Systematic Characterization of Lysine Post-translational Modification Sites Using MUscADEL.
Bioinformatics
Deep learning
Long short-term memory
Lysine
Machine learning
Post-translational modification
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2022
2022
Historique:
entrez:
13
6
2022
pubmed:
14
6
2022
medline:
16
6
2022
Statut:
ppublish
Résumé
Among various types of protein post-translational modifications (PTMs), lysine PTMs play an important role in regulating a wide range of functions and biological processes. Due to the generation and accumulation of enormous amount of protein sequence data by ongoing whole-genome sequencing projects, systematic identification of different types of lysine PTM substrates and their specific PTM sites in the entire proteome is increasingly important and has therefore received much attention. Accordingly, a variety of computational methods for lysine PTM identification have been developed based on the combination of various handcrafted sequence features and machine-learning techniques. In this chapter, we first briefly review existing computational methods for lysine PTM identification and then introduce a recently developed deep learning-based method, termed MUscADEL (Multiple Scalable Accurate Deep Learner for lysine PTMs). Specifically, MUscADEL employs bidirectional long short-term memory (BiLSTM) recurrent neural networks and is capable of predicting eight major types of lysine PTMs in both the human and mouse proteomes. The web server of MUscADEL is publicly available at http://muscadel.erc.monash.edu/ for the research community to use.
Identifiants
pubmed: 35696083
doi: 10.1007/978-1-0716-2317-6_11
doi:
Substances chimiques
Proteome
0
Lysine
K3Z4F929H6
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
205-219Informations de copyright
© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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