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\begin{document}

\title{Unveiling the biological interface of protein complexes by mass
spectrometry-coupled methods}



\author[1]{Goeun Shin}%
\author[1]{Sung In Lim}%
\affil[1]{Pukyong National University}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Most biomolecules become functional and bioactive by forming protein
complexes through interaction with ligands that are diverse in size,
shape, and physicochemical properties. In the complex biological milieu,
the interaction is ligand-specific, driven by molecular sensing and
recognition of a binding interface localized within a protein structure.
Mapping interfaces of protein complexes is a highly sought area of
research as it delivers fundamental insights into proteomes and
pathology and hence strategies for therapeutics. While X-ray
crystallography and electron microscopy still serve as a gold standard
for structural elucidation of protein complexes, artificial and static
analytic nature thereof often results in a non-native interface that
otherwise might be negligible or non-existent in biological environment.
In recent years, the mass spectrometry-coupled approaches, chemical
crosslinking (CLMS) and hydrogen-deuterium exchange (HDMS), have become
valuable analytic complements to traditional techniques.~These methods
explicitly identify hot residues and motifs embedded in binding
interfaces, in particular,~for which~the interaction is predominantly
dynamic, transient, and/or caused by~an~intrinsically disordered
domain.~Here we review the principal role of CLMS and HDMS in protein
structural biology with a particular emphasis on the contribution~of
recent examples~to exploring biological interfaces. In addition, we
describe recent studies that utilized these methods to expand our
understanding~of protein complex formation and related biological
processes and to increase probability of structure-based drug design.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

Viability of all forms of life is crucially reliant on homeostasis of
protein interactome. Therefore, elucidation of networks involving
protein-protein or protein-ligand interactions is a cornerstone for
understanding the complexity of biological processes. Additionally,
comprehensive informatics of protein interactions serves as a useful
template for drug discovery and drug repositioning (Payandeh and Volgraf
2021; Scott et al., 2016; Wang et al., 2021). A multitude of methods
have been developed to study the protein interactome with high
throughput and accuracy (Rao et al., 2014). While the protein
interactome illustrates the global network and relation of proteins and
ligands, mechanistic insight at molecular level into each set of protein
interactions further requires high-resolution technologies capable of
probing an array of critical motifs and residues, defined herein as an
interface, that are collectively responsible for accurate molecular
recognition and energetic stabilization.

Elementary bioanalytic methods readily available in most molecular
biology laboratories are size exclusion chromatography (SEC) and native
PAGE which examine if biomolecules under investigation do recognize each
other, albeit with little information of binding interfaces. These
methods macroscopically detect a change in mobility of biomolecules
through a matrix, attributed by size increase and charge variation upon
molecular association. Quantitative measurements of kinetics and
thermodynamics parameters involved in molecular interactions require
real-time techniques. Surface plasmon resonance (SPR) and bio-layer
interferometry (BLI) employ sophisticated optical biosensors that
monitor minute changes of resonance signals, or light interference
quantitatively dependent upon intermolecular binding events (Dysinger
and King 2012; Rich and Myszka 2011). Data processing from repetitive
measurements in different concentrations of an analyte yields the rates
of association and dissociations as well as the strength of the
interaction in terms of affinity (K\textsubscript{D}). Microscale
thermophoresis (MST) technique is quick to derive K\textsubscript{D}from
variations of a molecular movement of a capillary-filled, fluorescently
labeled ligand throughout microthermal gradient, affected by its
association with an analyte and subsequent changes in physical
properties (Seidel et al., 2013). Isothermal titration calorimetry (ITC)
is a unique method that investigates the interaction between
biomolecules without labeling or immobilization (Pierce et al., 1999).
By measuring differential changes in the heat supplied to the sample
cell, as compared to the blank cell, in which two binding partners are
incrementally mixed, ITC provides useful parameters related to the
bimolecular interaction including binding stoichiometry, enthalpy and
entropy changes as well as affinity. Each quantitative method has its
advantages, and might serve as a standard technique depending on the
amount, purity, and physicochemical properties of biomolecules under
investigation. Despite being more informative than SEC and PAGE, these
methods are still far from defining the binding interface which is
mainly responsible for various interaction parameters obtained in the
measurements. Structural information of a binding interface at atomic
resolution can be derived by implementing X-ray crystallography, nuclear
magnetic resonance spectroscopy (NMR), or cryo-electron microscopy
(Cryo-EM). While X-ray crystallography is the most predominant in
accessibility and practice thus far, the size of the crystallographic
interface is usually smaller and the interface water content is higher
in general compared to the interface under native environment, referred
to herein as `biological interface', due to non-physiological but
crystallization-favored conditions inflicted on a protein complex
(Schreiber 2020). Recent advances of NMR and Cryo-EM in instrumentation
and software reliably extract structural information of protein
complexes in pseudo-physiological conditions which can then be processed
to reconstruct preexisting interfacial crystal contacts into a
near-native state or even newly visualize the binding interfaces not
resolved in X-ray crystallography. Cyro-EM and NMR provide unique and
additional advantages of facile sample preparation and delivery of
structural dynamics data, respectively. Still, variable and dynamic
natures of binding interfaces embedded in protein complexes complicates
the interpretation of spectroscopic data with reasonable resolution and
fidelity, rendering mechanistic details of many protein interactions as
yet to be explored.

Aside from the high resolution, another critical attribute expected from
structural information of protein complexes is the high fidelity which
presents the most probable and representative snapshot of biological
interfaces under in-solution native environment, contrasting to a static
condition in which X-ray crystallography and electron microscopy are
generally best suited. Despite high resolution provided by static
methods, however, a static interface features non-physiological water
content and accessibility due to non-specificity caused by invasive
conditions such as a wide range of pH, high ion strength, high protein
concentration, all of which are detrimental to obtaining high-fidelity
interfacial landscape. In addition to such empirical artifacts, a static
interface often loses fidelity due to inherent natures of certain
protein interactions which could be dynamic, transient, weak, or
mediated by an intrinsically disordered domain that adopts no static
conformation. Thus, in combination of X-ray crystallography and electron
microscopy, supplementary methods to obtain structural data from the
native solution-phase are of great necessity to probe a biological
interface with both high resolution and fidelity.

Chemical crosslinking (CLMS) and hydrogen-deuterium exchange (HDMS),
both of which are coupled to mass spectrometry, have become unique and
complementary analytic methods to address abovementioned unmet needs in
structural biology. CLMS and HDMS are particularly valuable for
unveiling hot spots in interfaces that otherwise would be cryptic or of
poor fidelity, thus enabling the construction of a new interfacial
landscape of an unknown protein complex or reconstitution of a
preexisting protein interface, and subsequently aid in the
structure-based design of novel modulators of binding interfaces with a
high success rate (Arkin et al., 2014; Chen et al., 2012). In this
review, we briefly describe how CLMS and HDMS map the interfaces of
protein complexes with high fidelity and their integration with other
structural information to yield native-like high-order structures
assembled via biological interfaces. We then discuss recent efforts that
successfully employed CLMS and/or HDMS to reveal dynamic, transient, or
intrinsically disordered interfaces found in various protein complexes
that had been challenging to probe via X-ray crystallography and
electron microscopy.

