A genetic algorithm for energy minimization in bio-molecular systems

Energy minimization algorithms for bio-molecular systems are critical to applications such as the prediction of protein folding. Conventional energy minimization methods such as the steepest descent method and conjugate gradient method suffer from the drawback that they can only locate energy minima that are extremely dependent on the initial parameter settings of the computation. We present an energy minimization algorithm based on genetic algorithms that largely overcomes this drawback of conventional methods because it provides an effective mechanism, through crossover and mutation, to explore new regions of the parameter space without being dependent on a single, preselected parameter setting. This allows the algorithm to cross local energy barriers not surmountable by conventional methods. The algorithm significantly increases the probability of reaching deeper energy minima. Tests show that the genetic algorithm based approach can achieve much lower final energy than conventional methods. Our genetic algorithm approach differs from other genetic algorithm based approaches in that we do not use the genetic algorithm to directly compute molecular conformations but instead compute a set of parameters to be used in conjunction with the molecular dynamics simulation package GROMOS96