王初 Chu Wang

主要通过化学蛋白质组学、生物化学和计算生物学等多种跨领域的技术和手段，大规模发掘蛋白质组中被内源生物小分子或外源化学药物分子特异修饰的功能位点和靶点，并深入研究这些修饰对蛋白质的结构、功能以及其所在的细胞内新陈代谢和信号传导通路的影响。这些研究将有力地推动在后基因组时代人们对大量未知蛋白功能注释的进程，揭示其在各种新陈代谢通路中的关键作用，以及从分子水平上解释多种病理环境的形成和诱因，为相关的药物和治疗手段的研发提供理论基础。目前主要研究方向包括：1.发展高效的化学探针和定量质谱方法，实现活细胞内蛋白质翻译后修饰的精准探测，研究了它们参与细胞命运调控的生物学功能。2.建立活性小分子靶标鉴定的组学方法，在复杂生物样品内发现天然产物、内源代谢物、外源药物的作用靶点，揭示它们的分子机制。3.研发计算化学生物学方法，发掘全新的蛋白质活性中心、翻译后修饰和小分子结合位点，实现对蛋白质功能的精准调控。

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.

近5年的论文：
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