Thus, the SSFEP and SILCS LGFE approaches represent viable alternatives for actively driving ligand design during drug discovery and development. © 2016 Wiley Periodicals, Inc.