Ion Mobility - Mass Spectrometry Methods for Studying Membrane ProteinsbySarah M. Fantin

A dissertation submitted in partial fulfillmentof the requirements for the degree ofDoctor of Philosophy(Chemistry)in the University of Michigan
2020

Doctoral Committee:
Professor Brandon T. Ruotolo, Chair
Professor Phillip Andrews
Professor Robert T. Kennedy
Professor Bing YeSarah M. Fantin
fantins@umich.edu
ORCID iD: 0000-0002-2509-1143© Sarah M. Fantin
2020

Dedication
This thesis is dedicated to Dorothy Evans. If you had not supported me applying toUniversity of Michigan, and told me that I had to go if I got int, this would not have happened.
Good call G-Ma.

ii

Acknowledgements
I am grateful to have so many advocates, supporters, and collaborators during my academic
career. I first thank Professor Brandon Ruotolo for the countless valuable experiences he has
enabled during my thesis work. From advising on research directions, to mentoring on career paths,
to creating opportunities to advance our skills, it obvious that he respects his lab group and puts
great effort into our training. I thank him for providing so many opportunities to present myresearch and develop my education skills. In my five years of graduate school, he has managed tojuggle advising a full lab group, planning a conference from scratch, and the addition of three
newborns at home, while still finding time to talk with me about the struggles of membraneproteins and be a reassuring “cheerleader” that my experiments are in fact useful. Thank you forbeing an amazing mentor.
Next, I thank my committee members and collaborating faculty who have helped shape myresearch and career trajectory. My committee members of Professor Robert Kennedy, Professor
Bing Ye, and Professor Phil Andrews have given me important feedback on interpreting myexperimental results and steered the direction of this thesis through my candidacy and data
meeting. I would additionally like to thank Professor Robert Kennedy, Professor Charles Sanders,and Professor Anne McNeil for their support in advancing my professional career.
Much of the work presented in this thesis is collaborative, and I have had the pleasure ofworking with supportive and engaged collaborators out of my university. Professor Shelagh
Ferguson-Miller and Dr. Jian Liu provided samples and valuable discussion for Chapter 2.
Professor Melanie Ohi and Dr. Pramod Yadav supplied samples for Chapters 3 and 5 as well asiii

help inform our experiments. Professor Charles Sanders, Dr. Geoffrey Li, and Dr. Hui Huangprovided samples and direction for Chapters 3, 4, and 5, and performed cellular assays for Chapter
5. I would also like to thank my collaborators within the Ruotolo group, particularly Kristine
Parson (Chapters 2, 3, 5), Brock Juliano (Chapter 3), and Iliana Hampton (Chapter 5).
Being a part of the Ruotolo lab group has been an extraordinary time and part of that isfueled by the extraordinary scientists I have been able to call colleagues. First, my original mentor
Shuai Niu. I am not sure you realized that “to TSPO” would lead to at least three students worthof membrane protein related thesis projects, but I thank you for it. All five years I kept your advice
about rage quitting experiments at heart, but unfortunately, I have never caught up to your skill
level in Dota 2. To past lab members Joseph Eschweiler, Jessica Rabuck-Gibbons, Yuwei Tian,
Daniel Polasky (Dan), and Sugyan Dixit (Suggie), thank you for your valuable lab leadership andsetting the standard for years to come. Dan, your commitment to “making code do the work” hasso greatly impacted the lab, and both you and Suggie’s contributions to the CIUSuite2 project
enabled all of the work in this thesis. Suggie, thank you for letting me talk to you for long enoughthat I answered my own questions, being there for me during candidacy, and letting me sing thecool part of Creep so loudly. Now to sum up one of the most amazing experiences of grad schoolin a few sentences, mentoring, and in turn being mentored by, Kristine Parson and Iliana Hampton.
Kristine, working with you on the TSPO project and learning more about nanodiscs through yourproject was inspiring to me and really kept me motivated to push onwards. Iliana, yourcontributions to Chapter 5 so far and willingness to take on so much as a rotator are impressive,
and I am excited to see how your thesis takes shape! Since I don’t think I’m allowed to write
paragraphs about everyone, I would also like to thank these friends and colleagues: Varun Gadkari(for your dedication to the lab and leadership as a post-doc), Chunyi Zhao (for all of the fun timesiv

we have had over the past five years), Cara D’Amico (for always teaching us something outsideof native MS at group meeting and being a freaking awesome bridesmaid), Daniel Vallejo (foranswering all of my illustrator questions and pushing for great causes even outside of lab), Yilin
Han (for never allowing there to be a dull moment and being my ASMS buddy), Carolina RojasRamirez (for sharing memes, caring about MS so much, and being my number one conference
roommate), Brock Juliano (for blowing up micelles with lasers and being such a good friend),
Chae Kyung Jeon (for being ambitious enough to take on a much needed role in lab and for being
so supportive in our sport), and Rosendo Villafuerte (for making my last year of grad school somuch fun and being your wonderful self).
The professors I had in my undergraduate career led me to where I am today and continue
to support me throughout my academic career. Thank you to the chemistry department atUniversity of Indianapolis, including my undergraduate research advisor Professor Brad Neal, forpreparing me well for graduate school. You are all the kind of mentors I aspire to be! Additionally,
I thank Professor Michael Johnson at University of Kansas for his support during my NSF-REU
which sold me on graduate school.
Lastly, I thank my family and friends for their support. Thank you to my parents, John andRenee Fantin, who have supported me during grad school and are always ready to celebrateachievements. I also thank my sisters Leah and Heidi for listening to my problems and asking me
science questions that make me feel like I’m useful. Thank you to my extended family in Michigan,living near you has been a lot of fun. A huge thank you to my honorary sister, Harleen Athwal.
You have had my back since undergrad and you are still my motivator when times get rough.
Finally, to my husband Nathan, thank you for everything including moving to Michigan with me,v

chauffeuring me so I wouldn’t have to ride the bus, and listening to enough science talk that I think
you could probably pass as a chemist.

vi

Table of Contents
Dedication ___________________________________________________________________ ii
Acknowledgements ____________________________________________________________ iii
List of Figures ________________________________________________________________ ix
List of Appendices ___________________________________________________________ xiii
Abstract ____________________________________________________________________ xiv
Chapter 1 Introduction _________________________________________________________ 1
1.1 Membrane proteins and human disease _______________________________________ 1
1.2 Structure of membrane proteins _____________________________________________ 2
1.3 Membrane proteins are challenging analytes ___________________________________ 4
1.3.1 Solubilization methods _________________________________________________ 5
1.3.2 Membrane proteins are underrepresented in structural databases ________________ 6
1.4 Biophysical techniques for studying membrane protein structure ___________________ 7
1.5 Mass spectrometry of membrane proteins _____________________________________ 8
1.5.1 Structural proteomics, labeling, and lipidomics _____________________________ 10
1.5.2 Native ion mobility - mass spectrometry __________________________________ 12
1.6 Summary ______________________________________________________________ 14
Chapter 2 CIU Classifies Ligand Binding of Integral Membrane Translocator Protein (TSPO) 16
2.1 Introduction ____________________________________________________________ 16
2.2 Methods _______________________________________________________________ 18
2.2.1 Membrane Protein Sample Preparation ___________________________________ 18
2.2.2 CIU Experiments ____________________________________________________ 19
2.2.3 High Resolution MS Lipid Extraction Experiments__________________________ 21
2.3 Results and Discussion ___________________________________________________ 22
2.3.1 Analyzing CIU Data for TSPO:Ligand Complexes __________________________ 22
2.3.2 Data Processing Reduces Micellar Chemical Noise__________________________ 24
2.3.3 Ligand Binding Generally Stabilizes TSPO ________________________________ 26
2.3.4 CIU Classification of Ligand Binding Location_____________________________ 28
vi

2.3.5 CIU Differentiates TSPO:Lipid Complexes Based on Chain Saturation __________ 30
2.4 Conclusions ____________________________________________________________ 33
2.5 Acknowledgments _______________________________________________________ 34
Chapter 3 Ion Mobility - Mass Spectrometry Reveals the Role of Peripheral Myelin Protein
Dimers in Peripheral Neuropathy ________________________________________________ 35
3.1 Introduction ____________________________________________________________ 35
3.2 Methods _______________________________________________________________ 37
3.2.1 Membrane Protein Sample Preparation ___________________________________ 37
3.2.2 Native IM-MS_______________________________________________________ 38
3.2.3 High Resolution MS Lipid Extraction Experiments__________________________ 39
3.3 Results and Discussion ___________________________________________________ 40
3.3.1 Quantifying Abundance of PMP22 Dimers ________________________________ 40
3.3.2 CIU Reveals Differential Stabilities of Monomeric PMP22 Mutants ____________ 42
3.3.3 Mutant PMP22 Dimeric Complexes Are More Stable Than WT _______________ 44
3.3.4 Gas Phase Stability Data Correlate with Cellular and in vivo Assays ____________ 46
3.3.5 Encapsulation within SCOR Bicelles Engenders Mutant PMP22 Homodimer
Formation_______________________________________________________________ 48
3.4 Conclusions ____________________________________________________________ 50
3.5 Acknowledgements ______________________________________________________ 51
Chapter 4 Collision Induced Unfolding Differentiates Functional Variants of the KCNQ1
Voltage Sensor Domain _______________________________________________________ 52
4.1 Introduction ____________________________________________________________ 52
4.2 Methods _______________________________________________________________ 54
4.2.1 Membrane Protein Sample Preparation ___________________________________ 54
4.2.2 IM-MS and CIU Experiments __________________________________________ 55
4.3 Results and Discussion ___________________________________________________ 56
4.3.1 IM-MS of KCNQ1 VSD_______________________________________________ 56
4.3.2 Differences in CIU features correlate to KCNQ1 VSD variant function __________ 57
4.3.3 Quantifying differences in CIU fingerprints of KCNQ1 VSD variants throughcomprehensive analysis (RMSD) ____________________________________________ 59
4.3.4 CIU Classification of phenotypic function _________________________________ 61
4.3.5 CIU reveals evidence of mutant destabilization _____________________________ 62vii

4.4 Conclusions ____________________________________________________________ 64
Chapter 5 Systematically Studying Membrane Proteins Liberated from Different Solubilization
Techniques _________________________________________________________________ 66
5.1 Introduction ____________________________________________________________ 66
5.2 Methods _______________________________________________________________ 68
5.2.1 Membrane Protein Sample Preparation ___________________________________ 68
5.2.2 Native MS and CIU Experiments ________________________________________ 70
5.3 Results and Discussion ___________________________________________________ 71
5.3.1 IM-MS of TSPO from micelles and nanodiscs _____________________________ 71
5.3.2 Studying the effects of solubilization technique on CIU of monotopic membraneprotein CYP 3A4 _________________________________________________________ 73
5.3.3 Comparing the unfolding trajectories of transmembrane protein complexes between
SCOR bicelles and detergent micelles_________________________________________ 76
5.3.4 Comparison of CIU from PMP22 containing soluble construct liberated from micelles,
bicelles, and nanodisc _____________________________________________________ 79
5.4 Conclusions ____________________________________________________________ 82
5.5 Acknowledgments _______________________________________________________ 84
Chapter 6 Conclusions and Future Directions ______________________________________ 85
6.1 Conclusions ____________________________________________________________ 85
6.2 Future Directions ________________________________________________________ 87
6.2.1 High-throughput membrane proteins screens _______________________________ 87
6.2.2 CIU of membrane proteins from diverse solubilization methods________________ 88
Appendices _________________________________________________________________ 90
References _________________________________________________________________ 127

viii

List of Figures

Figure 1-1. Types of membrane proteins ....................................................................................... 2
Figure 1-2. Transmembrane protein functional classes.. ............................................................... 3
Figure 1-3. Solubilization techniques for membrane proteins employed in this work. ................. 5
Figure 1-4. Percentage of membrane proteins in the eukaryotic proteome and percentage ofmembrane proteins in the PDB. ...................................................................................................... 6
Figure 1-5. Percentage of total membrane protein structure entries across four databases. .......... 7
Figure 1-6. Percent of membrane protein structure reported in databases by method per year..... 7
Figure 1-7. Nanoelectrospray ionization (nESI) of membrane proteins.. ...................................... 9
Figure 1-8. Examples phospholipids............................................................................................ 11
Figure 1-9. Instrument diagram of an IM-MS instrument ........................................................... 12
Figure 1-10. Collision induced unfolding (CIU).. ....................................................................... 13
Figure 2-1. CIU analysis workflow for membrane protein complexes........................................ 22
Figure 2-2. A Gaussian noise filter for membrane protein CIU Data.. ........................................ 24
Figure 2-3. Net CIU stabilities of TSPO:ligand complexes......................................................... 27
Figure 2-4. CIU based classification of TSPO ligands.. .............................................................. 29
Figure 2-5. CIU based classification of lipid chain saturation in TSPO complexes. ................... 31
Figure 3-1. Native IM-MS of PMP22 reveals differences in dimeric abundances. ..................... 40
Figure 3-2. CIU data for monomeric PMP22. ............................................................................. 43
Figure 3-3. CIU data for dimeric PMP22. ................................................................................... 45ix

Figure 3-4. Correlation between monomer CIU50 stability values and PMP22 trafficking
efficiencies .................................................................................................................................... 47
Figure 3-5. WT and L16P PMP22 liberated from SCOR bicelles. .............................................. 49
Figure 4-1. Native IM-MS of the KCNQ1 VSD.. ........................................................................ 56
Figure 4-2. CIU fingerprints of KCNQ1 VSD variants. .............................................................. 58
Figure 4-3. Comprehensive difference analysis of KCNQ1 VSD variants CIU fingerprints ...... 60
Figure 4-4. CIU based classification of KCNQ1 VSD functional variants ................................. 61
Figure 4-5. CIU50 stability analysis of KCNQ1 variants. ........................................................... 63
Figure 5-1. Native ion mobility-mass spectrometry of the TSPO from micelles and nanodiscs..72
Figure 5-2. CIU of CYP 3A4 liberated from detergent micelles and nanodiscs ......................... 75
Figure 5-3. CIU of L16P PMP22 dimeric complexes liberated from detergent micelles and
SCOR bicelles.. ............................................................................................................................. 78
Figure 5-4. CIU of WTtag PMP22 monomers liberated from detergent micelles, SCOR bicelles,
and POPC nanodiscs ..................................................................................................................... 80
Figure 6-1. Example of optimized solubilization agents across different mimetics. ................... 89
Appendix: Figure I-1. Mass analysis of TSPO and TSPO ligand complexes ............................ 90
Appendix: Figure I-2. Detergent screen of TSPO ...................................................................... 91
Appendix: Figure I-3. Width analysis for Gaussian denoising................................................... 92
Appendix: Figure I-4. Feature detection of Gaussian fitted CIU data ........................................ 93
Appendix: Figure I-5. Mass spectra of ligand bound TSPO ...................................................... 94
Appendix: Figure I-6. CIU fingerprints of TSPO:ligand complexes ......................................... 96
Appendix: Figure I-7. HRMS of lipid extraction from TSPO .................................................... 97
Appendix: Figure I-8. Classification of TSPO complexes by lipid head group charge ............. 98xAppendix: Figure II-1. Mass analysis of PMP22 variant monomers and dimer ........................ 99
Appendix: Figure II-2. Sequence location of PMP22 mutations studied in this work ............. 100
Appendix: Figure II-3. Mass spectra of PMP22 variants released from C12E8 micelles ........ 101
Appendix: Figure II-4. HRMS identifies thrombin cleavage related additions to PMP22 ...... 102
Appendix: Figure II-5. 9+ monomer and 13+ dimer CIU fingerprints for all variants studied inthis work...................................................................................................................................... 103
Appendix: Figure II-6. Correlative plots .................................................................................. 104
Appendix: Figure II-7. Mass spectra of G150D PMP22 released from SCOR bicelles .......... 105
Appendix: Figure II-8. Denoising of bicelle and G150D micelle data. ................................... 106
Appendix: Figure IV-1. Measured masses of KCNQ1 VSD variants ...................................... 112
Appendix: Figure IV-2. Denoising of KCNQ1 fingerprints ..................................................... 113
Appendix: Figure IV-3. Mass spectra of KCNQ1 variants. ..................................................... 114
Appendix: Figure IV-4. Comprehensive difference plots for all KCNQ1 variants .................. 115
Appendix: Figure IV-5. Trafficking and expression of KCNQ1 variants ................................ 116
Appendix: Figure V-1. Expected and observed masses for all protein forms studied in Chapter 5..................................................................................................................................................... 117
Appendix: Figure V-2. IM-MS datasets of TSPO liberated from various solubilization
conditions .................................................................................................................................... 118
Appendix: Figure V-3. Mass spectra and IM-MS datasets of CYP 3A4 liberated from micelles
and nanodiscs. ............................................................................................................................. 119
Appendix: Figure V-4. Mass spectra and IM-MS datasets of L16P PMP22 liberated frommicelles and SCOR bicelles ........................................................................................................ 120

xiAppendix: Figure V-5. Mass spectra and IM-MS datasets of WTtag PMP22 liberated frommicelles, SCOR bicelles, and POPC nanodiscs. ......................................................................... 121
Appendix: Figure V-6. Comprehensive difference analysis of WTtag PMP22 9+ CIU
fingerprints from C12E8 micelles, SCOR bicelles, and POPC nanodiscs ................................. 122

xii

List of AppendicesI. Chapter 2 Supporting Information........................................................................................ 85
II. Chapter 3 Supporting Information ...................................................................................... 94
III Chapter 3 Supporting Methods ......................................................................................... 102
IV. Chapter 4 Supporting Information................................................................................... 107
V Chapter 5 Supporting Information ..................................................................................... 112
VI. Chapter 5 Supporting Methods ......................................................................................... 118

xiii

Abstract
Membrane proteins are important therapeutic targets which play vital roles in cellularfunction. As protein structure is closely linked to its function, the characterization of membraneproteins is vital to elucidating their involvement in disease and potential druggability. Despite this,membrane proteins are underrepresented in structural databases due to challenges associated with
their hydrophobicity and difficulty in obtaining high purity samples. There is an imminent needfor methods to study the dynamic structure, folding, and ligand interactions of membrane proteins.
Native mass spectrometry (MS) has emerged as a method capable of handling the polydispersityof membrane proteins samples to yield details of their structure and function. Particularly, ion
mobility MS (IM-MS) has shown promise in assessing the organization of membrane protein
complexes and, through collision induced unfolding (CIU) experiments, the relative stability ofgas phase membrane proteins. However, challenges remain in developing robust, high-throughput
methods for the study of membrane proteins through IM-MS.
In Chapter 2, we develop a workflow for studying ligand binding of the integral
translocator protein (TSPO) with the aim of revealing the identity of an endogenous ligand. Aspart of this workflow, a system for automated de-noising of CIU fingerprints is presented, allowing
for the quantitative stability characterization of eight different lipids and ligands. For the first time,quantitative CIU measurements of stability and classification are used to determine that the ligands
endogenously bound to TSPO are a mixture of phospholipid isoforms bearing the PG head group.
In Chapter 3, we present a workflow for elucidating key differences among disease associatedprotein variants through IM-MS. Seven mutant forms of peripheral myelin protein (PMP22) arexivshown to possess differences in dimer formation and gas phase stability that correlate withpreviously published data about cellular trafficking and disease state. Further, from our analysis,
we construct a potential mechanism of how dysregulation of PMP22 leads to disease in which
mutations cause destabilization of PMP22 monomers leading to the formation of dimericcomplexes that are poorly trafficked in cells. In Chapter 4, we present methods to study threemutant forms of the voltage sensing domain of the KCNQ1 potassium channel. We find that CIUof the KCNQ1 mutants classify well according to their observed level of trafficking in cells,
indicating a role for specific structural triggers in KCNQ1 trafficking. The function-basedclassification represents a more broadly applicable method for studying structure differences inproteins with many known mutations.
In Chapter 5, we explore the fundamental relationship between membrane protein
solubilization techniques and IM-MS. The integral transmembrane proteins, PMP22 and TSPO,
and a monotopic membrane protein, cytochrome P450 3A4, are prepared in multiple solubilization
techniques, including micelles, bicelles and, nanodiscs. Noticeable differences in oligomerization
are found for TSPO samples liberated from detergent micelles and nanodiscs, and for shifts in theobserved charges states are noted. Upon CIU analysis of the complexes, we find that differencesin unfolding trajectories and gas phase stability exist for both PMP22 and CYP3A4 when liberated
from the varying solubilization agents, which we interpret to relate to the local protein
environment. Our experiments do not show that one solubilization method is universally superior
at preserving the gas phase structure of membrane proteins as each protein system had different
patterns of compaction in terms of CCS, number of CIU transitions, and stability across thesolubilization methods.

xvChapter 1 Introduction

Membrane proteins play vital roles in many cellular functions including cell signaling,transport, and enzymatic reactions. Embedded in biological membranes, these proteins exist incomplex environments, where interactions with lipids, sterols, and other proteins regulate theirfunction.1 Additionally, the folding and structure of membrane proteins are intimately linked totheir environmental interactions.2 Methods to study membrane proteins are essential tounderstanding how they perform their cellular functions and, as many membrane proteins are
implicated in disease states, how pharmaceuticals may be developed to correct their dysfunction.3
However, due to their insolubility in aqueous solutions and the complex environments in which
they exist natively, membrane proteins are challenging analytes. Here I review some of the generalproperties, challenges, and methods for studying membrane proteins, with an emphasis on native
ion mobility mass spectrometry (IM-MS), which is the focus of this dissertation.

1.1 Membrane proteins and human disease
Membrane proteins represent over 60% of therapeutic drug targets and nearly 80% of drugs
approved by the FDA act on membrane proteins.3 These therapeutic protein targets vary widely in
structure, function, and the types of diseases they are implicated in. One example of a wellcharacterized membrane protein target is the serotonin transporter, on which multiple classes ofantidepressants act. The role of the serotonin transporter in depression,4,5 its structure and mode of
drug binding,6–9 and its protein-protein interactions have all been reported.10 Contrastingly, for1many other membrane proteins implicated in disease much less is understood about their exact
biological roles and how disruption of these roles cause disease states. One example of this is the
mitochondrial 18 kDa Translocator Protein (TSPO), which is implicated in a range of illnesses
including neuroinflammation,11,12 cancer,13,14 and cardiovascular disease,15 based on changes in
its expression levels and the observation of disease-correlated mutations. While TSPO is proposed
to be involved in the transport of cholesterol and porphyrin, researchers are actively working toelucidate TSPO-related disease eitologies.16 Peripheral myelin protein 22 (PMP22) is anotherexample of a membrane protein where mutations have been correlated to a range of disease states,
including Charcot-Marie-Tooth 1,17–19 but the mechanism by which these mutations lead to various
forms of neuropathy is not wholly understood.20 For both TSPO and PMP22, no FDA approved
drugs currently exist.

1.2 Structure of membrane proteins
Membrane proteins can be classified broadly into two categories, integral and peripheral,
based on the way they interact with the membrane.21 Peripheral proteins do not embed in themembrane and instead interact withthe polar lipid head groups on thesurface of the membrane in areversible fashion, as seen in Figure1-1. Integral proteins have one or
moreregionsoftheproteinembedded in the membrane andFigure 1-1. Types of membrane proteins. Integral proteins embed in the lipid
bilayer while peripheral proteins interact with only the surface of themembrane. Transmembrane integral proteins span the entire membrane andmonotopic integral proteins only penetrate part of the bilayer.

interact with the hydrophobic tails of the lipids in the bilayer in addition to the polar lipid head2

groups. Integral proteins can be further divided into classes of transmembrane, which span theentire length of the bilayer, and monotopic proteins, which permanently associate only with one
leaflet of the bilayer. For transmembrane proteins, the structure of the regions within the membrane
contain either α-helical or β-strand motifs, often rich in hydrophobic residues.22–24 These motifs
commonly manifest as α-helical bundles and β-barrels when multiple membrane spanning domains
are present.

Figure 1-2. Transmembrane protein functional classes. From left to right: top-down and membrane view of a tetrameric voltage
gated potassium channel (PDB 6EBM), membrane view of a β-2 adrenergic receptor GPCR (PDB 5D5A), membrane view of abitopic cytochrome p450 (PDB 4LXJ).

