CoChat
Sign Up
Cited by 2Advanced Proteomics Techniques and ApplicationsMass Spectrometry Techniques and ApplicationsAdvanced Biosensing Techniques and Applications

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Open access
Yildiz, Pelin, Ozcan, Sureyya·2023·Talanta
DOI 10.1016/j.talanta.2023.124878

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.talanta.2023.124878

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 81

  1. Patel, 2014, Role of proteomics in biomarker discovery, 39, 10.1016/B978-0-12-800168-4.00003-2 · DOI
  2. Serna, 2019, Targeted multiplex proteomics for molecular prescreening and biomarker discovery in metastatic colorectal cancer, Sci. Rep., 9, 1, 10.1038/s41598-019-49867-7 · DOI · Paper page
  3. Duncan, 2010, The pros and cons of peptide-centric proteomics, Nat. Biotechnol., 28, 659, 10.1038/nbt0710-659 · DOI
  4. Addona, 2009, Multi-site assessment of the precision and reproducibility of multiple reaction monitoring–based measurements of proteins in plasma, Nat. Biotechnol., 27, 633, 10.1038/nbt.1546 · DOI · Paper page
  5. van Duijl, 2021, Development and provisional validation of a multiplex LC-MRM-MS test for timely kidney injury detection in urine, J. Proteome Res., 20, 5304, 10.1021/acs.jproteome.1c00532 · DOI
  6. Arora, 2019, Serum biomarkers identification by iTRAQ and verification by MRM: S100A8/S100A9 levels predict tumor-stroma involvement and prognosis in Glioblastoma, Sci. Rep., 9, 1, 10.1038/s41598-019-39067-8 · DOI
  7. Kim, 2023, Simultaneous quantification of apolipoprotein C-iii O-glycoforms by protein-MRM, J. Proteome Res., 22, 91, 10.1021/acs.jproteome.2c00490 · DOI
  8. Whiteaker, 2021, Targeted mass spectrometry enables multiplexed quantification of immunomodulatory proteins in clinical biospecimens, Front. Immunol., 12, 4719, 10.3389/fimmu.2021.765898 · DOI
  9. Plubell, 2022, Putting humpty dumpty back together again: what does protein quantification mean in bottom-up proteomics?, J. Proteome Res., 21, 891, 10.1021/acs.jproteome.1c00894 · DOI · Paper page
  10. Lange, 2008, Selected reaction monitoring for quantitative proteomics: a tutorial, Mol. Syst. Biol., 4, 222, 10.1038/msb.2008.61 · DOI
  11. Liebler, 2013, Targeted quantitation of proteins by mass spectrometry, Biochemistry, 52, 3797, 10.1021/bi400110b · DOI
  12. Gallien, 2011, Selected reaction monitoring applied to proteomics, J. Mass Spectrom., 46, 298, 10.1002/jms.1895 · DOI
  13. Picotti, 2012, Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions, Nat. Methods, 9, 555, 10.1038/nmeth.2015 · DOI · Paper page
  14. Mohammed, 2017, PeptideTracker: a knowledge base for collecting and storing information on protein concentrations in biological tissues, Proteomics, 17, 10.1002/pmic.201600210 · DOI
  15. Farrah, 2012, PASSEL: the P eptide A tlas SRM experiment library, Proteomics, 12, 1170, 10.1002/pmic.201100515 · DOI
  16. Schubert, 2013, The Mtb proteome library: a resource of assays to quantify the complete proteome of Mycobacterium tuberculosis, Cell Host Microbe, 13, 602, 10.1016/j.chom.2013.04.008 · DOI
  17. Kusebauch, 2016, Human SRMAtlas: a resource of targeted assays to quantify the complete human proteome, Cell, 166, 766, 10.1016/j.cell.2016.06.041 · DOI · Paper page
  18. Sharma, 2014, Panorama: a targeted proteomics knowledge base, J. Proteome Res., 13, 4205, 10.1021/pr5006636 · DOI
  19. Whiteaker, 2014, CPTAC Assay Portal: a repository of targeted proteomic assays, Nat. Methods, 11, 703, 10.1038/nmeth.3002 · DOI · Paper page
  20. Jadav, 2023, ProteoExcelTP: development of a simple excel-based tool for surrogate peptide selection in mass spectrometry based targeted proteomics, J. Pharm. Biomed. Anal., 222, 10.1016/j.jpba.2022.115105 · DOI
