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Seminars in Cancer Biology 23 (2013) 243–251
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Seminars in Cancer Biology
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Review
The structural network of inflammation and cancer: Merits and challenges
Emine Guven Maiorov a , Ozlem Keskin a , Attila Gursoy a , Ruth Nussinov b,c,d,∗
a
Center for Computational Biology and Bioinformatics, College of Engineering, Koc University, Rumelifeneri Yolu, Sariyer, Istanbul, Turkey
Basic Science Program, SAIC-Frederick, Inc., United States
c
National Cancer Institute, Center for Cancer Research Nanobiology Program, Frederick, MD 21702, United States
d
Sackler Inst. of Molecular Medicine, Department of Human Genetics and Molecular Medicine, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
b
a r t i c l e
i n f o
Keywords:
TLR
NF-␬B
Inflammation
Cancer
Inflammation and cancer link
Structural pathway
MyD88
Structural data
a b s t r a c t
Inflammation, the first line of defense against pathogens can contribute to all phases of tumorigenesis, including tumor initiation, promotion and metastasis. Within this framework, the Toll-like receptor
(TLR) pathway plays a central role in inflammation and cancer. Although extremely useful, the classical representation of this, and other pathways in the cellular network in terms of nodes (proteins) and
edges (interactions) is incomplete. Structural pathways can help complete missing parts of such diagrams: they demonstrate in detail how signals coming from different upstream pathways merge and
propagate downstream, how parallel pathways compensate each other in drug resistant mutants, how
multi-subunit signaling complexes form and in particular why they are needed and how they work, how
allosteric events can control these proteins and their pathways, and intricate details of feedback loops
and how kick in. They can also explain the mechanisms of some oncogenic SNP mutations. Constructing
structural pathways is a challenging task. Here, our goal is to provide an overview of inflammation and
cancer from the structural standpoint, focusing on the TLR pathway. We use the powerful PRISM (PRotein Interactions by Structural Matching) tool to reveal important structural information of interactions
in and within key orchestrators of the TLR pathway, such as MyD88.
© 2013 Elsevier Ltd. All rights reserved.
Proteins perform their functions via interactions; among these,
interactions with other proteins are the most common, and they
control cell life. Thus, there is no wonder that a major effort by the
scientific community has targeted the modeling of protein–protein
interactions. The availability of structural data relating to the mode
of protein interactions is crucial to the understanding of how
Abbreviations: TLR, Toll-like receptor; PRISM, PRotein Interactions by Structural Matching; IAP, inhibitors of apoptosis; ECM, extracellular matrix; ROS, reactive
oxygen species; IBD, inflammatory bowel disease; NSAIDs, non-steroidal antiinflammatory drugs; TGF-␤, transforming growth factor-1 beta; PTEN, phosphatase
and tensin homologue; VHL, von Hippel Lindau; Treg s, regulatory T cells; MDSCs,
myeloid-derived suppressor cells; TAMs, tumor-associated macrophages; EGF, epidermal growth factor; VEGF, vascular endothelial growth factor; PRRs, pattern
recognition receptors; PAMPs, pathogen-associated molecular patterns; DAMPs,
damage-associated molecular patterns; HMGB1, high mobility group box-1; LRR,
leucine-rich repeats; TIR, Toll/IL-1R; MyD88, myeloid differentiation factor 88; Mal,
MyD88 adaptor-like; TRIF, TIR domain containing adaptor inducing interferon-␤;
TRAM, TRIF-related adaptor molecule; SARM, sterile ␣ and heat-armadillo motifs;
IRAK, interleukin-1 receptor-associated kinases; IKK, I␬B-kinase; TNF-␣, tumor
necrosis factor-alpha; IL-6, interleukin-6; IRFs, interferon regulatory factors; IFN-␣,
interferon alpha; IFN-␤, interferon beta; RANK, receptor activator of NF-␬B; Ubc13,
ubiquitin-conjugating enzyme E2N; PTMs, post-translational modifications.
∗ Corresponding author at: National Cancer Institute, Frederick, MD, United States.
Tel.: +1 301 846 5579; fax: +1 301 846 5598.
E-mail addresses: [email protected] (E. Guven Maiorov), [email protected]
(O. Keskin), [email protected] (A. Gursoy), [email protected] (R. Nussinov).
