1
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Sun B, Liu Z, Liu J, Zhao S, Wang L, Wang F. The utility of proteases in proteomics, from sequence profiling to structure and function analysis. Proteomics 2023; 23:e2200132. [PMID: 36382392 DOI: 10.1002/pmic.202200132] [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] [Received: 08/11/2022] [Revised: 11/08/2022] [Accepted: 11/08/2022] [Indexed: 11/18/2022]
Abstract
In mass spectrometry (MS)-based bottom-up proteomics, protease digestion plays an essential role in profiling both proteome sequences and post-translational modifications (PTMs). Trypsin is the gold standard in digesting intact proteins into small-size peptides, which are more suitable for high-performance liquid chromatography (HPLC) separation and tandem MS (MS/MS) characterization. However, protein sequences lacking Lys and Arg cannot be cleaved by trypsin and may be missed in conventional proteomic analysis. Proteases with cleavage sites complementary to trypsin are widely applied in proteomic analysis to greatly improve the coverage of proteome sequences and PTM sites. In this review, we survey the common and newly emerging proteases used in proteomics analysis mainly in the last 5 years, focusing on their unique cleavage features and specific proteomics applications such as missing protein characterization, new PTM discovery, and de novo sequencing. In addition, we summarize the applications of proteases in structural proteomics and protein function analysis in recent years. Finally, we discuss the future development directions of new proteases and applications in proteomics.
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Affiliation(s)
- Binwen Sun
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Zheyi Liu
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
| | - Jin Liu
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Shan Zhao
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
| | - Liming Wang
- Engineering Research Center for New Materials and Precision Treatment Technology of Malignant Tumors Therapy, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Engineering Technology Research Center for Translational Medicine, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
- Division of Hepatobiliary and Pancreatic Surgery, Department of General Surgery, Second Affiliated Hospital, Dalian Medical University, 467 Zhongshan Road, Dalian, 116027, China
| | - Fangjun Wang
- CAS Key Laboratory of Separation Sciences for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 463 Zhongshan Road, Dalian, 116023, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing, 100049, China
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2
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Zhu H, Jiang S, Zhou W, Chi H, Sun J, Shi J, Zhang Z, Chang L, Yu L, Zhang L, Lyu Z, Xu P, Zhang Y. Ac-LysargiNase efficiently helps genome reannotation of Mycolicibacterium smegmatis MC2 155. J Proteomics 2022; 264:104622. [DOI: 10.1016/j.jprot.2022.104622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/10/2022] [Accepted: 05/16/2022] [Indexed: 10/18/2022]
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3
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Zhou WJ, Wei ZH, He SM, Chi H. pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power. J Proteomics 2022; 251:104414. [PMID: 34737111 DOI: 10.1016/j.jprot.2021.104414] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/13/2021] [Accepted: 10/25/2021] [Indexed: 11/26/2022]
Abstract
Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. We have proposed a validation method pValid before, but its scope of application is limited because two features used in pValid are related to open database search and sub-optimal peptide candidates for tandem mass spectra, and the performance on complex datasets still has room for improvement. In this study, we developed a more comprehensive validation method, pValid 2, to break these limitations by removing the two features and bringing in a new feature related to the retention time predicted by a deep learning-based method pPredRT. pValid 2 yielded an average false positive rate of 0.03% and an average false negative rate of 1.37% on three testing datasets, better than those of pValid, and flagged 8.47% to 11.31% more incorrect identifications than pValid on two complex datasets. Moreover, pValid 2 flagged almost all decoy identifications in validating the open-search datasets. In addition, the function of validating identifications given by MaxQuant and MS-GF+ was implemented in pValid 2, and the validation results showed that pValid 2 performed dramatically better than three metabolic labeling validation methods. Further considering its cost-effectiveness as a pure computational approach, pValid 2 has the potential to be a widely used validation tool for peptide identifications of any proteome search engines in shotgun proteomics. SIGNIFICANCE: Identification results given by shotgun proteomics are vital to life science research. The correctness of identifications deeply affects the precision of the subsequent studies about protein structures and functions, protein-protein interactions, pathogenic mechanism, and targeted drugs. Thus, validating the correctness of identifications is crucial and urgent. In 2019, we developed an identification credibility validation method named pValid, whose false positive rate (FPR) is 0.03% and false negative rate (FNR) is 1.79%, comparable to those of the gold standard, i.e., the Synthetic-peptide validation method. However, pValid can only be used for validating the results from pFind, and its validation performance on a few complex datasets still has room for improvement. So, in this submission, we proposed pValid 2, a more comprehensive computational validation method that can validate identifications from any proteome search engines with increased discriminating power.
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Affiliation(s)
- Wen-Jing Zhou
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Zhuo-Hong Wei
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Si-Min He
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Hao Chi
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China; University of Chinese Academy of Sciences, Beijing, China.
