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Protein interaction networks as starting points to identify novel
antimicrobial drug targets
Roya Zoraghi1 and Neil E Reiner1,2
Novel classes of antimicrobials are needed to address the
challenge of multidrug-resistant bacteria. Current bacterial
drug targets mainly consist of specific proteins or subsets of
proteins without regard for either how these targets are
integrated in cellular networks or how they may interact with
host proteins. However, proteins rarely act in isolation, and the
majority of biological processes are dependent on interactions
with other proteins. Consequently, protein–protein interaction
(PPI) networks offer a realm of unexplored potential for nextgeneration drug targets. In this review, we argue that the
architecture of bacterial or host–pathogen protein
interactomes can provide invaluable insights for the
identification of novel antibacterial drug targets.
Addresses
1
Division of Infectious Diseases, Department of Medicine, University of
British Columbia, Vancouver, Canada
2
Department of Microbiology and Immunology, University of British
Columbia, Vancouver, Canada
Corresponding author: Reiner, Neil E ([email protected])
Current Opinion in Microbiology 2013, 16:566–572
This review comes from a themed issue on Antimicrobials
Edited by Robert EW Hancock and Hans-Georg Sahl
For a complete overview see the Issue and the Editorial
Available online 10th August 2013
1369-5274/$ – see front matter, # 2013 Elsevier Ltd. All rights
reserved.
networks supported by specific protein–protein interactions (PPIs) [5,6,7,8]. The application of this type of
strategy has great potential for expanding our understanding of pathways and subnetworks of biological interest.
Analysis of these interactions can identify essential gene
networks and highly connected essential hub proteins and
in both cases suggest new classes of novel antibacterial
drug targets. A widely believed phenomenon known as the
centrality–lethality rule proposed that deleting a highly
connected protein (hub) is more likely to be lethal to an
organism than deleting a lowly connected protein (nonhub), based on genome-wide gene deletion studies [9].
Therefore, given that highly connected hub proteins are
generally essential for network integrity and stability, they
are expected to be less prone to genetic mutations conferring drug resistance emergence due to the network
centrality–lethality rule [10,11,12,13]. Experience tells
us that network analysis will often identify evolutionarily
conserved proteins, as potential novel targets and in these
cases, structural differences between prokaryotes/bacteria
and eukaryotes/man can be attributed to amino acid substitution or insertion/deletion (Indel) can be exploited to
selectively target the bacterial protein [14]. Our recent
studies illustrate these principles and indicated for the first
time that, essential, highly connected bacterial hub
proteins, such as MRSA pyruvate kinase (PK), with structural differences from their mammalian orthologs, have
clear potential as novel, high quality antibacterial drug
targets [15].
http://dx.doi.org/10.1016/j.mib.2013.07.010
Experimental measurements of protein
interactions
Introduction
Recent increases in antibiotic resistance among bacterial
pathogens coupled with a dearth of new antibiotic development over the past three decades or more, have
created major challenges in the clinic. The majority of
recent target-driven drug discovery approaches have
focused exclusively on unique pathogen-specific proteins
due to toxicity concerns, but still have the potential development of antibiotic resistance [1,2–4]. To minimize
risk for the latter, new antibiotic development strategies
based on modern integrative knowledge of bacterial cellular processes and mechanisms of bacterial pathogenesis
are critically needed. One such strategy is to use genomewide protein–protein interaction networks (PIN) in bacteria (the bacterial interactome) or host–pathogen interaction networks for initial target selection. This has the
potential to provide invaluable insights into systems
biology by allowing the analysis of biomolecular
Current Opinion in Microbiology 2013, 16:566–572
Several high-throughput (HT) experimental methodologies have been developed to detect PPIs at a proteome-wide scale thus enabling network structures to be
defined [16–18,19]. Techniques that are widely used to
measure PPIs in a HT fashion are bacterial or yeast twohybrid (Y2H) screening, tandem affinity purification
(TAP) experiments on tagged proteins coupled to mass
spectrometry (MS), and combined optical biosensor (BIAcore) coupled to MS analysis.
