Molecular and cellular approaches for the detection of protein-protein interactions: Latest techniques and current limitations

Article (PDF Available)inThe Plant Journal 53(4):610-35 · March 2008with80 Reads
DOI: 10.1111/j.1365-313X.2007.03332.x · Source: PubMed
Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597-609). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.
Molecular and cellular approaches for the detection of
protein–protein interactions: latest techniques and
current limitations
Sylvie Lalonde
, David W. Ehrhardt, Dominique Loque
, Jin Chen, Seung Y. Rhee and Wolf B. Frommer
Carnegie Institution, 260 Panama Street, Stanford, CA 94305, USA
Received 15 June 2007; revised 18 September 2007; accepted 20 September 2007.
For correspondence (fax þ1 650 325 6857; e-mail
Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including
architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential
components of post-genomic toolkits needed for understanding biological processes at a systems level. Over
the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein
interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide
tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques
range from co-localization of tags, which may be limited by the optical resolution of the microscope, to
fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on
the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved
complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques
described in this review, such as surface plasmon resonance, provide detailed information on physical
properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are
amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods,
this review emphasizes techniques that can be applied to determine interactions involving membrane proteins,
including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with
high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and
Thelen (2008; this issue, pp. 597–609). In addition, we discuss the use of interaction data to construct interaction
networks and as the basis for the exciting possibility of using to predict interaction surfaces.
Keywords: protein interaction, fluorescence resonance energy transfer, fluorescence correlation spectro-
scopy, split GFP, split ubiquitin, co-localization.
Macromolecular interactions such as protein–protein inter-
actions (PPI) are fundamental for all biological processes,
ranging from the formation of cellular structures and
enzymatic complexes to the regulation of signaling path-
ways. Proteins frequently function as stable or transient
complexes with other proteins (Alberts, 1998; Grigoriev,
2003; Kerrien et al., 2007). Interactions between proteins
can serve diverse functions such as conferring specificity to
interactions between enzymes and substrates in signal
transduction events, protection of proteins from their
environment, facilitation of substrate channeling, or build-
ing molecular machines such as the cytoskeleton (Huang
et al., 2001; Islam et al., 2007; Kozer et al., 2007). Some
proteins function as obligatory oligomers, for example the
Escherichia coli tryptophan repressor that forms two sym-
metric tryptophan-binding sites at the dimer interface
(Schevitz et al., 1985). Similarly, K
channels form a single
pore from four similar or identical subunits (MacKinnon,
1991; Long et al., 2005). Such oligomeric interactions pro-
vide the possibility of novel properties, as exemplified by
the Ca
/calmodulin-dependent protein kinase II (CaMKII).
This consists of 12 identical subunits, each of which is able
610 ª 2008 The Authors
Journal compilation ª 2008 Blackwell Publishing Ltd
The Plant Journal (2008) 53, 610–635 doi: 10.1111/j.1365-313X.2007.03332.x
to phosphorylate its neighbor. Modeling showed that the
interplay between the autocatalytic phosphorylation of
CaMKII and removal of phosphate groups by protein
phosphatase produces two stable states of CaMKII at basal
free calcium levels, enabling it to act as a switch involved in
memory and as a decoder of calcium spike patterns (Mitra
et al., 2004; Shen et al., 2000). A second example of emer-
gent properties due to oligomerization is provided by the
ammonium transporter AtAMT1, where trans-activation
among constituent subunits allows for rapid non-linear
shutdown of transport activity and memory of activity state
et al., 2007).
Protein interactions are characterized by kinetic and
thermodynamic parameters. Some proteins interact with
high affinity, forming stable interactions, such as TEM1-b-
lactamase and its inhibitor protein (Reichmann et al., 2007).
Other proteins interact more dynamically, requiring a lower-
affinity binding. Typically, these are proteins that serve
regulatory roles, including G-protein-coupled receptors,
protein kinases, and cell surface receptors that are activated
by dimerization (Pellicena and Kuriyan, 2006). The methods
described in this review differ in their sensitivity, specificity,
and ability to detect interactions of differing affinity; thus
selection of a suitable method is crucial for a given inves-
tigation. The detection of weak or transient interactions may
require special techniques such as cross-linking (Trakselis
et al., 2005). For example, data obtained in heterologous
systems often overexpress the two partners and thus an
interaction detected in such a system may not reflect a native
or conditional occurrence in situ. It is also important to
decide whether the objective is to determine the interaction
between a protein pair or to analyze for the existence of
protein in larger complexes before choosing a method. Two-
hybrid screens measure direct binary interactions, while
immunoprecipitation-based methods and fluorescence
resonance energy transfer (FRET) measure the presence of
a bait protein in a complex or in the vicinity of the prey
(Figure 1). Therefore, datasets derived from different meth-
ods each have their characteristics and thus are expected to
share only partial overlap; extensive follow-up using differ-
ent approaches is therefore required to generate a compre-
hensive interaction map (Rual et al., 2005).
The structural basis of protein interactions
The binding of two proteins is determined by the shape and
chemistry of the binding surface, i.e. the amino acid com-
position and tertiary structure of the proteins (Perozzo et al.,
2004; Reichmann et al., 2007). Protein interactions can occur
between identical or non-identical polypeptides (homo- and
hetero-oligomers). Protein interactions can be further
classified into obligate and non-obligate complexes or,
depending on the lifetime of the complex, into transient or
permanent complexes (Nooren and Thornton, 2003). Pro-
teins that interact typically have complementary surfaces,
and the forces that stabilize the interaction are identical to
those that play a role in protein folding as well as in inter-
actions of proteins with small molecules: ionic interactions,
dipole interactions, hydrogen bonds, van der Waals
forces, hydrophobic interactions, and also water-mediated
Figure 1. Types of protein complexes.
(a) A variety of proteins occur as monomers, homodimers, homotrimers, homotetramers, or even larger homomeric complexes (e.g. CaMKII as a dodecamer;
Rosenberg et al., 2006).
(b) Proteins may assemble as hetero-oligomers consisting of homo- or hetero-oligomers.
(c) Indirect interactions: for example scaffolding proteins such as INAD (Wang and Montell, 2007) can bind various monomeric or oligomeric proteins, as for example
in the signalosome (Wei and Deng, 2003). The methods used to identify protein interactions differ with respect to the type of interaction they detect and thus yield
non-overlapping datasets. The yeast two-hybrid or mating-based split ubiquitin systems identify binary interactions only (as in a or b); while affinity
chromatography/mass spectrometry and fluorescence resonance energy transfer identify both binary complexes as well as complexes in which two proteins
interact via a third partner (e.g. trimer or tetramer in c).
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interactions between residues on the surface of the two
partners (Reichmann et al., 2007).
Stable protein–protein interfaces consist of a set of
modules (Reichmann et al., 2005). Well-studied examples
include the nanomolar-affinity-interaction between TEM1
b-lactamase and its protein inhibitor, b-lactamase inhibitor
protein (Reichmann et al., 2005), the RNase BARNASE and
its inhibitor BARSTAR (Vaughan et al., 1999; Reichmann
et al., 2005), and interferon IFN-a2 and its receptor IFNA-R1
(Reichmann et al., 2005, 2007). The interaction surface
between TEM1 b-lactamase and its inhibitor consists of five
modules, where each module comprises a cluster of inter-
acting residues (Reichmann et al., 2005). Complex formation
is very fast, and the complex is highly stable with a K
in the
femtomolar range. An important question is thus whether
the molecular interactions that contribute to formation and
stability of the complex are made up from an additive set of
atomic interactions, or whether the interaction is made from
more complex networks of cooperative interactions (Reich-
mann et al., 2007). Surprisingly, individual bonds at the
complex interaction surface can be altered or obliterated by
mutation without major effects on the formation of the
overall complex and with only small effects on binding
affinity. This has been shown for a variety of proteins, e.g.
the binding of IFN-a2 to the IFNA-R1 receptor (Reichmann
et al., 2005, 2007). A careful analysis introducing single
mutations alone or in combinations demonstrated cooper-
ativity as well as intercluster additivity between the interface
modules (Reichmann et al., 2005). In contrast, the weak
interaction between the bacterial signaling components
CheA and CheY with an affinity of 2 l
is characterized by
fewer contacts between the two proteins, which are
organized into only a single cluster. Thus in addition to
knowledge of the thermodynamic and kinetic properties of a
given interaction pair, structural information is important for
understanding the evolution of protein interaction networks.
Systematic analysis of protein interactions combined with
structural information may ultimately help to develop
methods that will allow more accurate predictions of inter-
actions and their properties (Shoemaker and Panchenko,
Interactomics: the importance of systematic
generation of protein interaction maps
From the discussion above, it is obvious that a compre-
hensive map of both low- and high-affinity PPI among sol-
uble and membrane proteins in the cell would be an
invaluable asset for the understanding of biological pro-
cesses and molecular mechanisms at the systems biology
level. Such a map needs to include both binary protein
interactions as well as larger complexes (Figure 1). Knowl-
edge of the protein interaction network is a crucial pre-req-
uisite for understanding most cellular functions; especially
the regulatory and signaling networks. Primary goals of the
post-genomic era are: (i) the assignment of functions to each
of the genes encoded in a given genome and (ii) their inte-
gration into metabolic and regulatory networks. While
transcriptomics and proteomics are progressing rapidly,
collection of other essential information for building these
network maps the mapping of protein interactions (inter-
actome or associome), the profiling of intermediates (Meyer
et al., 2007), ions (Baxter et al., 2007), and metabolic flux
(Wiechert et al., 2007) will be a major focus of research in
the coming years.
Network analysis requires methods amenable to high
throughput (HT), such as yeast two-hybrid (Y2H) assays and
affinity purification–mass spectrometry (AP-MS) for per-
forming systematic screens (Table 1; Miernyk and Thelen,
2008; this issue, pp. 597–609). Benefits of HT analyses are
Table 1 Methods for analyzing protein–protein interactions
In vitro In vivo
Affinity purification AP-MS AP-MS
Genetic test systems Y2H
, mbSUS
a, b
, CytoTrap
Fluorescence FRET
Plasmon resonance Quantitative SPR (Boozer et al., 2006)
Crystal structure Structure of complex -
Calorimetry Quantitative analysis of protein interactions
AFM Detection and quantitative analysis of protein interactions
NMR Quantitative analysis of large complexes (Sprangers and Kay, 2007) STINT-NMR (Burz et al., 2006)
Protein arrays Identification and analysis of selectivity of protein interactions
(Korf and Wiemann, 2005)
AP-MS, affinity purification-mass spectrometry; Y2H, yeast two-hybrid; mbSUS, mating-based split ubiquitin system; FRET, fluorescence
resonance energy transfer; BRET, bioluminescence resonance energy transfer; AFM, atomic force microscopy; SPR, surface plasmon resonance;
STINT-NMR, mapping structural interactions using in-cell NMR spectroscopy.
Suitable for membrane proteins.
Amenable to high throughput.
612 Sylvie Lalonde et al.
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
that a single lab or small consortium with extensive expe-
rience in one method can carry out a whole or sub-genome
screen and generate a complete dataset collected under
comparable conditions in which the complement of all
tested proteins serves as a multiparallel internal control,
thus reducing the number of potential artifacts. A potential
drawback of HT analysis may be that typically only a limited
number of replica tests are performed (a single run of a
matrix of 30,000 · 30,000 proteins covering the genome of a
higher eukaryote adds up to close to a billion individual
binary tests). Another drawback is that protein interactions
are typically scored in an all-or-nothing scheme. Yeast two-
hybrid analyses and screens, for example, often just score
auxotrophy versus prototrophy using a binary code (Miller
et al., 2005; Uetz et al., 2000). Furthermore, in the Y2H as
well as other protein tagging approaches, test proteins are
often overexpressed, thus modifying the relative concentra-
tions of potential interaction partners from the in vivo state.
