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通过应用于分子动力学轨迹的静电学方法测定蛋白质的 pKa 值。

pKa values in proteins determined by electrostatics applied to molecular dynamics trajectories.

机构信息

Institute of Chemistry and Biochemistry, Freie Universität Berlin , Fabeckstrasse 36A, 14195 Berlin, Germany.

出版信息

J Chem Theory Comput. 2015 Jun 9;11(6):2827-40. doi: 10.1021/acs.jctc.5b00123. Epub 2015 May 19.

DOI:10.1021/acs.jctc.5b00123
PMID:26575575
Abstract

For a benchmark set of 194 measured pKa values in 13 proteins, electrostatic energy computations are performed in which pKa values are computed by solving the Poisson-Boltzmann equation. In contrast to the previous approach of Karlsberg(+) (KB(+)) that essentially used protein crystal structures with variations in their side chain conformations, the present approach (KB2(+)MD) uses protein conformations from four molecular dynamics (MD) simulations of 10 ns each. These MD simulations are performed with different specific but fixed protonation patterns, selected to sample the conformational space for the different protonation patterns faithfully. The root-mean-square deviation between computed and measured pKa values (pKa RMSD) is shown to be reduced from 1.17 pH units using KB(+) to 0.96 pH units using KB2(+)MD. The pKa RMSD can be further reduced to 0.79 pH units, if each conformation is energy-minimized with a dielectric constant of εmin = 4 prior to calculating the electrostatic energy. The electrostatic energy expressions upon which the computations are based have been reformulated such that they do not involve terms that mix protein and solvent environment contributions and no thermodynamic cycle is needed. As a consequence, conformations of the titratable residues can be treated independently in the protein and solvent environments. In addition, the energy terms used here avoid the so-called intrinsic pKa and can therefore be interpreted without reference to arbitrary protonation states and conformations.

摘要

对于 13 种蛋白质中的 194 个实测 pKa 值基准集,进行了静电能计算,其中 pKa 值是通过求解泊松-玻尔兹曼方程计算得出的。与 Karlsberg(+) (KB(+)) 先前的方法不同,后者本质上使用了带有侧链构象变化的蛋白质晶体结构,而本方法(KB2(+)MD)使用了来自四个分子动力学(MD)模拟的蛋白质构象,每个模拟持续 10 ns。这些 MD 模拟使用不同的特定但固定的质子化模式进行,这些模式被选择用于忠实地采样不同质子化模式的构象空间。计算和实测 pKa 值之间的均方根偏差(pKa RMSD)从使用 KB(+)时的 1.17 pH 单位降低到使用 KB2(+)MD 时的 0.96 pH 单位。如果在计算静电能之前,使用介电常数 εmin = 4 对每个构象进行能量最小化,则 pKa RMSD 可以进一步降低至 0.79 pH 单位。计算所基于的静电能表达式已重新表述,使得它们不涉及混合蛋白质和溶剂环境贡献的项,并且不需要热力学循环。因此,可以在蛋白质和溶剂环境中独立处理可滴定残基的构象。此外,此处使用的能量项避免了所谓的固有 pKa,因此可以在不参考任意质子化状态和构象的情况下进行解释。

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