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4
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Wu S, Sun J, Wang X, Xu F, Chi H, Li Y, Zhong B, Xie Y, Yan Z, Chang L, Wang D, He F, Wu J, Zhang Y, Xu P. Open-pFind Verified Four Missing Proteins from Multi-Tissues. J Proteome Res 2020; 19:4808-4814. [PMID: 33172275 DOI: 10.1021/acs.jproteome.0c00370] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The Chromosome-Centric Human Proteome Project (C-HPP) was launched in 2012 to perfect the annotation of human protein existence by identifying stronger evidence of the expression of missing proteins (MPs) at the protein level. After an 8 year effort all over the world, the number of MPs in the neXtProt database significantly decreased from 5511 (2012-02-24) to 1899 (2020-01-17). It is now more difficult to provide confident evidence of the remaining MPs because of their specific characteristics, including low abundance, low molecular weight, unexpected modifications, transmembrane structure, tissue-expression specificity, and so on. A higher resolution mass spectrometry (MS) interpretation engine might provide an opportunity to identify these buried MPs in complex samples by the combination with multi-tissue large-scale proteomics. In this study, open-pFind was used to dig MPs from 20 pairs of healthy human tissues by Wang et al. ( Mol. Syst. Biol. 2019, 15 (2), e8503) combined with our large-scale testis data set digested by three enzymes (Glu-C, Lys-C, and trypsin) with specificity for different amino acid residues ( J. Proteme Res. 2019, 18 (12), 4189-4196). A total of 1 535 536 peptides with 17 283 477 peptide-spectrum matches (PSMs) were mapped to 14 279 protein entries at a false discovery rate of <1% at the PSM, peptide, and protein levels. A total of 103 MP candidates were identified, among which 86 candidates had more unique peptide numbers compared with our single testis tissue. After rigorous screening, manual checks, peptide synthesis, and matching with documented peptides from PeptideAtlas, we validated four MPs, P0C7T8 (duodenum and small intestine), Q8WWZ4 (stomach and rectum), Q8IV35 (fallopian tube), and O14921 (tonsil), at the protein level. All MS raw files have been deposited to the ProteomeXchange with identifier PXD021391.
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Affiliation(s)
- Shujia Wu
- School of Basic Medical Science, Key Laboratory of Combinational Biosynthesis and Drug Discovery of Ministry of Education, School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Jinshuai Sun
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Xi Wang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing 100080, China
| | - Feng Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Hao Chi
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Beijing 100080, China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Bowen Zhong
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Yuping Xie
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Zhonghua Yan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Dongxue Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Junzhu Wu
- School of Basic Medical Science, Key Laboratory of Combinational Biosynthesis and Drug Discovery of Ministry of Education, School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China
| | - Ping Xu
- School of Basic Medical Science, Key Laboratory of Combinational Biosynthesis and Drug Discovery of Ministry of Education, School of Basic Medical Sciences, Wuhan University, Wuhan 430072, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Research Unit of Proteomics & Research and Development of New Drug of Chinese Academy of Medical Sciences, Institute of Lifeomics, Beijing 102206, China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
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5
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Omenn GS, Lane L, Overall CM, Cristea IM, Corrales FJ, Lindskog C, Paik YK, Van Eyk JE, Liu S, Pennington SR, Snyder MP, Baker MS, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project. J Proteome Res 2020; 19:4735-4746. [PMID: 32931287 DOI: 10.1021/acs.jproteome.0c00485] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States.,Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Ileana M Cristea
- Princeton University, Princeton, New Jersey 08544, United States
| | | | | | | | | | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | | | | | - Mark S Baker
- Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | - Ruedi Aebersold
- ETH-Zurich and University of Zurich, 8092 Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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6
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Is It Possible to Find Needles in a Haystack? Meta-Analysis of 1000+ MS/MS Files Provided by the Russian Proteomic Consortium for Mining Missing Proteins. Proteomes 2020; 8:proteomes8020012. [PMID: 32456206 PMCID: PMC7356824 DOI: 10.3390/proteomes8020012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 05/18/2020] [Accepted: 05/19/2020] [Indexed: 12/04/2022] Open
Abstract
Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).
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7
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Soh WT, Demir F, Dall E, Perrar A, Dahms SO, Kuppusamy M, Brandstetter H, Huesgen PF. ExteNDing Proteome Coverage with Legumain as a Highly Specific Digestion Protease. Anal Chem 2020; 92:2961-2971. [PMID: 31951383 PMCID: PMC7075662 DOI: 10.1021/acs.analchem.9b03604] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
![]()
Bottom-up
mass spectrometry-based proteomics utilizes proteolytic
enzymes with well characterized specificities to generate peptides
amenable for identification by high-throughput tandem mass spectrometry.