In Y2H method pairs of proteins to be tested for interaction are expressed as fusion proteins in yeast, as they are
fused to a DNA-binding domain and to a transcriptional
activator domain, respectively. Any interaction between
them is detected by the formation of a functional transcription factor. The main criticism applied to the Y2H
screen is the possibility of a high number of false positive
(and false negative) identifications. Overexpression of the
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Analysis of PINs to identify antimicrobial targets Zoraghi and Reiner 567
fusion proteins may cause unnatural protein concentrations, certain interactions may be inhibited by the
fused parts in the N-terminus of the proteins, some
interactions may not happen in yeast or in nucleus as
they are not in their native organism or compartment.
Because of the combined effects of all error sources
results derived by Y2H have to be interpreted with
caution.
In TAP-tag screen individual proteins are tagged and
used as ‘hooks’ to biochemically purify whole protein
complexes which are further separated and their components identified by mass spectrometry. TAP based
screen has higher efficiency and accuracy compared to
Y2H method. Because of the high degree of specificity
conferred by the tandem purification, number and
quantity of contaminating proteins are low. In TAP tag
method several members of a complex can be tagged
giving an internal check for consistency. In TAP-tag
screen, due to excess amount of tagged protein and high
sensitivity of mass spectrometry, a stringent criterion
must be applied to assign confidence scores for detection
of interacting proteins (e.g. proteins with two or more
unique peptides were usually considered as interacting
partner). However, since TAP tag method requires two
successive tandem steps of protein purification, transient
PPIs and some complexes that are not present under the
given conditions might be readily missed.
Y2H methods capture the direct physical binary interactions, whether these are transient or stable. Thus, Y2H
interaction maps can be viewed as a large static representation of the PPIs that can potentially form in the cell,
independent of its physiological state. However, since the
two interacting proteins are over expressed in this assay,
the observed interactions may not be present in the wild
type cells. Other limitations of the Y2H approach include
the difficulty of detecting interactions involving membrane proteins [20].
On the other hand, TAP-MS which defines the total
spectrum of complexes for a particular tagged protein
is an approach that is performed under near-physiological
conditions by analysis of stable protein interactions that
are expressed at native levels in the cell. This approach
provides information on the functional dynamics of the
complexes [17]. In this aspect, the two approaches can be
highly complementary. Combined optical biosensor
(BIAcore) and MS analysis is a relatively low throughput
assay that enables detection of formation and dissociation
of binary and multicomponent complexes in real-time
without labels.
High rates of false-positive and false-negative (missing)
interactions are still the most frequent pitfalls for each of
these experimental approaches. In order to evaluate
whether an interaction is biologically relevant various
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supporting approaches such as annotation, cellular localization and messenger RNA (mRNA) expression levels
can be utilized [21,22,23,24,25], while ultimately
other evidence of functional interactions or antibody pull
down methods can be performed to confirm specific
interactions.
Computational approaches for predicting
protein interactions
A variety of computational approaches have been developed over the past few years to predict genome-wide
protein interactions based on various attributes and data
types such as interaction-ortholog (interolog), domain
compositions, gene coexpression, coevolution of fusion
proteins and phylogenetic profiles [26,27]. Among these
the interolog method which deduces interactions from
experimentally determined interactomes of the orthologs
in other species is widely used even though it is limited to
interactions among the most conserved proteins. The
assumption is that if two homologs interact in one species
they will also likely interact in a related or even more
distant species. Generally, one protein interacts with its
partner by binding one of its structural domains to the
structural domain(s) in its target protein. In other words,
proteins interact with each other through domain–domain
interactions (DDIs). Since PPIs are generally assumed to
be mediated by DDIs, PPIs can be predicted for proteins
that contain experimentally determined DDIs in other
species even in the absence of interacting orthologs [28].
Coevolution computational approaches rely mainly on the
hypothesis that interacting proteins tend to evolve in a
constrained fashion, since mutations in one protein may
affect its ability to interact with and thus affect another
protein. Coevolution can be detected by searching for
pairs of proteins that are fused in some organism or have
similar phylogenetic distances to members of their
respective families. Protein fusions may be detected by
searching for two non-homologous proteins that align to
different regions of another protein [29]. The possible
yield of a significant number of false fusion events due to
the existence of these conserved domains is the limitation
of this approach. Proteins with similar phylogenetic profiles are effectively coevolving, since they are often found
together in organisms. However, it is difficult to identify
subtle differences in the evolution of paralogs using
phylogenetic profiles [30]. The recently developed
three-dimensional (3D) structure-based approaches (i.e.
an algorithm termed PrePPI) can be used to predict PPIs
with high accuracy (e.g. >50%) and coverage [31,32].
