Propagating annotations of molecular networks using in silico fragmentation

By Ricardo R. da Silva, Mingxun Wang, Louis-Félix Nothias, J. J. Hooft, A. Caraballo-Rodríguez, E. Fox, Marcy J. Balunas, J. Klassen, N. Lopes, Pieter C. Dorrestein. PLoS Comput. Biol., 2018

299 citations recorded by the source corpus.

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f05fbb968a1e58bac0c57479b65d45af0d62fb58

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In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Feature-Based Molecular Networking in the GNPS Analysis Environment (2019, methodology): “NAP 33 uses MetFrag/MetFusion 37,42 for the prediction of putative structures and the network topology to rerank structure predictions by propagating the expected structural similarity.”
MolNetEnhancer: Enhanced Molecular Networks by Integrating Metabolome Mining and Annotation Tools (2019, methodology): “Metabolite annotation and identification tools have benefited from these advances as now more MS2 spectra per sample can be queried in reference libraries in order to find candidate structures or submitted to in silico tools that propose a putative structure [1–9].”