Post Transactional Modification (PTM) is a vital process which plays an important role in a wide range of biological interactions. One of the most recently identified PTMs is Malonylation. It has been shown that Malonylation has an important impact on different biological pathways including glucose and fatty acid metabolism. Malonylation can be detected experimentally using mass spectrometry. However, this process is both costly and time-consuming which has inspired research to find more efficient and fast computational methods to solve this problem. This paper proposes a novel approach, called SEMal, to identify Malonylation sites in protein sequences. It uses both structural and evolutionary-based features to solve this problem. It also uses Rotation Forest (RoF) as its classification technique to predict Malonylation sites. To the best of our knowledge, our extracted features as well as our employed classifier have never been used for this problem. Compared to the previously proposed methods, SEMal outperforms them in all metrics such as sensitivity (0.94 and 0.89), accuracy (0.94 and 0.91), and Matthews correlation coefficient (0.88 and 0.82), for Homo Sapiens and Mus Musculus species, respectively. SEMal is publicly available as an online predictor at: http://brl.uiu.ac.bd/SEMal/.

交易后修饰（PTM）是至关重要的过程，在广泛的生物相互作用中起着重要作用。最近发现的PTM之一是丙二酰化。已经显示，丙二酰化作用对包括葡萄糖和脂肪酸代谢的不同生物途径具有重要影响。丙二酸化可以使用质谱实验地检测。但是，该过程既昂贵又耗时，这激发了研究人员寻找更有效，更快速的计算方法来解决该问题的灵感。本文提出了一种新方法，称为SEMal，用于识别蛋白质序列中的丙二酰化位点。它同时使用基于结构和基于进化的功能来解决此问题。它还使用旋转森林（RoF）作为其分类技术来预测丙二酰化位点。据我们所知，我们提取的特征以及我们使用的分类器从未用于此问题。与以前提出的方法相比，SEMal在所有指标上都优于它们，例如灵敏度（0.94和0.89），准确性（0.94和0.91）和Matthews相关系数（0.88和0.82）。智人和小家鼠物种。SEMal可作为在线预测变量公开获得，网址为：http：//brl.uiu.ac.bd/SEMal/。