Department of Physics and Astronomy, Computational Biophysics and Bioinformatics, Clemson University, Clemson, South Carolina.
Proteins. 2018 Dec;86(12):1277-1283. doi: 10.1002/prot.25608. Epub 2018 Oct 26.
DelPhiPKa is a widely used and unique approach to compute pK 's of ionizable groups that does not require molecular surface to be defined. Instead, it uses smooth Gaussian-based dielectric function to treat computational space via Poisson-Boltzmann equation (PBE). Here, we report an expansion of DelPhiPKa functionality to enable inclusion of salt in the modeling protocol. The method considers the salt mobile ions in solvent phase without defining solute-solvent boundary. Instead, the ions are penalized to enter solute interior via a desolvation penalty term in the Boltzmann factor in the framework of PBE. Hence, the concentration of ions near the protein is balanced by the desolvation penalty and electrostatic interactions. The study reveals that correlation between experimental and calculated pK 's is improved significantly by taking into consideration the presence of salt. Furthermore, it is demonstrated that DelphiPKa reproduces the salt sensitivity of experimentally measured pK 's. Another new development of DelPhiPKa allows for computing the pK 's of polar residues such as cysteine, serine, threonine and tyrosine. With this regard, DelPhiPKa is benchmarked against experimentally measured cysteine and tyrosine pK 's and for cysteine it is shown to outperform other existing methods (DelPhiPKa RMSD of 1.73 vs RMSD between 2.40 and 4.72 obtained by other existing pK prediction methods).
DelPhiPKa 是一种广泛使用且独特的方法,可用于计算不需要定义分子表面的可电离基团的 pK '。相反,它使用基于平滑高斯的介电函数通过泊松-玻尔兹曼方程(PBE)处理计算空间。在这里,我们报告了 DelPhiPKa 功能的扩展,以实现将盐纳入建模协议中。该方法考虑了溶剂相中的盐移动离子,而无需定义溶质-溶剂边界。相反,离子通过在 PBE 框架中的玻尔兹曼因子中引入去溶剂化罚分项而被惩罚进入溶质内部。因此,通过去溶剂化罚分和静电相互作用来平衡蛋白质附近离子的浓度。研究表明,通过考虑盐的存在,实验和计算的 pK '之间的相关性得到了显著改善。此外,还证明了 DelphiPKa 再现了实验测量的 pK '的盐敏感性。DelPhiPKa 的另一个新发展允许计算半胱氨酸、丝氨酸、苏氨酸和酪氨酸等极性残基的 pK '。在这方面,DelPhiPKa 与实验测量的半胱氨酸和酪氨酸 pK '进行了基准测试,并且对于半胱氨酸,它表现优于其他现有方法(DelPhiPKa 的 RMSD 为 1.73,而其他现有 pK 预测方法获得的 RMSD 在 2.40 和 4.72 之间)。