Trypsin, which cuts specifically after the basic residues lysine and
arginine, is the predominant enzyme used for proteome digestion, although
proteases with alternative specificities are required to detect sequences
that are not accessible after tryptic digest. Here, we show that the
human cysteine protease legumain exhibits a strict substrate specificity
for cleavage after asparagine and aspartic acid residues during in-solution
digestions of proteomes extracted from Escherichia
coli, mouse embryonic fibroblast cell cultures, and Arabidopsis thaliana leaves. Generating peptides
highly complementary in sequence, yet similar in their biophysical
properties, legumain (as compared to trypsin or GluC) enabled complementary
proteome and protein sequence coverage. Importantly, legumain further
enabled the identification and enrichment of protein N-termini not
accessible in GluC- or trypsin-digested samples. Legumain cannot cleave
after glycosylated Asn residues, which enabled the robust identification
and orthogonal validation of N-glycosylation sites based on alternating
sequential sample treatments with legumain and PNGaseF and vice versa.
Taken together, we demonstrate that legumain is a practical, efficient
protease for extending the proteome and sequence coverage achieved
with trypsin, with unique possibilities for the characterization of
post-translational modification sites.
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Affiliation(s)
- Wai Tuck Soh
- Department of Biosciences , University of Salzburg , 5020 Salzburg , Austria
| | - Fatih Demir
- Central Institute for Engineering, Electronics and Analytics, ZEA-3 , Forschungszentrum Jülich , 52428 Jülich , Germany
| | - Elfriede Dall
- Department of Biosciences , University of Salzburg , 5020 Salzburg , Austria
| | - Andreas Perrar
- Central Institute for Engineering, Electronics and Analytics, ZEA-3 , Forschungszentrum Jülich , 52428 Jülich , Germany
| | - Sven O Dahms
- Department of Biosciences , University of Salzburg , 5020 Salzburg , Austria
| | - Maithreyan Kuppusamy
- Central Institute for Engineering, Electronics and Analytics, ZEA-3 , Forschungszentrum Jülich , 52428 Jülich , Germany
| | - Hans Brandstetter
- Department of Biosciences , University of Salzburg , 5020 Salzburg , Austria
| | - Pitter F Huesgen
- Central Institute for Engineering, Electronics and Analytics, ZEA-3 , Forschungszentrum Jülich , 52428 Jülich , Germany.,Cologne Excellence Cluster on Cellular Stress Responses in Aging Associated Diseases, Medical Faculty and University Hospital , University of Cologne , 50931 Cologne , Germany.,Institute for Biochemistry, Faculty of Mathematics and Natural Sciences , University of Cologne , 50674 Cologne , Germany
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8
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Deutsch EW, Lane L, Overall CM, Bandeira N, Baker MS, Pineau C, Moritz RL, Corrales F, Orchard S, Van Eyk JE, Paik YK, Weintraub ST, Vandenbrouck Y, Omenn GS. Human Proteome Project Mass Spectrometry Data Interpretation Guidelines 3.0. J Proteome Res 2019; 18:4108-4116. [PMID: 31599596 DOI: 10.1021/acs.jproteome.9b00542] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Human Proteome Organization's (HUPO) Human Proteome Project (HPP) developed Mass Spectrometry (MS) Data Interpretation Guidelines that have been applied since 2016. These guidelines have helped ensure that the emerging draft of the complete human proteome is highly accurate and with low numbers of false-positive protein identifications. Here, we describe an update to these guidelines based on consensus-reaching discussions with the wider HPP community over the past year. The revised 3.0 guidelines address several major and minor identified gaps. We have added guidelines for emerging data independent acquisition (DIA) MS workflows and for use of the new Universal Spectrum Identifier (USI) system being developed by the HUPO Proteomics Standards Initiative (PSI). In addition, we discuss updates to the standard HPP pipeline for collecting MS evidence for all proteins in the HPP, including refinements to minimum evidence. We present a new plan for incorporating MassIVE-KB into the HPP pipeline for the next (HPP 2020) cycle in order to obtain more comprehensive coverage of public MS data sets. The main checklist has been reorganized under headings and subitems, and related guidelines have been grouped. In sum, Version 2.1 of the HPP MS Data Interpretation Guidelines has served well, and this timely update to version 3.0 will aid the HPP as it approaches its goal of collecting and curating MS evidence of translation and expression for all predicted ∼20 000 human proteins encoded by the human genome.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology , Seattle , Washington 98109 , United States
| | - Lydie Lane
- SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine , University of Geneva , CMU, Michel Servet 1 , 1211 Geneva 4 , Switzerland
| | - Christopher M Overall
- Centre for Blood Research, Departments of Oral Biological & Medical Sciences, and Biochemistry & Molecular Biology, Faculty of Dentistry , The University of British Columbia , Vancouver , BC V6T 1Z4 , Canada
| | - Nuno Bandeira
- Center for Computational Mass Spectrometry and Department of Computer Science and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences , University of California San Diego , La Jolla , California 92093 , United States
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Science , Macquarie University , Macquarie Park , NSW 2109 , Australia
| | - Charles Pineau
- Univ. Rennes , Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail) - UMR_S 1085 , F-35042 Rennes cedex , France
| | - Robert L Moritz
- Institute for Systems Biology , Seattle , Washington 98109 , United States
| | - Fernando Corrales
- Functional Proteomics Laboratory, Centro Nacional de Biotecnología , Spanish Research Council , ProteoRed-.ISCIII , Madrid 117 , Spain
| | - Sandra Orchard
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus , Hinxton , Cambridge CB10 1SD , U.K
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Department of Medicine , Cedars Sinai Medical Center , Los Angeles , California 90048 , United States
| | - Young-Ki Paik
- Yonsei Proteome Research Center , Yonsei University , 50 Yonsei-ro , Sudaemoon-ku , Seoul 03720 , Korea
| | - Susan T Weintraub
- The University of Texas Health Science Center at San Antonio , San Antonio , Texas 78229 , United States
| | - Yves Vandenbrouck
- Univ. Grenoble Alpes , CEA, INSERM, IRIG-BGE, U1038 , F-38000 Grenoble , France
| | - Gilbert S Omenn
- Institute for Systems Biology , Seattle , Washington 98109 , United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health , University of Michigan , Ann Arbor , Michigan 48109-2218 , United States
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9
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Paik YK, Overall CM, Corrales F, Deutsch EW, Lane L, Omenn GS. Toward Completion of the Human Proteome Parts List: Progress Uncovering Proteins That Are Missing or Have Unknown Function and Developing Analytical Methods. J Proteome Res 2019; 17:4023-4030. [PMID: 30985145 PMCID: PMC6288998 DOI: 10.1021/acs.jproteome.8b00885] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Young-Ki Paik
- Yonsei Proteome Research Center, College of Life Science and Technology, Yonsei University
| | - Christopher M Overall
- Centre for Blood Research, Departments of Oral Biological & Medical Sciences and Biochemistry & Molecular Biology, Faculty of Dentistry, University of British Columbia
| | - Fernando Corrales
- Functional Proteomics Laboratory National Center of Biotechnology, CSIC
| | | | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine, CMU, University of Geneva
| | - Gilbert S Omenn
- Institute for Systems Biology, Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics & School of Public Health, University of Michigan
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10
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Choong WK, Chen CT, Wang JH, Sung TY. iHPDM: In Silico Human Proteome Digestion Map with Proteolytic Peptide Analysis and Graphical Visualizations. J Proteome Res 2019; 18:4124-4132. [PMID: 31429573 DOI: 10.1021/acs.jproteome.9b00350] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
When conducting proteomics experiments to detect missing proteins and protein isoforms in the human proteome, it is desirable to use a protease that can yield more unique peptides with properties amenable for mass spectrometry analysis. Though trypsin is currently the most widely used protease, some proteins can yield only a limited number of unique peptides by trypsin digestion. Other proteases and multiple proteases have been applied in reported studies to increase the number of identified proteins and protein sequence coverage. To facilitate the selection of proteases, we developed a web-based resource, called in silico Human Proteome Digestion Map (iHPDM), which contains a comprehensive proteolytic peptide database constructed from human proteins, including isoforms, in neXtProt digested by 15 protease combinations of one or two proteases. iHPDM provides convenient functions and graphical visualizations for users to examine and compare the digestion results of different proteases. Notably, it also supports users to input filtering criteria on digested peptides, e.g., peptide length and uniqueness, to select suitable proteases. iHPDM can facilitate protease selection for shotgun proteomics experiments to identify missing proteins, protein isoforms, and single amino acid variant peptides.