Mass spectrometry-coupled techniques, CLMS and HDMS, provide novel means
of landscaping a biological interface. In contrast to abovementioned
spectroscopic techniques focusing on electronic or electromagnetic
properties of respective atoms, CLMS and HDMS delve into reciprocal
behavior of residues with other residues or matters surrounding them
under a native condition. The resultant mass changes are detected to
calculate corresponding inter-residue distances or solvent
accessibility. Although structural information obtained from CLMS and
HDMS is generally intermediate in resolution, it provides improved
fidelity by repositioning known residues in a static interface or
identifying unknown residues residing in a dynamic, transient, or
disordered interfacial region.

\textbf{2. Mapping biological interfaces by CLMS}

A crosslinking reagent makes a short covalent bridge between two
biomolecules in proximity, i.e. freezing them, that occurs when they are
bound through specific molecular contacts, thereby usefully aiding the
identification of an unknown binding partner interacting with a host
protein, especially in proteome-wide studies (Figure 1) (G\selectlanguage{ngerman}ötze et al.,
2019; Holding 2015; Piersimoni and Sinz 2020). Due to poor site-specific
reactivity and/or lack of highly sensitive analytical tools,
residue-level elucidation of a binding interface via chemical
crosslinking had not been possible until controlled crosslinking
chemistry and high-sensitivity mass spectrometry emerged into practice
(Mishra et al., 2020; Yang et al., 2016).

Among a variety of chemical reactivity available, to date, a
photo-activated functionality such as diazirine provides the most
effective resolution of crosslinks due to \emph{in situ} initiation and
the fast kinetics of chemical steps. Additionally, a carbene formed from
diazirine upon UV irradiation shows an equivalent crosslinking
reactivity to all proteinogenic amino acids, avoiding biased estimation
in the following mass spetrometric analyses. An unnatural amino acid
containing a photo-activated residue (UAA) can be genetically and
site-specifically inserted into a protein or a photo-activated moiety
can be synthetically incorporated into a peptide or a small molecule
(Chen et al., 2020; Pham et al., 2013; Yang et al., 2016).
Alternatively, a natural amino acid residue such as amine or thiol in a
protein is derivatized with N-hydroxysuccinimide (NHS) ester in a
heterobifunctional linker that also contains a photo-activated group for
a subsequent crosslinking reaction to a binding partner.

A conventional homobifunctional linker bearing NHS esters is still
widely applicable not only for its ease of manipulation but also for
newly introduced functionalities like MS-cleavable moiety, isotope
labeling, or well-defined spatial constraints which, to a greater
extent, facilitate automated mass data analyses and computational
modeling with high confidence (Iacobucci et al., 2018; Ihling et al.,
2020; Rappsilber 2011). For more details about the workflow of CLMS,
crosslinking chemistries, and data processing which are beyond the scope
of this review, interested readers are referred to several recent
reviews (Chavez et al., 2019; Holding 2015; Piersimoni and Sinz 2020).

\textbf{2.1. Interfaces revealed by UAA incorporation and crosslinking}

Incorporation of a photo-reactive UAA into a target protein in live
cells followed by controlled crosslinking \emph{in situ} provides a
great advantage of identifying target protein-specific binding
partner(s) in a native environment (Figure 2A). Importantly, it
circumvents the need for target protein extraction and purification in
aqueous solution before crosslinking, which is almost impossible for
some proteins, especially those in cellular membranes like G
protein-coupled receptors (GPCRs). GPCRs are the largest family of
integral membrane proteins with a characteristic structure comprising
seven transmembrane helix folds, three intracellular loops together with
the N-terminus, and three extracellular loops together with the
C-terminus (Cvicek et al., 2016). Membranous and flexible natures of
GPCRs usually compromise the sample preparation for classical structure
analyses that require a substantial sample amount and purity. Even
though dozens of GPCR structures have been determined, most of them are
believed to represent a single snapshot of GPCR that otherwise would
wiggle vigorously in a native environment.

CXC chemokine receptor 4 (CXCR4) is a pathologically and clinically
important GPCR in cancers, inflammation, and viral infection. To probe
the binding site of T140, a 14-residue cyclic peptide HIV-1 entry
blocker, CXCR4 was site-specifically mutated with a photoactivatable
UAA, p-benzoyl-l-phenylalanine (BzF), at various positions one-by-one in
live cells (Grunbeck et al., 2011). Co-incubation of the fluorescently
labeled T140 with CXCR4-transfected cells was followed by UV exposure to
trap a CXCR4-T140 complex. A series of experiments for each CXCR4 mutant
showed that the crosslinked complex was formed only when BzF had been
incorporated at Phe189. Based on a characteristic 3 Å-long reactivity
distance of BzF as well as the positional information gained from the
crosslinking analyses, molecular modeling could successfully derive a
more accurate model of the CXCR4-T140 interface modified from available
crystal structures. Similarly, strategic incorporation of
photo-crosslinking UAA allowed identification of contact residues of
CCR5 in complex with a small molecule HIV-1 entry inhibitor, maraviroc
(Grunbeck et al., 2012). Newly discovered contact residues previously
not recognized in the computational modeling could serve as a cue to
reconstruct the interface taking the dynamic allosteric binding of
maraviroc into account (Grunbeck et al., 2012). Although the
UAA-mediated crosslinking studies mentioned above lacked the mass
spectrometric analysis, a genetically well-defined position of
crosslinking and the ability to incorporate the UAA in any position of
interest could make a significant contribution to remodeling GPCR-ligand
structures to display the biological interface. The most attractive
aspect of the method in studying the structural biology of GPCRs lies in
that important contact sites can be identified even when GPCR behaves
dynamically in a native cellular environment in response to different
biological stimulation. By analyzing 25 different mutants of the
angiotensin II type 1 receptor (AT1R) containing a photo-reactive UAA,
Gagnon et al. demonstrated that AT1R exhibits structurally differential
binding modes, i.e., distinct conformations or residues, with an
intracellular \selectlanguage{greek}β-\selectlanguage{english}arrestin regulator depending on the presence of an
extracellular ligand, angiotensin II (Gagnon et al., 2019).