Moreover, the use of heterologous systems can eliminate
competing activities that exist in the native system and can
also introduce novel competitors. Analysis of interactions in
extracts, as typically performed in AP-MS experiments, may
bring together proteins from different compartments in
‘non-crowded’ environments that do not reflect the in vivo
situation. Therefore, overexpression and the elimination of
competing interaction partners or the co-expression of
proteins residing in different cellular compartments can
lead to the detection of interactions that will not occur in
vivo. Interactions detected in such screens are therefore
designated ‘potential interactions’. For cases in which a
protein is found to interact with several or many other
proteins, as may be expected for scaffolding proteins,
orthogonal assays are required to determine the relevance
of an interaction in vivo.
Taken together, HT screens are necessary to obtain an
overview of the potential interactome, but extensive follow-
up is required to unambiguously identify false positive and
negative results and determine those interactions that are
relevant for cellular function.
The special case of membrane proteins
Membrane proteins play crucial roles in many biological
processes. They control cell permeability (influx and efflux)
for a myriad of compounds and are responsible for sensing
chemical and physical stimuli from the environment (nutri-
ents, hormones, pH, pathogens, etc.) to allow the organism
to acclimate to changing conditions and to coordinate
transport and metabolism. Despite the importance of
membrane proteins (which represent 20–30% of the Ara-
bidopsis proteome; Schwacke et al., 2003) in carrying out
functions such as transport, vesicular trafficking, energiza-
tion, homeostasis, or signaling, little is known about their
interactions with each other or with other proteins (Ludewig
et al., 2003; Obrdlik et al., 2004; Reinders et al., 2002a). Yeast
two-hybrid assays are depleted of membrane protein inter-
actions (Xia et al., 2006), because in the classical Y2H system
the activation domain of a transcription factor, when fused
to a membrane protein, will be retained at the membrane,
and thus rendered unavailable for reconstitution of a func-
tional transcription factor in the nucleus (Figure 2a). More-
over, membrane proteins are often toxic when expressed in
E. coli, leading to under-representation of membrane pro-
tein open reading frames (ORFs) in cDNA expression
libraries (Frommer and Ninnemann, 1995). Biochemical
assays require optimization of solubilization in detergents
(Kalipatnapu and Chattopadhyay, 2005) and subsequent
reconstitution in lipid bilayers. Therefore, alternative meth-
ods, such as the split ubiquitin system (Obrdlik et al., 2004)
and advanced biochemical methods are required to provide
maps of interactions between membrane proteins as well as
the interface between membrane and soluble proteins.
High-throughput interactomics tools
To systematically analyze protein complexes at a sub- or full-
genome level, several methods have been adapted for HT
screens: Y2H systems, the mating-based split-ubiquitin
system (mbSUS), and affinity purification of protein com-
plexes followed by identification of proteins by mass
spectroscopy (AP-MS; Miernyk and Thelen, 2008; this issue,
pp. 597–609). Yeast two-hybrid and AP-MS methods have
successfully been used to determine significant parts of
protein interaction networks in Saccharomyces cerevisiae
(Gavin et al., 2002; Ho et al., 2002; Ito et al., 2000; Krogan
et al., 2006; Miller et al., 2005; Uetz et al., 2000), Caenor-
habditis elegans (Li et al., 2004; Walhout et al., 2000),
Drosophila melanogaster (Formstecher et al., 2005; Giot
et al., 2003; Stanyon et al., 2004), bacteria (Bartel et al., 1996;
Rain et al., 2001), Homo sapiens (Rual et al., 2005; Stelzl
et al., 2005), and Arabidopsis thaliana (de Folter
et al., 2005;
Popescu et al., 2007). As mentioned before, AP-MS methods
detect the presence of primary or secondary interactions
within a complex, whereas two-hybrid systems measure
direct binary interactions (Figure 1).
Most of the detection systems are based on the reconsti-
tution of a function of the two halves of a split protein. The
canonical Y2H system consists of two components: the
DNA-binding domain (DBD) from a transcription factor
(generally Gal4 or LexA) fused to protein ‘X’, and the
transcription activation domain (TAD; generally Gal4 or
B42) fused to protein ‘Y’ (Figure 2a). When both chimeric
proteins are co-expressed and localized to the nucleus and if
protein ‘X’ interacts with protein ‘Y’, they reconstitute a
functional transcription factor that activates transcription of
marker genes in the nucleus. Since the first Y2H system
developed by Fields and Song (1989), several modifications
have been made to improve the quality of the data, including
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
the insertion of upstream activation sequences (UAS) into
different promoters of the marker genes, use of low copy
plasmids, implementation of multiple markers (URA3, HIS3,
ADE2, lacZ, GFP), and use of negative selection of de novo
autoactivators, for example the counter-selectable reporter
CYH2 (for comprehensive reviews on Y2H see Vidalain et al.
(2004) and Vidal (2005)).
Alternatively, protein complexes can be purified and
analyzed by AP-MS complementing large-scale PPI datasets
obtained by Y2H (Cusick et al., 2005). Gavin et al. (2006)
analyzed PPI in S. cerevisiae using a tandem affinity
purification (TAP) tag consisting of a calmodulin-binding
domain, a protease cleavage site (TEV), and a protein A tag
fused to 5500 ORFs. All C-terminal fusions were introduced
into yeast by homologous recombination in order to express
the tagged proteins under their native promoter in the native
chromosomal environment. The tagged protein is then
isolated by affinity purification along with its interacting
partners, and their identities are determined by mass
spectroscopy. As pointed out above, proteins from different
compartments may associate in the extract, leading to false
positives. Obviously, no ideal HT tool exists at present,
Figure 2. Comparison of the mating-based split
ubiquitin (mbSUS) and yeast two-hybrid (Y2H)
(a) Classical Y2H system: a transcription factor is
split into the activation (green arrow) and DNA-
binding domain (blue zigzag line), which are
fused to proteins ‘X’ and ‘Y’. Interaction between
the two partners will lead to activation of tran-
scription of several reporter genes (Ade2, His3,
and LacZ).
(b) The mbSUS: when protein ‘X’ (red or brown
sphere) and protein ‘Y’ (purple or blue sphere)
interact, a functional ubiquitin protein is recon-
stituted from the two domains (Nub, green
hollow sphere; and Cub-PLV, green sphere with
protrusion and the artificial transcription factor
PLV as blue DNA binding and activation zigzag
arrow). The protein fused to the Cub-PLV chi-
mera must either be an integral transmembrane
protein (purple sphere, top left), a peripheral
membrane protein (blue, center), or attached to
the membrane, e.g. by a lipid anchor (purple
sphere with anchor, top left), otherwise Y-Cub-
PLV can enter the nucleus and create a report in
the absence of an interaction partner. In all three
cases, interaction will lead to reconstitution of a
‘functional’ ubiquitin that will be recognized by
endogenous ubiquitin-specific proteases (UBPs),
leading to release of the artificial transcription
factor PLV (blue zigzag arrow). The transcription
factor, which contains a nuclear localization
signal, will enter the nucleus, bind to operators
(uas) to activate the transcription of several
reporter genes (Ade2, His3, and LacZ).
614 Sylvie Lalonde et al.
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
making extensive follow-up necessary to demonstrate the in
vivo existence and relevance of interactions detected by any
of the methods described here.
The mating-based split-ubiquitin system (mbSUS)
for membrane protein interactions
To circumvent the problems associated with the analysis of
membrane proteins using the classical Y2H, the mbSUS was
developed (Figure 2b; Miller et al., 2005; Obrdlik et al.,
2004). The split-ubiquitin system is similar to the classical
Y2H as it uses yeast as a heterologous system and has a
similar read-out, but it allows the detection of interactions of
membrane proteins. The interaction must occur at the
cytosolic face of any of the yeast membranes, including the
nuclear envelope, endoplasmic reticulum (ER), Golgi, vacu-
ole, mitochondria, and plasma membrane.
The system solves the problem of a classical Y2H that a
transcription factor will be non-functional when fused to a
membrane protein but still makes use of reconstitution of
two halves of a split protein, here ubiquitin (split-ubiquitin
system, SUS). The concept of SUS relies on the release of a
transcription factor from the membrane if two membrane
proteins interact. The SUS uses a ubiquitin split into two
halves: The N-terminal domain of ubiquitin (Nub) can
reconstitute a functional ubiquitin when co-expressed with
its other C-terminal half (Cub) (Johnsson and Varshavsky,
1994). Nub mutants such as NubG (containing mutation
Ile13Gly) with reduced affinity to Cub reconstitute the full-
length ubiquitin only when brought into its vicinity via
interaction of the two fusion partners. In SUS, protein ‘X’
is fused to the NubG and protein ‘Y’ is fused to the Cub fused
to an artificial transcription factor composed of a tag (IgG-
binding domains of Staphylococcus aureus protein A), LexA
DNA-binding domain, and the activation domain of VP16
(PLV) (Stagljar et al., 1998). When ‘X’ interacts with ‘Y’, the
Nub and Cub moieties are brought together and a functional
ubiquitin molecule can be reconstituted, triggering action of
endogenous ubiquitin-specific proteases, thus cleaving the
reconstituted ubiquitin from their fused membrane proteins
and releasing the transcription factor PLV into the cytosol.
The transcription factor diffuses into the nucleus where it
activates transcription of marker genes (Figure 2b). The SUS
system was further improved to make it amenable for HT by
using a mating approach to bring together bait and prey in
one cell (mbSUS; Miller et al., 2005; Obrdlik et al., 2004).
Practical considerations for mbSUS analysis
The first critical step for mbSUS analysis is cloning of the
membrane protein ORF. As mentioned above, membrane
proteins are often toxic when expressed in E. coli, and the
first step for the generation of Nub/Cub fusions is the cloning
in E. coli (Frommer and Ninnemann, 1995). There are two
ways around this problem: (i) cloning PCR products directly
in yeast by using in vivo recombination (Miller et al., 2005;
Obrdlik et al., 2004) or (ii) using secure E. coli vectors as
typically used in the Gateway technology (Invitrogen, In vivo cloning in yeast is fas-
ter since it eliminates an intermediate cloning step in E. coli
because yeast is directly co-transformed with the PCR
product (having homologous overhang) and the linearized
vector. However, it requires additional steps to verify the
sequence of the PCR-derived inserts in both the NubG and
Cub-PLV vectors. The Gateway technology takes advan-
tage of the commercially available Gateway entry vectors
which carry multiple rrnB sequences acting as transcrip-
tional termination signals upstream of the insertion site,
therefore reducing read-through expression and thus toxic-
ity in E. coli. Alternatively, usage of an E. coli strain reducing
the plasmid copy number may also reduce potential toxicity
(e.g. CopyCutter from Epicentre Biotechnologies, http://
The currently available vectors for mbSUS (available from
the Arabidopsis Biological Resource Center (ABRC); http://
html) offer the possibility of cloning the gene of interest into
either low- or high-copy plasmids (X-NubG or NubG-X) or a
low-copy plasmid (Y-Cub-PLV). Therefore, the expression
level (in yeast) of fused proteins can be manipulated via
choice of the copy number of the plasmid. The use of a
methionine-regulated promoter further expands the control
over the expression level of Y-Cub-PLV. An HT pilot screen
(Obrdlik et al., 2004) indicated that the low-copy vector
provides more stringent conditions; while the use of high-
copy plasmids establishes less stringent conditions but
offers higher sensitivity (Grefen et al., 2007).
