Mass Spectrometry-Based Chemical Proteomics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
PROVIDES STRATEGIES AND CONCEPTS FOR UNDERSTANDING CHEMICAL PROTEOMICS, AND ANALYZING PROTEIN FUNCTIONS, MODIFICATIONS, AND INTERACTIONS--EMPHASIZING MASS SPECTROMETRY THROUGHOUT Covering mass spectrometry for chemical proteomics, this book helps readers understand analytical strategies behind protein functions, their modifications and interactions, and applications in drug discovery. It provides a basic overview and presents concepts in chemical proteomics through three angles: Strategies, Technical Advances, and Applications. Chapters cover those many technical advances and applications in drug discovery, from target identification to validation and potential treatments. The first section of Mass Spectrometry-Based Chemical Proteomics starts by reviewing basic methods and recent advances in mass spectrometry for proteomics, including shotgun proteomics, quantitative proteomics, and data analyses. The next section covers a variety of techniques and strategies coupling chemical probes to MS-based proteomics to provide functional insights into the proteome. In the last section, it focuses on using chemical strategies to study protein post-translational modifications and high-order structures. * Summarizes chemical proteomics, up-to-date concepts, analysis, and target validation * Covers fundamentals and strategies, including the profiling of enzyme activities and protein-drug interactions * Explains technical advances in the field and describes on shotgun proteomics, quantitative proteomics, and corresponding methods of software and database usage for proteomics * Includes a wide variety of applications in drug discovery, from kinase inhibitors and intracellular drug targets to the chemoproteomics analysis of natural products * Addresses an important tool in small molecule drug discovery, appealing to both academia and the pharmaceutical industry Mass Spectrometry-Based Chemical Proteomics is an excellent source of information for readers in both academia and industry in a variety of fields, including pharmaceutical sciences, drug discovery, molecular biology, bioinformatics, and analytical sciences.
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Table of Contents
Preface
1 Protein Analysis by Shotgun Proteomics1
1.1 Introduction
1.2 Overview of Shotgun Proteomics
1.3 Sample Preparation
1.4 Peptide Separation and Data Acquisition
1.5 Informatics
References
2 Quantitative Proteomics for Analyses of Multiple Samples in Parallel with Chemical Perturbation
2.1 Introduction
2.2 Relative and Absolute Label‐Free Quantitation Strategies
2.3 Stable Isotope‐Based Quantitative Proteomics
2.4 Conclusion
2.5 Methodology
2.6 Notes
Acknowledgments
References
3 Chemoproteomic Analyses by Activity‐Based Protein Profiling
3.1 Introduction
3.2 How ABPP Works
3.3 ABPP Probe Design
3.4 ABPP and Mass Spectrometry for Chemoproteomics
3.5 ABPP Applications and Recent Advances
3.6 ABPP Applied to Drug Discovery
3.7 Comparative, Competitive, and Convolution ABPP
3.8 Conclusions and The Outlook of ABPP
Acknowledgments
References
4 Activity‐Based Probes for Profiling Protein Activities
4.1 Introduction
4.2 Design of Activity‐Based Probes
4.3 Analytical Platforms for ABPP
4.4 Classes of Enzymes Studied by ABPP
4.5 Conclusions
Acknowledgment
References
5 Chemical Probes for Proteins and Networks
5.1 Introduction
5.2 Application of Metabolic Chemical Probes to Lipidated Protein Networks
5.3 Chemical Probes for Target Identification
5.4 Protocol
5.5 Notes
References
6 Probing Biological Activities with Peptide and Peptidomimetic Biosensors
6.1 Introduction
6.2 Peptide Biosensors for Assignment and Characterization of Enzymatic Reactions and Substrate Specificity
6.3 Screening Inhibitors and Detecting Ligand Interactions
6.4 Diagnostic and Clinical Applications
6.5 Profiling Enzymatic Activity
6.6 Protocol
6.7 Conclusion
References
7 Chemoselective Tagging to Promote Natural Product Discovery
7.1 Introduction
7.2 Nonreversible Mass Spectrometry Tags
7.3 Reversible Enrichment Tags
7.4 Conclusions
7.5 Protocol for Enrichment of Carboxylic‐Acid‐Containing Natural Products
References
8 Identification and Quantification of Newly Synthesized Proteins Using Mass Spectrometry‐Based Chemical Proteomics
8.1 Introduction
8.2 Protein Labeling to Study Newly Synthesized Proteins
8.3 Global Identification of Newly Synthesized Proteins by Noncanonical Amino Acids and MS
8.4 Comprehensive Quantification of Newly Synthesized Proteins by MS
8.5 Materials
8.6 Methods
Acknowledgments
References
9 Tracing Endocytosis by Mass Spectrometry
9.1 Introduction
9.2 Clathrin‐Mediated Endocytosis
9.3 Mass Spectrometry as a Tool to Study Endocytosis
9.4 Protocols for TITAN
9.5 Conclusion and Future Directions
References
10 Functional Identification of Target by Expression Proteomics (FITExP)
10.1 Introduction
10.2 FITExP Protocol
References
11 Target Discovery Using Thermal Proteome Profiling
11.1 Introduction
11.2 Thermodynamics of Ligand Binding as a Measure of Target Engagement
11.3 Thermal Proteome Profiling – Proteome‐wide Detection of Drug–Target Interactions
11.4 Experimental Formats
11.5 Experimental Protocol
11.6 Reagents
11.7 Present Challenges with TPP
11.8 CETSA to TPP – Where Are We Heading?
References
12 Chemical Strategies to Glycoprotein Analysis
12.1 Introduction
12.2 Sample Preparation Strategies for Glycoproteomics
12.3 MS Analysis
12.4 Conclusions
References
13 Proteomic Analysis of Protein–Lipid Modifications: Significance and Application
13.1 Introduction
13.2 Chemical Proteomic Approach to Identify Lipidated Proteins
13.3 Protocol for Proteomic Analysis of Prenylated Proteins
References
14 Site‐Specific Characterization of Asp‐ and Glu‐ADP‐Ribosylation by Quantitative Mass Spectrometry
14.1 Introduction
14.2 Materials
14.3 Methods
14.4 Notes
Acknowledgments
References
15 MS‐Based Hydroxyl Radical Footprinting: Methodology and Application of Fast Photochemical Oxidation of Proteins (FPOP)
15.1 Introduction
15.2 Generation of Hydroxyl Radicals
15.3 Fast Photochemical Oxidation of Proteins (FPOP)
15.4 Applications of FPOP
15.5 Conclusions
References
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Probes highlighted in current literature that have made unique contrib...
Chapter 5
Table 5.1 Overview of different quantitative methods that can be applied for che...
Chapter 6
Table 6.1 A list of 15 JmjC‐domain containing proteins with their reported subst...
Chapter 13
Table 13.1 Formula for click reaction.
Table 13.2 MS analysis parameters and filters.
Table 13.3 Comparison of putative prenylated proteins identified in
Plasmodium fa
...
