Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences
IF 3.4 4区 生物学Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
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引用次数: 0
Abstract
The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation
翻译后修饰是调节各种细胞过程的重要分子调控机制。蛋白质的丙二酰化是赖氨酸/k 残基的一种可逆翻译后修饰,与细胞调控和致病机理等多种生物功能有关。这种修饰在新陈代谢途径、线粒体功能、脂肪酸氧化和其他生命过程中起着至关重要的作用。然而,准确鉴定丙二酰化位点对于了解丙二酰化的分子机制至关重要,而实验鉴定可能是一项具有挑战性且成本高昂的任务。最近,有人提出了基于机器学习(ML)的方法来解决这一问题。事实证明,这些方法在提高准确性的同时,也降低了成本和时间限制。然而,这些方法也存在一些具体的缺陷,包括蛋白质序列特征提取不当、高维特征和底层分类器效率低下。因此,迫切需要有效的预测和计算方法。在本研究中,我们对用于预测蛋白质序列中丙二酰化位点的现有预测模型、工具和基准数据集进行了全面分析和综述,并进行了比较研究。综述包括基准数据集的规格、对特征和编码方法的解释、对预测方法及其嵌入的 ML 或深度学习模型的描述,以及对该领域现有工具的描述和比较。为了评估和比较工具的预测能力,我们根据最新的数据库提取了一批新数据,并根据提取的数据对工具进行了评估。最后,我们提出了一种由多个分类器(包括经典 ML 模型和深度学习模型)组成的混合架构,以组合预测结果。与本研究中的所有预测工具相比,这种方法表现出更好的性能(本手稿中介绍的模型源代码可在 https://github.com/Malonylation 中获取)。数据库网址:https://github.com/A-Golshan/Malonylation
期刊介绍:
Huge volumes of primary data are archived in numerous open-access databases, and with new generation technologies becoming more common in laboratories, large datasets will become even more prevalent. The archiving, curation, analysis and interpretation of all of these data are a challenge. Database development and biocuration are at the forefront of the endeavor to make sense of this mounting deluge of data.
Database: The Journal of Biological Databases and Curation provides an open access platform for the presentation of novel ideas in database research and biocuration, and aims to help strengthen the bridge between database developers, curators, and users.