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Qianqian J, Bo J, Yukui Z, Lihua Z. Metal Organic Layers-Based Sample Preparation Method for Membrane Proteome Analysis. Methods Mol Biol 2025; 2908:111-123. [PMID: 40304906 DOI: 10.1007/978-1-0716-4434-8_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2025]
Abstract
As a central platform for exchange of signaling communication and substance between cells and within cells, mapping of membrane proteome can provide new insights into the pathophysiology of cancer and other diseases, explore potential drug targets and biomarkers for prognosis or diagnosis, and ultimately contribute to the development of precision medicine. However, mass spectrometry-based identification of membrane proteins poses many challenges due to the intrinsic properties of membrane proteins, especially high hydrophobicity and low abundance. Herein, we described a metal organic layers (MOL)-based sample preparation method for membrane proteome analysis in living cells, involving the cell surface engineering process through the exquisite interaction between MOL and phospholipid bilayers on the membrane and the following biomembrane fusion process, providing a simple and robust tool for dissection of membrane proteome with high specificity. This chapter details the preparation of such metal organic layers and enrichment workflow of HeLa membrane proteins.
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Affiliation(s)
- Jiang Qianqian
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jiang Bo
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
| | - Zhang Yukui
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
| | - Zhang Lihua
- State Key Laboratory of Medical Proteomics, National Chromatographic R. & A. Center, CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China
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Wei Q, Li J, He QY, Chen Y, Zhang G. Identifying PE2 and PE5 Proteins from Existing Mass Spectrometry Data Using pFind. J Proteome Res 2024; 23:2323-2331. [PMID: 38865581 DOI: 10.1021/acs.jproteome.3c00674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
The Chromosome-Centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome. Currently, the human proteome still contains approximately 2000 PE2-PE5 proteins, referring to annotated coding genes that lack sufficient protein-level evidence. During the past 10 years, it has been increasingly difficult to identify PE2-PE5 proteins in C-HPP approaches due to the limited occurrence. Therefore, we proposed that reanalyzing massive MS data sets in repository with newly developed algorithms may increase the occurrence of the peptides of these proteins. In this study, we downloaded 1000 MS data sets via the ProteomeXchange database. Using pFind software, we identified peptides referring to 1788 PE2-PE5 proteins. Among them, 11 PE2 and 16 PE5 proteins were identified with at least 2 peptides, and 12 of them were identified using 2 peptides in a single data set, following the criteria of the HPP guidelines. We found translation evidence for 16 of the 11 PE2 and 16 PE5 proteins in our RNC-seq data, supporting their existence. The properties of the PE2 and PE5 proteins were similar to those of the PE1 proteins. Our approach demonstrated that mining PE2 and PE5 proteins in massive data repository is still worthy, and multidata set peptide identifications may support the presence of PE2 and PE5 proteins or at least prompt additional studies for validation. Extremely high throughput could be a solution to finding more PE2 and PE5 proteins.
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Affiliation(s)
- Qianzhou Wei
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou 510632, China
| | - Jiamin Li
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou 510632, China
| | - Qing-Yu He
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou 510632, China
| | - Yang Chen
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou 510632, China
| | - Gong Zhang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes and MOE Key Laboratory of Tumor Molecular Biology, Institute of Life and Health Engineering, Jinan University, Guangzhou 510632, China
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Liu X, Cai F, Zhang Y, Luo X, Yuan L, Ma H, Yang M, Ge F. Interactome Analysis of ClpX Reveals Its Regulatory Role in Metabolism and Photosynthesis in Cyanobacteria. J Proteome Res 2024; 23:1174-1187. [PMID: 38427982 DOI: 10.1021/acs.jproteome.3c00610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2024]
Abstract
Protein homeostasis is essential for cyanobacteria to maintain proper cellular function under adverse and fluctuating conditions. The AAA+ superfamily of proteolytic complexes in cyanobacteria plays a critical role in this process, including ClpXP, which comprises a hexameric ATPase ClpX and a tetradecameric peptidase ClpP. Despite the physiological effects of ClpX on growth and photosynthesis, its potential substrates and underlying mechanisms in cyanobacteria remain unknown. In this study, we employed a streptavidin-biotin affinity pull-down assay coupled with label-free proteome quantitation to analyze the interactome of ClpX in the model cyanobacterium Synechocystis sp. PCC 6803 (hereafter Synechocystis). We identified 503 proteins as potential ClpX-binding targets, many of which had novel interactions. These ClpX-binding targets were found to be involved in various biological processes, with particular enrichment in metabolic processes and photosynthesis. Using protein-protein docking, GST pull-down, and biolayer interferometry assays, we confirmed the direct association of ClpX with the photosynthetic proteins, ferredoxin-NADP+ oxidoreductase (FNR) and phycocyanin subunit (CpcA). Subsequent functional investigations revealed that ClpX participates in the maintenance of FNR homeostasis and functionality in Synechocystis grown under different light conditions. Overall, our study provides a comprehensive understanding of the extensive functions regulated by ClpX in cyanobacteria to maintain protein homeostasis and adapt to environmental challenges.