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Affiliation(s)
- Wai-Kok Choong
- Institute of Information Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Ching-Tai Chen
- Institute of Information Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Jen-Hung Wang
- Institute of Information Science , Academia Sinica , Taipei 11529 , Taiwan
| | - Ting-Yi Sung
- Institute of Information Science , Academia Sinica , Taipei 11529 , Taiwan
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11
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Zhou WJ, Yang H, Zeng WF, Zhang K, Chi H, He SM. pValid: Validation Beyond the Target-Decoy Approach for Peptide Identification in Shotgun Proteomics. J Proteome Res 2019; 18:2747-2758. [PMID: 31244209 DOI: 10.1021/acs.jproteome.8b00993] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
As the de facto validation method in mass spectrometry-based proteomics, the target-decoy approach determines a threshold to estimate the false discovery rate and then filters those identifications beyond the threshold. However, the incorrect identifications within the threshold are still unknown and further validation methods are needed. In this study, we characterized a framework of validation and investigated a number of common and novel validation methods. We first defined the accuracy of a validation method by its false-positive rate (FPR) and false-negative rate (FNR) and, further, proved that a validation method with lower FPR and FNR led to identifications with higher sensitivity and precision. Then we proposed a validation method named pValid that incorporated an open database search and a theoretical spectrum prediction strategy via a machine-learning technology. pValid was compared with four common validation methods as well as a synthetic peptide validation method. Tests on three benchmark data sets indicated that pValid had an FPR of 0.03% and an FNR of 1.79% on average, both superior to the other four common validation methods. Tests on a synthetic peptide data set also indicated that the FPR and FNR of pValid were better than those of the synthetic peptide validation method. Tests on a large-scale human proteome data set indicated that pValid successfully flagged the highest number of incorrect identifications among all five methods. Further considering its cost-effectiveness, pValid has the potential to be a feasible validation tool for peptide identification.
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Affiliation(s)
- Wen-Jing Zhou
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology, CAS , Beijing , China 100190.,University of Chinese Academy of Sciences , Beijing , China 100049
| | - Hao Yang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology, CAS , Beijing , China 100190.,University of Chinese Academy of Sciences , Beijing , China 100049
| | - Wen-Feng Zeng
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology, CAS , Beijing , China 100190.,University of Chinese Academy of Sciences , Beijing , China 100049
| | - Kun Zhang
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology, CAS , Beijing , China 100190.,University of Chinese Academy of Sciences , Beijing , China 100049
| | - Hao Chi
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology, CAS , Beijing , China 100190.,University of Chinese Academy of Sciences , Beijing , China 100049
| | - Si-Min He
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) , Institute of Computing Technology, CAS , Beijing , China 100190.,University of Chinese Academy of Sciences , Beijing , China 100049
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12
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Omenn GS, Lane L, Overall CM, Corrales FJ, Schwenk JM, Paik YK, Van Eyk JE, Liu S, Snyder M, Baker MS, Deutsch EW. Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project. J Proteome Res 2018; 17:4031-4041. [PMID: 30099871 PMCID: PMC6387656 DOI: 10.1021/acs.jproteome.8b00441] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The Human Proteome Project (HPP) annually reports on progress throughout the field in credibly identifying and characterizing the human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2018-01-17, the baseline for this sixth annual HPP special issue of the Journal of Proteome Research, contains 17 470 PE1 proteins, 89% of all neXtProt predicted PE1-4 proteins, up from 17 008 in release 2017-01-23 and 13 975 in release 2012-02-24. Conversely, the number of neXtProt PE2,3,4 missing proteins has been reduced from 2949 to 2579 to 2186 over the past two years. Of the PE1 proteins, 16 092 are based on mass spectrometry results, and 1378 on other kinds of protein studies, notably protein-protein interaction findings. PeptideAtlas has 15 798 canonical proteins, up 625 over the past year, including 269 from SUMOylation studies. The largest reason for missing proteins is low abundance. Meanwhile, the Human Protein Atlas has released its Cell Atlas, Pathology Atlas, and updated Tissue Atlas, and is applying recommendations from the International Working Group on Antibody Validation. Finally, there is progress using the quantitative multiplex organ-specific popular proteins targeted proteomics approach in various disease categories.
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Affiliation(s)
- Gilbert S. Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, Michigan 48109-2218, United States
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, CMU, Michel-Servet 1, 1211 Geneva 4, Switzerland
| | - Christopher M. Overall
- Life Sciences Institute, Faculty of Dentistry, University of British Columbia, 2350 Health Sciences Mall, Room 4.401, Vancouver, BC Canada V6T 1Z3
| | | | - Jochen M. Schwenk
- Science for Life Laboratory, KTH Royal Institute of Technology, Tomtebodavägen 23A, 17165 Solna, Sweden
| | - Young-Ki Paik
- Yonsei Proteome Research Center, Room 425, Building #114, Yonsei University,50 Yonsei-ro, Seodaemoon-ku, Seoul 120-749, Korea
| | - Jennifer E. Van Eyk
- Advanced Clinical BioSystems Research Institute, Cedars Sinai Precision Biomarker Laboratories, Barbra Streisand Women’s Heart Center, Cedars-Sinai Medical Center, Los Angeles, CA 90048, United States
| | - Siqi Liu
- Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX 75390-9148, United States
| | - Michael Snyder
- Department of Genetics, Stanford University, Alway Building, 300 Pasteur Drive, 3165 Porter Drive, Palo Alto, 94304, United States
| | - Mark S. Baker
- Department of Biomedical Sciences, Macquarie University, NSW 2109, Australia
| | - Eric W. Deutsch
- Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5263, United States
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13
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Siddiqui O, Zhang H, Guan Y, Omenn GS. Chromosome 17 Missing Proteins: Recent Progress and Future Directions as Part of the neXt-MP50 Challenge. J Proteome Res 2018; 17:4061-4071. [PMID: 30280577 DOI: 10.1021/acs.jproteome.8b00442] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The Chromosome-centric Human Proteome Project (C-HPP), announced in September 2016, is an initiative to accelerate progress on the detection and characterization of neXtProt PE2,3,4 "missing proteins" (MPs) with a mandate to each chromosome team to find about 50 MPs over 2 years. Here we report major progress toward the neXt-MP50 challenge with 43 newly validated Chr 17 PE1 proteins, of which 25 were based on mass spectrometry, 12 on protein-protein interactions, 3 on a combination of MS and PPI, and 3 with other types of data. Notable among these new PE1 proteins were five keratin-associated proteins, a single olfactory receptor, and five additional membrane-embedded proteins. We evaluate the prospects of finding the remaining 105 MPs coded for on Chr 17, focusing on mass spectrometry and protein-protein interaction approaches. We present a list of 35 prioritized MPs with specific approaches that may be used in further MS and PPI experimental studies. Additionally, we demonstrate how in silico studies can be used to capture individual peptides from major data repositories, documenting one MP that appears to be a strong candidate for PE1. We are close to our goal of finding 50 MPs for Chr 17.
