Protein structure prediction using a combination of sequence-based alignment, constrained energy minimization, and structural alignment.

ABSTRACT

We present a novel approach to protein structure prediction in which fold recognition techniques are combined with ab initio folding methods. Based on the predicted secondary structure, one of two different protocols is followed. For mostly alpha proteins, global optimization and sampling of a statistical energy function is used to generate many low-energy structures; these structures are then screened against a fold library. Any structural matches are then selected for further refinement. For proteins predicted to have significant beta-content, sequence and secondary structure-based alignment is used to identify candidate templates; spatial constraints are then extracted from these templates and used, along with the statistical energy function, in the global sampling and optimization program. Successes and failures of both protocols are discussed.