Chapter 15
Table 15.1 Rate constants of reactions of amino acids with hydroxyl radicals and...
Table 15.2 Rate constants for reactions of amino acids and hydrated electrons.
List of Illustrations
Chapter 1
Figure 1.1 Typical workflow of a bottom‐up proteomics experiment. Proteins are...
Figure 1.2 Example of MudPIT setup and 12‐step MudPIT gradient table. Buffer A...
Figure 1.3 Common fragmentation methods used in bottom‐up proteomics and the t...
Figure 1.4 Comparison of three most commonly used peptide identification strat...
Figure 1.5 Examples of different labeling strategy for quantitation, where * d...
Chapter 2
Figure 2.1 Comparison of MS1‐ and MS2‐based quantitation with duplex mdDiLeu.
Figure 2.2 Decision tree for picking the appropriate tag system for a research...
Figure 2.3 Synthesis strategies for the 4‐plex DiAla and DiVal tags.
Figure 2.4 Tag isotopic structures for all chemical tags discussed, including ...
Chapter 3
Figure 3.1 ABPP allows for the isolation, identification, and quantification o...
Figure 3.2 The structure of an activity‐based probe. The recognition element, ...
Figure 3.3 Workflow of an ABPP experiment with a mechanism‐based probe. The AB...
Figure 3.4 (A) Examples of mechanism‐based, reactivity‐based, and photoaffinit...
Figure 3.5 Workflow for analyzing peptides using a cleavable linker to identif...
Chapter 4
Figure 4.1 Summary of the key components of an activity‐based probe (ABP), wit...
Figure 4.2 Schematic representation of a typical ABPP experiment.
Figure 4.3 The general catalytic mechanism of serine hydrolases.
Figure 4.4 Fluorophosphonate probes reported by Cravett et al.
Figure 4.5 Acylation mechanism of cysteine proteases.
Figure 4.6 The chemical structures of AOMK probes.
Figure 4.7 The general catalytic mechanism of metallohydrolases.
Figure 4.8 Acylation mechanism of cysteine proteases.
Figure 4.9 Catalytic mechanism of β‐retaining glycosidases.
Figure 4.10 Glycosidase (LacZ) labeling with activity‐based probe 6Az2FGalF.
Figure 4.11 Acylation mechanism of kinases by acyl phosphate probes.
Figure 4.12 (a) Mechanism of kinase inactivation by wortmannin. (b) Chemical s...
Figure 4.13 The general mechanism of dephosphorylation by PTPs.
Figure 4.14 Mechanism of quinone‐methide‐based probes for PTPs.
Figure 4.15 Chemical structures of various PTP inactivators and activity‐based...
Figure 4.16 The covalent modification of PTPs with a PVS probe.
Chapter 5
Figure 5.1 (a) A standard workflow for chemical proteomics. Cells are trea...
Figure 5.2 (a) Metabolically incorporated probes to detect
N
‐myristoylatio...
Figure 5.3 (a) Typical spike‐in SILAC workflow. A designed probe, in compe...
Figure 5.4 (A) Addition of an alkyne tag to zerumbone allows for enrichmen...
Figure 5.5 (a) Performing a
t
‐test allows proteins that are significantly ...
Chapter 6
Figure 6.1 General examples of peptide biosensors being used to assign and cha...
Figure 6.2 Illustrations depicting how peptide biosensors can detect ligand in...
Figure 6.3 Depictions of peptide biosensors being used for diagnostic and heal...
Figure 6.4 Several examples of peptide biosensors being used to profile enzyma...
Figure 6.5 Experimental workflow for using the Abltide biosensor in a cell‐bas...
Figure 6.6 Structure and sequence of the full length Abltide biosensor used in...
Chapter 7
Figure 7.1 Structures of representative natural products displaying functional...
Figure 7.2 Mass‐spectrometry‐based analysis of extracts treated with chemosele...
Scheme 7.1 Solid‐support, CuAAC‐based chemoselective probes for natural produc...
Figure 7.3 Thiol tags for the detection of electrophile natural products.
Scheme 7.2 Dibrominated aminooxy probe for the discovery of carbonyl‐containin...
Figure 7.4 Mass‐spectrometry‐based analysis of extracts treated with reversibl...
Scheme 7.3 Chemoselective enrichment resins for the isolation of oxygen‐contai...
Figure 7.5 A pyridyl disulfide‐functionalized solid‐support enrichment probe f...
Scheme 7.4 Synthesis of carboxylic acid enrichment resin
27
.
Chapter 8
Figure 8.1 Protein labeling using noncanonical amino acids.
Figure 8.2 Schematic QuaNCAT workflow.
Figure 8.3 Enrichment of newly synthesized proteins in the S phase and measure...
Figure 8.4 Half‐lives of newly synthesized proteins in the S phase. (a) Abunda...
Chapter 9
Figure 9.1 Diagram showing the phases of endocytosis: ligand binding, coat pro...
Figure 9.2 (a) Schematic representation of the functionalized dendrimer. (b) E...
Figure 9.3 Spatiotemporal information for dendrimer‐interacting proteins invol...
Figure 9.4 Synthesis of masked aldehyde handle.
Figure 9.5 Synthetic route for functionalized dendrimer.
Chapter 10
Figure 10.1 General workflow of the FITExP method: (a) a panel of cell lines i...
Figure 10.2 Example of determination of the EC50 – 48 h for one drug using a f...
Chapter 11
Figure 11.1 Drug binding influences several physiochemical properties of a pro...
Figure 11.2 Thermodynamics of ligand binding. Proteins in solution exist in an...
Figure 11.3 Design of thermal proteome profiling (TPP) experiments. (i) Choice...
Figure 11.4 From CETSA to TPP – where will the journey take us? – Since the ti...
Chapter 12
Figure 12.1 Generalized N‐glycan structure. (a) All N‐glycans share a pentasac...
Figure 12.2 Glycopeptide enrichment strategies. (a) Lectin affinity utilizes l...
Figure 12.3 Oxonium ions commonly used for glycopeptide identification. Fragme...
Chapter 13
Figure 13.1 A general chemical proteomic strategy to characterize lipid‐modifi...
Figure 13.2 Bio‐orthogonal ligation strategies with azide‐modified lipid analo...
Figure 13.3 Bio‐orthogonal ligation strategies with alkyne‐modified lipid anal...
Figure 13.4 Commonly used bio‐orthogonal chemical reporters for protein lipida...
Chapter 14
Figure 14.1 The general strategy for characterization of the Asp/Glu‐PARylated...
Figure 14.2 An overview of the workflow for the quantitative characterization ...
Chapter 15
Figure 15.1 (a) Photo of National Synchrotron Light Source II (NSLS‐II) ...
Figure 15.2 Coupling of synchrotron hydroxyl radical labeling and MS for prote...
Figure 15.3 Schematic showing the apparatus for FPOP. (a) The protein solution...
