The structural network of Interleukin-10 and its implications in inflammation and cancer
© Acuner-Ozbabacan et al.; licensee BioMed Central Ltd. 2014
Published: 20 May 2014
Inflammation has significant roles in all phases of tumor development, including initiation, progression and metastasis. Interleukin-10 (IL-10) is a well-known immuno-modulatory cytokine with an anti-inflammatory activity. Lack of IL-10 allows induction of pro-inflammatory cytokines and hinders anti-tumor immunity, thereby favoring tumor growth. The IL-10 network is among the most important paths linking cancer and inflammation. The simple node-and-edge network representation is useful, but limited, hampering the understanding of the mechanistic details of signaling pathways. Structural networks complete the missing parts, and provide details. The IL-10 structural network may shed light on the mechanisms through which disease-related mutations work and the pathogenesis of malignancies.
Using PRISM (a PRotein Interactions by Structural Matching tool), we constructed the structural network of IL-10, which includes its first and second degree protein neighbor interactions. We predicted the structures of complexes involved in these interactions, thereby enriching the available structural data. In order to reveal the significance of the interactions, we exploited mutations identified in cancer patients, mapping them onto key proteins of this network. We analyzed the effect of these mutations on the interactions, and demonstrated a relation between these and inflammation and cancer. Our results suggest that mutations that disrupt the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and α2-macroglobulin (A2M) may enhance inflammation and modulate anti-tumor immunity. Likewise, mutations that weaken the A2M-APP (amyloid precursor protein) association may increase the proliferative effect of APP through preventing β-amyloid degradation by the A2M receptor, and mutations that abolish the A2M-Kallikrein-13 (KLK13) interaction may lead to cell proliferation and metastasis through the destructive effect of KLK13 on the extracellular matrix.
Prediction of protein-protein interactions through structural matching can enrich the available cellular pathways. In addition, the structural data of protein complexes suggest how oncogenic mutations influence the interactions and explain their potential impact on IL-10 signaling in cancer and inflammation.
Inflammation by innate immunity is the first line of defense against pathogenic infections . It is also involved in all phases of cancer development, including tumor initiation, promotion and metastatic dissemination [2–4]. By triggering immunosuppressive mechanisms, inflammation creates a tissue microenvironment which permits tumor growth and metastasis . Inflammatory cells provide growth factors that sustain proliferation, and survival factors that allow escape from apoptosis; it also contributes to extracellular matrix (ECM) modifying enzymes, and to pro-angiogenic factors that facilitate angiogenesis, invasion and ultimately metastasis .
Several lines of evidence link cancer and inflammation, emphasizing that chronic inflammation contributes to tumor initiation and progression [5, 6]. Chronic inflammatory bowel disease predisposes individuals to colon cancer  and individuals with chronic hepatitis are more prone to develop hepatocellular carcinoma . Chronic Helicobacter pylori infection and the resulting inflammation is associated with gastric cancer ; chronic bronchitis with lung cancer; and pancreatitis with pancreas cancer . Additionally, long term use of non-steroidal anti-inflammatory drugs (NSAIDs)  which inhibit pro-inflammatory cytokines, like TNF-α and IL-1β, decrease cancer incidence .
Identified in 1989 , IL-10 is an anti-inflammatory cytokine that modulates the immune response: if IL-10 is not present or functional, inflammation becomes possible. It restricts the immune response to pathogens and prevents damage to the host. Secreted by immune cells, IL-10 diversely affects cell types in the immune system. Although it terminates inflammatory responses by suppressing monocyte/macrophage function, it also acts as an immunostimulator to promote Th2 response. IL-10 regulates growth and/or differentiation of B cells, NK cells, cytotoxic and helper T cells, mast cells, granulocytes, dendritic cells, keratinocytes, and endothelial cells. Additionally, it stimulates immunoglobulin secretion, and promotes antibody class switching . Therefore, IL-10 has both immune suppressive (anti-inflammatory) and immune stimulatory roles (B and T-cell development).
IL-10 deficiency increases the production of IL-1 (a pro-inflammatory cytokine) and in the absence of IL-10, IL-1 promotes tumor growth in mice . IL-10 also prevents development of regulatory T cells (Tregs) and Myeloid-derived suppressor cells (MDSCs) [14–17]. IL-10 deficiency leads to an increase in the number of Tregs and MDSCs in tumor tissue. Tregs and MDSCs have suppressive roles against tumor-specific immunity that favor tumor growth [4, 14, 15]. Apart from its anti-inflammatory roles, it is associated with activation of anti-tumor immunity . The presence of IL-10 leads to tumor regression and increase in tumor-specific immunogenicity . In contrast, some studies proposed that blockage of IL-10 signaling promote anti-tumor immunity . These controversial findings stem from the pleiotropic effects of IL-10 and different experimental models (human or animal, in vitro or in vivo, solid or hematological tumors, presence of exogenous or endogenous IL-10 or IL-10 inhibitors, etc.) and the varying site of IL-10 production .