1044-579X/$ – see front matter © 2013 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.semcancer.2013.05.003
function is executed, and its control. When tiled together in the
framework of the cell, it helps obtain the information flow from
the extracellular space, through the membrane, the cytoplasm, and
into the nucleus, to turn genes on and off. Signaling is not linear:
pathways merge and diverge, signals integrate, and the proteins,
which are the nerve centers of these pathway crossings, are frequently involved in cancer. Mutations deregulating these proteins
are often responsible for turning on, and keeping on, entire pathways. They can also deregulate proteins that act as repressors of
over-expression and activity.
The community has invested much effort in constructing pathways and in devising simple and clear ways of presenting them.
The most common among these is the so-called nodes-and-edges
representation, where nodes are the proteins and edges their interactions. This representation has been extremely useful since it
essentially provides an overview of which proteins interact, in
much the same way as a reference guide. At the same time, such
pathway diagrams also suffer from limitations [1], since the information that they provide is incomplete, and does not allow in-depth
understanding of the processes and their regulation. In contrast,
structural networks which piece together the structures of individual proteins are much more powerful [2]. Structural networks
allow understanding of how pathways cross through key hub proteins; which domains are involved in the interactions and which
residues. They provide insight into the question of whether the
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pathways are controlled allosterically; how constitutive gain- and
loss-of-function mutations can turn the protein on or off; and how
parallel pathways can take over during drug resistance. Structural
pathways are also very useful in drug discovery, because they can
help forecast the global effects on the cell [3–6]; and they can help
in selecting drug targets which are not the direct ‘ailing’ dysfunctional protein [7,8]. Such drugs exploit conformational ensembles
[9,10]; they may also make use of network dynamics [11].
Constructing the structural network of major pathways in the
cell is challenging: it entails putting together available structures
(determined by crystallography, NMR, and high quality models)
when data about their interactions exist in the literature, and when
these data are missing, predicting them. It necessitates modeling
the linear pathways as well as pathway integration and branching;
it also necessitates predicting if the protein can act as a dimer –
homo or hetero, and possible oligomerization modes. To grasp the
enormity of the challenge, recall that hub proteins, such as p53,
can interact with tens or hundreds of partners; Raf can work as a
monomer and as a dimer; and EGFR has two dimeric states, symmetric (inactive) and asymmetric (active). However, the interaction
surfaces are often unknown [1,12–14].
So why are structural pathways important? Structural pathways
predict new interactions, not observed in ‘classical’ pathway maps;
in particular those involving scaffolding proteins; they allow identification of parallel pathways in drug resistant mutants; they may
discover positive feedback loops, altering core processes, as in the
case of inhibitors of apoptosis (IAP) [15]. They also provide interaction details, which allow identification of oncogenic mutations;
help drug discovery; and allow assembling multi-subunit signaling
complexes, such as the key complex MyD88.
A current goal in our lab is to take steps toward accomplishing
the overarching task of modeling protein interactions in cancerrelated pathways and the cancer network in the cell and mapping
oncogenic mutations associated with these. Within this framework, here we aim to provide an overview of inflammation and
cancer from this structural standpoint. Below, we first provide a
broad description of the inflammation and cancer link that we
aim to model, and some examples as they relate to this link. We
then explain the challenge in modeling protein interactions on a
large scale, particularly when seeking to also detect new interactions, which are not known a priori. We follow by explaining
PRISM [16,17], the method that we use to model these pathways,
its advantages and shortcomings.
Computational structural biology helps experiment by providing more complete information and leads. It is our belief
that a complete map of key cellular pathways with structural data - including multimolecular associations of adaptor
proteins - is critical to fully understand cancer cell biology
with the abnormality originating from deregulation of signaling
pathways.