Figure 15.4 Numerical simulations of HO
•
concentration vs. time. FPOP la...
Figure 15.5 Schematic of the custom‐built flow system for carbene footprinting...
Figure 15.6 Crystal structure of the exEGFR (b) complexed with Adnectin (a), P...
Figure 15.7 Mass spectra of intact, FPOP‐labeled Aβ
1–42
(+5 charge) as a...
Figure 15.8 Schematic of a custom‐built two‐laser, pump/probe system on an FPO...
Figure 15.9 (a) Schematic illustration of a Nanodisc with a transmembrane prot...
Guide
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Mass Spectrometry‐Based Chemical Proteomics
Edited by
W. Andy Tao
Purdue University
West Lafayette, IN, US
Ying Zhang
Fudan University
Shanghai, China
Copyright
This edition first published 2019
© 2019 John Wiley & Sons Inc.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
The right of W. Andy Tao and Ying Zhang to be identified as the authors of the editorial material in this work has been asserted in accordance with law.
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Library of Congress Cataloging‐in‐Publication Data
Names: Tao, W. Andy, 1970‐ editor. | Zhang, Ying, 1983‐ editor.Title: Mass spectrometry‐based chemical proteomics / edited by W. Andy Tao(Purdue University, West Lafayette, IN, US), Ying Zhang (Fudan University, Shanghai, China).Other titles: Mass spectrometry based chemical proteomicsDescription: First edition. | Hoboken, NJ : Wiley, 2019. | Includes bibliographical references and index. | Identifiers: LCCN 2019001991 (print) | LCCN 2019003559 (ebook) | ISBN 9781118970218 (Adobe PDF) | ISBN 9781118970201 (ePub) | ISBN 9781118969557 (hardcover)Subjects: LCSH: Proteins–Spectra. | Proteomics. | Molecular biology. | Spectrum analysis.Classification: LCC QP551 (ebook) | LCC QP551.M311945 2019 (print) | DDC 572/.6–dc23LC record available at https://lccn.loc.gov/2019001991
Cover design: Wiley
Cover Images: Top: © prettyboy80/Getty Images, Middle: © monsitj/Getty Images, Bottom: Courtesy of W. Andy Tao
Preface
The field of proteomics is evolving from cataloging proteins in various biological systems to elucidating the cellular functions of the proteins in both normal and pathological processes. Quantitative proteomics, based on either isotope labeling or label‐free, facilitates globally profiling changes in protein expression levels, but the abundance of a protein does not directly correlate with its activity. Mass spectrometry (MS)‐based chemical proteomics has emerged as an important high‐throughput tool for the study of functional proteomes of interest. Technical advances in MS instrumentation have allowed for the accurate and sensitive detection of proteins among complex biological matrices such as cell lysates and body fluids, complemented by chemical strategies and detection methods that give desirable specificity and versatility to the process.
Beyond quantitative comparison of proteomes, chemical strategies have been developed to tailor specific applications, including identification and validation of novel drug targets, quantification of ligand–protein or protein–protein interaction affinity, understanding the pathways involving in the drug action, measuring the kinase activity or screening the enzyme inhibitor, profiling posttranslational modifications (PTMs) in large‐scale and so on. In recent years, we have observed multiple robust and high‐throughput chemical proteomics approaches, which have greatly expanded the scope of proteomics research. This book thus seeks to outline the basic principle of chemical proteomics, summarize the recent developments in this fast‐evolving field, and provide a timely overview of the current outlook of the technology for the students and researchers who are interested in understanding the basics and utilizing the tool in their respective areas.
This book is divided into three parts. The first part of book including Chapters 1 and 2 describes the basic principle of MS‐based proteomics and commonly used high‐throughput techniques, focusing on shotgun/bottom‐up proteomics (Chapter 1), and quantitative proteomics covering label‐free quantitation, metabolic labeling, chemical stable isotope labeling, and strategies to select the appropriate labeling approach for the intended proteomic analysis (Chapter 2).
The second part of book (Chapters 3–11) covers a variety of techniques and strategies coupling chemical probes to MS‐based proteomics to provide functional insights into the proteome. Among this part, Chapters 3–6 place more emphasis on the techniques of classical chemical proteomics while Chapters 7–11 elaborate on the novel applications and expansion of chemical proteomics in broad term. Chapters 3 and 4 introduce the classical chemical proteomics approach of activity‐based protein profiling (ABPP) that uses site‐directed small chemical probes to directly measure the functional state of proteins in vitro and in vivo, including the general strategies to design probes, analytical platforms used in ABPP, and classes of enzyme studied by ABPP. The technique was uniquely presented in these two chapters by a MS expert and by chemical biologists, respectively. Chapter 5 focuses on key technologies of using metabolic and tagged probes for global profiling of protein networks and targeted identification. Chapter 6 shows how to use peptides as the biosensors to measure or report highly diverse information from biological systems and especially highlights and summarizes recent major discoveries in this field and a detailed protocol is included for quantitative measurement of the Bcr–Abl tyrosine kinase activity using a substrate peptide biosensor in a chronic myeloid leukemia (CML) cell line as a working example. From Chapter 7, expansion and applications of chemical proteomics are presented. Chapter 7 addresses the challenge of discovering extremely low abundant or unstable natural products through chemical proteomics and discusses the development of chemoselective probes (nonreversible and reversible chemoselective probes) to tag secondary metabolites by means of their functional group identity. Chapter 8 reviews the existing methods for the study of newly synthesized proteins, with an emphasis on protein labeling with noncanonical amino acids to allow for the use of bio‐orthogonal chemistry to enrich newly synthesized proteins for MS‐based analyses. Chapter 9 introduces a new chemical proteomics strategy termed Tracing Internalization and Trafficking of NAnomaterials (TITAN) for studying the endocytosis process, through chemically tracing nanoparticle cellular uptake and transportation and revealing real‐time protein−nanoparticle interactions. While all above chapters deal with the utility of probes or chemical labels to couple with proteomics, Chapters 10–11 introduce approaches without designing of functional probes. Chapter 10 describes a new approach called Functional Identification of Target by Expression Proteomics (FITExP) for the effective identification of drug target candidates in view of the observation that for the protein target of a small‐molecule drug, the abundance change in late apoptosis is exceptional compared to the expectations based on the abundances of coregulated proteins. Chapter 11 demonstrates a strategy called Thermal Proteome Profiling (TPP) that measures the heat‐induced denaturation of proteins and identifies drug‐bound targets based on altered thermal stability without the requirement of modifying the ligand molecules for studying the protein–ligand binding.
