Zhu Haodong, Tong Xupeng, Wang Qi, Li Aijing, Wu Zubao, Wang Qiqi, Lin Pei, Yao Xinsheng, Hu Liufang, He Liangliang, Yao Zhihong
State Key Laboratory of Bioactive Molecules and Druggability Assessment; International Cooperative Laboratory of Traditional Chinese Medicine Modernization and Innovative Drug Development of Ministry of Education (MOE) of China; Guangdong Basic Research Center of Excellence for Natural Bioactive Molecules and Discovery of Innovative Drugs; Guangdong Province Key Laboratory of Pharmacodynamic Constituents of TCM and New Drugs Research, College of Pharmacy, Jinan University, Guangzhou 510632, China.
Guangxi Key Laboratory of Special Biomedicine; School of Medicine, Guangxi University, Nanning 530004, China.
Acta Pharm Sin B. 2025 Jun;15(6):3210-3225. doi: 10.1016/j.apsb.2025.03.050. Epub 2025 Apr 4.
Effective annotation of drug metabolites using liquid chromatography-mass spectrometry (LC-MS) remains a formidable challenge. Herein, a metabolic reaction-based molecular networking (MRMN) strategy is introduced, which enables the "one-pot" discovery of prototype drugs and their metabolites. MRMN constructs networks by matching metabolic reactions and evaluating MS spectral similarity, incorporating innovations and improvements in feature degradation of MS spectra, exclusion of endogenous interference, and recognition of redundant nodes. A minimum 75% correlation between structural similarity and MS similarity of neighboring metabolites was ensured, mitigating false negatives due to spectral feature degradation. At least 79% of nodes, 49% of edges, and 97% of subnetworks were reduced by an exclusion strategy of endogenous ions compared to the Global Natural Products Social Molecular Networking (GNPS) platform. Furthermore, an approach of redundant ions identification was refined, achieving a 10%-40% recognition rate across different samples. The effectiveness of MRMN was validated through a single compound, plant extract, and mixtures of multiple plant extracts. Notably, MRMN is freely accessible online at https://yaolab.network, broadening its applications.
使用液相色谱 - 质谱联用（LC - MS）对药物代谢物进行有效注释仍然是一项艰巨的挑战。在此，引入了一种基于代谢反应的分子网络（MRMN）策略，该策略能够“一锅法”发现原型药物及其代谢物。MRMN通过匹配代谢反应和评估质谱相似性来构建网络，在质谱特征降解、排除内源性干扰以及识别冗余节点方面进行了创新和改进。确保相邻代谢物的结构相似性和质谱相似性之间至少有75%的相关性，减少了由于光谱特征降解导致的假阴性。与全球天然产物社会分子网络（GNPS）平台相比，通过内源性离子排除策略，至少减少了79%的节点、49%的边和97%的子网。此外，改进了冗余离子识别方法，在不同样品中实现了10% - 40%的识别率。通过单一化合物、植物提取物以及多种植物提取物混合物验证了MRMN的有效性。值得注意的是，可通过https://yaolab.network在线免费访问MRMN，从而拓宽了其应用范围。