An important feature to consider before cloning ORFs into
the mbSUS vectors is the topology of the membrane protein.
In order to produce a read-out in the nucleus, the Cub-PLV
and NubG fusions both must be present in the cytosol. Thus
fusions have to be made accordingly, and at present, the
system does not allow for analysis of proteins in which both
N- and C-termini are located inside an organelle or outside
the cell. For large-scale screens, structural information and
prediction tools may be used to evaluate the potential
topologies, as has been done systematically for Arabidopsis
and rice (Oryza sativa) membrane proteins (http://aramem The detector domains must be
fused to either a cytosolic N- or C-terminus; suitable vectors
for N- or C-terminal fusions are available (http://www. Obviously, Cub-PLV must be fused to an
integral or membrane-associated protein (Figure 2b); a
soluble protein fused to the Cub-PLV moiety would diffuse
into the nucleus without having to interact with the NubG
fused protein and activate the transcription of the markers
leading to a false-positive read-out. Since membrane protein
predictions are not sufficiently accurate, especially when
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only a single hydrophobic domain is present or when a
protein contains a hydrophobic leader peptide (Xia et al.,
2006), it is necessary to control for activation of the reporters
in the absence of NubG. It is possible to artificially add a
transmembrane domain or a membrane anchor to also allow
the use of soluble proteins as Cub-PLV fusions. In contrast,
both membrane or soluble proteins can be used as NubG
fusions, enabling us to test not only the complement of
membrane protein/membrane protein interactions, but also
to target the interface between the membrane and the
cytosol, which includes most of the interactions important
for the initial steps of signaling cascades.
Potential pitfalls
A major source of false-positive output of the Y2H system is
caused by activation of transcription of the reporter genes by
the protein fused to the DBD independently of the protein
fused to TAD (Rual et al., 2005). A second source of false-
positives in HT datasets is de novo autoactivators, which
may represent up to 10% of the baits (protein fused to the
DBD). De novo autoactivators emerge during the course of
the screen by spontaneous mutations (Rual et al., 2005).
Strategies for reducing artifacts include verification that
several or all reporters score positive, by counter-selection
of CYH2-containing vectors on cycloheximide (currently not
implemented in the mbSUS), or by addition of 3-amino-
1,2,4-triazole (3-AT) to increase the stringency of HIS3
selection (3-AT is a competitive inhibitor of imidazole-glyc-
erol-phosphate dehydratase, the HIS3 gene product). Fur-
thermore, the reliability of the data can be increased
significantly by retesting the original ORF-fusion clones to
exclude mutations during the selection phase.
In the case of the mbSUS, Y-Cub-PLV fusions comprise a
functional transcription factor; thus if the fusion protein is
not prevented from diffusion into the nucleus via attachment
to a membrane, transcription of the reporter genes will be
activated in the absence of X-NubG. For example proteins
that contain a hydrophobic core but no membrane domain
may bioinformatically be classified as membrane proteins
by mistake, thus mbSUS analysis will yield a positive read-
out in the absence of NubG. In addition, false positives may
arise from proteolysis of the fusion and release of the PLV
transcription factor by unknown processes (e.g. the quality
control mechanisms in the ER). Strategies to eliminate both
types of false positives include testing for reporter activity in
the absence of NubG or mating of each Y-Cub-PLV fusion
with soluble free NubG. False negatives may arise from low
abundance of the Y-Cub-PLV due to low expression or low
stability of the fusion protein, or to a lack of accessibility of
the PLV to ubiquitin protease cleavage. In contrast to the
G mutant (NubG), the wild-type N-terminal ubiquitin
domain (NubWT) can readily interact with the C-terminal
ubiquitin domain. Thus co-expression of the Y-Cub-PLV
fusion with NubWT may be used to test for Y-Cub-PLV
expression and PLV accessibility without the need for fused
proteins to interact (Figure 3). In certain cases, fine tuning of
the expression of the Cub-PLV fusion can be achieved by
using the methionine-repressible promoter and titration
using different methionine concentrations in the medium
and/or using 3-aminotriazole, or using other Nub affinity
mutants (Raquet et al., 2001) will allow optimization of the
selection conditions for autoactivators or for clones showing
low expression.
Strategies for HT analysis
The large number of assays that need to be performed in
order to determine the whole complement of potential pro-
tein interactions require HT technologies. For Y2H, one
approach is to use standard liquid handling robotics (Miller
et al., 2005). The number of individual assays to be
Figure 3. Scheme for controls to determine false-positive and false-negative
read-out from mating-based split ubiquitin system (mbSUS) analyses.
Protein ‘Y’ (which is putatively inserted or anchored in one of the cellular
membranes) is fused to Cub-PLV. In a first step, some of the false-positive
data can be eliminated by testing for reporter activity (HIS3 prototrophy, red
chromophore formation for ADE2 or lacZ activity) either in the absence of
NubG or by mating with yeast cells expressing soluble NubG (without a
fusion partner). In a second step, functional expression of the Cub-PLV fusion
as well as accessibility of Cub-PLV for interaction is tested by mating with cells
expressing the soluble wild-type version of Nub (NubWT), which interacts
with Cub-PLV fusions in the absence of an interaction partner for protein ‘Y’.
616 Sylvie Lalonde et al.
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performed can be reduced by 2D and 3D pooling strategies
(Jin et al., 2006; Rual et al., 2005; Zhong et al., 2003). One
potential pooling strategy for mbSUS screens would be to
create subpools of the Cub library, and to perform a first set
of screens of Cub subpools interacting with individual NubG
proteins, followed by a round of deconvolution for each pool
yielding a positive read-out. This requires testing each NubG
fusion against the individual members of the pools it was
found to interact with. For mbSUS screens, the optimal pool
size was estimated to be about five.
An alternative pooling strategy was developed for screen-
ing the human genome interactome (Rual et al., 2005). In
this approach, each bait is mated to individual pools of 188
TAD-ORFs in a microplate and positive colonies were
identified from each pool. Subsequently, the positive colo-
nies were retested and sequenced to identify the interacting
prey (TAD-ORF). Potential interactors were then again
retested from original clones (Rual et al., 2005). The choice
of the pooling strategy depends on the number of interac-
tions detected in a matrix as well on the promiscuity of
protein interactions in the collection and thus has to be
designed on the basis of pre-screens for each individual
A disadvantage of many of the large-scale Y2H screens is
that the output is not quantitative but rather a visual binary
score of prototrophy. The information content of Y2H
screens could be improved by determining growth curves
quantitatively using a fluorescent marker such as GFP. While
the reporter output may not necessarily correlate with the
affinity of the underlying interaction, the quantitative data
may help to reduce artifacts and improve standardization
over multiple assays performed over the data collection
period. Titration of the promoters may provide additional
insights into properties of the interactions.
Fluorescence spectroscopy and imaging technologies
for analysis of protein–protein interactions
Neither in vitro tests nor Y2H provide data on the interaction
of proteins in their native environment. New imaging tech-
nologies, coupled with the development of genetically
encoded fluorescent proteins (FPs) and the increasing
capability of software for image acquisition and analysis,
have enabled in vivo studies of protein functions and pro-
cesses. Genetically encoded FPs are at the core of a variety of
approaches to probe PPI in living cells (Table 2). The most
Table 2 Imaging-based methods for detecting protein–protein interactions by fluorescence resonance energy transfer (FRET)
Method Specific potential Important points
Filter-based fluorescence
intensity ratio imaging
Simple system allowing for fluorescence
bleed-through corrections
Image captures (sensitized emission) is
rapid and very suited for time-lapse or 3D
Need to capture image from a sample and two
reference images (donor alone and acceptor alone)
Stoichiometry of donor and acceptor is difficult to
establish in live cell imaging
Pixel shifts caused by filter change
If sensitized emission is captured: correction for
bleed-through need to be done either by following
capturing emission from controls or by spectral
Measures are equipment-specific, determination of
FRET requires calibration (Vogel et al., 2006)
Ratio imaging by spectral
Requires either a confocal microscope
with spectrophotometric capacity or
multiple bandpass filters
Algorithm for spectral unmixing may not be
implemented appropriately when FRET occurs in
the sample
Acceptor photobleaching Simple system; can be performed on a
wide-field system
Best when combined with an independent
second method
Long bleaching time in wide-field microscopy can
induce phototoxicity and reduce cell viability
Does not correct for fluorescence bleed-through
Destructive, non-dynamic
Physical properties of fluorescent proteins may
compromise FRET measurements
Fluorescence lifetime
imaging (FLIM)
Considered the gold standard for FRET
analysis of protein interactions
Independent of fluorophore stoichiometry
Can determine dynamic interactions
Both the time-domain and the frequency-domain
mode require specialized equipment
Frequency domain: assumption is that the donor
has a single exponential decay which is not
necessarily the case with biological samples
(i.e. cyan fluorescent protein lifetime fits better a
double exponential)
Time domain: requires long exposures which can
cause photodamage, thus typically requires fixation
which may lead to artifacts
Anisotropy and anisotropy
Can measure homo-FRET and thus permits
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
popular methods are (i) co-localization of two labeled pro-
teins, (ii) FRET measurements where protein ‘X’ is fused to a
donor FP while protein ‘Y’ is fused to an acceptor FP, and (iii)
protein-fragment complementation assays (PCA) consisting
of a split protein that reconstitutes a function upon interac-
tion of protein ‘X’ and protein ‘Y’ fused to the different
moieties of the split protein (Figure 4).
Fluorescence resonance energy transfer (FRET)
Fluorescence resonance energy transfer refers to a quantum
mechanical effect between a given pair of fluorophores, i.e. a
fluorescent donor and an acceptor, where, upon excitation
of the donor, energy is transferred from the donor to the
acceptor in a non-radiative manner via dipole-dipole cou-
pling (resonance) (Fo
rster, 1948; Jares-Erijman and Jovin,
2003). As a result of FRET between a donor and acceptor a
portion of the energy absorbed by the donor is emitted in a
spectral window that is characteristic of the acceptor. Fluo-
rescence resonance energy transfer is characterized by the
efficiency of the energy transfer, E, which is defined as the
fraction of the photons absorbed by the donor and trans-
ferred to the acceptor (Figure 4a). E is a function of the
inverse sixth power of the distance (r) between the two
fluorophores [E = R
+ r
)]. The distance at which
energy transfer is 50% is known as the Fo
rster distance (R
and is a unique property of a given FRET pair. R
depends on
the extent of spectral overlap (overlap integral) between
donor emission and acceptor absorption (>30%; J(k)), the
quantum yield of the donor (Q
), the refractive index (n)of
the medium, and the relative orientation of the dipole mo-
ment (j) of the donor and acceptor: R
= 9.78 · 10
(Jares-Erijman and Jovin, 2003; Lakowicz, 2006).
Because of its exquisite dependence on molecular distance,
FRET has been described as a molecular ruler (Stryer, 1978),
which operates in the range of 1–10 nm; a distance relevant
for most molecules engaged in complex formation or con-
formational changes. Although the contribution of the di-
pole orientation compromises FRET as an accurate measure
of molecular distance, FRET is capable of resolving molec-
ular interactions and conformations with a spatial resolution
exceeding the inherent diffraction limit of conventional
optical microscopy (Jares-Erijman and Jovin, 2003).
