Large-scale comparative assessment of computational predictors for lysine post-translational modification sites

Abstract

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.

Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks

“…Unlike the traditional ML algorithms that require pre-defined features, DL is capable of learning sparse representation in a self-taught manner with the inclusion of multiple hidden layers. DL has been applied to the prediction of various kinds of modification sites and demonstrated great performances [14], such as protein phosphorylation [15], [16], lysine malonylation [17], protein nitration and nitrosylation [18] and RNA N6-methyladenosine [19]- [21]. In this study, we constructed a DL architecture, dubbed pKcr, for the prediction of Kcr sites on the papaya proteome.…”

A deep dense inception network for protein beta‐turn prediction

“…The pretrained network is effective in learning some more general beta‐turn features; then the transfer learning technique can transfer the base network to some more specific models that can classify nine‐class beta‐turns. We have also demonstrated some techniques for tuning deep neural networks on small data classification problems, which may be useful in other areas of biological sequence analyses with imbalanced data sets, such as genomic analysis, poly‐signal identification, post‐translational modification prediction, and so forth.…”

Deep Learning in Proteomics

“…Deep learning has been used in the prediction of PTM sites for phosphorylation, [96][97][98][99][100] ubiquitination, [101,102] acetylation, [103][104][105][106] glycosylation, [107] malonylation, [108,109] succinylation, [110,111] glycation, [112] nitration/nitrosylation, [113] crotonylation [114] and other modifications [115][116][117]224] as shown in Table 3. MusiteDeep, the first deep learning-based PTM prediction tool, provides both general phosphosite prediction and kinase-specific phosphosite prediction for five kinase families, each with more than 100 known substrates.…”