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Characterization and Identification of Lysine Phosphoglycerylation Sites Based on Sequence-Based Features. iDPGK is a web server for identifying phosphoglycerylation sites. Protein phosphoglycerylation is the reaction in which a 1,3-bisphosphoglyceric acid (1,3-BPG) is attached to a lysine residue of a protein molecule, and thus to form a 3-phosphoglyceryl-lysine (pgK). Phosphoglycerylation is a reversible and non-enzymatic post-translational modification (PTM). It has been shown that the process of lysine phosphoglycerylation plays important regulatory roles in glucose metabolism and glycolytic process. However, studies in this field have been limited by the difficulty of experimentally identifying modified site specificity in lysine phosphoglycerylation (PGK). To facilitate this process, several tools have been proposed for the computational identification of PGK sites.

A Machine Learning Recommendation Method and Webtool for the Prediction of Functional Post-Translational Modifications | ABSTRACT Post-translational Modifications (PTMs), chemical or proteinaceous covalent alterations to the side chains of amino acid residues in proteins, are a rapidly expanding feature class of significant importance in cell biology. Due to a high burden of experimental proof and the lack of effective means for experimentalists to prioritize PTMs by functional significance, currently less than ~2% of all PTMs have an assigned biological function. Here, we describe a new artificial neural network model, SAPH-ire TFx for the functional prediction of experimentally observed eukaryotic PTMs. Unlike previous functional PTM prioritization models, SAPH-ire TFx is optimized with both receiver operating characteristic (ROC) and recall metrics that maximally capture the range of diverse feature sets comprising the functional modified eukaryotic proteome

An integrative database of the expression dynamics of proteins in response to hypoxia in animals.

FLEXIQuant-LF is a tool for robust regression to quantify protein modification extent in label-free proteomics data.

DeepCSO is an online prediction tool for the recognition of Cysteine S-sulphenylation (CSO) sites. DeepCSO contains three LSTM models: two species-specific models and one general model.

GlyConnect Compozitor is a glycan composition visualising and comparative tool associated with the GlyConnect database.

A Database of Experimentally Identified O-GlcNAc Sites and Proteins.

ProteinBERT is a protein language model pretrained on ~106M proteins from UniRef90. The pretrained model can be fine-tuned on any protein-related task in a matter of minutes. ProteinBERT achieves state-of-the-art performance on a wide range of benchmarks. ProteinBERT is built on TenforFlow/Keras.