Guiding probabilistic search of the protein conformational space with structural profiles.
The roughness of the protein energy surface poses a significant challenge to search algorithms that seek to obtain a structural characterization of the native state. Recent research seeks to bias search toward near-native conformations through one-dimensional structural profiles of the protein native state. Here we investigate the effectiveness of such profiles in a structure prediction setting for proteins of various sizes and folds. We pursue two directions. We first investigate the contribution of structural profiles in comparison to or in conjunction with physics-based energy functions in providing an effective energy bias. We conduct this investigation in the context of Metropolis Monte Carlo with fragment-based assembly. Second, we explore the effectiveness of structural profiles in providing projection coordinates through which to organize the conformational space. We do so in the context of a robotics-inspired search framework proposed in our lab that employs projections of the conformational space to guide search. Our findings indicate that structural profiles are most effective in obtaining physically realistic near-native conformations when employed in conjunction with physics-based energy functions. Our findings also show that these profiles are very effective when employed instead as projection coordinates to guide probabilistic search toward undersampled regions of the conformational space.
Department of Computer Science, George Mason University, 4400 University Drive Fairfax, VA 22030, USA.
Brian
B
Olson
Kevin
K
Molloy
S Farid
SF
Hendi
Amarda
A
Shehu
D000465:Algorithms; D008958:Models, Molecular; D009010:Monte Carlo Method; D011336:Probability; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*
D011506:Proteins