  21. Mohammed, 2014, PeptidePicker: a scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments, J. Proteonomics, 106, 151, 10.1016/j.jprot.2014.04.018 · DOI
  22. Tsiatsiani, 2015, Proteomics beyond trypsin, FEBS J., 282, 2612, 10.1111/febs.13287 · DOI
  23. Dau, 2020, Proteomics using protease alternatives to trypsin benefits from sequential digestion with trypsin, Anal. Chem., 92, 9523, 10.1021/acs.analchem.0c00478 · DOI
  24. Zhong, 2020, Ultrafast enzymatic digestion of proteins by microdroplet mass spectrometry, Nat. Commun., 11, 1, 10.1038/s41467-020-14877-x · DOI
  25. Wu, 2018, Lys-C/Arg-C, a more specific and efficient digestion approach for proteomics studies, Anal. Chem., 90, 9700, 10.1021/acs.analchem.8b02448 · DOI
  26. Wiśniewski, 2018, Multiple-enzyme-digestion strategy improves accuracy and sensitivity of label-and standard-free absolute quantification to a level that is achievable by analysis with stable isotope-labeled standard spiking, J. Proteome Res., 18, 217
  27. Varnum, 2011, Plasma biomarkers for detecting hodgkin's lymphoma in HIV patients, PLoS One, 6, 10.1371/journal.pone.0029263 · DOI
  28. van Rees, 2018, Evidence of microglial activation following exposure to serum from first-onset drug-naïve schizophrenia patients, Brain Behav. Immun., 67, 364, 10.1016/j.bbi.2017.10.003 · DOI
  29. Sleddering, 2014, Proteomic analysis in type 2 diabetes patients before and after a very low calorie diet reveals potential disease state and intervention specific biomarkers, PLoS One, 9, 10.1371/journal.pone.0112835 · DOI
  30. Sepiashvili, 2014, Integrated omic analysis of oropharyngeal carcinomas reveals human papillomavirus (HPV)–dependent regulation of the activator protein 1 (AP-1) pathway, Mol. Cell. Proteomics, 13, 3572, 10.1074/mcp.M114.041764 · DOI
  31. Ozcan, 2017, Towards reproducible MRM based biomarker discovery using dried blood spots, Sci. Rep., 7, 1, 10.1038/srep45178 · DOI
  32. Martin, 2013, Dried blood spot proteomics: surface extraction of endogenous proteins coupled with automated sample preparation and mass spectrometry analysis, J. Am. Soc. Mass Spectrom., 24, 1242, 10.1007/s13361-013-0658-1 · DOI
  33. Kim, 2017, Serum fibronectin distinguishes the early stages of hepatocellular carcinoma, Sci. Rep., 7, 1
  34. Norrgran, 2009, Optimization of digestion parameters for protein quantification, Anal. Biochem., 393, 48, 10.1016/j.ab.2009.05.050 · DOI
  35. Burkhart, 2012, Systematic and quantitative comparison of digest efficiency and specificity reveals the impact of trypsin quality on MS-based proteomics, J. Proteonomics, 75, 1454, 10.1016/j.jprot.2011.11.016 · DOI
  36. Saveliev, 2013, Trypsin/Lys-C protease mix for enhanced protein mass spectrometry analysis, Nat. Methods, 10, 10.1038/nmeth.f.371 · DOI
  37. Huesgen, 2014, LysargiNase mirrors trypsin for protein C-terminal and methylation-site identification, Nat. Methods, 12, 55, 10.1038/nmeth.3177 · DOI
  38. Vandermarliere, 2013, Getting intimate with trypsin, the leading protease in proteomics, Mass Spectrom. Rev., 32, 453, 10.1002/mas.21376 · DOI
  39. Wu, 2018, Lys-C/Arg-C, a more specific and efficient digestion approach for proteomics studies, Anal. Chem., 90, 9700, 10.1021/acs.analchem.8b02448 · DOI
  40. Klammer, 2006, Effects of modified digestion schemes on the identification of proteins from complex mixtures, J. Proteome Res., 5, 695, 10.1021/pr050315j · DOI
  41. Whiteaker, 2018, Targeted mass spectrometry enables robust quantification of FANCD2 mono-ubiquitination in response to DNA damage, DNA Repair, 65, 47, 10.1016/j.dnarep.2018.03.003 · DOI
  42. Chavez, 2018, Chemical crosslinking mass spectrometry analysis of protein conformations and supercomplexes in heart tissue, Cell Syst, 6, 136, 10.1016/j.cels.2017.10.017 · DOI