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Affiliation(s)
- Omer Siddiqui
- Department of Electronic Engineering and Computer Science , University of Michigan , Ann Arbor , Michigan 48109 , United States.,Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Hongjiu Zhang
- Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , Michigan 48109 , United States.,Department of Internal Medicine , University of Michigan , Ann Arbor , Michigan 48109 , United States
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics , University of Michigan , Ann Arbor , Michigan 48109 , United States.,Department of Internal Medicine , University of Michigan , Ann Arbor , Michigan 48109 , United States.,Department of Human Genetics and School of Public Health , University of Michigan , Ann Arbor , Michigan 48109 , United States
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14
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Sun J, Shi J, Wang Y, Chen Y, Li Y, Kong D, Chang L, Liu F, Lv Z, Zhou Y, He F, Zhang Y, Xu P. Multiproteases Combined with High-pH Reverse-Phase Separation Strategy Verified Fourteen Missing Proteins in Human Testis Tissue. J Proteome Res 2018; 17:4171-4177. [PMID: 30280576 DOI: 10.1021/acs.jproteome.8b00397] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Subsequent to conducting the Chromosome-Centric Human Proteome Project, we have focused on human testis-enriched missing proteins (MPs) since 2015. For protein coverage to be enhanced, a multiprotease strategy was used for separation of samples by 10% SDS-PAGE. For the separating efficiency to be improved, a high-pH reverse phase (RP) separation strategy was applied to fractionate complex samples in this study. A total of 11,558 proteins was identified, which is the largest proteome data set for single human tissue sample so far. On the basis of this large-scale data set, we verified 14 MPs (PE2) in neXtProt (2018-01) after spectrum quality analysis, isobaric post-translational modification, and single amino acid variant filtering, and synthesized peptide matching. Tissue expression analysis showed that 3 of 14 MPs were testis-specific proteins. Functional analysis showed that 10 of 14 MPs were closely related to liver tumor, liver carcinoma, and hepatocellular carcinoma. Another 100 MPs were listed as candidates but required additional verification information. All MS data sets have been deposited into the ProteomeXchange with the identifier PXD009737.
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Affiliation(s)
- Jinshuai Sun
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences , Hebei University , Baoding , Hebei 071002 , China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Jiahui Shi
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences , Hebei University , Baoding , Hebei 071002 , China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Yihao Wang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Yang Chen
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Degang Kong
- Department of Hepatopancreatobiliary Surgery , The Second Affiliated Hospital of Tianjin Medical University , Tianjin 300211 , China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Fengsong Liu
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences , Hebei University , Baoding , Hebei 071002 , China
| | - Zhitang Lv
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences , Hebei University , Baoding , Hebei 071002 , China
| | - Yue Zhou
- Demo Laboratory of Thermofisher Scientific China , Shanghai 200120 , China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China
| | - Yao Zhang
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources, School of Life Sciences , Sun Yat-Sen University , Guangzhou 510275 , China
| | - Ping Xu
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences , Hebei University , Baoding , Hebei 071002 , China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206 , China.,Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science , Wuhan University , Wuhan 430072 , China
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15
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Jeong SK, Kim CY, Paik YK. ASV-ID, a Proteogenomic Workflow To Predict Candidate Protein Isoforms on the Basis of Transcript Evidence. J Proteome Res 2018; 17:4235-4242. [PMID: 30289715 DOI: 10.1021/acs.jproteome.8b00548] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
One of the goals of the Chromosome-Centric Human Proteome Project (C-HPP) is to map and characterize the functions of protein isoforms produced by alternative splicing of genes. However, identifying alternative splice variants (ASVs) via mass spectrometry remains a major challenge, because ASVs usually contain highly homologous peptide sequences. A routine protein sequence analysis suggests that more than half of the investigated proteins do not generate two or more uniquely mapping peptides that would enable their isoforms to be distinguished. Here, we develop a new proteogenomics method, named "ASV-ID" (alternative splicing variants identification), which enables identification of ASVs by using a cell type-specific protein sequence database that is supported by RNA-Seq data. Using this workflow, we identify 1935 distinct proteins under highly stringent conditions. In fact, transcript evidence on these 841 proteins helps us distinguish them from other isoforms, despite the fact that these proteins are not predicted to make 2 or more uniquely mapping peptides. We also demonstrate that ASV-ID enables detection of 19 differently expressed isoforms present in several cell lines. Thus, a new workflow using ASV-ID has the potential to map yet-to-be-identified difficult protein isoforms in a simple and robust way.
