Mapping the Edges of Mass Spectral Prediction: Evaluation of Machine Learning EIMS Prediction for Xeno Amino Acids
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388963%3A_____%2F25%3A00635552" target="_blank" >RIV/61388963:_____/25:00635552 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1021/acs.analchem.5c00286" target="_blank" >https://doi.org/10.1021/acs.analchem.5c00286</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1021/acs.analchem.5c00286" target="_blank" >10.1021/acs.analchem.5c00286</a>
Alternative languages
Result language
angličtina
Original language name
Mapping the Edges of Mass Spectral Prediction: Evaluation of Machine Learning EIMS Prediction for Xeno Amino Acids
Original language description
Mass spectrometry is one of the most effective analytical methods for unknown compound identification. By comparing observed m/z spectra with a database of experimentally determined spectra, this process identifies compound(s) in any given sample. Unknown sample identification is thus limited to whatever has been experimentally determined. To address the reliance on experimentally determined signatures, multiple state-of-the-art MS spectra prediction algorithms have been developed within the past half decade. Here we evaluate the accuracy of the NEIMS spectral prediction algorithm. We focus our analyses on monosubstituted alpha-amino acids given their significance as important targets for astrobiology, synthetic biology, and diverse biomedical applications. Our general intent is to inform those using generated spectra for detection of unknown biomolecules. We find predicted spectra are inaccurate for amino acids beyond the algorithms training data. Interestingly, these inaccuracies are not explained by physicochemical differences or the derivatization state of the amino acids measured. We thus highlight the need to improve both current machine learning based approaches and further optimization of ab initio spectral prediction algorithms so as to expand databases for structures beyond what is currently experimentally possible, even including theoretical molecules.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10406 - Analytical chemistry
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Analytical Chemistry
ISSN
0003-2700
e-ISSN
1520-6882
Volume of the periodical
97
Issue of the periodical within the volume
19
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
10282-10288
UT code for WoS article
001483270200001
EID of the result in the Scopus database
2-s2.0-105004756073