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ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis
BACKGROUND: Cross-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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510197/ https://www.ncbi.nlm.nih.gov/pubmed/37730532 http://dx.doi.org/10.1186/s12859-023-05473-z |
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author | Zhou, Chen Dai, Shuaijian Lai, Shengzhi Lin, Yuanqiao Zhang, Xuechen Li, Ning Yu, Weichuan |
author_facet | Zhou, Chen Dai, Shuaijian Lai, Shengzhi Lin, Yuanqiao Zhang, Xuechen Li, Ning Yu, Weichuan |
author_sort | Zhou, Chen |
collection | PubMed |
description | BACKGROUND: Cross-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. RESULTS: In 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. CONCLUSIONS: ECL 3.0 source code is available at https://github.com/yuweichuan/ECL-PF.git. A quick tutorial is available at https://youtu.be/PpZgbi8V2xI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05473-z. |
format | Online Article Text |
id | pubmed-10510197 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105101972023-09-21 ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis Zhou, Chen Dai, Shuaijian Lai, Shengzhi Lin, Yuanqiao Zhang, Xuechen Li, Ning Yu, Weichuan BMC Bioinformatics Software BACKGROUND: Cross-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. RESULTS: In 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. CONCLUSIONS: ECL 3.0 source code is available at https://github.com/yuweichuan/ECL-PF.git. A quick tutorial is available at https://youtu.be/PpZgbi8V2xI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-023-05473-z. BioMed Central 2023-09-20 /pmc/articles/PMC10510197/ /pubmed/37730532 http://dx.doi.org/10.1186/s12859-023-05473-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Zhou, Chen Dai, Shuaijian Lai, Shengzhi Lin, Yuanqiao Zhang, Xuechen Li, Ning Yu, Weichuan ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
title | ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
title_full | ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
title_fullStr | ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
title_full_unstemmed | ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
title_short | ECL 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
title_sort | ecl 3.0: a sensitive peptide identification tool for cross-linking mass spectrometry data analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10510197/ https://www.ncbi.nlm.nih.gov/pubmed/37730532 http://dx.doi.org/10.1186/s12859-023-05473-z |
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