Transmembrane proteins are uniquely suited to perform several functions in the cell
including transporting ions and conveying signals across the lipid bilayer. Accordingly, mosttransmembrane proteins fall into one of three functional categories: transporters, receptors, andenzymes (Figure 1-2).25 As biological membranes are generally impermeable to most polar
molecules, transporter proteins are key for regulating the flow of molecules in and out of cells andorganelles.26 Families of transporters are sensitive to different environmental conditions, such asvoltage gated ion channels which respond to charge differences across the membrane.27 Alltransporters characterized thus far pass through the membrane multiple times and are thus multipass transmembrane proteins. The receptor category of transmembrane proteins is responsible for
initiating cellular responses upon specific ligand binding.28 G-protein coupled receptors (GPCRs)

3

are one family of receptor transmembrane proteins of great importance to the pharmaceuticalindustry, as 33% of all small molecule drugs target this protein class.3. While GPCRs are multipass proteins with seven transmembrane domains, some transmembrane receptor proteins passthrough the membrane only once, referred to as bitopic or single-pass proteins.25 Membrane
enzymes are often bitopic proteins, including many of the cytochrome P450 monooxygenases thatare responsible for metabolizing many small molecules.29,30 Note that cytochrome P450s can also
be monotopic (e.g. CYP 3A4).31

1.3 Membrane proteins are challenging analytes
Despite their importance, membrane proteins are understudied in comparison to their soluble
counterparts.32 This is due to a combination of factors that make membrane proteins challenging
analytes. Membrane proteins are difficult to express and purify, which can lead to low yields andimpure protein samples which are not amenable to traditional biophysical techniques for structure
determination.33,34 Additionally, the intrinsic hydrophobicity of membrane proteins makes thempoorly water soluble and unable to retain their native structure in aqueous solution. As such, asolubilization method must typically be employed to mimic their native hydrophobic environment
in order to carry out detailed structure and function characterization efforts.35,36 Further
complicating the study of membrane proteins is the complexity of their native environment, where
they may interact with other proteins,37 endogenous ligands,38,39 or lipids.40,41 These challenges
have all contributed to the dramatic underrepresentation of membrane proteins in structural
databases.4241.3.1 Solubilization methods
To conserve the native structure of membrane proteins in the absence of a cell membrane,
multiple solubilization techniques have been developed. The three methods used in this thesis:
detergent micelles, bicelles, and nanodiscs, are shown in Figure 1-3. Historically, detergent
micelles have been the most popular technique for solubilizing purified membrane proteins andthey remain widely used today.34,42 Detergents can have varying structures and charge, but all
possess the ability to form a micelle above certain critical concentration.33 This concentration isreferred to as the critical micelle concentration or CMC in membrane protein purification andanalysis protocols. With the optimization of the detergent choice, concentration, and solution
conditions, a membrane protein can be encapsulated within detergent micelles, whereinhydrophobic protein residues interact with hydrophobic parts of the detergent and the polar headgroups of the detergent solubilize the complex. 42,43

Figure 1-3. Solubilization techniques for membrane proteins employed in this work.

More complex solubilization methods aim to better mimic the native environment of
membrane proteins through creating a lipid bilayer. There are many approaches to create thisbilayer, each with advantages and disadvantages,44–46 but only bicelles and nanodiscs will be
described. In both bicelles and nanodiscs, the membrane protein is encapsulated by a lipid bilayer,
5which consists of one or more lipid type. In bicelles, detergents are used to surround the edges ofthe bilayer to create a fluid, yet discrete, bilayer structure.47 In nanodiscs, a scaffold protein or
peptide is used in lieu of detergents.48 For both methods, the thickness of the bilayer,49 overall
sizes of the particles,48,50 and composition of lipids in the bilayer51,52 can be optimized for protein
incorporation.

1.3.2 Membrane proteins are underrepresented in structural databases
It is estimated that up to 30% of proteins in eukaryotic cells are membrane proteins, but onlya small fraction of these proteins have solved structures indatabases.32,33,53 For example, as of 2015, only 3% of unique structuresin the Protein Data Bank (PDB) were membrane proteins.54 Protein
structure is intimately linked to function,55 and thus correcting thisdisparity has been a major area of research over the last few decades.
Towards this end, membrane protein specific structural databases havebeen created in addition to the PDB, including mpstruc,56 Orientationsof Proteins in Membranes (OPM),57 and PDBTM.58 These databases
contain almost 12,000 unique membrane protein structures.

Figure 1-4. Percentage of membraneproteins in the eukaryotic proteome
and percentage of membraneproteins in the PDB.

Advances in structure determination techniques, specifically throughadvances in electron microscopy (EM), have allowed the proportion of transmembrane (TM)proteins in structural databases to eclipse that of monotopic and peripheral proteins combined, as
shown in Figure 1-5.32 Transmembrane α-helical proteins comprise 57% of total reported
structures across all databases, while β-barrel structures comprise only 11%, which looselytranslates to their expected representation in the mammalian proteomes.32,53,59 It is important to6note that for all of these reported structures, the fulltransmembrane region or protein sequence is notnecessarily present. Some may also include engineered
additions to the protein that aim to increase the proteinsstability or amenability to the structure determination
Figure 1-5. Percentage of total membrane protein
structure entries across four databases.

technique, which may or may not alter

proteinstructure.60

1.4 Biophysical techniques for studying membrane protein structure
The majority of membrane protein structures entered across the PDB and membrane
specific databases were obtained through X-ray crystallography.32 In X-ray crystallography, apurified protein is first crystallized and then subjected to X-ray diffraction analysis. The patternformed during diffraction is then used
to map the electron density of thecrystal, allowing for production of
atomic resolution 3D structures. For
membrane proteins, this process iscomplicated by both the difficulty offorming

crystalsfrommembraneproteins and the typically low purity

Figure 1-6. Percent of membrane protein structure reported in databases
by method per year. Adapted with permission from Shimizu et al. 2018.

and concentration of membrane proteins.33 A major advancement enabling the success ofmembrane protein crystallography was the development of lipid cubic phase (LCP)
methodologies,61 which has enabled the structural determination of membrane protein complexes,

7

such as the beta-2-adrenegic G protein complex.62 Ligands or lipids interacting with membraneproteins have also been observed through this method, however, the electron density alone is notalways sufficient to identify these small molecules.14
As mentioned above, the number of membrane protein structures derived from EM
methods has significantly grown over the past 5 years (Figure 1-6).32 In this method, a crystal is
not required and individual particles are placed in a grid, or within vitrified ice (cryoEM),
irradiated with electrons. The structures produced by cryoEM can possess comparable resolution
to those produced by X-ray crystallography, however, the success of this approach is limited forproteins under 100 kDa.63–65 Conversely, nuclear magnetic resonance spectroscopy (NMR), whereradio frequencies are used to excite nuclear spin states for atoms within a protein sample and theobserved shifts in observed spin frequency are used to measure atomic proximity, can be used to
study the dynamics of membrane proteins up to approximately 40 kDa.66 In NMR, a crystal is not
required and multiple protein conformations can be observed leading to measurements capable ofcapturing dynamic protein motions, but pure and homogenous samples are still required. Both EM
and NMR are also compatible with membrane proteins solubilized in lipid bilayer mimetics suchas bicelles and nanodiscs.67–69 In light of the challenges associated with high resolution structural
techniques, a plethora of spectroscopic and biophysical assays have been developed to study the
structure membrane proteins,42 including surface plasmon resonance,70 circular dichroism,71 andfluorescence assays.72
1.5 Mass spectrometry of membrane proteins
Mass spectrometry has recently emerged as a method especially suited for the analysis ofnative membrane proteins, due in part to its ability to handle complex mixtures and lower sample
concentration requirements.52,54,73–76 In MS, analytes are ionized and transported to a mass

8

analyzer and detector. The ionization of membrane proteins has been mainly performed throughFigure 1-7. Nanoelectrospray ionization (nESI) of membrane proteins. In addition to membrane proteins encapsulated in theirsolubilization techniques, the solubilization agents themselves can ionize.

electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI),77 however
for the purposes of this work we will focus on ESI methodologies, which dominate the analysis ofnative protein samples. ESI can be used to study denatured, yet intact, membrane proteins or
peptides generated through protein digests of membrane proteins. In contrast, native MS ofmembrane proteins, where the 3D structure of the membrane protein is left largely unchanged, canbe optimally performed through nano-electrospray ionization (nESI), where smaller droplets areproduced and heating of the sample is typically not needed to initiate ionization. Membrane protein
ions generated through ESI or nESI for native MS analysis are most often still encapsulated inwithin solubilization agents, and therefore collisional activation must be applied to remove bounddetergents or lipids from the target membrane protein ion.78 Additionally, the independent
solubilization agents themselves, e.g. detergents, also ionize and can thus result in an abundanceof noise signals in the resulting native mass spectra. While time-of-flight (ToF) mass analyzers
have shown great success in this field, higher resolution technologies, such as Orbitrap mass
analyzers, can be helpful for resolving such noise signals from intended membrane protein
analytes.

9

1.5.1 Structural proteomics, labeling, and lipidomics
Proteomics, the organism-wide study of protein expression, can be used to identify
membrane proteins through their sequence,59 elucidate post-translational modifications,79 andreveal interactions between membrane proteins. MS-based proteomics methods typically identify
peptide fragments originating from larger proteins through their measured mass and sequencing
information derived from tandem MS (MS/MS) methodologies, where one m/z is selected withina first mass analyzer and then fragmented using a variety of activation techniques prior to secondstage mass analysis. Overall, proteomics methods can be divided into those that approach the
problem from a bottom-up perspective, where peptides are generated by digestion of a protein in
solution are the primary analytes, and those that analyze proteins in a top-down frame of reference,
where denatured proteins are the primary analytes, and identification is highly dependent on
fragment ions generated within the mass spectrometer. Although proteomics has been widely used
to study soluble proteins, the low expression level of many membrane proteins, incompatibility
with traditional urea based denaturation techniques, and a limited number of charged residues with
their sequences have hampered the success of membrane proteomics.77 Additionally, thedetergents used to solubilize membrane proteins can create abundant noise signals in the mass
spectra and, particularly for top-down proteomics, require additional energy within the instrument
to remove from the intended analytes. Advancements in membrane enrichment techniques,80,81
detergent removal steps,82 and development of MS compatible detergents83 have all aided theprogress of this field in recent years. However, the current technology is not sufficient tocharacterize the diverse membrane proteome in its entirety,59 and the study of native membraneproteins through proteomics is still an active area of research with relatively few publishedmanuscripts.84

10Deeper structural insights can be gained from membrane protein digests through labelingtechniques, where the solvent accessible sites of a native protein in solution are labeledpermanently or reversibly prior to digestion. Hydrogen-deuterium exchange (HDX), chemical
crosslinking, and fast photochemical oxidation (FPOP),85 have all been used to probe the tertiary
structure and interactions of membrane proteins with proteins86 and solubilization agents.87
As lipids are known to interact with and change the functionalityof membrane proteins,41,74,88 lipidomics is an important field adjacent tomembrane proteomics. While lipids are much smaller than proteins, they
are vastly complex with over 40,000 biological lipids cataloged in the
database LIPID MAPS.89 A challenge for MS-based lipidomics
techniques is that some lipids are isobaric, and therefore cannot be
differentiated by intact mass alone. MS/MS methods can be employed
to enhance the identification of lipids, including some which areisobaric.90 Databases and software have been curated using libraries ofknown MS/MS spectra to assist in the identification of lipids.91,92 One
limitation of lipidomics in respect to structural membrane protein
analysis is that lipids observed to co-purify with membrane proteins may
or may not represent evidence of functional protein-lipid complexes.

Figure
1-8.
Examples
phospholipids illustrating thediversity among lipids in bothhead group (orange) and chains(blue).

Additionally, information on where such lipids interact with the protein, their stoichiometries, andbinding strengths cannot be directly obtained through these experiments alone.

11

1.5.2 Native ion mobility - mass spectrometry
Recently, native MS has been shown to be a powerful tool for probing membrane protein
structure and function.54,74,75,93,94 After nESI, membrane proteins can be liberated from a variety
of detergent and solubilization agents in the gas phase using different techniques for activation,such as collision induced dissociation (CID). It is important to note that not all solubilization agentsare equally effective in this endeavor, and screening detergents, solution conditions, andFigure 1-9. Instrument diagram of an IM-MS instrument, Synapt G2 (Waters, Milford MA). After nESI, membrane proteins aretransported through the quadrupole still encapsulated in their polydisperse solubilization agents. Membrane proteins are liberated
from the agents in the trap collision cell and then separated in a travelling wave IM cell. Additional energy can be added in thetransfer collision cell to dissociate clusters of solubilization agents before time-of-flight mass analysis.

optimizing instrument parameters is a necessity for striking the delicate balance between theremoval of solubilization agents and retention of native structure.95 However, with successful
optimization, native MS has been used to study discrete lipid binding,74,94,96 ligand binding,97 andspecific protein-protein interactions associated with a wide range of membrane proteins.52,75,98
The addition of an ion mobility separation to mass spectrometry instruments (IM-MS) is
beneficial for native MS of membrane proteins. IM separates protein ions according to their sizeand shape by recording their drift through a neutral buffer gas under the influence of a weak electric

12

field. The output of IM analyses is often the determination of collision cross section (CCS), or theorientationally averaged size, of ions which can be compared to cross sections calculated from

Figure 1-10. Collision induced unfolding (CIU). The arrival time distribution (ms) for a specific m/z is extracted from the IMMS data set. As the CV is ramped in the trap of the instrument the protein unfolds, which can be visualized through CIU
fingerprints. Software allows for automated analysis of CIU fingerprints including the detection of features and generation of
sigmoidal fits between features yielding CIU50 values.

structures found within in databases or produced through molecular dynamics simulations.99
Additionally, IM-MS platforms, such as the Waters Synapt G2 (Milford, MA, USA) used in thiswork (Figure 1-9), include supplemental trapping regions that provide opportunities to perform
CID for both liberation of membrane proteins from their solubilization agents and breaking up freeclusters of solubilization agents, which can greatly increase signal quality.
The energy experienced by ions in these trapping regions is a function of an accelerating or
collision voltage (CV), and, ideally, optimized solubilization systems can be removed at relatively
low CVs. At CVs higher than the threshold for removal of the solubilization agents, the membraneprotein can experience collision induced unfolding (CIU), and this unfolding can be tracked
through the resulting IM arrival time distribution.100 As seen in Figure 1-10, these data can beplotted as CIU fingerprints with CV on the x-axis and arrival time on the y-axis with intensity
indicated by a color scale. Recent software developments allow for the automated detection offeatures, or regions of conformational stability in CV space, as well as the fitting of sigmoidal

13

functions between features to output discrete values where transitions occur (CIU50 values).101
CIU experiments are valuable for assessing the relative gas phase stability of membrane protein
complexes74,102 and have been used in the past to classify soluble protein systems.73 Despite thesuccessful characterization of a range of membrane protein systems, many IM-MS and CIU
datasets reported in the literature exhibit solubilization agent related noise,40,103 and currently,
membrane protein CIU has only been demonstrated with protein liberated from detergent micelles.
Methods for minimizing and handling detergent, lipid, and scaffold related noise are key toenabling CIU of membrane protein solubilized in more complex lipid mimetics.
IM-MS methods for studying membrane proteins and membrane protein complexes havesample concentration requirements in the low nanomolar range, which is significantly lower thanhigh resolution structural techniques such as X-Ray crystallography.61,95 IM-MS is also compatible
with many different membrane protein solubilization methods over a broad range of protein
sizes.104 While IM-MS methods have been shown to yield dynamic information on membraneprotein structure, atomic level protein structure cannot be solved through IM-MS alone, and thus
IM-MS methods are often combined with traditional biophysical techniques.105,106 Additionally,
membrane protein IM-MS and CIU are still relatively new fields, both gaining popularity within
the last decade, and their full potential is still actively being explored.100

1.6 Summary
This thesis focuses on developing and applying IM-MS methodologies topharmaceutically relevant membrane proteins. It can be divided into two parts: 1) developing andapplying workflows for elucidating biologically relevant information from therapeutic targets

14through IM-MS and 2) exploring the fundamental effects of membrane proteins solubilization
agents on gas phase protein structure and unfolding.
Chapters 2, 3, and 4 are aimed at studying the structural biology of therapeutically
relevant membrane proteins. In Chapter 2, a workflow for de-noising of CIU fingerprints and theidentification of an endogenously bound ligand to TSPO is revealed. This work has been
published as Fantin, S. M, Parson, K. F., Niu, S., Liu, J., Polasky, D. A., Dixit, S. M.,
Ferguson-Miller, S. M., and Ruotolo, B. T. Collision Induced Unfolding Classifies Ligands
Bound to the Integral Membrane Translocator Protein. Anal. Chem. 2019, 91 (24), 1546915476. Chapter 3 elucidates a possible mechanism of disease for mutant forms of PMP22
through observed differences in protein oligomeric state and gas phase stability. In Chapter 4,
gas phase unfolding of mutant forms of the voltage sensing domain (VSD) of KCNQ1 isdemonstrated as a means to classify mutants according to their cellular trafficking behavior.
Chapter 5 focuses on the effects of solubilization technique, namely micelles, bicelles andnanodiscs, on gas phase protein structure and unfolding. We find that there are significant
differences in protein oligomeric state, charge state, and unfolding trajectories when suchanalytes are prepared using different solubilization techniques, which calls into question how
such agents should be used to preserve native membrane protein structure in general. In Chapter
6 we conclude by summarizing our findings and their impact on the field of nMS, and we
propose future directions related to high throughput screening and the expansion of thesolubilization agent study described in Chapter 5.

15

Chapter 2 CIU Classifies Ligand Binding of Integral Membrane Translocator Protein(TSPO)(Adapted with permission from: Sarah M. Fantin, Kristine F. Parson, Shuai Niu, Jian Liu,
Daniel A. Polasky, Sugyan M. Dixit, Shelagh M. Ferguson-Miller, and Brandon T. Ruotolo.
Collision Induced Unfolding Classifies Ligands Bound to the Integral Membrane TranslocatorProtein. Analytical Chemistry 2019 91 (24), 15469-15476. Copyright 2019 American Chemical
Society.)

2.1 Introduction
Membrane proteins play central roles in cellular signaling, molecular trafficking, and awide variety of enzymatic reactions.107 As such, membrane proteins represent a disproportionately
large number of current drug targets with respect to their predicted abundance in the humangenome.108,109 Despite their importance, the poor solubility of membrane proteins in aqueous
solvents often limits their expression and purification yields,35,110

resulting in a deficit ofmembrane protein structural knowledge. Specifically, membrane proteins currently comprise only
3% of unique entries in the Protein Data Bank.54 Recent advancements in NMR, X-Ray, and EM
methods have moved to close this gap; however, many challenges remain in assessing membraneprotein dynamics, polydispersity, and ligand binding in a high-throughput manner.111–114
Recently, native mass spectrometry (MS) has been deployed to address the uniquechallenges associated with studying the structure and function of membrane proteins. Typicalexperimental workflows involve the micellar solubilization of membrane proteins, the use of nanoelectrospray ionization (nESI) to produce micelle-protein complex ions, and the subsequent16

breakdown of detergent micelle ions using collisional activation to reveal membrane protein as
well as smaller surfactant cluster ion populations. Insights into membrane protein complex
organization,84,115,116 lipid binding,96,117,118 and membrane mimetic performance52,118–121 have been
gained by leveraging the ability of MS to manage the complex mixtures that typically result fromthis workflow. Ion mobility-mass spectrometry (IM-MS), which sorts ions according to both theirorientiationally-averaged size and m/z ratio, can be used to capture large, global changes inmembrane protein structure associated with their function.122 IM-MS enables the use of collisioninduced unfolding (CIU), where collisional activation is utilized to unfold isolated protein ions in
the gas phase. CIU of membrane proteins can be sensitive to ligand binding, and gas-phasestability shifts have been linked to lipid binding selectivity.106,123,124 Despite these successes, many
challenges hinder the broader implementation of CIU methodologies for membrane protein
complexes. Generating tandem MS of sufficient quality and detail to identify ligands ejected frommembrane proteins is currently difficult, if not impossible, to achieve. In addition, chemical noise
generated by protein solubilization agents can overlap with signals associated with small
membrane protein ligand complexes, creating a convoluting signal population within CIU
fingerprints and obfuscating key stability information.
An example of one such small membrane protein complex is the 36 kDa dimer oftranslocator protein (TSPO), which plays key roles in stress response, cancer, neurodegenerative
conditions, and cardiovascular diseases13,15,125,126. Multiple classes of ligands have been proposed
to bind to specific regions of the TSPO surface, including cholesterol, protoporphyrin, lipids, andsmall drug-like molecules.127,128 Despite its plurality of proposed ligands, TSPO is still an orphan
receptor, in that there is currently no consensus regarding the identity of its endogenous ligands,

17and many questions remain surrounding the potential allosteric impact of ligand binding on TSPO
structure.129
Here, we develop CIU data analysis strategies and deploy these techniques to characterize
TSPO-ligand complexes. We quantify stability shifts associated with lipid and porphyrin binding
to TSPO, and observe a stronger stability shift upon protoporphyrin IX (PPIX) binding whencompared to any of the lipid complexes we detect. Furthermore, we build a CIU classification
scheme capable of differentiating two sets of TSPO:ligand complexes by their proposed binding
region. Finally, we utilize CIU fingerprint classification and high resolution mass spectrometry(HRMS) to characterize an endogenous TSPO ligand, putatively identifying the bound material asa mixture of phosphotidylglycerol (PG) isomers having different levels of chain saturation. We
conclude by discussing the general utility of our enhanced CIU methods for the study of membraneprotein complexes.

2.2 Methods

2.2.1 Membrane Protein Sample Preparation
TSPO bearing the A138F point mutation was purified and expressed using establishedprotocolsfrom

Rhodobacter

sphaeroides.128

All

lipids(1,2-dioleoyl-sn-glycero-3-

phosphoethanolamine [PE], 1',3'-bis[1,2-dimyristoyl-sn-glycero-3-phospho]-glycerol [CDL], 1palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine
phosphocholine

[PC],

[PS],

1-palmitoyl-2-oleoyl-glycero-3-

1,2-dipalmitoyl-sn-glycero-3-phospho-(1'-rac-glycerol)

dipalmitoyl-sn-glycero-3-phospho-(1'-myo-inositol)[PI],

and

[PG],

1,2-

1,2-dioleoyl-sn-glycero-3-

phosphate[PA]) were purchased from Avanti Polar Lipids (Alabaster, AL). Ammonium acetate,18

Protoporphyrin IX, Octyl β-D-glucopyranoside (OG), and chloroform were purchased from Sigma
Aldrich (St. Louis, MO). Isopropanol and methanol were purchased from Fisher Scientific(Hampton, NH). For all native MS experiments, 30 μM TSPO was buffer and detergent exchanged
simultaneously from 50 mM Tris, 150 mM NaCl , 0.20% DM (N-dodecyl β-D-maltoside), pH 8.0
to 40 mM OG , 200 mM ammonium acetate, pH 8.0 using 100 kDa Amicon Ultra-0.5 centrifugal
filter units (MilliporeSigma, Burlington, MA). Detergent screening and lipid binding studies were
performed following established protocols,95 where the protein:lipid molar ratio was optimized foreach lipid to be between 1:1 and 1:3, using an incubation period of 10 minutes per binding
experiment. Protoporphyrin IX (PPIX) was dissolved in ammonium acetate buffer and added to
TSPO in a light-free environment at an optimized protein:ligand ratio of 1:10. An endogenous
ligand was noted to be present in some samples provided, and from these samples, replicates ofthe endogenous ligand (UNK) binding were generated without further preparation.

2.2.2 CIU Experiments
All CIU data was collected using a Synapt G2 HDMS IM-Q-ToF mass spectrometer(Waters, Milford, MA), with a direct infusion nESI source set to positive ion mode. Our instrument
settings were tuned to generate intact protein ions while completely dissociating detergent adducts
prior to the IM separator, including appropriately tuned settings for the source temperature (40°C), helium cell gas flow (100 mL/min), and the sampling cone (120 V). Trapping cell wave
velocity and height were 116 m/s and 0.1 V. IMS wave velocity and height were 700 m/s and 32.5
V. Transfer cell wave velocity and height were 300 m/s and 10 V, with an accelerating potentialof 70 V used to dissociate empty micelles. Collision cross section analysis was performed byestablished methods with the protein standards concavalin A, avidin, and β-lactoglobulin.