Photo-reactive UAA incorporation technique in live cells readily allows
investigation of a dynamic interface hidden in the transmembrane domain
which is much more challenging to recombinantly prepare \emph{in
vitro}than the extracellular domain. CGRP is a ligand that binds to the
CLR/RAMP1 receptor. While the crystal structure of the extracellular
domain of CLR/RAMP1 in complex with CGRP and corresponding contact
residues had been available, the interface located in the transmembrane
domain remained to be probed. Crosslinked residue screening by
photo-reactive UAA crosslinker incorporation at multiple potential
contact sites in the CLR transmembrane helix domain identified major
contact residues, providing insight into the extent of CGRP penetration
into the transmembrane core of CLR (Simms et al., 2018). It should be
noted that, besides GPCR, most protein complexes recombinantly
expressible in E. coli or mammalian cells can benefit from techniques
reviewed above for investigation of biological interfaces regardless of
their size, cellular location, and complexity (Bridge et al., 2019;
Owens et al., 2019; Rubino et al., 2020).

Since biochemical data obtained from the UAA-mediated crosslinking are
an array of band shift patterns resulting from various
crosslinked-target proteins or fragments in the immunoblotted gel
electrophoresis, the output is generally moderate or low in
three-dimensional conformation and resolution. The crosslinking data can
be best utilized when integrated with other structural information to
give a refined structure with high resolution and fidelity. For example,
the class B corticotropin-releasing factor receptor type 1 (CRF1R) was
mapped by genetically incorporating an UAA crosslinker systematically to
characterize the difference in a conformational change and an
interfacial landscape, when CRF1R was stimulated by an agonist or
antagonist. Differences in band shift patterns were evident in western
blotting, and the inter-residue distance constraints, a characteristic
of the crosslinked chemical structure, estimated for all of the
crosslinked residues were applied to computationally generate
conformational models of the agonist- and the antagonist-bound CRF1R
complex. Extensive sampling of conformations led to optimized structure
models for both complexes, revealing that CRF1R adopted distinct binding
interfaces and local conformations to engage the agonist and the
antagonist, respectively (Seidel et al., 2017). Remarkably, the
predicted models have been found to be very compatible with the
high-resolution cryo-EM structures obtained years later (Liang et al.,
2020; Ma et al., 2020). A unique hormone-binding motif of the insulin
receptor could be recognized from the crosslinking data in conjunction
with the preexisting crystal structure of the apo-insulin receptor. As
was expected from the crystal contacts, a photo-reactive UAA
incorporated in the typical \selectlanguage{greek}β-\selectlanguage{english}strands 2 and 3 in the L1 domain of the
insulin receptor \selectlanguage{greek}α-\selectlanguage{english}subunit was found to be crosslinked to the other
\selectlanguage{greek}α-\selectlanguage{english}subunit in an apo-state. However, in a holo-state where the insulin
receptor was bound to the insulin, the crosslinks were made to the
insulin, implying a dynamic interface change caused by the induced fit
of the insulin (Whittaker et al., 2012). Similarly, a recent study
performed a crystal structure-based photo-crosslinking analysis of the
macromolecular complex of nuclear pore proteins, Nup82, Nup116, and
Nup159. Of note, crosslinking patterns demonstrated that, in comparison
to the crystal interfaces, the interfaces of Nup82 in contact with
Nup116 and 159 were significantly different in areas and residues
involved (Shin and Lim 2020). Interestingly, some contact residues in
the interface engaging Nup82 and Nup116 in the ternary complex seemed to
lose contacts when Nup82 and Nup116 formed the binary complex by
themselves without Nup159, indicating a dynamic nature of the interface
not locally isolated but wriggling in conjunction with overall
structural changes. Abovementioned and related studies are summarized in
the header row of `photo-crosslinking UAA in live cells' (Table 1).

\textbf{2.2. Interfaces revealed by UAA incorporation and crosslinking
coupled to MS}

While the UAA-mediated photo-crosslinking technique facilitates a scan
of interfacial residues on one side of the biomolecular complex,
specific locations of the crosslinks made at the other binding molecules
remain elusive. This is because photo-reactive UAA can be
site-specifically incorporated in the target protein but its reactivity
upon irradiation towards a binding partner protein is non-specific.
Analytic improvements made through enzymatic fragmentation of the
crosslinked complex followed by fingerprinting by high-resolution mass
spectrometry opened up a new arena of probing two-sided binding
interfaces of a protein complex with better resolution and fidelity
(Figure 2B). For example, to investigate the mechanistic details of
lipopolysaccharide transport through the integral membrane protein
LptD/E complex, LptE was site-specifically modified with an UAA
photo-crosslinker at various positions. The covalent intermolecular
complexes LptD/E formed upon irradiation were observed in Western
blotting and then subjected to tryptic digestion followed by
high-resolution two-dimensional MALDI-TOF/TOF spectrometry. The UAA
incorporated at the 150\textsuperscript{th} position of LptE was found
to be linked to S538 in LptD, specifically suggesting that a putative
extracellular loop of the LptD \selectlanguage{greek}β-\selectlanguage{english}barrel spanning a residue S538 was a
major motif of interaction with LptE (Freinkman et al., 2011). In case a
ligand is a small molecule that is amenable to derivatization with a
photo-reactive group, the interface mapping can be performed without
generating multiple protein mutants bearing a photo-reactive UAA.
Diazirine-bearing cholesterol acting as a photoactivatable crosslinker
as well as a cofactor for the glycine receptor was employed to
investigate how the landscape encompassing contact residues between the
cholesterol and the glycine receptor would vary depending on a
cholesterol concentration. Crosslinks obtained after trypsin digestion
were analyzed by the multidimensional MS to identify specific amino
acids of the glycine receptor linked to the cholesterol with high
sensitivity and subsequently map the cholesterol-protein interface
altering as a function of cholesterol composition (Ferraro and Cascio
2018). A bifunctional UAA bearing a diazirine, for photo-crosslinking,
and a terminal alkyne group, for bioorthogonal tagging, has been
reported (Yang et al., 2020). A ligand harboring the bifunctional UAA
captured a target protein by photo-crosslinking and then could be tagged
with an affinity motif such as biotin by alkyne-azide click chemistry.
This strategy might enable the enrichment of ligand-target peptide
conjugates after digestion and purification for high resolution MS,
allowing efficient mapping of biological interfaces on both sides of a
ligand and a target.

Although the mass spectrometry greatly enhances the abundance of
structural information obtained from the crosslinking, it often suffers
from a high rate of false-positive hits and from missed true-positive
hits due to complicated mass spectra to interpret, in particular when
the photo-crosslinking is performed under living conditions, i.e., with
high contamination backgrounds. To increase the confidence level of data
analyses, a novel photo-reactive UAA bearing a transferable chemical
label has been developed. The UAA introduced site-specifically to a
target protein, upon interaction with a binding partner, makes a
crosslink and then undergoes an oxidative cleavage leaving the label on
the interface of a binding partner. This technique enables the
identification of true-positive mass fragments with high confidence and
the exact contact residues on both sides of interfaces simultaneously
(He et al., 2017; Yang et al., 2016; Yang et al., 2017).