The advantages of FRET over co-localization
Resolution is defined as the smallest distance between two
points within an image, which can be separated and still be
distinguished. Resolution depends on the wavelength of the
light imaged and on the numerical aperture (NA) of the
objective (1.22k/2NA for wide-field epifluorescence). For a
high-NA objective, the resolution is thus about 200 nm at
best. If we assume a sphere with a diameter of 200 nm
(corresponding to a volume of 4.2 · 10
fl; Figure 5a), it
could contain up to 140,000 densely packed GFP molecules
[volume of a single GFP molecule is about 30 · 10
fl; a very
similar discussion is presented by Vogel et al. (2006)]. This
simple calculation demonstrates that there are many ways
that two proteins could be contained within a co-localized
volume without physically interacting. Fluorescence reso-
nance energy transfer increases the apparent resolution by
restricting the volume occupied by two interrogated flu-
orophores. The FRET volume as calculated by Vogel et al.
(2006) is 4 · 10
fl, which is much smaller than the optical
resolution volume but still large enough space to contain
about 100 GFP molecules. Thus, co-localization even by
FRET may be one of several pieces of circumstantial
evidence for a PPI, but on its own is insufficient to conclude
that two proteins are in a complex. To illustrate this,
two proteins may reside in the same vesicle, one in the
lumen and the other on the surface, so they will appear
Figure 4. Comparison of fluorescence resonance energy transfer (FRET) and
protein fragment complementation assay (PCA) [split fluorescent protein (FP)
or bimolecular fluorescence complementation (BiFC) methods].
(a) FRET: when two proteins ‘X’ (blue cone) and ‘Y’ (orange ball) are in
sufficiently close vicinity (2–8 nm), e.g. in the case of an interaction between
the two fusion partners (direct or indirect), resonance energy transfer will
occur between the donor fluorophore (blue cylinder) and the acceptor
fluorophore (yellow cylinder).
(b) In the PCA (also named split-FP or BiFC), a fluorescent protein, e.g. GFP or
Venus, is split into two halves (yellow and orange half-cylinders). When
expressed separately, the split-FP halves do not form a functional fluoro-
phore. However, when the fused proteins ‘X’ and ‘Y’ interact, a FP is
reconstituted creating a stable and quasi-irreversible complex generating a
functional fluorophore. Irreversibility is advantageous for sensitivity, but
increases the possibility of artifacts, especially when the fused proteins are
618 Sylvie Lalonde et al.
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
co-localized, but they do not interact. Or, one protein may be
evenly distributed in the plasma membrane, whereas the
other is highly localized to puncta, where it physically inter-
acts with the evenly distributed protein. In both cases, addi-
tional evidence for interaction/non-interaction is required.
There is a ‘critical concentration’ of the acceptor at which
chance diffusion alone will place an acceptor within an
distance of a donor. For a cyan fluorescent protein
(CFP)/yellow fluorescent protein (YFP) FRET pair, with an R
of 5.4 nm, the ‘critical concentration’ of YFP is about 2.8 m
and this corresponds to only about 6.7 YFP molecules in the
FRET volume. Due to the increased chance of random
collisions in a two-dimensional space, this is an even more
important consideration when we consider interactions that
occur in a restricted plane such as a membrane (Figure 5b)
or in a restricted volume inside an organelle. These simple
calculations demonstrate that there is a need to carefully
evaluate positive FRET results to exclude the possibility that
FRET is due to random collisions. Energy transfer efficiency
can be estimated fairly easily (see below) and can be
calibrated (Koushik et al., 2006; Thaler et al., 2005; Vogel
et al., 2006). The observed values should be compared with
a theoretical value for the donor–acceptor pair to evaluate
the potential of random collisions. In addition, independent
methods may have to be applied to verify interactions
suggested by positive FRET results. The need for appropri-
ate controls also applies to negative FRET results. Low
transfer efficiency may be caused by the absence of a
molecular interaction, by a stoichiometry of donor-to-accep-
tor other than 1:1 (Vogel et al., 2006), or by excitation of the
acceptor in the donor excitation channel (bleed-through).
Methods for FRET determination and analysis
of FRET changes
Most methods evaluate energy transfer efficiency as the
relative fluorescence intensity of the donor FP in the pres-
ence or absence of the acceptor FP. The most popular
methods employed are: (i) filter-based FRET (ratio-imaging/
sensitized emission), (ii) spectral imaging, (iii) acceptor
photobleaching, (iv) lifetime measurements (fluorescence
lifetime microscopy, FLIM), and (v) a combination of the
above (Table 2). The various measurement modes were re-
cently reviewed by Jares-Erijman and Jovin (2003, 2006).
Filter-based FRET acquires fluorescence intensity of the
donor (excitation- and emission-specific filters), acceptor
(excitation- and emission-specific filters) and acceptor-sen-
sitized emission (excitation of the donor and capture of
acceptor emission) by using either two-filter or three-filter
configurations. Filter-based FRET is probably the most
problematic method if FRET is not intramolecular (when
the donor is not physically linked to the acceptor, a case that
applies to most PPI studies) because it requires acquisition
and registration of multiple images, correction for spectral
bleed-through as well as knowledge of the stoichiometry of
donor and acceptor. In many studies, only the sensitized
emission (emission of the acceptor after excitation of the
donor) is measured. However, when the donor is not
physically linked to the acceptor, this method neither
considers bleed-through (donor emission passed through
the acceptor emission filter and direct excitation of acceptor
by donor excitation filter) nor the concentration of the FPs. A
better technique involves acquisition of multiple images
from different samples expressing donor alone, the acceptor
alone or both the donor and the acceptor: (i) donor excita-
tion–donor emission, (ii) acceptor excitation–acceptor
emission, (iii) donor excitation–acceptor emission. This
Figure 5. The optical path resolution is critical for the interpretation of
imaging-based interaction analyses.
(a) Objects (e.g. GFP molecules) are shown in a three-dimensional space at a
scale corresponding to the maximal optical resolution of 200 nm for
conventional fluorescence microscopy. Since the volume of a pixel is
significantly larger than the volume of a single GFP molecule, co-localization
within a pixel cannot be used as a proof of a protein–protein interaction (Vogel
et al., 2006). Thus the lower the resolution, the lower the confidence will be. At
the highest resolution, none of the objects (red and yellow) within the sphere
(black meshed structure) can be resolved, whereas objects at opposite sides
of the sphere are resolved. Due to the form of the point-spread function,
objects in different z-planes will appear to be co-localized. Apparently, the
higher the expression, the higher the possibility of false-positive data.
(b) A similar limitation applies to membrane proteins; however, here proteins
are limited to a two-dimensional space (Fung and Stryer, 1978). In this case
the reduced degrees of freedom and diffusion in a plane increase the chance
of random collisions and produce a positive read-out for proteins that
normally do not interact. This also applies to split fluorescent protein
The detection of protein–protein interactions 619
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
normalization provides for stringent correction for bleed-
through fluorescence (Berney and Danuser, 2003). It also
allows for estimation of donor/acceptor stoichiometry and
for the presence of FRET and non-FRET signals in each
acquired image (Berney and Danuser, 2003; Gordon et al.,
1998). The method relies, however, on the assumption that
the cellular concentration of the samples expressing donor
alone is the same as the concentration of the donor in cells
co-expressing donor and acceptor. If the same promoters
are used for expression of donor and acceptor, competition
for limiting factors may occur, leading to differences in
donor expression levels. In live cell imaging, determination
of donor levels in the presence or absence of acceptor
may be difficult to achieve since it requires acquisition of
three images from three different transformed lines; and
expression levels will probably vary in their individual
An alternative to filter-based FRET measurements is
spectral imaging followed by linear unmixing (Zimmermann
et al., 2003). Emission spectra for each pixel are acquired
(e.g. using a confocal microscope equipped with spectral
sensor or using a slit-scanning spectral system such as the
SpectralDV) and deconvoluted by spectral unmixing to
obtain the ‘pure’ emission for each fluorophore. This system
has the additional advantage that it can correct for autoflu-
orescence. Although this method corrects for spectral bleed-
through and autofluorescence, depending on the algorithm
used, it may underestimate the contribution of the donor
and overestimate the acceptor when FRET occurs (Thaler
et al., 2005). Vogel and colleagues have implemented a
method in which the emission spectra are captured at two
different excitation wavelengths in order to calculate the
contribution of energy transfer (Thaler et al., 2005). More-
over, they implemented a FRET calibration system that can
be applied to normalize data and make them comparable
between different imaging systems (Vogel et al., 2006).
Since in live cell imaging the proportion of the two
fluorophores cannot always be established reliably, an
alternative method for measuring FRET is to determine the
emission intensity of the donor before and after acceptor
photobleaching (also named acceptor photobleaching or
donor dequenching). This method requires the donor to be
relatively photostable while the acceptor is photolabile.
Assuming that the donor is not affected by the light used to
bleach the acceptor, emission from the donor increases after
photobleaching of the acceptor when FRET occurs. With
patterned illumination it is possible to photobleach a defined
area of the sample making it possible to measure emission
of the donor both with and without an acceptor in a single
image. Acceptor photobleaching method is sensitive to
incomplete bleaching of the acceptor; if the acceptor is
bleached to only 30% of its original intensity, this can create
an error of up to 50% in quantifying FRET (Berney and
Danuser, 2003). When using a wide-field microscope with a
conventional mercury arc light source, bleaching times can
be as long as 20 min; thus diffusion of FPs might be a
significant problem and 100% photobleaching might be
difficult to achieve. Furthermore, one has to consider
phototoxicity and possible loss of cell viability. Intense laser
light may reduce exposure times but present a similar if not
greater hazard for phototoxicity. Photobleaching can be
performed on fixed cells to avoid diffusion of the FPs from
unbleached areas; however, fixatives such as formaldehyde
have been shown to differentially quench FP fluorescence
and fixation may cause artifacts due to cross-linking of
proteins during the fixation process (Chen et al., 2006).
An alternative to measuring fluorescence intensity to
estimate FRET efficiency is fluorescence lifetime imaging
(FLIM). This has the advantage of being largely independent
of fluorophore concentrations as it measures the relaxation
time of an excited fluorophore after a short pulse of
excitation light rather than the number of photons emitted
(Biskup et al., 2007; Wouters, 2006). Fluorescence lifetime
imaging is also independent of light scattering and refrac-
tion in different regions of the specimen (Bastiaens and
Squire, 1999). The fluorescence lifetime corresponds to the
average time a fluorophore remains in the excited state
following excitation and shows a lifetime characteristic for a
given FP (Lakowicz, 2006). Fluorescence decays exponen-
tially. Fluorescence resonance energy transfer withdraws
energy from the donor and thus leads to a reduction of its
lifetime. Two methods are used for measuring fluorescence
lifetime: time- and frequency-domain FLIM. Both methods
are limited by the small number of photons recorded and
thus may require high-intensity excitation light. Time-
domain measurements use a pulsed laser for excitation
and time-resolved image acquisition to quantify donor
lifetime directly. The fluorophore is excited by femtosecond
light pulses and the time at which photon arrives after each
pulse is measured, yielding a histogram of decay times
(Lakowicz, 2006; van Munster and Gadella, 2005). Advanced
methods that distinguish FRET from other parameter
changes such as quenching employ spectrally resolved
fluorescence lifetime measurements with use of streak
cameras or time-correlated single-photon counting (Biskup
et al., 2007). The frequency-domain method measures life-
times indirectly using excitation of the sample/probe by
continuous light with sinusoidally modulated intensity cou-
pled with sinusoidally modulated detection. Lifetimes are
then calculated as a function of phase and amplitude
changes of the signal (Lakowicz, 2006). Time-domain FLIM
has been successfully used in plant cells to demonstrate the
interaction between the two receptor-like kinases BRI1 and
BAK1 (SERK3, Russinova et al., 2004), the AAA-ATPase
CDC48A and the receptor-like kinase SERK1 (Aker et al.,
2006), and the AvrA10-dependent interaction between the
transcription factor WRKY2 and the MLA10 receptor (Shen
et al., 2007). Typically, integration times for exposure for
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these analyses were in the range of 60–120 sec. Frequency-
domain FLIM has also been successfully used to analyze the
interaction of G-protein subunits in plants (Adjobo-Hermans
et al., 2006). A potential draw back of frequency-domain
FLIM is that decay is measured indirectly. This may be
problematic, since typically only a fraction of the donor
associates with an acceptor, leading to overlaid decay
components for donors alone and donors in the FRET
vicinity to an acceptor (Biskup et al., 2007). These multicom-
ponent decays can be readily resolved with time-domain
analysis, while for frequency-domain analysis the decays
need to be differentiated using acquisition at multiple
frequencies of the modulation of the excitation light
(Redford and Clegg, 2005). Typically, time- and frequency-
domain methods are considered to be similar with respect to
the detection of a given number of photons detected and are
comparable over several orders of magnitude (Gratton et al.,
2003; Philip and Carlsson, 2003). Two factors need to be
considered when choosing between the two systems: the
need for speed of acquisition in the case of dynamic
interactions versus the sensitivity required at low fluoro-
phore concentrations. Time-domain FLIM has been reported
to have a higher signal-to-noise ratio for dim samples but
required extended integration, while frequency-domain
FLIM may be advantageous when rapid image acquisition
of brighter samples is required to study dynamic processes
(Gratton et al., 2003; Philip and Carlsson, 2003). Taken
together, the recent development of new hardware for FLIM
detection provides opportunities to localize and characterize
PPIs efficiently.