19

Theoretical collision cross sections of dimeric TSPO were calculatedusing IMPACT.99,130,131 All
CIU analyses were performed by increasing the trap collision voltage in a stepwise manner 50 –140 V in 5 V increments. CIU data from the 9+ charge state of the TSPO dimer was extracted into
a text-based format using TWIMExtract132, then processed and analyzed using CIUSuite 2.133 Data
processing included two rounds of 2D Savitzky-Golay smoothing with a window of five bins andinterpolation of the collision voltage axis by a factor of four.
Gaussian de-noising and the subsequent classification of Gaussian processed CIU data was
performed using a modified version of the classification module of CIUSuite 2, which we term
CIUSuite 2.1, in conjunction with manual feature selection. Information regarding the algorithms
that underpin CIUSuite 2.1 software is available elsewhere.134 Briefly, we modified the standard
classification algorithm within CIUSuite by both standardizing and normalizing input CIU data as
well as updating our approach to Gaussian fitting of IM ATDs. Our data normalization procedure
is designed so that the most intense signals across different classes receive equal weight within
resulting classifiers. We then standardize this data to a normal distribution possessing both a zeromean and unit variance.135 This approach prevents bias in the support vector machine (SVM)
approach that we utilize to generate downstream classifiers and dramatically improves therobustness of the classifiers produced. In CIUSuite 2.1, the normalization and standardization steps
described above have been implemented for both raw CIU data and the Gaussian features that
result from our de-noising procedures.133 For Gaussian data, fitted peak centroids, widths, andamplitudes were standardized separately at each collision voltage. If multiple peaks were present
at a given collision voltage, their attributes were averaged for standardization. The Gaussian fitting
module of CIUSuite 2 was also updated as part of this work to utilize the optimized fitting resultsfrom the analyses of previous collision voltages as the initial guesses for the next collision voltage

20data processed. Since CIU features are typically stable over several collision voltage steps, our
CIUSuite 2.1 approach results in higher quality initial guesses in all cases, except for those IM
datasets where new features first appear. In general, this new Gaussian fitting approach results inmore consistent and higher quality fits in the noisy membrane protein CIU datasets presented here.

2.2.3 High Resolution MS Lipid Extraction Experiments
30 μL of 1:2 chloroform:methanol solution was added to 2.5 μ L of 3.3 mM TSPO in 50
mM Tris, 150 mM NaCl , 0.20% DM, pH 8.0. This solution was then vortexed and allowed toincubate for 10 minutes on ice. The resulting solution was dried under a stream of nitrogen gas
and resuspended in 40 μL1:1 isopropanol:methanol, sonicated for 30 minutes, and then directly
infused into a nESI source of an Orbitrap Fusion Lumos Tribrid MS (Thermo Scientific
Incorporated, Waltham, MA). MS spectra were collected in positive and negative mode, with mass
selection and fragmentation performed for any peak of interest at HCD energy values between 0
and 40. MS/MS data was analyzed using LipidMaps.89

21

2.3 Results and Discussion

2.3.1 Analyzing CIU Data for TSPO:Ligand Complexes
Previous reports have demonstrated the capabilities of CIU to capture stability shifts within
membraneproteins

binding.122,123,136
robust,upon

However,quantitative

ligandgenerating

descriptionsofmembrane protein complexes using CIU
remains a significant challenge, prompting
the creation of custom CIU data analysis
software.133,136 In this report, we present a
new workflow for the automated quantitive
analysis of membrane protein CIU data,
which we utilize to extract stability values
for a range of TSPO:ligand complexes,
wherein we first detect complex ions using
native MS, then perform CIU and assign
each feature observed according to their
voltage ranges and centroid IM drift times,
allowing us to fit sigmoid functions to thetransitions between the CIU features

Figure 2-1. CIU analysis workflow for membrane protein complexes.
A,B. Representative MS data for dimer TSPO:ligand complexes,
where triangles denote TSPO monomers and circles represent PI(purple) and PG (green). C,D,E. CIU fingerprints of specificprotein:ligand complex stoichiometries are analyzed throughautomated feature detection (shaded boxes) and CIU50 transition
fitting (sigmoid traces). Here, 9+ dimer TSPO (MS not shown) and
TSPO:ligand complexes are analyzed. F. CIU50 values are extracted
across replicates, N = 3, in order to quantify complex stability andassess error, in this case clearly indicating the stabilization of TSPO
upon binding both PI and PG lipids as opposed to when no lipid ispresent.

22

observed, and lastly extract the inflection points for each sigmoid fit, which we term the CIU50
value.
Two example MS datasets for TSPO:ligand complexes are shown in Figure 2-1, bothacquired under activating conditions so as to dissociate the majority of interfering surfactant
clusters and simplify MS interpretation. While both monomer and dimer states of the protein are
observed over multiple charge states, the data shown in subsequent panels focuses on 9+ TSPO
dimer signals. CIU experiments involve the stepwise increase of the field strength associated with
the injection of ions into the trapping region preceding IM separation. Thus, ions accumulate
excess kinetic energy, which is subsequently converted to internal modes via collisions with Argas, leading directly to protein ion unfolding. Plots of IM drift time versus the voltage are used todefine the ion kinetic energies used to initiate CIU (CIU Fingerprints). These serve to track the
structural transitions undergone by the ions as a function of their internal energies (Figure 2-1C).
CIU fingerprints typically display multiple features related to protein ion unfolding, and we use
CIUSuite2.1 software to detect and define these structural states in terms of their centroid drift
times, and the range of collision voltages over which they appear. We then fit the transitions
between the CIU features observed and extract the inflection points, or CIU50 values, for thesefits from the data collected (Figure 2-1C-E). The example data shown in Figure 2-1 indicates thatthe transition between the most compact CIU feature and its nearest neighbor occurs at higher
collision voltage values when PG or PI lipids are bound to the TSPO ions studied here (Figure 21D, E). In order to display and compare CIU stability data, we typically project bar charts thattrack each CIU50 recorded for the initial unfolding transition, although the CIU50 values forhigher-energy transitions can also be readily compared in this fashion if the intensity of the highest
energy feature reaches 50% of the maximum signal.

23

2.3.2 Data Processing Reduces Micellar Chemical Noise
Previous reports have studied the roleofdetergentinpreserving

native-like

conformationsforgas-phase

membraneproteins ions,95,137 indicating that detergent
choice can dramatically impact IM-MS data
qualityandionstability

measurements.54,122,138 Data quality can also
be impacted by surfactant-related chemicalnoise, where stable surfactant cluster ions can
produce high intensity signals and excess
detergent adduction can obfuscate protein
data. Although the impact of surfactantrelated noise on the native MS analyses ofmembrane proteins can be observed in multiple
previous reports,40,78,119,138 we have found suchnoise to present a particular challenge for the
IM-MS analysis of smaller membrane protein
complexes, where significant overlap between

Figure 2-2. A Gaussian noise filter for membrane protein CIU
Data. A. IM-MS data for TSPO-ligand complexes where
surfactant micelle-related noise overlaps with the signals for the
9+ charge state of TSPO (boxed). B. Resulting CIU fingerprints
recorded for the noise-contaminated 9+ TSPO signal. C, D.
Gaussian fitting of interpolated TSPO IM drift time data
recorded at a single collision voltage indicates that surfactant
noise typically exhibits broader peak widths than TSPO signals,
allowing informative protein signals (blue) to be sorted fromnoise (red) across all collision voltages scanned to create thecomplete CIU fingerprint. E. Performing feature detection on
the Gaussian fitted peaks in panel D allows for the robust
identification of three features within the resulting CIU
fingerprint (see Figure S3 for more detail). F. Reconstruction of
the features detected from the Gaussian fits gives a de-noised
fingerprint.

micelle cluster ion signals and protein signals can exist. TSPO dimers, at 36 kDa, are an example
of one such small membrane protein complex, where CIU analysis is dramatically complicated bychemical noise arising from OG detergent ions, which we have observed to be an optimal detergent

24

choice for native MS of TSPO (Figure I-2). For the TSPO data discussed in this report, we havedeveloped a CIUSuite 2.1 approach that automatically detects and removes surfactant noise fromCIU data using a combination of data interpolation and Gaussian filtering.
Figure 2-2A displays a typical TSPO IM-MS dataset in the presence of OG detergent,
where we observe signals identified as TSPO monomer and dimer over a range of charge statesindicative of native like structures (5-12+). We chose to evaluate dimeric TSPO as it has been
identified as the biologically active form.129 Furthermore, our native IM-MS data reveals evidenceof ligand binding exclusively within dimeric TSPO. We focused our analysis on signals assigned
to the 9+ charge state of the TSPO dimer based on their signal intensities relative to other dimer
ion signals and their lack of m/z overlap with TSPO monomer signals. Additionally, the collisioncross section recorded for 9+ TSPO, 2751 Å2, is in good agreement with theoretical estimates
based on the available X-ray structure, 2741 Å2, indicating native-like folding for ions under these
conditions (PDB 4UC1).14 A broad set of signals associated with OG surfactants and clusters isobserved to overlap significantly with the TSPO 9+ peak (Figure 2-2A), producing CIU
fingerprints contaminated with micellar noise, which increases in intensity relative to protein
signals detected at higher collision voltages (Figure 2-2B).
Automated Gaussian fitting applied to interpolated IM data acquired for 9+ TSPO dimerions detects two main classes of ions readily differentiated based on their IM peak widths andintensities (Figure 2-2C), with broadened, low-intensity features belonging primarily to surfactant
noise. We utilized this difference to identify the noise signals detected across all collision voltages
scanned during the CIU experiment, developing a scoring algorithm based on peak width andintensity to do so (Figure 2-2D, I-3). While the signals assigned as noise appear at a variety of drift
times, the most intense, and thus problematic, noise signals are those that occupy drift times similar25

to those observed for protein ions. In the data shown in Figure 2-2, OG-related cluster ion noise
signals appear at IM drift times ranging from 13.0 -15.5 ms across all collision voltages probed
during CIU. In addition, we observe that the positioning of OG cluster ions in drift time-m/z spacechanges as a function of the collision voltage setting used to acquire CIU data, further complicating
data analysis. Despite the challenge presented by this complex noise distribution, following ourGaussian de-noising procedure, we are able to accurately detect and quantify three TSPO CIU
features free of interfering noise (Figure 2-2E, Figure I-4). These features can be used toreconstruct de-noised fingerprints from the Gaussian fits used during the de-noising process(Figure 2-2F), which can be used for CIU50 and classification analysis, in a manner impossible toachieve using the original CIU data alone.133

2.3.3 Ligand Binding Generally Stabilizes TSPO
Both lipids and protoporphyrins have known binding sites on the surface of the TSPO
dimer, but little is known regarding the biophysical consequences of TSPO ligand binding. Figure
2-3A indicates the locations for these binding sites which are based on a combination of crystal
structures and computational modeling. Specifically, protoporphyrins occupy a discrete pocket
between transmembrane helicies I and II and underneath loop 1, while phospholipids likely attach
to both the hydrophobic dimer interface as well as a series large, hydrophobic grooves in the26

membrane

embeddedregionoftheprotein.129 Using native IM-MS we wereable to evaluate nine TSPO ligands,
including a diverse set of eight lipids and
PPIX, observing a wide range of binding
stoichiometries to TSPO dimers for eachligand tested (Figure 2-3, S4). All TSPO
complexes

studiedhere,

exceptPC

complexes, exhibited the same number of
CIU features when compared to the unbounddimer, and resulted in various degrees ofstabilization upon complex formation (FigureII-6). Our CIUSuite2.1 workflow was used toobtain de-noised CIU50 values for each

Figure 2-3. Net CIU stabilities of TSPO:ligand complexes. Afterincubation with a selected ligand, CIU50 values for 9+ TSPO dimerions were quantified, N = 3, in a range of liganded states (key,bottom). The CIU50 of unbound TSPO (no lipid present) was
subtracted from the CIU50 values extracted for TSPO:ligand
complexes to generate a measurement of net CIU stability. Among
lipids, subsequent binding events tend to increase TSPO stability,with some lipids acting to stabilize the complex to a greater extent
than others. The PPIX binding site identified from X-ray structures
occupies a separate site from that identified for lipids (inset PDB
4UC1),46 and TSPO:PPIX complexes exhibit enhanced stabilities
when compared to the lipids screened here. An unidentified
endogenous ligand (UNK) exhibits CIU stabilities with the greatest
similarity to the majority of lipids analyzed in this report.

complex observed.
Figure 2-3 displays CIU50 values associated with the first unfolding transition observed,
collected in triplicate for PPIX, six lipids (CDL, PA, PE, PI, PS, PG), and a previously unidentified
endogenous ligand, labeled UNK. All of these ligands exhibited binding stoichiometries withdimeric TSPO ranging from 1:2-1:5. In order to compare CIU50 values across all complexes
measured, Figure 2-3 plots CIU50 values that are a product of subtracting the equivalent unbound
TSPO CIU50 value. This net CIU stabilization measurement reveals that PPIX binding stabilizes
gas-phase TSPO to a greater extent than any protein-lipid complex detected and discrete lipid
bound states appear to differentially stabilize the TSPO dimer. As noted above, TSPO-PC

27

complexes provide a notable exception to this trend, in that lipid binding appears to destabilize thecomplex and alter the observed CIU pathway significantly (Figure I-6).
The presence of TSPO-UNK complexes allows us to investigate the ability of CIU toclassify an unknown membrane protein ligand, in this case, extracted endogenously fromexpressed TSPO samples. A cursory analysis of the CIU data shown in Figure 2-3 indicatesstability values for UNK complexes similar to most of the lipids screened in our experiments,
excluding the lipids PS and CDL. Although we are unable to measure the mass of UNK in native
MS data with high accuracy (740 ± 20 Da), such MS data supports the conclusion that the unknown
ligand is likely not related to PPIX. Taking the 1:1 and 1:2 TSPO dimer:lipid complex CIU stability
values observed as a guide, UNK CIU50 values are within error of those recorded for equivalent
TSPO complexes containing both PG and PA. As an MS/MS analysis of UNK could not be
achieved due to the presence of significant surfactant-related chemical noise, we performed lipid
extraction experiments aimed at identifying all endogenous lipids co-purified with TSPO samples.
These experiments revealed a range of PG isoforms as the principle endogenous lipids present
within these TSPO samples (Figure I-7).

2.3.4 CIU Classification of Ligand Binding Location
As depicted in Figure 2-3, prior research has determined that TSPO possesses multiple
binding surfaces for ligand attachment, including separate sites for drug and cholesterolattachment, not depicted.128 TSPO shares this quality with a plurality of membrane protein drug
targets, and rapidly confirming binding location on a target protein surface often represents asignificant challenge associated with the discovery of new therapeutics.73,139,140 As such, weinvestigated the ability of CIU to provide such information. We began by utilizing de-noised and28interpolated CIU fingerprints acquired for 1:1
TSPO dimer:ligand complexes as a training
dataset which can be sorted into two classes
based on proposed binding location: PPIX andlipid-associated complexes. Using CIUSuite
2.1, we built a classification scheme, where
each voltage was evaluated for its ability todifferentiate

the

two

trainingclasses,

generating a score for each test (Figure 2-4A).
By considering both the absolute value anderror of the score generated for each voltage,
the region shown in the gray box in Figure 24A was automatically selected for a “leave oneout” cross-validation step (Figure 2-4A, inset)
which serves to evaluate the robustness of an
identified classifier through evaluation withdata not included in the training set.133 Our
analysis indicated that when the voltages
ranging from 116.25 V to 125 V are used, themaximum classification accuracy of 0.99 is
obtained, as measured by the area under thecorresponding receiver operating characteristic

Figure 2-4. CIU based classification of TSPO ligands. A. We
begin by scoring each voltage for its ability to differentiate all
lipids from PPIX, N=3. Voltages shown within the gray box
were those selected for a ‘leave one out’ cross-validation test,
shown in the inset plot, where we tracked accuracy achieved
with the training data (blue),non-training data (green), and thearea under the ROC curve (red) as a function of the number ofvoltages included in the classification scheme. Based on thesetests, two voltages (*) were selected for the final classification.
B. A plot of linear discriminant space resulting from theclassification structure described in A, illustrating clear
separation of the CIU responses related to lipids from thoserecorded from PPIX. C. A bar chart tracking the probability of
our class assignments for additional replicates that were not partof the dataset used to build the classification engine in A, where
error bars shown reflect the assigned probability from eachvoltage. Three CIU replicates recorded for TSPO-UNK
complexes provide high probability lipid assignments.

curve (AUC-ROC).

29

These eight voltages are used to build a CIU classification scheme and the results of thelinear discriminant (LD) analysis are plotted on the resulting LD axis, where we observe clear
separation between PPIX and lipid CIU signatures (Figure 2-4B). To further test the strength of
our classification scheme, additional replicates not included in our original training set were input
into our classification structure as “unknowns”. We analyzed the results of this mock unknownclassification experiment by assessing the probability associated with the ligand class assignment
indicated by CIUSuite 2.1 (Figure 2-4C). All of our mock unknowns analyzed in this way classify
correctly.

In addition, data for TSPO-UNK complexes resulted in high probability lipid

classifications, as predicted by the analysis of our CIU50 values displayed in Figure 2-3. The data
shown in Figure 2-4 represents the first time CIU data has been used to classify ligands topreviously identified disparate binding locations within a membrane protein complex, and the first
instance of using CIU to identify an unknown ligand class with a quantified level of confidence.

2.3.5 CIU Differentiates TSPO:Lipid Complexes Based on Chain Saturation
Lipids can play functional roles upon binding to membrane proteins, engendering thepossibility of specific binding pockets, where the detailed structure of the intended binder may
affect its ability to interact with the protein.40,124,139 The structure of lipids can include variationsin head group, chain length, and chain saturation, all of which can exert biological effects.141
Therefore, we proceeded to evaluate the ability of CIU data to classify TSPO:lipid complexes
according to their specific chemical structures, finding the greatest correlations in our CIU data tolipid chain saturation levels.
As lipids are often distinguished by their head group, we began by grouping the lipids weanalyzed in our study based on their overall physiological charge. However, all attempts to build30

a classification scheme based on lipid
physiological charge resulted in poor crossvalidation testing and incorrect assignment ofsomelipids(FigureI-8).

Notingthesimilarities between the CIU data collected for
TSPO complexes containing PE and PA(Figure I-8 C), and that TSPO possesses largehydrophobic lipid binding grooves,14 we next
focused on lipid tail attributes in order toclassify TSPO:lipid complex CIU data. To
construct our classification scheme, we sorted
our 1:1 TSPO dimer:lipid CIU data into
groups based on lipid tail saturation levels,
including saturated (PG, PI), one double bondcontaining (PS), and two double bondcontaining (PA, PE) lipids. Data for CDL and PC
complexes were left out of classification efforts,
as when compared with the majority of the lipids
studied here, they possessed either a different
number of chains or exhibited evidence of asignificantly altered CIU pathway, respectively.
Our general procedure for constructing our threeway lipid saturation-based classification scheme

31

Figure 2-5. CIU based classification of lipid chain saturationin TSPO complexes. A. Each voltage was scored for its ability
to differentiate the following three classes: saturated lipids(PG, PI), lipids with one double bond (PS) and lipids with two
double bonds (PA, PE), N = 3 for each ligand. Voltages in thegray box were selected for cross-validation runs, where theinset plot is formatted identically to the one shown in Figure
4. Based on this analysis, one voltage (*) was found to provide
the most accurate classification scheme. B. A three-way CIU
classification depicted as decision regions on linear
discriminant axes, illustrating the separation of all threeclasses. C. A bar chart plotting the probability of correct
assignment for lipids replicates not included in the training
data evaluated in A, where the true saturation of each lipid isshown using a color code as indicated. Each lipid is correctly
assigned, and three replicates of UNK, suspected to have a PG
head group, show the most likely assignment to be thesaturated isoform, followed by isoforms containing a single
double bond

was similar to the approach described above for building our two-way lipid/PPIX classification
scheme (Figure 2-4). A notable difference between our lipid saturation level based classification
scheme and our two-way lipid/PPIX classification structure is that the optimally differentiating
region of our CIU data identified is at lower voltage values than those selected as optimal for
lipid/PPIX discrimination. Our cross-validation testing demonstrated that using CIU data acquired
at one voltage, 88.75 V, provides a classification scheme of greatest accuracy, resulting in a 0.72
AUC-ROC (Figure 2-5A). Although the accuracies measured in our cross-validation runs for thisscheme are lower than those generated in Figure 2-4, this is expected when evaluating a three-way
classification, where assignments are not binary (Figure 2-5B).
In our three-way classification scheme, the assignment probabilities for each CIU dataset
are shown as a stacked bar graph in Figure 2-5C for nine datasets not included in our training data.
While mock unknowns are assigned to chain saturation classes with a range of probabilities, thelowest of which is associated with a PA dataset shown, all CIU datasets are correctly assigned tothe appropriate chain saturation class. This includes CIU data for PC, a sample for which no
replicates were included in the training dataset. The CIU replicates for UNK can be assigned withmoderate confidence as a saturated lipid (with probabilities of 74%, 72% and 62% for the threedatasets acquired), where the second most likely lipid class assignment includes one degree ofchain unsaturation (Figure 2-5C). The ambiguity of these assignments can be rationalized using
HRMS lipid extraction data (Figure I-7). While these data identified saturated PG (16:0) as thedominant endogenous lipid form present within these TSPO samples, other PG isoforms, including
many with one degree of unsaturation, were also identified. While the HRMS data cannot assign
which of these lipids is bound within the TSPO:UNK complexes detected in our experiments, it ispossible that UNK is comprised of a mixture of PG isoforms, containing both saturated chains and32those containing one double bond. Our proposed assignment for UNK is also in agreement with
X-ray data analysis reported for a TSPO mutant.14 Overall, the data shown in Figure 2-5 represents
the first example of using CIU data to classify protein complexes in terms of the chemical
structures of the bound ligands, and provides a foundation for future membrane protein complex
analyses where CIU data is leveraged to aid in the identification of unknown endogenous ligands.

2.4 Conclusions
CIU is a promising technique for studying ligand binding within membrane proteins,
despite challenges related to chemical noise. We have designed data analysis workflows aimed
specifically at addressing these noise related challenges, and have applied these workflows to gain
new insight into the ligand binding behavior of TSPO. Complexes containing ligands known tobind at two separate sites were detected as possessing differential stabilities using CIU, where
PPIX binding provides a greater degree of gas-phase stabilization for TSPO than any of the lipids
assessed here. While our data alone does not enable us to rule out the presence of rearranged
TSPO structures under our conditions, such information suggests that a stronger intermolecularinteractions exist within TSPO:PPIX complexes than when compared to equivalent lipid binding
events, and furthermore indicates that TSPO ligand complexes retain a strong memory of theirnative-state structures in the gas-phase.
Furthermore, we constructed two CIU-based classification schemes for TSPO andobserved the successful differentiation of lipid binders from PPIX, as well as sub-classes of lipids
based on their levels of chain saturation. These include iso-mass lipids (e.g, POPC and POPS, bothhaving an average mass of 760 Da), which we are able to confidently differente based on CIU
classifiers. The construction of our classification schemes also revealed that level of lipid chain

33

saturation, rather than charge, dictates the gas-phase unfolding of TSPO. This observation iscongruent with the large, hydrophobic lipid binding grooves on the surface of TSPO observed in
X-ray structures for the complex.31 By combining CIU data at the level of intact protein complexes
with HRMS data collected on extracted lipids co-isolated with TSPO, we propose the identity ofa detected unknown endogenous ligand to be a heterogeneous population of PG isoforms
containing various states of chain saturation. Such a determination would be challenging, if not
impossible, to achieve with contemporary MS/MS approaches. Clearly, rapid advancements in
CIU technology are acting to position this approach within the broader structural MS field, as auniquely flexible biophysical tool, capable of assessing the composition, stability, and structure ofmembrane protein assemblies within complex mixtures.
2.5 Acknowledgments
Membrane protein research in the Ruotolo lab is supported by the National Institute of
Health under Grants GM105942 and S10 OD021619. The authors further thank Gabriela
Grigorean (UM, Chemistry MS Facility) for her assistance with HRMS data acquisition.

