Chu Wang

Through chemical proteomics, biochemistry, and computational biology, we conduct large-scale discovery of functional sites and targets in the proteome that are specifically modified by endogenous bioactive small molecules or exogenous chemical drug molecules, and deeply investigate the effects of these modifications on protein structure, function, and cellular metabolism and signal transduction pathways. These studies will strongly promote the annotation of numerous unknown protein functions in the post-genomic era, reveal their key roles in various metabolic pathways, and explain the formation and causes of various pathological conditions at the molecular level, providing a theoretical basis for the development of related drugs and therapeutic approaches. Current main research directions include: 1. Developing efficient chemical probes and quantitative mass spectrometry methods to achieve precise detection of post-translational modifications of proteins in living cells, and studying their biological functions in regulating cell fate. 2. Establishing omics methods for identifying active small molecule targets, discovering the action targets of natural products, endogenous metabolites, and exogenous drugs in complex biological samples, and revealing their molecular mechanisms. 3. Developing computational chemical biology methods to discover novel protein active centers, post-translational modifications, and small molecule binding sites, achieving precise regulation of protein functions.

Publications in the Last Five Years：
Zhang, F., Cheng, Y., Xue, B., Gao, Y., Liu, Y., & Wang, C. (2024). MetalNet2: an enhanced server for predicting metal-binding sites in proteomes. National science review, 11(12), nwae391. https://doi.org/10.1093/nsr/nwae391
Zeng, X., Wei, T., Wang, X., Liu, Y., Tan, Z., Zhang, Y., Feng, T., Cheng, Y., Wang, F., Ma, B., Qin, W., Gao, C., Xiao, J., & Wang, C. (2024). Discovery of metal-binding proteins by thermal proteome profiling. Nature chemical biology, 20(6), 770–778. https://doi.org/10.1038/s41589-024-01563-y
Cheng, Y., Wang, H., Xu, H., Liu, Y., Ma, B., Chen, X., Zeng, X., Wang, X., Wang, B., Shiau, C., Ovchinnikov, S., Su, X. D., & Wang, C. (2023). Co-evolution-based prediction of metal-binding sites in proteomes by machine learning. Nature chemical biology, 19(5), 548–555. https://doi.org/10.1038/s41589-022-01223-z