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Protein Processing, Post-Translational

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GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome.

Bioinformatics (Oxford, England)
MOTIVATION: Glycosylation is a ubiquitous type of protein post-translational modification (PTM) in eukaryotic cells, which plays vital roles in various biological processes (BPs) such as cellular communication, ligand recognition and subcellular reco...

MlyPredCSED: based on extreme point deviation compensated clustering combined with cross-scale convolutional neural networks to predict multiple lysine sites in human.

Briefings in bioinformatics
In post-translational modification, covalent bonds on lysine and attached chemical groups significantly change proteins' physical and chemical properties. They shape protein structures, enhance function and stability, and are vital for physiological ...

Sul-BertGRU: an ensemble deep learning method integrating information entropy-enhanced BERT and directional multi-GRU for S-sulfhydration sites prediction.

Bioinformatics (Oxford, England)
MOTIVATION: S-sulfhydration, a crucial post-translational protein modification, is pivotal in cellular recognition, signaling processes, and the development and progression of cardiovascular and neurological disorders, so identifying S-sulfhydration ...

Enhanced O-glycosylation site prediction using explainable machine learning technique with spatial local environment.

Bioinformatics (Oxford, England)
MOTIVATION: The accurate prediction of O-GlcNAcylation sites is crucial for understanding disease mechanisms and developing effective treatments. Previous machine learning (ML) models primarily relied on primary or secondary protein structural and re...

Improved prediction of post-translational modification crosstalk within proteins using DeepPCT.

Bioinformatics (Oxford, England)
MOTIVATION: Post-translational modification (PTM) crosstalk events play critical roles in biological processes. Several machine learning methods have been developed to identify PTM crosstalk within proteins, but the accuracy is still far from satisfa...

Sitetack: a deep learning model that improves PTM prediction by using known PTMs.

Bioinformatics (Oxford, England)
MOTIVATION: Post-translational modifications (PTMs) increase the diversity of the proteome and are vital to organismal life and therapeutic strategies. Deep learning has been used to predict PTM locations. Still, limitations in datasets and their ana...

DeepPRMS: advanced deep learning model to predict protein arginine methylation sites.

Briefings in functional genomics
Protein methylation is a form of post-translational modifications of protein, which is crucial for various cellular processes, including transcription activity and DNA repair. Correctly predicting protein methylation sites is fundamental for research...

TransPTM: a transformer-based model for non-histone acetylation site prediction.

Briefings in bioinformatics
Protein acetylation is one of the extensively studied post-translational modifications (PTMs) due to its significant roles across a myriad of biological processes. Although many computational tools for acetylation site identification have been develo...

Lactylation prediction models based on protein sequence and structural feature fusion.

Briefings in bioinformatics
Lysine lactylation (Kla) is a newly discovered posttranslational modification that is involved in important life activities, such as glycolysis-related cell function, macrophage polarization and nervous system regulation, and has received widespread ...

Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.

Database : the journal of biological databases and curation
The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety...