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Advances in Artificial Intelligence
Volume 2012 (2012), Article ID 674832, 19 pages
Research Article

Basin Hopping as a General and Versatile Optimization Framework for the Characterization of Biological Macromolecules

1Department of Computer Science, George Mason University, Fairfax, VA 22030, USA
2Department of Bioengineering, George Mason University, Fairfax, VA 22030, USA

Received 29 June 2012; Revised 23 September 2012; Accepted 19 October 2012

Academic Editor: Zhiyuan Luo

Copyright © 2012 Brian Olson et al. 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.


Since its introduction, the basin hopping (BH) framework has proven useful for hard nonlinear optimization problems with multiple variables and modalities. Applications span a wide range, from packing problems in geometry to characterization of molecular states in statistical physics. BH is seeing a reemergence in computational structural biology due to its ability to obtain a coarse-grained representation of the protein energy surface in terms of local minima. In this paper, we show that the BH framework is general and versatile, allowing to address problems related to the characterization of protein structure, assembly, and motion due to its fundamental ability to sample minima in a high-dimensional variable space. We show how specific implementations of the main components in BH yield algorithmic realizations that attain state-of-the-art results in the context of ab initio protein structure prediction and rigid protein-protein docking. We also show that BH can map intermediate minima related with motions connecting diverse stable functionally relevant states in a protein molecule, thus serving as a first step towards the characterization of transition trajectories connecting these states.