Comprehensive identification of protein disulfide bonds with pepsin/trypsin digestion, Orbitrap HCD and Spectrum Identification Machine.


Journal

Journal of proteomics
ISSN: 1876-7737
Titre abrégé: J Proteomics
Pays: Netherlands
ID NLM: 101475056

Informations de publication

Date de publication:
30 04 2019
Historique:
received: 11 10 2018
revised: 04 12 2018
accepted: 12 12 2018
pubmed: 18 12 2018
medline: 9 7 2020
entrez: 18 12 2018
Statut: ppublish

Résumé

Disulfide bonds (SS) are post-translational modifications important for the proper folding and stabilization of many cellular proteins with therapeutic uses, including antibodies and other biologics. With budding advances of biologics and biosimilars, there is a mounting need for a robust method for accurate identification of SS. Even though several mass spectrometry methods have emerged for this task, their practical use rests on the broad effectiveness of both sample preparation methods and bioinformatics tools. Here we present a new protocol tailored toward mapping SS; it uses readily available reagents, instruments, and software. For sample preparation, a 4-h pepsin digestion at pH 1.3 followed by an overnight trypsin digestion at pH 6.5 can maximize the release of SS-containing peptides from non-reduced proteins, while minimizing SS scrambling. For LC/MS/MS analysis, SS-containing peptides can be efficiently fragmented with HCD in a Q Exactive Orbitrap mass spectrometer, preserving SS for subsequent identification. Our bioinformatics protocol describes how we tailored our freely downloadable and easy-to-use software, Spectrum Identification Machine for Cross-Linked Peptides (SIM-XL), to minimize false identification and facilitate manual validation of SS-peptide mass spectra. To substantiate this optimized method, we've comprehensively identified 14 out of 17 known SS in BSA. SIGNIFICANCE: Comprehensive and accurate identification of SS in proteins is critical for elucidating protein structures and functions. Yet, it is far from routine to accomplish this task in many analytical or core laboratories. Numerous published methods require complex sample preparation methods, specialized mass spectrometers and cumbersome or proprietary software tools, thus cannot be easily implemented in unspecialized laboratories. Here, we describe a robust and rapid SS mapping approach that utilizes readily available reagents, instruments, and software; it can be easily implemented in any analytical core laboratories, and tested for its impact on the research community.

Identifiants

pubmed: 30557666
pii: S1874-3919(18)30439-1
doi: 10.1016/j.jprot.2018.12.010
pmc: PMC6414265
mid: NIHMS1007941
pii:
doi:

Substances chimiques

Disulfides 0
Peptides 0
Trypsin EC 3.4.21.4
Pepsin A EC 3.4.23.1

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

78-86

Subventions

Organisme : NINDS NIH HHS
ID : P30 NS046593
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM067640
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM112415
Pays : United States
Organisme : NIH HHS
ID : S10 OD025047
Pays : United States

Informations de copyright

Copyright © 2018 Elsevier B.V. All rights reserved.

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Auteurs

Chuanlong Cui (C)

Center for Advanced Proteomics Research and Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University - New Jersey Medical School and Cancer Institute of New Jersey, Newark, NJ 07103, USA.

Tong Liu (T)

Center for Advanced Proteomics Research and Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University - New Jersey Medical School and Cancer Institute of New Jersey, Newark, NJ 07103, USA.

Tong Chen (T)

Center for Advanced Proteomics Research and Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University - New Jersey Medical School and Cancer Institute of New Jersey, Newark, NJ 07103, USA.

Johanna Lu (J)

Center for Advanced Proteomics Research and Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University - New Jersey Medical School and Cancer Institute of New Jersey, Newark, NJ 07103, USA.

Ian Casaren (I)

Center for Advanced Proteomics Research and Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University - New Jersey Medical School and Cancer Institute of New Jersey, Newark, NJ 07103, USA.

Diogo Borges Lima (DB)

Mass Spectrometry for Biology Unit, Institut Pasteur, CNRS USR 2000, Paris, France.

Paulo Costa Carvalho (PC)

Laboratory for Structural and Computational Proteomics, Carlos Chagas Institute, Fiocruz Paraná, Brazil.

Annie Beuve (A)

Department of Pharmacology, Physiology and Neuroscience, Rutgers University - New Jersey Medical School, Newark, NJ 07103, USA.

Hong Li (H)

Center for Advanced Proteomics Research and Department of Microbiology, Biochemistry and Molecular Genetics, Rutgers University - New Jersey Medical School and Cancer Institute of New Jersey, Newark, NJ 07103, USA. Electronic address: liho2@rutgers.edu.

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Classifications MeSH