Even though interactions derived using computational
approaches are generally not as reliable as those measured
experimentally, a combination of computational
approaches provides a more complete and accurate understanding of protein interactions in combination with
experimental data [25,33,34,35].
Current Opinion in Microbiology 2013, 16:566–572
568 Antimicrobials
Protein interaction databases
Over the past few years, due to the availability of a very
large number of PPIs generated by both the experimental
approaches and computational prediction, several protein
interaction databases have been developed. Literature
mining is used to systematically search for these individual interactions, with the results captured in databases
such as the Database of Interacting Proteins (DIP), the
Biomolecular Interaction Network (BIND), Human
Protein Reference (HPRD), the microbial protein interaction (MPIDB), Systems biology of the innate Immune
response (InnateDB), the Molecular Interaction (MINT),
Mammalian Protein–Protein Interaction (MIPS), Systems biology of the innate Immune response (InnateDB),
host–pathogen interaction (HPIDB), IntAct, BioGRID
and STRING [36–39]. Several factors limit the coverage
of some of these PPI databases. As discussed above, the
presence of high false-positive and false-negative rates in
some datasets generated using Y2H systems (e.g. some
PPI in bacteria) limits the reliability of these interactions.
Furthermore PPIs are often measured under different
conditions from those in vivo and those conditions under
which the interactions identified are poorly defined
[25,40,41].
Bacterial and host–pathogen interactomes as
source of antibacterial drug target
Bacterial and host–pathogen interactomes (PHI) have
great potential to shed light on pathogen biology, virulence pathways and as a unique resource to identify
potential new drug targets. Despite this potential, systematic genome-wide PPI derived experimentally are
currently available for a limited number of pathogens
[6,42]. Among the published proteome-wide bacterial
interactomes are Helicobacter pylori, Campylobacter jejuni,
Treponema pallidum (causing syphilis), Escherichia coli,
Mycobacterium tubercolusis, Mycoplasma pneumonia, Staphylococcus aureus (MRSA), Streptococcus pneumonia and the
Pseudomonas aeruginosa predicted interactome [31,43–49].
The E. coli and M. pneumonia interactomes were analyzed
using large-scale TAP-MS corresponding respectively to
80% and 60% coverage of the annotated open reading
frames. For the other pathogens Y2H screens were used
which generated interactions leading to 46%, 80% and
70% of the predicted bacterial proteomes for respectively
H. pylori, C. jejuni and T. pallidum. The M. tuberculosis
interactome was characterized using a B2H screen resulting in 74% coverage of known proteins. The large-scale
PPI analysis done using a pull-down strategy combined
with quantitative MS in the hospital-acquired strain of
MRSA-252 resulted in a PIN involving 22.5% of MRSA
proteins (Figure 1). In fact, none of these bacterial interactomes are complete in the sense that they have not
captured all of the expected biological interactions.
Indeed, it has been estimated that none of them covers
more than 20% or 30% of all interactions, primarily
because most of these studies used only a single approach,
Current Opinion in Microbiology 2013, 16:566–572
Figure 1
Protein-protein Interaction Network forthe MRSA
non-hub
proteins
hub proteins
known
antimicrobial
targets
hubs- known
antimicrobial
targets
Current Opinion in Microbiology
2D representation of the developed MRSA partial PIN. Hub proteins are
shown in yellow and non-hub in blue. Established antimicrobial drug
targets are shown in red if they were classified as non-hubs and in purple
if they were categorized as hubs
Reprinted from Cherkasov A, et al.: Mapping the protein interaction
network in methicillin-resistant Staphylococcus aureus. J Proteome
Res 2011, 10:1139–1135. Used with permission.
which would detect only a subset of interactions [50]. On
the other hand, depending on the experimental protocol
used, the interactions measured using HT assays tend to
generate a considerable fraction of false-positive interactions [17,21,41,51]. Hence, the higher-confidence
interactions coverage based on correlations with biological significance are considered to be only a subset,
25%, of all detected interactions in PPI studies [52].
