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    Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories

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    JILSA20110300008_14802159.pdf
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    Description:
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    Type
    Article
    Authors
    Chikalov, Igor
    Yao, Peggy
    Moshkov, Mikhail cc
    Latombe, Jean-Claude
    KAUST Department
    Applied Mathematics and Computational Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2011-08-31
    Online Publication Date
    2011-08-31
    Print Publication Date
    2011
    Permanent link to this record
    http://hdl.handle.net/10754/594706
    
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    Abstract
    Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. H-bonds involving atoms from residues that are close to each other in the main-chain sequence stabilize secondary structure elements. H-bonds between atoms from distant residues stabilize a protein’s tertiary structure. However, H-bonds greatly vary in stability. They form and break while a protein deforms. For instance, the transition of a protein from a nonfunctional to a functional state may require some H-bonds to break and others to form. The intrinsic strength of an individual H-bond has been studied from an energetic viewpoint, but energy alone may not be a very good predictor. Other local interactions may reinforce (or weaken) an H-bond. This paper describes inductive learning methods to train a protein-independent probabilistic model of H-bond stability from molecular dynamics (MD) simulation trajectories. The training data describes H-bond occurrences at successive times along these trajectories by the values of attributes called predictors. A trained model is constructed in the form of a regression tree in which each non-leaf node is a Boolean test (split) on a predictor. Each occurrence of an H-bond maps to a path in this tree from the root to a leaf node. Its predicted stability is associated with the leaf node. Experimental results demonstrate that such models can predict H-bond stability quite well. In particular, their performance is roughly 20% better than that of models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a given conformation. The paper discusses several extensions that may yield further improvements.
    Citation
    Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories 2011, 03 (03):155 Journal of Intelligent Learning Systems and Applications
    Publisher
    Scientific Research Publishing, Inc,
    Journal
    Journal of Intelligent Learning Systems and Applications
    DOI
    10.4236/jilsa.2011.33017
    Additional Links
    http://www.scirp.org/journal/PaperDownload.aspx?DOI=10.4236/jilsa.2011.33017
    ae974a485f413a2113503eed53cd6c53
    10.4236/jilsa.2011.33017
    Scopus Count
    Collections
    Articles; Applied Mathematics and Computational Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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