Ahmad Wakil, Arafat Easin, Taherzadeh Ghazaleh, Sharma Alok, Dipta Shubhashis Roy, Dehzangi Abdollah, Shatabda Swakkhar
Department of Computer Science and Engineering, United International University, United City, Madani Avenue, Dhaka 1212, Bangladesh.
Institute for Bioscience and Biotechnology Research, University of Maryland, College Park, MD, 20742, USA.
IEEE Access. 2020;8:77888-77902. doi: 10.1109/access.2020.2989713. Epub 2020 Apr 22.
Post Translational Modification (PTM) is considered an important biological process with a tremendous impact on the function of proteins in both eukaryotes, and prokaryotes cells. During the past decades, a wide range of PTMs has been identified. Among them, malonylation is a recently identified PTM which plays a vital role in a wide range of biological interactions. Notwithstanding, this modification plays a potential role in energy metabolism in different species including Homo Sapiens. The identification of PTM sites using experimental methods is time-consuming and costly. Hence, there is a demand for introducing fast and cost-effective computational methods. In this study, we propose a new machine learning method, called Mal-Light, to address this problem. To build this model, we extract local evolutionary-based information according to the interaction of neighboring amino acids using a bi-peptide based method. We then use Light Gradient Boosting (LightGBM) as our classifier to predict malonylation sites. Our results demonstrate that Mal-Light is able to significantly improve malonylation site prediction performance compared to previous studies found in the literature. Using Mal-Light we achieve Matthew's correlation coefficient (MCC) of 0.74 and 0.60, Accuracy of 86.66% and 79.51%, Sensitivity of 78.26% and 67.27%, and Specificity of 95.05% and 91.75%, for Homo Sapiens and Mus Musculus proteins, respectively. Mal-Light is implemented as an online predictor which is publicly available at: (http://brl.uiu.ac.bd/MalLight/).
翻译后修饰（PTM）被认为是一个重要的生物学过程，对真核生物和原核生物细胞中蛋白质的功能都有巨大影响。在过去几十年中，已经鉴定出了各种各样的PTM。其中，丙二酰化是最近鉴定出的一种PTM，它在广泛的生物相互作用中起着至关重要的作用。尽管如此，这种修饰在包括智人在内的不同物种的能量代谢中发挥着潜在作用。使用实验方法鉴定PTM位点既耗时又昂贵。因此，需要引入快速且经济高效的计算方法。在本研究中，我们提出了一种新的机器学习方法，称为Mal-Light，以解决这个问题。为了构建这个模型，我们使用基于双肽的方法，根据相邻氨基酸的相互作用提取基于局部进化的信息。然后，我们使用轻量级梯度提升（LightGBM）作为分类器来预测丙二酰化位点。我们的结果表明，与文献中先前的研究相比，Mal-Light能够显著提高丙二酰化位点的预测性能。使用Mal-Light，我们分别在智人和小家鼠蛋白质上实现了马修斯相关系数（MCC）为0.74和0.60，准确率为86.66%和79.51%，灵敏度为78.26%和67.27%，以及特异性为95.05%和91.75%。Mal-Light作为一个在线预测器实现，可在以下网址公开获取：(http://brl.uiu.ac.bd/MalLight/)。
