Amino acids peptides and proteins in organic chemistry analysis and function of amino acids and peptides analysis and function of amino acids and peptides

Edited by
Andrew B. Hughes
Amino Acids, Peptides
and Proteins in
Organic Chemistry


Further Reading
Pignataro, B. (ed.)

Fessner, W.-D., Anthonsen, T.

Ideas in Chemistry and
Molecular Sciences

Modern Biocatalysis

Advances in Synthetic Chemistry
2010

Stereoselective and Environmentally
Friendly Reactions
2009
ISBN: 978-3-527-32071-4

ISBN: 978-3-527-32539-9

Tulla-Puche, Judit / Albericio,
Fernando (eds.)

The Power of Functional Resins
in Organic Synthesis

Lutz, S., Bornscheuer, U. T. (eds.)

Protein Engineering Handbook
2 Volume Set
2009
ISBN: 978-3-527-31850-6

2008
ISBN: 978-3-527-31936-7

Castanho, Miguel / Santos, Nuno (eds.)

Eicher, T., Hauptmann, S., Speicher, A.

Peptide Drug Discovery and
Development

The Chemistry of Heterocycles
Structure, Reactions, Synthesis, and
Applications
2011
ISBN: 978-3-527-32868-0 (Hardcover)
ISBN: 978-3-527-32747-8 (Softcover)

Royer, J. (ed.)

Asymmetric Synthesis of
Nitrogen Heterocycles
2009
ISBN: 978-3-527-32036-3

Translational Research in Academia
and Industry
2011
ISBN: 978-3-527-32891-8

Sewald, N., Jakubke, H.-D.

Peptides: Chemistry and
Biology
2009
ISBN: 978-3-527-31867-4

JNicolaou, K. C., Chen, J. S.

Drauz, K., Gröger, H., May, O. (eds.)

Classics in Total Synthesis III

Enzyme Catalysis in Organic
Synthesis

New Targets, Strategies, Methods

Third, Completely Revised and
Enlarged Edition

3 Volumes
2011
ISBN: 978-3-527-32547-4

2011
ISBN: 978-3-527-32958-8 (Hardcover)
ISBN: 978-3-527-32957-1 (Softcover)


Edited by
Andrew B. Hughes

Amino Acids, Peptides and Proteins
in Organic Chemistry
Volume 5 - Analysis and Function of
Amino Acids and Peptides


The Editor
Andrew B. Hughes
La Trobe University
Department of Chemistry
Victoria 3086
Australia

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V

Contents
List of Contributors
1
1.1
1.1.1
1.1.2
1.1.3
1.1.4
1.1.5
1.2
1.2.1
1.2.2
1.2.3
1.3
1.3.1
1.3.2
1.3.3
1.3.4
1.4
1.4.1
1.4.2
1.5
1.5.1
1.6

2
2.1
2.1.1
2.1.2

XV

Mass Spectrometry of Amino Acids and Proteins 1
Simin D. Maleknia and Richard Johnson
Introduction 1
Mass Terminology 1
Components of a Mass Spectrometer 4
Resolution and Mass Accuracy 6
Accurate Analysis of ESI Multiply Charged Ions 10
Fragment Ions 11
Basic Protein Chemistry and How it Relates to MS 21
Mass Properties of the Polypeptide Chain 21
In Vivo Protein Modifications 21
Ex Vivo Protein Modifications 26
Sample Preparation and Data Acquisition 28
Top-Down Versus Bottom-Up Proteomics 28
Shotgun Versus Targeted Proteomics 28
Enzymatic Digestion for Bottom-Up Proteomics 29
Liquid Chromatography and Capillary Electrophoresis for
Mixtures in Bottom-Up 30
Data Analysis of LC-MS/MS (or CE-MS/MS) of Mixtures 32
Identification of Proteins from MS/MS Spectra of Peptides 32
De Novo Sequencing 35
MS of Protein Structure, Folding, and Interactions 36
Methods to Mass-Tag Structural Features 37

Conclusions and Perspectives 40
References 40
X-Ray Structure Determination of Proteins and Peptides
Andrew J. Fisher
Introduction 51
Light Microscopy 51
X-Rays and Crystallography at the Start 52

51


VI

Contents

2.1.3
2.1.4
2.2
2.2.1
2.2.2
2.2.3
2.2.4
2.2.5
2.3
2.3.1
2.3.2
2.3.3
2.3.4
2.4
2.4.1

2.4.2
2.4.3
2.4.4
2.4.5
2.4.6
2.4.7
2.5
2.5.1
2.5.2
2.6
2.6.1
2.6.2
2.6.3
2.7
2.7.1
2.7.2
2.7.3

