Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP(3) MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP(3) MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP(3) MS Workflow adds improved computing of the MS(2) spectra in an LC-MS/MS data set and provides tools for automatic [M + H](+) ion deconvolution using fragmentation rules; chemical structural annotation against MS(2) databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP(3) MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.
NP(3) MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.
NP(3) MS 工作流程:一个开源软件系统,利用非靶向代谢组学增强基于天然产物的药物发现
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作者:Bazzano Cristina F, de Felicio Rafael, Alves Luiz Fernando Giolo, Costa Jonas Henrique, Ortega Raquel, Vieira Bruna Domingues, Morais-Urano Raquel Peres, Furtado Luciana Costa, Ferreira Everton L F, Gubiani Juliana R, Berlinck Roberto G S, Costa-Lotufo Leticia V, Telles Guilherme P, B B Trivella Daniela
| 期刊: | Analytical Chemistry | 影响因子: | 6.700 |
| 时间: | 2024 | 起止号: | 2024 May 14; 96(19):7460-7469 |
| doi: | 10.1021/acs.analchem.3c05829 | 研究方向: | 代谢 |
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