As demonstrated in studies reviewed above, the UAA-mediated crosslinking
method genetically installs a reactive chemical handle directly into a
predicted interfacial residue of interest, and induces interfacial
crosslinkage of a protein complex in a controlled manner. As it require
no exogenous chemical reagents at the time of crosslinking, diffusional
and steric limitations that otherwise would adversely affect data
fidelity can be avoided. Despite such unique advantages it provides,
however, some cares need to be taken to address a potential risk of poor
expression yield of UAA-incorporated proteins and/or structural
perturbation of a native interface by UAA mutation. Abovementioned and
related studies are summarized in the header row of `photo-crosslinking
UAA' (Table 1).

\textbf{2.3. Interfaces revealed by a chemical crosslinker coupled to
MS}

A bifunctional crosslinker with a specific reactivity towards amino acid
residues or naturally occurring bonds in biomolecules can be usefully
employed to `freeze' a protein complex in the solution phase. The
technique avoids the need for mutagenesis or derivatization of
interacting molecules which sometimes interferes with a natural binding
mode of interaction, and necessitates integrative analyses in tandem
with mass spectrometry due to rich but complex dataset generated upon
crosslinking. A chemical crosslinker is generally applied to a purified
biomolecular complex to eventually obtain comprehensive distance
information between residues or bonds thereof (Figure 2B). The
intrinsically disordered domain (IDD) of a protein has little propensity
to form a fixed or ordered conformation. Whereas some IDDs are involved
in functional molecular interaction, their flexible structure or
transient binding behavior often limits the investigation of binding
interfaces by conventional spectroscopic methods. A chemical linker
approach has proven effective to address this issue. The tumor
suppressor p53 which is known for its inherent structural flexibility
and resistance to crystallization was subjected to the chemical
crosslinking using an amine-reactive bifunctional linker coupled to mass
spectrometry. The analyses uncovered novel information regarding the
dimer-dimer interface of the p53 tetramer as well as a large degree of
flexibility of the C-terminal domain and DNA-dependent conformational
changes (Arlt et al., 2017; Arlt et al., 2015).

Moreover, amine-specific linkers with different reactive groups and
spacer distances were utilized to dissect the flexible or disordered
interfaces of macromolecular complexes such as MICAL3, ELKS, and Rab8A;
SLAIN2, CLASP2, and ch-TOG (Liu et al., 2017); NLRP1 and DPP9
(Hollingsworth et al., 2021); Rpn2 and Rpn13 (Gong et al., 2020). Recent
developments in linker chemistry have produced crosslinker reagents that
are cleavable, photo-activatable, or heterobifunctional, permitting
controlled crosslink reactions, low background, improved sensitivity
(Beard et al., 2021; Krist and Statsyuk 2015), and even in a native
cellular environment (Xie et al., 2021). For instance, a crosslinker
bearing a photoreactive moiety as well as a reducible spacer have been
developed. A bait protein labeled with the crosslinker at a defined
residue is photo-crosslinked to a prey protein after which a reductive
removal of a bait protein leaves a thiol-containing fragment of the
crosslinker onto the target that can be located by MS (Horne et al.,
2018; Mintseris and Gygi 2020). This technique improves MS sensitivity
by eliminating the background from bait peptides and, depending on the
site of crosslinker labeling and spacer length, would provide structural
information surrounding the interfacial region.

A substantial amount of interfacial information attained in these
biological environments was completely new or contradictory to
information previously obtained from the crystallographic or cryo-EM
structures, enabling remodeling or refinement by the combinatorial data
processing. The technique also successfully probed interfacial residues
responsible for protein oligomerization, which had been poorly resolved
by crystallography (Karag\selectlanguage{ngerman}öz et al., 2017). Abovementioned and related
studies are summarized in the header rows of `photo-crosslinker' and
`chemical crosslinker' (Table 1).

\textbf{3. Mapping biological interfaces by HDMS}

Hydrogens in amides constituting a protein backbone undergo continuous
exchange with hydrogens in a solvent. The exchange rate of each amide
hydrogen varies significantly, depending on solvent accessibility
(Figure 1). By incubating a protein complex in a solvent containing
excess deuterium oxide (D\textsubscript{2}O), the exchange rate can be
quantitatively measured by a mass increase of the complex, a basic
principle of hydrogen-deuterium exchange coupled to mass spectrometry
(HDMS). Regional mass changes measurable by proteolytic fragments of the
complex followed by high-quality MS can be translated into residue- or
peptide-level solvent accessibilities which are useful to infer overall
protein conformation and, in particular, potential interfacial areas
where the exchange rate differs in a bound state relative to an unbound
state.

In comparison to CLMS, HDMS is more applicable to any size of a protein
complex and involves relatively simple data interpretation. On the other
hand, HDMS does not freeze the complex but monitors the time course of
changes which might not be significant for unstructured or transient
interfaces where H-D exchange is too fast to detect. For more details
about the work-flow of HDMS and data processing which are beyond the
scope of this review, interested readers are referred to several reviews
elsewhere (Anderson et al., 2018; Masson et al., 2019; Zhang et al.,
2012).

\textbf{3.1. Antibody-epitope interface}

Success factors for discovery of biopharmaceuticals include
understanding the epitope-efficacy relationship in addition to other
parameters such as affinity and manufacturability (Puchades et al.,
2019; Sun et al., 2021; Zhu et al., 2019). HDMS is robust, high in
throughput (Qi et al., 2021), ensuring quick and efficient determination
of binding regions in a near-native physicochemical condition --- a
feature very attractive to the fast-paced pharmaceutical R\&D sector. In
this regard, HDMS has made significant contributions to probing binding
interfaces of antibody-antigen complexes, i.e., epitope mapping, in
industry as well as academia (Figure 3A) (Masson et al., 2017).

In particular, exceptional applicability of HDMS to biomacromolecules is
useful for mapping an epitope for an antigen-antibody complex which is
usually as large-sized as more than 100 kDa --- far beyond a measurable
limit of NMR analysis. Huang et al. subjected a homotrimeric cytokine
(90 kDa), TL1A, complexed with three antibodies (450 kDa) at an
equimolar binding stoichiometry to HDMS for the epitope mapping. The
analysis allowed residue-level determination of a discontinuous epitope
within a predicted binding interface of TL1A and its cognate receptor
DR3, rationalizing a strong potency of the antibody as a TL1A antagonist
(Huang et al., 2018). The epitope mapping by HDMS is less idiosyncratic
and resource-intensive, permitting comparative epitope analyses for a
high-molecular-weight antigen targeted by two distinct antibodies. For
instance, the epitopes of a large homotrimeric food allergen, cashew 11S
globulin allergen (286 kDa), bound to either of two monoclonal
antibodies, were mapped by the solution-phase HDMS (Zhang et al., 2011).
Due to the large size and complexity of the antigen-antibody complex,
the epitope mapping was inaccessible by NMR or X-ray crystallography.
Desirably, HDMS was readily applicable to the complex, yielding
comparative epitope analyses between two different antibodies in
parallel, and required less overall effort than the conventional epitope
mapping strategies such as mutagenesis and peptide scanning (Gershoni et
al., 2007; Ozohanics and Ambrus 2020; Ständer et al., 2021).

