Computational Protein Design with an MMGBSA Energy Function
Résumé
Protein design aims at conceiving new proteins or modifying existing ones to obtain a given function. Computational approaches are a valuable help for protein design, to rationalize the predictions and guide the experimental tests. Computational protein design (CPD) has sparked important methodological efforts and obtained spectacular successes such as the creation of a protein with a new fold or enzyme active site engineering. The main difficulty of CPD lies in the astronomical number of possible sequences and conformations, of the order of (20x10)^100 for a protein with 100 amino acids. Another key element for CPD success is the energy function used to evaluate and select the sequences and conformations.
Our approach of CPD is based on an atomic model of the protein structure and a molecular mechanics energy function. An important aspect is the solvent treatment, represented as a dielectric continuum with a Generalized-Born term, supplemented by a term proportional to the solvent accessible surface area. The key elements of our implementation are: 1) the protein backbone is maintained fixed, 2) the side-chain conformational space is reduced to a discrete library of rotamers, 3) the energy function is decomposed into interaction pairs. The first step consists in calculating a matrix of interactions between each pair of rotamers. In the next step, the sequence-conformation space is explored with an optimization algorithm. Energy evaluations in this second step are fast thanks to the pre-calculation of the energy matrix. The implementation of our CPD procedure is presented, as well as applications to the prediction of side-chain conformations and to the design of full protein sequences.