Applying NMR compound identification using NMRfilter to match predicted to experimental data
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SHORT COMMUNICATION
Applying NMR compound identification using NMRfilter to match predicted to experimental data Stefan Kuhn1 · Simon Colreavy‑Donnelly1 · Lucas Eliseu de Andrade Silva Quaresma2 · Ezequiel de Andrade Silva Quaresma2 · Ricardo Moreira Borges2 Received: 11 May 2020 / Accepted: 11 November 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Introduction Metabolomics is the approach of choice to guide the understanding of biological systems and its molecular intricacies, but compound identification is yet a bottleneck to be overcome. Objective To assay the use of NMRfilter for confidence compound identification based on chemical shift predictions for different datasets. Results We found comparable results using the lead tool COLMAR and NMRfilter. Then, we successfully assayed the use of HMBC to add confidence to the identified compounds. Conclusions NMRfilter is currently under development to become a stand-alone interactive software for high-confidence NMR compound identification and this communication gathers part of its application capabilities. Keywords NMR · Compound identification · Metabolomics · Dereplication · NMRfilter
1 Introduction Today, much of the effort in life science can be summarized as understanding biological systems, and so, metabolomics has emerged as the approach of choice. From fundamental ecological and interaction studies to precision medicine, metabolomics has been applied with promising results due to its scrutiny to establish statistically supported biomarkers when different groups are compared (Wishart 2019). Although much was accomplished in experimental design, mathematical modeling, and statistical protocols, one major bottleneck is yet to be solved: Unequivocal compound identification. Natural product research is another major area where this is important (Hubert et al. 2017). Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11306-020-01748-1) contains supplementary material, which is available to authorized users. * Ricardo Moreira Borges [email protected] 1
School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK
Walter Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil
2
When dealing with samples consisting mainly of primary metabolites, such as in biofluids, methods for compound identification based on formal databases are straightforward. Complex Mixture Analysis by NMR (COLMAR; http://spin. ccic.ohio-state. edu/index. php/colmar ) (Bingol et al. 2015) is a leading system that runs a matching algorithm for chemical shift comparison using the Biological Magnetic Resonance Data Bank (BMRB) and the Human Metabolome Database (HMDB). COLMAR is successfully and broadly used across the literature for compound identification yielding confidence parameters. The one drawback of such databasedriven methods is its strong reliance on how comprehensive those datab
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