  43. Polson, 2003, Optimization of protein precipitation based upon effectiveness of protein removal and ionization effect in liquid chromatography–tandem mass spectrometry, J. Chromatogr., B, 785, 263, 10.1016/S1570-0232(02)00914-5 · DOI
  44. Fang, 2015, Controlling nonspecific trypsin cleavages in LC-MS/MS-based shotgun proteomics using optimized experimental conditions, Analyst, 140, 7613, 10.1039/C5AN01505G · DOI
  45. Sottrup-Jensen, 1989, α-Macroglobulins: structure, shape, and mechanism of proteinase complex formation, J. Biol. Chem., 264, 11539, 10.1016/S0021-9258(18)80094-1 · DOI
  46. Rehman, 2013, Alpha-2-Macroglobulin: a physiological guardian, J. Cell. Physiol., 228, 1665, 10.1002/jcp.24266 · DOI
  47. An, 2009, Determination of glycosylation sites and site-specific heterogeneity in glycoproteins, Curr. Opin. Chem. Biol., 13, 421, 10.1016/j.cbpa.2009.07.022 · DOI
  48. Gupta, 2019, Cerebrospinal fluid proteomics for identification of α2-macroglobulin as a potential biomarker to monitor pharmacological therapeutic efficacy in dopamine dictated disease states of Parkinson's disease and schizophrenia, Neuropsychiatric Dis. Treat., 2853, 10.2147/NDT.S214217 · DOI
  49. Varma, 2017, Alpha-2 macroglobulin in Alzheimer's disease: a marker of neuronal injury through the RCAN1 pathway, Mol. Psychiatr., 22, 13, 10.1038/mp.2016.206 · DOI
  50. Kovacs, 2000, α2-Macroglobulin in late-onset Alzheimer's disease, Exp. Gerontol., 35, 473, 10.1016/S0531-5565(00)00113-3 · DOI
  51. Cater, 2019, Alpha-2-Macroglobulin, a hypochlorite-regulated chaperone and immune system modulator, Oxid. Med. Cell. Longev., 2019, 1, 10.1155/2019/5410657 · DOI
  52. Meyer, 2012, Human α2-macroglobulin-another variation on the venus flytrap, Angew. Chem. Int. Ed., 51, 5045, 10.1002/anie.201201104 · DOI
  53. Burgess, 2008, Prostate cancer serum biomarker discovery through proteomic analysis of alpha-2 macroglobulin protein complexes, proteomics -, Clin. Appl., 2, 1223, 10.1002/prca.200780073 · DOI
  54. Yoshino, 2019, Molecular form and concentration of serum α2-macroglobulin in diabetes, Sci. Rep., 9, 10.1038/s41598-019-49144-7 · DOI
  55. Housley, 1968, Alpha-2-Macroglobulin levels in disease in man, J. Clin. Pathol., 21, 27, 10.1136/jcp.21.1.27 · DOI
  56. Eubanks, 2011, Identification of α2-macroglobulin as a major serum ghrelin esterase, Angew. Chem. Int. Ed., 50, 10699, 10.1002/anie.201104512 · DOI
  57. Bateman, 2021, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Res., 49, D480, 10.1093/nar/gkaa1100 · DOI · Paper page
  58. Gaudet, 2017, The neXtProt knowledgebase on human proteins: 2017 update, Nucleic Acids Res., 45, D177, 10.1093/nar/gkw1062 · DOI · Paper page
  59. Pundir, 2016, UniProt tools, Curr. Protoc. Bioinforma., 53, 10.1002/0471250953.bi0129s53 · DOI
  60. Song, 2017, Targeted proteomic assays for quantitation of proteins identified by proteogenomic analysis of ovarian cancer, Sci. Data, 4, 1, 10.1038/sdata.2017.91 · DOI
  61. Marrero, 2012, The crystal structure of human α2-macroglobulin reveals a unique molecular cage, Angew. Chem. Int. Ed., 51, 3340, 10.1002/anie.201108015 · DOI
  62. Rigsby, 2016, Using the P y MOL application to reinforce visual understanding of protein structure, Biochem. Mol. Biol. Educ., 44, 433, 10.1002/bmb.20966 · DOI
  63. Swaney, 2010, The value of using multiple proteases for large-scale mass spectrometry-based proteomics, J. Proteome Res., 9, 1323, 10.1021/pr900863u · DOI
  64. Lin, 2008, Sodium-deoxycholate-assisted tryptic digestion and identification of proteolytically resistant proteins, Anal. Biochem., 377, 259, 10.1016/j.ab.2008.03.009 · DOI
  65. Proc, 2010, A quantitative study of the effects of chaotropic agents, surfactants, and solvents on the digestion efficiency of human plasma proteins by trypsin, J. Proteome Res., 9, 5422, 10.1021/pr100656u · DOI