One of the novel characteristics of immunoglobulin G (IgG) is the
FcRn-mediated recycling that is responsible for long circulation
half-lives of IgG. Understanding their structural mechanisms of
interaction is crucially important for optimizing pharmacokinetics of
therapeutic antibodies (Liu 2018). As revealed in the X-ray
crystallography (Oganesyan et al., 2014), the Fc region was mainly
involved in FcRn interaction. However, a lack of structural information
on a full-length IgG (150 kDa) complexed with FcRn (50 kDa,
extracellular domain) in solution raised a possibility of potential
binding interfaces located at the F\textsubscript{ab} region. Jensen et
al. studied the interaction between a full-length human IgG1 and human
FcRn via HDMS, and identified several loci at the
F\textsubscript{ab}region which were substantially protected from
hydrogen/deuterium exchange in the presence of FcRn. Some of the loci
were found even in V\textsubscript{H} and V\textsubscript{L} domain
remote from the Fc region, implying a landscape of the FcRn-IgG binding
interface extended throughout the whole IgG structure or conformational
dynamics of the F\textsubscript{ab} region relevant to the FcRn binding
(Jensen et al., 2015). Future studies might provide supplementary CLMS
datasets that include defined distance information between FcRn and the
protected loci of IgG detected by HDMS, and therefore should scrutinize
either possibility unambiguously.

A unique analytic feature of HDMS is its ability to directly monitor the
interfacial dynamics at various protein concentrations in solution.
Antibodies are prone to reversible self-association when highly
concentrated (Yadav et al., 2011). Arora et al. investigated two
antibody samples at low and high concentrations by HDMS in parallel, and
could define major interfacial hot spots in CDRs that are related to the
concentration-dependent reversible self-association (Arora et al.,
2015). Furthermore, the HDMS results demonstrated that
C\textsubscript{H1}-C\textsubscript{H2} interface at the hinge region,
distal to the self-association interface, exhibited significant local
backbone flexibility in a concentration-dependent manner, presumably
attributed by long-range, distant dynamic coupling effects.
Abovementioned and related studies are summarized in Table 2.

\textbf{3.2. Oligomer interface}

Protein oligomerization occurs in biological systems, with about 30-35\%
of total proteins involved (Gabizon and Friedler 2014; Kumari and Yadav
2019). Thus, the ability to regulate protein oligomerization by
introducing mutations in oligomerization interfaces or engaging a
specific modulator should prove useful for studying the biological role
of protein oligomerization of interest (Kumari and Yadav 2019). On the
one hand, there is a variety of circumstances in which a process of
protein folding is incomplete or interrupted, eventually leading to
formation of pathological or pathogenic protein oligomers (Figure 3B).
For instance, the development of cognitive disorders such as Alzheimer's
disease (AD) and Parkinson's disease (PD) is typically accompanied by
the formation and accumulation of amyloid fibrils in the patient's brain
(Araki et al., 2019; Brown et al., 2020; Salahuddin et al., 2021). In
this sense, artificial or natural modulators capable of inhibiting or
reversing aberrant protein oligomerizations would be of great
prophylactic or therapeutic measures to treat relevant diseases
(Galzitskaya 2019; Lee et al., 2017). Above all, understanding the
underlying mechanisms of protein misfolding is one of the primary steps
for the development of novel therapeutic strategies. In the study using
HDMS by Stephens et al., the unstructured amyloid protein, \selectlanguage{greek}α-\selectlanguage{english}synuclein
(aSyn) involved in PD, exhibited the highest solvent protection at the
C-terminus, indicative of a fold at the C-terminal domain that had been
presumed to play a role in modulating aggregation but without structural
evidences (Stephens et al., 2018).

Aggregation of tau into the paired helical filament (PHF) is a
characteristic feature of AD, and the way Cys-mediated disulfide bond is
formed, either intermolecularly or intramolecularly, serves as a
critical element in tau fibrillation. To understand a molecular feature
of aggregation-resistance of tau conformer, Jebarupa et al. synthesized
the intramolecular Cys cross-linked tau monomer by oxidation and
analyzed the rearranged conformational dynamics by HDMS. As a result,
they found that, due to induced intramolecular H-bonding, the oxidized
tau exhibited increased conformational rigidity and reduced
accessibility in the core of the oligo-inducing interface that otherwise
would have made an intermolecular H-bonded \selectlanguage{greek}β-\selectlanguage{english}sheet formation and
subsequently tau fibrillation (Jebarupa et al., 2019). Other than the
disulfide bond, aggregation propensity of tau was investigated in the
context of its extent of phosphorylation by Zhu et al. Time-resolved
electrospray ionization (TRESI) mass spectrometry in combination with
hydrogen/deuterium exchange (TRESI-HDX) is responsive to dynamic,
temporary, and weak hydrogen bond interactions, as well as solvent
accessibility, both of which are influenced by residual structure ---
biases in their native conformational ensembles of intrinsically
disordered proteins. Authors used TRESI-HDX to characterize the native
structural ensembles of a full-length tau, one of the main amyloidogenic
species in AD, offering a detailed picture of the conformational changes
that occur upon hyperphosphorylation by a kinase GSK-3\selectlanguage{greek}β \selectlanguage{english}(Zhu et al.,
2015). Increased deuterium uptake of the hexapeptide motif (H2) of
hyperphosphorylated tau sampled appropriately such that aggregates were
not significantly populated at this time pointed to a dominant role for
H2 in GSK-3\selectlanguage{greek}β-\selectlanguage{english}mediated increases in tau amyloidogenic propensity,
consistent with the conclusion of a previously reported loss-of-function
mutagenesis study (von Bergen et al., 2000).

Aggregation-prone apolipoprotein E4 (ApoE4) is a major risk factor for
AD and cardiovascular diseases. Huang et al. coupled HDMS to gas-phase
electron-transfer dissociation fragmentation (Zehl et al., 2008) to
achieve single amino acid resolution and specify residues responsible
for self-association of ApoE4. Despite the lack of a determined crystal
structure as a reference due to a high tendency of ApoE4 to aggregate,
the method could tune the analytic concentrations appropriately and
identify 15 residues in the C-terminal domain deemed critically situated
in ApoE4 oligomerization interface (Huang et al., 2011).

