首页> 外文期刊>Free Radical Biology and Medicine: The Official Journal of the Oxygen Society >Factors influencing protein tyrosine nitration--structure-based predictive models.
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Factors influencing protein tyrosine nitration--structure-based predictive models.

机译:影响蛋白质酪氨酸硝化的因素-基于结构的预测模型。

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摘要

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).
机译:基于20种蛋白质的3D结构特征创建了探索蛋白质中酪氨酸硝化的模型,这些蛋白质具有高分辨率的X射线晶体学或NMR数据,并且在氧化应激下已通过实验证明了35种酪氨酸的硝化作用。先前工作中建议的增强硝化作用的因素已通过定量结构描述符进行了研究。相邻的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,到最接近带电侧链杂原子的距离对应于可疑硝化物种在酪氨酸和带电之间形成氢键桥所需的距离。氨基酸。这表明这种桥在酪氨酸硝化中起非常重要的作用。对于掩埋的酪氨酸和对于硝基而言空间不足的酪氨酸,通常会阻碍硝化。对于体外硝化,在某种程度上有利于具有能够稳定自由基的杂原子或不饱和中心的封闭环境。根据硝化条件,已经开发了四种基于定量结构的模型来预测位点特异性酪氨酸硝化。最好的模型,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),灵敏度为60/71(11个假阳性)。

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