X-Ray Crystallography Today 53
Limitations of X-Ray Crystallography 54
Growing Crystals 55
Why Crystals? 55
Basic Methods of Growing Protein Crystals 55
Protein Sample 59
Preliminary Crystal Analysis 59
Mounting Crystals for X-Ray Analysis 61
Symmetry and Space Groups 62
Crystals and the Unit Cell 62
Point Groups 65
Space Groups 66

Asymmetric Unit 67
X-Ray Scattering and Diffraction 67
X-Rays and Mathematical Representation of Waves 67
Interaction of X-Rays with Matter 70
Crystal Lattice, Miller Indices, and the Reciprocal Space 73
X-Ray Diffraction from a Crystal: Bragg’s Law 75
Bragg’s Law in Reciprocal Space 77
Fourier Transform Equation from a Lattice 79
Friedel’s Law and the Electron Density Equation 80
Collecting and Processing Diffraction Data 82
Data Collection Strategy 82
Symmetry and Scaling Data 83
Solving the Structure (Determining Phases) 83
Molecular Replacement 83
Isomorphous Replacement 85
MAD 88
Analyzing and Refining the Structure 90
Electron Density Interpretation and Model Building 90
Protein Structure Refinement 91
Protein Structure Validation 93
References 94

3

Nuclear Magnetic Resonance of Amino Acids, Peptides,
and Proteins 97
Andrea Bernini and Pierandrea Temussi
Introduction 97
Active Nuclei in NMR 98
Energy Levels and Spin States 98

Main NMR Parameters (Glossary) 99
Chemical Shift 99
Scalar Coupling Constants 100
NOE 100
RDC 101

3.1
3.1.1
3.1.2
3.1.3
3.1.3.1
3.1.3.2
3.1.3.3
3.1.3.4


Contents

3.2
3.2.1
3.2.2
3.2.3
3.2.4
3.2.5
3.2.6
3.3
3.3.1
3.3.2
3.3.3
3.3.4

3.3.4.1
3.3.4.2
3.3.4.3
3.3.5
3.3.6
3.3.6.1
3.3.6.2
3.3.6.3
3.3.6.4
3.4
3.4.1
3.4.2
3.4.3
3.4.3.1
3.4.3.2
3.4.3.3
3.4.3.4
3.4.3.5
3.4.4
3.4.5
3.4.5.1
3.4.5.2
3.4.5.3
3.5

4
4.1
4.2
4.3

Amino Acids 101
Historical Significance 101
Amino Acids Structure 101
Random Coil Chemical Shift 102
Spin Systems 105
Labile Protons 110
Contemporary Relevance: Metabolomics 112
Peptides 113
Historical Significance 113
Oligopeptides as Models for Conformational Transitions
in Proteins 114
Bioactive Peptides 116
Choice of the Solvent 117
Transport Fluids 118
Membranes 120
Receptor Cavities 122
Ensemble Calculations 125
Selected Examples from the Major Fields of Bioactive Peptides
Aspartame 125
Opioids 126
Transmembrane Helices 127
Cyclopeptides 128
Proteins 129
An Alternative to or a Validation of Diffractometric
Methods? 129
Protein Spectra 129
Wüthrich’s Protocol 130
Sample Preparation 131
Recording NMR Spectra 131
Sequential Assignment 131

Conformational Constraints 132
Model Building 134
Recent Developments 134
Selected Structures 136
Superoxide Dismutases 137
Malate Synthase G 137
Interactions 138
Conclusions 145
References 146
Structure and Activity of N-Methylated Peptides 155
Raymond S. Norton
Introduction 155
Conformational Effects of N-Methylation 157
Effects of N-Methylation on Bioactive Peptides 159

125

VII


VIII

Contents

4.3.1
4.3.2
4.3.3
4.3.4
4.4

Thyrotropin-Releasing Hormone 159
Cyclic Peptides 159
Somatostatin Analogs 160
Antimalarial Peptide 161
Concluding Remarks 162
References 163

5

High-Performance Liquid Chromatography of Peptides
and Proteins 167
Reinhard I. Boysen and Milton T.W. Hearn
Introduction 167
Basic Terms and Concepts in Chromatography 169
Chemical Structure of Peptides and Proteins 173
Biophysical Properties of Peptides and Proteins 173
Conformational Properties of Peptides and Proteins 176
Optical Properties of Peptides and Proteins 176
HPLC Separation Modes in Peptide and Protein
Analysis 177
SEC 178
RPC 179
NPC 181
HILIC 181
ANPC 183
HIC 184
IEX 187
AC 188
Method Development from Analytical to Preparative Scale
Illustrated for HP-RPC 189

