Potential of genetic algorithms in protein folding and protein engineering simulations.

Genetic algorithms are very efficient search mechanisms which mutate, recombine and select amongst tentative solutions to a problem until a near optimal one is achieved. We introduce them as a new tool to study proteins. The identification and motivation for different fitness functions is discussed. The evolution of the zinc finger sequence motif from a random start is modelled. User specified changes of the lambda repressor structure were simulated and critical sites and exchanges for mutagenesis identified. Vast conformational spaces are efficiently searched as illustrated by the ab initio folding of a model protein of a four beta strand bundle. The genetic algorithm simulation which mimicked important folding constraints as overall hydrophobic packaging and a propensity of the betaphilic residues for trans positions achieved a unique fold. Cooperativity in the beta strand regions and a length of 3-5 for the interconnecting loops was critical. Specific interaction sites were considerably less effective in driving the fold.