Choice of fluorophores
The choice of the optimal FRET pair lies in the different
physical properties of the FPs. Ideal FPs will have a high
quantum yield, a high extinction coefficient, a large Stokes
shift, good photostability, low sensitivity to the cellular
environment (ionic interactions, pH), and, for FLIM, a suitable
lifetime that can be measured with the available equipment.
Furthermore, these features should be similar for both the
donor and acceptor fluorophores, and the excitation and
emission spectra of the FRET pair should be separated as far
as possible. For recent reviews on FPs and their properties,
see Shaner et al. (2004, 2005) and Dixit et al. (2006).
Another criterion for the choice of fluorophores for FRET
is the extent of overlap between the emission spectrum of
the donor and the excitation spectrum of the acceptor. On
the one hand it is advantageous to obtain a FRET pair with a
large spectral overlap, since the Fo
rster equation says that
transfer efficiency depends on the overlap of donor emission
and acceptor excitation. On the other hand, depending on
the Stokes shift of the two fluorophores, a large spectral
overlap between donor emission and acceptor excitation
can lead to contamination of the acceptor-sensitized
emission with the donor emission (bleed-through). For
example, the CFP/YFP FRET pair commonly used for genet-
ically encoded sensors displays a large spectral overlap;
however, because the Stokes shift for YFP is small, signif-
icant bleed-through may be observed. Alternatively, a
fluorescence filter set has to be used that is shifted towards
longer wavelengths, reducing the fluorescence intensity
since a smaller fraction of the YFP emission spectrum can be
collected. The GFP-S
T/YFP pair, which has a larger overlap
integral compared with CFP/YFP, yields a higher energy
transfer efficiency. However, due to bleed-through, FRET
cannot be measured reliably using filters but has to be
acquired by determining the emission spectra followed by
linear spectral unmixing (Zimmermann et al., 2002). When
the Stokes shift is small and there is significant overlap
between the excitation and emission spectra of a single
fluorophore, as in the case of YFP, homotransfer can occur.
Homotransfer is defined as energy transfer between two
identical molecules (Lakowicz, 2006). In summary, the ideal
pair will have a large spectral overlap integral regarding
donor emission and acceptor excitation, while excitation of
the acceptor is minimal to permit maximal FRET and
emission from the acceptor should be minimal in the donor
Among the different available FPs, the CFP/YFP pair is the
most popular for FRET measurements (Rizzo et al., 2006).
However, blue fluorescent protein (BFP)/GFP (Heim and
Griesbeck, 2004; Mitra et al., 1996), CyPet/Ypet (Nguyen
and Daugherty, 2005), MiCy/mKO (Karasawa et al., 2004),
T-Sapphire/mOrange (Shaner et al., 2004), cerulean/YFP
(Aker et al., 2006, 2007), and mVenus (Venus-A
mStrawberry (Adjobo-Hermans et al., 2006) have also been
used. Blue fluorescent protein was replaced fairly rapidly by
CFP because of CFP’s higher quantum yield and enhanced
photostability. Furthermore, the excitation wavelength of
BFP (380 nm) is more cytotoxic than CFP (434 nm) and
autofluorescence of plant material below 420 nm is more
likely to be a problem. Although CFP is a better donor than
BFP it is still dimly fluorescent compared with YFP. Another
disadvantage of CFP for lifetime FRET measurements is that
its decay kinetic fits a double exponential, complicating
interpretation of lifetime results (Rizzo et al., 2006). The
standard CFP and YFP are known to form weak dimers; thus
it is recommended to use monomeric variants to exclude
artifacts [EYFP-A
K (mYFP) and ECFP-A
K (mCFP), Zach-
arias et al., 2002; ]. Alternatives to CFP are Cerulean (Rizzo
et al., 2004), Azurite (Mena et al., 2006) and mTFP1 (Teal
fluorescent protein, Ai et al., 2006) all of which are brighter
(higher quantum yield and higher extinction coefficient) and
have single-component decay kinetics. Cerulean is more
susceptible to photobleaching than CFP, while mTFP1
appears to be as photostable as EGFP, making it the most
stable cyan FP. mTFP1 has the additional advantage of
having its excitation maximum at 462 nm and can thus be
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
excited with lasers commonly installed on confocal micro-
scopes. Similarly, mCitrine (mYFP Q
M) and Venus are
better alternatives to YFP since they are less sensitive to the
ionic conditions, including changes in pH or chloride within
the physiologically relevant range (Griesbeck et al., 2001;
Nagai et al., 2002).
Practical considerations for FRET measurements
The proper use of FRET efficiency measurements to char-
acterize molecular interactions requires that correction be
made for: (i) bleed-through fluorescence (excitation of the
acceptor fluorophore through the donor excitation filter and
donor emission signal through the acceptor emission filter)
and (ii) stoichiometry of donor and acceptor fluorophores.
The simplest case for FRET measurements is if the fluoro-
phores are covalently coupled, as is the case with genetically
encoded FRET sensors (Lalonde et al., 2005). In this case it
may not even be necessary to correct for bleed-through
(Deuschle et al., 2006; Fehr et al., 2005; Gu et al., 2006;
Okumoto et al., 2005). Whenever the relative levels of the
FRET partners are not equimolar, FRET measurements are
problematic. Correction techniques for the relative levels of
donor and acceptor fluorophores have been developed
(Berney and Danuser, 2003; Gordon et al., 1998). The rule
described above for co-localization, i.e. that the concentra-
tion of donor and/or acceptor should not reach the ‘critical
concentration’, e.g. about 2.8 m
for soluble CFP/YFP FRET
pairs, and a significantly lower value for the ‘critical con-
centration’ for membrane proteins also applies to all FRET
measurements. Besides the proper choice of FP pair for
FRET, the excitation, dichroic, and emission filters need to be
chosen carefully to maximize excitation and emission and
minimize bleed-through (Shaw, 2006).
Traditionally, FRET has been used as a spectroscopic ruler
to measure the distance between two sites on a protein, such
as intrinsic tryptophan acting as donor and an acceptor dye
covalently bound to the protein (Stryer, 1978). The wide-
spread availability of spectrofluorometers and fluorescence
microscopes together with the advent of genetically
encoded fluorophores has made FRET measurements com-
paratively accessible and affordable. Energy transfer effi-
ciency can be estimated fairly easily and be compared with a
theoretical value for the donor–acceptor pair to evaluate the
potential for random collision. In most cases, rather than
measuring the actual FRET efficiency a proxy is determined,
in the simplest case the ratio of the peak emission of the two
fluorophores (Vogel et al., 2006). A number of factors affect
the apparent energy transfer, such as the relative dipole
orientation (if it is not random; j
2/3), the
ratio of
the partners (if different from 1:1), protein concentrations
above the critical level, and technical problems such as
bleed-through. While the theoretical achievable transfer
efficiency may be 50%, the measured efficiency could be
10%; thus extensive additional information is needed before
conclusions on the molecular interaction can be drawn.
Considering photophysical properties of FPs and the possi-
bility of homotransfer (if donor concentration reaches the
‘critical concentration’), it is good practice to verify that
donor quenching upon FRET also results in an increased
fluorescence intensity of the acceptor or in a decrease in the
lifetime of the donor (Subramaniam et al., 2003), or to obtain
actual values of FRET for different FRET pairs by using
calibration systems as developed by Vogel’s group (Koushik
et al., 2006; Thaler et al., 2005). The different methods for
measuring FRET efficiency have been compared using
different set-ups and microscope/electronic configurations
(Gordon et al., 1998; Koushik et al., 2006; Pelet et al., 2006;
Rizzo et al., 2006). Taken together, PPI analysis using
fluorophore-based assays requires careful control of a
number of parameters to exclude artifacts.
Bioluminescence resonance energy transfer (BRET)
Bioluminescence resonance energy transfer (BRET) is simi-
lar to FRET in that energy transfer occurs between a donor
and acceptor (Subramanian et al., 2006; Xu et al., 1999). The
method is suitable for the analysis of protein interactions in
extracts and is suitable for imaging at least at lower resolu-
tion (Xu et al., 2007). The major difference of BRET lies in the
RET donor, which in the case of BRET makes use of lucifer-
ase which catalyses the oxidation of luciferin to emit light.
The energy of the reaction can be transferred by RET to an
acceptor (e.g. GFP or YFP) if luciferase and the fluorophore
are within a radius of 50 A
. The most frequently used BRET
pairs are coelenterazine/GFP (or YFP) or the DeepBlueC/
UV-GFP (though DeepBlueC does not appear to work in
plants; Subramanian et al., 2004). Because this reaction
occurs in the dark, it does not require excitation light, hence
there is no risk of photodamage, no acceptor photobleach-
ing, no fluorescence bleed-through, and, due to the lack of
excitation, no problems caused by autofluorescence of the
sample. Despite the apparently higher sensitivity of BRET
over FRET, the emission generated is limited in intensity,
requiring long integration times; thus while BRET can be
visualized at the tissue and cellular levels with a sensitive
camera (e.g. modified electron bombardment CCD) it cannot
be used for analyzing dynamic interactions since exposure
times are prohibitive (Xu et al., 2007).
Fluorescence correlation spectroscopy (FCS)
Fluorescence correlation spectroscopy (FCS) measures
fluctuations in fluorescence intensity caused by the diffusion
or conformational changes of fluorescently labeled mole-
cules in a small interrogated volume, typically created by a
confocal microscope (Lakowicz, 2006). Fluorescence corre-
lation spectroscopy can be used to measure several prop-
622 Sylvie Lalonde et al.
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erties of a labeled molecule including the number of mole-
cules in the interrogated volume, their diffusion rate, flow
rate, aggregate formation, and rotational dynamics (with
polarized light) (Schwille et al., 1999). In a typical applica-
tion, as a diffusing fluorophore moves into the interro-
gated volume a burst of photons begins due to multiple
cycles of excitation and emission, and ends when the flu-
orophore leaves the interrogated volume. The duration of
bursts is correlated with the diffusion rate. Although a
confocal microscope is used to create the excitation vol-
ume (interrogated volume) in which the single molecule is
observed, this method is not an imaging technique that
provides spatial information in a living cell; it is rather
used to study molecular interactions in vitro and in vivo
(Goedhart et al., 2000; Hink et al., 2003; Ko
hler et al., 2000).
