SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.
SVM-SulfoSite: A support vector machine based predictor for sulfenylation sites.
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DOI：
10.1038/s41598-018-29126-x
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发表时间：
2018-07-26
影响因子：
4.6
通讯作者：
Kc DB
中科院分区：
文献类型：
--
作者：
Al-Barakati HJ;McConnell EW;Hicks LM;Poole LB;Newman RH;Kc DB
蛋白质S-亚磺酰化是由半胱氨酸残基上的游离巯基氧化而产生的，最近已成为一种重要的翻译后修饰，其调节参与各种生理和病理过程的蛋白质的结构和功能。通过改变修饰的半胱氨酸残基的大小和理化性质，亚磺酰化可以以几种不同的方式影响蛋白质的细胞功能。因此，快速准确地识别蛋白质中假定的亚磺酰化位点的能力将为各种细胞环境中蛋白质功能的氧化还原依赖性调节提供重要的见解。虽然自下而上的蛋白质组学方法，如串联质谱法（MS/MS），提供了丰富的信息，全球变化的亚磺酰化状态的蛋白质，MS/MS为基础的实验往往是劳动密集型的，昂贵的和技术上的挑战。因此，为了补充现有的蛋白质组学方法，研究人员开发了一系列计算工具来识别蛋白质上假定的亚磺酰化位点。然而，现有的方法通常遭受低准确性、特异性和/或灵敏度。在这项研究中，我们开发了SVM-SulfoSite，一种新的亚磺酰化预测工具，使用支持向量机（SVM）来识别五个特征类别中的亚磺酰化的关键决定因素：二进制代码，理化性质，k空间氨基酸对，氨基酸组成和高质量的理化指标。使用10倍交叉验证，SVM-SulfoSite实现了95%的灵敏度和83%的特异性，总体准确度为89%，Matthew相关系数（MCC）为0.79。同样，使用实验鉴定的亚磺酰化位点的独立测试集，我们的方法在准确性、灵敏度、特异性和MCC方面的得分分别为74%、62%、80%和0.42，受试者工作特征（ROC）曲线下面积为0.81。此外，在并排比较中，SVM-SulfoSite的表现与现有的亚磺酰化预测工具一样好或更好。总之，这些结果表明，我们的方法代表了一个强大的和互补的技术，先进的探索蛋白质S-亚磺酰化。
Protein S-sulfenylation, which results from oxidation of free thiols on cysteine residues, has recently emerged as an important post-translational modification that regulates the structure and function of proteins involved in a variety of physiological and pathological processes. By altering the size and physiochemical properties of modified cysteine residues, sulfenylation can impact the cellular function of proteins in several different ways. Thus, the ability to rapidly and accurately identify putative sulfenylation sites in proteins will provide important insights into redox-dependent regulation of protein function in a variety of cellular contexts. Though bottom-up proteomic approaches, such as tandem mass spectrometry (MS/MS), provide a wealth of information about global changes in the sulfenylation state of proteins, MS/MS-based experiments are often labor-intensive, costly and technically challenging. Therefore, to complement existing proteomic approaches, researchers have developed a series of computational tools to identify putative sulfenylation sites on proteins. However, existing methods often suffer from low accuracy, specificity, and/or sensitivity. In this study, we developed SVM-SulfoSite, a novel sulfenylation prediction tool that uses support vector machines (SVM) to identify key determinants of sulfenylation among five feature classes: binary code, physiochemical properties, k-space amino acid pairs, amino acid composition and high-quality physiochemical indices. Using 10-fold cross-validation, SVM-SulfoSite achieved 95% sensitivity and 83% specificity, with an overall accuracy of 89% and Matthew’s correlation coefficient (MCC) of 0.79. Likewise, using an independent test set of experimentally identified sulfenylation sites, our method achieved scores of 74%, 62%, 80% and 0.42 for accuracy, sensitivity, specificity and MCC, with an area under the receiver operator characteristic (ROC) curve of 0.81. Moreover, in side-by-side comparisons, SVM-SulfoSite performed as well as or better than existing sulfenylation prediction tools. Together, these results suggest that our method represents a robust and complementary technique for advanced exploration of protein S-sulfenylation.
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影响因子：
--
作者：
Ismail HD;Jones A;Kim JH;Newman RH;Kc DB
通讯作者：
Kc DB
影响因子：
--
作者：
Ismail HD;Newman RH;Kc DB
通讯作者：
Kc DB
DOI：
10.1093/protein/2.3.185
发表时间：
1988-09-01
期刊：
PROTEIN ENGINEERING
影响因子：
--
作者：
BIOU, V;GIBRAT, JF;GARNIER, J
通讯作者：
GARNIER, J
影响因子：
2.9
作者：
MAXFIELD, FR;SCHERAGA, HA
通讯作者：
SCHERAGA, HA
影响因子：
20.1
作者：
Chung HS;Wang SB;Venkatraman V;Murray CI;Van Eyk JE
通讯作者：
Van Eyk JE