Development of an Analytical Method 190
Scaling Up to Preparative Chromatography 196
Fractionation 198
Analysis of the Quality of the Fractionation 198
Multidimensional HPLC 198
Purification of Peptides and Proteins by MD-HPLC
Methods 200
Fractionation of Complex Peptide and Protein Mixtures
by MD-HPLC 202
Operational Strategies for MD-HPLC Methods 202
Off-line Coupling Mode for MD-HPLC Methods 202
On-Line Coupling Mode for MD-HPLC Methods 203
Design of an Effective MD-HPLC Scheme 203
Orthogonality of Chromatographic Modes 203
Compatibility Matrix of Chromatographic Modes 205
Conclusions 206
References 207

5.1
5.2
5.3
5.3.1
5.3.2
5.3.3
5.4
5.4.1
5.4.2
5.4.3
5.4.4
5.4.5

5.4.6
5.4.7
5.4.8
5.5
5.5.1
5.5.2
5.5.3
5.5.4
5.6
5.6.1
5.6.2
5.6.3
5.6.3.1
5.6.3.2
5.6.4
5.6.4.1
5.6.4.2
5.7


Contents

6

6.1
6.1.1
6.1.2
6.1.3
6.2
6.2.1

6.2.2
6.2.3
6.3
6.3.1
6.3.2

7
7.1
7.2
7.3
7.4
7.4.1
7.4.2
7.4.3
7.5
7.5.1
7.5.1.1
7.5.1.2
7.6
7.6.1
7.6.1.1
7.6.1.2
7.6.1.3
7.6.1.4
7.7
7.7.1
7.7.2
7.7.3

Local Surface Plasmon Resonance and Electrochemical Biosensing

Systems for Analyzing Functional Peptides 211
Masato Saito and Eiichi Tamiya
Localized Surface Plasmon Resonance (LSPR)-Based Microfluidics
Biosensor for the Detection of Insulin Peptide Hormone 211
LSPR and Micro Total Analysis Systems 211
Microfluidic LSPR Chip Fabrication and LSPR Measurement 212
Detection of the Insulin–Anti-Insulin Antibody Reaction
on a Chip 213
Electrochemical LSPR-Based Label-Free Detection of Melittin 215
Melittin and E-LSPR 215
Fabrication of E-LSPR Substrate and Formation of the Hybrid
Bilayer Membrane 215
Measurements of Membrane-Based Sensors for Peptide Toxin 217
Label-Free Electrochemical Monitoring of b-Amyloid (Ab)
Peptide Aggregation 218
Alzheimer’s Ab Aggregation and Electrochemical
Detection Method 218
Label-Free Electrochemical Detection of Ab Aggregation 219
References 221
Surface Plasmon Resonance Spectroscopy in the Biosciences 225
Jing Yuan, Yinqiu Wu, and Marie-Isabel Aguilar
Introduction 225
SPR-Based Optical Biosensors 225
Principle of Operation of SPR Biosensors 226
Description of a SPR Instrument 228
Sensor Surface 228
Flow System 229
Detection System 230
Application of SPR in Immunosensor Design 230
Assay Development 232

Immobilization of the Analyte to a Specific Chip Surface 232
Assay Design 233
Application of SPR in Membrane Interactions 234
General Protocols for Membrane Interaction
Studies by SPR 236
Liposome Preparation 236
Formation of Bilayer Systems 236
Analyte Binding to the Membrane System 237
Membrane Binding of Antimicrobial Peptides by SPR 238
Data Analysis 240
Linearization Analysis 240
Numerical Integration Analysis 241
Steady-State Approximations 242

IX


X

Contents

7.8

Conclusions 243
References 244

8

Atomic Force Microscopy of Proteins 249
Adam Mechler

Foreword 249
Importance of Asking the Right Question 250
AFM 250
Principle and Basic Modes of Operation 250
How Does a Tip Tap? 251
Bioimaging Highlights 253
Protein Oligomerization, Aggregation, and Fibers 253
Membrane Binding and Lysis 255
Ion Channel Activity 257
Protein–DNA-Specific Binding 261
Issues 261
Resolution 262
Imaging Force 263
Repetitive Stress 264
Artifacts Related to too Low Free Amplitude 265
Transient Force and Bandwidth 266
Accuracy of Surface Tracking 266
Step Artifacts 268
Force Measurements 269
Liquid Imaging 269
Sample Preparation for Bioimaging 272
Adhesion 272
Physical Entrapment 273
Chemical Binding 274
Outlook 274
References 275

8.1
8.1.1
8.2

8.2.1
8.2.2
8.3
8.3.1
8.3.2
8.3.3
8.3.4
8.4
8.4.1
8.4.2
8.4.3
8.4.4
8.4.5
8.4.6
8.4.7
8.5
8.6
8.7
8.7.1
8.7.2
8.7.3
8.8

