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影响酪氨酸硝化的因素——基于结构的预测模型。

Factors influencing protein tyrosine nitration--structure-based predictive models.

机构信息

Department of Medicinal Chemistry and Institute for Structural Biology and Drug Discovery, Massey Cancer Center, Virginia Commonwealth University, Richmond, VA 23298, USA.

出版信息

Free Radic Biol Med. 2011 Mar 15;50(6):749-62. doi: 10.1016/j.freeradbiomed.2010.12.016. Epub 2010 Dec 21.

Abstract

Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high-resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged side chain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines for which there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases, predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives).

摘要

基于具有高分辨率 X 射线晶体学或 NMR 数据的 20 种蛋白质的 3D 结构特征,以及在氧化应激条件下已通过实验证明对 35 个总酪氨酸进行硝化的情况,已经创建了用于探索蛋白质中酪氨酸硝化的模型。在先前的工作中,使用定量结构描述符检查了增强硝化的因素。相邻酸性和碱性残基的作用很复杂:对于大多数被硝化的酪氨酸,与最近的带电荷侧链的杂原子的距离与怀疑的硝化物种在酪氨酸和带电荷氨基酸之间形成氢键桥所需的距离相对应。这表明这种桥在酪氨酸硝化中起着非常重要的作用。对于被埋藏的酪氨酸和硝基基团空间不足的酪氨酸,硝化通常受到阻碍。对于体外硝化,具有附近杂原子或不饱和中心的封闭环境可以稳定自由基,因此更有利。已经针对硝化的不同条件开发了四个基于定量结构的模型,用于预测特定酪氨酸硝化的位置。最佳模型适用于体外和体内情况,可预测 35 个酪氨酸硝化中的 30 个(阳性预测值),并且具有 60/71(11 个假阳性)的敏感性。

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