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Affiliation(s)
- Xin Liu
- School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan 430023, China
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- Hubei Key Laboratory of Animal Nutrition and Feed Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Fangfang Cai
- School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan 430023, China
- Hubei Key Laboratory of Animal Nutrition and Feed Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Yumeng Zhang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
- Department of Basic Research, Research-And-Development Center, Sinopharm Animal Health Corporation Ltd., Wuhan 430074, China
| | - Xuan Luo
- School of Animal Science and Nutritional Engineering, Wuhan Polytechnic University, Wuhan 430023, China
- Hubei Key Laboratory of Animal Nutrition and Feed Science, Wuhan Polytechnic University, Wuhan 430023, China
| | - Li Yuan
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Haiyan Ma
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Mingkun Yang
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
| | - Feng Ge
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
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Cao X, Sun S, Xing J. A Massive Proteogenomic Screen Identifies Thousands of Novel Peptides From the Human "Dark" Proteome. Mol Cell Proteomics 2024; 23:100719. [PMID: 38242438 PMCID: PMC10867589 DOI: 10.1016/j.mcpro.2024.100719] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 01/01/2024] [Accepted: 01/16/2024] [Indexed: 01/21/2024] Open
Abstract
Although the human gene annotation has been continuously improved over the past 2 decades, numerous studies demonstrated the existence of a "dark proteome", consisting of proteins that were critical for biological processes but not included in widely used gene catalogs. The Genotype-Tissue Expression project generated more than 15,000 RNA-seq datasets from multiple tissues, which modeled 30 million transcripts in the human genome. To provide a resource of high-confidence novel proteins from the dark proteome, we screened 50,000 mass spectrometry runs from over 900 projects to identify proteins translated from the Genotype-Tissue Expression transcript model with proteomic support. We also integrated 3.8 million common genetic variants from the gnomAD database to improve peptide identification. As a result, we identified 170,529 novel peptides with proteomic evidence, of which 6048 passed the strictest standard we defined and were supported by PepQuery. We provided a user-friendly website (https://ncorf.genes.fun/) for researchers to check the evidence of novel peptides from their studies. The findings will improve our understanding of coding genes and facilitate genomic data interpretation in biomedical research.
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Affiliation(s)
- Xiaolong Cao
- Department of Anesthesiology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Siqi Sun
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA
| | - Jinchuan Xing
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA; Human Genetic Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, New Jersey, USA.
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Li Z, Li S, Luo M, Jhong JH, Li W, Yao L, Pang Y, Wang Z, Wang R, Ma R, Yu J, Huang Y, Zhu X, Cheng Q, Feng H, Zhang J, Wang C, Hsu JBK, Chang WC, Wei FX, Huang HD, Lee TY. dbPTM in 2022: an updated database for exploring regulatory networks and functional associations of protein post-translational modifications. Nucleic Acids Res 2021; 50:D471-D479. [PMID: 34788852 PMCID: PMC8728263 DOI: 10.1093/nar/gkab1017] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 10/08/2021] [Accepted: 10/13/2021] [Indexed: 01/02/2023] Open
Abstract
Protein post-translational modifications (PTMs) play an important role in different cellular processes. In view of the importance of PTMs in cellular functions and the massive data accumulated by the rapid development of mass spectrometry (MS)-based proteomics, this paper presents an update of dbPTM with over 2 777 000 PTM substrate sites obtained from existing databases and manual curation of literature, of which more than 2 235 000 entries are experimentally verified. This update has manually curated over 42 new modification types that were not included in the previous version. Due to the increasing number of studies on the mechanism of PTMs in the past few years, a great deal of upstream regulatory proteins of PTM substrate sites have been revealed. The updated dbPTM thus collates regulatory information from databases and literature, and merges them into a protein-protein interaction network. To enhance the understanding of the association between PTMs and molecular functions/cellular processes, the functional annotations of PTMs are curated and integrated into the database. In addition, the existing PTM-related resources, including annotation databases and prediction tools are also renewed. Overall, in this update, we would like to provide users with the most abundant data and comprehensive annotations on PTMs of proteins. The updated dbPTM is now freely accessible at https://awi.cuhk.edu.cn/dbPTM/.
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Affiliation(s)
- Zhongyan Li
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Shangfu Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Mengqi Luo
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jhih-Hua Jhong
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Wenshuo Li
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Lantian Yao
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuxuan Pang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Zhuo Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Rulan Wang
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China.,School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Renfei Ma
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jinhan Yu
- Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Yuqi Huang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Xiaoning Zhu
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Qifan Cheng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Hexiang Feng
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Jiahong Zhang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Chunxuan Wang
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Justin Bo-Kai Hsu
- Department of Medical Research, Taipei Medical University Hospital, Taipei 110, Taiwan
| | - Wen-Chi Chang
- Institute of Tropical Plant Sciences and Microbiology, National Cheng Kung University, Tainan 701, Taiwan
| | - Feng-Xiang Wei
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,Department of Cell Biology, Jiamusi University, Jiamusi 154007, China.,Shenzhen Children's Hospital of China Medical University, Shenzhen 518172, China
| | - Hsien-Da Huang
- The Genetics Laboratory, Longgang District Maternity & Child Healthcare Hospital of Shenzhen City, Shenzhen 518172, China.,School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
| | - Tzong-Yi Lee
- School of Life and Health Sciences, The Chinese University of Hong Kong, Shenzhen 518172, China.,Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Shenzhen 518172, China
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