IL-10 is a dimeric cytokine  that signals through a tetrameric transmembrane receptor complex, consisting of two IL-10RA (also known as IL-10R1) and two IL-10RB (also known as IL-10R2) proteins [22, 23]. Both receptors belong to the class II receptor family, and consist of three domains: an intracellular domain, a transmembrane domain, and an extracellular domain . The receptor complex assembles sequentially: IL-10RA, with higher affinity, binds to IL-10 first and then IL-10RB . IL-10 binding to the extracellular domain of IL-10RA leads to phosphorylation of JAK1 (Janus Kinase-1) and TYK2 (Tyrosine Kinase-2), that interacts with IL-10RA and IL-10RB , respectively. Specific tyrosine residues on the intracellular domain of the IL-10RA are then phosphorylated by these kinases. The STAT3 (Signal Transducer and Activator of Transcription-3) transcription factor binds to those tyrosine residues and gets phosphorylated. Activated STAT3 then translocates to the nucleus as a homodimer, and activates transcription of anti-apoptotic and cell-cycle-progression genes [25, 26].
IL-10 has many functional partners, one of which is the α2-macroglobulin (A2M). IL-10 forms a stable complex with activated A2M . A2M is a large homotetrameric glycoprotein  in the plasma, and in the extracellular space. It is a proteinase inhibitor, and has a peptide stretch, called the 'bait region'. Cleavage of the bait region by a proteinase leads to a conformational change in the protein that causes proteinase trapping, and receptor-mediated endocytosis of the A2M-proteinase complex . A2M is also a cytokine transporter. Many cytokines, including IL-10, and growth factors bind to A2M non-covalently in vivo . When A2M forms complexes with IL-10, TGF-β (anti-inflammatory cytokines) and IFN-γ, it accelerates the appearance of these cytokines in the blood [31, 32]. A2M in its native form increases the half-life of bound cytokines in the plasma by protecting them from proteolysis . Thus, at sites of inflammation, A2M concentration rises as a response to an increase in proteinase level. . A2M also contributes to the anti-inflammatory response of IL-10 by preventing its destruction.
IL-10 and IL-10Rs are able to interact with many partner molecules in the signaling network; however, their detailed protein structural interactions, as well the corresponding mutational mechanisms have not been well illustrated. In this study, we constructed the structural pathway based on protein-protein interactions (PPIs). The commonly used node-and-edge description of pathways, where nodes represent proteins and edges the interactions between them, are useful, but do not provide structural interaction detail [33, 34]. Further, in many cases, such as IL-10 and the receptors in this study, the available structural interaction data of the proteins are incomplete. However, recently developed computational structural approaches, such as PRISM (PRotein Interactions by Structural Matching tool), are capable of predicting PPI and can help filling in the gaps. PRISM [35, 36] is a motif-based protein-protein interaction prediction tool which uses a knowledge-based strategy to construct and analyze structural PPI networks. PRISM is based on the notion that evolution has exploited favorable structural motifs adapting them to different functions, in protein folds and at protein-protein-interfaces [37–39], lending robustness to its predictions. PRISM has predicted protein interactions successfully [40, 41] for different pathways, like apoptosis , ubiquitination , MAPK [41, 44], the Toll-like receptor pathways,  and for identifying drug off-targets . The success of PRISM is very close to %100 (87 out of 88 cases) in rigid-body prediction . Recently we have further enhanced it by introducing ensemble docking, by exploiting different conformations, and PRISM could predict two thirds of the 'difficult' cases of a docking benchmark dataset . Here, we applied this enhanced PRISM protocol to construct the structural protein-protein interaction (PPI) network of IL-10 centered signaling. Importantly, the analysis was able to identify mutations falling in the interfaces and to predict their effects on interactions such as of IL-10 with its receptors, IL-10 with A2M, and A2M with APP and KLK13. This allowed us to enrich the structural interaction data of IL-10 with its partners, and to analyze the mechanisms of mutations that lead to inflammation, and cancer through their impact on predicted interactions.