1. Inflammation and cancer
Inflammation by innate immunity, which is required to fight
microbial infections, heal wounds, and maintain tissue homeostasis, can lead to the hallmarks of cancer [18–20]. Several recent
studies suggested that inflammation has an important role in all
phases of tumor development, including tumor initiation, tumor
promotion, invasion, metastatic dissemination, and evading the
immune system [18,19,21]. Inflammation causes cellular stress and
may trigger DNA damage or genetic instability [21], and chronic
inflammation can contribute to primary genetic mutations and
epigenetic mechanisms that initiate malignant cell transformation
[20–22]. Tumor-promoting effects of inflammation alter tissue
homeostasis, predisposing individuals to cancer [21,23,24]. The
connection between inflammation and cancer has been established
by Wirchow already in 1863, who noticed that cancers originate
at sites of chronic inflammation and tumors have “lymphoreticular infiltrate” [20,21,25,26]. Inflammation establishes a tissue
microenvironment, which tolerates tumor growth and metastasis by setting immunosuppressive mechanisms [21]. Therefore,
inflammation not only induces carcinogenesis but also makes
immune cells incapable of destroying tumor cells. A key initiator
of a major pathway leading to inflammation is the TLR pathway
(displayed in Fig. 1).
Inflammatory cells supply growth factors to maintain proliferation, survival factors to escape from apoptosis, pro-angiogenic
factors and extracellular matrix (ECM) modifying enzymes that
enable angiogenesis, invasion and metastasis [18]. Inflammatory
cells can also secrete reactive oxygen species (ROS) that induce
mutations, lead to failure of DNA repair, activation of oncogenes,
and ultimately cancer [18,20,21]. ROS further activates inflammatory genes and takes part in tumorigenesis which is regulated by
c-MYC, K-Ras, and Wnt signaling pathways [18].
The first evidence that connected inflammation with cancer
was that inflammatory diseases such as inflammatory bowel disease (IBD) increase cancer susceptibility [27]. Additional evidence
comes from the tumor microenvironment, which has inflammatory
cells, cytokines and chemokines. In addition, long term administration of non-steroidal anti-inflammatory drugs (NSAIDs) leads to
a decrease in the number of relapses and newly acquired tumors
[18]. Moreover, pathways of inflammation function downstream
of oncogenic mutations (MYC, RAS, BRAF, and RET), suggesting that
these oncogenic mutations lead to activation of an inflammatory
pathway [28]. Last but not least, inhibiting inflammatory cytokines
and chemokines such as TNF-␣, IL-1␤, or essential transcription factors, such as NF-␬B and STAT3, decrease the appearance of cancer
[24]. Most of these proteins that connect inflammation and cancer
are also members of TLR-pathway.
The TLR pathway in Fig. 1 is presented in the classical nodeand-edge form. As we noted above, such pathway diagrams are
informative and have been enormously useful in compiling and
laying out the interaction maps. As can be seen in Fig. 1, they
provide the interconnectivity between the proteins and outline the
major signaling flow. Nonetheless, the information that they are
able to provide is incomplete. Structural pathways can fill in gaps;
add missing proteins and interactions, and provide the interaction
details, which can help in figuring out parallel pathways, and the
conformational mechanisms that control them. These are important for understanding function under physiological conditions and
oncogenic mutations and drug resistance in disease.
The link between inflammation and cancer appears to stem
from two pathways [29], the intrinsic and extrinsic inflammation
pathways, as shown in Fig. 2. Intrinsic inflammation is initiated
by mutations that lead to activation of oncogenes and inactivation of tumor suppressors (tumor-promoter role) [24]. On the other
hand, in the extrinsic pathway, infection or inflammation precedes
cancer and increases the risk of cancer (tumor-initiator role) [24].
The similarity between cancer tissue and inflamed tissue involves
angiogenesis and tissue infiltrating leukocytes, such as lymphocytes, macrophages and mast cells [18,19].
RAS, MYC, RET, and BRAF are among the oncogenes that stimulate inflammation [21]. Besides their roles in tumor initiation
and promotion, oncogenes impact the relationship between the
tumor and its microenvironment. The RAS-RAF signaling pathway
is involved in most cancer types, and the activated proteins along
the pathway induce the secretion of pro-inflammatory cytokines
and chemokines [30]. Another oncogene, MYC, has a critical role
in tissue remodeling of ECM [24,31]. RET, a tyrosine kinase, can
be constitutively activated by mutations even in the absence of its
ligands.
E. Guven Maiorov et al. / Seminars in Cancer Biology 23 (2013) 243–251
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Fig. 1. Toll-like receptor pathway in inflammation (adapted from KEGG [65] and science signaling [66]), in traditional node-and-edge representation, where nodes represent
proteins and edges represent interactions between proteins.
