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

doi:10.1186/s12859-023-05473-z

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

BMC Bioinformatics. 2023 Sep 20;24(1):351. doi: 10.1186/s12859-023-05473-z.

Authors

Chen Zhou¹, Shuaijian Dai¹, Shengzhi Lai¹, Yuanqiao Lin¹, Xuechen Zhang¹, Ning Li^{2

3}, Weichuan Yu^{4

5}

Affiliations

¹ Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
² Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
³ HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, China.
⁴ Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China. eeyu@ust.hk.
⁵ HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, China. eeyu@ust.hk.

Abstract

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 .

Keywords: Cross-linking mass spectrometry; Database searching; Protein feedback; Proteomics.

MeSH terms

Algorithms
Cross-Linking Reagents
Data Analysis
Peptides*
Tandem Mass Spectrometry*

Substances

Peptides
Cross-Linking Reagents

Abstract

MeSH terms

Substances

Grants and funding