9
9.1
9.2
9.2.1
9.2.2
9.2.3
9.2.4

9.2.5
9.3
9.3.1
9.3.2
9.3.3

Solvent Interactions with Proteins and Other Macromolecules 277
Satoshi Ohtake, Yoshiko Kita, Kouhei Tsumoto, and Tsutomu Arakawa
Introduction 277
Solvent Applications 280
Research 280
Precipitation 287
Chromatography 288
Protein Refolding 296
Formulation 297
Solvent Application for Viruses 300
Isolation and Purification of Viruses 301
Stabilization and Formulation of Viruses 302
Inactivation of Viruses 309


Contents

9.4
9.4.1
9.4.2
9.5
9.5.1
9.5.1.1
9.5.1.2

9.5.2
9.5.2.1
9.5.3
9.6
9.6.1
9.6.2
9.7

Solvent Application for DNA 310
Isolation and Purification of DNA 310
Stability of DNA in a Cosolvent System 312
Mechanism 314
Physical Mechanism 315
Hydration 315
Excluded Volume 318
Thermodynamic Interaction 322
Group Interaction: Model Compound Solubility 322
Preferential Interaction 328
Protein–Solvent Interactions in Frozen and Freeze-Dried Systems 342
Frozen Systems 342
Freeze-Dried System 345
Conclusions 348
References 349

10

Role of Cysteine 361
Lalla A. Ba, Torsten Burkholz, Thomas Schneider, and Claus Jacob
Sulfur: A Redox Chameleon with Many Faces 361
Three Faces of Thiols: Nucleophilicity, Redox Activity, and

Metal Binding 365
Towards a Dynamic Picture of Disulfide Bonds 371
Chemical Protection and Regulation via S-Thiolation 374
‘‘Dormant’’ Catalytic Sites 378
Peroxiredoxin/Sulfiredoxin Catalysis and Control Pathway 379
Higher Sulfur Oxidation States: From the Shadows to
the Heart of Biological Sulfur Chemistry 384
Cysteine as a Target for Oxidants, Metal Ions, and
Drug Molecules 388
Conclusions and Outlook 390
References 391

10.1
10.2
10.3
10.4
10.5
10.6
10.7
10.8
10.9

11
11.1
11.2
11.3
11.4
11.5
11.6
11.7

11.7.1
11.7.2
11.7.3
11.7.4

Role of Disulfide Bonds in Peptide and Protein Conformation 395
Keith K. Khoo and Raymond S. Norton
Introduction 395
Probing the Role of Disulfide Bonds 396
Contribution of Disulfide Bonds to Protein Stability 396
Role of Disulfide Bonds in Protein Folding 397
Role of Individual Disulfide Bonds in Protein Structure 399
Disulfide Bonds in Protein Dynamics 401
Disulfide Bonding Patterns and Protein Topology 403
Conservation and Evolution of Disulfide Bonding Patterns 403
Conservation of Disulfide Bonds 404
Cysteine Framework and Disulfide Connectivity 404
Non-Native Disulfide Connectivities 407

XI


XII

Contents

11.8
11.9

Applications 408

Conclusions 409
References 410

12

Quantitative Mass Spectrometry-Based Proteomics 419
Shao-En Ong
Introduction 419
Quantification in Biological MS 420
Label-Free Approaches in Quantitative MS Proteomics 423
SIL in Quantitative Proteomics 425
Identifying Proteins Interacting with Small Molecules with
Quantitative Proteomics 430
Conclusions 433
References 434

12.1
12.2
12.2.1
12.2.2
12.3
12.4

13

13.1
13.2
13.3
13.3.1
13.3.1.1

13.3.1.2
13.3.1.3
13.3.2
13.3.2.1
13.3.2.2
13.3.2.3
13.3.3
13.3.3.1
13.3.3.2
13.3.3.3
13.3.4
13.3.4.1
13.3.4.2
13.3.4.3
13.3.4.4
13.3.4.5
13.3.4.6
13.4
13.4.1

Two-Dimensional Gel Electrophoresis and Protein/Polypeptide
Assignment 439
Takashi Manabe and Ya Jin
Introduction 439
Aim of Protein Analysis and Development of 2-DE
Techniques 439
Current Status of 2-DE Techniques 441
Denaturing 2-DE for the Separation of Polypeptides 442
Principle 442
Procedures 444