As oncogenes, tumor suppressor proteins also modulate the
production of pro-inflammatory elements. Transforming growth
factor-1 beta (TGF-␤), a tumor suppressor protein, accelerates tumorigenesis by increasing tumor-promoting inflammation.
Other examples of such tumor suppressors are phosphatase and
tensin homologue (PTEN) and von Hippel Lindau (VHL) proteins
[24,28,32].
Tumors are not composed of solely homogenous cancer cells,
but are rather heavily infiltrated by cells, both the innate and adaptive immune cells, like macrophages, and mast cells [19,23]. In
addition to immune cells, epithelial cells, endothelial cells, fibroblasts and stromal cells, infiltrate tumor tissue [18]. Mast cells
produce compounds that preserve chronic inflammation and support tumor development [33]. Some of the inflammatory cells,
namely, regulatory T cells (Treg s), myeloid-derived suppressor
cells (MDSCs) and tumor-associated macrophages (TAMs) have
immune suppressive roles [19,24]. MDSCs and TGF-␤ released by
cancer cells may inhibit cytotoxic T cells and natural killer cells
[19]. Inflammatory cells also secrete epidermal growth factor, EGF
(tumor growth factor), vascular endothelial growth factor, VEGF
(pro-angiogenic factor), and matrix degrading enzymes, which help
tissue-remodeling, angiogenesis and metastasis [19].
2. TLRs, key adaptors, and key transcription factors
Fig. 2. The extrinsic and intrinsic pathways of inflammation.
Toll-like receptors (TLRs) regulate both the innate and the adaptive immune systems and have an essential role in inflammation
[22]. TLRs detect the presence of pathogens and initiate inflammation. TLRs belong to pattern recognition receptors (PRRs) and they
recognize highly conserved motifs, pathogen-associated molecular patterns (PAMPs), such as lipopolysaccharide of gram-negative
bacteria and damage-associated molecular patterns (DAMPs),
such as high mobility group box-1 (HMGB1) protein or heat
shock proteins [20,22,34,35]. Hence, pathogen-derived compounds
and endogenous molecules of damaged tissue serve as ligands
for TLRs. TLRs are single-pass trans-membrane receptors with
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Fig. 3. Modeled interactions of MyD88. MyD88 (in red color) has TIR and death domains (DD), PDB IDs; 4dom:A and 3mop:F, respectively. The dashed line between the
two domains represents the missing part in the crystal structure of the MyD88 protein. The boxes mark the locations of the respective interactions. MyD88 interacts with
bridging adaptor protein Mal/TIRAP through its TIR domain. However, it uses its DD to interact with IRAK4 (PDB ID: 3mop: F, J, N chains), IRF7 (3qu3:A), TRAF6 (3hcs:A),
and FADD (1e41:A). MyD88 uses different DD interfaces to interact with IRF7, TRAF6 and IRAK4, thus those interactions can take place simultaneously. On the other hand,
it uses overlapping interfaces to interact with IRAK4 and FADD. Therefore, MyD88 can interact either with IRAK4 or FADD, suggesting that at any given time, MyD88 either
activates the apoptosis pathway or the NF-␬B pathway, but not both. Interactions are modeled by PRISM [16], except for the DD-IRAK4 (3mop) where the crystal structure
of the complex is available. (For interpretation of the references to color in the text, the reader is referred to the web version of the article.)
extracellular leucine-rich repeats (LRR) and cytoplasmic Toll/IL1R (TIR) domain [20]. TLRs are expressed in a variety of cell
types, including innate immune cells, T and B lymphocytes (adaptive immune cells), endothelial cells, epithelial cells, fibroblasts
and cancer cells [20]. Up to now, 11 mammalian and 13 murine
TLRs have been identified [20]. Some of the TLRs are found on
plasma membrane (TLR1, TLR2, TLR4, TLR5, TLR6, TLR10), whereas
others are located on endosomal membranes (TLR3, TLR7, TLR8,
TLR9) [20,36]. Upon binding with their ligands on extracellular
LRR, TLRs form homo- or hetero-dimers, recruit some TIR domaincontaining adaptor proteins to their cytoplasmic TIR domains
and activate downstream signaling pathways [20]. TLRs have five
TIR-containing adaptor proteins, Myeloid differentiation factor
88 (MyD88), MyD88 adaptor-like (Mal, also known as TIRAP),
TIR domain containing adaptor inducing interferon-␤ (TRIF, also
known as TICAM-1), TRIF-related adaptor molecule (TRAM, also
known as TICAM-2) [20] (see Fig. 1), and sterile ␣ and heatarmadillo motifs (SARM) [37]. MyD88 is the common adaptor for
almost all TLRs, except TLR3 and also IL-1R family members [38].