Amyloid fibrils formed by \selectlanguage{greek}β-\selectlanguage{english}2-microglobulin (\selectlanguage{greek}β\selectlanguage{english}2m) are an inevitable
symptom of kidney failure-induced dialysis in patients' joints. Borotto
et al. used HDMS to reveal a few structural insights into metal-induced
amyloid development of \selectlanguage{greek}β\selectlanguage{english}2m. They reported that the Cu(II) binding to
Asp59 is required for the formation of amyloid-competent dimers, as well
as cis-trans isomerization of the His31-Pro32 amide bond essential for
the formation of the amyloidogenic conformer. In contrast, Ni(II) only
binds to His31 and does not cause structural changes favorable for
producing oligomers or amyloids. Interestingly, the dimer formation was
observed in response to Zn(II) binding in a similar fashion with
Cu(II)-induced \selectlanguage{greek}β\selectlanguage{english}2m dimerization, but did not lead to the pathway to the
amyloid. Focused investigation into the dimer interfaces by HDMS found
out the Zn(II)-induced dimer interface quite different from the
Cu(II)-induced dimer interface in terms of stability and local
interfacial areas involved (Borotto et al., 2017). Abovementioned and
related studies are summarized in Table 2.

\textbf{3.3. Protein-lipid interface}

Membrane proteins are notoriously insusceptible to high-purity
preparation for structural analyses due to their lipophilic nature and
propensity to deformation when not in contact with membranes. Detergent
micelles and vesicles are usually employed as standard formulations to
isolate membrane proteins in a stable and functional state, thus
allowing HDMS to probe conformational dynamics and molecular
interactions of membranes proteins. Readers are referred to recent
review articles that discussed the accomplishments of HDMS in structural
biology of membrane proteins (Kaiser and Coin 2020; Martens and Politis
2020). Here we focus on the role of HDMS in exploring a heterogeneous
interface between a membrane protein and phospholipids surrounding it.
Hydrogen-deuterium exchange analysis is one of the least invasive
methods that requires neither reactive chemicals nor harsh conditions,
and thus readily operational under any preparative condition optimized
for specific membrane proteins that behave in a reversible (peripheral
protein) or permanent (integral protein) manner (Figure 3C). Moreover,
neither high sample purity nor large sample quantity is essential for
HDMS owing to recent advances in instrumentation and data processing
software (Martens et al., 2019).

The power of HDMS, free of labeling or crosslinking, was well utilized
in the study of transmembrane regions within an integral membrane
protein. A GPCR model, \selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}-adrenergic receptor
(\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}AR) was prepared in micelles for HDMS analyses, and
the overall HDMS profile showed that the transmembrane regions
surrounded by lipids was much lower in deuterium exchange than the
exposed regions, correlating well with the predicted seven-transmembrane
structure of GPCRs (Duc et al., 2015). In another study, transporter
proteins prepared in nanodiscs with different lipid compositions were
analyzed by HDMS for changes in conformational equilibrium (Martens et
al., 2018). It was found out that the charge-conserved, lipid-contacting
interfaces of transporters were responsible for the conformational shift
whose equilibrium was significantly affected by the lipid composition.

Tumor suppressor phosphatase and tensin homolog (PTEN) interacts with
cell membranes in a switchable manner depending on dynamic
conformational ensembles affected by phosphorylation (Jang et al.,
2021). In addition to active dynamics, PTEN possesses intrinsically
disordered tails at both termini which are considered important as
membrane binding elements but not crystallizable. HDMS could unveil a
novel mechanism of membrane interaction by PTEN, a behavior specifically
driven by the membrane-binding interface at the N-terminal tail in which
the extent of deuterium exchange was significantly dependent upon
co-incubation of the lipid vesicles interacting with PTEN (Masson et
al., 2016). Similarly, HDMS was employed to map the interface of
sphingosine kinase 1 (SK1) and membrane vesicles. SK1 is an enzyme that
catalyzes the conversion of sphingosine in membranes to
sphingosine-1-phosphate (S1P). The study identified a positively charged
motif on SK1 responsible for electrostatic interactions with membranes,
and further demonstrated a contiguous interface, comprising an
electrostatic site and a hydrophobic site, that interacted with
membrane-associated anionic phospholipids (Pulkoski-Gross et al., 2018).
More recently, a 390-kDa heterotetrameric lipid kinase Vps34 examined by
HDMS has revealed its so-called `aromatic finger' that interacts
directly with lipid membranes and regulates the catalytic activity. A
decreased rate of deuterium exchange in the finger region in the
presence of lipid vesicles served as a signature for its defined role.
Abovementioned and related studies are summarized in Table 2.

\textbf{4. Probing biological interfaces by integrative approaches}

While CLMS and HDMS may stand alone as the techniques to map the
biological interface, their integration aided by computational modeling
with the dataset obtained from X-ray crystallography, EM, or NMR can
significantly improve spatial resolution and coverage of binding
interfaces to provide comprehensive illustrations. For example, Zhang et
al. first implemented CLMS and HDMS together to extract previously
unknown information on the epitope and paratope interface of a
programmed cell death-1 (PD-1) and the corresponding antagonistic
antibody. In the next round of refinement, the suggested critical
binding residues and distance restraints were utilized to build
high-confidence binding models through molecular docking onto the
crystal structure (Zhang et al., 2020). While the epitope-paratope
relationships revealed by the approach were generally comparable with
those assigned by the crystal structure, one cryptic loop in PD-1 that
had not been crystallographically resolved due to its flexible nature
was found to be a non-epitope. The study demonstrated a complementary
role of CLMS and HDMS for accurate and detailed examination of a binding
interface. The same research group described a similar integrated
approach combining CLMS, HDMS, and molecular docking to probe the
binding interface of interleukin 7 (IL-7) complexed with its receptor
IL-7R\selectlanguage{greek}α. \selectlanguage{english}While the predicted model was generally in accordance with the
crystal structure, the approach newly discovered the C-terminal binding
region of IL-7, highlighting the value of integrative approaches to
obtain a high-confidence structural model (Zhang et al., 2019).

Interpretation of HDMS data using a predetermined crystal structure as a
template provides greater insight into reversible changes in regional
flexibility of binding interfaces. A junctional epitope antibody VHH6,
specifically recognizing a neo-epitope created only at the junction in
which IL-6 and gp80 are interlocked, was considered a molecular clamp as
shown in the ternary crystal structure. To understand the effect of VHH6
clamping on the structural flexibility of the junctional interface of
IL-6-gp80, Adams et al. compared the amounts of deuterium exchange
therein in the presence or absence of VHH6 (Adams et al., 2017). The
presence of VHH6 increased the rigidity in the local region spanning the
junction, stabilizing a transient interface between IL-6 and gp80.

Likewise, advances in CLMS techniques are better refining a
low-resolution interface solved by other methods into medium- to
high-resolution details. By applying multiple orthogonal crosslinking
chemistries to a target protein complex, Mintseris and Gygi could attain
a higher crosslinking density and improved sequence coverage (Mintseris
and Gygi 2020). Self-consistent analytic results could be mapped onto
cryo-EM models to define the interaction interface with high resolution
and confidence.

