doi: 10.7763/IJMLC.2012.V2.116
Modelling Hydrogen Bond Stability by Regression Trees
Igor Chikalov1
, , ,
Jean-Claude Latombe2
- 1Mathematical and CS & Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
- 2Computer Science Department, Stanford University, Stanford, CA 94305, USA
Abstract
Hydrogen bonds (H-bonds) play a key role in both the formation and stabilization of protein structures. However, H-bonds greatly vary in stability. Different 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 32 attributes. A trained model is constructed in the form of a regression tree. 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.
Keywords
- Molecular dynamics
- machine learning
- regression tree
How to Cite
Igor Chikalov, Mikhail Moshkov, Peggy Yao, and Jean-Claude Latombe, "Modelling Hydrogen Bond Stability by Regression Trees," International Journal of Machine Learning, vol. 2, no. 3, pp. 213-218, 2012. https://doi.org/10.7763/IJMLC.2012.V2.116
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).