Mal serves as a bridging adaptor between MyD88 and TLRs. Binding surfaces of both MyD88 and TLRs are electropositive, thus they
repel each other. However, Mal, with an electronegative surface,
brings MyD88 and TLRs together [38–40]. Likewise, TRAM acts as a
bridge between TLR3 and TRIF.
When stimulated, TLRs follow one of two pathways, as displayed
in Fig. 1. In the first, MyD88-dependent pathway, TLRs associate
with MyD88 and Mal proteins through their TIR domains, recruiting
serine/threonine kinases IRAKs (interleukin-1 receptor-associated
kinases) to stimulate the TRAF6/IKK complex and MAPKK, which
result in activation of MAP kinases (such as ERK, JNK, and p38
[41,42]), NF-␬B and AP-1 [20,34]. If NF-␬B is bound to its inhibitor
I␬B, it is located in the cytoplasm. When I␬B-kinase (IKK) phosphorylates I␬B, NF-␬B dissociates from I␬B and translocates to
the nucleus to activate transcription of cytokines and chemokines,
such as tumor necrosis factor-alpha (TNF-␣), interleukin-6 (IL6), IL-12, IL-1␤ and adhesion molecules [24,34,41,42]. However,
in the second, TRIF-dependent pathway, stimulated TLR recruits
TRIF and TRAM and results in the dimerization of interferon regulatory factors (IRFs), producing interferon alpha and beta (IFN-␣
and IFN-␤) [20]. Structural pathways should be able to capture the
dimerization and the altered interactions, which is not the case
in the node-and-edge representation. Normal immune defenses of
the host mediate NF-␬B activation and cytokine and chemokine
expression, leading to acute inflammation. If not finely tuned, it
can contribute to the onset of cancer [20].
Fig. 3 displays the structural details of MyD88 signaling model.
MyD88 interacts with Mal through its TIR domain, and IRAK4, IRF7,
TRAF6, and FADD through its DD. MyD88 interactions with IRAK4,
IRF7, and TRAF6 can co-exist since they use different interfaces.
But MyD88 cannot interact with both IRAK4 and FADD simultaneously because they use overlapping interfaces and thus have a
steric clash (Fig. 4). This means that either apoptosis or NF-␬B pathway is activated by MyD88 interaction, but not both. Similarly,
Fig. 5 demonstrates interactions of TRAF6. TRAF6 associates with
RANK (Receptor activator of NF-␬B) and CD40 through the same
interface close to its N-terminal end. Thus these interactions cannot take place at the same time. In addition, TRAF6 binds to Ubc13
(ubiquitin-conjugating enzyme E2 N) through an interface, which
is next to its C-terminal end. Since the 3D structure of full-length
TRAF6 has not been identified yet, it is not known whether its Nterminal and C-terminal ends are close to each other. Therefore, it is
still unclear whether the TRAF6 interactions with Ubc13 and CD40
E. Guven Maiorov et al. / Seminars in Cancer Biology 23 (2013) 243–251
Fig. 4. MyD88 uses shared interfaces to interact with IRAK4 and FADD. MyD88DD (PDB ID: 3mop:F) can interact either with IRAK4 (3mop:J) or FADD (1e41:A),
but not with both simultaneously as they use overlapping interfaces. MyD88-FADD
interaction is modeled by PRISM [16]. The figure illustrates the steric clashes that
would appear if both interactions were to take place at the same time.
or RANK can take place simultaneously. This example stresses, once
again, the necessity for full-length 3D structures of proteins and the
importance of structural pathways.
Chemokines and cytokines attract tumor-promoting infiltrates
(Treg s, TAMs, etc.) and facilitate angiogenesis and dissemination
[24]. Some early transformed cells express chemokine receptors
that contribute to their survival and invasion. For example, a
chemokine receptor CXCR4 is often expressed on tumor cells and
the quantity of this receptor is indicator of the metastatic potential
[15,24].
