Pairwise decomposition of an MMGBSA energy function for computational protein design.
Computational protein design (CPD) aims at predicting new proteins or modifying existing ones. The computational challenge is huge as it requires exploring an enormous sequence and conformation space. The difficulty can be reduced by considering a fixed backbone and a discrete set of sidechain conformations. Another common strategy consists in precalculating a pairwise energy matrix, from which the energy of any sequence/conformation can be quickly obtained. In this work, we examine the pairwise decomposition of protein MMGBSA energy functions from a general theoretical perspective, and an implementation proposed earlier for CPD. It includes a Generalized Born term, whose many-body character is overcome using an effective dielectric environment, and a Surface Area term, for which we present an improved pairwise decomposition. A detailed evaluation of the error introduced by the decomposition on the different energy components is performed. We show that the error remains reasonable, compared to other uncertainties.
Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
Thomas
T
Gaillard
Thomas
T
Simonson
D019295:Computational Biology; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D013816:Thermodynamics
D011506:Proteins
Generalized Born; Protein design; energy decomposition; molecular mechanics; surface area