Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles-Reference-Cited by-同舟云学术

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Systematic Assessment of Deep Learning-Based Predictors of Fragmentation Intensity Profiles

Published:2024-05-10 Issue:6 Volume:23 Page:1983-1999
ISSN:1535-3893
Container-title:Journal of Proteome Research
language:en
Short-container-title:J. Proteome Res.

Author:

Hamaneh Mehdi B.¹^ORCID,Ogurtsov Aleksey Y.¹,Obolensky Oleg I.¹,Yu Yi-Kuo¹^ORCID

Affiliation:

1. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States

Funder

U.S. National Library of Medicine

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jproteome.3c00857

Reference27 articles.

1. Size Dependent Fragmentation Chemistry of Short Doubly Protonated Tryptic Peptides

2. Collision-induced thermochemistry of reactions of dissociation of glycyl-homopeptides-An experimental and theoretical analysis

3. N-Protonated Isomers and Coulombic Barriers to Dissociation of Doubly Protonated Ala8Arg

4. Using dissociation energies to predict observability of b- and y-peaks in mass spectra of short peptides

5. Using dissociation energies to predict observability of b- and y-peaks in mass spectra of short peptides. II. Results for hexapeptides with non-polar side chains

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