A Markov-chain model description of binding funnels to enhance the ranking of docked solutions

Authors

  • Mieczyslaw Torchala,

    1. Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
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    • Mieczyslaw Torchala and Iain H. Moal contributed equally to this work.

  • Iain H. Moal,

    1. Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
    2. Life Science Department, Joint BSC-IRB Research Program in Computational Biology, Barcelona Supercomputing Center, Barcelona, Spain
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  • Raphael A. G. Chaleil,

    1. Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
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  • Rudi Agius,

    1. Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
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  • Paul A. Bates

    Corresponding author
    1. Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, London, United Kingdom
    • Correspondence to: Paul A. Bates, Biomolecular Modelling Laboratory, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London WC2A 3LY, UK. E-mail: paul.bates@cancer.org.uk

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ABSTRACT

Within the crowded, seemingly chaotic environment of the cell, proteins are still able to find their binding partners. This is achieved via an ensemble of trajectories, which funnel them towards their functional binding sites, the binding funnel. Here, we characterize funnel-like energy structures on the global energy landscape using time-homogeneous finite state Markov chain models. These models are based on the idea that transitions can occur between structurally similar docking solutions, with transition probabilities determined by their difference in binding energy. Funnel-like energy structures are those containing solutions with very high equilibrium populations. Although these are found surrounding both near-native and false positive binding sites, we show that the removal of nonfunnel-like energy structures, by filtering away solutions with low maximum equilibrium population, can significantly improve the ranking of docked poses. Proteins 2013; 81:2143–2149. © 2013 Wiley Periodicals, Inc.

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