The last part of book (Chapters 12–15) focuses on using chemical strategies to study different protein PTMs or protein high‐order structures. Chapter 12 describes chemical strategies to glycoprotein analyses, focusing on sample preparation and MS analyses. Chapter 13 reviews a variety of proteomic analyses using metabolic labeling in conjunction with enrichment for several lipid modifications, and it provides a detailed protocol for the identification of prenylated proteins from Plasmodium falciparum. Chapter 14 describes an integrative proteomic platform using boronate‐affinity chromatography for enriching the Asp‐ and Glu‐ADP‐ribosylated proteins and the quantitative characterization of protein ADP‐ribosylation on a global scale. Chapter 15 sums up the recent advances of MS‐based protein footprinting as an effective analytical technique for characterizing protein high‐order structures and especially focuses on the application of the fast photochemical oxidation of proteins to observe protein conformational changes.
We thank all the contributors, who are leading researchers in chemical proteomics and developers or experts of these methods, for sharing their expertise in this area and related protocols. We hope that the critical review and the methodologies described in each chapter will be valuable guidance for researchers who are either new to the field or already working on some aspect of chemical proteomics, and we also hope this book will contribute to the further development and wider applications of chemical proteomics approaches.
01 January 2019
Dr. W. Andy Tao
Departments of Biochemistry and Chemistry
Purdue University
West Lafayette, IN 47907USA
Dr. Ying Zhang
Institutes of Biomedical Sciences
Fudan University
Shanghai 200032
P.R. China
1Protein Analysis by Shotgun Proteomics1
Yu Gao1, and John R. Yates III2
1College of Pharmacy, University of Illinois at Chicago, Chicago, IL, USA
2Department of Molecular Medicine, Scripps Research, La Jolla, CA, USA
1.1 Introduction
1.1.1 Terminology
In mass‐spectrometry‐based protein analysis, there are two major strategies, the top‐down method and the bottom‐up method [1, 2]. The terms “top” and “bottom” refer to the complexity of the analyte, namely the more complex “protein” and less complex “peptide.” In top‐down protein analysis, the intact protein is directly analyzed by mass spectrometer. Mass information and fragment ions are generated from the intact protein ions and are then used for direct protein identification and characterization. In comparison, bottom‐up method starts with digesting the protein into peptides by either chemical or enzymatic digestion. The peptide product is then analyzed by a tandem mass spectrometer, and the peptide molecular weight and fragmentation information is matched back to the original protein or protein mixture. When a mixture of proteins is analyzed by a bottom‐up method, it is also called shotgun proteomics, owing to the similarity to shotgun genomic sequencing.
1.1.2 Power of Shotgun Proteomics
In a typical shotgun proteomics experiment performed on a modern instrument, one should expect to identify anywhere from 1000 to 10 000 proteins from a mammalian cell lysate [3, 4]. In comparison, a typical top‐down experiment is able to identify hundreds or a thousand proteins with a similar sample, but it require extensive fractionation to simplify the protein mixtures entering the mass spectrometer [5–7]. In top‐down proteomics, intact protein is highly complex in terms of molecular weight, charge state, hydrophobicity, molecular structure (shape), and so on, therefore, it is hard to find optimal conditions for ideal separation, fragmentation, and detection of all proteins presented in the sample.
1.1.3 Advantage of Shotgun Proteomics
Comparing to intact protein, a peptide is a much more unified class of analyte, with a narrow range of molecular weight and charge state. Because most digested peptides are denatured, peptides also have a more unified shape [8, 9]. Therefore, starting with peptides instead of the intact protein presents advantages over the top‐down method, including more robust liquid‐chromatography (LC) separation for peptides, more uniform electrospray ionization, more complete fragmentation in tandem mass spectrometry (MS/MS), and easier interpretation of the simplified fragmentation patterns. Due to these advantages, bottom‐up/shotgun proteomics method has become the easier strategy for protein analysis over the past two decades. However, these advantages also come with some nontrivial challenges in sample preparation, peptide separation, data acquisition, and informatics [10–12]. This chapter will discuss typical procedures of shotgun proteomics experiment and some recent advances regarding existing challenges.
1.2 Overview of Shotgun Proteomics
A typical shotgun proteomics experiment consists of three main steps: (i) sample preparation, (ii) mass spectrometry data acquisition, and (iii) data processing. The sample preparation step transforms the biological sample to a peptide mixture. The data acquisition step obtains MS/MS data from the peptide mixture. The final data processing step performs statistical and mathematical analyses to elucidate the identity and quantity of peptide and protein (Figure 1.1).
Figure 1.1 Typical workflow of a bottom‐up proteomics experiment. Proteins are first separated from biological samples, then digested into peptides. An LC–MS/MS system is typically used to fractionate and fragment peptides. The acquired mass spectra are then matched to existing peptide sequence using a database search algorithm and then inferred back to proteins.
In the sample preparation step, a protein mixture is first obtained by separating protein and nonprotein contents from a biological sample such as cell lysate or serum. The separated protein mixture is then chemically modified (reduced and then alkylated) to break all Cys–Cys disulfide bonds in order to linearize protein. Protease, for example, trypsin, is then added to the modified protein mixture to digest protein into peptides. After digestion, the peptide mixture is often loaded onto a C18 column and then washed to remove nonpeptide contents (salts, buffers, chaotropes, etc.).
Once the sample is digested and cleaned, an LC–MS system is used to fractionate peptides to increase the amount of MS/MS data obtained from the peptide mixture. As digested protein mixtures can create very complicated and complex peptide mixtures, to better resolve peptide mixture, various types of separation columns have been used either alone or in combinations, including reversed phase (RP), strong‐cation exchange (SCX), size exclusion (SEC), hydrophilic interaction liquid chromatography (HILIC), and affinity purification. In general, the final separation method prior to introduction of peptides into electrospray ionization is reversed‐phase as this method removes salts and other small‐molecule interferants. The separated peptides are then ionized and injected into the mass spectrometer for analysis. In this step, the peptide mixture is first temporally separated by LC, then spatially separated by the electrical fields. This separation cascade provides enough resolution to separate hundreds of thousands of peptide species within hours.
In the final data processing step, the data obtained for each detected peptide species, including MS (whole mass) and tandem MS/MS (fragmentation masses) data, is analyzed by algorithms that search sequence databases to match spectra to the original protein sequence. If desired, the data can also be further analyzed for quantitation by either “labeled” or “label‐free” methods.
1.3 Sample Preparation
1.3.1 Protein Separation
1.3.1.1 Overview
To analyze proteins from a complex biological sample, protein often needs to be separated from interfering small molecules and nucleotides. This is often done by nonspecific protein extraction such as protein precipitation or centrifugation [13–16]. Some of the most commonly used reagents/solvents/systems for protein precipitation include trichloroacetic acid (TCA)/water, chloroform/methanol, acetone, phenol/ammonium acetate/methanol, and so on. These methods can effectively separate the protein from other molecules such as salts, lipids, detergents (often introduced during lysis), DNA/RNA, and even the aqueous buffer. Therefore, the proteins are purified and concentrated for further processing. Centrifugation method such as sucrose gradient is also very useful for this purpose, but due to its lower throughput and efficiency, it is often used in combination of protein precipitation method to isolate proteins from specific cell organelles.
