HOW TO DERIVE A PROTEIN-FOLDING POTENTIAL - A NEW APPROACH TO AN OLD PROBLEM

In this paper we introduce a novel method of deriving a pairwise poten tial for protein folding. The potential is obtained by an optimization procedure that simultaneously maximizes thermodynamic stability for a ll proteins in the database. When applied to the representative datase t of proteins and with the energy function taken in pairwise contact a pproximation, our potential scored somewhat better than existing ones. However, the discrimination of the native structure from decoys is st ill not strong enough to make the potential useful for nb initio foldi ng. Our results suggest that the problem lies with pairwise amino acid contact approximation and/or simplified presentation of proteins rath er than with the derivation of potential. We argue that more detail of protein structure and energetics should be taken into account to achi eve energy gaps. The suggested method is general enough to allow us to systematically derive parameters for more sophisticated energy functi ons. The internal control of validity for the potential derived by our method is convergence to a unique solution upon addition of new prote ins to the database. The method is tested on simple model systems wher e sequences are designed, using the preset ''true'' potential, to have low energy in a dataset of structures. Our procedure is able to recov er the potential with correlation r approximate to 91% with the true o ne and we were able to fold all model structures using the recovered p otential. Other statistical knowledge-based approaches were tested usi ng this model and the results indicate that they also can recover the true potential with high degree of accuracy. (C) 1996 Academic Press L imited