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Atomistic models for free energy evaluation of drug binding to membrane proteins.


The binding of various molecules to integral membrane proteins with optimal affinity and specificity is central to normal function of cell. While membrane proteins represent about one third of the whole cell proteome, they are a majority of common drug targets. The quest for the development of computational models capable of accurate evaluation of binding affinities, decomposition of the binding into its principal components and thus mapping molecular mechanisms of binding remains one of the main goals of modern computational biophysics and related drug development. The primary scope of this review will be on the recent extension of computational methods for the study of drug binding to membrane proteins. Several examples of such applications will be provided ranging from secondary transporters to voltage gated channels. In this mini-review, we will provide a short summary on the breadth of different methods for binding affinity evaluation. These methods include molecular docking with docking scoring functions, molecular dynamics (MD) simulations combined with post-processing analysis using Molecular Mechanics/Poisson Boltzmann (Generalized Born) Surface Area (MM/PB(GB)SA), as well as direct evaluation of free energies from Free Energy Perturbation (FEP) with constraining schemes, and Potential of Mean Force (PMF) computations. We will compare advantages and shortcomings of popular techniques and provide discussion on the integrative strategies for drug development aimed at targeting membrane proteins.


  • Publisher Full Text
  • Publisher Full Text
  • Authors

    Durdagi S, Zhao C, Cuervo JE, Noskov SY


    Current medicinal chemistry 18:17 2011 pg 2601-11


    Drug Design
    Ether-A-Go-Go Potassium Channels
    HIV Protease
    Ion Channels
    Membrane Proteins
    Models, Molecular
    Molecular Dynamics Simulation
    Protein Binding

    Pub Type(s)

    Journal Article
    Research Support, Non-U.S. Gov't



    PubMed ID