The diffusion rate depends on the size of the molecule and
its interaction with other molecules; this dependence makes
FCS a valuable method for measuring a wide range of
binding interactions such as PPI. Because the diffusion time
scales with the cubic root of molecular mass, interaction of
binding partners must result in a significant increase in mass
to be detected by FCS. In fact, dimerization, or simple
doubling of mass, is difficult to detect as it causes an
increase in the diffusion coefficient of only 26% (Bacia et al.,
2006; Lakowicz, 2006; Meseth et al., 1999). Thus, the smaller
of the binding partners should always be the labeled
component to maximize the increase in mass upon interac-
tion and binding. An alternative methodology, which can be
applied to interactions that result in small changes in mass,
is fluorescence cross-correlation spectroscopy (FCCS; Schw-
ille et al., 1997). In FCCS interacting partners are labeled with
different fluorophores and the intensity fluctuations of the
two species are cross-correlated. If the two molecules
interact, their intensities will tend to fluctuate together. It
should be noted that FCCS does not measure the physical
interaction of two partners but calculates the probability of
having both partners in the same restricted volume at the
same time.
Practical considerations for FCS measurements
Fluorescence correlation spectroscopy measurements are
affected by light scattering, autofluorescence, photobleach-
ing of the fluorophores, the detection limit of the micro-
scope, and photodamage of living material (Schwille et al.,
1999). As is the case with all fluorescence microscopy, the
power of the excitation energy must also be adjusted care-
fully to find the best compromise between signal detection
on the one hand and photobleaching/phototoxicity on the
other. Light scattering at the cell wall can be a special
problem when analyzing plant cells. Two-photon fluores-
cence excitation has several advantages for in vivo analysis
in plants: (i) a smaller interrogated volume reduces both
light scattering and phototoxicity and (ii) longer excitation
wavelengths can reduce phototoxicity and improve depth
penetration (Schwille et al., 1999).
The amplitude of fluctuations in fluorescence intensity is
inversely proportional to the number of molecules mea-
sured at the same time, meaning that high concentrations
will diminish the effect of fluctuations and result instead in
the measurement of an average intensity (Mu
ller et al.,
2003). For plant cells, Hink et al. (2003) used concentrations
below 5 l
, while Schwille et al. (1997) recommend the use
of concentration ranges of 1–100 n
. As a consequence of
the low concentration of the proteins used, interactions can
only be detected if they have sufficiently high affinity for the
binding to occur in the concentration range suitable for FCS/
FCCS. It is important to note that FCS/FCCS may be limited
by interactions where diffusion is relatively rapid. For
example, proteins in the membrane may diffuse slowly
and not transit the interrogation volume frequently enough
to provide reliable fluctuations over experimentally practical
integration periods. In live cells, cytoplasmic streaming
(which is particularly rigorous in plant cells) may violate
assumptions of diffusion being the chief determinant of
fluorophore motion.
The choice of fluorescent proteins for FCS applications in
plant cells entails many of the same consideration that are
true for FRET; increasing fluorophore detectability by using
enhanced versions of GFP (Schwille et al., 1999), CFP and
YFP (Hink et al., 2003), and S65TmGFP4 (Ko
hler et al., 1997),
and avoiding fluorescent protein variants that have a
tendency to oligomerize on their own. However, by contrast
to FRET, in FCCS it is important that the two fluorophores
have non-overlapping spectra to avoid energy transfer and
bleed-through fluorescence. In fact, if energy transfer (or
photobleaching) occurs, the apparent diffusion coefficient
will decrease. Other important considerations, such as
proper alignment of the observation volumes for each
wavelength for FCCS are outlined by Schwille et al. (1997,
Protein fragment complementation assays (PCA)
The two-hybrid systems described above are based on the
concept that two separately expressed domains of a split
protein cannot complement each other except if the local
concentration is increased (see above discussion on ‘critical
concentration’). In the case of the classical Y2H system, a
transcription factor is split genetically into its DNA-binding
and transcription activation domains. The two domains do
not appear to be able to reconstitute a functional transcrip-
tion factor without the help of an interaction. The inability to
reconstitute is most probably due to the absence of a stable
interaction interface in the absence of the covalent linkage of
the two subunits (see discussion above on ‘structural basis
of protein interaction’) as is the case for split-GFP, split-
ubiquitin, or split b-galactosidase. The two domains even do
The detection of protein–protein interactions 623
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
not reassemble when a nuclear localization signal is added
as in the case of Y2H vectors. It is also possible that the
halves cannot fold by themselves, although the observation
of diffractable crystals suggests that the activation domain
can fold on its own (Chattopadhyaya and Pal, 2004). In
contrast, the two halves of the much simpler ubiquitin do
reconstitute a ‘functional’ ubiquitin on their own that is
recognized by the ubiquitin-specific proteases (Johnsson
and Varshavsky, 1994). Only by introducing mutations can
the affinity (or the folding) of the two halves towards each
other be reduced to make them suitable for interaction
screens. It has been known for many years that when pro-
teins are split into two polypeptide chains and these pep-
tides are co-expressed in a cell, a significant portion can
reconstitute to form a functional protein and this reconsti-
tution from fragments has been used to study protein fold-
ing (de Prat-Gay, 1996). This phenomenon has also been
used to study protein structure, folding, function, and evo-
lution. One of the classical examples is a-complementation
of b-galactosidase in E. coli (Galarneau et al., 2002; Rossi
et al., 1997; Spotts et al., 2002). Other examples include the
extracellular yeast invertase (Schonberger et al., 1996), as
well as lactose and sucrose transporters (Bibi and Kaback,
1992; Reinders et al., 2002b; Wrubel et al., 1994), b-lactam-
ase (Galarneau et al., 2002), luciferase (Paulmurugan et al.,
2002; Remy and Michnick, 2006), dihydrofolate reductase
(DHFR; Pelletier et al., 1998, 1999), TEV protease (Wehr et al.,
2006), and FP variants (Ghosh et al., 2000; Hu et al., 2002).
Some complementation assays require the presence of an
exogenous substrate (e.g. b-lactamase, luciferase, b-galac-
tosidase, DHFR). The molecular mechanism(s) for the
reconstitution of functional proteins from unfolded domains
is largely unknown, and multiple folding pathways may
exist. One may assume that the interfaces formed between
the domains are sufficient to reconstitute a stable protein, or
to induce folding of the two separate polypeptides post-
Similar to the cases described above, FPs can also be split
and reconstituted, a process that may be driven in part by
b-strand addition, a feature of a number of PPIs (Ghosh
et al., 2000; Wrubel et al., 1994). However, reconstitution of
split FPs is quasi-irreversible with a half-life time estimated
at 10 years and cannot be used for dynamic studies (Mag-
liery et al., 2005). N-GFP and C-GFP (split at 157–158) do not
seem to reassociate when combined at concentrations of
100 l
in solution or when co-expressed in bacteria (Ghosh
et al., 2000). The reconstitution assays (BiFC or split-GFP)
can be used to study a variety of processes such as protein
folding (Cabantous et al., 2005), and similar to other two-
hybrid systems as a tool to detect protein interactions with
subcellular resolution (Ghosh et al., 2000). It has been
proposed that the reconstitution from FP fragments has an
advantage over FRET interactions studies by having a higher
dynamic range, with practically no fluorescence in the
absence of an interaction to high levels of fluorescence after
fusion to interacting proteins. The absence of fluorescence
of the non-interacting domains has been ascribed to the
inability of the two separate domains to fold by themselves
(Magliery et al., 2005).
An implicit condition of these systems is that the two
halves do not reconstitute by themselves and that only the
interaction of protein ‘X’ with protein ‘Y’ (where X and Y are
fused to the split domains of an FP, respectively) triggers the
reconstitution of an FP. It is known, however, that the large
N-terminal fragment of split-GFP can pre-form a chromo-
phore under certain conditions (Demidov and Broude, 2006).
Moreover, certain fragment combinations can spontane-
ously reconstitute in the absence of attenuators (Cabantous
et al., 2005). Moreover, when expressed from the strong
CaMV35S promoter using the Nicotiana benthamiana tran-
sient infiltration system, the soluble Venus halves yield
significant fluorescence levels even in the absence of a
fusion partner (Figure 6; SL and WBF, unpublished). The
reconstituted FP shows a similar subcellular localization to
natively expressed GFP with localization to the nucleus. A
similar autoassembly of the two FP halves or assembly of
the N-terminal fusion with a protein-of-interest and the free
C-terminal domain has been observed in plant cytosol and
Figure 6. Autoreconstitution of split-Venus in plants.
Split-halves of Venus (nVenus: amino acids 1–155; cVenus: amino acids 155–238), when co-expressed, reconstitute a functional fluorescent protein in the absence of
fusion partners (SL and WF, unpublished results). Tobacco (Nicotiana benthamiana) leaves were infiltrated with an Agrobacterium tumefaciens suspension (OD 0.1)
of each construct (35S-nVenus-Nos and 35S-cVenus-Nos) and imaged 60 h post-infiltration using a Nipkow spinning disk confocal microscope (for method see
Deuschle et al., 2006): (a) Venus channel; (b) chlorophyll fluorescence; (c) merge. Bar represents 20 lm. No fluorescent signal was obtained when the halves were
expressed alone.
624 Sylvie Lalonde et al.
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even when the halves were targeted to the ER (Cabantous
et al., 2005; Walter et al., 2004; Zamyatnin et al., 2006).
These data suggest that in the plant systems used, the
‘critical concentration’ (see discussion in sections on
co-localization and FRET) is reached such that the two
halves can reconstitute a fluorescent protein in the absence
of fusion partners. As a consequence it is only possible to
observe differences in the rate of formation of the reconsti-
tuted and correctly folded split FP. Since the dissociation
constant of complex formation is negligible, reconstitution
is driven only by the on rate (association constant). Thus any
factor that enhances formation of a functional fluorophore
will affect the time when fluorescence becomes detectable
after infiltration/transfection (Table 3). Factors that contrib-
ute to changes in the association rate include association of
fusion partners, effects of the fusion on protein steady-state
levels of the chimeras, and effects of the fusions on folding.
Their steady-state levels, the expression level of the chi-
mera, protein fragment turnover, accessibility of the frag-
ments for reassembly, changes in free diffusion, and effects
of the fusion on the (pre-) folding of GFP halves will affect the
on rate. In other words, the split-FP system measures a
number of parameters such as binding of fusion partners,
protein folding, protein turnover, and accessibility for inter-
action, etc. Given the existence of these multiple factors that
could contribute to the rate of reconstitution, from first
principles, the split-FP system does not measure protein
interactions alone but measures all of the parameters
mentioned above. Additional experiments are necessary to
differentiate which of the factors is responsible for increased
reconstitution rates, with the binding affinity of the fusion
partners being only one of several possibilities. The tests
used for distinguishing between these possibilities include
the use of mutations in the fusion partners, which affect
formation of the fluorophore. However, these mutations
may affect any of the other parameters as well, such as
protein stability. It is important to note here also that at least
high-affinity complexes are typically not affected by single-
point mutations (Reichmann et al., 2007). Lowering the
expression level as well as novel split-FP systems in which
the dissociation rate is increased may constitute means to
improve the suitability for interaction, folding, and stability
Quantitative analysis of protein interactions
A variety of methods have been developed to detect and
quantify PPI both in vitro and in vivo (Table 1), including
surface plasmon resonance (SPR; BIAcore, McDonnell,
2001; Schuck, 1997b), isothermal titration calorimetry (ITC;
Kumaran and Jez, 2007; Perozzo et al., 2004; ), and analytical
ultracentrifugation (reviewed in Lakey and Raggett, 1998).
