Mini-review: Recent advances in post-translational modification site prediction based on deep learning

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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes

Published on: May 22, 2018

Lingkuan Meng^1,2, Wai-Sum Chan¹, Lei Huang¹

¹Department of Computer Science, City University of Hong Kong, Hong Kong Special Administrative Region.

Computational and Structural Biotechnology Journal

|July 21, 2022

Abstract:

Post-translational modifications (PTMs) are closely linked to numerous diseases, playing a significant role in regulating protein structures, activities, and functions. Therefore, the identification of PTMs is crucial for understanding the mechanisms of cell biology and diseases therapy. Compared to traditional machine learning methods, the deep learning approaches for PTM prediction provide accurate and rapid screening, guiding the downstream wet experiments to leverage the screened information for focused studies. In this paper, we reviewed the recent works in deep learning to identify phosphorylation, acetylation, ubiquitination, and other PTM types. In addition, we summarized PTM databases and discussed future directions with critical insights.

Keywords:

AAindex, Amino acid index ATP, Adenosine triphosphate AUC, Area under curve Ac, Acetylation BE, Binary encoding BLOSUM, Blocks substitution matrix Bi-LSTM, Bidirectional LSTM CKSAAP, Composition of k-spaced amino acid Pairs CNN, Convolutional neural network CNNOH, CNN with the one-hot encoding CNNWE, CNN with the word-embedding encoding CNNrgb, CNN red green blue CV, Cross-validation DC-CNN, Densely connected convolutional neural network DL, Deep learning DNNs, Deep neural networks Deep learning E. coli, Escherichia coli EBGW, Encoding based on grouped weight EGAAC, Enhanced grouped amino acids content IG, Information gain K, Lysine KNN, k nearest neighbor LASSO, Least absolute shrinkage and selection operator LSTM, Long short-term memory LSTMWE, LSTM with the word-embedding encoding M.musculus, Mus musculus MDC, Modular densely connected convolutional networks MDCAN, Multilane dense convolutional attention network ML, Machine learning MLP, Multilayer perceptron MMI, Multivariate mutual information Machine learning Mass spectrometry NMBroto, Normalized Moreau-Broto autocorrelation P, Proline PSP, PhosphoSitePlus PSSM, Position-specific scoring matrix PTM, Post-translational modifications Ph, Phosphorylation Post-translational modification Prediction PseAAC, Pseudo-amino acid composition R, Arginine RF, Random forest RNN, Recurrent neural network ROC, Receiver operating characteristic S, Serine S. typhimurium, Salmonella typhimurium S.cerevisiae, Saccharomyces cerevisiae SE, Squeeze and excitation SEV, Split to Equal Validation ST, Source and target SUMO, Small ubiquitin-like modifier SVM, Support vector machines T, Threonine Ub, Ubiquitination Y, Tyrosine ZSL, Zero-shot learning

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