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Protein Processing, Post-Translational

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Site-specific prediction of O-GlcNAc modification in proteins using evolutionary scale model.

PloS one
Protein glycosylation, a vital post-translational modification, is pivotal in various biological processes and disease pathogenesis. Computational approaches, including protein language models and machine learning algorithms, have emerged as valuable...

PSSM-Sumo: deep learning based intelligent model for prediction of sumoylation sites using discriminative features.

BMC bioinformatics
Post-translational modifications (PTMs) are fundamental to essential biological processes, exerting significant influence over gene expression, protein localization, stability, and genome replication. Sumoylation, a PTM involving the covalent additio...

Res-GCN: Identification of protein phosphorylation sites using graph convolutional network and residual network.

Computational biology and chemistry
An essential post-translational modification, phosphorylation is intimately related with a wide range of biological activities. The advancement of effective computational methods for correctly recognizing phosphorylation sites is important for in-dep...

PhosBERT: A self-supervised learning model for identifying phosphorylation sites in SARS-CoV-2-infected human cells.

Methods (San Diego, Calif.)
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus, which mainly causes respiratory and enteric diseases and is responsible for the outbreak of coronavirus disease 19 (COVID-19). Numerous studies have demonstr...

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Proteins
Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulati...

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...

DeepKlapred: A deep learning framework for identifying protein lysine lactylation sites via multi-view feature fusion.

International journal of biological macromolecules
Lysine lactylation (Kla) is a post-translational modification (PTM) that holds significant importance in the regulation of various biological processes. While traditional experimental methods are highly accurate for identifying Kla sites, they are bo...

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...

Enhancing Arabidopsis thaliana ubiquitination site prediction through knowledge distillation and natural language processing.

Methods (San Diego, Calif.)
Protein ubiquitination is a critical post-translational modification (PTM) involved in diverse biological processes and plays a pivotal role in regulating physiological mechanisms and disease states. Despite various efforts to develop ubiquitination ...

Stacking based ensemble learning framework for identification of nitrotyrosine sites.

Computers in biology and medicine
Protein nitrotyrosine is an essential post-translational modification that results from the nitration of tyrosine amino acid residues. This modification is known to be associated with the regulation and characterization of several biological function...