Modern plasmon resonance instrumentation enables the
characterization of interactions at least in moderate
throughput. Quantitative information on protein interactions
in the pico- to nanomolar affinity range can also be obtained
by mass spectrometry, such as matrix-assisted laser
desorption ionization time-of-flight mass spectrometry
(MALDI-TOF), and novel quantitative approaches are being
developed in this area, e.g. intensity fading (Krogan et al.,
2006; Yanes et al., 2007). The free energies of protein–
protein association as determined using electrospray ion-
ization mass spectrometry (ESI-MS) correlate accurately
with values obtained by solution enzyme assays (Krish-
naswamy et al., 2006). The sensitivity of the mass spec-
trometry approaches can be increased to detect weak and
transient interactions using chemical cross-linking ap-
proaches (Vasilescu and Figeys, 2006). Another method
that can be applied for the detection of PPIs is atomic
force microscopy (AFM; Hinterdorfer and Dufrene, 2006)
which measures the specific interaction forces involved in
protein interactions (Lin et al., 2005), and which has been
used to quantify the dissociation kinetics of protein com-
plexes, for example for the SNARE complex (Yersin et al.,
Surface plasmon resonance (SPR)
Probably the simplest method for the analysis of thermo-
dynamic and kinetic parameters of protein interactions in
vitro is surface plasmon resonance (SPR). Surface plasmon
resonance measures the change in refractive index of a
solvent near a surface (typically a gold film) that occurs
during complex formation or dissociation (McDonnell, 2001;
Schuck, 1997a). One partner (the bait) is bound to the surface
of a chip that is coated with the gold foil, in the case of
proteins via affinity tags such as nickel-chelation of a His-
tagged protein (Rich and Myszka, 2000). Thus a prerequisite
for application of SPR is that sufficient protein can be
obtained in a heterologous expression system. The chip is
mounted onto the instrument which measures SPR using an
evanescent wave in real time and the chip is perfused with a
buffer using a microfluidic device. Then a solution contain-
ing the prey ligand is added. If the prey binds to the bait, the
SPR signal changes, producing a new equilibrium due to
association of bait and prey. After this new steady state has
been reached, the chip is perfused with a buffer solution, the
ligand dissociates and a new equilibrium will be established.
Table 3 Parameters measured with the split fluorescent protein (FP)
Contribution of fusion partners to FP association rate
Contribution of fusion partners to FP maturation
Contribution of fusion partners to (pre-)folding of FP halves
Contribution of fusion partners to available number of FP halves
Contribution of fusion partners to stability of FP halves
Contribution of FP halves to stability of fusion partners
Steric accessibility of FPs in fusions for reconstitution
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From this reversible reaction, the association and dissocia-
tion as well as the binding constants can be determined.
Modern instruments (e.g. BIAcore) provide for the analysis
of interactions between multiple baits and prey with med-
ium throughput. Obviously, this method is best suited for
the analysis of interactions of soluble proteins since it is
difficult to bind a membrane protein, e.g. a transporter, in its
native conformation to the chip. As pointed out above,
knowledge of the thermodynamic and kinetic parameters
would be a highly valuable asset for our understanding of
protein interactions, and in order to obtain insights into
the detection range regarding affinities of the various HT
Databases for protein interactions
Physical protein interaction data are available for most
model organisms. However, the number of PPI data gener-
ated from experimental approaches varies widely depend-
ing on the organism. Compared with animal systems, very
few experimentally derived interaction data for plants are
available today. Several repositories for PPI data exist,
including IntAct (Hermjakob et al., 2004b), bioGRID (Stark
et al., 2006), BIND (Gilbert, 2005), DIP (Xenarios et al., 2002),
KEGG (Franca-Koh et al., 2006), MINT (Chatr-Aryamontri
et al., 2007), and MIPS (Pagel et al., 2005). Both hand-
curated data from the literature and bulk data from HT
screens are available in these databases. In addition, TAIR
has some Arabidopsis protein interaction data curated from
the literature (
proteinInteract.jsp). In addition, new databases such as the
database for Kinetic Data of Bio-molecular Interactions
(KDBI) provide kinetic data of PPI derived from literature
curation. The KDBI contains information about binding or
reaction events, participating molecules (name, synonyms,
molecular formula, classification, SWISS-PROT AC or CAS
number), binding or reaction equation, and kinetic data (Ji
et al., 2003) The proteomics community has developed and
adapted a standard for protein interaction data in XML called
Proteomics Standards Initiative Molecular Interaction (PSI-
MI; Hermjakob et al., 2004a). Several open-source, free
software applications for visualizing and analyzing protein
interaction data exist. Some of the popular ones include
Cytoscape (Shannon et al., 2003) and Osprey (Breitkreutz
et al., 2003).
Construction and analysis of protein interaction networks
The PPI data can be utilized largely in two ways. One can
start with a protein or complex of interest and determine
which proteins physically interact. Alternatively, one can
analyze the entire network of the interacting proteins of a
system to learn about any high-level organizing principles of
complexes and interacting proteins. Here, we briefly
describe the methods and findings of the study of PPI net-
works from this global perspective.
Genome-wide PPI networks are currently available for
several model organisms such as S. cerevisiae, C. elegans,
and D. melanogaster (Hermjakob et al., 2004b). Although
these networks represent only a fraction of the complete
interactomes, investigation of these networks is a first step
towards a systems-biology understanding of cells and
organisms. In S. cerevisiae, physical interactions between
proteins have been identified with large-scale HT experi-
ments using the Y2H method as well as direct purification of
complexes using AP-MS analyses (Ito et al., 2001; Uetz et al.,
2000). The number of PPI data for S. cerevisiae has been
increasing at a rapid rate, currently totaling 18 272 interac-
tions among 4920 proteins in DIP database (as of 6 Septem-
ber 2007). Similarly, a large fraction of PPI data exist for
C. elegans (Hermjakob et al., 2004b; Li et al., 2004; Xenarios
et al., 2002) and D. melanogaster (Giot et al., 2003; Her-
mjakob et al., 2004b; Xenarios et al., 2002). At present,
experimentally derived plant protein interactions are much
fewer than animals. For A. thaliana, there are about 1800
interactions among 1000 proteins that are curated from
literature available from TAIR, IntAct, and BIND. Large-scale
HT protein interaction projects had not been published for
plants at the time of this writing, though there are at least
two projects under way to generate large-scale interaction
data (, J. Ecker, Salk Institute
for Biological Studies, La Jolle, CA, USA, personal
communication). Meanwhile, attempts have been made to
extrapolate potential interaction data for Arabidopsis from
interacting orthologs in S. cerevisiae, C. elegans, D. mela-
nogaster, and
H. sapiens (Donaldson et al., 2003; Geisler-Lee
et al., 2007). A total of 1159 high-confidence, 5913 medium-
confidence, and 12907 low-confidence interactions were
predicted for 3617 proteins (Geisler-Lee et al., 2007). For
O. sativa, 8902 interactions for 1879 proteins have been
predicted (Yu et al., 2005).
When a substantial portion of an interactome is available,
it becomes feasible to study the interactome using graph
topological/theoretical analysis methods to obtain insights
into PPI properties from a systems view. A PPI network is
typically represented as a graph G =(V, E) (Harary, 1969)
where each vertex (a node representing a protein) in V (total
number of proteins in the network) is connected to an
interacting protein by a line (called an edge in graph
analysis) in E (total number of interactions in a network)
(Figure 7a). Such protein interactions may be derived from
individual experimental datasets, public databases, or with
the help of PPI prediction tools. Graph visualization and
analysis tools such as Graphviz (Gansner and North, 2000)
and Cytoscape (Shannon et al., 2003) can be used to draw
PPI networks in two or even three dimensions. Some graph
visualization tools, including Pajek (Batagelj and Mrvar,
1998) and LGL (Adai et al., 2004), apply a force-directed
626 Sylvie Lalonde et al.
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
graphical representation (representing vertices as physical
objects that exert forces on each other such that vertices that
are connected by an edge attract to ensure that they are
placed closely and pairs of graph vertices repulse from other
pairs to ensure that non-related vertices are placed at larger
distances. The resulting graph layout is an energy-minimal
state of the force system.) guided by a minimal spanning
tree (the portion of the network that represents a tree where
all the vertices are connected fully and the number of edges
is less than or equal to that of every other spanning tree) of
the network and are useful in visualizing and exploring large
biological networks.
As a result of the analysis of existing PPI networks, it
has been shown that PPI networks from large-scale,
systematic experiments have similar scale-free (most pro-
teins interact with just a few partners and a small number
of proteins interact with many partners) and small-world
topology (the shape of the network is significantly different
from random graph models) (Guelzim et al., 2002; Milo
et al., 2002; Yeger-Lotem et al., 2004). Common topological
and biological features of these PPI networks lead to
numerous biological hypotheses, which will be described
briefly in this section.
Small-world networks (Watts and Strogatz, 1998) were
discovered according to their clustering coefficient (a mea-
sure that weighs the cohesiveness of a graph) and their
mean-shortest path length (average length of the shortest
paths between two vertices). Small-world networks, as
compared with random graphs with the same scale (same
number of vertices in a graph), are characterized by cluster-
Figure 7. Protein–protein interaction (PPI) net-
(a) Scale-free topology of Saccharomyces cere-
visiae interaction network from 1004 proteins
and 948 interactions (http://depts.washing-; Uetz et al., 2000). Drawn with
Cytoscape (Shannon et al., 2003).
(b) Power-law degree distribution shows that the
PPI network is a scale-free network. The R
of 0.9637 indicates that the linear trendline fits
the data vary well.
The detection of protein–protein interactions 627
ª 2008 The Authors
Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
ing coefficients that are significantly higher, and mean
shortest-path length much lower than those in random
graphs. By definition, small-world networks have a high
portion of cliques (a graph in which every vertex is
connected to every other vertex in the graph) and sub-
graphs (a graph whose vertex and edge sets are subsets of
those in the larger graph G) that are a few edges short of
being cliques. This follows from the requirement of a high
clustering coefficient. Secondly, the majority of pairs of
vertices will be connected by at least one short path. This
follows from the requirement that the mean shortest path
length be small. If the average length of the shortest paths
for every two vertices is small, then by chance any two
vertices will be connected by a short path. Here we assume
the shortest path follows normal distribution and the
standard deviation is not high. Barabasi and Albert hypoth-
esize that the prevalence of small-world networks in biolog-
ical systems may reflect an evolutionary advantage of such
architecture, since small-world networks are more robust to
perturbations than other networks (Barabasi and Albert,
1999). In this case, this feature would provide an advantage
to biological systems that are subject to damage by muta-
tion or viral infection.
In addition to being a of a small-world type, biological
networks, including PPI networks, tend to be scale-free
(Guelzim et al., 2002; Milo et al., 2002; Yeger-Lotem et al.,
2004). In scale-free networks, the number of connections
per protein follows a power-law distribution (Figure 7b)
such that most vertices have only a few connections, while a
few number of vertices are highly connected (often called
‘hubs’). These hubs can shape the way in which the network
operates because the hubs and their direct neighbors
occupy the majority of the network. Since hubs interconnect
multiple sub-networks, it is not surprising that deletion of
such hubs is often lethal, which may be expected because
the loss of a centrally connected component probably
affects multiple cellular processes (Jeong et al., 2001).