1.3.1.2 2D‐Gel Approach
Two‐dimensional polyacrylamide gel electrophoresis (2D PAGE) is a robust, orthogonal approach, popularly applied for the simultaneous separation and fractionation of complex protein mixtures that have been recovered from biological samples for proteomic analysis [17, 18]. The method allows separation of several thousand proteins, on the basis of their molecular mass and the isoelectric point in a single gel. It used to be one of the most widely used methods for protein separation, and it has been used in studies related to proteins and protein complexes [19]. Once the separation is achieved via 2D PAGE, a protein spot or band can be visualized and then extracted. Coomassie brilliant blue or silver staining is commonly used for protein visualization. Coomassie brilliant blue is generally preferred over silver staining as it is a reversible stain and compatible with MS analysis [20]. Despite greater sensitivity, due to its limited compatibility and nonlinearity with the signal, silver staining may give disappointing results [21]. After protein visualization, the gel spots are digested with trypsin and identified by either protein fingerprinting using matrix‐assisted laser desorption/ionization–mass spectrometry (MALDI–MS) or via peptide sequencing using LC–MS. Although 2D electrophoresis is associated with the start of proteomics and is still widely used for various purposes, large‐scale proteomics is now associated with advanced separation and mass spectrometry technologies for protein identification. Technologies like LC–MS/MS, which offer superior separations, have taken over from 2D gel‐based methods.
1.3.1.3 Separation of Membrane Protein
Membrane proteins are integral parts of the nucleus and cell membranes. They are permanently anchored to the outer surface of the membrane or embedded into the lipid bilayer and are actively involved in many crucial cell functions, including transportation of ions and molecules, cellular communication via active cell signaling and cell interactions [22]. It is estimated that 20−30% of genes in most genomes are related to membrane proteins and hence are responsible for pathological induction of many deadly diseases, such as cancer, neurodegenerative disorders, diabetes, and so on [23]. As a result, these proteins have become the major targets of modern drugs. However, separating these proteins is a very challenging task, due to their high hydrophobicity and low abundance [24, 25]. Development of new technologies to identify and characterize membrane proteins was an important issue in proteomics.
Traditionally, membrane proteins are separated by sucrose gradient and similar methodologies that isolate subcellular membrane proteins directly from cell and tissues [26]. For tissue samples, Smolders et al. [27] have reported the use of biotinylation tagging on small tissue samples to overcome various problems that are commonly being encountered, such as poor extraction efficiency, weaker enrichment, sample contamination, and sample exhaustion. For cell samples, by using carefully optimized condition and specific neutral detergent (CA‐630), Pankow et al. showed that it is possible to preserve membrane proteins in their native state during cell lysis [28, 29]. Membrane proteins together with their interactors could be co‐immunoprecipitated in such condition and subsequently characterized by LC–MS.
1.3.1.4 Subcellular Fractionation
Separating membrane proteins with high specificity is not easy, more generally, probing the proteome in any subcellular location is a challenging task [30, 31]. This often involves the separation of specific organelle or compartments, such as nucleus and mitochondria, by biochemical methods or centrifugation from the whole cell extracts [32]. The specificity and the efficiency of the separation are often the major limiting factors of subcellular proteome measurements. Organelles with lipid bilayer membranes, such as nuclei and mitochondria, can be efficiently separated by either centrifugation or flow cytometry with preserved integrity [33]. After separation, further analysis could be carried out on separated intact organelles, such as mitochondria from the murine heart and skeletal muscles, respectively, to study posttranslational modifications (PTMs) like phosphorylation and carboxylation, and so on [34–37]. Other cellular apparatus such as endoplasmic reticulum, Golgi, and lysosome have been studied much less frequently due to difficulties in separation [38–42]. Separation of these organelles is often complicated and lacks specificity and efficiency [30, 31]. Moreover, the protein contents of these organelles are also believed to be more transitory and dynamic [38, 41]. Overall, from the data that is being accumulated so far from all the subcellular proteomic analysis, it has been quite evident that the purity of subcellular organelle is of utmost importance to produce quality data.
1.3.1.5 Protein Enrichment
In many cases, a subset of proteins, such as low‐abundance proteins or proteins with PTM, needs to be specifically enriched from the cell lysate to achieve better quantification and identification. This is largely due to the mismatch of a very high dynamic range of protein expression and a limited dynamic range provided by the mass spectrometer. Therefore, protein enrichment techniques such as affinity purification, by either antibody or other affinity methods, are often performed during sample preparation for the detection of low‐abundance proteins from complex samples.
1.3.1.6 Phosphoprotein
Phosphorylated proteins are often of low abundance, but they play a vital role in cell signaling [43]. Enrichment by affinity purification, using either immobilized metal affinity chromatography (IMAC) or metal oxide affinity chromatography (MOAC), can effectively improve phosphoprotein identification by several orders of magnitude [44, 45]. The method is based upon the high affinity of phosphate groups to cations such as Zn2+, Fe3+, Ti4+, and so on. The approach has been successfully used for both off‐line and on‐line separation of phosphoproteins and phosphopeptides. Metal oxide such as titanium dioxide (TiO2) can be used as a very robust chelating agent and thus provide specific phosphopeptide enrichment [46]. To overcome nonspecific chelation, esterification of acidic residues before IMAC enrichment may significantly improve the specificity of the enrichment [46, 47]. Immunoprecipitation is another technique that has been used for enrichment of phosphorylated proteins [48, 49]. By using highly specific antiphosphotyrosine antibodies, phosphoproteins containing phosphotyrosine can be enriched with high specificity. However, the high specificity of antibodies is also associated with enrichment bias toward a certain type of phosphorylation or certain peptide sequences [50, 51]. Therefore, two or more antibodies can be used together to target different phosphorylation sites.
1.3.1.7 Glycoprotein
Another very common PTM is mono‐ or oligosaccharide glycosylation of serine, threonine, and asparagine residues of proteins [52]. Similar to phosphorylation, glycoproteins benefit from enrichment before mass spectrometry analysis [53, 54]. Some of the most common glycoprotein enrichment techniques include HILIC, ion exchange chromatography, lectin‐based affinity purification, antibody‐based affinity purification, and the formation of covalent interaction such as hydrazide and boronic acid chemistry [55–60]. Among all existing techniques, ZIC‐HILIC (zwitterionic hydrophilic interaction liquid chromatography) based glycopeptide enrichment has been shown to be highly efficient and specific in the separation of N‐glycopeptides when compared with several other techniques [61, 62]. However, due to the diverse nature of complex glycans, different types of techniques are often quite complimentary to each other. Combination of multiple techniques could significantly improve glycoprotein identification and quantitation results.
