Interior-point methods for minimization of potential energy functions of polypeptides

Abstract

Interior-point methods to minimize nonlinear, nonconvex potential energy function of proteins constrained by the bounds on dihedral angles are proposed. The first approach uses a barrier function to transform the original problem into a sequence of subproblems. First-order necessary conditions are used to generate the direction of descent for the subproblem being solved. The second approach utilizes the term for Lennard-Jones 6-12 potential in the energy function as the barrier function. The two proposed solution approaches have been utilized to solve a number of polypeptide structures. The performance of the solution methods is compared with that of a genetic algorithm implementation and other methods from the literature. The comparison shows the proposed approaches to be computationally inexpensive and capable of providing good quality solutions. The proposed solution approaches can be adapted and extended to solve optimization problems arising out of other areas such as molecular structure prediction and peptide docking.