These global features of the PPI network suggest general
principles of the architecture of the network, but do not
help in elucidating what makes one PPI network different
from another. To do this, it is important to analyze features
of the network at a more local level. Apart from the global
topological characteristics, complex PPI networks are very
different from each other in several respects such as size,
shape, and connectivity, but they all share striking local
properties: the presence of many small dense sub-net-
works, called network modules (also called clusters, Alon,
2003). A network module is defined as a set of vertices that
have strong interactions and a common biological function
(e.g. a ribosome, proteosome, or photosystem I). A module
has boundary vertices (vertices interacting with other
vertices outside a module) that control the input/output
interactions (interactions between vertices inside a module
and vertices outside a module) with the rest of the network.
A module also has internal vertices (vertices that do not
interact with any vertex outside a module) that do not
significantly interact with vertices outside the module.
Modules in PPI networks of different sizes have been found
using different methods, included the highly connected
sub-graphs (HCS; Hartuv et al., 2000) and detection of
molecular complexes (MCODE; Bader and Hogue, 2003).
Modular biological networks may have an advantage over
non-modular network by having the capacity to be more
readily reconfigured to adapt to new conditions. The
modules in PPI networks also make the networks more
robust to perturbation by isolating the perturbation to the
module that is affected (Gerhart and Kirschner, 1997;
Lipson et al., 2002). Recent analysis on experimentally
derived PPI networks showed that with increasing number
of proteins in a PPI network, the number of vertices in
individual modules increases while the number of identi-
fied modules decreases (Przulj et al., 2004).
Another useful local property of networks is the existence
of network motifs, which are significantly recurring local
topological patterns (Milo et al., 2002). If modules represent
functional units, network motifs could be said to represent
structural units of a network. While relatively less widely
studied than the global topological features, network motifs
can lead to a better understanding of various classes of
complex networks, as some network motifs may be partic-
ular to specific classes of networks. For example, certain
triad and tetrad motifs are found to appear commonly in
gene transcription networks of S. cerevisiae and E. coli but
not in any other kinds of networks (Milo et al., 2002). In
addition, network motifs can also unravel the basic struc-
tural elements that underlie the hierarchical and modular
architecture of complex natural networks such as PPI
networks. It is interesting to note that similarity in network
motif topology does not necessarily stem from duplication.
Evolution, by constant tinkering, appears to converge on
these network motifs in different non-homologous systems,
presumably because they are optimally suited to carry out
key functions (Wagner, 2003). Network motifs can be
detected by frequent sub-graph mining algorithms such as
mfinder (Kashtan et al., 2004), hSigGram, vSigGram (Kura-
mochi and Karypis, 2004), and FPF (Schreiber and Schwob-
bermeyer, 2004), which compare the patterns found in the
target network with those found in suitably randomized
networks. Once a dictionary of network motifs and their
functions is established, one could envision researchers
detecting network motifs in new networks just as protein
domains are currently used in annotating new protein
sequences (Alon, 2003).
Assessing the accuracy of high-throughput PPI data
As described in the section ‘Practical considerations for
mbSUS analysis’, it is important to minimize the number of
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Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
‘false positives’ and ‘false negatives’ in the PPI data and to
evaluate the reliability of the data obtained. It is very
important to obtain experimental values for the reliability of
the data, e.g. by carrying out independent replicates and
independent confirmation by alternative methods, to enable
bioinformatic analysis of the reliability of a network. Bioin-
formatic methods for assessing the reliability of each can-
didate PPI as well as the network have been developed. Two
common methods for detecting false positives are based on
data integration and topology.
Data integration-based methods assess the accuracy of
PPI data by taking advantage of the difference in results
derived from different approaches. For example, Bader et al.
(2004) developed a quantitative method to compute confi-
dence values for PPIs with a logistic regression approach (a
statistical regression model for binomially distributed
response/dependent variables). A training set is generated
by comparing networks from Y2H and AP-MS experiments.
Pairs of proteins that are directly connected to each other in
an AP-MS network and are less than two edges apart in the
Y2H network were selected as positive examples, and
proteins that are directly connected in one network but far
apart in the second network were selected as negative
examples. These training sets were used to build a hyper-
plane (a high-dimensional generalization of the concept of a
line in Euclidean plane geometry and a plane in three-
dimensional Euclidean geometry) that maximally separates
the high-confidence pairs from the rest using a logistic
regression model. The logistic regression model is then
used to predict confidence scores for pair-wise interactions
in the full dataset. The high-confidence interactions in
Bader’s experiments show high agreement with manually
curated database annotations. This approach relies on the
use of comprehensive datasets, which at present are avail-
able only for a few organisms. A major drawback of this
approach is that the two methods detect different types of
protein complexes: while Y2H methods determine binary
PPIs, AP-MS experiments identify complexes, in which two
proteins do not have to interact directly. Moreover, it has to
be taken into account that the PPIs detected with the two
methods may actually differ biologically, for example with
respect to their ability to detect PPIs differing in kinetics and
binding affinities (see the description of properties of PPIs).
Topology-based methods model the expected topological
characteristics of true PPI networks, and then devise math-
ematical measures to assess the reliability of candidate
interactions. For example, Saito and colleagues developed a
series of computational measures called interaction gener-
alities (IG1 and IG2) (Saito et al., 2002, 2003) to assess the
reliability of PPI. The IG1 measure is based on the idea that
proteins that appear to have many interacting partners,
which in turn have no further interactions, were likely to be
false positives (Saito et al., 2002). This is a reasonable model
for Y2H data, as ‘sticky’ proteins or proteins that accumulate
beyond the ‘critical’ concentration in Y2H assays may
interact with proteins non-specifically or autoactivate the
reporter without interacting with another protein. These
proteins appear to interact with a large number of random
proteins in the experimental data. IG1 is a local measure,
which does not consider the topological properties of the
protein interaction network beyond the candidate protein
pair. As such, it only addresses the ‘sticky protein’ error but
does not correct other types of experimental error that could
also lead to false positives such as PPIs detected between
overexpressed proteins. The IG2 measure (Saito et al., 2003)
was developed to incorporate topological properties of
interactions beyond the candidate interacting pairs by
considering the five possible topological relationships of a
third protein, C, with a candidate interacting pair (A, B),
which increases the statistical power of determining reli-
ability. The IG2 measure uses the weighted sum of the five
topological components with respect to C. The weights were
assigned a priori by performing a principal components
analysis (a technique used to reduce multidimensional
datasets to lower dimensions by keeping lower-order prin-
cipal components and ignoring higher-order ones: such low-
order components often contain the ‘most important’
aspects of the data) on the entire interaction network.
Experimental results demonstrated that IG2 performed
better as measured by more coherence in gene ontology
(GO) functional, processing, and component annotations of
the interacting proteins than IG1 (Saito et al., 2003).
Because of the importance of protein interactions for biol-
ogy, a wide spectrum of advanced methods have been
developed to collect the complete interactomes of organ-
isms from all kingdoms. The generation of interactome
maps for plants is lagging behind animal systems, and
systematic large-scale network analysis remains a major
necessity for systems biology. Independent of the organism
of interest, membrane proteins are typically depleted from
genome-wide analyses for technical reasons. Thus, data on
both membrane protein/membrane protein interactions as
well as the interface between membrane proteins and the
soluble interactome are largely missing despite their
importance for advancing our understanding of the com-
munication of cells with their environment. The mbSUS and
AP-MS methods tailored for membrane protein interactions
will be a means to fill this gap. All of the HT analyses will
require extensive follow-up to verify the existence and rel-
evance of the data. In addition to verification, two areas that
will require specific attention in the near future will be
analysis of localized interactions within a cell and determi-
nation of the structural, thermodynamic, and kinetic prop-
erties of these interactions. Localized reactions are critical,
since many signaling processes occur in small regions of a
The detection of protein–protein interactions 629
ª 2008 The Authors
Journal compilation ª 2008 Blackwell Publishing Ltd, The Plant Journal, (2008), 53, 610–635
cell, as exemplified by localized signaling processes in che-
motaxis of Dictyostelium (Franca-Koh et al., 2006). Tools for
systematic determination of structures and for analysis of
the biochemical properties of the interactions are available,
including SPR, NMR, and AFM. More sophisticated statisti-
cal analyses to address the accuracy of the HT PPI and bio-
informatic mining and analysis to relate the PPI information
to biological context and derive new principles underlying
these processes will be required. Ultimately integration of
PPI data with other datasets from transcriptomics, meta-
bolomics including fluxomics (Wiechert et al., 2007), phos-
phoproteomics (Nu
hse et al., 2004), and comparative
genomics will enable the quantitative modeling of biological
processes in plant cells to facilitate our understanding of the
interplay between molecular machineries and biological
processes in biological systems.
The authors want to thank C. Biskup (University of Jena, Germany)
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grant support for the large-scale membrane protein interactome
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    • "The challenge has been and remains how to determine when this is the case. While a range of publications simply show exemplary images of black negative controls and bright positive samples without biochemical validation of expression (as exemplified in Fig. 1E), a growing number of researchers have come to view BiFC with greater caution due to these inherent complications (Lalonde et al., 2008; Grefen and Blatt, 2012a; Horstman et al., 2014). Here, rather than enumerating the different (individual expression ) vector systems that have been reported since the original development of BiFC, which unfortunately have done little to tackle the issues associated with the spontaneous reassembly of split fluorescent protein fragments and its irreversibility, this section addresses the issues around the use of BiFC and their potential solutions. "
    [Show abstract] [Hide abstract] ABSTRACT: Identifying key players and their interactions is fundamental for understanding biochemical mechanisms at the molecular level. The ever-increasing number of alternative ways to detect protein-protein interactions (PPIs) speaks volumes about the creativity of scientists in hunting for the optimal technique. PPIs derived from single experiments or high-throughput screens enable the decoding of binary interactions, the building of large-scale interaction maps of single organisms, and the establishment of cross-species networks. This review provides a historical view of the development of PPI technology over the past three decades, particularly focusing on in vivo PPI techniques that are inexpensive to perform and/or easy to implement in a state-of-the-art molecular biology laboratory. Special emphasis is given to their feasibility and application for plant biology as well as recent improvements or additions to these established techniques. The biology behind each method and its advantages and disadvantages are discussed in detail, as well as the design, execution, and evaluation of PPI analysis. We also aim to raise awareness about the technological considerations and the inherent flaws of these methods, which may have an impact on the biological interpretation of PPIs. Ultimately, we hope this review serves as a useful reference when choosing the most suitable PPI technique.
    Full-text · Article · Apr 2016
    • "Membrane protein complexes are notoriously difficult to study using traditional highthroughput techniques (Lalonde et al., 2008). Intact membrane complexes are difficult to " pull down " using conventional affinity purification/mass spectrometry-based systems. "
    [Show abstract] [Hide abstract] ABSTRACT: Identifying the entire set of complexes is essential not only to understand complex formations, but also to map the high level organisation of the cell. Computational prediction of protein complexes faces several challenges including the lack of sufficient protein interactions, presence of noise in protein interaction datasets and difficulty in predicting small and sparse complexes. These challenges are covered in most reviews of complex prediction methods. However, an important challenge that needs to be addressed is the prediction of membrane complexes. These are often ignored because existing protein interaction detection techniques do not detect interactions between membrane proteins. But, recently there have been several new experimental techniques including MY2H that are capable of detecting membrane protein interactions. In the light of this new data, we discuss here new challenges and the kind of open problems that need to be solved to effectively detect membrane complexes.
    Full-text · Article · Jun 2015 · Plant physiology
    • "Membrane protein complexes are notoriously difficult to study using traditional highthroughput techniques (Lalonde et al., 2008). Intact membrane complexes are difficult to " pull down " using conventional affinity purification/mass spectrometry-based systems. "
    [Show description] [Hide description] DESCRIPTION: Challenges and open problems in computational prediction of protein complexes: the case of membrane complexes
    File · Research · May 2015 · Plant physiology
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