title: Protein side chain conformation predictions with an MMGBSA energy function.
abstract: The prediction of protein side chain conformations from backbone coordinates is an important task in structural biology, with applications in structure prediction and protein design. It is a difficult problem due to its combinatorial nature. We study the performance of an "MMGBSA" energy function, implemented in our protein design program Proteus, which combines molecular mechanics terms, a Generalized Born and Surface Area (GBSA) solvent model, with approximations that make the model pairwise additive. Proteus is not a competitor to specialized side chain prediction programs due to its cost, but it allows protein design applications, where side chain prediction is an important step and MMGBSA an effective energy model. We predict the side chain conformations for 18 proteins. The side chains are first predicted individually, with the rest of the protein in its crystallographic conformation. Next, all side chains are predicted together. The contributions of individual energy terms are evaluated and various parameterizations are compared. We find that the GB and SA terms, with an appropriate choice of the dielectric constant and surface energy coefficients, are beneficial for single side chain predictions. For the prediction of all side chains, however, errors due to the pairwise additive approximation overcome the improvement brought by these terms. We also show the crucial contribution of side chain minimization to alleviate the rigid rotamer approximation. Even without GB and SA terms, we obtain accuracies comparable to SCWRL4, a specialized side chain prediction program. In particular, we obtain a better RMSD than SCWRL4 for core residues (at a higher cost), despite our simpler rotamer library. Proteins 2016; 84:803-819. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Gaillard
authors.affiliation: Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Panel
authors.affiliation: Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, Palaiseau, 91128, France.
authors.forename: Thomas
authors.initials: T
authors.lastname: Simonson
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D000818:Animals; D030562:Databases, Protein; D006801:Humans; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D055672:Static Electricity; D013816:Thermodynamics
chemical_list: D000596:Amino Acids; D011506:Proteins
keywords: Generalized Born; computational protein design; molecular mechanics; side chain placement; surface area