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实验室

11:00-12:00, Friday, November 2, 2018


Speaker: Alex MacKerell Ph.D.

Professor,

University of Maryland, Baltimore

Topic: Can CADD Drive GPCR Drug Design? Rapid Estimation of Relative Binding Affinities Based on Pre-computed Ensembles

Host:     Niu Huang, Ph.D.

Abstract

Rapid, accurate estimation of relative ligand affinities offers the potential to allow computational methods to direct drug design and development.  Towards this goal we have developed two methods based on pre-computed ensembles; Site Identification by Ligand Competitive Saturation (SILCS) and Single-Step Free Energy Perturbation (SSFEP).  SILCS is based on computational functional group affinity mapping (FragMaps) of proteins using oscillating μex Grand Canonical Monte Carlo/Molecular Dynamics (GCMC/MD) simulations that take into account contributions from protein desolvation, functional group desolvation, protein flexibility as well as functional group-protein interactions.  The method can be applied to a range of macromolecules including those with deep or full inaccessible binding pockets such GPCRs and nuclear receptors. Grid Free Energy (GFE) FragMaps obtained from the GCMC/MD simulations may be used both qualitatively and quantitatively to direct ligand design.  In SSFEP a lead compound-protein complex is subjected to MD simulations from which an ensemble of ligand-protein conformations is obtained.  Similar calculations are done on the ligand in solution.  Free energy differences associated with small chemical modifications of the lead compound may then be evaluated using the free energy perturbation formulation in the context of single step perturbations. Both SILCS and SSFEP allow for rapid scoring of 1000s of transformations on a daily time frame offering the potential to identify synthetically accessible ligands thereby facilitating decisions concerning compounds for synthesis and testing. An overview of the SSFEP and SILCS methodologies will be presented along with application of the methods in the context of lead compound identification and optimization in the context of GPCRs.