Full Protein Sequence Redesign with an MMGBSA Energy Function
Résumé
Computational protein design aims to create proteins with novel properties. A key element is the energy or scoring function used to select the sequences and conformations. We study the performance of an "MMGBSA" energy function, which combines molecular mechanics terms, a generalized Born and surface area (GBSA) solvent model, with approximations that make the model pairwise additive. Our approach is implemented in the Proteus software. The use of a physics-based energy function ensures a certain model transferability and explanatory power. As a first test, we redesign the sequence of nine proteins, one position at a time, with the rest of the protein having its native sequence and crystallographic conformation. As a second test, all positions are designed together. The contributions of individual energy terms are evaluated, and various parametrizations are compared. We find that the GB term significantly improves the results compared to simple Coulomb electrostatics but is affected by pairwise decomposition errors when all positions are designed together. The SA term, with distinct energy coefficients for nonpolar and polar atoms, makes a decisive contribution to obtain realistic protein sequences and can partially compensate for the absence of a GB term. With the best GBSA protocol, we obtain nativelike protein cores and Superfamily recognition of almost all of our sequences.