Reconstruction of the structural PPI network of IL-10 centered signaling
We used the String server  for selecting the first and second-degree neighbors of IL-10. Only interactions with experimental evidence and confidence score larger than 0.4 (the default confidence value) were considered. There were 4 first-degree and 45 second-degree neighbor proteins of IL-10 (Additional file 1). Overall, there were 50 proteins comprising the IL-10 centered protein-protein interaction network.
We used PRISM [35, 36] for modeling protein-protein interactions in the IL-10 centered network. PRISM searches for the motifs on the target protein surfaces similar to known interactions considering both geometrical complementarities and evolutionary conservation of hot spots. It treats proteins with at least 15 residues. To model an interface, PRISM requires the 3D structures of the proteins of interest (for further details of the PRISM protocol, see ). 39 of these 50 proteins have structural data in the PDB (corresponding to 958 PDB chains) and we could build homology models for the remaining 10. IGHV3-6's sequence information could not be found, so this protein is not included in our analysis (Additional file 1 Table S1). The I-TASSER server  was used for homology modeling and the top 5 models generated by the server were included in the predictions.
We reduced the redundancy of similar interface architectures for each protein, using TM (template modeling)-align . PDB structures having TM-scores larger than 0.5 and RMSD under 2.5Å were classified. Then, a representative PDB structure was assigned for each similar structure group and we ended up with 127 representative structures for 39 target proteins. The final IL-10 centered network is composed of 49 proteins and 70 interactions (Additional file 1 and 2).
Mapping oncogenic mutations onto the interfaces of predicted protein-protein complexes and in silico mutagenesis
Results and discussion
The distribution of COSMIC "missense" and "coding silent" mutations mapped onto the target structures in IL-10 centered network.
The relation between the interaction of IL-10 with its receptors and the implications in inflammation & cancer
Comparison of the binding energy scores for wild type and mutant structures
Binding Energy Score
IL-10 (1j7vL) (wt)
IL-10RA (1j7vR) (wt)
IL-10 (2ilkA) (wt)
IL-10RB (3lqmA) (wt)
IL-10 (2ilkA) (wt)
IL-10RB (3lqmA) (R198W)
IL-10 (2ilkA) (wt)
IL-10RB (3lqmA) (E41*)
IL-10 (2ilkA) (wt)
A2M (1bv8) (wt)
IL-10 (2ilkA) (Q56*)
A2M (1bv8) (wt)
A2M (4acqC) (wt)
APP (2llmA) (wt)
A2M (4acqC) (R945Q)
APP (2llmA) (wt)
hK13 (model) (wt)
A2M ( 1bv8A) (wt)
hK13 (model) (R236L)
A2M ( 1bv8A) (wt)
The association of IL-10 with A2M and its implications for inflammation and cancer
The interaction between A2M with APP and its implications in cancer
Here, we focus on the interaction between A2M, one of the interaction partners of IL-10, and β-amyloid precursor protein (APP). Extracellular cleavage of APP produces C99, a cell membrane-bound fragment, which is further cleaved by γ-secretase, and releases the intracellular domain of APP to produce amyloid-β (Ab) . Ab forms a complex with native A2M and the complex is internalized by the A2M receptor, a low density lipoprotein receptor-related protein (LRP), and degraded .. According to the PRISM results, A2M (PDB ID: 4acqC, residues 24-1474) forms a stable complex with APP (PDB ID: 2llmA, residues 686-726) with a binding energy score of -39.12. The template interface used in the prediction is 1fftAC (ubiquinol oxidase from Escherichia coli). APP is an integral type I transmembrane protein with a single transmembrane domain (residues 700-723), a large extracellular ectodomain (residues 18-699), and a short cytoplasmic tail (residues 724-770) .
The structural interaction of A2M with KLK13, and its implication in cancer
Although its function is still unclear, KLK13 is used as a new cancer biomarker in various cancers, including prostate , breast , ovarian , salivary gland , testicular , and non-small cell lung  cancers. This protein may be involved in the promotion of cancer cell growth, angiogenesis, metastasis and invasion. The ramification of the R263L mutation on KLK13 is observed in carcinoma from the COSMIC database [49, 50]. We investigated whether this mutation disrupts the interaction of KLK13 - A2M, created a structurally predicted mutant KLK13 protein and minimized its energy. An interaction between the minimized form of wild type KLK13 and A2M is predicted with an energy score of -87.73 (implying that the effect of minimization is insignificant). However, PRISM could not find a favorable interaction between mutant KLK13 and A2M (the best prediction has an energy score of +19.48, Table 2), suggesting disruption of the interaction. Our data is consistent with the prediction from the HotPoint server. Disruption of this interaction can allow KLK13 to react with other proteins, which may lead to cleavage in the major components of the extracellular matrix  and help in the promotion of cancer cell growth, metastasis and invasion.
