Suppr超能文献

基于密度泛函理论的短链醇脱氢酶反应活性预测

DFT-based prediction of reactivity of short-chain alcohol dehydrogenase.

作者信息

Stawoska I, Dudzik A, Wasylewski M, Jemioła-Rzemińska M, Skoczowski A, Strzałka K, Szaleniec M

机构信息

Institute of Biology, Pedagogical University of Cracow, Podchorążych 2, 30-084, Kraków, Poland.

Jerzy Haber Institute of Catalysis and Surface Chemistry, Polish Academy of Sciences, Niezapominajek 8, 30-239, Kraków, Poland.

出版信息

J Comput Aided Mol Des. 2017 Jun;31(6):587-602. doi: 10.1007/s10822-017-0026-5. Epub 2017 May 26.

Abstract

The reaction mechanism of ketone reduction by short chain dehydrogenase/reductase, (S)-1-phenylethanol dehydrogenase from Aromatoleum aromaticum, was studied with DFT methods using cluster model approach. The characteristics of the hydride transfer process were investigated based on reaction of acetophenone and its eight structural analogues. The results confirmed previously suggested concomitant transfer of hydride from NADH to carbonyl C atom of the substrate with proton transfer from Tyr to carbonyl O atom. However, additional coupled motion of the next proton in the proton-relay system, between O2' ribose hydroxyl and Tyr154 was observed. The protonation of Lys158 seems not to affect the pKa of Tyr154, as the stable tyrosyl anion was observed only for a neutral Lys158 in the high pH model. The calculated reaction energies and reaction barriers were calibrated by calorimetric and kinetic methods. This allowed an excellent prediction of the reaction enthalpies (R = 0.93) and a good prediction of the reaction kinetics (R = 0.89). The observed relations were validated in prediction of log K obtained for real whole-cell reactor systems that modelled industrial synthesis of S-alcohols.

摘要

采用簇模型方法,运用密度泛函理论(DFT)方法研究了来自芳香油杆菌的短链脱氢酶/还原酶(S)-1-苯乙醇脱氢酶催化酮还原的反应机理。基于苯乙酮及其八个结构类似物的反应,研究了氢化物转移过程的特征。结果证实了先前提出的氢化物从NADH伴随转移到底物的羰基C原子,同时质子从酪氨酸转移到羰基O原子的过程。然而,观察到质子中继系统中,O2'核糖羟基和Tyr154之间的下一个质子存在额外的耦合运动。Lys158的质子化似乎不影响Tyr154的pKa,因为仅在高pH模型中中性的Lys158情况下观察到稳定的酪氨酸阴离子。通过量热法和动力学方法对计算得到的反应能量和反应势垒进行了校准。这使得对反应焓的预测非常出色(R = 0.93),对反应动力学的预测也很好(R = 0.89)。所观察到的关系在预测模拟S-醇工业合成的实际全细胞反应器系统的log K时得到了验证。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8853/5487757/7e7b21da0d74/10822_2017_26_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验