PTransIPs: Identification of phosphorylation sites based on protein pretrained language model and Transformer. (arXiv:2308.05115v1 [q-bio.QM])

Phosphorylation is central to numerous fundamental cellular processes,
influencing the onset and progression of a variety of diseases. Identification
of phosphorylation sites is thus an important step for understanding the
molecular mechanisms of cells and virus infection, which potentially leads to
new therapeutic targets. In this study, we present PTransIPs, a novel deep
learning model for the identification of phosphorylation sites. PTransIPs
treats amino acids in protein sequences as words in natural language,
extracting unique encodings based on the types …

arxiv bio cells cellular diseases identification infection language language model leads pretrained language model processes protein transformer understanding virus