Structural PPI networks indicate not only which proteins interact, but also how they interact and the location of the interaction sites. Computational techniques allow us to predict PPIs, mutate proteins and investigate the effect of those mutations on the PPIs. Here we constructed the structural PPI network to explore mutational and pathogenesis mechanisms in inflammatory diseases and cancer. We focused on the IL-10 centered network, as IL-10 is a well-known cytokine with an anti-inflammatory activity and relation to cancer. Currently available structural data of the IL-10 pathway are incomplete, with only 2 interactions available in the PDB. We utilized homology modeling to obtain the missing protein structures and a motif-based PPI prediction tool to complete the missing network parts. First we modeled the structures of 10 proteins and then provided models for 40 additional interactions. Although PRISM has a high prediction accuracy, its success to predict interactions is dependent on the conformation of the proteins given. We exploited the structures in the PDB. If the PDB does not include a conformation close to the bound form of the protein, PRISM cannot predict the interaction. That is why we missed some interactions on the network. As the PDB gets richer of structures and different conformations, the success of PRISM to predict interactions will increase. However, the structural PPI network was extended form 2 interactions to 42 interactions via predictions. This allowed us to investigate the effect of clinically observed cancer mutations on our IL-10 centered network. Comparing the interaction models of the wild type and mutant proteins, we observed that specific mutations disrupt the interactions, such as between IL-10 and its receptor, IL-10 and A2M, and A2M and its partners, which may disrupt immune regulation in cancer. We discovered that mutations of the residues, which were clinically observed in cancers as hot spots, change the binding energy and abolished or weakened the interactions. Disruption of the interactions of IL-10 with its receptors (IL-10RA and IL-10RB) and α-2-macroglobulin (A2M) may lead to enhanced inflammation, which could promote tumor growth; blockage of the A2M-APP interaction may lead to cancerous cellular proliferation through free APP; blockage of A2M-KLK13 (hK13) interaction can increase free hK13, which can promote cancer cell growth, metastasis and invasion through damage in the extracellular matrix. Collectively, by merging mutational and structural data - available and predicted using our powerful PRISM tool - and combining it with functional data, we are able to reveal the consequences of weakening or abolishing key interactions, and obtain experimentally-testable mechanisms of oncogenic mutations in the IL-10 network.
Zhong Chen and Carter Van Waes are funded by NIDCD/NIH Intramural projects Z01-DC-00073 and Z01-DC-00074. 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. This work is partially supported by the Scientific and Technological Research Council of Turkey (TUBITAK) Grant Number 113E164. 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 U.S. Government. This research was supported (in part) by the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.
The publication costs for this article were funded by a grant from NIDCD/NIH Intramural projects Z01-DC-00073 and Z01-DC-00074 and 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.
This article has been published as part of BMC Genomics Volume 15 Supplement 4, 2014: SNP-SIG 2013: Identification and annotation of genetic variants in the context of structure, function, and disease. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcgenomics/supplements/15/S4
Table S2.The list of interactions in the IL-10 protein-protein interaction network. 2
- Guven Maiorov E, Keskin O, Gursoy A, Nussinov R: The structural network of inflammation and cancer: Merits and challenges. Seminars in cancer biology. 2013, 23 (4): 243-251.PubMedView ArticleGoogle Scholar
