THE LZX LAB
Post-translational modification database and predicting tool construction
Our group are mainly interested in revealing the importance of post-translational modifications (PTMs). We have done a lot of work in this area, including databases construction, predicting algorithm design, and molecular biology research. We have published a quantitative phosphoproteome database (qPhos), and are preparing an updated database for 10 types of PTMs (qPTM). Meanwhile, we have presented an integrative database for protein cysteine modifications in eukaryotes (iCysMod). Several deep learning/machine learning based predicting tools have been developed. such as HAT/HDAC-specific lysine acetylation sites (Deep-PLA), S-glutathionylation sites (DeepGSH), calpain-specific cleavage events (DeepCalpain), and sequences critical for phase separation (dSCOPE). Recently, a deep learning-based tool to predict cysteine modification sites (pCysMod) is also in preparation. With these databases and tools, we are trying to generate a high confident PTM regulation network, which will definitely promote the instinct mechanism research of PTM dysregulation in tumor genesis and progression.
We focus on the systematic analysis of genomic, transcriptomic and proteomic changes during tumor genesis and development, as well as epigenetics and post-translational modifications (PTMs). We aim to explore the molecular mechanisms of PTM networks, find prognostic or diagnostic biomarkers for cancers, and reveal their clinical significance. We are now carrying on a multi-omics project in gastric cancer, which may reveal the potential therapy targets for the diagnostic and prognostic for gastric cancer. We also cooperate with other laboratories to explore the influence of PTMs on signal pathways and the corresponding mechanisms.
Despite the intrinsic genomic characteristics, we areinterested in how other factors influence human health, especially the tumors. We firstly focused on the important role of gut microbiota, and developed a human gut metagenome database (gutMEGA) based on the literature that ever published. We also pay attention to the virus in tumors, and a database for Epstein-Barr virus (dbEBV), which is related to nasopharyngeal carcinoma, has been finished. Moreover, we are considering a database for all cancer associated virus.