Aims: Post-Translational Modifications (PTMs), which include more than 450 types, can be regarded as the fundamental cellular regulation.

Background: Recently, experiments demonstrated that the lysine malonylation modification is a significant process in several organisms and cells. Meanwhile, malonylation plays an important role in the regulation of protein subcellular localization, stability, translocation to lipid rafts and many other protein functions.

Objective: Identification of malonylation will contribute to understanding the molecular mechanism in the field of biology. Nevertheless, several existing experimental approaches, which can hardly meet the need of the high speed data generation, are expensive and time-consuming. Moreover, some machine learning methods can hardly meet the high-accuracy need in this issue.

Methods: In this study, we proposed a method, named MSIT that means malonylation sites identification tree, utilized the amino acid residues and profile information to identify the lysine malonylation sites with the tree structural neural network in the peptides sequence level.

Results: The proposed algorithm can get 0.8699 of F1 score and 89.34% in true positive ratio in E. coli. MSIT outperformed existing malonylation site identification methods and features on different species datasets.

Conclusion: Based on these measures, it can be demonstrated that MSIT will be helpful in identifying candidate malonylation sites.

目标：包括450多种类型的翻译后修饰（PTM），可以被视为基本的细胞调节。

背景：最近，实验表明，赖氨酸的丙二酰化修饰在几种生物和细胞中是一个重要的过程。同时，丙二酰化在调节蛋白亚细胞定位，稳定性，向脂筏的转运以及许多其他蛋白功能中起重要作用。

目的：丙二酰化的鉴定将有助于理解生物学领域的分子机制。然而，几种现有的实验方法既昂贵又费时，几乎不能满足高速数据生成的需要。此外，某些机器学习方法几乎无法满足此问题中的高精度需求。

方法：在这项研究中，我们提出了一种名为MSIT的方法，该方法表示丙二酰化位点识别树，利用氨基酸残基和谱图信息通过树结构神经网络在肽序列水平上鉴定赖氨酸的丙二酰化位点。

结果：该算法在大肠杆菌中可获得F699分数0.8699，真阳性率89.34％。MSIT在不同物种数据集上的表现优于现有的丙二酰化位点识别方法和功能。

结论：基于这些措施，可以证明MSIT将有助于识别候选的丙二酰化位点。