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    Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes

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    License
    Attribution 4.0 International
    Date Issued
    2018-04-12
    Publisher Version
    10.1038/s41598-018-23982-3
    Author(s)
    Zarbafian, Shahrooz
    Moghadasi, Mohammad
    Roshandelpoor, Athar
    Nan, Feng
    Li, Keyong
    Vakili, Pirooz
    Vajda, Sandor
    Kozakov, Dmytro
    Paschalidis, Ioannis Ch.
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    Permanent Link
    https://hdl.handle.net/2144/39074
    Version
    Published version
    Citation (published version)
    Shahrooz Zarbafian, Mohammad Moghadasi, Athar Roshandelpoor, Feng Nan, Keyong Li, Pirooz Vakili, Sandor Vajda, Dima Kozakov, Ioannis Ch Paschalidis. 2018. "Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes." Scientific Reports, Volume 8, Issue 1, https://doi.org/10.1038/s41598-018-23982-3
    Abstract
    We propose a novel stochastic global optimization algorithm with applications to the refinement stage of protein docking prediction methods. Our approach can process conformations sampled from multiple clusters, each roughly corresponding to a different binding energy funnel. These clusters are obtained using a density-based clustering method. In each cluster, we identify a smooth “permissive” subspace which avoids high-energy barriers and then underestimate the binding energy function using general convex polynomials in this subspace. We use the underestimator to bias sampling towards its global minimum. Sampling and subspace underestimation are repeated several times and the conformations sampled at the last iteration form a refined ensemble. We report computational results on a comprehensive benchmark of 224 protein complexes, establishing that our refined ensemble significantly improves the quality of the conformations of the original set given to the algorithm. We also devise a method to enhance the ensemble from which near-native models are selected.
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    Attribution 4.0 International
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    • ENG: Mechanical Engineering: Scholarly Papers [248]
    • ENG: Electrical and Computer Engineering: Scholarly Papers [265]
    • BU Open Access Articles [3866]


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