A dramatic contribution of HDMS in harmony with CLMS to modeling the
interface of an unstable macromolecular complex was presented by Shuka
et al (Shukla et al., 2014). While the human
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}adrenergic receptor (\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}AR) and
\selectlanguage{greek}β-\selectlanguage{english}arrestin-1 had crystal structures reported individually, the
\selectlanguage{greek}β\selectlanguage{english}\textsubscript{2}AR-\selectlanguage{greek}β-\selectlanguage{english}arrestin-1 complex was intolerant to experimental
conditions in X-ray crystallography or EM with only low-resolution map
for the overall conformation available. Remarkably, constraints provided
by HDMS and CLMS were mapped onto the preexisting data in an integrative
manner, resulting in the three-dimensional reconstruction of the complex
that displayed an unexpectedly extensive interface and a crucial
involvement of the finger loop in \selectlanguage{greek}β-\selectlanguage{english}arrestin-1 in the complex formation.
More recently, such a combined approach investigated the mechanism of
client binding of the periplasmic chaperone SurA and identified dynamic
inter-domain interfaces that underwent substantial structural
reorganization in response to the substrate binding interface being
occupied by a client OmpX (Calabrese et al., 2020).

\textbf{5. Conclusions and Perspectives}

Efforts to map a proteome-wide PPI network have made significant
contributions to our basic understanding of biological systems (Rual et
al., 2005). Scientists increasingly have deeper and comprehensive
insights into aberrant PPIs occurring in various pathological conditions
to strategically come up with effective therapeutic avenues (Ryan and
Matthews 2005). To this end, rational design of a potent modulator that
best counteracts abnormal PPIs would require detailed knowledge on a
binding interface of PPI with high fidelity comprising not only atomic
resolution but also ``reality'' existing in native environments. CLMS
and HDMS discussed herein are not competent enough for producing atomic
resolution of an interface comparable to that by X-ray crystallography
and EM. However, in terms of reality, they are especially useful in
extracting structural information in biological, native environment
including contact residues at interfaces, inter-residue distances,
peptide-level solvent accessibilities, and temporal dynamics of
interfacial areas, from which refined and near-physiological interfacial
landscape can be remodeled preferably using a preexisting structure as a
template. Exciting computational advances in data processing and
structural biology are greatly improving the fidelity of integration of
data obtained from both convention methods and CLMS/HDMS. High-fidelity
information of an interface thus acquired would serve as an excellent
cornerstone, increasing the likelihood of successful rational drug
discovery. Designer agonists or antagonists that recognize hot residues
in an interface responsible for a targeted PPI in a specific
pathological condition are expected to outperform conventional binders
with regard to both potency and efficacy.

Limitations of CLMS and HDMS remain to be overcome in future advances.
Specifically, CLMS users often suffer from excessive crosslinking
condition optimization and non-specific reactions that would compromise
precise measurement of spatial distances. Back exchange of deuterium in
HDMS hampers the reliable interpretation of solvent accessibility
dataset, necessitating effective sample treatment protocols by tricky
trial and error approach. Developments of new crosslinkers with
residue-specificity, bifunctionality, and/or reduced background mass
signals are expected to address the current challenges of CLMS (Ding et
al., 2016; Leitner et al., 2014; L\selectlanguage{ngerman}össl et al., 2014; Schneider et al.,
2018). Developments of improved workflows in HDMS should prove to be
innovative in the coming era (Hamuro and Zhang 2019; Lau et al., 2019;
Oganesyan et al., 2018). Together with novel MS instrumentation and
computational data processing techniques, all the efforts will continue
to advance CLMS and HDMS as excellent polishing tools for high-fidelity
structural determination of protein complexes and binding interfaces
thereof, thereby fueling the growth of structural proteomics and
biology, and, further, the advent of structure-based drug design regime
with exceptional reliability and probability (Figure 4).

\textbf{Declaration of Competing Interests}

The authors declare that they have no competing interests that can
influence the work reported in this review article.

\textbf{Data Availability Statement}

Data sharing not applicable to this article as no datasets were
generated or analysed during the current study

\textbf{Acknowledgments}

This work was supported by the Basic Science Research Program through
the National Research Foundation of Korea (NRF) funded by the Ministry
of Science, ICT \& Future Planning (2022R1C1C1010344).

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\textbf{Legends of Figures}

\textbf{Figure 1.} Structural information obtainable from CLMS and HDMS.
In CLMS, a crosslinking reagent makes a covalent, site-specific bridge
between binding proteins. The crosslinked complex is subjected to mass
spectrometry to provide contact residues or inter-residue distance. In
HDMS, the hydrogen (H)-deuterium (D) exchange rate in amides of binding
proteins varies depending on solvent accessibility. Relative to residues
or areas in non-interfaces, those in binding interfaces generally
exhibit lower D/H exchange, a mass change identifiable by mass
spectrometry.

\textbf{Figure 2.} Crosslinking techniques employed in CLMS to unveil
biological interfaces. (A) Photo-reactive unnatural amino acid (UAA)
site-specifically incorporated into a predetermined position is
irradiated by UV when proteins are interacting. The formation of a
crosslink observed by gel electrophoresis or mass spectrometry indicates
the UAA-incorporated site is within a potential binding interface. (B) A
photo-activatable or chemical crosslinker with defined geometry and
bifunctional, residue-specific reactivity forms a crosslink between
interacting proteins. Mass analyses provides an abundance of
inter-residue distance constraints surrounding interfacial regions.

\textbf{Figure 3.} Major protein complexes and their native interfaces
investigated by HDMS. (A) Antibody-antigen complex. (B) Self-associated
protein oligomers. (C) Membrane proteins associated with cell membranes.

\textbf{Figure 4.} Computer-aided, integrative structural modeling to
provide near-physiological landscape of binding interfaces of proteins
complexes with high resolution and fidelity. Design of PPI modulators
based on biological interfaces thus obtained would allow new drug
discovery with higher potency and efficacy.

