Evidence map›Paper›PMID 32043875›Full record

ArticleAnalytical chemistry2020

Predicting Electrophoretic Mobility of Proteoforms for Large-Scale Top-Down Proteomics.

Daoyang Chen, Rachele A Lubeckyj, Zhichang Yang, Elijah N McCool, Xiaojing Shen, Qianjie Wang, Tian Xu, Liangliang Sun

Abstract read
In one paragraph

Article in Analytical chemistry, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Capillary Electrophoresis-Mass Spectrometry for Top-Down Proteomics.Annual review of analytical chemistry (Palo Alto, Calif.) · 2025
    Review
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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

8 authors.

Daoyang ChenDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.
Rachele A LubeckyjDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.
Zhichang YangDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.
Elijah N McCoolDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.
Xiaojing ShenDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.ORCID 0000-0003-2079-9115
Qianjie WangDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.
Tian XuDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.
Liangliang SunDepartment of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States.ORCID 0000-0001-8939-5042

Funding

Quantitative proteomics of Zebrafish embryos and blastomeresR01GM125991 · NIGMS · MICHIGAN STATE UNIVERSITY · PI SUN, LIANGLIANG · 2018 to 2022
$1.7M
NIGMS NIH HHS R01 GM125991
6 · The paper itself

Abstract

Large-scale top-down proteomics characterizes proteoforms in cells globally with high confidence and high throughput using reversed-phase liquid chromatography (RPLC)-tandem mass spectrometry (MS/MS) or capillary zone electrophoresis (CZE)-MS/MS. The false discovery rate (FDR) from the target-decoy database search is typically deployed to filter identified proteoforms to ensure high-confidence identifications (IDs). It has been demonstrated that the FDRs in top-down proteomics can be drastically underestimated. An alternative approach to the FDR can be useful for further evaluating the confidence of proteoform IDs after the database search. We argue that predicting retention/migration time of proteoforms from the RPLC/CZE separation accurately and comparing their predicted and experimental separation time could be a useful and practical approach. Based on our knowledge, there is still no report in the literature about predicting separation time of proteoforms using large top-down proteomics data sets. In this pilot study, for the first time, we evaluated various semiempirical models for predicting proteoforms' electrophoretic mobility (μ

Indexed as

ElectrophoresisProteomics

Identifiers

PMID32043875
PMCPMC7543059

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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.