School of Basic Medical Science, Qingdao University, Dengzhou Road, Qingdao, Shandong, China.
Medicinal Chemistry, Leiden Academic Centre for Drug Research,Einsteinweg, Leiden, The Netherlands.
Brief Bioinform. 2019 Nov 27;20(6):2267-2290. doi: 10.1093/bib/bby089.
Lysine post-translational modifications (PTMs) play a crucial role in regulating diverse functions and biological processes of proteins. However, because of the large volumes of sequencing data generated from genome-sequencing projects, systematic identification of different types of lysine PTM substrates and PTM sites in the entire proteome remains a major challenge. In recent years, a number of computational methods for lysine PTM identification have been developed. These methods show high diversity in their core algorithms, features extracted and feature selection techniques and evaluation strategies. There is therefore an urgent need to revisit these methods and summarize their methodologies, to improve and further develop computational techniques to identify and characterize lysine PTMs from the large amounts of sequence data. With this goal in mind, we first provide a comprehensive survey on a large collection of 49 state-of-the-art approaches for lysine PTM prediction. We cover a variety of important aspects that are crucial for the development of successful predictors, including operating algorithms, sequence and structural features, feature selection, model performance evaluation and software utility. We further provide our thoughts on potential strategies to improve the model performance. Second, in order to examine the feasibility of using deep learning for lysine PTM prediction, we propose a novel computational framework, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs), using deep, bidirectional, long short-term memory recurrent neural networks for accurate and systematic mapping of eight major types of lysine PTMs in the human and mouse proteomes. Extensive benchmarking tests show that MUscADEL outperforms current methods for lysine PTM characterization, demonstrating the potential and power of deep learning techniques in protein PTM prediction. The web server of MUscADEL, together with all the data sets assembled in this study, is freely available at http://muscadel.erc.monash.edu/. We anticipate this comprehensive review and the application of deep learning will provide practical guide and useful insights into PTM prediction and inspire future bioinformatics studies in the related fields.
赖氨酸翻译后修饰（PTMs）在调节蛋白质的多种功能和生物过程中起着至关重要的作用。然而，由于基因组测序项目产生的测序数据量庞大，系统地鉴定整个蛋白质组中不同类型的赖氨酸 PTM 底物和 PTM 位点仍然是一个主要挑战。近年来，已经开发了许多用于赖氨酸 PTM 鉴定的计算方法。这些方法在其核心算法、提取的特征和特征选择技术以及评估策略方面表现出高度的多样性。因此，迫切需要重新审视这些方法并总结它们的方法学，以改进和进一步开发从大量序列数据中识别和表征赖氨酸 PTM 的计算技术。考虑到这一目标，我们首先对 49 种最新的赖氨酸 PTM 预测方法进行了全面调查。我们涵盖了对成功预测器的发展至关重要的各种重要方面，包括操作算法、序列和结构特征、特征选择、模型性能评估和软件实用性。我们进一步提出了提高模型性能的潜在策略。其次，为了检验使用深度学习进行赖氨酸 PTM 预测的可行性，我们提出了一种新的计算框架，称为 MUscADEL（用于赖氨酸 PTM 的多可扩展准确深度学习器），使用深度、双向、长短时记忆递归神经网络对人类和小鼠蛋白质组中的八种主要类型的赖氨酸 PTM 进行准确和系统的映射。广泛的基准测试表明，MUscADEL 在赖氨酸 PTM 特征描述方面优于当前的方法，证明了深度学习技术在蛋白质 PTM 预测中的潜力和优势。MUscADEL 的网络服务器以及本研究中组装的所有数据集均可在 http://muscadel.erc.monash.edu/ 免费获得。我们预计，这项全面的综述和深度学习的应用将为 PTM 预测提供实用指南和有用的见解，并激发相关领域的未来生物信息学研究。