1.3.1.8 AP–MS and Interactome
In the past decade, affinity purification under nondenaturing conditions coupled to mass spectrometry (AP–MS) has become a popular technique for the identification of target proteins and interactor proteins [63–67]. When a protein is captured under nondenaturing conditions by affinity purification, its interactors are often captured together. By carefully evaluating background proteins and nonspecific interactors by using appropriate controls, specific interactors can often be differentiated. Iterative AP–MS experiments of multiple members of the same protein complex can also be used to cross‐validate true interactions. Recently, two large protein interactome maps were published, both using large‐scale AP–MS. [63–65] Together, the two interactome studies cover more than 6000 bait proteins with more than 12 000 interactor proteins from human cells (HeLa and HEK293T). This information provides invaluable knowledge about protein–protein interaction, protein dynamics, and cellular behaviors including the function of multienzyme complexes, the cross‐talk between cells and tissues, and the function of enzymes.
Sample protocol for simple protein separation from cultured cell:
Harvest cell, wash the cell with PBS buffer a few times to remove extracellular fluid and centrifuge down the cell pellet.
Add urea lysis buffer (30 mM Tris, 8 M urea, 2 M thiourea, 4% CHAPS, add 1 tablet of cOmplete™ Mini EDTA‐free protease inhibitor per 10 ml solution, add benzonase to digest DNA) to the cell pellet in a volume ratio of approximately 3 : 1 to 5 : 1 (buffer to pellet). Pippette up and down to resuspend pellet in buffer.
Sonicate on ice for 30 seconds, cool down for 60 seconds, repeat three times.
Centrifuge down pellet debris on max spin speed, take supernatant, and use Bradford assay or UV to determine protein concentration in the supernatant.
To a supernatant of 100 μl, add 400 μl methanol, vortex well, then add 100 μl chloroform. Vortex well again and add 300 μl water. Sample should look cloudy at this point. Vortex well and centrifuge at 14 000
g
for two minutes.
Pipette off the top aqueous layer. Protein exists between layers and may be visible as a thin wafer.
Add 400 μl methanol, vortex well, centrifuge at 14 000
g
for three minutes, then pipette out methanol as much as possible without disturbing the protein pellet (at the bottom).
Speed‐vac to remove the remaining organic solvent. Avoid drying for too long or the pellet may be harder to resolubilize.
Protein Modification
1.3.2.1 Overview
One of the main purposes of protein modification in the sample preparation stage is to linearize the protein and thus facilitate downstream protein digestion and later protein inference. This mainly involves reduction of Cys–Cys disulfide bond by DTT or TCEP and then alkylation by either chloro‐ or iodo‐acetamide to prevent re‐formation. However, in some special cases, Cys–Cys disulfide bonds can be preserved for structure elucidation [68]. Modification of the peptide N‐terminus and the E‐amine of lysine are often used to add “tags” for “labeled‐quantitation” (will be discussed later in Section 1.5.2) such as dimethyl labeling, isobaric tags for relative and absolute quantitation (iTRAQ), and tandem mass tag (TMT) labeling [69–72]. Another important reason for protein modification is to preserve protein–protein interaction information before digestion. This generally involves the chemical modification of adjacent proteins. The classic technique is chemical crosslinking, which uses chemical reactions to convert noncovalent, transient protein interaction to covalent, permanent chemical bonds [73]. A recent development is the proximity labeling methods using a fused, promiscuous biotin ligase or a peroxidase to label proteins in close proximity [74, 75].
1.3.2.2 Reduction of Disulfide Bond and Alkylation
Typically, after protein separation, the disulfide linkage is first reduced by DTT or TCEP. Breaking the Cys–Cys bond linearizes the protein and therefore prevents the formation of branched peptides that contain a disulfide bond after digestion. However, in some special cases, where the disulfide bonds are located is needed to elucidate either protein structure or protein interaction, these disulfide bonds can be either preserved or partially reduced for downstream analysis. Alkylation of the free Cys by either chloro‐ or iodo‐acetamide prevents the re‐formation of the disulfide bond. It is worth noting that iodoacetamide can quickly alkylate other amino acids as well as the N‐terminus due to its higher reactivity [76]. Therefore, chloroacetamide is often used as an alternative to prevent off‐target alkylation.
1.3.2.3 Chemical Crosslinking
Another important application of protein modification prior to digestion is to preserve information regarding protein interaction and nearest neighbors. This may generally be associated with chemical modification, such as crosslinking by converting noncovalent, transient protein interactions to a permanent, covalent linkage. In a typical protein crosslinking experiment, the protein complex is chemically crosslinked in their simplest form, to be suitable for further digestion. This is generally achieved with the incubation of purified protein complex along with the crosslinking reagent; which replaces noncovalent interactions of the surface exposed to amino acid residues, with the covalent one [77–80]. Then the protein sample is then digested with the help of a suitable protease and is further separated and analyzed by LC–MS/MS. Moreover, it has been shown that with the combination of novel chemical crosslinkers and advanced data analysis platforms, it is possible to obtain structural information of protein complexes and protein–RNA complexes from crosslinked proteomics analysis [81]. This ability to undertake a large‐scale analysis of crosslinked peptides from complex mixtures of protein has been one of the major developments in the field.
1.3.2.4 Proximity Labeling
Proximity analysis provides a way to investigate proteins in the vicinity of a protein that may or may not be interacting by introducing covalent tags to neighboring proteins of the bait. Typically, a promiscuous biotin ligase is fused with the bait protein and then expressed together with the bait in the cell. Upon the addition of biotin, the biotin ligase will label adjacent proteins with biotins. The cell is then lysed and enriched for biotinylated proteins by streptavidin beads. By carefully comparing the biotinylated proteins with control, proteins in the proximity of the bait protein can be identified. The approach is very interesting because it can be used in living cells and allowing direct investigation of physiologically relevant interactions. The two prerequisites for the method to be successfully accomplished are appropriate matching with the fusion protein and isolation of specifically labeled protein. Three different types of enzymes have been used extensively for proximity labeling; the BirA biotin ligase to introduce a biotin into proteins in proximity to BirA (BioID), horseradish peroxidase (HRP) to introduce hydroxyl groups in adjacent proteins (APEX), and an engineered ascorbate peroxidase for faster labeling [82–88]. Various versions of these enzymes have been developed, providing faster and cleaner labeling. It is worth noting that a variation of this method can also be used on fixed tissue and fixed cell samples using specific antibodies and an HRP‐conjugated secondary antibody [89].
1.3.3 Protein Digestion
The key step of bottom‐up proteomics is the protein digestion, which converts vastly different proteins into peptides that are more uniform in size, shape, and charge. Digestion is often allowed to occur at different levels and also with different combinations of proteases. Commonly used proteases for digestion are trypsin, chymotrypsin, elastase, and endoproteases such as Lys‐C, Lys‐N, and Arg‐C. The most commonly used enzyme is trypsin. Trypsin is a highly specific serine protease, active at an optimum pH of 8 and at 37 °C. It cuts at the C‐terminal side of lysine (K) and arginine (R), except when proline (P) is on the carboxyl side of Arg or Lys. Both specificity and speed of hydrolysis are reduced when acidic residues are present at either side of the cleavage site. The specificity of trypsin ensures that most trypsin‐digested peptides have at least two positively charged residues (two ends being R–R, R–K, K–R, or K–K), which is helpful for the downstream peptide identification by LC–MS/MS. Several other strategies can be implemented on a routine basis to enhance the quality of digestion and improve protein identification. Addition of MS‐compatible surfactants helps to better solubilize and unfold proteins. Various commercially available surfactants such as ProteaseMAX, Invitrosol, Rapigest, and so on, can be added to reduce protein digestion time and to digest proteins that are difficult to digest otherwise.