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16
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He C, Sun J, Shi J, Wang Y, Zhao J, Wu S, Chang L, Gao H, Liu F, Lv Z, He F, Zhang Y, Xu P. Digging for Missing Proteins Using Low-Molecular-Weight Protein Enrichment and a “Mirror Protease” Strategy. J Proteome Res 2018; 17:4178-4185. [DOI: 10.1021/acs.jproteome.8b00398] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Cuitong He
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
| | - Jinshuai Sun
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Jiahui Shi
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Yihao Wang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
| | - Jialing Zhao
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
- Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science, Wuhan University, Wuhan 430072, China
| | - Shujia Wu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
- Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science, Wuhan University, Wuhan 430072, China
| | - Lei Chang
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
| | - Huiying Gao
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
| | - Fengsong Liu
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Zhitang Lv
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
| | - Fuchu He
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
| | - Yao Zhang
- State Key Laboratory of Biocontrol, Guangdong Key Laboratory of Plant Resources, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
| | - Ping Xu
- State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing Proteome Research Center, Beijing 102206, China
- Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding, Hebei 071002, China
- Key Laboratory of Combinational Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, School of Pharmaceutical Science, Wuhan University, Wuhan 430072, China
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17
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Melaine N, Com E, Bellaud P, Guillot L, Lagarrigue M, Morrice NA, Guével B, Lavigne R, Velez de la Calle JF, Dojahn J, Pineau C. Deciphering the Dark Proteome: Use of the Testis and Characterization of Two Dark Proteins. J Proteome Res 2018; 17:4197-4210. [DOI: 10.1021/acs.jproteome.8b00387] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Nathalie Melaine
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Emmanuelle Com
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Pascale Bellaud
- H2P2 Core Facility, UMS BioSit, Univ Rennes, Rennes F-35040, France
| | - Laetitia Guillot
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Mélanie Lagarrigue
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Nick A. Morrice
- Sciex, Phoenix House Lakeside Drive Centre Park, Warrington WA1 1RX, U.K
| | - Blandine Guével
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | - Régis Lavigne
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
| | | | - Jörg Dojahn
- Sciex, Landwehrstr. 54, 64293 Darmstadt, Germany
| | - Charles Pineau
- Univ Rennes, Inserm, EHESP, Irset (Institut de Recherche en Santé, Environnement et Travail)—UMR S 1085, F-35042 Rennes cedex, France
- Protim, Univ Rennes, F-35042 Rennes, France
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18
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Paik YK, Overall CM, Deutsch EW, Van Eyk JE, Omenn GS. Progress and Future Direction of Chromosome-Centric Human Proteome Project. J Proteome Res 2018; 16:4253-4258. [PMID: 29191025 DOI: 10.1021/acs.jproteome.7b00734] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
This special issue of JPR celebrates the fifth anniversary of the Chromosome-Centric Human Proteome Project (C-HPP). We present 27 manuscripts in four categories: (i) Metrics of Progress and Resources, (ii) Missing Protein Detection and Validation, (iii) Analytical Methods and Quality Assessment, and (iv) Protein Functions and Disease. We briefly introduce key messages from each paper, mostly from C-HPP teams and some from the Biology and Disease-driven HPP. From the first few months of the C-HPP NeXt-MP50 Missing Proteins Challenge, authors report 73 missing protein detections that meet the HPP guidelines using several novel approaches. Finally, we discuss future directions.