34

Chapter 3 Ion Mobility - Mass Spectrometry Reveals the Role of Peripheral Myelin
Protein Dimers in Peripheral Neuropathy
3.1 Introduction
The misfolding of membrane proteins is implicated in the mechanisms of multiple
debilitating diseases, including cystic fibrosis and retinitis pigmentosa.142–145 Often, specificsequence membrane protein mutations are associated with disease states, with variant forms
exhibiting altered stability and cellular trafficking.146 Unfortunately, due to the challenges
associated with preparing and handling pure, highly concentrated membrane protein samples,
detailed structural information on such targets is often lacking, especially for disease mutant forms.
Further, as some membrane proteins associated with misfolding diseases have hundreds ofmutations of interest,144 there is a clear need for high-throughput methods to assess dynamic
structural changes in membrane protein folding.
Peripheral myelin protein 22 (PMP22) is such a membrane protein, for which misfolding
and trafficking of mutant variants have been implicated in disease.20 PMP22 is a tetra-span integral
membrane glycoprotein predominately expressed in Schwann cells which are the principal glial
cells of the peripheral nervous system (PNS), where they produce myelin.147–149 In addition toaccounting for ~5% of the protein found in the myelin sheath surrounding PNS nerve axons,
PMP22 is thought to regulate intracellular Ca2+ levels,150 apoptosis,151 linking of actincytoskeleton with lipid rafts,152 epithelial intercellular junctions formation,153 myelin formation,154
lipid metabolism and cholesterol trafficking.155 Dysregulation and misfolding of PMP22 has been
identified as a key factor in multiple neurodegenerative disorders, such as Charcot Marie Tooth
disease types 1A and E, as well as Dejerine-Sottas Syndrome17,19,20,156. Like a number of other35

disease-linked membrane proteins,157 the trafficking of PMP22 is known to be inefficient, with
only 20% of the wild-type (WT) protein reaching its final membrane destination in Schwann
cells.158 Previously, it has been shown through a range of biophysical analyses that diseaseassociated PMP22 mutations lower protein stability and further reduce trafficking, however themechanism by which these mutant forms of PMP22 cause disease is still not well understood. 20
Additionally, a high-resolution structure of PMP22 has not yet been published.
Native mass spectrometry has recently been demonstrated to overcome sample purity andconcentration barriers to reveal critical details of membrane protein structure and function.54,95,159
Through the use of nano-electrospray (nESI), intact membrane proteins are ionized within
detergent micelles or other membrane mimetics,119,138,160,161 which can then be removed from themembrane protein ions within the instrument. This method has been used to elucidate oligomeric
information,40,116,162 complex organization,52,163 and lipid interactions 93,96,118 of diverse membraneproteins. The addition of ion mobility separations to mass spectrometry (IM-MS), provides dataon the orientationally-averaged size of analytes,99 and enables collision induced unfolding (CIU)
experiments.100 In CIU, the energies experienced by gas phase protein ions are ramped in a step
wise fashion causing unfolding. These dynamic measurements have been shown to be sensitive toligand binding,164,165 glycosylation,166,167 and disulfide bonding166 in soluble proteins, as well as
selective lipid and small molecule binding in membrane proteins.94,102,106,168 While CIU can clearly
capture subtle structural changes in proteins, its performance in discerning membrane protein
stability as a function of protein mutation has yet to be described.
Here, we demonstrate the ability of native mass spectrometry and CIU to detect key
differences in the stability and complex formation of PMP22 variants, leading to new insights into
the mechanism of PMP22 dysregulation in disease. We quantify the propensity of PMP22 to36

dimerize across WT and seven disease-associated point mutations. We find that mutations
associated with severe disease states form significantly more dimer than WT. Through CIU, wequantify the stability of gas phase monomeric and dimeric PMP variants and find that PMP variants
bearingmutations connected to severe neuropathy exhibit the lowest relative monomer

conformational stability. Interestingly, we observe that all dimers of mutant PMP22 forms are
more stable than WT PMP22 dimeric complexes. We continue by comparing our results topreviously published biophysical datasets and find that our monomeric PMP22 gas phase stability
values correlate well with cellular trafficking data.20 Lastly, we probe the effects of solubilization
agents on PMP22 by characterizing its dimerization within sphingomyelin and cholesterol rich
(SCOR) bicelles. We find that dimeric PMP22 complexes persist within SCOR bicelles and thatthe differences we observed in dimer abundance between WT and severe mutant protein forms areintensified. We conclude by proposing a new mechanism of PMP22 dysregulation in severe
neurodegenerative diseases by which PMP22 monomers are destabilized and more prone to form
poorly trafficked dimeric complexes than WT PMP22.

3.2 Methods

3.2.1 Membrane Protein Sample Preparation
PMP22 WT and mutant variants (S22F, A67T, T118M, G93R, L16P, H12Q, and G150D)
were expressed in E. Coli. using protocols adapted from Schlebach et al.169 Octaethylene Glycol
Monododecyl Ether (C12E8) and n-Dodecyl-β-D-Melibioside (DDMB) were purchased from
Anatrace. Ammonium acetate was purchased from Sigma Aldrich (St. Louis, MO). Detailed37

information on the purification of PMP22, preparation of sphingolipid and cholesterol rich (SCOR)
bicelles, and incorporation of PMP22 into SCOR bicelles can be found in the supporting
information.

For detergent-based native MS experiments, 50 µM PMP22 was buffer anddetergent-exchanged simultaneously from 50 mM Tris buffer pH 8.0, 0.15% β -n-decyl maltoside(DM), 15 mM imidazole and 1 mM TCEP buffer and 0.1% β -n-dodecylmaltoside (DDM) to
0.02% C12E8 (~4 x CMC), 200 mM ammonium acetate pH 8.0 using 10 kDa Amicon Ultra-0.5
centrifugal filter units (MilliporeSigma, Burlington, MA). Detergent screening was performed
following established protocols.95 For SCOR bicelles native MS experiments, 40 µM PMP22 in
10 mM acetate buffer (pH 5.0) containing 100 mM NaCl, 0.2 % SCOR bicelles (q = 0.33), 1 mM
EDTA, 5 mM TCEP, and 0.3 mM DDMB was exchanged into 200 mM ammonium acetate pH 8.0
using 10 kDa Amicon Ultra-0.5 centrifugal filter units.

3.2.2 Native IM-MS
All IM-MS data was collected using a Synapt G2 HDMS IM-Q-ToF mass spectrometer(Waters, Milford, MA), with a direct infusion nESI source set to positive ion mode. Instrument
settings were tuned to dissociate solubilization agents with minimal perturbation to protein
structure prior to the IM separator, including appropriately tuned settings for the source
temperature (30° C), source gas flow (50 mL/min), and the sampling cone (120 V). Trapping cellwave velocity and height were 116 m/s and 0.1 V. IMS wave velocity and height were 250 m/s
and 15 V. Transfer cell wave velocity and height were 300 m/s and 10 V, with an accelerating
potential of 70 V used to dissociate empty micelles and bicelles. Collision cross section analysis
was performed by using IMSCal-19v4, a program written in C. Theoretical collision cross sectionsof monomeric PMP were calculated using a previously published homology model170 and

38

IMPACT.99,130,131 Percent dimer calculations were performed at trap collision voltages of 80V to
minimize the impact of surfactant related noise. Arrival times distributions of charge states thatwere known to be uniquely monomer or dimer, as indicated in Figure 3-1A and Figure II-3, wereextracted using to a text-based format using TWIMExtract.132 For data where excess detergent andlipid noise was present at low arrival times, the extraction window was set to begin at the drift
times indicated in Figure II-8. To calculate the percent dimer, the sum of the extracted dimers isdivided by the monomer sum added to the dimer sum. All CIU analyses were performed intriplicate by increasing the trap collision voltage in a stepwise manner from 5 – 150 V in 5 V
increments. CIU data from the 9+ and 13+ charge states of monomeric and dimeric PMP22,
respectively, were extracted using TWIMExtract132, then processed and analyzed using CIUSuite
2.133 Data processing included three rounds of 2D Savitzky-Golay smoothing with a window of
five bins and interpolation of the collision voltage axis by a factor of four.

3.2.3 High Resolution MS Lipid Extraction Experiments
PMP22 S22F was prepared as described above for IM-MS experiments. HRMS
experiments were performed on an Orbitrap Fusion Lumos Tribid MS (ThermoFisher, San Jose,
CA). Instrument settings were optimized to dissociate the detergent micelle to facilitate detectionof the PMP22 signal. The in-source fragmentation voltages were enabled and set to 100 V. The
transfer tube temperature was also increased to 325 degrees. The ions were subjected to higherenergy C-Trap Dissociation (HCD) at an energy of 22.5% to further liberate the PMP22 from thedetergent micelle. All HRMS spectra were collected at a resolution 120000 FWHM at 200 Th andwith the rf frequency set to 30 %.

39

3.3 Results and Discussion

3.3.1 Quantifying Abundance of PMP22 Dimers
The

existence

of

PMP22

homodimers formed by WT and mutant
PMP22

variants,169,171

as

well

as

aggregation-like higher order species,172–174
has been inferred from low resolution mass
separations

andbiophysicalassays.

Therefore, we endeavored to use native mass
spectrometry,which

enablesthecharacterization of oligomeric state throughdetectable differences in the m/z of protein
ions, to analyze PMP22 oligomeric species.
While PMP22 has not previously beenstudied via native MS, we find that PMP22
liberated from C12E8 detergent micelles
reveals two distributions of protein related
signals with charge states of 10+ to 6+ and 15+
to 11+, as shown in Figure 3-1A. The two
distributions indicate that multiple protein
oligomeric states are present, and the masses
calculated from the charge state distributions,Figure 3-1. Native IM-MS of PMP22 reveals differences indimeric abundances. A. Mass spectra of PMP22 liberated fromdetergent micelles produces populations of monomer and dimer
protein at charge state ranges of 6-10+ and 11-15+, respectively.
Charge states were identified as containing only monomer(green), only dimer (blue), or a mix of monomer and dimer(gray). B. Intensities for PMP22 signals identified as monomer
or dimer were used to calculate the percent dimer in wild-type(WT) PMP22, as well as seven mutant PMP22 variants, N = 3.
Notably, L16P, H12Q, and G150D, mutants correlated withsevere disease phenotypes, exhibit significantly more dimer(41.0 ± 0.4%, 37.3 ± 0.6%, 28.1 ± 1.5%) than WT (23.2 ± 0.3%).

40

19.4 kDa and 39.1 kDa, correspond well to those expected for monomeric and dimeric PMP22(Table S1). The data presented here is the first time a PMP22 homodimer has been confirmed andcharacterized. Notably, the calculated collision cross section (CCS) for monomeric PMP22 inthese experiments was 1595 Å2, which agrees well with the theoretical cross section calculated forfolded PMP22 based on a previously published homology model of 1535 Å2.170 The CCS
agreement, as well as the range of charge states observed, indicate that PMP22 monomers retain
compact conformations when subjected to IM-MS analysis.
The observation of a PMP22 homodimer prompted us to quantify its relative abundanceacross a range of disease associated PMP22 mutants. As there is some overlap between the IMMS signals corresponding to PMP22 monomer and dimer, in order to calculate the abundance of
dimer, we filtered all signals detected using both IM and MS data leaving only those signals that
could be identified as uniquely resulting from monomer or dimer PMP22 (Figure 3-1A). For
example, our measurements indicate a 23.2 ± 0.3% dimer population for WT PMP22.
Of the 44 pathogenic missense mutations known to exist for PMP22, we chose sevenmutations occurring throughout its sequence based on their prevalence, disease phenotype
relationship, and previous biophysical assay behavior (Figure II-2). Two mutants, S22F and A67T,
are associated with hereditary neuropathy with liability for pressure palsies (HNPP), which isconsidered a mild neuropathy.17,175 Previous assays have found that S22F and A67T have
trafficking efficiencies and folding stabilities comparable to WT PMP22.20 Another set of mutants,
T118M and G93R, are associated with Charcot-Marie-Tooth 1 disease (CMT1), a moderate formof neuropathy.156,176 These mutations were observed to cause a reduction in cellular trafficking andstability relative to WT,20 and recent computational modelling of T118M PMP22 indicate that the
mutation causes changes to structural stability.177 Relative quantitation of the dimer states for these41four PMP22 mutations exhibit abundances within error of each other, but significantly lower thanthat of WT PMP22 (Figure 3-1B).
The final three mutants analyzed in this work, L16P, H12Q, and G150D, are associatedwith Dejerine-Sottas Syndrome (DSS), a severe neuropathic condition. These mutations werepreviously found to cause a dramatic reduction in cellular trafficking for PMP22, as well as
decreased stability.20,177–179 Strikingly, we observed that all three of these mutant PMP22 forms
exhibited a significantly larger amount of dimer than WT PMP22 or mutations corresponding tomild or moderate disease phenotypes (Figure 3-1B). The severe disease mutations H12Q and L16P
exhibited 41.0 ± 0.4% and 37.3 ± 0.6% dimer, respectively, while G150D shows a more moderateincrease in dimer relative to WT, at 28.1 ± 1.5%. As H12Q and L16P occur in the firsttransmembrane spanning helix and G150D in the fourth transmembrane spanning helix (Figure II2), these differences in dimeric abundance may indicate that the mutation site Q12 and P16
residues are associated with the PMP22 homodimer interface.

3.3.2 CIU Reveals Differential Stabilities of Monomeric PMP22 Mutants
The folding and stability of PMP22 mutants have been linked to their mistrafficking, androle in neuropathies.145 However, as a high-resolution structure of PMP22 has not yet been
determined, measurements related to the folding of PMP22 have largely been made from bulk
solution,169

biochemical,

biophysicaland cellular assays,20and molecular dynamic

simulations.170,177 While chemical modification has been used to specifically study WT PMP22
dimers,169 many reports convolute the various oligomeric states of PMP22. The gas phase
unfolding of membrane proteins has been previously reported to inform on the conformational
dynamics and stability of protein structures,94,168 and it allows for independent analysis of discrete

42

protein forms that are separated in m/z. Advances in CIU analysis software have also enabled theautomated identification of features, and the fitting of sigmoidal transitions between such
identified features.133 We proceeded to use this approach to evaluate the role of mutations in thestability of monomeric and dimeric PMP22.
The feature observed at the lowest
drift time and collision voltage represents themost folded and native-like conformationalfamily observed for the PMP22 monomer inour experiments, while features appearing at
longer IM arrival times correspond to a more
unfolded PMP22 conformations. Sigmoidal
curves fit to the transitions between CIU
features, allow us to compute inflection
points, or CIU50 values, that we use tocompare the relative stabilities of PMP22
variants.133,168
Figure 3-2A displays averaged CIU
fingerprints, N = 3, with detected features(left) and sigmoidal fits (right) for 9+
monomeric PMP22 WT, as well as example
mutants

from

each

diseaseseverity

phenotype. While a CIU50 of 19.8 ± 0.4V is

Figure 3-2. CIU data for monomeric PMP22. A. Analysis of 9+
monomer WT PMP22 and all mutant PMP22 forms tested identify
two features between 5V and 45V. Fitting a sigmoid to thesefeatures allows for CIU50 determinations. B. CIU50 values
recorded for monomeric WT and mutant PMP22, N = 3, orderedby the disease phenotype severity engendered by the indicatedmutant.

observed for WT PMP22, variants of PMP22

43

possessing the A67T, G93R, and L16P mutations all exhibit a range of CIU50 values significantly
lower than that observed for WT. These lower CIU50 values indicate that PMP22 mutants aredestabilized in a manner correlated with the severity of the neuropathy they induce. A clear
example of this trend is apparent upon comparing CIU data collected for WT PMP22 (teal) and
L16P PMP22 (pink), where the feature appearing at longer drift times in the L16P PMP22 data
begins at significantly decreased collision voltage values when compared to the analogues featureappearing in the WT fingerprint, resulting in an average CIU50 of 16.1 ± 0.3V for the mutant
protein.
Destabilization is observed for all PMP22 mutants studied here, except for S22F, where
each mutant protein monomer exhibits an average CIU50 value significantly below the lower
bound of error defined for our WT CIU50 measurement, (Figures 3-2B and II-5). The observationthat monomer S22F PMP22 exhibits a stability comparable to WT PMP22 is interesting in light ofpreviously published data concluding that this variant may be trafficked more efficiently than WT
and exhibits unique metal binding properties.20 We note that the most destabilized class of PMP22
variants are those associated with severe neuropathy (pink). Specifically, H12Q and L16P PMP22
exhibit CIU50 values of 16.1 ± 0.3V, and 15.4 ± 0.7V, which are both significantly less than thestability we record for the WTPMP22. This observation agrees well with previous data recorded
for these mutatants;20,170,177 however, this study represents the first direct stability measurementsof mutant PMP22 monomers

3.3.3 Mutant PMP22 Dimeric Complexes Are More Stable Than WT
Both While the mistrafficking of PMP22 is a common theme in its proposed roles inneuropathic disease,145,171,180 the underlying mechanism involved, as well as the fate of proteins44

which do not get properly trafficked, are still active areas of research. Proposed as either a cause
or a consequence of mistrafficking, PMP22
oligomerization has been linked to the etiology
and possible treatment modalities for its
associated importance of PMP22 oligomers to
its dysregulation we assessed the stability of
PMP22

homodimers

detectedinourexperiments as a function of protein mutation.
To study the CIU of PMP22 dimers, weselected the 13+ charge state, as it appears
across all datasets and cannot overlap withmonomeric ions. Automated feature analysis
detectedtwo

conformational

populations

between 5V and 60V for all WT and mutant
PMP22 dimer fingerprints. Sigmoidal fits
between these two states yielded reproducible
CIU50 values (Figure 3-3A). We interpreted
these data similarly to the monomericfingerprints described in Figure 3-2, where
CIU50 values were compared across dimeric

Figure 3-3. CIU data for dimeric PMP22. A. Analysis of the 13+
WT PMP22 dimer and all mutant PMP22 forms exhibit two
features between 5V and 60. B. CIU50 values for WT and mutant
PMP22 dimers, N = 3, ordered by disease phenotype severity.

fingerprints for all PMP22 variants (Figures 3-3B and II-5).
In contrast to monomeric data, we find that all variant dimers exhibit enhanced stability
relative to WT. Specifically, we record WT PMP22 dimer CIU50 of 25.6 ± 0.4V, while all mutant

45

variants studied here exhibit significantly larger values, ranging from 29.3 ± 1.4V to 36.8V ± 3.0V.
Note that equal levels of homodimer stabilization is observed for all mutant variants, despite thedifferences between mutants in relative abundance of homodimeric complexes (Figure 3-1B).
Additionally, the stability measurements reported by the CIU50s in Figure 3-3 reflect the unfoldingof the intact dimeric complexes, not dissociation events. However, no clear differences between
CIU50s of dimeric PMP22 mutants associated with different disease phenotypes is observed.
Given the literature precedence for PMP22 oligomers,172–174 our findings clearly support the notionthat destabilized mutant PMP22 monomers are also more apt to form stabilized dimeric states, andthis equilibrium plays a role in disease state severity. This insight is further supported by thegeneral correlations we observe between disease severity, monomeric stability, dimer stability, andthe quantitative amounts of PMP22 dimers detected in our experiments.

3.3.4 Gas Phase Stability Data Correlate with Cellular and in vivo Assays
To probe how our results fit with prior findings associated with PMP22 mutations, we
performed a series of correlative analyses where we plotted our native MS and CIU data presented
in Figures 3-1 and 3-2 against previously published cellular and in vivo data.20 For these analyses,
we included prior data tracking cellular trafficking efficiencies, nerve conduction velocities(NCV), and Gibbs free energy of Zn(II) binding (ΔΔGapp,total), which informs directly on thesolution phase folding of PMP22.
We first assessed how our monomer stability data and dimer abundance values correlated
to patient NCVs, as such data presents the most direct link to disease phenotype. Previously, NCVs
have been shown to correlate well with both trafficking efficiency (R2 = 0.88) and ΔΔGapp,total (R2= 0.64) in an analysis of a larger set of ten PMP22 mutants.20 While we find no significant

46

correlation between NCVs and the abundanceof dimeric complexes measured here (Figure
II-6B), we do observe a correlation between
the CIU50 monomeric stability of PMP22 andpatient NCVs (R2 = 0.57, Figure II-6A). The
nature of this relationship is proportional,
where a lower monomeric CIU50 value isassociated with slower nerve conduction
velocities. We next explored relationships

Figure 3-4. Correlation between monomer CIU50 stability values
and PMP22 trafficking efficiencies, N = 3.20 CIU50 values are fromthe 9+ monomeric charge state of PMP22 and describe the relative
stability of the protein forms. Mutations associated with different
disease severities are color coded. A quadratic regression (y =0.3538x2 - 8.4629x + 47.218) fit is shown for the data, along witherrors for each dataset, producing a correlation coefficient of 0.80,
which shows a positive correlation between monomeric protein
stability and trafficking efficiency

between the ΔΔGapp,total and our measurements, but found no significant correlations for either the
PMP22 monomeric stability values or our abundance measurements for PMP22 dimers (Figures
II-6D,E). We interpret this result as stemming from the foundational differences underpinning thetwo measurements analyzed in these separate studies. First, the prior data relies specifically on
Zn(II) binding to PMP22, and Zn(II) was not present in our native IM-MS experiments. Secondly,in the CIU experiments we are extracting stability information from specific oligomeric species,
where the solution phase experiments are likely sampling complex mixture of PMP22 oligomers.
We find that cellular trafficking efficiency data correlates strongly with our monomer
CIU50 values (R2 = 0.80, Figure 3-4) . The quadratic regression shown in Figure 3-4 exhibits arelationship wherein less stable PMP22 monomers are associated with reduced cellular trafficking.
Additionally, we find that PMP22 trafficking efficiency is anti-correlated with the dimer
abundance values recorded from our native IM-MS measurements (R2 = 0.66, Figure II-6F).
Notably, the mutants associated with severe forms of neuropathy, H12Q, L16P and G150D, cluster
at the extremes of these plots, all exhibiting low trafficking efficiencies, low monomeric stabilities,47

slow NCV values, and abundant dimers. The above correlations clearly indicate the importance of
PMP22 dimers, an oligomeric state ultimately fed by the stability of the preceding monomers, inpredicting PMP22-associated disease phenotypes.

3.3.5 Encapsulation within SCOR Bicelles Engenders Mutant PMP22 Homodimer
Formation
Lipids are recognized as key regulators of membrane proteins through selective
interactions or association with the membrane itself.40,181–183 Many biochemical processes arethought to be driven by partitioning of membrane proteins into nanodomains containing saturated
lipids, sphingolipids, and cholesterol.184,185 The myelin membrane in which PMP22 natively
resides is rich in cholesterol and sphingomyelin, and the protein is thought to play an importantrole in cholesterol homeostasis.155,186,187 Recently, a novel bicelle construct aiming to imitate theordered membrane environment encountered natively by PMP22 has been described.188 These
sphingomyelin and cholesterol rich (SCOR) bicelles have been found to be compatible with a wide
range of biophysical measurement techniques, including native IM-MS.188 To evaluate the effectof a more native-like lipid environment on the formation of PMP22 homodimers, we employed
SCOR bicelles to prepare samples for native IM-MS analysis.
In Figure 3-5A, an example plot of IM drift time versus m/z for PMP22 L16P liberated
from SCOR bicelles is shown, and we observe signals associated with monomer (green), dimer(blue), and overlapping PMP22 oligomeric states (gray). Additionally, noise signals associatedwith lipid and detergent clusters can be observed in the lower IM drift times and m/z values (not48shown). Due to this noise, the SCOR
bicelle-related data shown in Figure 3-5C,E
were

backgroundsubtractedforvisualization purposes, and prior to ourquantitative analysis of the PMP22 dimers
observed, data were de-noised (Figure II-8).
MS data collected for these samples
exhibited evidence of enhanced levels of
adduction (Figure 3-5B-E), which likely
stems for the sample preparation procedures
associated with SCOR bicelles used in theseexperiments (also observed in Figure II-7).
Figure 3-5F displays our quantitative
analysis of PMP22 dimers for WT, L16P,
and G150D variants liberated from micelles
and

SCOR

bicelles.

While

minor

differences exist in the amount of dimer
observed for the mutants between our
micelle and bicelle in our measurements, we
detect significant differences in the relative

Figure 3-5. WT and L16P PMP22 liberated from SCOR bicelles. A. IM-MS
of L16P liberated from SCOR bicelles reveals populations of monomer anddimer PMP22 as well as lipid and detergent related noise (low m/z noise notshown). Such noise was systematically excluded from all further analysis(Figure II-8). B-E. Mass spectra of WT PMP22 liberated from either detergent
micelles or SCOR bicelles (as indicated), followed by similar data collectedfor L16P PMP22. Monomeric PMP22 (green) and dimeric PMP22 (blue) ispresent in each spectra, but in varying amounts. F. Measured percent dimer
values from native IM-MS. Significant differences in these values areobserved between mutants and solubilization techniques

abundance of WT dimers ejected from SCOR bicelles when compared with our micelle data, which
produced values of 22.7 ± 0.3% PMP22 dimer in micelles and only 5.6 ± 0.6% dimer in SCOR
bicelles. This data further demonstrates that the increased levels of dimer we detect are not an49artifact associated with the location of the mutation studied, but rather a function overall structureand stability of the PMP22 variant. Overall, these observations demonstrate the importance of thenative membrane and lipid environment on PMP22 dimer formation, and are further evidence that
PMP22 homodimers complexes are strongly correlated with mutations that give rise to severe
neuropathies.

3.4 Conclusions
The role of PMP22 in neuropathies is complex, with its folding, stability, trafficking, andaggregation all previously indicated as important predictive links to its role indisease.20,172,177,178,189 Native IM-MS and CIU have allowed us to identify PMP22 homodimers as
a new candidate for inclusion among these factors. Our measurements enabled the stabilities of
PMP22 monomers to be untangled from their associated oligomers, and they suggest that thosemutations that engender the least stable monomers also produce the most stable dimers. These
results indicate that those monomers that are least stable, and thus most likely to form dimers, arealso those most likely to accumulate a significant dimeric population. The likelihood that theseresults bear relevance under native cellular environments is high, as our SCOR bicellemeasurements reveal a more dramatic difference in dimer amounts in WT and disease-associatedmutants. Furthermore, if these observations are combined with our analyses that highlightcorrelations between our results and previous measurements of PMP22 trafficking efficiency, amechanistic link can be formed between protein misfolding, dimer production, and a loss offunctional PMP22 within the cellular membrane, leading to direct consequences in NCV and theoverall severity of the neuropathy experienced by patients.