  66. Park, 2007, Energetics-based protein profiling on a proteomic scale: identification of proteins resistant to proteolysis, J. Mol. Biol., 368, 1426, 10.1016/j.jmb.2007.02.091 · DOI
  67. Rafiee, 2020, Protease‐resistant streptavidin for interaction proteomics, Mol. Syst. Biol., 16, 10.15252/msb.20199370 · DOI
  68. Oberg, 1998, Papaya glutamine cyclase, a plant enzyme highly resistant to proteolysis, adopts an all‐β conformation, Eur. J. Biochem., 258, 214, 10.1046/j.1432-1327.1998.2580214.x · DOI
  69. Lee, 2011, Targeted mass spectrometric approach for biomarker discovery and validation with nonglycosylated tryptic peptides from N-linked glycoproteins in human plasma, Mol. Cell. Proteomics, 10, 10.1074/mcp.M111.009290 · DOI
  70. Undin, 2016, Mechanistic investigation of the on-surface enzymatic digestion (oSED) protein adsorption detection method using targeted mass spectrometry, Analyst, 141, 1714, 10.1039/C5AN02091C · DOI
  71. Bian, 2020, Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC–MS/MS, Nat. Commun., 11, 1, 10.1038/s41467-019-13973-x · DOI
  72. Qian, 2009, Large-scale multiplexed quantitative discovery proteomics enabled by the use of an 18O-labeled “universal” reference sample, J. Proteome Res., 8, 290, 10.1021/pr800467r · DOI
  73. Piehowski, 2013, Sources of technical variability in quantitative LC–MS proteomics: human brain tissue sample analysis, J. Proteome Res., 12, 2128, 10.1021/pr301146m · DOI
  74. MacLean, 2010, Skyline: an open source document editor for creating and analyzing targeted proteomics experiments, Bioinformatics, 26, 966, 10.1093/bioinformatics/btq054 · DOI · Paper page
  75. Chiva, 2014, Influence of the digestion technique, protease, and missed cleavage peptides in protein quantitation, J. Proteome Res., 13, 3979, 10.1021/pr500294d · DOI
  76. Awasthi, 2022, Proteomic discovery and validation of novel fluid biomarkers for improved patient selection and prediction of clinical outcomes in Alzheimer's disease patient cohorts, Proteomes, 10, 26, 10.3390/proteomes10030026 · DOI
  77. Kuzyk, 2009, Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma, Mol. Cell. Proteomics, 8, 1860, 10.1074/mcp.M800540-MCP200 · DOI · Paper page
  78. Perrin, 2013, Quantitative label-free proteomics for discovery of biomarkers in cerebrospinal fluid: assessment of technical and inter-individual variation, PLoS One, 8, 10.1371/journal.pone.0064314 · DOI
  79. Maia, 2020, Simple peptide quantification approach for MS-based proteomics quality control, ACS Omega, 5, 6754, 10.1021/acsomega.0c00080 · DOI
  80. Anjo, 2019, Use of recombinant proteins as a simple and robust normalization method for untargeted proteomics screening: exhaustive performance assessment, Talanta, 205, 10.1016/j.talanta.2019.120163 · DOI
  81. Wells, 2005, Collision‐induced dissociation (CID) of peptides and proteins, Methods Enzymol., 402, 148, 10.1016/S0076-6879(05)02005-7 · DOI

Metadata

Authors
Yildiz, P., & Ozcan, S.
ORCID
Venue
Talanta
Publisher
Elsevier BV
Published
2023-12
Volume
265
Pages
124878
ISSN
0039-9140 (ISSN-L)
DOI
10.1016/j.talanta.2023.124878
OpenAlex
W4381885406
Cited by
2
References
4
Citations by year
2025: 1 · 2024: 1
FWCI
0.18
Type
journal article
Language
en
Open access
Licence
Locations
  • Talanta (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed (publishedVersion)
  • OpenMETU (Middle East Technical University) (submittedVersion, cc-by-nc-nd)
Funders
Orta Doğu Teknik Üniversitesi
Topics
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
Keywords
alpha2-macroglobulin, targeted mass spectrometry, unique peptides, matrix effects, concentration effects, protein quantification
MeSH terms
alpha-Macroglobulins, Humans, Peptides, Mass Spectrometry, Proteomics, Pregnancy-Associated alpha 2-Macroglobulins, Proteolysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review