Guiding Conformation Space Search Towards Biologically Relevant Regions Using All-Atom Energy Evaluations

The most significant impediment for protein structure prediction is the inadequacy of conformation space search methods. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. To alleviate this problem, we present model-based search, a novel conformation space search method. Model-based search uses highly accurate information obtained during search to build an approximate, partial model of the energy landscape. Model-based search aggregates information in the model as it progresses, and in turn uses this information to guide exploration towards regions most likely to contain a near-optimal minimum. We validate our method by predicting the structure of 32 proteins, ranging in length from 49 to 213 amino acids. Our results demonstrate that model-based search is more effective at finding lowenergy conformations in high-dimensional conformation spaces than existing search methods. The lower-energy conformations found by our method also correspond to higher-accuracy structure predictions.