Evidence map›Paper›PMID 40623297›Full record

ArticleJournal of the American Society for Mass Spectrometry2025

Assigning Peptide Structure from Ion-Mobility Mass Spectrometry Collision Cross Section Data.

Mithony Keng, Kenneth M Merz

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. PEAS: An Application for Autonomous Precision Conformation Sampling.Journal of chemical information and modeling · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mithony KengDepartment of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.ORCID 0000-0002-9850-0120
Kenneth M MerzDepartment of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States.ORCID 0000-0001-9139-5893

Funding

Portal for Open Computational Metabolomics Tools - Yr 4 U2C SupplementU2CES030167 · NIEHS · UNIVERSITY OF GEORGIA · PI SCHROEDER, FRANK CLEMENS · 2018 to 2021
$4.2M
NIEHS NIH HHS U2C ES030167
6 · The paper itself

Abstract

The ion mobility (IM) technique coupled with traditional mass spectrometry (IM-MS) has introduced a practical tool for the characterization of peptide analyte ions by exploiting the difference in their collision cross section (CCS) values. CCS holds molecular level information that can be used to computationally assign the conformation(s) of a molecular system. However, a reliable and accurate method for peptide structure prediction remains a challenge because peptides exhibit dynamic gas-phase intramolecular interactions, and hence, the methods used will need to account for this. In this work, we systematically assessed the performance of a computational workflow involving both classical and density functional theory (DFT) steps to elucidate the peptide structure. Not unexpectedly, extensive enumeration of available peptide conformations was critical to obtain high-quality results. Due to the size of the systems studied and the large numbers of conformers that needed to be optimized, we initially chose the D3-B3LYP/6-31G(d) level of theory and obtained good agreement between experimental and computed CCS values. However, in several cases, suboptimal accuracies were observed, but we found that increasing the basis set used to 6-31G(d,p) was able to improve our agreement with experiment. Altogether, we demonstrated that accurate peptide structure assignment is achievable with adequate sampling of the conformational space and using the appropriate quantum mechanical level of theory to account for intramolecular interactions in the gas phase.

Indexed as

Ion Mobility SpectrometryMass SpectrometryPeptidesDensity Functional TheoryIonsModels, MolecularProtein ConformationIonsPeptides

Identifiers

PMID40623297
PMCPMC12333332

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.