Li Tong, Froeyen Matheus, Herdewijn Piet
Laboratory for Medicinal Chemistry, Rega Institute for Medical Research, Minderbroedersstraat 10, B-3000 Leuven, Belgium.
J Mol Graph Model. 2008 Jan;26(5):813-23. doi: 10.1016/j.jmgm.2007.04.007. Epub 2007 May 3.
A thorough investigation of different roles of Escherichia coli type I signal peptidase residues binding to lipopeptide inhibitor has been performed by a combination of computational alanine scanning mutagenesis and free energy decomposition methods. PB and GB models are both used to evaluate the binding free energy in computational alanine scanning method and only GB model can be used to decompose the binding free energy on a per-residue basis. The regression analysis between the PB and GB model and also between the computational alanine scanning and free energy decomposition have been reported with a correlation coefficient of 0.96 and 0.83, respectively, which suggest they are both in fair agreement with each other. Moreover, the contribution components from van der Waals, electrostatic interaction, non-polar and polar energy of solvation, have been determined as well as the effects of backbones and side-chains. The results indicate that Lys145 is the most important residue for the binding but also acts as a general base, activating Ser90 to increase its nucleophility, recognizing and stabilizing the binding of lipopeptide inhibitor to the E. coli SPase. The hydroxyl group of Ser88 plays a key role for the binding of the inhibitor. Ser90 contributes more to the intramolecular interaction than to the intermolecular interaction. Tyr143 and Phe84 contribute larger van der Waals interaction energies, indicating that these residues can be important for the selection based on the shape of the inhibitors. The contributions from other several interfacial residues of the E. coli SPase are also analyzed. This study can be a guide for the optimization of lipopeptide inhibitors and future design of new therapeutic agents for the treatment of bacterial infections.
通过计算丙氨酸扫描诱变和自由能分解方法相结合,对大肠杆菌I型信号肽酶残基与脂肽抑制剂结合的不同作用进行了深入研究。在计算丙氨酸扫描方法中,PB模型和GB模型均用于评估结合自由能,且只有GB模型可用于按残基分解结合自由能。据报道,PB模型与GB模型之间以及计算丙氨酸扫描与自由能分解之间的回归分析相关系数分别为0.96和0.83,这表明它们彼此之间的一致性较好。此外,还确定了范德华力、静电相互作用、非极性和极性溶剂化能的贡献成分以及主链和侧链的影响。结果表明,Lys145是结合中最重要的残基,同时还作为一个通用碱,激活Ser90以增加其亲核性,识别并稳定脂肽抑制剂与大肠杆菌信号肽酶的结合。Ser88的羟基在抑制剂结合中起关键作用。Ser90对分子内相互作用的贡献比对分子间相互作用的贡献更大。Tyr143和Phe84贡献了更大的范德华相互作用能,表明这些残基对于基于抑制剂形状的选择可能很重要。还分析了大肠杆菌信号肽酶其他几个界面残基的贡献。该研究可为脂肽抑制剂的优化以及未来治疗细菌感染的新型治疗药物的设计提供指导。