- Trinchieri G: Cancer and inflammation: an old intuition with rapidly evolving new concepts. Annu Rev Immunol. 2012, 30: 677-706.PubMedView ArticleGoogle Scholar
- Trinchieri G: Inflammation in cancer: a therapeutic target?. Oncology (Williston Park). 2011, 25 (5): 418-420.Google Scholar
- Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell. 2011, 144 (5): 646-674.PubMedView ArticleGoogle Scholar
- Vacchelli E, Galluzzi L, Eggermont A, Galon J, Tartour E, Zitvogel L, Kroemer G: Trial Watch: Immunostimulatory cytokines. Oncoimmunology. 2012, 1 (4): 493-506.PubMedPubMed CentralView ArticleGoogle Scholar
- Balkwill F, Coussens LM: Cancer: an inflammatory link. Nature. 2004, 431 (7007): 405-406.PubMedView ArticleGoogle Scholar
- Coussens LM, Werb Z: Inflammation and cancer. Nature. 2002, 420 (6917): 860-867.PubMedPubMed CentralView ArticleGoogle Scholar
- Bar D, Apte RN, Voronov E, Dinarello CA, Cohen S: A continuous delivery system of IL-1 receptor antagonist reduces angiogenesis and inhibits tumor development. FASEB journal : official publication of the Federation of American Societies for Experimental Biology. 2004, 18 (1): 161-163.Google Scholar
- Balkwill F, Mantovani A: Inflammation and cancer: back to Virchow?. Lancet. 2001, 357 (9255): 539-545.PubMedView ArticleGoogle Scholar
- Robinson SC, Coussens LM: Soluble mediators of inflammation during tumor development. Advances in cancer research. 2005, 93: 159-187.PubMedView ArticleGoogle Scholar
- Mantovani A, Allavena P, Sica A, Balkwill F: Cancer-related inflammation. Nature. 2008, 454 (7203): 436-444.PubMedView ArticleGoogle Scholar
- Fiorentino DF, Bond MW, Mosmann TR: Two types of mouse T helper cell. IV. Th2 clones secrete a factor that inhibits cytokine production by Th1 clones. The Journal of experimental medicine. 1989, 170 (6): 2081-2095.PubMedView ArticleGoogle Scholar
- Moore KW, de Waal Malefyt R, Coffman RL, O'Garra A: Interleukin-10 and the interleukin-10 receptor. Annual review of immunology. 2001, 19: 683-765.PubMedView ArticleGoogle Scholar
- Tanikawa T, Wilke CM, Kryczek I, Chen GY, Kao J, Nunez G, Zou W: Interleukin-10 ablation promotes tumor development, growth, and metastasis. Cancer research. 2012, 72 (2): 420-429.PubMedPubMed CentralView ArticleGoogle Scholar
- Merlo P, Cecconi F: XIAP: inhibitor of two worlds. The EMBO journal. 2013, 32 (16): 2187-2188.PubMedPubMed CentralView ArticleGoogle Scholar
- Germano G, Allavena P, Mantovani A: Cytokines as a key component of cancer-related inflammation. Cytokine. 2008, 43 (3): 374-379.PubMedView ArticleGoogle Scholar
- Allavena P, Germano G, Marchesi F, Mantovani A: Chemokines in cancer related inflammation. Experimental cell research. 2011, 317 (5): 664-673.PubMedView ArticleGoogle Scholar
- Wilke CM, Wei S, Wang L, Kryczek I, Kao J, Zou W: Dual biological effects of the cytokines interleukin-10 and interferon-gamma. Cancer immunology, immunotherapy : CII. 2011, 60 (11): 1529-1541.PubMedView ArticleGoogle Scholar
- Mocellin S, Marincola FM, Young HA: Interleukin-10 and the immune response against cancer: a counterpoint. Journal of leukocyte biology. 2005, 78 (5): 1043-1051.PubMedView ArticleGoogle Scholar
- Kim BG, Joo HG, Chung IS, Chung HY, Woo HJ, Yun YS: Inhibition of interleukin-10 (IL-10) production from MOPC 315 tumor cells by IL-10 antisense oligodeoxynucleotides enhances cell-mediated immune responses. Cancer immunology, immunotherapy : CII. 2000, 49 (8): 433-440.PubMedView ArticleGoogle Scholar
- Sun Z, Luo Q, Ye D, Chen W, Chen F: Role of toll-like receptor 4 on the immune escape of human oral squamous cell carcinoma and resistance of cisplatin-induced apoptosis. Molecular cancer. 2012, 11: 33-PubMedPubMed CentralView ArticleGoogle Scholar
- Sabat R, Grutz G, Warszawska K, Kirsch S, Witte E, Wolk K, Geginat J: Biology of interleukin-10. Cytokine & growth factor reviews. 2010, 21 (5): 331-344.View ArticleGoogle Scholar
- Asadullah K, Sterry W, Volk HD: Interleukin-10 therapy--review of a new approach. Pharmacological reviews. 2003, 55 (2): 241-269.PubMedView ArticleGoogle Scholar
- Kotenko SV, Krause CD, Izotova LS, Pollack BP, Wu W, Pestka S: Identification and functional characterization of a second chain of the interleukin-10 receptor complex. The EMBO journal. 1997, 16 (19): 5894-5903.PubMedPubMed CentralView ArticleGoogle Scholar
- Riley JK, Takeda K, Akira S, Schreiber RD: Interleukin-10 receptor signaling through the JAK-STAT pathway. Requirement for two distinct receptor-derived signals for anti-inflammatory action. The Journal of biological chemistry. 1999, 274 (23): 16513-16521.PubMedView ArticleGoogle Scholar
- Donnelly RP, Dickensheets H, Finbloom DS: The interleukin-10 signal transduction pathway and regulation of gene expression in mononuclear phagocytes. Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research. 1999, 19 (6): 563-573.View ArticleGoogle Scholar
- Garber TR, Gonias SL, Webb DJ: Interleukin-4 and IL-10 bind covalently to activated human alpha2-macroglobulin by a mechanism that requires Cys949. Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research. 2000, 20 (2): 125-131.View ArticleGoogle Scholar
- Sheikh AM, Chauhan V, Tsiouris JA, Mehta PD, Burguess K, Fenko MD, Spivack W, Vaughan M, Malik M: Elevated levels of serum alpha(2) macroglobulin in wild black bears during hibernation. Biochimie. 2003, 85 (10): 1027-1032.PubMedView ArticleGoogle Scholar
- Rehman AA, Ahsan H, Khan FH: alpha-2-Macroglobulin: a physiological guardian. Journal of cellular physiology. 2013, 228 (8): 1665-1675.PubMedView ArticleGoogle Scholar
- Zhevago NA, Samoilova KA: Pro- and anti-inflammatory cytokine content in human peripheral blood after its transcutaneous (in vivo) and direct (in vitro) irradiation with polychromatic visible and infrared light. Photomedicine and laser surgery. 2006, 24 (2): 129-139.PubMedView ArticleGoogle Scholar
- Borth W, Scheer B, Urbansky A, Luger TA, Sottrup-Jensen L: Binding of IL-1 beta to alpha-macroglobulins and release by thioredoxin. Journal of immunology. 1990, 145 (11): 3747-3754.Google Scholar
- Borth W: Alpha 2-macroglobulin. A multifunctional binding and targeting protein with possible roles in immunity and autoimmunity. Annals of the New York Academy of Sciences. 1994, 737: 267-272.PubMedView ArticleGoogle Scholar
- Mosca R, Ceol A, Aloy P: Interactome3D: adding structural details to protein networks. Nature methods. 2013, 10 (1): 47-53.PubMedView ArticleGoogle Scholar
- Tuncbag N, Gursoy A, Keskin O: Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces. Physical biology. 2011, 8 (3): 035006-PubMedView ArticleGoogle Scholar
- Ogmen U, Keskin O, Aytuna AS, Nussinov R, Gursoy A: PRISM: protein interactions by structural matching. Nucleic Acids Research. 2005, 33: W331-W336.PubMedPubMed CentralView ArticleGoogle Scholar
- Tuncbag N, Gursoy A, Nussinov R, Keskin O: Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM. Nature Protocols. 2011, 6 (9): 1341-1354.PubMedView ArticleGoogle Scholar
- Tsai CJ, Lin SL, Wolfson HJ, Nussinov R: Protein-protein interfaces: Architectures and interactions in protein-protein interfaces and in protein cores. Their similarities and differences. Critical Reviews in Biochemistry and Molecular Biology. 1996, 31 (2): 127-152.PubMedView ArticleGoogle Scholar
- Keskin O, Nussinov R, Favorable scaffolds: proteins with different sequence, structure and function may associate in similar ways. Protein Engineering Design & Selection. 2005, 18 (1): 11-24.View ArticleGoogle Scholar
- Keskin O, Gursoy A, Ma B, Nussinov R: Principles of protein-protein interactions: What are the preferred ways for proteins to interact?. Chemical Reviews. 2008, 108 (4): 1225-1244.PubMedView ArticleGoogle Scholar
- Tuncbag N, Keskin O, Nussinov R, Gursoy A: Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement. Proteins-Structure Function and Bioinformatics. 2012, 80 (4): 1239-1249.View ArticleGoogle Scholar
- Kuzu G, Gursoy A, Nussinov R, Keskin O: Exploiting Conformational Ensembles in Modeling Protein-Protein Interactions on the Proteome Scale. Journal of Proteome Research. 2013, 12 (6): 2641-2653.PubMedPubMed CentralView ArticleGoogle Scholar
- Ozbabacan SEA, Keskin O, Nussinov R, Gursoy A: Enriching the human apoptosis pathway by predicting the structures of protein-protein complexes. Journal of Structural Biology. 2012, 179 (3): 338-346.View ArticleGoogle Scholar
- Kar G, Keskin O, Nussinov R, Gursoy A: Human proteome-scale structural modeling of E2-E3 interactions exploiting interface motifs. J Proteome Res. 2012, 11 (2): 1196-1207.PubMedPubMed CentralView ArticleGoogle Scholar
- Kuzu G, Keskin O, Gursoy A, Nussinov R: Constructing structural networks of signaling pathways on the proteome scale. Current Opinion in Structural Biology. 2012, 22 (3): 367-377.PubMedView ArticleGoogle Scholar
- Engin HB, Keskin O, Nussinov R, Gursoy A: A strategy based on protein-protein interface motifs may help in identifying drug off-targets. J Chem Inf Model. 2012, 52 (8): 2273-2286.PubMedPubMed CentralView ArticleGoogle Scholar
- Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, et al: STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013, 41 (Database): D808-815.PubMedPubMed CentralView ArticleGoogle Scholar
- Roy A, Kucukural A, Zhang Y, I-TASSER: a unified platform for automated protein structure and function prediction. Nat Protoc. 2010, 5 (4): 725-738.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang Y, Skolnick J, TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Res. 2005, 33 (7): 2302-2309.PubMedPubMed CentralView ArticleGoogle Scholar
- DuPage M, Jacks T: Genetically engineered mouse models of cancer reveal new insights about the antitumor immune response. Current opinion in immunology. 2013, 25 (2): 192-199.PubMedPubMed CentralView ArticleGoogle Scholar
- Forbes SA, Bindal N, Bamford S, Cole C, Kok CY, Beare D, Jia M, Shepherd R, Leung K, Menzies A, et al: COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic acids research. 2011, 39 (Database): D945-950.PubMedPubMed CentralView ArticleGoogle Scholar
- Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer discovery. 2012, 2 (5): 401-404.PubMedView ArticleGoogle Scholar
- Tuncbag N, Keskin O, Gursoy A: HotPoint: hot spot prediction server for protein interfaces. Nucleic acids research. 2010, 38 (Web Server): W402-406.PubMedPubMed CentralView ArticleGoogle Scholar
- Van Durme J, Delgado J, Stricher F, Serrano L, Schymkowitz J, Rousseau F: A graphical interface for the FoldX forcefield. Bioinformatics. 2011, 27 (12): 1711-1712.PubMedView ArticleGoogle Scholar
- Krieger E, Koraimann G, Vriend G: Increasing the precision of comparative models with YASARA NOVA--a self-parameterizing force field. Proteins. 2002, 47 (3): 393-402.PubMedView ArticleGoogle Scholar
- Forbes SA, Bhamra G, Bamford S, Dawson E, Kok C, Clements J, Menzies A, Teague JW, Futreal PA, Stratton MR: The Catalogue of Somatic Mutations in Cancer (COSMIC). Curr Protoc Hum Genet. 2008, Chapter 10 (Unit 10 11):Google Scholar
- Schuster-Bockler B, Bateman A: Protein interactions in human genetic diseases. Genome biology. 2008, 9 (1): R9-PubMedPubMed CentralView ArticleGoogle Scholar
- Chiti F, Dobson CM: Protein misfolding, functional amyloid, and human disease. Annual review of biochemistry. 2006, 75: 333-366.PubMedView ArticleGoogle Scholar
- Wang X, Wei X, Thijssen B, Das J, Lipkin SM, Yu H: Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nature biotechnology. 2012, 30 (2): 159-164.PubMedPubMed CentralView ArticleGoogle Scholar
- Engin HB, Guney E, Keskin O, Oliva B, Gursoy A: Integrating structure to protein-protein interaction networks that drive metastasis to brain and lung in breast cancer. PloS one. 2013, 8 (11): e81035-PubMedPubMed CentralView ArticleGoogle Scholar
- Acuner Ozbabacan SE, Gursoy A, Nussinov R, Keskin O: The structural pathway of interleukin 1 (IL-1) initiated signaling reveals mechanisms of oncogenic mutations and SNPs in inflammation and cancer. PLOS Comp Biol. 10 (2): e1003470-Google Scholar
- Yoon SI, Logsdon NJ, Sheikh F, Donnelly RP, Walter MR: Conformational changes mediate interleukin-10 receptor 2 (IL-10R2) binding to IL-10 and assembly of the signaling complex. J Biol Chem. 2006, 281 (46): 35088-35096.PubMedView ArticleGoogle Scholar
- Muller UC, Zheng H: Physiological functions of APP family proteins. Cold Spring Harbor perspectives in medicine. 2012, 2 (2): a006288-PubMedPubMed CentralView ArticleGoogle Scholar
- Narita M, Holtzman DM, Schwartz AL, Bu G: Alpha2-macroglobulin complexes with and mediates the endocytosis of beta-amyloid peptide via cell surface low-density lipoprotein receptor-related protein. Journal of neurochemistry. 1997, 69 (5): 1904-1911.PubMedView ArticleGoogle Scholar
- Yerbury JJ, Kumita JR, Meehan S, Dobson CM, Wilson MR: alpha2-Macroglobulin and haptoglobin suppress amyloid formation by interacting with prefibrillar protein species. The Journal of biological chemistry. 2009, 284 (7): 4246-4254.PubMedView ArticleGoogle Scholar
- Hansel DE, Rahman A, Wehner S, Herzog V, Yeo CJ, Maitra A: Increased expression and processing of the Alzheimer amyloid precursor protein in pancreatic cancer may influence cellular proliferation. Cancer research. 2003, 63 (21): 7032-7037.PubMedGoogle Scholar
- Meng JY, Kataoka H, Itoh H, Koono M: Amyloid beta protein precursor is involved in the growth of human colon carcinoma cell in vitro and in vivo. International journal of cancer Journal international du cancer. 2001, 92 (1): 31-39.PubMedView ArticleGoogle Scholar
- Takayama K, Tsutsumi S, Suzuki T, Horie-Inoue K, Ikeda K, Kaneshiro K, Fujimura T, Kumagai J, Urano T, Sakaki Y, et al: Amyloid precursor protein is a primary androgen target gene that promotes prostate cancer growth. Cancer research. 2009, 69 (1): 137-142.PubMedView ArticleGoogle Scholar
- Pietrzik CU, Hoffmann J, Stober K, Chen CY, Bauer C, Otero DA, Roch JM, Herzog V: From differentiation to proliferation: the secretory amyloid precursor protein as a local mediator of growth in thyroid epithelial cells. Proceedings of the National Academy of Sciences of the United States of America. 1998, 95 (4): 1770-1775.PubMedPubMed CentralView ArticleGoogle Scholar
- Yousef GM, Chang A, Diamandis EP: Identification and characterization of KLK-L4, a new kallikrein-like gene that appears to be down-regulated in breast cancer tissues. Journal of Biological Chemistry. 2000, 275 (16): 11891-11898.PubMedView ArticleGoogle Scholar
- Diamandis EP, Yousef GM: Human tissue kallikreins: A family of new cancer biomarkers. Clinical Chemistry. 2002, 48 (8): 1198-1205.PubMedGoogle Scholar
- Darling MR, Jackson-Boeters L, Daley TD, Diamandis EP: Human kallikrein 13 expression in salivary gland tumors. International Journal of Biological Markers. 2006, 21 (2): 106-110.PubMedGoogle Scholar
- Kapadia C, Yousef GM, Mellati AA, Magklara A, Wasney GA, Diamandis EP: Complex formation between human kallikrein 13 and serum protease inhibitors. Clinica Chimica Acta. 2004, 339 (1-2): 157-167.View ArticleGoogle Scholar
- Petraki CD, Gregorakis AK, Papanastasiou PA, Karavana VN, Luo LY, Diamandis EP: Immunohistochemical localization of human kallikreins 6, 10 and 13 in benign and malignant prostatic tissues. Prostate Cancer and Prostatic Diseases. 2003, 6 (3): 223-227.PubMedView ArticleGoogle Scholar
- Chang A, Yousef GM, Scorilas A, Grass L, Sismondi P, Ponzone R, Diamandis EP: Human kallikrein gene 13 (KLK13) expression by quantitative RT-PCR: an independent indicator of favourable prognosis in breast cancer. British Journal of Cancer. 2002, 86 (9): 1457-1464.PubMedPubMed CentralView ArticleGoogle Scholar
- Scorilas A, Borgono CA, Harbeck N, Dorn J, Schmalfeldt B, Schmitt M, Diamandis EP: Human kallikrein 13 protein in ovarian cancer cytosols: A new favorable prognostic marker. Journal of Clinical Oncology. 2004, 22 (4): 678-685.PubMedView ArticleGoogle Scholar
- Luo LY, Yousef G, Diamandis EP: Human tissue kallikreins and testicular cancer. Apmis. 2003, 111 (1): 225-233.PubMedView ArticleGoogle Scholar
- Planque C, Li L, Zheng YG, Soosaipillai A, Reckamp K, Chia D, Diamandis EP, Goodglick L: A multiparametric serum kallikrein panel for diagnosis of non-small cell lung carcinoma. Clinical Cancer Research. 2008, 14 (5): 1355-1362.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.