\textbf{Figure 1.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 4.}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Table 1} . Examples of biological interfaces or interfacial
residues uncovered by CLMS.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Crosslinking methods\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Protein complex\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Protein complex\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Interaction\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Findings\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
Ref.\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Photo-crosslinking UAA in live cells (No MS involved)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
CXCR4(GPCR)-T140\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein- small molecule\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein- small molecule\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
F189 is important for binding T140 ligand\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Grunbeck et al., 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
CCR5(GPCR)-maraviroc\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein- small molecule\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein- small molecule\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
New contact residues identified\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Grunbeck et al., 2012)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PGRMC1-Insig-1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
New interfaces identified\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Suchanek et al., 2005)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
AT1R(GPCR)-arrestin\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ligand-dependent interface change, newly found intracellular loop
engagement in the interface\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Gagnon et al., 2019)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Antibody- EGFR\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Roles of interfacial residues in binding\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Bridge et al., 2019)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
MurJ-LipidII\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-lipid\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-lipid\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Critical interfacial residues responsible for transient LipidII
binding\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Rubino et al., 2020)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Photo-crosslinking UAA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
LptD-LptE\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Critical interfacial residues\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Freinkman et al., 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DegP-substrate\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
New substrates identified by a photoactivatable UAA at the substrate
recognition site\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(He et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Histone H3-various ligands\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Weak and transient PPIs identified\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Yang et al., 2020)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
HdeA dimer\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
pH-dependent changes in the dimer interface\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Yang et al., 2016)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
HdeA - DegP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
HdeA binding to multiple interfacial regions in DegP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Rho-RTKN\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RTKN interaction with RohA through a similar binding interface as that
on PKN\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Photo-crosslinker, cleavable, MS label\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
MAP kinase - MKK4 peptide\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-peptide\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-peptide\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Unique binding interface of an intrinsically disordered peptide within a
folded protein domain\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Parker et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
E6AP(E3) - UbcH7(E2)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Previously unrecognized catalytic residues of the E6AP ligase\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Krist and Statsyuk 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Photo-crosslinker, heterobifunctional\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SurA-Unfolded OmpX\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Multi-site binding of OmpX to SurA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Calabrese et al., 2020)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sky-OmpA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Unique residues and sub-regions involved in transient interaction
between Skp and denatured OmpA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Horne et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Photo-activatable cholesterol\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
GlyR - cholesterol\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-lipid\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-lipid\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Unique cholesterol binding interfaces of GlyR Cholesterol
concentration-dependent changes in the interface\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Ferraro and Cascio 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Lys/Asp/Glu-cross-reactive chemical linker\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Yeast proteasome\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Dynamic and disordered interface of subunits (RPN2, RPN13)\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Mintseris and Gygi 2020)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BAX oligomer\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The unstructured N terminus and newly exposed \selectlanguage{greek}α\selectlanguage{english}1 of BAX adjacent to one
another in the oligomeric state\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Hauseman et al., 2020)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Heterobifunctional chemical crosslinker\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RAP1B-Glycan\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-glycan\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-glycan\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Different contact residues of RAP1B with glycan variants\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Xie et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
MDA5 filament\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Configuration of filaments\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Wu et al., 2013)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Amine-reactive chemical linker\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
p53 tetramer\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
DNA-dependent changes in the dimer-dimer interface\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Arlt et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
p53 tetramer\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Flexible C-terminal\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Arlt et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
NLRP1-DPP9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
CLMS data used for validation of the atomic model revealed by the
cryo-EM\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Hollingsworth et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SLAIN2, CLASP2, ch-TOG\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SLAIN2 showed crosslink patterns contradictory to previous x-ray crystal
structure\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
(Liu et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
hIRE1\selectlanguage{greek}α \selectlanguage{english}LD oligomer\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Critical residues in the oligomeric interface\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Karag\selectlanguage{ngerman}öz et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Structure refinement from published dataset\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Rpn2-Rpn13\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Protein-protein\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
The intrinsically disordered C-terminal interface of Rpn2\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
(Gong et al., 2020)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2} . Examples of biological interfaces or interfacial
residues uncovered by HDMS.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Type of interaction\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Protein complex\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Findings\strut
\end{minipage} & \begin{minipage}[b]{0.22\columnwidth}\raggedright\strut
Ref.\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Antibody-antigen\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Fab-GP41 MPER\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Induction of conformational changes in both the MPER and paratope during
the process of core epitope recognition\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Kim et al., 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG1-FcRn\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Potential FcRn-binding interfaces in the Fab region\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Jensen et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG4-TL1A\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
A discontinuous epitope within the predicted interaction interface of
TL1A and DR3\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Huang et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Fab-VEGF\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The identified epitope overlapping with the binding interface of the
biological receptor of VEGF\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Comamala et al., 2020)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG-CD20 peptide\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
CDR3 (VH) functioning as the dominant antigen docking motif\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Uhrik et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Fab-IL23\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Peptide-level epitopes identified\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Li et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Camelid single domain antibody-IL6/gp80 complex\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The junctional epitope present only in the IL6/gp80 complex, not in the
individual components\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Adams et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG-fHbp\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Four immunogenic regions located on the N- and C-termini of fHbp\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(St\selectlanguage{ngerman}änder et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG-transglutaminase 2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Main epitopes targeted by celiac disease autoantibodies\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Iversen et al., 2014)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG-rAna o 2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Conformational and linear epitopes recognized respectively by two
different antibodies\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Zhang et al., 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG-ANXA1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Interaction region within domain III of ANXA1 in Ca2+-bound
conformation\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Gramlich et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Antibody-Zika envelop\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Three spatially distinct epitopes for the six mAbs\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Adhikari et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Fab- cytochrome c\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The epitope being in good agreement with the contact residues identified
by the X-ray crystallographic structure\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Coales et al., 2009)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG- rAna o 2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Five discontinuous, conformational epitopes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Guan et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG- rAna o 2\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Four regions identified as potential epitopes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Zhang et al., 2013)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
IgG1 self-association\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Major protein-protein interfaces associated with the
concentration-dependent self-oligomerization of IgG\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Arora et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Self-oligomer\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Apolipoprotein E\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Oligomerization via the C-terminal regions\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Huang et al., 2011)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Tau\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Hyperphosphorylation-induced interface formation in the microtubule
binding repeat region\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Zhu et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\selectlanguage{greek}β-\selectlanguage{english}2-Microglobulin\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The region spanned by Arg45-Leu54 serving as the interface for the
dimer\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Borotto et al., 2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\selectlanguage{greek}α-\selectlanguage{english}synuclein\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The C-terminus acting as a fold\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Stephens et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
A\selectlanguage{greek}β\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Sequence-specific engagement of side-chains of residues located at the
N-terminal part in a network of oligomer-stabilizing interactions.\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Przygo\selectlanguage{polish}ń\selectlanguage{english}ska et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Protein-lipid\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
PTEN\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The membrane-binding helix in the N-terminal of PTEN\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Masson et al., 2016)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\selectlanguage{greek}β\selectlanguage{english}2AR\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The transmembrane domains embedded in bicellar region\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Duc et al., 2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Homologous transporters: XylE, LacY and GlpT\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
Conserved networks of charged residues regulated by interactions with
surrounding phospholipids, acting as molecular switches for the
conformational transition\strut
\end{minipage} & \begin{minipage}[t]{0.24\columnwidth}\raggedright\strut
(Martens et al., 2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
PTEN\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
The salt bridge interactions between the basic residues and the lipids
as a major driving force in stabilizing the protein-membrane
interface\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Jang et al., 2021)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
Sphingosine kinase 1\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
A positively charged motif responsible for electrostatic interactions
with membranes\strut
\end{minipage} & \begin{minipage}[t]{0.22\columnwidth}\raggedright\strut
(Pulkoski-Gross et al., 2018)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Graphical Abstract}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