Although incomplete, these bacterial PPI maps can
nevertheless be mined for subnetworks of biological interest, such as essential gene networks that suggest candidate drug targets [53,54,55]. Considering the
immense number and diversity of bacterial pathogen
species that exist, huge reservoirs of bacterial PPIs have
yet to be identified. However, comparative analyses of
PPI maps including those generated using model bacteria
have provided insights into the function and evolution of
proteins and their regulatory networks in other pathogenic species. This is of particular value given the difficulty in obtaining complete coverage in HT screens, and
the lack of suitable screening systems for many pathogenic bacterial species [7,17,22].
PINs are typically represented by a graph in which the
nodes are the different proteins, and edges represent the
physical interactions between these proteins (Figure 1). In
a scale-free network, most proteins participate in only a few
interactions (termed ‘nodes’), while a few proteins (termed
‘hubs’) participate in dozens of interactions indicating that
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Analysis of PINs to identify antimicrobial targets Zoraghi and Reiner 569
a few hubs bind numerous small nodes. Scale free networks
are resistant to random failure but vulnerable to targeted
attack, specifically against hubs. In all bacterial PPIs studies completed to date, the higher-confidence PINs exhibit scale-free properties, indicating that the observed
interactions are non-random with the characteristic presence of highly connected hub proteins [56]. Analysis of
several experimentally derived validated PPI maps from a
variety of organisms have suggested that proteins with
related functions tend to cluster into highly interconnected
subnetworks or modules that are conserved among species,
suggesting that they represent important functional pathways or protein complexes [57–59]. Thus PPI can sometimes be used to predict the biological role of
uncharacterized bacterial proteins based on the functions
of interacting proteins [53,60,61].
Analysis of bacterial PINs also revealed that hub proteins
are more likely to be essential for growth or viability than
non-hub proteins [13]. Moreover, structural analysis has
suggested that the more binding sites a protein has, the
more likely it is to be essential [9,62]. Thus, network
topology may provide one way of estimating the potential
importance of particular gene products and may be useful
in searches for new candidate drug targets. To date the
hub proteins that are essential for network integrity in
bacterial PINs have been largely overlooked as drug
targets (Figure 1) [49]. Nevertheless, these empirically
derived bacterial PINs provide a rich source for identifying critical proteins essential for network stability, many
of which can be considered as potential antimicrobial
drug targets. For example, PK was identified as a potential novel drug target based upon it being a highly connected, essential hub in the MRSA PIN. Selective
targeting of the bacterial PK enzyme based on its discrete
features identified a class of potent MRSA PK inhibitors
(IC50 of 0.1 mM) with >1000-fold selectivity over human
PK isoforms. These novel anti-PK compounds were
found to possess exceptional antibacterial activities
against MRSA and other gram-positive genera including
Enterococci and Streptococci. On the other hand, analysis of
M. tuberculosis PIN suggested that the coordination of a
group of ATPase subunits of the ABC transporters, Ser/
Thr protein kinases (e.g. PknK) and a hypothetical
protein Rv1354c constitute a potential membrane-associated signaling pathway and all represent potential drug
targets. The potential function for Rv1354c in a bacterial
ubiquitous cyclic-di-GMP signaling pathway was
revealed, providing the basis for development of new
anti-tuberculosis drugs. It also suggested that the mycobacterial unique family of protein PE/PPE proteins with
linked functions to protein secretion and membrane
transport might be involved in pathogen growth and
virulence. Two WhiB-like transcriptional factors were
also found to be highly connected proteins in the M.
tuberculosis PIN, indicating that these genes might be core
regulators and novel targets [23,31,48,53,55,63].
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Host–pathogen interactomes: in nearly every case, virulence factors of bacterial pathogens are secreted products that enhance the survival of the bacteria and/or
damage the host. Systematic mapping of the host–
pathogen interactions has recently been done for a
number of viral [64,65] and bacterial pathogens including Bacillus anthracis, Francisella tularensis, and Yersinia pestis, using Y2H technology [66]. These studies
have revealed global and local networks that relate to
known biological properties and provide significant
insights into the host–pathogen interactions during infection. In addition, they serve as starting points for
systems biology modeling of the development of potential therapeutic and prophylactic interventions. Moreover, computationally conserved modules of human–
pathogen PPIs found across multiple networks reveal
commonalities as to how different pathogens interact
with crucial host pathways involved in inflammation and
immunity. At the same time, these host–pathogen
interactomes define target sets of proteins for understanding mechanisms of pathogenicity. Computational
analysis of host–pathogen PINs has indicated that both
viral and bacterial proteins tend to interact with human
proteins that are hubs (proteins with many interacting
partners) and bottlenecks (proteins that are central to
many pathways in the network) in the human PIN
[39,42,67,68]. Such a strategy probably allows the
pathogen to control and disrupt essential complexes
and defense pathways governing the host response
[69]. Thus, pathogen proteins that are observed to
interact with human proteins that are involved in functions related to the host response may suggest novel
bacterial targets for broad based immunotherapeutic
development [65,68,69–72].