Specific Features 445
Nondenaturing 2-DE for the Separation of Biologically Active
Proteins and Protein Complexes 445
Principle 445
Procedures 446
Specific Features 447
Blue-Native 2-DE for the Detection of Protein–Protein Interactions
Principle 448
Procedures 448
Specific Features 449
Visualization of Proteins Separated on 2-DE Gels 449
Fixing Before CBB, Silver, or Fluorescent Dye Staining 450
CBB Staining 450
Silver Staining 450
Reverse Staining with Zinc-Imidazole 451
Fluorescent Dye Staining 451
Quantitation 451
Development of Protein Assignment Techniques on 2-DE
Gels and Current Status of Mass Spectrometric Techniques 452
Development of Protein Assignment Techniques 452

448


Contents

13.4.2
13.4.2.1
13.4.2.2
13.4.2.3

13.5

14

14.1
14.2
14.2.1
14.2.2
14.2.3
14.2.3.1
14.2.3.2
14.3
14.4

MS-Based Assignment Techniques Utilizing Amino Acid
Sequence Databases 454
Sample Preparation for MS Analysis 455
MALDI-TOF-MS and PMF 456
MS/MS and Peptide Sequence Search 459
Conclusions 460
References 460
Bioinformatics Tools for Detecting Post-Translational Modifications
in Mass Spectrometry Data 463
Patricia M. Palagi, Erik Arhné, MarKus Müller, and Frédérique Lisacek
Introduction 463
PTM Discovery with MS 465
Detecting PTMs in MS and MS/MS Data 466
Discovering PTMs in MS or MS/MS Data 468
PTM Prediction Tools 469
From MS Data 469

From Sequence Data 469
Database Resources for PTM Analysis 470
Conclusions 473
References 473

Index

477

XIII



XV

List of Contributors
Marie-Isabel Aguilar
Monash University
Department of Biochemistry and
Molecular Biology
Wellington Road
Clayton, Victoria 3800
Australia
Tsutomu Arakawa
Alliance Protein Laboratories
3957 Corte Cancion
Thousand Oaks, CA 91360
USA
Erik Arhne
Swiss Institute of Bioinformatics

Proteome Informatics Group
1 rue Michel Servet
1211 Geneva 4
Switzerland
Lalla A. Ba
University of Saarland
School of Pharmacy
Division of Bioorganic Chemistry
Campus B 2.1
66123 Saarbrücken
Germany

Andrea Bernini
University of Siena
Department of Molecular Biology
via Fiorentina 1
53100 Siena
Italy
Reinhard I. Boysen
Monash University
ARC Special Research
Centre for Green Chemistry
Building 75, Wellington Road
Clayton, Victoria 3800
Australia
Torsten Burkholz
University of Saarland
School of Pharmacy
Division of Bioorganic Chemistry
Campus B 2.1

66123 Saarbrücken
Germany
Andrew J. Fisher
University of California
Departments of Chemistry and
Molecular & Cell Biology
One Shields Avenue
Davis, CA 95616
USA


XVI

List of Contributors

Milton T.W. Hearn
Monash University
ARC Special Research
Centre for Green Chemistry
Building 75, Wellington Road
Clayton, Victoria 3800
Australia
Patricia Hernandez
Swiss Institute of Bioinformatics
Proteome Informatics Group
1 rue Michel Servet
1211 Geneva 4
Switzerland
Claus Jacob
University of Saarland

School of Pharmacy
Division of Bioorganic Chemistry
Campus B 2.1
66123 Saarbrücken
Germany
Ya Jin
Ehime University
Faculty of Science
Department of Chemistry
2-5 Bunkyo-cho
Matsuyama City 790-8577
Japan

and
University of Melbourne
Department of Medical Biology
Parkville, Victoria 3010
Australia
Yoshiko Kita
Keio University
School of Medicine
Department of Pharmacology
35 Shinanomachi, Shinjuku-ku
Tokyo 160-8582
Japan
Frederique Lisacek
Swiss Institute of Bioinformatics
Proteome Informatics Group
1 rue Michel Servet
1211 Geneva 4

Switzerland
Simin D. Maleknia
University of New South Wales
School of Biological, Earth and
Environmental Sciences
Sydney, NSW 2052
Australia

Richard Johnson
Institute for Systems Biology
1441 N. 34th Street
Seattle, WA 98103-1299
USA

Takashi Manabe
Ehime University
Faculty of Science
Department of Chemistry
2-5 Bunkyo-cho
Matsuyama City 790-8577
Japan

Keith K. Khoo
The Walter and Eliza Hall Institute of
Medical Research
Structural Biology Division
1G Royal Parade
Parkville, Victoria 3052
Australia

Adam Mechler
La Trobe University
Department of Chemistry
Physical Sciences 3, Bundoora Campus
Bundoora
Victoria 3086
Australia


