Potential of genetic algorithms in protein folding and protein engineering simulations

Abstract

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 λ 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 β 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 β 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.