Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry(LCeMS)remains a formidable challenge.Herein,a metabolic reaction-based molecular networking(MRMN)strategy is introduced,wh...Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry(LCeMS)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^(2)spectral similarity,incorporating innovations and improvements in feature degradation of MS^(2)spectra,exclusion of endogenous interference,and recognition of redundant nodes.A minimum 75%correlation between structural similarity and MS^(2)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 ofMRMN 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.展开更多
基金was financially supported by the National Natural Science Foundation of China(U23A20500,82374011,82474050,82404818)the Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine(ZYYCXTU-D-202203,China)+6 种基金the Guangdong Basic and Applied Basic Research Foundation(2025A1515011795,2023A1515011144,2024A1515012714,2024A1515011699,China)the Guangzhou Basic and Applied Basic Research Foundation(2024A04J3398,China)the China Postdoctoral Science Foundation(2023M741395)the Postdoctoral Fellowship Program of CPSF(GZB20240274,China)the Natural Science Foundation of Guangxi(2025GXNSFBA069293,China)the Scientific Research Start-up Funding Project of Guangxi University(ZX01080033724006,China)supported by the Fundamental Research Funds for the Central Universities(China).
文摘Effective annotation of in vivo drug metabolites using liquid chromatography-mass spectrometry(LCeMS)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^(2)spectral similarity,incorporating innovations and improvements in feature degradation of MS^(2)spectra,exclusion of endogenous interference,and recognition of redundant nodes.A minimum 75%correlation between structural similarity and MS^(2)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 ofMRMN 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.