List of Contributors

Markus Mueller
Swiss Institute of Bioinformatics
Proteome Informatics Group
1 rue Michel Servet
1211 Geneva 4
Switzerland
Raymond S. Norton
Monash University
Monash Institute of Pharmaceutical
Sciences
Medicinal Chemistry and Drug Action
381 Royal Parade
Parkville, Victoria 3052
Australia
Satoshi Ohtake
Aridis Pharmaceuticals
Research and Development
5941 Optical Court
San Jose, CA 95138

USA
Shao-En Ong
University of Washington
Department of Pharmacology
Health Sciences Center, Room E-401
Seattle, WA 98195-7280
USA
Patricia M. Palagi
Swiss Institute of Bioinformatics
Proteome Informatics Group
1 rue Michel Servet
1211 Geneva 4
Switzerland
Masato Saito
Osaka University
Graduate School of Engineering
Department of Applied Physics
2-1 Yamadaoka, Suita
Osaka 565-0871
Japan

Thomas Schneider
University of Saarland
School of Pharmacy
Division of Bioorganic Chemistry
Campus B 2.1
66123 Saarbrücken
Germany
Eiichi Tamiya
Osaka University

Graduate School of Engineering
Department of Applied Physics
2-1 Yamadaoka, Suita
Osaka 565-0871
Japan
Pierandrea Temussi
Universitá di Napoli Federico II
Dipartimento di Chimica
Via Cinthia
Complesso Monte S. Angelo
80126 Napoli
Italy
and
National Institute for Medical Research
Division of Molecular Structure
The Ridgeway
London NW7 1AA
UK
Kouhei Tsumoto
University of Tokyo
Medical Proteomics Laboratory
4-6-1 Shirokanedai, Minato-ku
Tokyo 106-8639
Japan

XVII


XVIII

List of Contributors

Yinqiu Wu
New Zealand Institute for Plant and
Food Research
120 Mt Albert Rd, Sandringham
Auckland 1025
New Zealand

Jing Yuan
New Zealand Institute for Plant and
Food Research
East Street 3214, Hamilton
New Zealand


j1

1
Mass Spectrometry of Amino Acids and Proteins
Simin D. Maleknia and Richard Johnson

1.1
Introduction
1.1.1
Mass Terminology

Like most matter (with the exception of, say, neutron stars), proteins and peptides are
mostly made of nothing – an ephemeral cloud of electrons with very little mass
surrounding tiny and very dense atomic nuclei that contain nearly all of the mass (i.e.,

peptides and proteins are made of atoms). Atoms have mass and the unit of mass that
is most convenient to use is called the atomic mass unit (abbreviated amu or u) or in
biological circles a Dalton (Da). Over the years, physicists and chemists have argued
about what standard to use to define an atomic mass unit, but the issue seems to have
been settled in 1959 when the General Assembly of the International Union of Pure
and Applied Chemistry defined an atomic mass unit as being exactly 1/12 of the mass
of the most abundant carbon isotope (12 C) in its unbound lowest energy state.
Therefore, one atom of 12 C has a mass of 12.0000 u. Using this as the standard, one
proton has a measured mass of 1.00728 u and one neutron is slightly heavier at
1.00866 u. One 12 C atom contains six protons and six neutrons, the sum of which is
clearly more than the mass of 12.0000 u. A carbon atom is less than the sum of its
parts, and the reason is that the protons and neutrons in a carbon nucleus are in a
lower energy state than free protons and neutrons. Energy and mass are interchangeable via Einstein’s famous equation (E ¼ mc2), and so this “mass defect” is a
result of the nuclear forces that hold neutrons and protons together within an atom.
This mass defect also serves as a reminder of why people like A. Q. Khan are so
dangerous [1].
Each element is defined by the number of protons per nucleus (e.g., carbon atoms
always have six protons), but each element can have variable numbers of neutrons.
Elements with differing numbers of neutrons are called isotopes and each isotope
possesses a different mass. In some cases, the additional neutrons result in stable
isotopes, which are particularly useful in mass spectrometry (MS) in a method called

Amino Acids, Peptides and Proteins in Organic Chemistry. Vol.5: Analysis and Function of Amino Acids and Peptides.
First Edition. Edited by Andrew B. Hughes.
Ó 2012 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2012 by Wiley-VCH Verlag GmbH & Co. KGaA.


