Department of Biology, Laboratoire de Biochimie (CNRS UMR7654), Ecole Polytechnique, 91128, Palaiseau, France.
J Comput Chem. 2014 Jul 5;35(18):1371-87. doi: 10.1002/jcc.23637. Epub 2014 May 23.
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.
计算蛋白质设计（CPD）旨在预测新的蛋白质或修饰现有的蛋白质。由于需要探索巨大的序列和构象空间，因此计算上具有巨大的挑战性。通过考虑固定的骨架和离散的侧链构象集，可以降低难度。另一种常见的策略是预先计算成对的能量矩阵，从中可以快速获得任何序列/构象的能量。在这项工作中，我们从一般理论的角度检查了蛋白质 MMGBSA 能量函数的成对分解，以及早些时候为 CPD 提出的实现。它包括广义 Born 项，其多体特性通过有效介电环境克服，以及表面积项，我们提出了一种改进的成对分解。对不同能量成分分解引入的误差进行了详细评估。我们表明，与其他不确定性相比，该误差仍然合理。