Typically, when a single enzyme is used for digestion, the sequence coverage for proteins identified from a shotgun proteomics experiment is far less than 50%, that is, most of the amino acid sequence of that protein has not been detected by the mass spectrometer. The reason is that many of the peptides from the digested proteins are simply too long, too short, or hard to ionize, making them difficult to detect. To improve sequence coverage, multiple enzymes, including highly specific and nonspecific enzymes, can be used in combinations. Therefore, the same sequence is digested differently and produces various digested peptides, which improve the chance of detection by mass spectrometer analysis.
Sample protocol for peptide modification and digestion from protein:
Dissolve protein pellet in 8 M urea solution (for 2.4 g urea, add 1 ml 500 mM Tris pH 8.5, 2.2 ml water). For every 50–100 μg protein, add 60 μl of the above solution.
For 60 μl protein solution, add 0.3 μl 1 M TCEP to make a final concentration of 5 mM. Incubate at room temperature for 20 minutes with mild shaking.
For 60 μl protein solution, add 6.6 μl of 500 mM 2‐chloro‐acetamide and incubate at room temperature for 15 minutes, keep in the dark.
For 60 μl protein solution, dilute sample with 180 μl 100 mM Tris pH 8.5 buffer to 240 μl total. Add 2.4 μl 100 mM CaCl
2
to a final concentration of 1 mM. Add sequence‐grade trypsin solution (0.5 μg/μl) at 1 : 20 to 1 : 100 weight ratio (trypsin:protein).
Incubate at 37 °C in the dark for four hours to overnight.
Add 13.5 μl 90% formic acid to a 5% final concentration.
Centrifuge at max speed for 15 minutes, transfer the supernatant to a new tube, freeze at −80 °C, or directly send for LC–MS/MS analysis.
1.4 Peptide Separation and Data Acquisition
1.4.1 Peptide Separation
One of the most significant advantages by using bottom‐up proteomics is easier LC separation of peptides, comparing to intact proteins. After enzymatic digestion, digested peptides are much more uniform in shape, size, and charge than proteins. Using chromatographic techniques such as ion exchange (IXC), RP, or combinations of IXC and RP such as Multidimensional Protein Identification Technology (MudPIT), peptides can be efficiently separated by both their surface charge and hydrophobicity.
1.4.1.1 Reversed Phase (RP)
Today, the most commonly used technique for peptide separation is nanoelectrospray along with reversed‐phase nanoflow LC. The method involves direct loading of peptide fragments onto a nanoflow capillary column, wherein they are separated on the basis of differential hydrophobicity and are processed further. Once the separation is achieved, separated protein fragments are directly electrosprayed from capillary tip into the mass spectrometer. The efficient separation by high performance liquid chromatography (HPLC) or ultra performance liquid chromatography (UPLC), when combined with the advanced mass spectrometer, is sufficient to identify more than 1000 proteins within an hour. With a longer column and separation time, more than 5000 protein identification can also be achieved in certain cases by a single reversed‐phase LC–MS/MS system.
1.4.1.2 HILIC
HILIC separates peptides based on their hydrophilic interactions with an ionic resin and has found most application in peptide fractionation and PTM analysis. Electrostatic repulsion‐hydrophilic interaction chromatography (ERLIC) is a specific form of HILIC, using a weak anion exchange (WAX) resin. Unlike reversed phase liquid chromatography (RPLC), peptides are retained under two separation modes. Early in the organic to aqueous gradient, hydrophilic interactions dominate, as in HILIC and inversely to RPLC. However, as the aqueous content of the elution buffer is increased, basic peptides electrostatically repel the WAX resin while acidic peptides are retained until their hydrophilic interaction with the WAX resin is disrupted late in the gradient. These superimposed separation mechanisms with ERLIC distribute peptides over the gradient better than RPLC and outperform it based on peptide and protein identifications by higher confidence spectral matching of larger peptides.
1.4.1.3 MudPIT
After enzymatic digestion, the number of peptide species and the huge differences in quantity among protein species result in a highly complex peptide mixture. A combination of orthogonal separation methods, such as SCX and RP, helps to better separate different peptide species and therefore achieve better proteome coverage. In a typical MudPIT separation, peptide mixture is first loaded onto a short C18 + SCX capillary column (2.5 cm of C18 followed by 2.5 cm of SCX resin, as shown in Figure 1.2). The C18 + SCX column is then connected with an analytical C18 capillary column with a needle end for electrospray. In a typical 12‐step MudPIT experiment, the first step uses a gradient of buffer B to elute all peptides from the short 2.5 cm C18 column to the SCX resin. In all the subsequent steps, buffer C is first used to elute a portion of peptides with different surface charges from SCX resin to the analytical column. The eluted peptides are then analyzed by C18 analytical column with a gradient of buffer B.
Figure 1.2 Example of MudPIT setup and 12‐step MudPIT gradient table. Buffer A: 5% acetonitrile, 95% water, 0.1% formic acid. Buffer B: 95% acetonitrile, 5% water, 0.1% formic acid. Buffer C: 500 mM ammonium acetate in water, 5% acetonitrile, 0.1% formic acid.
1.4.1.4 Capillary Electrophoresis
Capillary electrophoresis has also re‐emerged as a complementary, more sensitive, and viable option in shotgun proteomics, largely due to improvements in electrospray interfaces. Fractionation of peptides prior to nLC‐ESI to improve comprehensiveness was initially performed online with SCX resin minimizing sample losses from transfers intrinsic to offline fractionation and autosamplers.
1.4.2 Peptide Ionization
After separation, peptides are then ionized by various ionization methods to the gas phase and enter the mass spectrometer. Shotgun proteomics allows implementation of two primary ionization methods for ionic charging and transfer peptide fragments into the gas phase, noted as nanoelectrospray (nESI) and MALDI. The technique of nanoelectrospray is widely used in analytical mass spectrometry of oligosaccharides, glycosides, and glycoproteins due to its ease of use and remarkable sensitivity. In bottom‐up proteomics, nESI provides excellent sensitivity with only a minimum amount of sample. In contrast to nanoelectrospray, MALDI offers both nondestructive vaporization as well as ionization of many large and small molecules. Although nanoelectrospray is the most commonly used method in shotgun proteomics, MALDI has been used more and more often for mass‐spectroscopy‐based imaging as it can provide spatial information together with the mass information.
