A new class of hybrid global optimization algorithms for peptide structure prediction: integrated hybrids

Abstract

A novel class of hybrid global optimization methods for application to the structure prediction in protein-folding problem is introduced. These optimization methods take the form of a hybrid between a deterministic global optimization algorithm, the αBB, and a stochastically based method, conformational space annealing (CSA), and attempt to combine the beneficial features of these two algorithms. The αBB method as previously extant exhibits consistency, as it guarantees convergence to the global minimum for twice-continuously differentiable constrained nonlinear programming problems, but can benefit from improvements in the computational front. Computational studies for met-enkephalin demonstrate the promise for the proposed hybrid global optimization method.