pSAM is a deep learning model, named prediction of SAMs, based on a hybrid convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) architecture with an attention mechanism to accurately predict the potential nuclear localization of proteins.

pKAKA is a protein language model for prioritizing kinase-disrupting variants in diseases

dSCOPE is a web server developed for detecting sequences critical for phase separation related proteins.

DrugCVar database, which integrated our manually curated cancer variant-drug targeting evidences from literature and the interpretations from the public resources. In total, 8,539 clinical evidences for cancer variant-drug targeting were integrated and classified into ten evidence tiers.

Deep-PLA is a web server developed for understanding the reversible enzyme-specific lysine acetylation regulation for histone acetyltransferases (HATs) including CREBBP, EP300, HAT1, KAT2A, KAT2B, KAT5 and KAT8, and deacetylases (HDACs) including HDAC1, HDAC2, HDAC3, HDAC6, SIRT1, SIRT2, SIRT3, SIRT6, and SIRT7.

DeepGSH is a web server based on deep learning to predict S-glutathionylation sites. Given a protein or a set of proteins in FASTA format, it can predict the possible S-glutathionylation sites in proteins.

DeepCalpain is a web server based on deep learning to predict calpain-specific cleavage events. Given a protein or a set of proteins in FASTA format, it can predict the possible cleavage sites of two major types of calpain, including m-calpain and μ-calpain.

In this study, we manually curated the experimentally iden-tified substrates and phospho-rylation sites of prkC from the published literature, and developed a novel software of prkC-PSP (prkC-specific Phosphorylation Sites Prediction) for the prediction of prkC-specific phosphorylation sites.