Like TLR3, TLR4 also uses the TRIF-dependent pathway [43],
but whether it can associate with Mal and TRAM simultaneously
247
is unknown [44]. There are two TIR domains to be occupied, one
on each TLR. TLRs dimerize when they bind their cognate ligands, again highlighting the merit of structural pathways which
could model these interactions. Evidence suggests that the MyD88dependent pathway takes place in the early stages of stimulation of
the TLR4 signaling pathway. By contrast, TRIF-dependent pathway
is activated at later stages [38]. TRAM can function in the absence
of MyD88 [45]. Kagan et al. suggested that MyD88- and TRIFdependent pathways are activated sequentially. They proposed a
mechanism in which TLR4 on the plasma membrane first associates
with Mal and MyD88, leading to NF-␬B activation. Then, TLR4 is
endocytosed and gets associated with TRAM and TRIF, leading to
transcription of IFN-␣ and IFN-␤ [46].
TLRs are highly expressed in tumor cells and they confer
chemoresistance, escape from immunity, and promote tumor
growth [47,48]. For instance, MyD88 and NF-␬B signaling through
TLR4 on ovarian cancer cells enable these cells to escape from
immunity and contribute to cancer progression [20,49]. On the
other hand, there is also evidence to the contrary: TLR activation
of cancer cells helps to inhibit the proliferation of cancer cells [20].
These conflicting results demonstrate the complexity of TLR action
on cancer cells. A complete structural TLR pathway can help to forecast what will be the outcome: inhibition or promotion of tumor.
As can also be seen in Fig. 1, MyD88 and NF-␬B are key orchestrators of TLR signaling which is important in cancer related
inflammation [42]. In advanced cancers, TAMs have defective NF␬B activation [24]. Inhibition of IKK-␤ in myeloid lineage reduced
cancer related inflammation in intestine and colitis-associated cancer [24].
Similar to NF-␬B, STAT3, another transcription factor, is constitutively active in cancer and immune cells and participates in
driving inflammation to cancer. It promotes survival, proliferation, and metastasis of cancer cells [21,24]. STAT3 down-regulates
Fig. 5. Interactions of TRAF6 protein. (a) Interaction of Ubc13 protein with TRAF6 (PDB ID: 3hcu, covering 50–159th amino acids of TRAF6), Uev1, and CHIP (2c2v). The
multimeric complex is constructed by structural alignment of Ubc13 proteins (3hcu:B and 2c2v:B). Ubc13 protein can associate with both Uev1 and CHIP or TRAF6 simultaneously, but it cannot bind to both CHIP and TRAF6 at the same time as they bind through the same interface. (b) Interaction of TRAF6 with CD40 (PDB ID: 1lb6, covering
347–504th amino acids of TRAF6) and RANK (1lb5) proteins. Since the same interface is shared by CD40 and RANK, these two interactions cannot take place at the same time.
However, it is not known whether TRAF6 can interact with Ubc13 and CD40 or RANK simultaneously because structure of whole-length TRAF6 has not been found yet, and
TRAF6 proteins shown on part (a) and (b) correspond to different portions of TRAF6. If these portions are in close proximity to each other, this may prevent simultaneous
interactions of TRAF6 with Ubc13 and CD40 or RANK. This shows the importance of 3D structures of proteins in revealing protein–protein interactions.
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Fig. 6. Structurally similar interfaces. (a) Interaction between TIAM1 and RAC1 (PDB ID: 1foe, chains A and B). (b) Interaction between CDC42 and Pleckstrin homology
domain (1ki1, chains C and D). The interface between TIAM1 and RAC1 is structurally very similar to the interface between CDC42 and Pleckstrin homology domain. Although
the global structures of the proteins differ, they use similar binding surfaces to interact with their partners. (c) Structural alignment of these two interfaces by MultiProt [67].
The two interfaces align almost perfectly (with 1.02 Å RMSD), thus, these interfaces are alike structurally, even though their sequences vary.
tumor suppressor protein p53, and thereby inhibits apoptosis. It
also stimulates VEGF and metalloprotease expressions, increasing
angiogenesis and tissue rearrangement [21].