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Affiliation(s)
- Young-Ki Paik
- Yonsei Proteome Research Center and Department of Biochemistry, Yonsei University
| | - Christopher M Overall
- Centre for Blood Research, Departments of Oral Biological & Medical Sciences and Biochemistry & Molecular Biology, Faculty of Dentistry, University of British Columbia
| | | | - Jennifer E Van Eyk
- Advanced Clinical BioSystems Research Institute , Department of Medicine, Cedars-Sinai Medical Centre
| | - Gilbert S Omenn
- Institute for Systems Biology.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health, University of Michigan
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19
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Schiza C, Korbakis D, Panteleli E, Jarvi K, Drabovich AP, Diamandis EP. Discovery of a Human Testis-specific Protein Complex TEX101-DPEP3 and Selection of Its Disrupting Antibodies. Mol Cell Proteomics 2018; 17:2480-2495. [PMID: 30097533 DOI: 10.1074/mcp.ra118.000749] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Revised: 07/11/2018] [Indexed: 01/01/2023] Open
Abstract
TEX101 is a testis-specific protein expressed exclusively in male germ cells and is a validated biomarker of male infertility. Studies in mice suggest that TEX101 is a cell-surface chaperone which regulates, through protein-protein interactions, the maturation of proteins involved in spermatozoa transit and oocyte binding. Male TEX101-null mice are sterile. Here, we identified by co-immunoprecipitation-mass spectrometry the interactome of human TEX101 in testicular tissues and spermatozoa. The testis-specific cell-surface dipeptidase 3 (DPEP3) emerged as the top hit. We further validated the TEX101-DPEP3 complex by using hybrid immunoassays. Combinations of antibodies recognizing different epitopes of TEX101 and DPEP3 facilitated development of a simple immunoassay to screen for disruptors of TEX101-DPEP3 complex. As a proof-of-a-concept, we demonstrated that anti-TEX101 antibody T4 disrupted the native TEX101-DPEP3 complex. Disrupting antibodies may be used to study the human TEX101-DPEP3 complex, and to develop modulators for male fertility.
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Affiliation(s)
- Christina Schiza
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Canada
| | - Dimitrios Korbakis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
| | - Efstratia Panteleli
- Department of Clinical Biochemistry, University Health Network, Toronto, Canada
| | - Keith Jarvi
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada; Department of Surgery, Division of Urology, Mount Sinai Hospital, Toronto, Canada
| | - Andrei P Drabovich
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Canada; Department of Clinical Biochemistry, University Health Network, Toronto, Canada
| | - Eleftherios P Diamandis
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada; Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Canada; Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada; Department of Clinical Biochemistry, University Health Network, Toronto, Canada.
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20
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Schlaffner CN, Pirklbauer GJ, Bender A, Steen JAJ, Choudhary JS. A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes. J Vis Exp 2018. [PMID: 29889196 PMCID: PMC6101353 DOI: 10.3791/57633] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
Cross-talk between genes, transcripts, and proteins is the key to cellular responses; hence, analysis of molecular levels as distinct entities is slowly being extended to integrative studies to enhance the understanding of molecular dynamics within cells. Current tools for the visualization and integration of proteomics with other omics datasets are inadequate for large-scale studies. Furthermore, they only capture basic sequence identify, discarding post-translational modifications and quantitation. To address these issues, we developed PoGo to map peptides with associated post-translational modifications and quantification to reference genome annotation. In addition, the tool was developed to enable the mapping of peptides identified from customized sequence databases incorporating single amino acid variants. While PoGo is a command line tool, the graphical interface PoGoGUI enables non-bioinformatics researchers to easily map peptides to 25 species supported by Ensembl genome annotation. The generated output borrows file formats from the genomics field and, therefore, visualization is supported in most genome browsers. For large-scale studies, PoGo is supported by TrackHubGenerator to create web-accessible repositories of data mapped to genomes that also enable an easy sharing of proteogenomics data. With little effort, this tool can map millions of peptides to reference genomes within only a few minutes, outperforming other available sequence-identity based tools. This protocol demonstrates the best approaches for proteogenomics mapping through PoGo with publicly available datasets of quantitative and phosphoproteomics, as well as large-scale studies.
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Affiliation(s)
- Christoph N Schlaffner
- Department of Neurobiology, F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School; Proteomic Mass Spectrometry, Wellcome Trust Sanger Institute, Wellcome Genome Campus; Centre for Molecular Informatics, Department of Chemistry, University of Cambridge;
| | - Georg J Pirklbauer
- Proteomic Mass Spectrometry, Wellcome Trust Sanger Institute, Wellcome Genome Campus
| | - Andreas Bender
- Centre for Molecular Informatics, Department of Chemistry, University of Cambridge
| | - Judith A J Steen
- Department of Neurobiology, F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School
| | - Jyoti S Choudhary
- Proteomic Mass Spectrometry, Wellcome Trust Sanger Institute, Wellcome Genome Campus; Functional Proteomics Group, Chester Beatty Laboratories, Institute of Cancer Research
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