Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories
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ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Extensions of Dynamic Programming, Machine Learning and Discrete Optimization Research Group
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2011-02-18Online Publication Date
2011-02-18Print Publication Date
2011Permanent link to this record
http://hdl.handle.net/10754/325468
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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.Citation
Chikalov I, Yao P, Moshkov M, Latombe J-C (2011) Learning probabilistic models of hydrogen bond stability from molecular dynamics simulation trajectories. BMC Bioinformatics 12: S34. doi:10.1186/1471-2105-12-S1-S34.Publisher
Springer NatureJournal
BMC BioinformaticsPubMed ID
21342565PubMed Central ID
PMC3044290ae974a485f413a2113503eed53cd6c53
10.1186/1471-2105-12-S1-S34
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