APPLYING EXPERIMENTAL-DATA TO PROTEIN FOLD PREDICTION WITH THE GENETIC ALGORITHM

Specific residue interactions as revealed from a few and readily avail able experiments can be quite important in shaping a protein's tertiar y topology by complementing basic and general folding principles. This experimental information is employed in structure prediction (maincha in topology) based on sequence knowledge and the genetic algorithm wit h its ability to optimize simultaneously many parameters. Examples inv estigated include the distribution of cysteinyl S-S bonds, protein sid e-chain ligands to iron-sulfur cages, cofactor-ligands, crosslinks amo ngst side-chains, and conserved hydrophobic and catalytic residues. Su ch interactions yield an improvement in the predicted topology (0.4-6. 6 Angstrom root mean square deviation in the positions of the backbone C-alpha-atoms relative to those observed) compared with those resulti ng from simulations relying only on basic protein folding principles, For several examples the resultant topology depended critically on kno wledge of the few and specific interactions such that the relationship between predicted and observed C-alpha-positions was near random with out their use. The combined methodology (experimental data and the gen etic algorithm) should prove helpful in settings where experiment and theory can cooperate in successive steps to elucidate an unknown struc ture.