Below, we describe how we go about the structural modeling of
these pathways and multimolecular associations related to them,
such as that of the key scaffolding protein assembly of MyD88
(Fig. 3).
3. An overview of the rationale and method for
constructing structural pathways
Constructing pathways [1,2,50,51], in particular structural pathways, is challenging. First, experimental structural data are limited,
which necessitates exploiting homology models. Homology models
lead to increased noise in the predictions [52]. Second, the accuracy of high-throughput data relating to interactions is unclear.
Third, ideally, conformational changes in the protein that take
place following allosteric events should be taken into account.
For example, binding of ligands to the LRR facing the endosomal
lumen of Toll-like receptor TLR9 dimer induce allosteric changes
in the cytoplasmic TIR domains of the dimer [53]. These conformational changes are responsible for activating TLR9 signaling rather
than receptor dimerization [53]. The presence of post-translational
modifications (PTMs) such as phosphorylation, methylation, and
acetylation may also lead to conformational change. For instance,
IRAK1 goes through a conformational change following phosphorylation by IRAK4 [54]. In the unphosphorylated state, IRAK1 is in a
closed-inactive state and cannot associate with MyD88 through its
death domain and TRAF6 through its C-terminal domain. When it is
phosphorylated by IRAK4, it undergoes a conformational transition
to an active open form. In the active state, IRAK1 can associate with
both MyD88 and TRAF6 [54]. In another example, C/EBP protein,
which plays an important role in inflammatory cytokines
transcription [55], undergoes a conformational change upon phosphorylation and gets activated [56]. Conformational changes,
regardless of their origin – allosteric binding or posttranslational
modifications – may alter the interactions with partner proteins.
Fourth, a structural pathway does not provide the protein availability at any given time; for example, some proteins are expressed only
during certain phases of the cell cycle. Similarly, protein localization can also come into play. If a protein is restricted to the nucleus,
it cannot interact with another protein present in the mitochondria.
Computational strategies to predict interactions between proteins, and thus to construct structural pathways, include docking
and template-based techniques (reviewed elsewhere [2]). In most
protein–protein docking methods, proteins are treated as rigid bodies in their native form. Consequently, prediction of interactions via
docking approaches is often poor [52]. A major hurdle in the prediction of protein–protein interactions is that a large number of
solutions are typically obtained, and in the absence of additional
biochemical data about the location of the binding site, it is very
difficult to distinguish between the native and other interactions.
Modeling of pathways encounters the additional difficulty of the
combinatorial complexity, since a large number of proteins would
have to be sampled. ‘Traditional’ docking approaches may not work
for three reasons: first, they will produce too many false positive
solutions, since they will always find complementary surfaces; second, their time requirements prohibit their application on a large
scale; and third, rigid body docking considers only minor flexibility.
Template-based PPI prediction techniques make use of previous
interface knowledge. In nature, there are thousands of different
proteins with diverse 3D structures, but the binding surfaces or
interfaces are more conserved compared to the global structures
E. Guven Maiorov et al. / Seminars in Cancer Biology 23 (2013) 243–251
249
Fig. 7. Shared and different interfaces. (a) IRAK4 interacts with MyD88 and IRAK2 through different interfaces (PDB ID: 3mop: F, J, N chains). Thus, these two interactions
can co-exist. (b) Modeled interactions of MEK1 protein from in MAPK pathway, with MP1 and MEK2 [68]. MP1 and MEK2 interact with MEK1 through shared interfaces.
Therefore, these two interactions cannot take place simultaneously because of the steric clash. (PDB IDs: 1s9i, 3e8n, 1sko, and 3dv3).
of proteins [57–61]. That is, different proteins use the similar
interfaces to interact with their partners (as displayed in Fig. 6);
thus, previously identified interfaces from structures of protein
complexes can be used to predict novel interactions. If the surfaces
of two proteins of interest structurally match with the template
interface, theoretically these two proteins can interact. Templatebased prediction methods take much less CPU time than docking
and they can be used for pathway-, or proteome-scale predictions.
The success of template-based prediction depends on whether the
interface is present in the template set.
