Guiding conformation space search with an all-atom energy potential.
The most significant impediment for protein structure prediction is the inadequacy of conformation space search. 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 toward 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 low-energy conformations in high-dimensional conformation spaces than existing search methods. The reduction in energy translates into structure predictions of increased accuracy.
Robotics and Biology Laboratory, Department of Computer Science, University of Massachusetts Amherst, Amherst, Massachusetts 01003-9264, USA.
T J
TJ
Brunette
Oliver
O
Brock
D008958:Models, Molecular; D009010:Monte Carlo Method; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*; D040681:Structural Homology, Protein; D013816:Thermodynamics
D011506:Proteins