Peter Minary, Stanford University

Introduction to Modern Algorithms for Conformational Sampling and Optimization in Computational Structural Biology
Our limited ability to sample conformational space severely restricts the use of structural information in computational biology and bioinformatics. In this tutorial, first we highlight two relevant limiting factors: (i) The roughness of the energy landscape over the conformational space of a biophysical structure. (ii) The large number of degrees of freedom in Cartesian space. Next, we will describe some of the most advanced sampling algorithms that overcome these limitations. Here, we will bring up some real world examples where the combination of proper algorithms enables the investigation of complex questions in molecular biology. Finally, we introduce our software package MOSAICS (Methodologies for Optimization and SAmpling In Computational Structural biology) so that interested users can tailor these algorithms to their own needs.

References

  1. Minary, P., and Levitt, M. 2010. Conformational Optimization Using Natural Degrees of Freedom: A Novel Stochastic Chain Closure Algorithm. Journal of Computational Biology 17(8), 993-1010.

  2. Minary, P., and Levitt, M. 2006. Discussion of the Equi-Energy Sampler. Annals of Statistics 34, 1638-1641.

  3. Geyer, C. J. 1991. Computing Science and Statistics, Proceedings of the 23rd Symposium on the Interface, 156-163.

  4. Swendsen, R. H., and Wang, J. S. 1986. Replica Monte Carlo Simulation of Spin Glasses. Physical Review Letters 57, 2607.

  5. Minary, P. MOSAICS: Methodologies for Optimization and SAmpling In Computational Structural biology. In preparation.