50

While we present no mechanistic evidence that explains how the formation of PMP22
dimers may negatively impact their cellular trafficking here, we can postulate multiple potential
mechanisms based on what is known about membrane protein trafficking. The trafficking of WT
PMP22 is known to be tightly regulated,158 and it is likely that the molecular machinery which
recognizes properly folded PMP22 may not similarly interact with dimeric PMP22. Additionally,
PMP22 dimers may be the first step in a more extensive aggregation pathway leading to disruptionof neuronal function. Beyond the implications for PMP22 biology, we suggest that the methods
described here could be more broadly applied to membrane proteins misfolding disorders, paving
the way for future insights into a host of debilitating human ailments.

3.5 Acknowledgements
Membrane protein research in the Ruotolo lab is supported by the National Institute of
Health under GM105942. HRMS research on the Orbitrap Fusion Lumos was supported by the
National Institutes of Health under S10OD021619. This work was also supported by NIH R01
NS095989 granted to the Sanders and Ohi lab.

51

Chapter 4 Collision Induced Unfolding Differentiates Functional Variants of the KCNQ1
Voltage Sensor Domain

4.1 Introduction
Membrane proteins maintain cellular homeostasis through regulating signaling, facilitating
ion transport across biological membranes, and performing a wide array of enzymatic reactions.107
Disruption of any of these vital functions can cause cellular dyshomeostasis and disease.190 The
point mutations can significantly alter membrane protein folding, trafficking, and activity, and thus
many such sequence variants have been linked to disease.145 With recent advances in genome
sequencing, more human membrane protein variants are being discovered, but the significance of
these variants is not always readily apparent.191,192 Methods for identifying structural differencesin these variants is complicated by their insolubility in aqueous solutions and difficulty in obtaining
pure, high concentration samples.33–35 As there can be hundreds of variants of interest for aparticular membrane protein,144,145,193 there is an active need for high-throughput compatible
methods to obtain such structural information on variants of unknown significance (VUS) andclassify them according to their functional and disease associations.
Mass spectrometry (MS) is readily capable of overcoming the complexity and challenges
associated with studying membrane proteins structure.85 In particular, native MS has enabled
insights into the ligand binding,74,117,194 local lipid environment,118 and protein-protein interactionsof membrane proteins.162 Coupling ion mobility (IM) separation with mass spectrometry (IM-MS)
produces information on the orientationally averaged size of ions which allows for the evaluationof structural changes within membrane proteins.52,122 The use of IM-MS also facilitates collision52

induced unfolding (CIU) experiments, in which protein ions are subjected to ramped collisionenergies within the instrument, causing unfolding.100 CIU experiments have been shown to besensitive to lipid binding in membrane proteins,102,168 and classification schemes built on CIU
experiments,101,195 which have differentiated disulfide bonding patterns134 and ligand binding
modes.196 However, the use of CIU to differentiate classes of membrane protein variants associatedwith disease have yet to be completely explored.
Here, we report the first use of CIU to differentiate variants of the 18 kDa voltage sensor
domain of the KCNQ1 voltage-gated potassium channel. KCNQ1 plays a key role in the tightly
controlled process of repolarization in cardiac cells197 and the pore opening is modulated by thetetraspan integral voltage sensor domain (VSD).198 Specific heritable mutations in the KCNQ1
VSD are associated with losses or gains of pore activity, where a loss of function (LOF) causesLong QT Syndrome (LQTS)199,200 and a gain of function (GOF) causes Short QT Syndrome(SQTS).201,202 Both conditions are associated with an increased risk for cardiac arrhythmias andsudden cardiac death.203,204 There are over 600 human variants of KCNQ1 associated with
LQTS.193 However, the significance of many of these variants and the mechanism by which themutations cause disregulation is not well understood.
In this work we use native IM-MS and CIU to differentiate KCNQ1 VSD variants, eachhaving different disease and functional significance. We first show that wild-type (WT) KCNQ1
VSD can be liberated from detergent micelles in a manner that retains native like folding in thegas-phase. Then we explore the CIU of WT KCNQ1 alongside three mutants: R231C which is
associated with SQTS and GOF,205,206 E115G which is associated with LQTS and LOF,207 and
H126L which was originally a VUS associated with LQTS, but has recently been shown to exhibit
LOF.146 Our feature detection and comprehensive difference analysis approach highlights changes

53

in the unfolding pathway of WT, GOF, and LOF KCNQ1 VSD variants, where our data support
the finding that H126L behaves most similar to the LOF type. We demonstrate the ability of CIU
classification to differentiate WT, GOF, and LOF KCNQ1 VSD variants and show that the H126L
fingerprints classify as LOF. Further, we quantify the relative stability of the KCNQ1 VSD
variants through CIU-based stability measurements and find preliminary evidence that the R231C
mutation is destabilized relative to WT, which we discuss in the context of cellular data showing
increased expression and cellular trafficking. Lastly, we project the utility of CIU methods inscreening large numbers of VUS and discuss the future of such technologies for advancing ourunderstanding of membrane protein function.

4.2 Methods

4.2.1 Membrane Protein Sample Preparation
WT KCNQ1 and the mutant variants H126L, E115G, and R231C were expressed in E. Coliand purified using protocols described elsewhere.146 Octaethylene Glycol Monododecyl Ether(C12E8) was purchased from Anatrace and ammonium acetates was purchased from Sigma
Aldrich (St. Louis, MO). For mass spectrometry experiments, 150 µM KNQ1 in buffer of 50mM
MES, 0.5 mM EDTA, 2 mM TCEP, and 56 µM DDM at pH 5.5 was simultaneously buffer anddetergent exchanged to 200 mM ammonium acetate buffer, pH 8.0, containing 0.02% C12E8 (~4x CMC) using 10 kDa Amicon Ultra-0.5 centrifugal filter units (MilliporeSigma, Burlington,MA). The final protein concentration used in mass spectrometry experiments was approximately
25 µM.

54

4.2.2 IM-MS and CIU Experiments
All IM-MS and CIU data was collected using a Synapt G2 HDMS IM-Q-ToF mass
spectrometer (Waters, Milford, MA), with a direct infusion nESI source set to positive ion mode.
Our instrument settings were tuned to generate intact protein ions while completely dissociating
detergent adducts prior to the IM separator, including appropriately tuned settings for the source
temperature (30° C), source gas flow (50 mL/min), and the sampling cone (120 V). ). Trappingcell wave velocity and height were 116 m/s and 0.1 V. IMS wave velocity and height were 250
m/s and 15 V. Transfer cell wave velocity and height were 300 m/s and 10 V, with an accelerating
potential of 70 V used to dissociate empty micelles and salt clusters. All CIU analyses were
performed by increasing the trap collision voltage in a stepwise manner 5 – 50 V in 5 V increments,
as severe signal loss was observed at voltages above 50V for mutant KCNQ1. CIU data from the
8+ charge state of KCNQ1 was extracted into a text-based format using TWIMExtract132, thenprocessed and analyzed using CIUSuite 2.1.134 Data processing included three rounds of 2D
Savitzky-Golay smoothing with a window of five bins and interpolation of the collision voltage
axis by a factor of four.
Protein stability values, referend to here as CIU50 values, were extracted as described
previously.168 For mutant KCNQ1, excess surfactant related noise was noted in the lower drift time
region from 35 - 50V. This noise was removed from the text-based files manually to eliminate any
biased contributions to RMSD analysis, as shown in Figure IV-2. To ensure the noise removal did
not alter the reported protein unfolding trajectory, CIU50 analysis was performed before and after
noise removal, and reported CIU50 values were identical. Classification of these denoised data
was performed using the “All Data” mode of CIUSuite 2.1, with automatic feature selection. The
algorithms that enable our CIU classification analysis are described in detail elsewhere.101,134,168

55

4.3 Results and Discussion

4.3.1 IM-MS of KCNQ1 VSD
Sequence variants associated with toxic
LOF or GOF occur throughout the sequence ofthe KCNQ1 VSD, as shown in Figure 4-1A.146
For our assay, we chose mutations from disparate
locations in the proteins sequence, with E115G insoluble helix S0, H126L in membrane spanning
helix S1, and R231C in membrane spanning helix
S4. The KCNQ1 potassium channel functions as
a tetrameric complex, where four pore domains
interact to form a functional channel.198 The
tetraspanning VSD of KCNQ1 is connected to thepore domain by a flexible linker between helices
S4 and S5, as well as a 99 residue soluble domain
before helix S1. The VSD is positioned at the
periphery of the pore, and as such is not thought tobe heavily involved in pore oligomerization.198
Figure 4-1B shows an example native IMMS dataset for WT KCNQ1, where a range ofsignals can be detected after successful dissociation

Figure 4-1. Native IM-MS of the KCNQ1 VSD. A.
Sequence and structure of the KCNQ1 VSD with residues
known to mutate and produce disease phenotypes
associated with LOF (red) or GOF (blue). Variants E115G,
H126L, and R231C are studied in this work. B,C.
Representative mass spectra and IM-MS data for WT
KCNQ1 at 80V trap collision voltage. One distribution of
charge states, 5-10+, is observed for all variants
corresponding to KCNQ1 VSD monomers (Figure IV-3).
The charge state envelope and compactness of signals in
IM space indicate native like folding, and the 8+ charge
state (orange) was chosen for further analysis.

of the detergent from the associated membrane protein of interest. MS analysis and charge state56

assignment of these signals reveals an ion population with an intact mass of 18208 ± 44 Da, which
agrees well with the expected sequence mass of a KCNQ1 VSD monomer, 18158 Da. The rangeof charge states we observe for KCNQ1, 5-10+, as well as the compactness of the signals in thetwo-dimensional IM-MS data set, indicate native like structure for the gas phase protein (Figure
4-1C, IV-3). Notably, for all four sequence variants studied here we observe monomodal IM
distributions at low collision energies for KCNQ1 VSD and find no evidence of oligomeric species(Figure IV-1). As has been documented for other IM-MS experiments of membraneproteins,40,78,119,138,168 we observe noise signals related to detergent and salt clusters that persist
despite sample and instrumental optimization (Figure 4-1C). These noise signals were more
intense in the IM-MS datasets recorded for the mutant protein forms, perhaps due to lower protein
concentrations used in these experiments in comparison to those involving WT. The 8+ charge
state of the KCNQ1 VSD (orange) was chosen for subsequent CIU experiments as it exhibits agood balance between high signal intensity, low noise overlap, and low charge state.

4.3.2 Differences in CIU features correlate to KCNQ1 VSD variant function
CIU analysis often focuses on defining regions of stability, or features, within the recordeddata.

CIU feature analysis has been used to characterize domain level unfolding208 andnoncovalent interactions106 within proteins, and we employed such an approach to interrogate the
CIU data collected for the four KCNQ1 VSD sequence variants discussed above. CIU fingerprintsof the KCNQ1 VSD 8+ charge state were collected in triplicate using a collision voltage range of
5-50V. While the interference from noise signals was minimal for WT KCNQ1 VSD, each variant
dataset contained noise appearing primarily at larger collision voltage (CV) values and at drift
times shorter than those occupied by the protein ion signals tracked in our experiments (Figure IV-

57

Figure 4-2. CIU fingerprints of KCNQ1 VSD variants. (Top) Fingerprints were collected for the 8+ charge state in triplicate for
WT, R231C, E115G, and H126L variants, from 5-50V, and then denoised and averaged to produce the images shown here.
(Bottom) Automated feature detection of the fingerprints finds three discrete features between 5-50V for WT and R231C, andtwo discrete features for E115G and H126L. The similarity in starting drift times of the four fingerprints indicate all forms begin
at similar orientationally averaged sizes.

2). These noise signals did not alter the CIU features detected in our fingerprints; however, toeliminate any bias in our workflow, detergent noise was uniformly removed as described in themethods section for all datasets interrogated in this study. Figure 4-2 shows the de-noised CIU
fingerprints (top) and detected features (below) for each KCNQ1 variant. For WT KCNQ1 VSD
fingerprints, three features are observed with median drift times of 16.4 ± 0.3 ms, 19.9 ± 0.0 ms,
and 21.8 ± 0.0 ms. Similarly, the GOF variant R231C fingerprints also exhibit three features withmedian drift times comparable to those observed for WT at 17.0 ± 0.3 ms, 20.0 ± 0.3 ms, and 22.7
± 0.2 ms respectively. The two LOF variants, E115G and H126L, show two features each at drift
times of 17 ± 0.0 ms and 21.0 ± 0.0 ms, and 16.4 ± 0.3 ms and 20.4 ± 0.5 ms, respectively.
The most compact CIU feature occurs at similar starting drift times for each KCNQ1 VSD
form studied here, which indicates that there are no significant differences in the orientationallyaveraged size of the gas phase structures at low collision energies. However, differences weredetected in the overall number of features occurring between 5-50V across the mutants studied
here, where specifically we detect two CIU features over the above voltage range for E115G and58

H126L, and three features for WT and R231C. The similarity observed in the CIU data recorded
for E115G and H126L is further apparent in the similar range of voltages values over which theirfeatures persist, as seen in Figure 4-2. These results mimic previous cellular assays which classify
these mutants together in terms of their LOF severity.146

4.3.3 Quantifying differences in CIU fingerprints of KCNQ1 VSD variants throughcomprehensive analysis (RMSD)
To further investigate the relative differences between KCNQ1 VSD variants, we
performed comprehensive difference analyses using a root mean squared deviation (RMSD)
approach. Such analyses have been used previously to quantify differences detected in CIU data
recorded for antibodies based on glycosylation state.166 Figure 4-3A shows a difference plot for
the averaged CIU fingerprints recorded for E115G and H126L KCNQ1 mutants. We find an
overall RMSD across these CIU fingerprints to be 6.6%, which is similar to RSMD values
encountered for comparisons between KCNQ1 technical replicates alone (a range from 6.4% to9.5%). In contrast, Figure 4-3B shows a similar CIU difference plot generated for averaged H126L
and R231C fingerprints. In this comparison, the regions of differences are more pronounced, withstronger R231C signal apparent at longer drift times relative to H126L. Unsurprisingly, we find alarger overall RMSD for this comparison, resulting a value almost three times that for the analysis
shown in Figure 4-3A, at 18.6%.
A comprehensive comparative analysis of our KCNQ1 variant CIU datasets reveals thosemutants that are most and least similar to one another (Figure 4-3). In this cross-comparison, we
see differences 2-3 times greater than the baseline RMSD values computed through the analysisof technical replicates of identical samples (think bordered boxes, values range from 6.4% for WT59

to 9.5% for H126L), as indicated by the color scale in
Figure 4-3C. Specifically, we observe no significant
differences for the CIU data recorded for H126L and
E115G, while the RMSD recorded for the comparisonsof H126L and R231C CIU data is significant.
Interestingly, the datasets exhibiting the largest RMSD
value in our cross-comparison CIU analyses are WT
and R231C, producing a value of 24.8%, despite thesimilarities observed between these data in Figure 42(also see Figure IV-4). A careful analysis of ourcomparative CIU data indicates that R231C isdestabilized relative to WT and other KCNQ1 VSD
variants. Overall, the comprehensive difference
analysis shown in Figure 4-3C indicates significant
differences between R231C and all other variants, aswell as similarities between E115G and H126L. These
results align with the fact that R231C is the lone GOF

Figure 4-3. Comprehensive difference analysis of
KCNQ1 VSD variants CIU fingerprints, N = 3 foreach variant. A, B. Example difference plots of a
low RMSD comparison (E115G and H126L, 6.6%)
and high RMSD comparison (H126L and R231C,
18.6%). C. Pairwise comparisons of WT, E115G,
H126L, and R231C. RMSD baseline values areshown in the thick bordered boxes. Differences of 23x above the baseline are considered significant. All
difference plots are shown in Figure IV-4.

mutant studied here, as well as prior trafficking andcellular expression data recorded for this variant (Figure IV-5).

60

4.3.4 CIU Classification of phenotypic function
As shown in Figure 4-1A, there are many possible
disease-associated variants of the KCNQ1 VSD.
Additionally, some former VUS forms of KCNQ1
were found to exhibit WT-like function, indicating
they may not contribute to disease.146 To probe theability of CIU to differentiate these phenotypes, we
endeavored to build and test a three-way CIU
classification scheme with the KCNQ1 variants
studied here, grouped into the following classes:
WT, gain of function (GOF), and loss of function(LOF). We started with at least three denoised
replicates each of WT, E115G, and R231C KCNQ1
VSD used in the training dataset to represent eachfunctional phenotype: WT, LOF, and GOF,
respectively (Figure 4-4A). Our efforts identified
the most differentiating region within the datasets as
those between 10-20V,

and cross validation

revealed that classifiers using only the 15V datasets
produced the greatest assignment accuracy (0.81
AUC-ROC, Figure 4-4A inset).
A classification scheme was built using thechosen 15V data and the results plotted linear

61

Figure 4-4. CIU based classification of KCNQ1 VSD
functional variants A. Each voltage is scored on its
ability to differentiate the three classes: WT, LOF, andGOF, using at least three WT, E115G, and R231C
replicates. All voltages were used in a ‘leave one out’
cross-validation test, shown in the inset plot, where we
tracked the accuracy achieved with the training data
(blue),non-training data (green), and the area under the
ROC curve (red) as a function of the number of voltages
included in the classification scheme. These tests
indicated one voltage (*) was best for classification B.
Using the voltage indicated in A, the training data set isplotted in linear discriminant and shows clear separationof the data into the three classes. C. The probability ofassignment for replicates not part of the training data set
is displayed in a bar chart. Each replicate is correctly
assigned, including replicates of the H126L variant for
which no example was included in the training set.

discriminant space in Figure 4-4B, where gray data points indicate the WT phenotype, red indicate
the LOF phenotype, and blue indicate the GOF phenotype. To test the performance of ourclassification scheme, we input CIU replicates to our classifier that were not part of the training
dataset as unknowns. The classification scheme was then used to analyze these unknowns, and theresulting probabilities associated with assigning each of these datasets to one of the three KCNQ1classes defined above is shown in Figure 4-4C. Data for WT, E115G, and R231C all classify as
expected, producing an assignment probability of at least 60% for the correct class. In order tofurther test our classifier, we employed it to analyze CIU data collected for the H126L variant,none of which was in our training data. Our H126L CIU data classifies, as expected, as an LOF
KCNQ1 variant,146 with a total class assignment probability of 60 ± 2%. As such, our results bothsupport prior assignment of H126L as an LOF KCNQ1 variant, and demonstrate the ability of CIU
fingerprints to classify KCNQ1 VSD mutants based on their associated phenotypic function.

4.3.5 CIU reveals evidence of mutant destabilization
To further probe and quantify differences among KNCQ1 VSD variants relative to WT,
we performed CIU50 analysis in order to quantify stability differences among the mutants studied
here. Due to the different number of CIU features detected across KCNQ1 VSD variants, WT and
R231C fingerprints were fit with two sigmoidal curves and E115G and H126L fingerprints were
fit with one sigmoidal curve, as shown in Figure 4-5A, during our analysis. As such, allcomparisons here will focus on the first CIU50 value recorded for WT and R231C variants. Figure
4-5B summarizes the average CIU50 values extracted for the first CIU transitions shown in Figure
4-5A. We record the stability of the WT as 25.8 ± 1.5V, which is within error of the stability values
we observe for the two LOF mutants E115G and H126L at 24.8 ± 0.3V and 24.0 ± 1.3V

62

respectively. The GOF mutant, R231C, exhibits CIU50 values
for the first transition that occur at significantly lower energies
relative to the other three variants, producing a value of 17.7 ±1.6V. In addition, the CIU50 we record for the second CIU
transition for R231C also occurs at lower average voltages
than WT, but the experimental error we compute for thesevalues renders the difference insignificant , with R231C
producing a value of 30.0 ± 4.9V and WT having a CIU50 of
37.0 ± 3.3V.
Overall, we take these CIU50 data to support theobservations made in Figures 4-2 and 4-3, in that the GOF
mutant R231C produces CIU data that is significantly different
from that observed from either WT or LOF KCNQ1 mutants,
primarily driven by the destabilization of R231C. This data isinteresting to consider in the context of cellular assays which
demonstrate that R231C KCNQ1 is a super-trafficker, and ishighly-expressed relative to the other variants studied here
(Figure IV-5). It is important to note that the construct used in
our MS assays only includes the VSD of KCNQ1, whereas
cellular assays track the entire KCNQ1 sequence, which is
tetrameric in its native state. As such, we propose that thedestabilization of the GOF R231C VSD variant may besomewhat ameliorated in the tetrameric state of KCNQ1, the63

Figure 4-5. CIU50 stability analysis of
KCNQ1 variants. A. Sigmoidal curves
are fit between identified features to
describe the transition, where WT and
R231C have two transitions and E115G
and H126L have one transition. B. A bar
chart of average CIU50 values extracted
from the inflection point determined foreach first transition detected, N = 3. Gray
is WT, blue is GOF, and red is LOF.

formation of which would be favored at high cellular concentrations. In addition, the proteinprotein interactions formed with cellular trafficking machinery may not depend on the VSD regionof KCNQ1. Future native IM-MS assays of full length KCNQ1 may further reveal how thedestabilization of R231C KCNQ1 results in increased trafficking and GOF.

4.4 Conclusions
CIU is amenable to the characterization of membrane proteins and many past studies havefocused mainly on its ability to characterize lipid binding behavior in such systems.94,106,168 Here
we describe a workflow for using CIU to classify membrane protein variants according to theirassociated functional role within a given phenotype using the KCNQ1 VSD as a model system.
We first performed comprehensive difference analysis on three types of KCNQ1 VSD variants:
WT, GOF, and LOF, and found strong evidence to support clustering our CIU data along the
known functional consequences associated with each variant analyzed. We then used an
established machine learning approach to build a classification scheme using CIU data recorded
for example WT, GOF, and LOF variants, and found that this approach was reliably able to groupfurther CIU data into classes based on the above phenotypes. Lastly, we explored the stability
differences associated with these variants using CIU50 analysis and found evidence that the GOF
R231C mutant is destabilized relative to WT and LOF variants for the KCNQ1 VSD.
Additionally, Native IM-MS is a method compatible with high throughput analyses, as is
our classification workflow. Using the methods presented here, we estimate that the native IM-MS
data collection needed to classify a VUS with our scheme could be performed in triplicate within
6 minutes. Clearly, our KCNQ1 classification scheme would be strengthened by the addition of
more known variants for each class prior to testing such high throughput analyses, where we

64envision that only the most differentiating regions of CIU space need be collected for each
unknown interrogated. We also project that this same type of CIU classification scheme could be
deployed to detect the five LOF classes described previously for KCNQ1.146 Overall, we projectthat the workflow presented here could be expanded to broadly classify KCNQ1 VUSs, or be
expanded to accommodate a wide range of membrane protein systems possessing variants thatlack functional annotation.

