Factors influencing protein tyrosine nitration--structure-based predictive models.
Factors influencing protein tyrosine nitration--structure-based predictive models.
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DOI：
10.1016/j.freeradbiomed.2010.12.016
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发表时间：
2011-03-15
影响因子：
7.4
通讯作者：
Kellogg, Glen E.
中科院分区：
文献类型：
--
作者：
Bayden, Alexander S.;Yakovlev, Vasily A.;Graves, Paul R.;Mikkelsen, Ross B.;Kellogg, Glen E.
探索蛋白质中酪氨酸硝化的模型已经基于20种蛋白质的3D结构特征创建，其中高分辨率X射线晶体学或NMR数据可用，并且35种总酪氨酸的硝化已经在氧化应激下实验证明。在以前的工作中，以提高硝化的因素进行了研究与定量结构描述符。相邻的酸性和碱性残基的作用是复杂的：对于大多数被硝化的酪氨酸，到最近带电侧链的杂原子的距离对应于疑似硝化物质在酪氨酸和带电氨基酸之间形成氢键桥所需的距离。这表明这种桥在酪氨酸硝化中起着非常重要的作用。对于被掩埋的酪氨酸和对于其中硝基空间不足的那些酪氨酸，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。四个定量的基于结构的模型，根据硝化的条件，已开发用于预测位点特异性酪氨酸硝化。最好的模型，在体外和体内的情况下，预测35个酪氨酸硝化（阳性预测值）的30个，并具有60/71（11假阳性）的灵敏度。
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 sidechain 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 where 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).
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影响因子：
7.3
作者：
Cozzini, P;Fornabaio, M;Mozzarelli, A
通讯作者：
Mozzarelli, A
影响因子：
3.5
作者：
Donnini, Sandra;Monti, Martina;Ziche, Marina
通讯作者：
Ziche, Marina
DOI：
10.1021/ci000036s
发表时间：
2001-01-01
期刊：
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
影响因子：
--
作者：
Izrailev, S;Agrafiotis, D
通讯作者：
Agrafiotis, D
影响因子：
2.9
作者：
Han, D;Canali, R;Cadenas, E
通讯作者：
Cadenas, E
影响因子：
5.6
作者：
Dairou, Julien;Pluvinage, Benjamin;Rodrigues-Lima, Fernando
通讯作者：
Rodrigues-Lima, Fernando