j 1 Mass Spectrometry of Amino Acids and Proteins

2

isotope dilution. Examples in the proteomic field that employ isotope dilution
methodology include the use of the stable isotopes 2 H, 13 C, 15 N, and 18 O, as applied
in methods such as ICAT (isotope-coded affinity tags) [2], SILAC (stable isotope
labeling with amino acids in cell culture) [3], or enzymatic incorporation of 18 O
water [4]. Whereas some isotopes are stable, others are not and will undergo
radioactive decay. For example, hydrogen with one neutron is stable (deuterium),
but if there are two additional neutrons (a tritium atom) the atoms will decay to
helium (two protons and one neutron) plus a negatively charged b-particle and a
neutrino. Generally, if there are sufficient amounts of a radioactive isotope to produce
an abundant mass spectral signal, the sample is likely to be exceedingly radioactive,
the instrumentation would have become contaminated, and the operator would likely
come to regret having performed the analysis. Therefore, mass spectrometrists will
typically concern themselves with stable isotopes. Each element has a different
propensity to take on different numbers of neutrons. For example, fluorine has nine
protons and always 10 neutrons; however, bromine with 35 protons is evenly split
between possessing either 44 or 46 neutrons. There are most likely interesting
reasons for this, but they are not particularly relevant to a description of the use of MS
in the analysis of proteins.
What is relevant is the notion of “monoisotopic” versus “average” versus
“nominal” mass. The monoisotopic mass of a molecule is calculated using the
masses of the most abundant isotope of each element present in the molecule. For
peptides, this means using the specific masses for the isotopes of each element that
possess the highest natural abundance (e.g., 1 H, 12 C, 14 N, 16 O, 31 P, and 32 S as shown
in Table 1.1). The “average” or “chemical” mass is calculated using an average of the
isotopes for each element, weighted for natural abundance. For elements found in
most biological molecules, the most abundant isotope contains the fewest neutrons
Table 1.1 Mass and abundance values for some biochemically relevant elements.

Element

Hydrogen

Average mass
1.008

Isotope
1

H
H
12
C
13
C
14
N
15
N
16
O
17
O
18
O
31
P
23
Na
32
S

33
S
34
S
36
S
2

Carbon

12.011

Nitrogen

14.007

Oxygen

15.999

Phosphorus
Sodium
Sulfur

30.974
22.990
32.064

Monoisotopic mass

Abundance (%)

1.00783
2.01410
12
13.00335
14.00307
15.00011
15.99491
16.99913
17.99916
30.97376
22.98977
31.97207
32.97146
33.96787
35.96708

99.985
0.015
98.90
1.10
99.63
0.37
99.76
0.04
0.200
100
100
95.02

0.75
4.21
0.02


1.1 Introduction

and the less abundant isotopes are of greater mass. Therefore, the monoisotopic
masses calculated for peptides are less than what are calculated using average
elemental masses. The term “nominal mass” refers to the integer value of the most
abundant isotope for each element. For example, the nominal masses of H, C, N, and
O are 1, 12, 14, and 16, respectively. A rough conversion between nominal and
monoisotopic peptide masses is shown as [5]:
Mc ¼ 1:000495 Á Mn

ð1:1Þ

Dm ¼ 0:03 þ 0:02 Á Mn =1000

ð1:2Þ

where Mc is the estimated monoisotopic peptide mass calculated from a nominal
mass, Mn. Dm is the estimated standard deviation at a given nominal mass. For
example, peptides with a nominal mass of 1999 would be expected, on average, to
have a monoisotopic mass of around 1999.99 with a standard deviation of 0.07 u.
Therefore, 99.7% of all peptides (3 standard deviations) at a nominal mass 1999
would be found at monoisotopic masses between 1999.78 and 2000.20 (Figure 1.1).

Figure 1.1 Predicting monoisotopic from
nominal molecular weights. Using the

equations from Wool and Smilansky [5],
peptides with nominal molecular weights of
1998, 1999, and 2000 would on average be
expected to have monoisotopic molecular

weights of 1998.99, 1999.99, and 2000.99 with
standard deviations of 0.07. The difference
between monoisotopic and nominal masses is
called the mass defect and this value scales with
mass.

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j 1 Mass Spectrometry of Amino Acids and Proteins

4

As can be seen, at around mass 2000, the mass defect in a peptide molecule is
just about one whole mass unit. Most of this mass defect is due to the large
number of hydrogen atoms present in a peptide of this size. The mass defect
associated with nitrogen and oxygen tends to cancel out, and carbon by definition
has no mass defect. The other important observation that can be made from this
example is that 99.7% of peptides with a nominal mass of 1999 will be found
between 1999.78 and 2000.20. Therefore, a molecule that is accurately measured
to be 2000.45 cannot be a standard peptide and must either not be a peptide at all
or is a peptide that has been modified with elements not typically found in
peptides.
1.1.2
Components of a Mass Spectrometer

