Evidence map›Paper›PMID 37730532›Full record

ArticleBMC bioinformatics2023

ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis.

Chen Zhou, Shuaijian Dai, Shengzhi Lai, Yuanqiao Lin, Xuechen Zhang, Ning Li, Weichuan Yu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

7 authors.

Chen ZhouDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Shuaijian DaiDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Shengzhi LaiDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Yuanqiao LinDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Xuechen ZhangDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Ning LiDivision of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
Weichuan YuDepartment of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. eeyu@ust.hk.

Funding

Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone project HZQB-KCZYB-2020083Hong Kong University of Science and Technology BGF.001.2023Innovation and Technology Commission of Hong Kong S.A.R. MHP/033/20Research Grants Council, University Grants Committee 16102422
6 · The paper itself

Abstract

backgroundCross-linking mass spectrometry (XL-MS) is a powerful technique for detecting protein-protein interactions (PPIs) and modeling protein structures in a high-throughput manner. In XL-MS experiments, proteins are cross-linked by a chemical reagent (namely cross-linker), fragmented, and then fed into a tandem mass spectrum (MS/MS). Cross-linkers are either cleavable or non-cleavable, and each type requires distinct data analysis tools. However, both types of cross-linkers suffer from imbalanced fragmentation efficiency, resulting in a large number of unidentifiable spectra that hinder the discovery of PPIs and protein conformations. To address this challenge, researchers have sought to improve the sensitivity of XL-MS through invention of novel cross-linking reagents, optimization of sample preparation protocols, and development of data analysis algorithms. One promising approach to developing new data analysis methods is to apply a protein feedback mechanism in the analysis. It has significantly improved the sensitivity of analysis methods in the cleavable cross-linking data. The application of the protein feedback mechanism to the analysis of non-cleavable cross-linking data is expected to have an even greater impact because the majority of XL-MS experiments currently employs non-cleavable cross-linkers.

resultsIn this study, we applied the protein feedback mechanism to the analysis of both non-cleavable and cleavable cross-linking data and observed a substantial improvement in cross-link spectrum matches (CSMs) compared to conventional methods. Furthermore, we developed a new software program, ECL 3.0, that integrates two algorithms and includes a user-friendly graphical interface to facilitate wider applications of this new program.

conclusionsECL 3.0 source code is available at https://github.com/yuweichuan/ECL-PF.git . A quick tutorial is available at https://youtu.be/PpZgbi8V2xI .

Indexed as

PeptidesTandem Mass SpectrometryAlgorithmsCross-Linking ReagentsData AnalysisCross-Linking ReagentsPeptidesCross-linking mass spectrometryDatabase searchingProtein feedbackProteomics

Identifiers

PMID37730532
PMCPMC10510197

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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.