Of interest, recent studies of host–bacterial and host–viral
interactomes revealed that both bacteria and viruses also
attack host proteins involved in metabolic processes as a
common infection strategy [68,73,74]. For instance,
human muscle pyruvate kinase isozyme (KPYM) that
functions in glycolysis is targeted by at least three bacterial (Bacillus, Francisella, and Yersinia) and three viral
genera (Hepatitis, Herpesviruses, and Papillo maviruses).
Alpha-enolase and LDHA/LDHB are two other bacteria–
virus-targeted human enzymes which also function in
glycolysis. In addition to glycolysis, some enzymes functioning in lipid metabolism and in protection against
oxidative-stress are among the common targets of bacteria
and viruses [67,68].
Targeting bacterial and host–pathogen
interactomes: myth or reality?
There are many bacterial physiological processes and
host–pathogen induced pathological processes that are
dependent on PPIs. Furthermore, some of these can be
induced or inhibited by external ligands, providing new
avenues for antibacterial drug targeting [7,23,31,63]. In
Current Opinion in Microbiology 2013, 16:566–572
570 Antimicrobials
general, targeting PPIs is more challenging than single
drug targets that naturally bind to small molecules [24].
Thus, PPIs have hotspots (small subsets of residues that
contribute most of the free energy of binding to both
natural partners and small molecules) which need to be
characterized [75]. Defined structures and characterization of contact surfaces in a protein complex will facilitate the design of small molecules that will inhibit
complex function [34]. In addition, interaction interfaces
are dynamic and can be more convoluted in solution than
they appear to be in cocrystal structures [76]. The properties of temporary complex interfaces are unique for each
interacting pair of proteins and can be considered as
analogs of the active site of an enzyme, representing
prospective targets for a new generation of antibacterial
drugs. In the last decade, numerous investigations were
undertaken to find or design small molecules that block
protein dimerization or protein–receptor interaction, or,
conversely, to induce protein dimerization [14,77]. Antibacterial PIN-based drug discovery would be crucially
augmented by the availability of orthogonal methods of
characterization including computational screening, fragment-based discovery, mutagenesis, epitope mapping
and structural biology [23,78–80,81].
Conclusions and future directions
Both bacterial and host–pathogen protein complexes
represent a reliable source of potential targets for novel
classes of antimicrobials. However, many more highly
validated interaction datasets are required to evaluate
the biological significance of individual interactions. In
particular it is imperative to distinguish between conserved and non-conserved (but biologically relevant)
hubs and separate them from false-positives and falsenegatives by combining different PPI technologies with
complementary experimental approaches and computational analyses. Currently, comparative studies
suggest that the development of accurate and complete
repertoires of bacterial PPIs is still in its infancy, but
given the progress that has been made and the importance of this targeting strategy, it is likely to receive
increased attention in the future. Moreover, to reduce
the likelihood of resistance development, targeting
conserved hubs in bacterial or pathogen–host interactomes is consistent with the recent trends in antibacdrug
discovery
favoring
potential
terial
polypharmacology (i.e. a single drug acting upon
multiple targets of a unique pathway or a single drug
acting upon multiple targets pertaining to multiple
pathways), over single target drugs [12,82].
Acknowledgements
This work was supported by CIHR award MOP-84582 and by funding from
Genome Canada and Genome British Columbia, Vancouver General
Hospital & University of British Columbia Hospital Foundation, through
the PRoteomics for Emerging PAthogen REsponse (PREPARE) Project.
Current Opinion in Microbiology 2013, 16:566–572
References and recommended reading
Papers of particular interest, published within the period of review,
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of special interest
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