Protein structure prediction using physical-based global optimization and knowledge-guided fragment packing

Abstract

We describe a new method to predict the tertiary structure of new-fold proteins. Our two-phase approach combines the knowledge-based fragment-packing with the minimization of a physics-based energy function. The method is one of the few attempts to use an all-atom physics-based energy function throughout all stages of the optimization. Information from the known proteins is utilized to guide the search through the vast conformational space. We tested this method in CASP6 and it produced the best prediction on one of the new-fold targets-T238, alpha-helical protein. After CASP6, we carried out a series of experiments to test and improve our method and we found that our method performed well on alpha-helical proteins.