65

Chapter 5 Systematically Studying Membrane Proteins Liberated from Different
Solubilization Techniques

5.1 Introduction
Membrane proteins are important biological and pharmacological analytes that play key
roles in maintaining cellular homeostasis and represent 60% of current therapeutic targets.3,107
Membrane proteins exist in complex cellular environments where their function can be modulated
by interactions with other proteins and lipids.141,209 Due to their complex native environments andintrinsic hydrophobicity, the purification of membrane proteins requires the use of solubilization
agents and often results in low protein yields.35 These factors make obtaining high resolution
structural information on membrane proteins difficult and they are currently underrepresented instructural databases in comparison to their soluble counterparts.33,34 Further, the function andstructure of membrane proteins has been shown to be sensitive to the solubilization techniqueused,69,210,211 resulting in increased use of more native-like lipid mimetics for membrane protein
solubilization.
Recently, native mass spectrometry (nMS) has been demonstrated capable of analyzing
membrane protein samples from a variety of solubilization methods to reveal structural andfunctional information.54,85 In nMS workflows, membrane proteins undergo nano-electrosprayionization (nESI) while protected by their solubilization agents before being collisionally ejectedfrom these agents inside the instrument. While the majority of the work in this field has been
performed

byliberating

gas

phase

membraneproteins

fromdetergent

micelles,40,78,93,96,97,122,137,168,194 membrane proteins housed in other solubilization techniques such66

as amphipols,119 bicelles,119 nanodiscs,52,118,162,212,213 styrene maleic acid lipid particles(SMALPs),214 and biological membrane vesicles161 have all been successfully detected throughnMS. The use of these techniques with nMS has enabled the study of elusive membrane proteincharacteristics such as annular lipid binding,118 oligomerization pathways,212 and complex
formation which span both the inner and outer cellular membranes.215 The addition of ion mobility(IM) separations to mass spectrometry allows for the measurement of the orientationally averaged
size of gas phase membrane proteins,99 and also enables collision induced unfolding experiments(CIU). In CIU experiments, the protein is collisionally activated by the stepwise ramping of anaccelerating potential, which causes it to unfold. The trajectory by which a gas phase analyte
unfolds can be tracked through the generation of CIU fingerprints, and recent software
advancements have presented workflows for the automated analysis of CIU fingerprints to informon the relative gas phase stability of ions.133 While multiple studies have used IM-MS and CIU toelucidatethestructuralchangesandrelativestabilityof

membraneproteincomplexes,76,94,106,122,136,168,216 to our knowledge the use of CIU on membrane proteins has beenpreviously limited to membrane proteins solubilized in detergent micelles.
Here, we employ IM-MS and CIU to systematically study membrane proteins solubilizedby multiple techniques. We first describe the use of IM-MS to screen nanodisc lipid conditions
towards the optimization of dimeric complex incorporation for the 18 kDa integral translocator
protein (TSPO). Given the ability of IM-MS and CIU to access structural information about
membrane proteins, we our second set of experiments probe the ability of IM-MS and CIU tocapture differences in the membrane proteins associated with their solubilization agents. We do
this through collecting CIU fingerprints for model protein systems which have been solubilizedusing at least two different methods, including detergent micelles, sphingomyelin and cholesterol

67rich (SCOR) bicelles,188 and lipid nanodiscs. Our three model protein systems, chosen based on
their representation of diverse membrane protein structure, are the monotopic cytochrome P450
3A4 protein, the dimeric L16P variant of the integral peripheral myelin protein (L16P PMP22),
and a form of WT PMP22 which contains a large, 11 kDa soluble tag (WTtag PMP22). We findthat significant differences exist between all CIU datasets collected for these proteins as a functionof their solubilization agents. Further, we find that these differences emphasize the importance ofcareful evaluation of solubilization agents on a protein to protein basis to best preserve gas phasestructure. We conclude by discussing how the data presented should be considered during
membrane protein nMS optimization and proposing further experiments to elucidate the role ofsolubilization agents in gas phase protein structure.

5.2 Methods

5.2.1 Membrane Protein Sample Preparation
TSPO bearing the A138F point mutation was purified and expressed using establishedprotocols from Rhodobacter sphaeroides.128 CYP 3A4 was expressed in E. Coli and purified using
protocols described elsewhere.217–219 PMP22 WTtag and the L16P mutant variant were expressedin E. Coli. using protocols adapted from Schlebach et al.220 For WTtag PMP22, the protein did notundergo the final thrombin cleavage step. Octaethylene glycol monododecyl ether (C12E8) and nDodecyl-β-D-Melibioside (DDMB) were purchased from Anatrace, Octyl β-D-glucopyranoside(OG),membrane

scaffold

protein

1D1(-),

ammoniumacetate,sodiumchloride,tris(hydroxymethyl)aminomethane (tris), sodium azide, and ethylenediaminetetraacetic acid
(EDTA) were purchased from Sigma Aldrich (St. Louis, MO). The lipids 1,2-dioleoyl-sn-glycero-683-phosphoethanolamine [DOPE], 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine [POPC], 1,2dipalmitoyl-sn-glycero-3-phospho-L-serine[DMPS],

and

1,2-dimyristoyl-sn-glycero-3-

phosphocholine [DMPC] were purchased from Avanti Polar Lipids (Alabaster, AL). All
membrane proteins were screened for appropriate detergent conditions.85
Samples housed in detergent micelles were simultaneously detergent and buffer exchanged
using 10 kDa Amicon Ultra-0.5 centrifugal filter units (MilliporeSigma, Burlington, MA), except
TSPO which was exchanged using a 100 kDa Amicon Ultra-0.5 centrifugal filter units(MilliporeSigma, Burlington, MA). Starting and ending buffers and detergents conditions prior tonative MS are as follows: 30 μM TSPO was exchanged from 50 mM Tris, 150 mM NaCl , 0.20%
DM (N-dodecyl β-D-maltoside), pH 8.0 to 40 mM OG , 200 mM ammonium acetate, pH 8.0, 50µM PMP22 was exchanged from 50 mM Tris, 0.15% DM, 15 mM imidazole, and 1 mM TCEP,
and 0.1% DDM, pH 8.0, to 0.02% C12E8 (~4 x CMC), 200 mM ammonium acetate, pH 8.0, 36µM CYP 3A4 was exchanged from 40 mM potassium phosphate, 20% glycerol, pH 7.4 to 40 mM
OG, 200 mM ammonium acetate, pH 7.4. Samples housed in SCOR bicelles were prepared as
described in the supplemental methods and were then buffer exchanged using 10 kDa Amicon
Ultra-0.5 centrifugal filter units. Bicelle samples were not detergent exchanged and the DDMB
concentration was held at 1 x CMC to preserve the bicelles q ratio (0.33) Specifically, 40 µM
PMP22 in 10 mM acetate buffer (pH 5.0) containing 100 mM NaCl, 0.2 % SCOR bicelle (q =0.33), 1 mM EDTA, 5 mM TCEP, and 0.3 mM DDMB was exchanged into 200 mM ammonium
acetate, 0.3 mM DDMB, pH 8.0 using 10 kDa Amicon Ultra-0.5 centrifugal filter units. TSPO
samples were incorporated into nanodiscs as described in the supplemental methods and were thenbuffer exchanged using Micro Biospin6 spin columns (BioRad, Hercules, CA) from standard MSP
buffer to 200 mM ammonium acetate, pH 7.4. Add DMPC, DPPS. CYP 3A4 samples were69

incorporated into POPC nanodiscs with the scaffold protein MSPE3D1 using a microfluidic
device.221
5.2.2 Native MS and CIU Experiments
All IM-MS and CIU data were collected using a Synapt G2 HDMS IM-Q-ToF mass
spectrometer (Waters, Milford, MA), with a direct infusion nESI source set to positive ion mode.
Our instrument settings were tuned for each protein system and mimetic to generate intact protein
ions while completely dissociating detergents, lipids and scaffold protein prior to the IM separator,
including appropriately tuned settings for the source temperature (30-40° C), source gas flow (50mL/min), and the sampling cone (120 V). The traveling wave height and wave velocities in thetrap, IM, and transfer region, as well as the helium cell flow rate, were identical for each protein
system across mimetics. For TSPO, the trapping cell wave velocity and height were 116 m/s and
0.1 V. IMS wave velocity and height were 700 m/s and 32.5 V. For PMP and CYP, trapping cellwave velocity and height were 116 m/s and 0.1 V, IMS wave velocity and height were 250 m/s
and 15 V, transfer cell wave velocity and height were 300 m/s and 10 V. An accelerating potentialof 70 V in the transfer region was used to dissociate empty solubilization agents for all systems
except CYP in nanodiscs, which only required 10 V. Experimental collision cross section analysis
was performed by using IMSCal-19v4, a program written in C, and, where possible, theoreticalcross sections were calculated from crystal structures14 and homology models178 using
IMPACT.99,222 All CIU analyses were performed by increasing the trap collision voltage in 5 V
increments across ranges tuned for each system. CIU data from selected charge states wereextracted into a text-based format using TWIMExtract,132 then processed and analyzed using
CIUSuite 2.1.133,221 Data processing included two or three rounds of 2D Savitzky-Golay smoothingwith a window of five bins and interpolation of the collision voltage axis by a factor of four.

705.3 Results and Discussion

5.3.1 IM-MS of TSPO from micelles and nanodiscs
TSPO is a penta-span integral mitochondrial protein proposed to be involved in cholesteroltransport and lipid transport.127,223 This protein is highly expressed in areas of inflammation and isassociated with multiple disease states, including traumatic brain injuries and cancer.16,129,224 TSPO
is thought to perform its function as a homodimeric complex and previous native mass
spectrometry studies have identified lipids endogenously bound to the dimer protein.168,194
Although it was found to bind multiple lipids across different lipid classes,168 all previous nMS
studies of TSPO have been performed using detergent micelles. Given its proposed roles in lipid
transport, we considered TSPO to be a good model system for investigating the effects ofsolubilization agents on nMS datasets. Here, we report the first nMS studies of TSPO liberated
from membrane mimetics, describe how nMS can be used to inform optimization process for
obtaining biologically relevant protein complexes and discuss the noise related challenges
presented by each method.
Figure 5-1A, B shows example nMS and IM-MS datasets for TSPO liberated from micelles
composed of the detergent OG. From these spectra, monomeric and dimeric TSPO can be observed
across the charge state ranges of 4 - 7+ and 8 – 11+, respectively. The CCS for 9+ TSPO dimer,
2751 Å2, agrees well with the expected CCS based on the available X-ray structure, 2741 Å2 (PDB
4UC1).14 While there is literature detailing the purification of TSPO into detergent micelles,128 to
our knowledge no protocol has been optimized for incorporating TSPO into a nanodisc. We
screened a range of lipid conditions for TSPO incorporation and found that despite optimizing theprotein:scaffold:lipid ratio, nanodiscs formed from the standard net neutral head group lipid,

71

DMPC, only allowed for the detection of monomeric TSPO (Figure V-2).225 This is an interestingobservation in the context of previous studies that observed potential destabilization of TSPO
dimeric complexes upon POPC binding.168 Additionally, nMS of nanodiscs formed with the net
negatively charged lipid DPPS showed significant amounts of dimeric complex but required
excessive trap collision voltages of 200 V to dissociate the protein from the nanodisc. Nanodiscs
formed with TSPO using the net neutral lipid DOPE were both amenable to nMS analysis andexhibited dimeric complexes, and were thus determined to be the optimal composition for further
experiments.

Figure 5-1. Native ion mobility-mass spectrometry of the TSPO from micelles and nanodiscs. A, B. Mass spectra and IM-MS
data of TSPO liberated from OG detergent micelles shows mainly dimeric protein from the charge states 8-11+ and somemonomeric protein at charge states 4-7+. Endogenously bound lipid can also be observed from the dimeric complexes, as wellas broad noise signals related to empty detergent micelles. C, D. Mass spectra and IM-MS of TSPO liberated from the optimized
nanodisc of POPE lipids shows relatively less dimer than the OG micelles at reduced charge states of 7-10+, and moremonomeric protein at charge states of 4-7+. Lipids are bound to both monomeric and dimeric protein in high stoichiometries,
indicating they are retained from the nanodisc as it dissociates. Noise related to overlapping to the scaffold protein and its
retained lipid can interfere with the TSPO signals.

The nMS and IM-MS data for TSPO liberated from DOPE nanodiscs are shown in Figure
5-1C, D. Comparing these optimized nanodisc samples to the detergent micelle nMS data, key

72

similarities and differences can be observed. First, the arrival times in IM space are generallycomparable for monomeric and dimeric species, indicating similar levels of compactness in thegas phase structures. Second, while dimeric complexes can be observed, the intensity of thesecomplexes in the optimized nanodisc conditions is much lower. Additionally, these dimer signals
for the nanodisc nMS experiments are slightly shifted to lower charge states, which can be
explained considering the potential differences in solvent accessible areas of the protein between
the two methods. Lastly, vastly different noise signals exist between the two methods which
present unique challenges. The micelle data shows a large region affected by noise signals which
creates an elevated baseline in the nMS, and is caused by the polydisperse empty micelles (Figure
5-1AB). The nanodisc data has more discrete noise signals related to the membrane scaffold
protein and various lipid:scaffold complex stoichiometries, which can overlap with signals ofinterest in both mass to charge and arrival time space (Figure 5-1D). These observations highlightthe different challenges associated with each technique as well as the importance of optimizing
nanodisc incorporation protocols for protein complexes.

5.3.2 Studying the effects of solubilization technique on CIU of monotopic membrane protein
CYP 3A4
While the field of membrane protein nMS has largely focused on multi-pass integral
proteins, such as TSPO, recent efforts have explored how these techniques can be extended toperipheral proteins.226 These proteins possess large aqueous domains in addition to regions whichinteract with but do not span, the lipid bilayer and their dual nature can complicate their biophysicalcharacterization.227 For this reason, single pass and monotopic membrane proteins, which areembedded in the membrane with a single alpha helix, are commonly studied in forms where the73sequence is truncated to exclude the alpha helix which can alter the structure of membraneassociated domains.227 However, to study the more biologically relevant protein form with the fullsequence, a method must be capable of handling the solubilization agents needed to retain thenative helix structure. Here, we use the full length monotopic protein cytochrome P450 as a model
system for testing the effects of micellar and nanodisc based solubilization techniques on the gas
phase stability of proteins possessing large aqueous domains.
The CYP family of proteins are enzymes important for the process of drug metabolism.217
They bind a wide variety of drugs and have been noted in the past to also bind detergent molecules.
228

We therefore screened for nMS detergent conditions in which there was no bound detergent

peak present, which led us to use the detergent OG for nMS of CYP 3A4. Figure V-3A showsmonomeric CYP 3A4 liberated from OG micelles at charge states of 13-17+, and while the peaks
are broad relative to soluble systems of comparable size,229 no distinct detergent binding isobserved. Mass analysis of centroid of these peaks corresponds to the mass of CYP 3A4 plus itsheme cofactor, indicating we mainly see the holo protein state. CYP 3A4 has been previously
studied in nanodiscs, and multiple incorporation protocols have been published.230,231 In Figure V3B, we show CYP 3A4 incorporated into POPC nanodiscs with the MSP3ED1 scaffold protein
which was produced using a microfluidic device.219 Peaks for CYP 3A4 liberated from thenanodisc are significantly more resolved than those from micellar conditions, and allow for theidentification of apo and holo CYP 3A4, as well as retained lipid bound states. These monomeric
CYP 3A4 peaks occur at charge states identical to the micellar data at 13-17+ and both IM-MS
data sets show compact signals indicating native-like folding. CCS measurements from the 16+
holo peak do show small differences in orientationally averaged size, with micellar CYP 3A4 at
5022 ± 26 Å2 and nanodisc CYP 3A4 at 4785 ± 25 Å2. We propose that these differences, as well74

as the broadness of the micellar nMS peaks, are related to increased glycerol, salt, or detergent
adduction in micellar CYP 3A4 samples.
To further investigate the differences between CYP 3A4 solubilized in micelles andnanodiscs, we next performed CIU experiments on the 16+ holo charge state as shown in Figure5-2. CIU fingerprints were generated in triplicate for nanodisc CYP 3A4 and, while multiple
replicates of CYP in OG micelles were collected, data quality was too poor to analyze in all but
one. Figure 5-2A shows the five features and four transitions observed for the CYP 3A4 micellar
replicates,withfeaturesbeginning

at

17.0

ms

and

Figure 5-2. CIU of CYP 3A4 liberated from detergent micelles and nanodiscs. All fingerprints shown are for the 16+ holo state.
A. Feature detection analysis of CYP liberated from OG micelles, N = 1, shows features of 17.0, 20.8, 22.0, 25.2, and 28.3 ms.
CIU 50 transitions fit to these features occur at 26.9, 38.4, 48.0, and 56.9 V. B. Feature detection analysis of CYP liberated from
POPC nanodiscs with the MSPE3D1 scaffold, N = 3, show features of 16.5 ± 0.3, 19.2 ± 0.2, 20.8 ± 0.2, 24.1 ± 0.5, and 27.2 ± 0.3
ms. CIU 50 transitions fit to these features occur at 24.1 ± 1.5, 31.6 ± 1.5, 38.5 ± 0.7, and 47.7 ± 0.7 V. C. RMSD plot of themicellar replicate subtracted from the averaged nanodisc fingerprint for CYP 3A4 shows an RMSD of 32.2%.

a first transition from the most native-like state occurring at 26.9V. Figure 5-2B shows the average
fingerprint for CYP 3A4 generated from the three POPC nanodisc replicates, which also fit to fivefeatures and four transitions. The first CYP 3A4 nanodisc features occurs at 16.5 ± 0.3 ms and thetransition from this first, most native-like feature to the first unfolded feature occurs at 24.1 ± 1.5V.
These data show that the protein undergoes a similar unfolding trajectory regardless of it being
solubilized in OG micelles or POPC nanodiscs, however, the starting drift time for the CIU

75

fingerprint is slightly longer for the micelles. This aligns with the CCS difference observed above
and could be a result of either increased adduction or a more unfolded starting state. However, theCIU50 transitions for the micellar CYP 3A4 are all shifted to higher CV relative to the nanodisc
CYP 3A4, which indicates that the micellar CYP 3A4 is stabilized relative to nanodisc. This canalso be observed in the comprehensive difference analysis plot in Figure 5-2C, where the blue
regions characteristic of the micellar CYP fingerprint are concentrated to the right of the nanodisc
fingerprints (red). As the lipid environment of CYP 3A4 is known to be important for its
function,211 we would expect that the protein released from the lipid environment be more stable
than that from the micelle. Instead, what we propose to observe here is the increased adduction of
CYP 3A4 under the detergent conditions causes enhanced stability relative to the nanodisc.
Adduction of soluble proteins has been previously shown to stabilize soluble proteins,232 and thus
it extends that the same phenomenon can occur for membrane proteins. Further work with thissystem will include additional detergent screening and buffer conditions to minimize adduction.

5.3.3 Comparing the unfolding trajectories of transmembrane protein complexes between
SCOR bicelles and detergent micelles
Membrane proteins exist in a complex lipid environment where they can interact with avariety of different lipid classes.2,141 While the nanodiscs we have discussed so far have been
comprised of one lipid, more biologically relevant bilayer mimetics have been recently
described.188,211,214 One example is sphingomyelin and cholesterol rich (SCOR) bicelles which aim
to mimic the more ordered, cholesterol dense regions of membranes.188 As lipids have been known
to influence the oligomeric complex formation of transmembrane proteins,40 we chose to study adimeric protein complex of peripheral myelin protein 22 (PMP22) liberated from SCOR bicelles

76and compare the stability of these complexes to those liberated from detergent micelles. We report
the first CIU measurements of protein liberated from SCOR bicelles.
Drawing from previous findings in Chapter 3 related to the relative amount of dimer
complex present in nMS of PMP22 SCOR bicelle samples, we chose to evaluate the L16P mutant
of PMP22. We also adopted the detergent screen and bicelle preparation protocols from Chapter 3
but extended the analysis to include performing CIU experiments with the L16P PMP22 liberated
from SCOR bicelles. Figure V-4 shows example nMS and IM-MS data sets for L16P PMP22
liberated from C12E8 micelles and SCOR bicelles. In both samples, signals corresponding tomonomeric protein, dimeric protein, or both are present at identical charge states. For PMP22
L16P liberated from the SCOR bicelles, considerable noise is present in the lower mass to charge
range. The 13+ dimeric PMP22 charge state was chosen for CIU analysis due to its intensity andthe minimal noise overlap. CCS analysis of the for the 13+ dimeric charge state of PMP22 showed
the protein liberated from SCOR bicelles to be more compact, at 2653 ± 13 Å2, versus 2953 ± 9
Å2 from C12E8 micelles (N = 1). This CCS analysis is preliminary evidence showing that gas
phase L16P PMP22 dimers adopt a different, more compact starting conformation when liberated
from SCOR bicelles than those liberated from C12E8 micelles, presumably due to interactions
with the lipids or different solution phase structures.
The 13+ dimeric L16P PMP22 charge state was chosen for CIU analysis due to its intensity
and the minimal noise overlap. Figure 5-3 shows the average of three replicates for both micellar
and bicellar L16P PMP22 with detected features and CIU50 transitions. Interestingly, the two
fingerprints are quite different in terms of the number of features and feature median drift time.
The micellar L16P PMP22 fingerprint shows two features, at 15.0 ± 0.3 ms and 23.5 ± 0.7 ms, andthe bicellar L16P PMP22 fingerprints show three features, at 17.3 ± 0.4 ms, 21.4 ± 0.2 ms, and77

25.5 ± 0.2 ms. These starting feature medians seem to contradict the CCS values above that show
bicellar L16P PMP22 dimers to be more compact than micellar L16P PMP22 dimers. However,as CCS analysis calibrates for day-to-day instrumental variations in arrival time distributions, it islikely the more accurate depiction of the relative starting compactness and we therefore conclude
that bicellar L16P PMP22 dimers are more compact that micellar L16P PMP22 dimers.
It is interesting to note that the difference between the first and second feature in micellar
L16P PMP22 dimers (8.5 ms) is very similar to the difference between the first and last feature ofbicellar L16P PMP22 dimers (8.27 ms). Additionally, the CIU50 of the micellar transition (28.9
V) is very similar to the midpoint between the two transitions of 18.3 ± 1.0 V and 38.6 ± 0.8 V(28.4 V). This could be evidence that the bicellar unfolding trajectory possesses similar starting

Figure 5-3. CIU of L16P PMP22 dimeric complexes liberated from detergent micelles and SCOR bicelles. All fingerprints shown
are for the 13+ dimeric charge state. A. Feature detection analysis of L16P PMP22 dimers liberated from C12E8 micelles, N = 3,
shows two features of 15.0 ± 0.3 ms and 23.5 ± 0.7 ms. CIU 50 transitions fit to these features occur at 28.9 ± 0.8 V. B. Featuredetection analysis of L16P PMP22 dimers liberated from SCOR bicelles, N = 3, show three features of 17.3 ± 0.4 ms, 21.4 ± 0.2
ms, and 25.5 ± 0.2 ms. CIU50 transitions fit to these features occur at 18.3 ± 0.9V and 38.6 ± 0.8 V. C. RMSD plot of the averaged
bicellar replicate subtracted from the averaged micellar fingerprint for L16P PMP22 dimers shows an RMSD of 24.6%, which isalmost 2x the replicate baseline RMSDs of 13.2% for L16P PMP22 dimers liberated from micelles and 4.5% for bicelles.

and ending conformations to the micellar protein, but also experiences an intermediate phase
Figure 5-4. CIU of WTtag PMP22 monomers liberated from detergent micelles, SCOR bicelles, and POPC nanodiscs. All
which either
does
notor is short
lived
terms detectionof voltage
spacefortag themicellarprotein.
fingerprints
shown
are fortheexist
9+ monomericchargestate. in
A. Feature
analysis
of WT
PMP22
monomers
liberatedThis
from
C12E8 micelles, N = 3, shows three features of 17.0 ± 0.2 ms, 19.9 ± 0.1 ms, and 21.7 ± 0.2 ms. CIU 50 transitions fit to thesefeaturesoccur at 27.8
± 1.5
V and presumably
41.2 ± 0.5 V B.occur
Featureindetection
analysis spaceof WTtag
PMP22 monomers
from
SCOR
intermediate
phase
would
the voltage
betweenfeaturesliberated
one andtwoofbicelles, N = 3, shows four features of 16.8 ± 0.2 ms, 19.8 ± 0.3 ms, 21.7 ± 0.2 ms, and 26.4 ± 0.2 ms. CIU 50 transitions fit tothese features occur at 22.4 ± 1.3 V, 29.2 ± 2.0 V, and 62.8 ± 0.7 V. C. Feature detection analysis of WTtag PMP22 monomers
liberated
fromPMP22
POPC nanodiscs,
N = 3,
shows three featuresof 20.0 ±the
0.5 ms,22.0 ± 0.3are
ms, andms. CIUto
50 detecttransitions
the L16P
micellarfingerprints,
in whichcentroids
not27.2
flat± 0.2
enougha
fit to these features occur at 27.8 ± 1.1 V and 44.7 ± 3.3 VFigure 5-5. CIU of L16P PMP22 dimeric complexes liberated fromdetergent micelles and SCOR bicelles. All fingerprints shown are for the 13+ dimeric charge state. A. Feature detection analysis of
L16P PMP22 dimers liberated from C12E8 micelles, N = 3, shows two features of 15.0 ± 0.3 ms and 23.5 ± 0.7 ms. CIU 50
transitions fit to these features occur at 28.9 ± 0.8 V. B. Feature detection analysis of L16P PMP22 dimers liberated from SCOR
bicelles, N = 3, show three features of 17.3 ± 0.4 ms, 21.4 ± 0.2 ms, and 25.5 ± 0.2 ms. CIU50 transitions fit to these features occur
78
at 18.3 ± 0.9V and 38.6 ± 0.8 V. C. RMSD plot of the averaged bicellar replicate subtracted from the averaged micellar fingerprintfor L16P PMP22 dimers shows an RMSD of 24.6%, which is almost 2x the replicate baseline RMSDs of 13.2% for L16P PMP22
dimers liberated from micelles and 4.5% for bicelles.

reliable feature. Regardless, the unfolding trajectories and starting gas phase structures aredifferent enough that a direct stability comparison using the CIU50 data is not appropriate. These
differences are highlighted in Figure 5-3C which shows the comprehensive difference analysis
between micellar and bicellar L16P PMP22 dimer CIU fingerprints. As these homodimericcomplexes were not previously reported before Chapter 3 of this thesis, further work in this systemshould focus on computational methods for generating dimeric structures to compare the CCS
values found here.

