Development and optimisation of a novel genetic algorithm for studying model protein folding

Abstract

Determination of the native state of a protein from its amino acid sequence is the goal of protein folding simulations, with potential applications in gene therapy and drug design. Location of the global mini- mum structure for a given sequence, however, is a dif- ficult optimisation problem. In this paper, we describe the development and application of a genetic algorithm (GA) to find the lowest-energy conformations for the 2D HP lattice bead protein model. Optimisation of the parameters of our ''standard'' GA program reveals that the GA is most successful (at finding the lowest-energy conformations) for high rates of mating and mutation and relatively high elitism. We have also introduced a number of new genetic operators: a duplicate preda- tor—which maintains population diversity by eliminat- ing duplicate structures; brood selection—where two ''parent'' structures undergo crossover and give rise to a brood of (not just two) offspring; and a Monte Carlo based local search algorithm—to explore the neigh- bourhood of all members of the population. It is shown that these operators lead to significant improvements in the success and efficiency of the GA, both compared with our standard GA and with previously published GA studies for benchmark HP sequences with up to 50 beads.