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Understanding Properties of Cofactors in Proteins: Redox Potentials of Synthetic Cytochromes b

氧化还原 血红素 化学 质子化 计算化学 结晶学 生物化学 有机化学 离子
作者
Ana P. Gámiz‐Hernández,Gernot Kieseritzky,Artur Galstyan,Ozgur Demir‐Kavuk,Ernst‐Walter Knapp
出处
期刊:ChemPhysChem [Wiley]
卷期号:11 (6): 1196-1206 被引量:13
标识
DOI:10.1002/cphc.200900889
摘要

Haehnel et al. synthesized 399 different artificial cytochrome b (aCb) models.1 They consist of a template-assisted four-helix bundle with one embedded heme group. Their redox potentials were measured and cover the range from −148 to −89 mV. No crystal structures of these aCb are available. Therefore, we use the chemical composition and general structural principles to generate atomic coordinates of 31 of these aCb mutants, which are chosen to cover the whole interval of redox potentials. We start by modeling the coordinates of one aCb from scratch. Its structure remains stable after energy minimization and during molecular dynamics simulation over 2 ns. Based on this structure, coordinates of the other 30 aCb mutants are modeled. The calculated redox potentials for these 31 aCb agree within 10 mV with the experimental values in terms of root mean square deviation. Analysis of the dependence of heme redox potential on protein environment shows that the shifts in redox potentials relative to the model systems in water are due to the low-dielectric medium of the protein and the protonation states of the heme propionic acid groups, which are influenced by the surrounding amino acids. Alternatively, we perform a blind prediction of the same redox potentials using an empirical approach based on a linear scoring function and reach a similar accuracy. Both methods are useful to understand and predict heme redox potentials. Based on the modeled structure we can understand the detailed structural differences between aCb mutants that give rise to shifts in heme redox potential. On the other hand, one can explore the correlation between sequence variations and aCb redox potentials more directly and on much larger scale using the empirical prediction scheme, which—thanks to its simplicity— is much faster.

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