5.3.4 Comparison of CIU from PMP22 containing soluble construct liberated from micelles,
bicelles, and nanodisc
Noting the marked differences between PMP22 dimers liberated from micelles andbicelles, we next aimed to compare PMP22 protein liberated from nanodiscs to our previous data
sets. However, as shown in Figure 5-1 with TSPO monomers, it can be difficult to distinguishsignals between the membrane scaffold protein, 22 kDa, and proteins of similar size such as the
19 kDa PMP22. Additionally, we wanted to first begin with a monomeric system to compare acrossall three solubilization techniques to avoid the challenges we found in generating sufficientcomplex signal for CIU, as was the case with TSPO. For these reasons we chose to study a versionof WT PMP22 which has an added 11 kDa of soluble tags, denoted throughout as WTtag PMP22.
The WTtag PMP22 also proved more amenable to nanodisc sample preparation for nMS, as thepenta-histidine tag included in the sequence allowed us to use a nickel affinity resin to separate
incorporated nanodiscs from empty nanodiscs. Note that for this reason, the histag free constructof MSP1D1(-) was utilized, allowing specificity in Ni affinity resin step.

79

Figure 5-10. CIU of WTtag PMP22 monomers liberated from detergent micelles, SCOR bicelles, and POPC nanodiscs. All
fingerprints shown are for the 9+ monomeric charge state. A. Feature detection analysis of WTtag PMP22 monomers liberated
from C12E8 micelles, N = 3, shows three features of 17.0 ± 0.2 ms, 19.9 ± 0.1 ms, and 21.7 ± 0.2 ms. CIU 50 transitions fit tothese features occur at 27.8 ± 1.5 V and 41.2 ± 0.5 V B. Feature detection analysis of WTtag PMP22 monomers liberated fromSCOR bicelles, N = 3, shows four features of 16.8 ± 0.2 ms, 19.8 ± 0.3 ms, 21.7 ± 0.2 ms, and 26.4 ± 0.2 ms. CIU 50 transitions
fit to these features occur at 22.4 ± 1.3 V, 29.2 ± 2.0 V, and 62.8 ± 0.7 V. C. Feature detection analysis of WTtag PMP22 monomers
liberated from POPC nanodiscs, N = 3, shows three features of 20.0 ± 0.5 ms, 22.0 ± 0.3 ms, and 27.2 ± 0.2 ms. CIU 50 transitions
fit to these features occur at 27.8 ± 1.1 V and 44.7 ± 3.3 V

As shown in Figure V-5, WTtag PMP22 can be successfully liberated and detected from
C12E8 micelles, SCOR bicelles, and POPC MSP1D1(-) nanodiscs. Monomeric WTtag PMP22 isobserved at charge states 7-15+ when solubilized in both micelles and bicelles, but the WTtag
PMP22 nanodisc IM-MS shows significant overlap with MSP1D1(-) signals in the higher m/z
range, reducing the observable charge states to 7-12+ (Figure V-25C). At lower trap collisionvoltages, signals related to lipid bound MSP1D1(-) can also interfere with the WTtag PMP22
signals. The 9+ charge state of WTtag PMP22 was found to be most suitable for CIU analysis due
to its distance in mass to charge space from the MSP1D1(-) signals. CCS analysis of the 9+ charge
80state for micelles and bicelles was found to yield similar values, at 2683 ± 12 Å2 and 2659 ± 12 Å2
respectively. Contrastingly, the CCS for the 9+ charge state liberated from nanodiscs was foundto be larger at 2793 ± 13 Å2. Given that the SCOR bicelles possess more biologically relevant
lipids than the POPC nanodiscs, one could hypothesize that the protein should be more compact
or folded when ejected from the bicelle. While we do observe the bicellar WTtag PMP22 monomer
to be most compact in terms of CCS, there is no available high-resolution structure for the WTtag
PMP22 construct to which we can compare our CCS values in order to assess their proximity tonative folding. It is also interesting to note that while the L16P PMP22 data shows compaction of
dimers liberated from the SCOR bicelles, the same trend is not observed here for WTtag PMP22
monomers.
CIU fingerprints for the 9+ of WTtag PMP22 solubilized in the micelles, bicelles, andnanodiscs were generated in triplicate and their averages with detected features and CIU50
transitions are shown in Figure 5-4. The WTtag PMP22 fingerprints from micelles and SCOR
bicelles possess three similar features at approximately 17.0 ms, 19.9 ms, and 21.7 ms (Figure 54 A, B). However, the most unfolded feature which occurs at 26.4 ms in the bicelle fingerprints is
not detected in the micelle fingerprint, as it does not reach the necessary relative intensity within
the bounds of the CV range. This information can be combined with the CIU50 values for the firstand second transitions of the datasets to show that the WTtag PMP22 liberated from detergent
micelles is more stabilized relative to SCOR bicelles. Interestingly, there are no apparent adduction
differences in these data as there were for the CYP 3A4 data in Figure 5-2. To further explore thesolubilization agent dependent changes in the CIU fingerprints of monomeric WTtag PMP22,
comprehensive differences analysis was performed between all three datasets as shown in Figure
V-6. The micellar and bicellar fingerprints are found to be the most similar between the three81

solubilization techniques, with an RMSD of 16.0%. This RMSD is still 3x the highest baseline
replicate RMSD of 5.8%.
The features detected in the WTtag PMP22 nanodisc fingerprints are much different thanthose detected in the micellar and bicellar fingerprints, with features at 20.0 ± 0.5 ms, 22.0 ± 0.3
ms, and 27.2 ± 0.2 ms (Figure 5-3C). The differences in starting CCS and starting features indicatethat the WTtag PMP22 monomers liberated from POPC nanodiscs are beginning at a more unfolded
state relative to the micelle and SCOR bicelles, and it is possible that the three features detected tofor the nanodisc fingerprint correspond to the second, third, and fourth feature of the micellar andbicellar fingerprints. Comprehensive difference analysis also shows that the nanodisc fingerprints
are significantly different from the micellar and bicellar fingerprints, with RMSDs of 25.4% and34.8% (Figure V-6), which are at least 2x greater than the nanodisc baseline replicate RMSD of
16.0%. While there are clear differences between the unfolding trajectories between all threesolubilization techniques, more experiments would help elucidate the cause of these differences.
For example, the addition of WTtag PMP22 liberated from POPC bicelles or nanodiscs with more
complex lipid mixtures could allow for the independent analysis of the lipid composition effectfrom the solubilization technique and vice versa. Further, the partly soluble protein construct, CYP
3A4, presented here would be an interesting comparison point for another full data set withmicelles, bicelles, and nanodiscs of a mostly hydrophobic transmembrane protein, such as TSPOor PMP22 without soluble tags.

5.4 Conclusions
NMS methods have been extended to membrane proteins solubilized using manysolubilization techniques, and here we have demonstrated that IM-MS and CIU methods can also82be extended from detergent micelles to more complex membrane mimetics. We show that IM-MS
can inform nanodisc optimization protocols pertaining to protein complexes using TSPO as amodel system. We report CIU fingerprints from SCOR bicelles and nanodiscs for multiple proteins
which are readily analyzed by existing software workflows. In the case of CYP 3A4, we observe
excess detergent, salt, and glycerol adduction cause increased stability of micellar samples relative
to nanodisc samples. We show that these methods can be used to study protein complexes with
L16P PMP22 and find preliminary evidence that SCOR bicelles may support more compact gas
phase dimers than C12E8 micelles. Additionally, we find that for WTtag PMP22, CIU fingerprints
from three different solubilization techniques exhibit differences which are not explained by thepresence of lipids alone. However, from our data we cannot determine whether the differences area function of the lipid composition, SCOR mixed lipids versus POPC, or the solubilization
methods used.
Overall, we find that CIU is sensitive to the solubilization technique a protein is housedin, as all CIU fingerprints gathered here for the same protein, PMP22, liberated from different
solubilization techniques were different to some extent. To further this area of research, studies toparse out of the contribution of lipid composition to the unfolding trajectories will be key, as ourcurrent data cannot rule out the possibility that a protein liberated from a POPC bicelles and POPC
nanodisc do not present the same CIU fingerprint. Additionally, extension to more diverse protein
systems, such as GPCRs, could be vital for defining the role of membrane protein nMS in theassessment of solubilization agents for applications such as cryo-EM or pharmaceutical screens.

835.5 Acknowledgments
Membrane protein research in the Ruotolo lab is supported by the National Institute of
Health under Grants GM105942. The author thanks collaborators Kristine F. Parson and Iliana L.
Hampton for their contributions to this chapter.

84

Chapter 6 Conclusions and Future Directions
6.1 Conclusions
Due to their important roles in cells, many disease states can be linked to the dysfunctionof membrane proteins and the majority of pharmaceuticals approved by the FDA target membraneproteins. Dynamic structural measurements of membrane proteins can help elucidate the role ofthese proteins in disease and how pharmaceutical interventions can be designed to treat them.
However, the challenges associated with their hydrophobic nature and complex nativeenvironments has severely limited the biophysical characterization of membrane proteins. This
thesis adds important workflows and analysis methods to the growing field of membrane protein
mass spectrometry aimed at overcoming the challenges associated with membrane proteincharacterization. Specifically, the comparative stability analyses and classification workflows
presented in this thesis demonstrate novel ways to study the effects of mutation, diseaseassociation, and ligand binding on gas phase membrane protein structure, which could be broadly
applied to membrane proteins beyond those described in this work.
One hinderance to the broader adoption of CIU techniques for membrane proteins is thelarge amount of noise which can be generated by solubilization agents. In Chapter 2, wedemonstrate a workflow for removing this noise from CIU fingerprints using Gaussian fitting andautomated feature detection, and we use this workflow to screen lipid and protoporphyrin binding
to a protein of therapeutic interest, TSPO. We further extend our method to characterize the CIU
of a previously unidentified endogenous lipid bound to TSPO. Through the use of CIU
classification schemes, stability analysis, and high-resolution MS (HRMS), we are able to identify

85the endogenous lipid as a set of lipids containing the PG head group with various chain lengths
and saturations. As the observance of endogenously bound lipids is common in membrane protein
MS, this workflow for identifying endogenous lipids through CIU and HRMS contributes to thelarger field of membrane protein MS. Our identification of the endogenous lipids as PG withvarying chain length is supported by recently published tandem HRMS workflows, in which lipids
ejected from native TSPO dimers were fragmented to yield detailed head group and chain length
distributions.194 Further, the denoising workflow presented in this chapter can be utilized to
manage noise related issues in CIU datasets from any protein origin.
There are multiple membrane proteins for which sequence mutations can cause pathogenic
misfolding and the stability of these mutant protein variants has been implicated in disease
etiology. While much progress has been made using CIU to characterize membrane protein ligand
binding, no studies have previously attempted to detect CIU differences in membrane proteinsbased on their sequence associated disease pathology. In Chapter 3 and 4, we demonstrate theability of IM-MS and CIU to detect differences in sequence variants of two disease associatedmembrane proteins, PMP22 and KCNQ1 VSD. We show that sequence variants of PMP22 canexhibit altered stability and dimeric complex formation, and that differences in dimeric complex
formation are intensified when the protein is liberated from SCOR bicelles, which mimic theproteins native lipid environment. Our data adds key pieces of information to previously publishedcellular biology assays and allows us to construct a mechanism of PMP22 disregulation leading toneuropathic disease. For KCNQ1 VSD, we build a classification scheme capable of differentiating
variants based on disease association and set up a framework for rapidly evaluating the hundredsof disease-associated protein variants. We also find preliminary evidence of destabilization of the
KCNQ1 VSD variant R231C, which has been shown to exhibit unique cellular trafficking and86

expression properties. Together, these chapters add to the knowledgebase of each respective
protein and lay the groundwork for extension to high-throughput screens of protein variants in
misfolding diseases.
Finally, as all CIU of membrane proteins reported to date have used detergent micelles assolubilization agents, we endeavored to assess the utility and compatibility of CIU with proteinsliberated from more complex membrane mimetics. We show that different membrane mimetics
present different noise related challenges, and that IM-MS can be used to inform nanodisc
incorporation protocol optimization for protein complexes. We also present the first CIU of
membrane proteins liberated from bicelles and nanodiscs in multiple protein systems. We find that
CIU is sensitive to the solubilization agent used for each membrane protein and describe theimportance of this data in the context of membrane protein solubilization agent screening.
Overall, this thesis presents multiple workflows related to denoising, membrane proteinclassification, and stability analysis which can be adopted by the growing field of membraneprotein MS. Throughout our experiments, we have also added valuable information to the fieldsof membrane protein misfolding diseases, endogenous lipid binding, and mimetic incorporation
protocols.

6.2 Future Directions

6.2.1 High-throughput membrane proteins screensAt odds with the intrinsic challenging nature of membrane proteins are the many types of
information which could be valuable to their characterization. Screens of small molecule binding
to membrane proteins, stability and disease association of the hundreds of known protein variants87

in membrane protein misfolding diseases, and lipid screening of membrane proteins to illuminate
biological interactions can all be imagined to add a dearth of information to our understanding of
membrane protein interactions and roles in disease. In this thesis, we have presented workflows
which demonstrate the capability of obtaining the information content that is garnered by each of
those screens. The experiments detailed here have been relatively low throughput due tolimitations in manual sample handling, with each experiment requiring a few hours of sample
preparation before the data can be collected. The use of automated sample handling methods
interfaced to the mass spectrometer would greatly increase the throughput of experiments, where
one can imagine many samples are prepared in tandem by researchers and then stored in wellplates for automated injection and analysis. Additionally, software advances now allow for batchprocessing of CIU data with the denoising processes described in this thesis, which could allow
for even shorter data collection times on the scale of seconds per instrument voltage scanned.
The demonstration of a high throughput membrane protein screens based on native IM-MS andCIU could enable the adoption of these technologies as routine analyses in research and industry.

6.2.2 CIU of membrane proteins from diverse solubilization methods
The utility of detergent micelle-based solubilization agents for native mass spectrometry(nMS) of membrane proteins has been thoroughly demonstrated in this thesis and work by others.
However, as micelles do not form bilayers similar to the membranes in which these proteins88natively reside, their wide use and thebiological conclusions drawn from proteinssolubilized in micelles use has been,rightfully, called into question. However, as
the field moves to embrace lipid-based
membrane mimetics, we ask if an artificial
bilayer formed of only one lipid is any“better” at mimicking the native folding andstructure of a membrane protein? Similarly,
how does the ability to preserve structure

Figure 6-1. Example of optimized solubilization agents acrossdifferent mimetics. Both lipid composition and mimetic type are
optimized to result in the most compact gas phase structure.

change when bilayers are formed with acomplexmixtureconstrained

by

an

of

lipidsartificial

that

are

boundary

Figure 6-2. Example of optimized solubilization agents acrossdifferent mimetics. Both lipid composition and mimetic type are
optimized to result in the most compact gas phase structure.

provided by membrane scaffold proteins or detergents in nanodiscs and bicelles? These questions
are difficult to address and remain largely unanswered. We see nMS and CIU as uniquely suited
to explore these questions and inform on the practical use of each of these solubilization methods.
In this thesis we have begun to address these questions and demonstrated that CIU is indeed
sensitive to the solubilization method of membrane proteins, but we assert that a morecomprehensive analysis of lipid composition and solubilization method for a diverse set ofmembrane proteins would greatly aid to the development of this field.

89

AppendicesI. Chapter 2 Supporting Information

TSPO dimer

Expected

Observed

35.95236.033TSPO monomer
17.97617.964
TSPO:Ligand Complexes Mass Analysis (in kDa)
Exp

Obs

Exp

1 BoundObs

Exp

2 BoundObs

Exp

3 BoundObs

Exp

4 BoundObs

5 Bound

TSPO: 14:0
CDL

37.29637.31638.55838.55739.82139.812----

TSPO: 18:1
PA

36.73336.76237.43337.47738.13338.183----

TSPO: 18:1
PE

36.77736.84337.52137.559

-

-

-

-

-

-

TSPO: 16:0
PI

36.84136.91137.64937.729

-

-

-

-

-

-

TSPO: 16:018:1 PS

36.79436.81937.55537.58738.31638.34939.07739.13139.83839.888

TSPO: 16:018:1 PC

36.79336.82237.55337.55838.31338.321----

TSPO: 16:0
PG

36.77836.77137.52337.50638.26838.24439.01339.013--TSPO:
Endogenous
Ligand-36.806-37.478

-

-

-

-

-

-

TSPO: PPIX

36.59636.68337.15837.235

-

-

-

-

-

-

Figure I-1 . Mass Analysis of TSPO and TSPO Ligand Complexes. Expected (Exp) andobserved (Obs) are listed for all complexes used in analysis (kDa) and the small
differences shown are attributed to the nature of native mass spectrometry
experiments. Expected TSPO:ligand complex masses were calculated by adding
the ligand weight to the value listed in the observed TSPO dimer mass (36.033
kDa). Note that when ligands are added to the solution, the peak corresponding to
the TSPO dimer alone tends to increase in mass slightly, indicating increased
adduction. This increased mass was used to measure the weight of the endogenous
ligand across replicates (740 ± 20 m/z).

90

Figure I-2 Detergent screen of TSPO. All IM-MS data shown in squared scale. Peaks labeledwith black lettering are consistent with masses expected for TSPO monomer or
dimer. Peaks labelled with red lettering are not consistent with TSPO complexes
and are most likely detergent signals. A. IM-MS of TSPO solubilized in OG (Octyl
β-D-glucopyranoside) shows dimer (blue triangles) at multiple charge states,
including the 9+. B-E. Other detergents screened include Cymal-5 (5-Cyclohexyl1-Pentyl-β-D-Maltoside), C12E8 (Dodecyl Octaethylene Glycol Ether), NG (nNonyl-β-D-Glucopyranoside), and OM (n-Octyl-β-D-Maltopyranoside). C12E8
and NG datasets showed no signals corresponding to TSPO monomer or dimer,while OM showed only low-intensity monomer signals that appear to be nonnative. Cymal-5 IM-MS does show TSPO dimer, but heavy adduction is present
even at high energies. Additionally, the Cymal-5 data show highly charged
monomer signals, indicative of non-native folding. Due to these factors, OG waschosen as the ideal detergent over Cymal-5.

91Figure I-3 Width analysis for Gaussian denoising. The arrival time for the apo 9+ TSPO dimer
can be extracted without interfering micellular noise at the lowest collision voltage
energies. From this data, a Gaussian distribution was fitted and the full width at
half maximum can be measured. These measurements (3ms) and a manually
optimized tolerance of (0.4 ms) were used as parameters in Gaussian fitting of full
CIU fingerprints using CIUSuite 2.1, and in combination with relative peakintensity was used to differentiate noise signals from TSPO signals, where noise
signals were over 3ms in width and lower intensity.

92

Figure I-4 Feature detection of Gaussian fitted CIU data. Gaussian centroids are originallyassigned as corresponding to either protein (blue) or noise- related signals (red).
These assignments are all automated and are a function of the input parametersfrom Figure I-3. Feature detection is performed on these Gaussian peaks to obtain
Figure 2-2E and this reduced data set is used for all CIU50 and classification
analysis. The feature detection parameters of feature length and tolerance weremanually optimized for each ligand set to obtain fits which most accuratelyreflected the fingerprint, and automated re-assignments as noise or protein weremade after feature detection. Highlighted by letters a-d in this figure are the four
possible outcomes for centroids after automated feature detection is performed. a.
These Gaussian centroids assigned as TSPO signals combine to form a feature,defined by the following input parameters: feature length (10 steps), allowed width(1 ms), and minimum collision voltage step gap (2 steps). b. These centroids
assigned as noise signals are not incorporated in any features and are excluded fromthe final dataset. c. This centroid was assigned as a TSPO related signal during
Gaussian fitting but is excluded from the final de-noised dataset as it is not part ofa feature. d. This centroid originally assigned as a noise signal is re-assigned as aprotein related peak due to its good agreement with surrounding TSPO peaks which
form a feature.

93

Figure I-5 Mass spectra of ligand bound TSPO. After incubation with the selected ligand(except in the case of the endogenous ligand), dimer TSPO can be observed incomplex with 2-5 ligands at charge states 8-10+. The 9+ charge state mainly usedin our analysis is labeled in blue. For clarity of peak assignment, these spectra were94

taken at high voltages and baseline corrected in MassLynx. A. 1',3'-bis[1,2dimyristoyl-sn-glycero-3-phospho]-glycerol [14:0 CDL]. B. 1,2-dioleoyl-snglycero-3-phosphate[18:1PA].
C.
1,2-dioleoyl-sn-glycero-3phosphoethanolamine [18:1 (Δ9-Cis) PE]. D. 1,2-dipalmitoyl-sn-glycero-3phospho-(1'-myo-inositol)[16:0 PI].
E. 1-palmitoyl-2-oleoyl-sn-glycero-3phospho-L-serine [16:0-18:1 PS]. F. Protoporphyrin IX (PPIX). G. 1,2dipalmitoyl-sn-glycero-3-phospho-(1'-rac-glycerol) [16:0 PG]. H. A previously
unknown endogenous ligand, proposed in this manuscript to be a group of lipids
possessing the PG head group. I. 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine[16:0-18:1 PC]

95

Figure I-6 CIU fingerprints of TSPO:ligand complexes. Averaged fingerprints, n=3, of the
one ligand bound state for each ligand. Fingerprints were collected in 5 V
increments from 50 -140 V, with each increment comprising scans averaged over
2 minutes. Above 140 V, signal quality was severely degraded and no additional
unfolding was able to be observed. Ligand binding causes some differences thatare observable by eye, such as stabilization of the first and second features byligands PPIX, PG, UNK, as well as the unique unfolding pathway of the lipid PC.
In all PC replicates, it seems that the most unfolded state occurs at lower voltages
than Apo TSPO, indicating destabilization.

96

Figure I-7 HRMS of lipid extraction from TSPO with endogenous lipid present. Each labeledpeak from positive and negative mode electrospray was isolated and fragmented atvarying HCD energies. Intact mass and fragment masses were considered whenusing LipidMaps to attempt to identify peaks. Lipid matches were identified forpeaks labeled with blue, and exact lipid chain information was obtained for all
except those marked with (*). Orange labeled peaks yielded no lipid identification
information.

97

Figure I-8 Classification of TSPO complexes by lipid head group charge. A. Each voltage
was scored for the ability to differentiate three classes: neutral lipids (PC, PE),
lipids with a net charge of -1 (PA, PG, PI, PS) and lipids with a net charge of -2(CDL), N = 3 for each ligand. Voltages in the gray box were selected for crossvalidation, inset plot, and one voltage (*) was chosen to build the scheme based on
accuracy. B. Three-way classification depicted as decision regions on linear
discriminant axes shows that some neutral replicates were classified as negatively
charged, indicating that the scheme built is not capable of reliably classifying
TSPO:lipid complexes based on head group charge. Attempts to manually identify
voltage combinations that may yield better separation gave similar or worse results,
where neutral lipids were often misclassified as negative (data not shown). C. Threereplicates that often classified incorrectly corresponded to the neutral lipid PE,
where the classification scheme grouped PE with the -1 charge lipids (PA, PI, PG,
PS). Looking at the features detected for PC, PE, and PA, it is clear that PE and PA
fingerprints possess very similar features. Although the typical physiologicalcharge of DOPE and DOPA is different, their shared attribute of chain unsaturation(2 double bonds) can be used effectively to classify these fingerprints. (Figure 25).
98

II. Chapter 3 Supporting Information

Figure II-1 Mass Analysis of PMP22 Variant Monomers and Dimers. Expected (Exp) andobserved (Obs) are listed for all complexes used in analysis (Da). Note that due tothe presence of two extra amino acid residues related to variations in thrombin
cleavage, the signals corresponding to the lowest mass to charge at each charge
state were chosen for mass analysis. More information on the additions can befound in Figure II-4.

99

Figure II-2 Sequence location of PMP22 mutations studied in this work. In addition to WT
PMP22, two mutations associated with the mild neuropathy HNPP (A67T and
S22F), two mutations associated with the moderate neuropathy CMT1 (T118M andG93R), and three mutations associated with the severe neuropathy DSS (L16P,
H12Q, and G150D) were characterized in this work.

100Figure II-3 Mass spectra of PMP22 variants released from C12E8 detergent micelles. For all
variants released from detergent micelles, charge states associated with monomer(green) and dimer (blue) were observed. Based on the charge states envelope, somesignals were determined to contain indistinguishable amounts of both monomer
and dimer (gray). These signals were excluded from percent dimer calculations.
Spectra shown here are background subtracted for visualization purposes, but data
de-noising for further analysis is only performed as described in Figure II-8.

