JILSA> Vol.3 No.3, August 2011
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Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories

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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 non-functional 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.

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Cite this paper

I. Chikalov, P. Yao, M. Moshkov and J. Latombe, "Learning Probabilistic Models of Hydrogen Bond Stability from Molecular Dynamics Simulation Trajectories," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 3, 2011, pp. 155-170. doi: 10.4236/jilsa.2011.33017.

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