# Computational Core

> **NIH NIH U2C** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2020 · $298,287

## Abstract

Computational Core - Project Summary
Compound identification, particularly for LC-MS based metabolomics, has mostly been viewed as a challenge
which must be tackled empirically. An alternative to exclusively experimental identification strategies is to
leverage computational approaches to aid in interpretation of MS and MS/MS data. Computational
approaches to interpretation of metabolite fragmentation under collision-induced dissociation (CID) type
conditions are improving, both because of progress generating in-silico MS/MS libraries, and in terms of library
search strategies which can better contend with the less-predictable nature and lower information content of
small-molecule fragmentation. To make headway on identification of unknown compounds in existing and
future metabolomics data sets, and to improve throughput and reduce cost of future compound identification
efforts, it is essential to use these tools alongside experimental approaches. A second challenge contributing to
the high proportion of unidentified features in untargeted metabolomics data is the abundance of redundant
(degenerate) features in electrospray ionization mass spectrometry data, which include isotopes, in-source
fragments and adducts. Presently existing tools are insufficient to contend with the complexity of fragments
and adducts, both predicted and unknown, that have been demonstrated to occur in electrospray ionization
mass spectrometry. A more useful tool would also facilitate analyst-aided interpretation of feature redundancy,
an important step for the careful systematic compound ID workflow to be performed by MCIDC.
Operating in coordination with the Administrative and Experimental Cores of MCIDC and with the Common
Funds Metabolomics Consortium, the MCIDC Computational Core will help address major challenges in the
field of untargeted metabolomics by carrying out the following Specific Aims: We will develop and apply a novel
software tool, Binner, to reduce degeneracy of features in untargeted metabolomics data. Effective use of
Binner will allow us to prioritize identification efforts on primary features, while allowing degenerate features to
be indexed as such in metabolite spectral databases and be more rapidly removed from future data sets. Next,
we will implement a novel probabilistic tandem mass spectral search strategy for small-molecule metabolites,
including a “Hybrid Search” approach. Our approach will allow detection of common structural motifs in
unknown metabolites and aid in determination of their identity. Working with the experimental core, we will
validate and refine this scoring algorithm using spectra of known metabolites from biological data.

## Key facts

- **NIH application ID:** 9940872
- **Project number:** 5U2CES030164-03
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Alexey I Nesvizhskii
- **Activity code:** U2C (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $298,287
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9940872

## Citation

> US National Institutes of Health, RePORTER application 9940872, Computational Core (5U2CES030164-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9940872. Licensed CC0.

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