A new hybrid algorithm for finding the lowest minima of potential surfaces: approach and application to peptides

A new algorithm is presented for finding the global minimum, and other low-lying minima, of a potential energy surface (PES) of biological molecules. The algorithm synergetically combines three well-known global optimization methods: the diffusion equation method (DEM), which involves smoothing the PES; a simulated annealing (SA) algorithm; and evolutionary programming (EP), whose population-oriented approach allows for a parallel search over different regions of the PES. Tests on five peptides having between 6 and 9 residues show that the code implementing the new combined algorithm is efficient and is found to outperform the constituent methods, DEM and SA. Results of the algorithm, in the gas phase and with the GBSA implicit solvent model, are compared with crystallographic data for the test peptides; good accord is found in all cases. Also, for all but one of the examples, our hybrid algorithm finds a minimum deeper than those obtained by a very extensive scan. TINKERs implementation of the OPLS-AA force field is employed for the structure prediction. The results show that the new algorithm is a powerful structure predictor, when a reliable potential function is available. Our implementation of the algorithm is time-efficient, and requires only modest computational resources. Work is underway on applications of the new algorithm to structural prediction of proteins and other biological macro-molecules.