At minimum, a mass spectrometer has an ionization source, a mass analyzer, an ion
detector, and some means of reporting the data. For the purposes here, there is no
need to go into any detail at all regarding the ion detection and although there are
many historically interesting methods of recording and reporting data (photographic
plates, UV-sensitive paper, etc.), nowadays one simply uses a computer. The ionization source and the mass analyzer are the two components that need to be well
understood.
Historically, ionization was limited to volatile molecules that were amenable to
gas phase ionization methods such as electron impact. Over time, other techniques
were developed that allowed for ionization of larger polar molecules – techniques
such as fast atom bombardment (FAB) or field desorption ionization. However,
these had relatively poor sensitivity requiring 0.1–1 nmol of peptide and, with the
exception of plasma desorption ionization – a technique that used toxic radioactive
californium, were generally not capable of ionizing larger molecules like proteins.
Remarkably, two different ionization methods were developed in the late 1980s that
did allow for sensitive ionization of larger molecules – electrospray ionization (ESI)
and matrix-assisted laser desorption ionization (MALDI). Posters presented at the
1988 American Society for Mass Spectrometry conference by John Fenn’s group
showed mass spectra of several proteins [6, 7], which revealed the general nature of
ESI of peptides and proteins. Namely, a series of heterogeneous multiply protonated ions are observed, where the maximum number of charges is roughly
dependent on the number of basic sites in the protein or peptide. Conveniently,
this puts the ions at mass-to-charge (m/z) ratios typically below 4000, which is a
range suitable for just about all mass analyzers (see below). In a series of papers
between 1985 and 1988, Hillenkamp and Karas described the essentials of
MALDI [8–10]. Also, Tanaka presented a poster at a Joint Japan–China Symposium
on Mass Spectrometry in 1987 showing a pentamer of lysozyme using laser
desorption from a glycerol matrix containing metal shavings [11]. These early
results showed the general nature of MALDI – singly charged ions predominate
and therefore the mass analyzer must be capable of measuring ions with very high
m/z ratios.

1.1 Introduction

It is desirable for users to have some basic understanding of the different types
of mass analyzers that are available. At one time multisector analyzers [12] were
well-liked (back when FAB ionization was popular), but quickly became dinosaurs
for protein work after the discovery of ESI. It was too difficult to deal with the
electrical arcs that tended to arise when trying to couple kiloelectronvolt source
voltages with a wet acidic atmospheric spray. ESI was initially most readily
coupled to quadrupole mass filters, which operated at much lower voltages.
Quadrupole mass filters [13], as the name implies, are made from four parallel
rods where at appropriate frequency and voltages, ions at specific masses can
oscillate without running into a rod or escaping from between the rods. Given a
little push (a few electronvolts potential) the oscillating ions will pass through the
length of the parallel rods and be detected at the other end. Both quadrupole mass
filters and multisector instruments suffer from slow scan rates and poor sensitivity due to their low duty cycle. Instrument vendors have therefore been busy
developing more sensitive analyzers. The ion traps [14, 15] are largely governed by
the same equations for ion motion as quadrupole mass filters, but possess a
greater duty cycle (and sensitivity). For those unafraid of powerful super cooled
magnets, and who possess sufficiently deep pockets to pay for the initial outlay
and subsequent liquid helium consumption, Fourier transform ion cyclotron
resonance (FT-ICR) provides a high-mass-accuracy and high-resolution mass
analyzer [16]. In this case, the ions circle within a very high vacuum cell under
the influence of a strong magnetic field. The oscillating ions induce a current in a
pair of detecting electrodes, where the frequency of oscillation is related to the
m/z ratio. Detection of an oscillating current is also performed in Orbitrap
instruments [17, 18], except in this case the ions circle around a spindle-shaped
electrode rather than magnetic field lines. The time-of-flight hybrid (TOF) mass
analyzer [19, 20] is, at least in principle, the simplest analyzer of all – it is an

empty tube. Ions are accelerated down the empty tube and, as the name implies,
the TOF is measured and is related to the m/z ratio (big ions move slowly and
little ones move fast).
Tandem MS is a concept that is independent of the specific type of mass analyzer,
but should be understood when discussing mass analyzers. As the name implies,
tandem MS employs two stages of mass analysis, where the two analyzers can be
scanned in various ways depending on the experiment. In the most common type of
experiment, the first analyzer is statically passing an ion of a specific mass into a
fragmentation region, where the selected ions are fragmented somehow (see
below) and the resulting fragment ions are mass analyzed by the second mass
analyzer. These so-called daughter, or product, ion scans are usually what are meant
when referring to an “MS/MS spectra.” However, there are other types of tandem
MS experiments that are occasionally performed. One is where the first mass
analyzer is statically passing a precursor ion (as in the aforementioned product ion
scan) and the second analyzer is also statically monitoring one, or a few, specific
fragment ions. This so-called selected reaction monitoring (SRM) experiment is
particularly useful in the quantitation of known molecules. There are other less
frequently used tandem MS scans (e.g., neutral loss scans) and it should be noted

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