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mUSP:通过特征增强方法预测的泛素化和 SUMO 化蛋白质组原位串扰的高精度图谱。

mUSP: a high-accuracy map of the in situ crosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach.

机构信息

Department of Chemistry, Nanchang University, 999 Xuefu Road, Nanchang, Jiangxi, China.

出版信息

Brief Bioinform. 2021 May 20;22(3). doi: 10.1093/bib/bbaa050.

Abstract

Reversible post-translational modification (PTM) orchestrates various biological processes by changing the properties of proteins. Since many proteins are multiply modified by PTMs, identification of PTM crosstalk site has emerged to be an intriguing topic and attracted much attention. In this study, we systematically deciphered the in situ crosstalk of ubiquitylation and SUMOylation that co-occurs on the same lysine residue. We first collected 3363 ubiquitylation-SUMOylation (UBS) crosstalk site on 1302 proteins and then investigated the prime sequence motifs, the local evolutionary degree and the distribution of structural annotations at the residue and sequence levels between the UBS crosstalk and the single modification sites. Given the properties of UBS crosstalk sites, we thus developed the mUSP classifier to predict UBS crosstalk site by integrating different types of features with two-step feature optimization by recursive feature elimination approach. By using various cross-validations, the mUSP model achieved an average area under the curve (AUC) value of 0.8416, indicating its promising accuracy and robustness. By comparison, the mUSP has significantly better performance with the improvement of 38.41 and 51.48% AUC values compared to the cross-results by the previous single predictor. The mUSP was implemented as a web server available at http://bioinfo.ncu.edu.cn/mUSP/index.html to facilitate the query of our high-accuracy UBS crosstalk results for experimental design and validation.

摘要

可逆的翻译后修饰(PTM)通过改变蛋白质的性质来协调各种生物过程。由于许多蛋白质被 PTM 多次修饰,因此鉴定 PTM 串扰位点已成为一个有趣的话题,并引起了广泛关注。在这项研究中,我们系统地破译了同一赖氨酸上同时发生的泛素化和 SUMO 化的原位串扰。我们首先收集了 3363 个位于 1302 个蛋白质上的泛素-SUMO 化(UBS)串扰位点,然后在残基和序列水平上研究了单修饰位点的核心序列基序、局部进化程度和结构注释的分布。鉴于 UBS 串扰位点的特性,我们因此开发了 mUSP 分类器,通过整合不同类型的特征,并通过递归特征消除方法进行两步特征优化,来预测 UBS 串扰位点。通过各种交叉验证,mUSP 模型的平均曲线下面积(AUC)值达到 0.8416,表明其具有较高的准确性和稳健性。相比之下,mUSP 的表现明显更好,与之前的单预测器的交叉结果相比,AUC 值提高了 38.41%和 51.48%。mUSP 已作为一个网络服务器实现,可在 http://bioinfo.ncu.edu.cn/mUSP/index.html 上访问,以方便查询我们高精度的 UBS 串扰结果,用于实验设计和验证。

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