Wallin Stefan
Department of Physics and Physical Oceanography, Memorial University, St. John's, NL, A1B 3X7, Canada.
Methods Mol Biol. 2017;1561:201-211. doi: 10.1007/978-1-4939-6798-8_12.
The computational peptide screening method is a Monte Carlo-based procedure to systematically characterize the specificity of a peptide-binding site. The method is based on a generalized-ensemble algorithm in which the peptide sequence has become a dynamic variable, i.e., molecular simulations with ordinary conformational moves are enhanced with a type of "mutational" move such that proper statistics are achieved for multiple sequences in a single run. The peptide screening method has two main steps. In the first, reference simulations of the unbound state are performed and used to parametrize a linear model of the unbound state free energy, determined by requiring that the marginal distribution of peptide sequences is approximately flat. In the second step, simulations of the bound state are performed. By using the linear model as a free energy reference point, the marginal distribution of peptide sequences becomes skewed towards sequences with higher binding free energies. From analyses of the sequences generated in the second step and their conformational ensembles, information on peptide binding specificity, relative binding affinities, and the molecular basis of specificity can be achieved. Here we demonstrate how the algorithm can be implemented and applied to determine the peptide binding specificity of a PDZ domain from the protein GRIP1.
计算肽筛选方法是一种基于蒙特卡洛的程序，用于系统地表征肽结合位点的特异性。该方法基于一种广义系综算法，其中肽序列已成为一个动态变量，即通过一种“突变”移动增强普通构象移动的分子模拟，以便在单次运行中对多个序列实现适当的统计。肽筛选方法有两个主要步骤。第一步，进行未结合状态的参考模拟，并用于对未结合状态自由能的线性模型进行参数化，这是通过要求肽序列的边际分布近似平坦来确定的。第二步，进行结合状态的模拟。通过将线性模型用作自由能参考点，肽序列的边际分布向具有更高结合自由能的序列倾斜。通过分析第二步中生成的序列及其构象系综，可以获得有关肽结合特异性、相对结合亲和力和特异性分子基础的信息。在这里，我们展示了如何实现该算法并将其应用于确定来自蛋白质GRIP1的PDZ结构域的肽结合特异性。
