Optimizing potentials for the inverse protein folding problem

Inverse protein folding, which seeks to identify sequences that fold into a given structure, has been approached by threading candidate sequences onto the structure and scoring them with database-derived potentials. The sequences with the lowest energies are predicted to fold into that structure. It has been argued that the limited success of this type of approach is not due to the discrepancy between the scoring potential and the true potential but is rather due to the fact that sequences choose their lowest-energy structure rather than structures choosing the lowest-energy sequences. Here we develop a non-physical potential 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 obtained by our method is more likely to produce successful predictions than the true potential.