Department of Chemistry and Biochemistry, University of Oklahoma, Norman, Oklahoma 73019, USA.
Proteins. 2011 Dec;79(12):3364-73. doi: 10.1002/prot.23080. Epub 2011 Jul 11.
Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy.
质子的摄取或释放控制着许多重要的生物过程,如能量转导、病毒复制和催化。准确的 pK(a)预测可以提供质子途径的信息,从而揭示详细的酸碱机制。基于物理的方法在分子动力学模拟的框架内不仅可以进行 pK(a)预测,还可以提供 pK(a)位移的物理起源信息,并提供关于离子化诱导构象松弛和大规模转变的详细信息。最近开发的连续恒 pH 分子动力学 (CPHMD) 方法就是这样一种方法,它已被证明是一种准确且稳健的天然可滴定残基 pK(a)预测工具。为了进一步检查 CPHMD 的准确性和局限性,我们盲目预测了在葡萄球菌核酸酶和变体的各种疏水区中引入的 87 个可滴定残基的 pK(a)值。预测值与实验值的均方根偏差为 1.69 pK 单位,只有两个 pK(a)值的误差大于 3.5 pK 单位。在计算的 pK(a)值误差的背景下分析滴定侧链的构象波动表明,明确处理构象灵活性和相关介电弛豫使 CPHMD 具有明显的优势。对误差来源的分析表明,通过提高去溶剂化能量计算的准确性,可以为埋藏最深的残基获得更准确的 pK(a)预测。此外,还发现当前 CPHMD 实现所基于的广义 Born 隐溶剂模型略微扭曲了局部构象环境,因此包含显式溶剂表示可能会提高准确性。