Department of Plant Biology, University of Szeged, Közép fasor 52, 6726, Szeged, Hungary.
Institute of Biochemical Plant Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstaedter Landstr. 1, D-85764, Oberschleißheim, München, Germany.
Plant Physiol Biochem. 2021 Oct;167:851-861. doi: 10.1016/j.plaphy.2021.09.011. Epub 2021 Sep 13.
The perception and transduction of nitric oxide (NO) signal is achieved by NO-dependent posttranslational modifications (PTMs) among which S-nitrosation and tyrosine nitration has biological significance. In plants, 100-1000 S-nitrosated and tyrosine nitrated proteins have been identified so far by mass spectrometry. The determination of NO-modified protein targets/amino acid residues is often methodologically challenging. In the past decade, the growing demand for the knowledge of S-nitrosated or tyrosine nitrated sites has motivated the introduction of bioinformatics tools. For predicting S-nitrosation seven computational tools have been developed (GPS-SNO, SNOSite, iSNO-PseACC, iSNO-AAPAir, PSNO, PreSNO, RecSNO). Four predictors have been developed for indicating tyrosine nitration sites (GPS-YNO2, iNitro-Tyr, PredNTS, iNitroY-Deep), and one tool (DeepNitro) predicts both NO-dependent PTMs. The advantage of these computational tools is the fast provision of large amount of information. In this review, the available software tools have been tested on plant proteins in which S-nitrosated or tyrosine nitrated sites have been experimentally identified. The predictors showed distinct performance and there were differences from the experimental results partly due to the fact that the three-dimensional protein structure is not taken into account by the computational tools. Nevertheless, the predictors excellently establish experiments, and it is suggested to apply all available tools on target proteins and compare their results. In the future, computational prediction must be developed further to improve the precision with which S-nitrosation/tyrosine nitration-sites are identified.
一氧化氮（NO）信号的感知和转导是通过 NO 依赖性的翻译后修饰（PTMs）实现的，其中 S-亚硝化和酪氨酸硝化具有生物学意义。在植物中，到目前为止，通过质谱已经鉴定了 100-1000 种 S-亚硝化和酪氨酸硝化蛋白。NO 修饰蛋白靶标/氨基酸残基的测定通常在方法学上具有挑战性。在过去的十年中，对 S-亚硝化或酪氨酸硝化位点知识的需求不断增长，这促使人们引入了生物信息学工具。为了预测 S-亚硝化，已经开发了七个计算工具（GPS-SNO、SNOSite、iSNO-PseACC、iSNO-AAPAir、PSNO、PreSNO、RecSNO）。已经开发了四个预测酪氨酸硝化位点的工具（GPS-YNO2、iNitro-Tyr、PredNTS、iNitroY-Deep），并且有一种工具（DeepNitro）可以预测两种 NO 依赖性 PTM。这些计算工具的优点是可以快速提供大量信息。在这篇综述中，已经在实验中鉴定了 S-亚硝化或酪氨酸硝化位点的植物蛋白上测试了可用的软件工具。这些预测器表现出不同的性能，与实验结果存在差异，部分原因是计算工具没有考虑三维蛋白质结构。然而，预测器可以很好地验证实验，建议将所有可用的工具应用于目标蛋白，并比较它们的结果。在未来，计算预测必须进一步发展，以提高 S-亚硝化/酪氨酸硝化位点识别的精度。