To address the modeling of the inflammation and cancer network, we propose to use PRISM (PRotein Interactions by Structural
Matching) [16,17], a motif-, knowledge-based algorithm. PRISM
exploits our observation that protein–protein interfaces are conserved, and consist of recurring architectural motifs, much like the
motifs observed in single chain proteins. In particular, interfaces
can be conserved even when the global structures and the functions
of the proteins are entirely different. Some examples for shared and
distinct interfaces are given in Fig. 7.
The difficulty in the prediction is compounded by the fact that
a particular protein can interact with other proteins via the same
or different interfaces (Fig. 7). If a protein interacts with its two
partners through the same interface, these two interactions are
mutually exclusive. Likewise, if the interfaces overlap, those interactions are also mutually exclusive. Conversely, if two partners bind
to a particular protein through different interfaces, with no overlap,
these interactions can co-exist [62–64].
As we noted above, hub proteins interact with tens or hundreds
of partners. Due to the limited surface area of a protein, a single protein cannot interact with all of its partners simultaneously.
Structural pathways provide the knowledge of which interactions
can take place simultaneously and thus the respective pathways coactivated/inhibited, and which are mutually exclusive [62]. Even
though the structures of many of these proteins are available in
the PDB, and of some in multiple forms (e.g. IRF3 and MAPK), their
interactions provide the cellular network, which ultimately is how
the cell is regulated. Fig. 7 depicts some of these key proteins and
their interactions.
4. Conclusions
Structural pathways are important. They are essential to the
understanding of how oncogenic mutations work and to figuring out alternative parallel pathways in drug resistant mutants.
Structural pathways also help to understand the inter-relationship
among linked phenomena, as in the case of inflammation and cancer. Cell biology provides a global overview of the behavior of the
cell, tissue and the organism under different sets of conditions; the
structures of single proteins and their coherent interactions provide
insight into the dynamic changes in the proteins, such as those taking place through post-translational modifications, binding events
and mutations, and into their interactions. While not addressed
in this review, it behoves us to recall that beyond the challenging
construction of structural pathways, there is also a need to obtain a
mechanistic insight into single proteins, their modifications, interactions and broadly, their changing landscapes. Why is insight into
the dynamic landscape of single proteins important? Perceiving
protein’s behavior can help to forecast allosteric transitions, and
regulation; it can help relate oncogenic mutations to their constitutive consequences. Oncogenic mutations bypass the autoinhibited
state by shifting the population from the physiologically favored
inactive state to an active state. In principle, this can take place
via three mechanisms: first, by stabilizing the active conformation,
as in the case of the T790M mutation in EGFR which promotes
the assembly of an enzymatically active kinase conformation by
250
E. Guven Maiorov et al. / Seminars in Cancer Biology 23 (2013) 243–251
stabilizing the hydrophobic R spine; second, by disrupting critical
interactions in the inactive conformation, thus destabilizing it with
respect to the active state, as in the case of the L858R mutation
in the hydrophobic core of inactive EGFR, which leads to the shift
of the population toward the active conformation; or third, by a
combination of both mechanisms, as in the case of L858R which
also stabilizes the ␣C-helix-in active conformation from an intrinsically disordered structure through heterodimerization, and thus
shifts the population even in the absence of ligand-induced receptor dimerization [3]. Such insight into the kinase landscape can help
to classify oncogenic mutations, and thus identify the origin of their
oncogenic mechanisms.
Structures, their associations, and their conformational ensembles are critical to fully understand the hallmarks of cancer,
pathways to cancer, the loss of control through constitutive mutations and the bevy of changing interactions; they are key to reveal
and understand in detail the steps that drive a normal cell to
become a cancer cell.
Here, we focused on the link between inflammation and cancer,
and how we go about constructing the structural pathways as a
paradigm for mapping cancer signaling pathways in the cell, which
is a major on-going project in our lab.
Conflict of interest statement
The authors declare that there are no conflicts of interest.
Acknowledgements
This project has been funded in whole or in part with Federal
funds from the National Cancer Institute, National Institutes of
Health, under contract number HHSN261200800001E. The content
of this publication does not necessarily reflect the views or policies
of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply
endorsement by the US Government. This research was supported
(in part) by the Intramural Research Program of the NIH, National
Cancer Institute, Center for Cancer Research. O.K. acknowledges the
support of Science Academy (of Turkey).
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