OPTIMIZING POTENTIALS FOR THE INVERSE PROTEIN-FOLDING PROBLEM

Inverse protein folding, which seeks to identify sequences that fold i nto a given structure, has been approached by threading candidate sequ ences onto the structure and scoring them with database-derived potent ials. The sequences with the lowest energies are predicted to fold int o that structure. It has been argued that the limited success of this type of approach is not due to the discrepancy between the scoring pot ential and the true potential but is rather due to the fact that seque nces choose their lowest-energy structure rather than structures choos ing the lowest-energy sequences. Here we develop a non-physical potent ial scheme optimized for the inverse folding problem. We maximize the average probability of success for a set of lattice proteins to obtain the optimal potential energy function, and show that the potential ob tained by our method is more likely to produce successful predictions than the true potential.