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 potential for protein folding. The potential is obtained by an optimization procedure that simultaneously maximizes thermodynamic stability for all proteins in the database. When applied to the representative dataset of proteins and with the energy function taken in pairwise contact approximation, our potential scored somewhat better than existing ones. However, the discrimination of the native structure from decoys is still not strong enough to make the potential useful for ab initio folding. Our results suggest that the problem lies with pairwise amino acid contact approximation and/or simplified presentation of proteins rather than with the derivation of potential. We argue that more detail of protein structure and energetics should be taken into account to achieve energy gaps. The suggested method is general enough to allow us to systematically derive parameters for more sophisticated energy functions. The internal control of validity for the potential derived by our method is convergence to a unique solution upon addition of new proteins to the database. The method is tested on simple model systems where sequences are designed, using the preset "true" potential, to have low energy in a dataset of structures. Our procedure is able to recover the potential with correlation r approximately 91% with the true one and we were able to fold all model structures using the recovered potential. Other statistical knowledge-based approaches were tested using this model and the results indicate that they also can recover the true potential with high degree of accuracy.