RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites
RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites
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DOI：
10.1016/j.csbj.2020.02.012
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发表时间：
2020-01-01
影响因子：
6
通讯作者：
Kc, Dukka
中科院分区：
文献类型：
--
作者：
AL-barakati, Hussam;Thapa, Niraj;Kc, Dukka
丙二酸化是最近出现的一种重要的赖氨酸修饰，调节多种生物活性，并与包括心血管疾病和癌症在内的几种广泛性疾病有关。然而，使用串联质谱仪进行传统的全球蛋白质组学分析可能会耗时、昂贵且具有技术挑战性。因此，为了补充和扩展现有的丙二酰化位点识别的实验方法，我们开发了两种新的基于随机森林和深度学习机器学习算法的丙二酰化位点预测计算方法，分别是RF-MaloSite和DL-MaloSite。DL-MaloSite需要初级氨基酸序列作为输入，而RF-MaloSite利用了一系列不同的生化、物理化学和基于序列的特征。虽然对性能指标的系统评估表明，‘RFMaloSite’和‘DL-MaloSite’在所有测试的指标中都表现良好，但我们的方法在准确性、敏感性和总体方法性能(通过马太相关系数评估)方面表现得特别好。例如，RF-MaloSite使用10倍交叉验证和独立测试集分别显示MCC分数为0.42和0.40。同时，基于10次交叉验证和独立设置，DL-MaloSite的MCC得分分别为0.51和0.49。重要的是，这两种方法的效率得分都与现有的丙二酸化位点预测方法持平或更高。这些位点的识别也可能为丙二酸化和其他赖氨酸修饰之间的串扰机制提供重要的见解，如乙酰化、戊二酸化和琥珀酸化。为了方便他们的使用，这两种方法都免费提供给Lps://github.comjdukkakcIDL-MaloSite-and-RF-MaloSite.的研究社区(C)2020作者。由Elsevier B.V.代表计算和结构生物技术研究网络出版。
Malonylation, which has recently emerged as an important lysine modification, regulates diverse biological activities and has been implicated in several pervasive disorders, including cardiovascular disease and cancer. However, conventional global proteomics analysis using tandem mass spectrometry can be time-consuming, expensive and technically challenging. Therefore, to complement and extend existing experimental methods for malonylation site identification, we developed two novel computational methods for malonylation site prediction based on random forest and deep learning machine learning algorithms, RF-MaloSite and DL-MaloSite, respectively. DL-MaloSite requires the primary amino acid sequence as an input and RF-MaloSite utilizes a diverse set of biochemical, physiochemical and sequence-based features. While systematic assessment of performance metrics suggests that both 'RFMaloSite' and `DL-MaloSite' perform well in all metrics tested, our methods perform particularly well in the areas of accuracy, sensitivity and overall method performance (assessed by the Matthew's Correlation Coefficient). For instance, RF-MaloSite exhibited MCC scores of 0.42 and 0.40 using 10-fold cross-validation and an independent test set, respectively. Meanwhile, DL-MaloSite was characterized by MCC scores of 0.51 and 0.49 based on 10-fold cross-validation and an independent set, respectively. Importantly, both methods exhibited efficiency scores that were on par or better than those achieved by existing malonylation site prediction methods. The identification of these sites may also provide important insights into the mechanisms of crosstalk between malonylation and other lysine modifications, such as acetylation, glutarylation and succinylation. To facilitate their use, both methods have been made freely available to the research community at Lps://github.comjdukkakcIDL-MaloSite-and-RF-MaloSite. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.