Malonylation is a recently discovered post‐translational modification (PTM) in which a malonyl group attaches to a lysine (K) amino acid residue of a protein. In this work, a novel machine learning model, SPRINT‐Mal, is developed to predict malonylation sites by employing sequence and predicted structural features. Evolutionary information and physicochemical properties are found to be the two most discriminative features whereas a structural feature called half‐sphere exposure provides additional improvement to the prediction performance. SPRINT‐Mal trained on mouse data yields robust performance for 10‐fold cross validation and independent test set with Area Under the Curve (AUC) values of 0.74 and 0.76 and Matthews’ Correlation Coefficient (MCC) of 0.213 and 0.20, respectively. Moreover, SPRINT‐Mal achieved comparable performance when testing on H. sapiens proteins without species‐specific training but not in bacterium S. erythraea. This suggests similar underlying physicochemical mechanisms between mouse and human but not between mouse and bacterium. SPRINT‐Mal is freely available as an online server at: http://sparks-lab.org/server/SPRINT-Mal/. © 2018 Wiley Periodicals, Inc.

中文翻译：

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使用序列和预测的结构特征预测蛋白质的赖氨酸丙二酰化位点

丙二酰化是最近发现的一种翻译后修饰 (PTM)，其中丙二酰基团连接到蛋白质的赖氨酸 (K) 氨基酸残基上。在这项工作中，开发了一种新的机器学习模型 SPRINT-Mal，通过使用序列和预测的结构特征来预测丙二酰化位点。发现进化信息和物理化学特性是两个最具辨别力的特征，而称为半球暴露的结构特征为预测性能提供了额外的改进。在小鼠数据上训练的 SPRINT-Mal 对 10 倍交叉验证和独立测试集产生了强大的性能，曲线下面积 (AUC) 值分别为 0.74 和 0.76，马修斯相关系数 (MCC) 分别为 0.213 和 0.20。而且，SPRINT-Mal 在没有物种特异性训练的情况下对 H. sapiens 蛋白质进行测试时取得了相当的性能，但在细菌 S. erythraea 中则没有。这表明小鼠和人类之间存在相似的潜在物理化学机制，但小鼠和细菌之间却没有。SPRINT-Mal 可作为在线服务器免费使用：http://sparks-lab.org/server/SPRINT-Mal/。© 2018 Wiley Periodicals, Inc.