Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting.
Annotation of natural product compound families using molecular networking topology and structural similarity fingerprinting.
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DOI:
10.1038/s41467-022-35734-z
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发表时间:
2023-01-19
影响因子:
16.6
通讯作者:
Linington, Roger G. G.
中科院分区:
文献类型:
--
作者:
Morehouse, Nicholas J. J.;Clark, Trevor N. N.;McMann, Emily J. J.;van Santen, Jeffrey A. A.;Haeckl, F. P. Jake;Gray, Christopher A. A.;Linington, Roger G. G.
Spectral matching of MS2 fragmentation spectra has become a popular method for characterizing natural products libraries but identification remains challenging due to differences in MS2 fragmentation properties between instruments and the low coverage of current spectral reference libraries. To address this bottleneck we present Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) which matches chemical similarity grouping in the Natural Products Atlas to grouping of mass spectrometry features from molecular networking. This approach assigns compound families to molecular networking subnetworks without the need for experimental or calculated reference spectra. We demonstrate SNAP-MS can accurately annotate subnetworks built from both reference spectra and an in-house microbial extract library, and correctly predict compound families from published molecular networks acquired on a range of MS instrumentation. Compound family annotations for the microbial extract library are validated by co-injection of standards or isolation and spectroscopic analysis. SNAP-MS is freely available at www.npatlas.org/discover/snapms. Comparing experimental mass spectra to reference spectra can enable natural product identification, but these spectral libraries are often incomplete and not universally applicable. Here, the authors present SNAP-MS, a tool that allows assigning compound families without experimental or calculated reference spectra.
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影响因子:
5.5
作者:
Capecchi A;Reymond JL
通讯作者:
Reymond JL
影响因子:
28.3
作者:
Nguyen DD;Melnik AV;Koyama N;Lu X;Schorn M;Fang J;Aguinaldo K;Lincecum TL Jr;Ghequire MG;Carrion VJ;Cheng TL;Duggan BM;Malone JG;Mauchline TH;Sanchez LM;Kilpatrick AM;Raaijmakers JM;De Mot R;Moore BS;Medema MH;Dorrestein PC
通讯作者:
Dorrestein PC
影响因子:
4.3
作者:
da Silva RR;Wang M;Nothias LF;van der Hooft JJJ;Caraballo-Rodríguez AM;Fox E;Balunas MJ;Klassen JL;Lopes NP;Dorrestein PC
通讯作者:
Dorrestein PC
影响因子:
7.4
作者:
Rasche, Florian;Scheubert, Kerstin;Boecker, Sebastian
通讯作者:
Boecker, Sebastian
影响因子:
7.4
作者:
Treutler, Hendrik;Tsugawa, Hiroshi;Balcke, Gerd Ulrich
通讯作者:
Balcke, Gerd Ulrich