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Scientific Research Publishing, Journal of Intelligent Learning Systems and Applications, 03(03), p. 155-170, 2011

DOI: 10.4236/jilsa.2011.33017

BioMed Central, BMC Bioinformatics, S1(12), 2011

DOI: 10.1186/1471-2105-12-s1-s34

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

Journal article published in 2011 by Igor Chikalov, Peggy Yao, Mikhail Moshkov, Jean-Claude Latombe
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Background: Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. They form and break while a protein deforms, for instance during the transition from a non-functional to a functional state. 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.Methods: This paper describes inductive learning methods to train protein-independent probabilistic models of H-bond stability from molecular dynamics (MD) simulation trajectories of various proteins. The training data contains 32 input attributes (predictors) that describe an H-bond and its local environment in a conformation c and the output attribute is the probability that the H-bond will be present in an arbitrary conformation of this protein achievable from c within a time duration ?. We model dependence of the output variable on the predictors by a regression tree.Results: Several models are built using 6 MD simulation trajectories containing over 4000 distinct H-bonds (millions of occurrences). Experimental results demonstrate that such models can predict H-bond stability quite well. They perform roughly 20% better than models based on H-bond energy alone. In addition, they can accurately identify a large fraction of the least stable H-bonds in a conformation. In most tests, about 80% of the 10% H-bonds predicted as the least stable are actually among the 10% truly least stable. The important attributes identified during the tree construction are consistent with previous findings.Conclusions: We use inductive learning methods to build protein-independent probabilistic models to study H-bond stability, and demonstrate that the models perform better than H-bond energy alone. 2011 Chikalov et al; licensee BioMed Central Ltd.