1.4.3 Mass Analyzer
Over the last two decades, important advances in mass spectrometers, development of front‐end automated methodologies, and completion of human genome project; applications for further peptide analysis, such as peptide identification, characterization, and so on, have greatly increased. In this regard, multiple automated instruments have been developed with hybrid technology, involving simultaneous separation, quantification, and data analysis. In this regard, some of the common mass analyzers that have proven to be adept in analysis of complex peptide mixtures can be noted as linear ion trap (LIT), Orbitrap, Fourier transform ion cyclotron resonance (FT‐ICR), quadrupole, and time of flight (TOF). All these mass analyzers allow easy isolation and accurate data measurement of peptide masses at different interfaces, using different mechanisms; by maintaining proportionate balance between speed and sensitivity. Out of these analyzers mentioned herewith, most advanced version of mass spectrometers exploited widely in the field of proteomics is LIT. Furthermore, it should be noted that ion trap mass spectrometry is performing a leading role in modern instrumental world, for being capable of identifying and quantifying high‐ and low‐molecular‐weight pure peptides, with the same sensitivity and specificity. Thus, linear‐ion‐trapped mass analyzer essentially serves the role of all, that is, ion selection, ion trapping, ion fragmentation as well as low‐resolution mass analysis. Identification of peptides within the sample is accessed with the help of data‐dependent acquisition on the basis of initially unbiased sampling. An upgraded version of LIT involves four elongated planer electrodes, mounted in parallel to maximize the potential of ion trapping in both radial and axial directions. Once the sample is ionized, peptide ions are being trapped within the LIT. A radiofrequency voltage applied within the trap is increased, thus initiating ion ejection from the ion trap to detectors outside of the quadrupoles. With the initial precursor ion scan, it is possible to identify abundant peptide precursor ion m/z values and then subsequently an identified peptide precursor ion is selected and then isolated by scanning out all other ions. Trapped ions are translationally excited causing collisions with the helium bath gas to vibrationally excite the ions, through conversion of translational energy into vibrational energy. As vibrational energy increases, covalent bonds begin to fragment and when this happens, the resulting fragment ions are no longer excited. Accordingly, the fragment ions are scanned out of the ion trap. Some scan strategies are implemented to create unbiased sampling of peptide ions by using a data‐independent acquisition with consecutive small (10–25 m/z) ion isolation windows. Additionally, sampling speed in ion traps improved from the 3D ion trap by the invention of the 2D LIT and then creation of the segmented 2D LIT to separate ion trapping and fragmentation from mass analysis. The segmented trap allows the use of different gas pressures
The 2D LIT has also been a useful technology to create hybrid mass spectrometers to combine ion trapping and MS/MS capability to mass analyzers where these steps are difficult or impossible to perform in the mass analyzer. For example, the 2D LIT was interfaced with a FT‐ICR mass analyzer to add a high‐resolution and high‐accuracy mass analyzer to the capabilities of the LIT. The LIT was also added to the Orbitrap mass analyzer to create a powerful hybrid mass spectrometer.
The Orbitrap mass analyzer detects the frequency of ion current produced by peptide ions, which oscillate along a central electrode with a frequency proportional to (m/z)−1/2. The frequency‐based signal can be measured repetitively without losing the peptide ion and therefore enhance the accuracy [117]. Fourier transformation is then used to convert the frequency signal to highly accurate m/z values. The introduction of Orbitrap mass analyzers significantly improved the analysis for PTMs and quantification with isotopic labeling.
FT‐ICR instruments, analyzers specifically working on the principle of mass to charge ratio (m/z) of ions, are still capable of a higher mass accuracy as compared to Orbitrap. However, the price and size of FT‐ICR instrument is often inferior when compared to Orbitrap instruments, which limit the use of FT‐ICR in many applications.
1.4.4 Peptide Fragmentation Method
Shotgun proteomics has advanced with improvements in mass spectrometry technology providing better data accuracy and quantification to achieve wider proteome coverage. Further improvements on proteome coverage could be achieved by advances in gas phase fragmentation methods for peptides. Although development is still going on, at present, various modern methods are available to allow ion fragmentation, providing different information about the structure and composition of given molecule. The most commonly used methods are still collision‐induced dissociation (CID) and collisionally activated dissociation (CAD).
1.4.4.1 CID/HCD
CID, a mass spectrometry technique, has been recognized as the most trusted choice for fragmenting gaseous molecular ions, due to its high efficiency, predictable fragmentation, and ease of use. The ions that are being generated through CID are exploited for several purposes.
This type of excitation is primarily associated with LIT and beam‐type collision activation, preferable to mass spectrometers such as triple quadruple. In ion trap instruments, the motion of precursor ions is increased by resonance excitation to create more forceful collisions of ions with neutral molecules. Resonance excitation is associated with 3D and 2D ion trap instruments. In general, commonly produced ions are b‐ and y‐type, leaving positive as well as negative charges on the N‐ and C‐terminus (Figure 1.3). As resonance excitation is based on the motion of ions in the trap and that motion is based on the m/z value of the ion as soon as a fragmentation event occurs, the resulting fragment ions fall off the excitation frequency and they are no longer excited.
Figure 1.3 Common fragmentation methods used in bottom‐up proteomics and the typical fragmentation ions generated from peptide.
Ion activation in a beam‐type instrument such as a triple quadrupole or a quadrupole TOF occurs when ions are passed through a quadrupole containing a high gas pressure (a few millitorr) and ions collide with the gas until sufficient vibrational energy is reached for fragmentation to occur. Unlike ion trapping instruments when fragment ions are produced, they continue to undergo energetic collisions as they pass through the quadrupole collision cell. A variation on the collision cell used in traditional beam instruments was developed for Orbitrap hybrids. In this device, the precursor ion is accelerated into the collision cell and then returned to the injection site effectively passing up and back in the cell. An advantage to fragmentation in the collision versus fragmentation in an ion trap is that a collision does not have a lower m/z cutoff and thus immonium ions can be detected and reporter ions from TMT‐like experiments can be observed.
1.4.4.2 ETD/ECD
Collision‐based fragmentation methods are driven by the input of vibrational energy into ions. This energy gets randomized throughout the bonds of the ion and then weakest bonds fragment. This is an ergodic process. Electron capture methods such as ECD and ETD result in fast fragmentation of ions probably at the site of electron capture or transfer and thus is considered to be nonergodic. The electrons that are being employed in this method are either thermal electrons or electrons transferred from negatively charged fluoranthene, generating mostly c‐ and z‐types of ions (Figure 1.3). ETD/ECD can generally provide a “softer” fragmentation method in the sense that labile PTMs such as phosphorylation and glycosylation are preserved. The localization of labile y‐CO2 and SO3 modifications can be identified by ECD fragmentation. Studies have shown that CID is efficient for phosphopeptide identification and ECD is better for phosphorylation site localization [90, 91]. When combined, complementary information and higher confidence are offered for identified phosphopeptides. ETD is also suggested to be more advantageous over CID method for the detection of phosphorylation and glycosylation sites due to retention of labile modification moieties.
