title: 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.
abstract: 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.
authors: Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street HSF II, Baltimore, Maryland, 21201.
authors: E Prabhu
authors: EP
authors: Raman
authors: Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street HSF II, Baltimore, Maryland, 21201.
authors: Sirish Kaushik
authors: SK
authors: Lakkaraju
authors: Medicine Design, Worldwide Research & Development, Pfizer Inc, 610 Main Street, Cambridge, Massachusetts, 02139.
authors: Rajiah Aldrin
authors: RA
authors: Denny
authors: Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, 20 Penn Street HSF II, Baltimore, Maryland, 21201.
authors: Alexander D
authors: AD
authors: MacKerell
mesh_terms: D004268:DNA-Binding Proteins / Q000737:chemistry / Q000378:metabolism*; D015195:Drug Design; D055808:Drug Discovery; D006801:Humans; D008024:Ligands; D062105:Molecular Docking Simulation; D056004:Molecular Dynamics Simulation; D011485:Protein Binding; D054852:Small Molecule Libraries / Q000737:chemistry* / Q000494:pharmacology*; D013816:Thermodynamics; D048051:p38 Mitogen-Activated Protein Kinases / Q000737:chemistry / Q000378:metabolism*
chemical_list: C512283:CC2D1A protein, human; D004268:DNA-Binding Proteins; D008024:Ligands; D054852:Small Molecule Libraries; D048051:p38 Mitogen-Activated Protein Kinases
keywords: CGenFF; CHARMM General Force Field; SILCS; SSFEP; ack1; computational chemistry; computer-aided drug design; molecular dynamics; p38 map kinase