OPTIMIZATION BIAS IN ENERGY-BASED STRUCTURE PREDICTION.

ABSTRACT: Physics-based computational approaches to predicting the structure of macromolecules such as proteins are gaining increased use, but there are remaining challenges. In the current work, it is demonstrated that in energy-based prediction methods, the degree of optimization of the sampled structures can influence the prediction results. In particular, discrepancies in the degree of local sampling can bias the predictions in favor of the oversampled structures by shifting the local probability distributions of the minimum sampled energies. In simple systems, it is shown that the magnitude of the errors can be calculated from the energy surface, and for certain model systems, derived analytically. Further, it is shown that for energy wells whose forms differ only by a randomly assigned energy

SUBMITTER: Petrella RJ 

PROVIDER: S-EPMC4278582 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature