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Article: Oncoviruses Can Drive Cancer by Rewiring Signaling Pathways Through Interface Mimicry.

Guven-Maiorov, Emine / Tsai, Chung-Jung / Nussinov, Ruth

2019 Volume 9, Page(s) 1236

Abstract: Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) ... ...

Abstract	Oncoviruses rewire host pathways to subvert host immunity and promote their survival and proliferation. However, exactly how is challenging to understand. Here, by employing the first and to date only interface-based host-microbe interaction (HMI) prediction method, we explore a pivotal strategy oncoviruses use to drive cancer: mimicking binding surfaces-interfaces-of human proteins. We show that oncoviruses can target key human network proteins and transform cells by acquisition of cancer hallmarks. Experimental large-scale mapping of HMIs is difficult and individual HMIs do not permit in-depth grasp of tumorigenic virulence mechanisms. Our computational approach is tractable and 3D structural HMI models can help elucidate pathogenesis mechanisms and facilitate drug design. We observe that many host proteins are unique targets for certain oncoviruses, whereas others are common to several, suggesting similar infectious strategies. A rough estimation of our false discovery rate based on the tissue expression of oncovirus-targeted human proteins is 25%.
Language	English
Publishing date	2019-11-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2019.01236
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Delineating functional mechanisms of the p53/p63/p73 family of transcription factors through identification of protein-protein interactions using interface mimicry.

Guven-Maiorov, Emine / Sakakibara, Nozomi / Ponnamperuma, Roshini M / Dong, Kun / Matar, Hector / King, Kathryn E / Weinberg, Wendy C

Molecular carcinogenesis

2022 Volume 61, Issue 7, Page(s) 629–642

Abstract: Members of the p53 family of transcription factors-p53, p63, and p73-share a high degree of homology; however, members can be activated in response to different stimuli, perform distinct (sometimes opposing) roles and are expressed in different tissues. ... ...

Abstract	Members of the p53 family of transcription factors-p53, p63, and p73-share a high degree of homology; however, members can be activated in response to different stimuli, perform distinct (sometimes opposing) roles and are expressed in different tissues. The level of complexity is increased further by the transcription of multiple isoforms of each homolog, which may interact or interfere with each other and can impact cellular outcome. Proteins perform their functions through interacting with other proteins (and/or with nucleic acids). Therefore, identification of the interactors of a protein and how they interact in 3D is essential to fully comprehend their roles. By utilizing an in silico protein-protein interaction prediction method-HMI-PRED-we predicted interaction partners of p53 family members and modeled 3D structures of these protein interaction complexes. This method recovered experimentally known interactions while identifying many novel candidate partners. We analyzed the similarities and differences observed among the interaction partners to elucidate distinct functions of p53 family members and provide examples of how this information may yield mechanistic insight to explain their overlapping versus distinct/opposing outcomes in certain contexts. While some interaction partners are common to p53, p63, and p73, the majority are unique to each member. Nevertheless, most of the enriched pathways associated with these partners are common to all members, indicating that the members target the same biological pathways but through unique mediators. p63 and p73 have more common enriched pathways compared to p53, supporting their similar developmental roles in different tissues.
MeSH term(s)	DNA-Binding Proteins/metabolism ; Humans ; Nuclear Proteins/genetics ; Nuclear Proteins/metabolism ; Transcription Factors/metabolism ; Tumor Protein p73/genetics ; Tumor Protein p73/metabolism ; Tumor Suppressor Protein p53/metabolism ; Tumor Suppressor Proteins/genetics ; Tumor Suppressor Proteins/metabolism
Chemical Substances	DNA-Binding Proteins ; Nuclear Proteins ; Transcription Factors ; Tumor Protein p73 ; Tumor Suppressor Protein p53 ; Tumor Suppressor Proteins
Language	English
Publishing date	2022-05-13
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	1004029-8
ISSN	1098-2744 ; 0899-1987
ISSN (online)	1098-2744
ISSN	0899-1987
DOI	10.1002/mc.23405
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Article ; Online: Structural host-microbiota interaction networks.

Guven-Maiorov, Emine / Tsai, Chung-Jung / Nussinov, Ruth

PLoS computational biology

2017 Volume 13, Issue 10, Page(s) e1005579

Abstract: Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular ... ...

Abstract	Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.
MeSH term(s)	Animals ; Computational Biology ; Humans ; Mice ; Microbiota/physiology ; Models, Molecular ; Molecular Mimicry ; Protein Interaction Maps/physiology ; Symbiosis/physiology ; Toll-Like Receptors
Chemical Substances	Toll-Like Receptors
Keywords	covid19
Language	English
Publishing date	2017-10-12
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1005579
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Article ; Online: Interface-Based Structural Prediction of Novel Host-Pathogen Interactions.

Guven-Maiorov, Emine / Tsai, Chung-Jung / Ma, Buyong / Nussinov, Ruth

Methods in molecular biology (Clifton, N.J.)

2018 Volume 1851, Page(s) 317–335

Abstract: About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host ... ...

Abstract	About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.
MeSH term(s)	Host-Pathogen Interactions/physiology ; Molecular Mimicry ; Protein Binding ; Viral Proteins/chemistry ; Viral Proteins/metabolism
Chemical Substances	Viral Proteins
Language	English
Publishing date	2018-10-05
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-4939-8736-8_18
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Article ; Online: Pathogen mimicry of host protein-protein interfaces modulates immunity.

Guven-Maiorov, Emine / Tsai, Chung-Jung / Nussinov, Ruth

Seminars in cell & developmental biology

2016 Volume 58, Page(s) 136–145

Abstract: Signaling pathways shape and transmit the cell's reaction to its changing environment; however, pathogens can circumvent this response by manipulating host signaling. To subvert host defense, they beat it at its own game: they hijack host pathways by ... ...

Abstract	Signaling pathways shape and transmit the cell's reaction to its changing environment; however, pathogens can circumvent this response by manipulating host signaling. To subvert host defense, they beat it at its own game: they hijack host pathways by mimicking the binding surfaces of host-encoded proteins. For this, it is not necessary to achieve global protein homology; imitating merely the interaction surface is sufficient. Different protein folds often interact via similar protein-protein interface architectures. This similarity in binding surfaces permits the pathogenic protein to compete with a host target protein. Thus, rather than binding a host-encoded partner, the host protein hub binds the pathogenic surrogate. The outcome can be dire: rewiring or repurposing the host pathways, shifting the cell signaling landscape and consequently the immune response. They can also cause persistent infections as well as cancer by modulating key signaling pathways, such as those involving Ras. Mapping the rewired host-pathogen 'superorganism' interaction network - along with its structural details - is critical for in-depth understanding of pathogenic mechanisms and developing efficient therapeutics. Here, we overview the role of molecular mimicry in pathogen host evasion as well as types of molecular mimicry mechanisms that emerged during evolution.
Language	English
Publishing date	2016-10
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1312473-0
ISSN	1096-3634 ; 1084-9521
ISSN (online)	1096-3634
ISSN	1084-9521
DOI	10.1016/j.semcdb.2016.06.004
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Zs.A 2918: Show issues

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Article ; Online: Prediction of Host-Pathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer.

Guven-Maiorov, Emine / Tsai, Chung-Jung / Ma, Buyong / Nussinov, Ruth

Journal of molecular biology

2017 Volume 429, Issue 24, Page(s) 3925–3941

Abstract: There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through ...

Abstract	There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of host-pathogen interactions (HPIs) and mapping the re-wired superorganism HPI network-with structural details-can provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
MeSH term(s)	Bacterial Proteins/metabolism ; Helicobacter Infections/complications ; Helicobacter Infections/metabolism ; Helicobacter Infections/microbiology ; Helicobacter pylori/physiology ; Host-Pathogen Interactions ; Humans ; Molecular Mimicry ; Protein Binding ; Protein Interaction Mapping ; Stomach Neoplasms/etiology ; Stomach Neoplasms/metabolism
Chemical Substances	Bacterial Proteins
Language	English
Publishing date	2017-10-26
Publishing country	England
Document type	Journal Article
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2017.10.023
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Article ; Online: Structural host-microbiota interaction networks.

Emine Guven-Maiorov / Chung-Jung Tsai / Ruth Nussinov

PLoS Computational Biology, Vol 13, Iss 10, p e

2017 Volume 1005579

Abstract	Hundreds of different species colonize multicellular organisms making them "metaorganisms". A growing body of data supports the role of microbiota in health and in disease. Grasping the principles of host-microbiota interactions (HMIs) at the molecular level is important since it may provide insights into the mechanisms of infections. The crosstalk between the host and the microbiota may help resolve puzzling questions such as how a microorganism can contribute to both health and disease. Integrated superorganism networks that consider host and microbiota as a whole-may uncover their code, clarifying perhaps the most fundamental question: how they modulate immune surveillance. Within this framework, structural HMI networks can uniquely identify potential microbial effectors that target distinct host nodes or interfere with endogenous host interactions, as well as how mutations on either host or microbial proteins affect the interaction. Furthermore, structural HMIs can help identify master host cell regulator nodes and modules whose tweaking by the microbes promote aberrant activity. Collectively, these data can delineate pathogenic mechanisms and thereby help maximize beneficial therapeutics. To date, challenges in experimental techniques limit large-scale characterization of HMIs. Here we highlight an area in its infancy which we believe will increasingly engage the computational community: predicting interactions across kingdoms, and mapping these on the host cellular networks to figure out how commensal and pathogenic microbiota modulate the host signaling and broadly cross-species consequences.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2017-10-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry.

Guven-Maiorov, Emine / Hakouz, Asma / Valjevac, Sukejna / Keskin, Ozlem / Tsai, Chung-Jung / Gursoy, Attila / Nussinov, Ruth

Journal of molecular biology

2020 Volume 432, Issue 11, Page(s) 3395–3403

Abstract: Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and ... ...

Abstract	Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.
MeSH term(s)	Bacteria/genetics ; Bacteria/pathogenicity ; Host Microbial Interactions/genetics ; Humans ; Internet ; Protein Interaction Mapping ; Software
Language	English
Publishing date	2020-02-13
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2020.01.025
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Article: Prediction of HostâPathogen Interactions for Helicobacter pylori by Interface Mimicry and Implications to Gastric Cancer

Guven-Maiorov, Emine / Buyong Ma / Chung-Jung Tsai / Ruth Nussinov

Journal of Molecular Biology. 2017,

2017

Abstract	There is a strong correlation between some pathogens and certain cancer types. One example is Helicobacter pylori and gastric cancer. Exactly how they contribute to host tumorigenesis is, however, a mystery. Pathogens often interact with the host through proteins. To subvert defense, they may mimic host proteins at the sequence, structure, motif, or interface levels. Interface similarity permits pathogen proteins to compete with those of the host for a target protein and thereby alter the host signaling. Detection of hostâpathogen interactions (HPIs) and mapping the re-wired superorganism HPI networkâwith structural detailsâcan provide unprecedented clues to the underlying mechanisms and help therapeutics. Here, we describe the first computational approach exploiting solely interface mimicry to model potential HPIs. Interface mimicry can identify more HPIs than sequence or complete structural similarity since it appears more common than the other mimicry types. We illustrate the usefulness of this concept by modeling HPIs of H. pylori to understand how they modulate host immunity, persist lifelong, and contribute to tumorigenesis. H. pylori proteins interfere with multiple host pathways as they target several host hub proteins. Our results help illuminate the structural basis of resistance to apoptosis, immune evasion, and loss of cell junctions seen in H. pylori-infected host cells.
Keywords	apoptosis ; carcinogenesis ; Helicobacter pylori ; host-pathogen relationships ; immune evasion ; immunity ; intercellular junctions ; models ; pathogens ; prediction ; proteins ; stomach neoplasms ; therapeutics
Language	English
Size	p. .
Publishing place	Elsevier Ltd
Document type	Article
Note	Pre-press version
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2017.10.023
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Article ; Online: Advances in template-based protein docking by utilizing interfaces towards completing structural interactome.

Muratcioglu, Serena / Guven-Maiorov, Emine / Keskin, Özlem / Gursoy, Attila

Current opinion in structural biology

2015 Volume 35, Page(s) 87–92

Abstract: The increase in the number of structurally determined protein complexes strengthens template-based docking (TBD) methods for modelling protein-protein interactions (PPIs). These methods utilize the known structures of protein complexes as templates to ... ...

Abstract	The increase in the number of structurally determined protein complexes strengthens template-based docking (TBD) methods for modelling protein-protein interactions (PPIs). These methods utilize the known structures of protein complexes as templates to predict the quaternary structure of the target proteins. The templates may be partial or complete structures. Interface based (partial) methods have recently gained interest due in part to the observation that the interface regions are reusable. We describe how available template interfaces can be used to obtain the structural models of protein interactions. Despite the agreement that a majority of the protein complexes can be modelled using the available Protein Data Bank (PDB) structures, a handful of studies argue that we need more template proteins to increase the structural coverage of PPIs. We also discuss the performance of the interface TBD methods at large scale, and the significance of capturing multiple conformations for improving accuracy.
MeSH term(s)	Molecular Docking Simulation/methods ; Protein Interaction Mapping/methods ; Proteins/chemistry ; Proteins/metabolism
Chemical Substances	Proteins
Language	English
Publishing date	2015-12
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	1068353-7
ISSN	1879-033X ; 0959-440X
ISSN (online)	1879-033X
ISSN	0959-440X
DOI	10.1016/j.sbi.2015.10.001
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