101

Figure II-4 HRMS identifies thrombin cleavage related additions to PMP22. PMP22 S22F
without any additions can be observed at the major peak of 1934.53 Th. PMP22
S22F with an additional two residues, proline and arginine, can be observed at theminor peak of 1954.24 Th. The presence of the additional residues is likely a resultof promiscuous cleavage at the thrombin cleavage site. Present in all micellesamples except G150D, which were prepared using thrombin from a different
company. CIU of data extracted with narrow windows from each individual aminoacid addition exhibited no significant differences, so to maximize signal intensity,
all CIU include the base protein and additions.

102

Figure II-5 9+ monomer and 13+ dimer CIU fingerprints for all variants studied in this work.
Averaged, N=3. A. Automated CIU feature detection (left) and CIU50 sigmoidaltransitions (right) for monomeric PMP22 reveal that all PMP22 mutations except
for S22F show some degree of destabilization relative to WT. B Automated CIU
feature detection (left) and CIU50 sigmoidal transitions (right) for dimeric PMP22
reveal that all PMP22 mutants are stabilized relative to WT.
103

Figure II-6 Correlative plots. Nerve conduction velocities (NCV), cellular trafficking
efficiencies, and ΔΔGapp,total from Schlebach et al. 2015. A, B. NCVs (m/s) versus
monomeric PMP22 CIU50 transition values (V) and percent dimer. Linear
regression analysis of NCV versus CIU50 yields R2 = 0.57. No significant
relationship between NCV and percent dimer is observed. C. Monomer CIU50
transition (V) versus percent dimer shows a linear inverse correlation with R2 =0.46 when all data are included. *When the WT PMP22 data point is removed fromthis data R2 = 0.61. D,E. ΔΔGapp,total (kcal/mol) versus monomer CIU50 transition(V) and percent dimer show no significant correlations. **ΔΔGapp,total is a measureof Zn(II) binding free energy associated with protein folding. F. Percent dimer
versus trafficking efficiency. An exponential fit yields R2 = 0.66.

104Figure II-7 Mass spectra of G150D PMP22 released from SCOR bicelles. Signals
corresponding to monomer (green), dimer (blue) and mixed monomer and dimer
signals (gray) are observed.

105

Figure II-8 Denoising of bicelle and G150D micelle data. A, B, C. Mass to charge versus
arrival time plots of PMP22 variants liberated from SCOR bicelles exhibited noise
signals associated with lipid and detergent clusters in the lower arrival time andmass to charge space. To minimize any bias towards monomeric PMP22 in thepercent dimer analysis, the data extract was parameterized to extract only thearrival time distribution starting at 18 ms. D. G150D micelle samples were preparedand stored in glycerol, which led to a similar clustering issue in the low mass tocharge range. G150D micelle arrival time distribution data was extracted startingat 17 ms. Note, this noise was not present in other micelle samples as there was noglycerol present, so no de-noising was performed.

106III. Chapter 3 Supporting Methods
Preparation of SCOR bicelles: The sphingolipid and cholesterol-rich (SCOR) bicelle usedin this study is composed of a lipid mixture consisting of 1,2-dimyristoyl-sn-glycero-3phosphocholine (DMPC), egg sphingomyelin (eSM), and cholesterol in a mole ratio of 4:2:1, andthe detergent -n-dodecylmelibioside (DDMB), such that the q ratio (lipid-to-detergent moleratio) is 0.33. Powdered samples of DMPC (CardenPharma), egg SM (NOF Corporation), andcholesterol (Sigma-Aldrich) necessary to make a 10-mL stock solution of 6% (w/v) SCOR bicelles
were first weighed out into a 15-mL glass vial and the mixture was dissolved in chloroform byvortexing. The solvent was then evaporated at 45 °C in a Smart Evaporator C1 (BioChromato)
until a gel-like substance remained. The lipid mixture was then lyophilized overnight to remove
the remaining solvent. The lyophilized mixture was resuspended in 15% (w/v) stock solution of
DDMB (Anatrace) in milliQ water at a volume necessary to bring the q ratio to 0.33. The volumeof the mixture was brought to 10 mL by adding milliQ water. The resulting mixture was thensubjected to a series of freeze-thaw cycles consisting of freezing in liquid nitrogen, followed bythawing in warm sonication bath, and extensive vortexing before freezing again, until it turned
clear (usually at least three cycles). The formation of SCOR bicelle was confirmed by dynamic
light scattering.
Expression of PMP22: Wild type and mutant PMP22 (i.e S22F, A67T, T118M, G93R,
L16P and H12Q) proteins were expressed in E. coli BL21(DE3) as described.1 Briefly, theplasmid containing cDNA encoding recombinant human PMP22 was transformed in the E. coli
BL21 star (DE3) cells and grown in minimal M9 media at 20 °C with agitation. 1 mM Isopropyl
β-D-1-thiogalactopyranoside (IPTG) was added when OD600 was ~0.8, to induce the expression107

of the PMP22 protein. Cells were harvested by centrifugation ~22 hours after induction and thecell pellets were flash frozen until use in purification.
Purification of human PMP22 in detergent micelles: Recombinant PMP22 proteins (i.e.
WT, S22F, A67T, T118M, G93R, L16P and H12Q) were purified on Ni-NTA column (Qiagen)as described1 with some modifications. To refold the protein, the EMPIGEN BB detergent (N,NDimethyl-N-dodecylglycine betaine, N-(Alkyl C10-C16)-N,N-dimethylglycine betaine, SigmaAldrich) was exchanged with Decyl-Maltoside (DM, Anatrace) by rinsing PMP22 bound to NiNTA resin with 200 ml of 50 mM Tris buffer (pH 8.0) contained 0.5% DM and 1mM TCEP
(Tris(2-carboxyethyl)phosphine, COMPANY). PMP22 and PMP22 mutants were eluted with 300
mM Imidazole dissolved in refolding buffer (5mM Imidazole, 50mM Tris buffer pH 8.0, 0.15%
DM and 1mM TCEP). Finally, after overnight thrombin cleavage the 50 ml protein solution was
passed over a second Ni-NTA column that was pre-equilibrated with 50mM Tris buffer (pH 8.0)
containing 0.15% DM and 1mM TCEP. Flow through was discarded and bound PMP22 was eluted
with 15 mM Imidazole dissolved in 50mM Tris buffer pH 8.0, 0.15% DM and 1mM TCEP. The
PMP22 proteins were concentrated to 1-2 mg/ml and stored at -80°C for use in mass spectrometry
studies. The final protein concentration was estimated by measuring the absorbance at 280 using
an extinction coefficient of 44,900 M−1cm−1.
Purification of PMP22 into SCOR Bicelles: Cells containing overexpressed PMP22 were
resuspended in lysis buffer (75 mM Tris-HCl (pH 7.5), 300 mM NaCl, 0.2 mM EDTA) at 20 mL
buffer per gram of cell pellet. Lysozyme (0.2 mg/mL), DNase (0.02 mg/mL), RNase (0.02
mg/mL), phenylmethanesulfonyl fluoride (PMSF, 1 mM), and magnesium acetate (5 mM) wereadded and the mixture was tumbled for at least 30 minutes at 4 °C, followed by probe-sonication
on ice for 10 minutes. To dissolve the protein from the membrane and inclusion bodies, lauryl108

betaine (empigen detergent, BOC Sciences) to a final concentration of 3%, as well as glycerol(15%) and tris(2-carboxyethyl)phosphine (TCEP, 1 mM), were added, and the mixture was rotated
at 4 C for at least an hour. The insoluble cell debris were then removed by centrifugation of thelysate at 20,000 rpm and 4 C for 30 minutes. The supernatant was collected and mixed with 0.5
mL of pre-equilibrated HisPurTM Ni-NTA Superflow Agarose resin (Thermo Scientific) per gramof cell pellet. The resulting mixture was rotated at 4 °C for ~14 hours.
The resin was then packed into a chromatography column and washed first with 5 column
volumes (CV) of purification buffer (40 mM HEPES (pH 7.5), 300 mM NaCl) containing 15%
glycerol, 3% empigen, and 1 mM TCEP, followed by purification buffer containing 40 mM
imidazole, 15% glycerol, 1.5% empigen, and 1 mM TCEP, until the measured absorbance at 280
nm reaches the baseline, indicating elution of weakly bound impurities (typically ~15 CV). The
empigen detergent was then exchanged with n-tetradecylphosphocholine (TDPC, Anatrace) by
washing the resin with 12 CV of 25 mM sodium phosphate buffer (pH 7.2) containing 0.2% TDPC
and 1 mM TCEP. The PMP22 fusion protein was then eluted from the resin using 50 mM Tris
buffer (pH 8) containing 500 mM imidazole, 0.2% TDPC and 1 mM TCEP.
The eluted protein was buffer exchanged twice with 20 mM sodium dihydrogen phosphatebuffer (pH 7.5) containing 1 mM TCEP to get rid of the imidazole. Each buffer exchange cycle
consisted of concentrating the solution to ~1 mL by centrifugation at 3,000 x g using a 10K MWCO
Amicon Ultra-15 centrifugal filter device (Millipore) and diluting back to ~15 mL. The final
solution was then concentrated to ~5 mL before loading into a pre-equilibrated HiTrap SP FF
cation exchange column (GE Healthcare) for cation exchange chromatography. The cationexchange chromatography serves two purposes: to remove a common E. coli stress protein YodA
that cooverexpressed and coeluted with the PMP22 fusion protein, and to replace the membrane109

mimetic from TDPC to SCOR bicelles. After loading the sample, the column was then washedwith at least 30 CV of 20 mM sodium dihydrogen phosphate buffer (pH 7.5) containing 1 mM
DTT, 0.2% SCOR bicelle (q = 0.33), and 0.3 mM β-n-dodecylmelibioside (DDMB, Anatrace) to
exchange the membrane mimetic. The PMP22 fusion protein in SCOR bicelles was eluted byrunning a gradient where the concentration of NaCl is increased from 0 to 1 M over the course of
14 CV.
Cleavage of the fusion partner of PMP22 was carried out by adding 1000 units of thrombin(Recothrom) to ~0.8 mg/mL of purified PMP22 fusion protein adjusted to pH 8.0 by addition of
20 mM Tris buffer (pH 8.0) prior to cleavage. The solution was rotated gently at room temperature
overnight. The His tag-containing fusion partner, as well as uncleaved PMP22 fusion protein, wereseparated from the cleaved PMP22 by performing another Ni affinity chromatography. Preequilibrated Ni-NTA resin (at 1 mL per 10 mg fusion protein) and 20 mM imidazole were added
to the cleavage reaction mixture before rotating for at least an hour at 4 C. The resin was packed
into a chromatography column and the flow through containing most of the cleaved PMP22 wascollected. Successive washes with 20 mM Tris buffer (pH 8.0) containing 0.2% SCOR bicelle (q= 0.33), 0.3 mM DDMB, 1 mM TCEP, and increasing concentrations of imidazole (20 mM, 30
mM, 40 mM, 250 mM) were carried out. The solution containing the cleaved PMP22 wasconcentrated before loading into a pre-equilibrated size exclusion column (GE Superdex 200
Increase 10/300 GL). Size exclusion chromatography was performed using 10 mM acetate buffer(pH 5.0) containing 100 mM NaCl, 0.2 % SCOR bicelle (q = 0.33), 1 mM EDTA, 5 mM TCEP,
and 0.3 mM DDMB to remove empty bicelles and to exchange the buffer. Fractions containing
PMP22 were combined and concentrated using 10K MWCO Amicon concentrator for use in native
ion mobility-mass spectrometry experiments. The final concentration of PMP22 in SCOR bicelles

110was determined by measuring the absorbance at 280 nm using an extinction coefficient of 41,940
M-1 cm-1.

111

IV. Chapter 4 Supporting Information
Masses of KCNQ1 VSD (Da)Expected Observed
Monomer
KCNQ1 VSD WT

18158

18208

KCNQ1 VSD R231C

18086

18152

KCNQ1 VSD E115G

18105

18131

KCNQ1 VSD H126L

18134

18181

Figure IV-1 . Measured masses of KCNQ1 VSD variants. Expected and observed masses for
KCNQ1 VSD WT, R231C, E115G, and H126L. As peak splitting related to singleamino acid truncations was observed (~175 Da), these masses are calculated fromthe peak without truncation, which is also the most intense peak.

112

Figure IV-2 . Denoising of KCNQ1 fingerprints. Noise was noted in the low mass to charge
and high voltage space, especially for mutant KCNQ1 forms which started atrelatively lower concentrations before buffer and detergent exchange (top,averaged fingerprints N = 3). This noise was manually removed from each
replicate’s text file to yield de-noised fingerprints, for which averaged examples
are shown (bottom, N=3).

113

Figure IV-3 . Mass spectra of KCNQ1 variants. A-D. Mass spectra for KCNQ1 VSD WT,
R231C, E115G, and H126L show the same charge state distribution of 5-10+
monomeric protein. Peak splitting of each charge state indicates a single amino acidtruncation (~180 Da) for WT KCNQ1 VSD and possible additional amino acidtruncations in the mutant species. CIU fingerprints generated with small extraction
windows only encompassing individual truncations exhibited no significant
differences, so the entire peak width was extracted for the CIU fingerprints shownin this work. These spectra are post processed through background subtraction to
add in visualization.

114

Figure IV-4 . Comprehensive difference plots for all KCNQ1 variants. A-F. De-noised
fingerprints were averaged, N = 3, for each KCNQ1 VSD form and comprehensive difference
analysis was performed using CIUSuite 2.1 to yield an RMSD for each pairwise comparison.
G. Summary of all RMSDs and baseline replicate RMSDs (thick border).

115

Figure IV-5 Trafficking and expression of KCNQ1 variants. Data collected by Hui Huang,
Sanders lab at Vanderbilt University. The cell surface expression (A), total
expression (B), and trafficking efficiency (C) of KCNQ1 mutants. HEK293 cells
transiently expressing WT or mutant KCNQ1 were first treated with PEconjugated antibody to stain surface KCNQ1 and then, following fixing andpermeabilization, with Alexa Fluor 647-conjugated antibody to stain intracellular
KCNQ1. Values are given as % of WT after correction for nonspecific staining.
Mutant trafficking efficiency %WT=[mutant surface/mutant total]/[WT
surface/WT total]*100. Results are expressed as mean ± S.E.M. of at least three
independent experiments. *Significantly different from WT KCNQ1, P<0.05.

116

V. Chapter 5 Supporting Information
Measured Masses
Expected

Observed

Expected

Monomer

Observed

Dimer

A138F TSPO (Micelle)

17976

18005

35952

36033

A138F TSPO (Nanodisc)

17976

17994

35952

36008

CYP 3A4 - holo (Micelle)

57561

57783

-

-

CYP 3A4 - holo (Nanodisc)

57561

57588

-

-

PMP22 L16P (Micelle)

19260

19265

38520

38800

PMP22 L16P (Bicelle)

19260

19480

38520

38998

PMP22 WT with tag (Micelle)

30353

30214

-

-

PMP22 WT with tag (Bicelle)

30353

30237

-

-

PMP22 WT with tag (Nanodisc)

30353

30304

-

-

Figure V-1 . Expected and observed masses for all protein forms studied in Chapter 5.

117

Figure V-2 IM-MS datasets of TSPO liberated from various solubilization conditions. A. Example
data of TSPO liberated from OG micelles show intense signals related to dimericcomplex. B,C,D. TSPO prepared in MSP1D1 nanodiscs with different lipid
compositions of DMPC, DOPE, and DPPS. DMPC and DOPE IM-MS are shown at
80V trap collision voltage and DPPS at 200V trap collision voltage as no protein could
be observed at lower energies for that lipid composition. Only DOPE nanodiscs exhibit
significant amounts of dimeric complex.

118

Figure V-3 Mass spectra and IM-MS datasets of CYP 3A4 liberated from micelles and nanodiscs.
A. CYP 3A4 can be observed with the charge state distribution of 13-17+ when liberated
from OG micelles. These peaks are broad enough that apo and holo protein forms cannot
be resolved, however the centroid of the peak aligns well with weight for the holo CYP
3A4. The peak broadness could be caused by detergent adduction. B. When liberated
from nanodiscs, the apo, holo, and lipid bound peaks of CYP 3A4 can be resolved forthe charge states 13-17+.

119

Figure V-4 . Mass spectra and IM-MS datasets of L16P PMP22 liberated from micelles and SCOR
bicelles. A, B. When released from C12E8 micelles or SCOR bicelles, monomeric(green), dimeric (blue), and overlapping monomeric and dimeric (gray) charge states of
L16P PMP22 can be detected. The 13+ dimeric charge state was chosen for CIU
analysis.

120

Figure V-5 Mass spectra and IM-MS datasets of WTtag PMP22 liberated from micelles, SCOR
bicelles, and POPC nanodiscs. A. When released from C12E8 micelles monomeric
WTtag PMP22 can be observed from 7-15+, along with detergent related noise and asmaller contaminant protein. This contaminant could be related to degraded forms of
the 11 kDa soluble tag, but we do not observe it to interact with the protein and thesignals do not interfere with the 9+ charge state CIU. ted. The 9+ monomeric charge
state was chosen for CIU analysis. B. When released from SCOR bicelles monomeric
WTtag PMP22 can be observed from 7-15+, along with detergent and lipid related noise
signals. C. When released from POPC nanodiscs, monomeric WTtag PMP22 can beobserved from 7-12+, however any higher charge state overlaps significantly with
MSP1D1 signals and cannot be distinguished. Some charge states can be separated in
IM space from MSP1D1 although they possess significant mass to charge overlap. The
9+ charge state is the most distant in mass to charge space from MSP1D1 signals andwas chosen for CIU analysis.

121

Figure V-6 Comprehensive difference analysis of WTtag PMP22 9+ CIU fingerprints from C12E8
micelles, SCOR bicelles, and POPC nanodiscs. Baselines replicate RMSDs are as
follows: micelle 2%, bicelle 6%, nanodisc 16%. A. Bicelle subtracted from micelle,
RMSD = 16.0%. B. Bicelle subtracted from nanodisc, RMSD = 25.4%. C. Micellesubtracted from nanodisc, RMSD = 34.8%.

122VI. Chapter 5 Supporting Methods
Nanodisc incorporation protocol. Except for CYP 3A4 which was incorporated into POPCMSPE3D1(-) nanodiscs using a microfluidic device, all proteins were incorporated into nanodiscs
using the protocol outlined by Sligar et al 2010.233 Successful incorporation conditions for TSPO
and WTtag are as follows: For TSPO, the DOPE:MSP1D1(+histag):TSPO ratio was 65:4:1, which
was incubated at DOPE Tm without TSPO for 30 minutes. The TSPO was added and incubated for
additional 30 minutes before being added to detergent removal resin (Biobeads) and incubated for
4 hours. Samples were prepared for nMS using 6 kDa or 40 kDa buffer exchange spin tubes. For
WTtag PMP22: the POPC:MSP1D1(-): WTtag PMP22 ratio was 65:4:1 and the mixture wasincubated on ice without WTtag PMP22 for 30 minutes. Then WTtag PMP22 and extra secondary
detergent (DDM) were added to the mixture and incubated for additional 60 minutes. This wasadded to detergent removal resin (Amberlite XAD) and incubated at 6° C while shaking overnight.
To remove empty nanodiscs, samples were then incubated with an equilibrated nickel affinity resinfor 30 minutes and eluted with 1 mM imidazole. Size exclusion chromatography was performedon the eluent using a Superdex increase 10/300 gL prepacked Tricorn column equilibrated in 200
mM ammonium acetate pH 7.4. Samples were concentrated using 10 kDa concentrators prior tonMS analysis.
SCOR bicelles incorporation protocol. Cells containing overexpressed PMP22 were
resuspended in lysis buffer (75 mM Tris-HCl (pH 7.5), 300 mM NaCl, 0.2 mM EDTA) at 20 mL
buffer per gram of cell pellet. Lysozyme (0.2 mg/mL), DNase (0.02 mg/mL), RNase (0.02
mg/mL), phenylmethanesulfonyl fluoride (PMSF, 1 mM), and magnesium acetate (5 mM) wereadded and the mixture was tumbled for at least 30 minutes at 4°C, followed by probe-sonication
on ice for 10 minutes. To dissolve the protein from the membrane and inclusion bodies, lauryl
123betaine (empigen detergent, BOC Sciences) to a final concentration of 3%, as well as glycerol(15%) and tris(2-carboxyethyl)phosphine (TCEP, 1 mM), were added, and the mixture was rotated
at 4 C for at least an hour. The insoluble cell debris were then removed by centrifugation of thelysate at 20,000 rpm and 4°C for 30 minutes. The supernatant was collected and mixed with 0.5
mL of pre-equilibrated HisPurTM Ni-NTA Superflow Agarose resin (Thermo Scientific) per gramof cell pellet. The resulting mixture was rotated at 4 °C for ~14 hours.
The resin was then packed into a chromatography column and washed first with 5 column
volumes (CV) of purification buffer (40 mM HEPES (pH 7.5), 300 mM NaCl) containing 15%
glycerol, 3% empigen, and 1 mM TCEP, followed by purification buffer containing 40 mM
imidazole, 15% glycerol, 1.5% empigen, and 1 mM TCEP, until the measured absorbance at 280
nm reaches the baseline, indicating elution of weakly bound impurities (typically ~15 CV). The
empigen detergent was then exchanged with n-tetradecylphosphocholine (TDPC, Anatrace) by
washing the resin with 12 CV of 25 mM sodium phosphate buffer (pH 7.2) containing 0.2% TDPC
and 1 mM TCEP. The PMP22 fusion protein was then eluted from the resin using 50 mM Tris
buffer (pH 8) containing 500 mM imidazole, 0.2% TDPC and 1 mM TCEP.
The eluted protein was buffer exchanged twice with 20 mM sodium dihydrogen phosphatebuffer (pH 7.5) containing 1 mM TCEP to remove the imidazole. Each buffer exchange cycle
consisted of concentrating the solution to ~1 mL by centrifugation at 3,000 x g using a 10 K
MWCO Amicon Ultra-15 centrifugal filter device (Millipore) and diluting back to ~15 mL. The
final solution was then concentrated to ~5 mL before loading into a pre-equilibrated HiTrap SP FF
cation exchange column (GE Healthcare) for cation exchange chromatography. The cationexchange chromatography serves two purposes: to remove a common E. coli stress protein YodA
that cooverexpressed and coeluted with the PMP22 fusion protein, and to replace the membrane124

mimetic from TDPC to SCOR bicelles. After loading the sample, the column was then washedwith at least 30 CV of 20 mM sodium dihydrogen phosphate buffer (pH 7.5) containing 1 mM
DTT, 0.2% SCOR bicelle (q = 0.33), and 0.3 mM β-n-dodecylmelibioside (DDMB, Anatrace) to
exchange the membrane mimetic. The PMP22 fusion protein in SCOR bicelles was eluted byrunning a gradient where the concentration of NaCl is increased from 0 to 1 M over the course of
14 CV.
Cleavage of the fusion partner of PMP22 was carried out by adding 1000 units of thrombin(Recothrom) to ~0.8 mg/mL of purified PMP22 fusion protein adjusted to pH 8.0 by addition of
20 mM Tris buffer (pH 8.0) prior to cleavage. The solution was rotated gently at room temperature
overnight. The His tag-containing fusion partner, as well as uncleaved PMP22 fusion protein, wereseparated from the cleaved PMP22 by performing another Ni affinity chromatography. Preequilibrated Ni-NTA resin (at 1 mL per 10 mg fusion protein) and 20 mM imidazole were added
to the cleavage reaction mixture before rotating for at least an hour at 4 °C. The resin was packed
into a chromatography column and the flow through containing most of the cleaved PMP22 wascollected. Successive washes with 20 mM Tris buffer (pH 8.0) containing 0.2% SCOR bicelle (q= 0.33), 0.3 mM DDMB, 1 mM TCEP, and increasing concentrations of imidazole (20 mM, 30
mM, 40 mM, 250 mM) were carried out. The solution containing the cleaved PMP22 wasconcentrated before loading into a pre-equilibrated size exclusion column (GE Superdex 200
Increase 10/300 GL). Size exclusion chromatography was performed using 10 mM acetate buffer(pH 5.0) containing 100 mM NaCl, 0.2 % SCOR bicelle (q = 0.33), 1 mM EDTA, 5 mM TCEP,
and 0.3 mM DDMB to remove empty bicelles and to exchange the buffer. Fractions containing
PMP22 were combined and concentrated using 10K MWCO Amicon concentrator for use in native125

ion mobility-mass spectrometry experiments. The final concentration of PMP22 in SCOR bicelles
was determined by measuring References

126

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