Pathogen mimicry of host protein-protein interfaces modulates immunity

Guven-Maiorov, Emine; Tsai, Chung‐Jung; Nussinov, Ruth

doi:10.1016/j.semcdb.2016.06.004

Cited by 43 publications

(39 citation statements)

References 84 publications

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“…Our short summary and the papers of this special issue [25,29,30,33,36,39,42,52,57] highlight the promises and challenges of using signaling networks (including RAS-related signaling events) in the description of cancer initiation and development, metastasis and the emergence of drug resistance. As key messages we emphasize that…”

Section: Discussionmentioning

confidence: 99%

“…The approaches outlined above and in the contributions of this special issue [25,29,30,33,36,39,42,52,57] may overcome the current Nietzschean dilemma of cancer cell targeting: “what does not kill me makes me stronger” [60]. …”

Section: Discussionmentioning

confidence: 99%

“…Disruption of the gut microbiota impairs the efficiency of immuno- and chemotherapies [56]. In the closing contribution of this special issue Ruth Nussinov and co-workers [57–59] explore a highly exciting special area of cell-cell interactions: the mimicry of host protein binding surfaces by pathogenic proteins. This mimicry leads to the rewiring and repurposing of the host signaling pathways contributing to carcinogenesis including the modulation of RAS-signaling.…”

Section: The Role Of Inter-cellular Signaling In Cancer Initiationmentioning

confidence: 99%

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Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy

Csermely

Korcsmáros

Nussinov

2016

Seminars in Cell & Developmental Biology

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Cancer initiation and development are increasingly perceived as systems-level phenomena, where intra- and inter-cellular signaling networks of the ecosystem of cancer and stromal cells offer efficient methodologies for outcome prediction and intervention design. Within this framework, RAS emerges as a ‘contextual signaling hub’, i.e. the final result of RAS activation or inhibition is determined by the signaling network context. Current therapies often ‘train’ cancer cells shifting them to a novel attractor, which has increased metastatic potential and drug resistance. The few therapy-surviving cancer cells are surrounded by massive cell death triggering a primordial adaptive and reparative general wound healing response. Overall, dynamic analysis of patient- and disease-stage specific intracellular and intercellular signaling networks may open new areas of anticancer therapy using multitarget drugs, drugs combinations, edgetic drugs, as well as help design ‘gentler’, differentiation and maintenance therapies.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: The Role Of Inter-cellular Signaling In Cancer Initiationmentioning

confidence: 99%

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Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy

Csermely

Korcsmáros

Nussinov

2016

Seminars in Cell & Developmental Biology

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“…The host interacts with microbiota through proteins, metabolites, small molecules and nucleic acids [8, 9]. The microbiota employs a range of effectors to modulate host cellular functions and immune responses.…”

Section: Introductionmentioning

confidence: 99%

“…One efficient way is molecular mimicry, which has been extensively reviewed in our recent study [9]. Molecular mimicry can take place at four levels: mimicking (i) both sequence and 3D structure of a protein, (ii) only structure without sequence similarity, (iii) sequence of a short motif–motif mimicry, and (iv) structure of a binding surface without sequence similarity–interface mimicry.…”

Section: Introductionmentioning

confidence: 99%

Structural host-microbiota interaction networks

2017

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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.

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Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host‐pathogen Interaction Network Parameters

Singh

Bhatnagar

2021

Molecular Informatics

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Host-pathogen interactions play a crucial role in invasion, infection, and induction of immune response in humans. In this work, four machine learning algorithms, namely Logistic regression, K-nearest neighbor, Support Vector Machine, and Random Forest were implemented for the classification of drug targets. The algorithms were trained using 3400 hosts and 3800 pathogen drug and nondrug target proteins as learning instances. For each protein, 68 pathogen and 73 host features were computed that included sequence, structure, biological and host-pathogen network centrality characteristics. The Random Forest classifier model achieved the best accuracy after 10-fold cross-validation. 99 % accuracy was achieved with a ROC-AUC score of 0.99 � 0.01 for both pathogen and host training sets. The Eigenvector Centrality of host-pathogen interactions and host-host interactions was the top feature in performing classification of pathogen and host targets respectively. Other features important for classification were the presence of catalytic and binding sites, low instability/ aliphatic index, and cellular location. The Random Forest classifier was then used for prediction of drug targets involved in Microbe Associated Cardiovascular Diseases. 331 host and 743 pathogen proteins were predicted as drug targets by the random forest model and can be validated experimentally for therapeutic intervention in Microbe Associated Cardiovascular Diseases.

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Pathogen mimicry of host protein-protein interfaces modulates immunity

Cited by 43 publications

References 84 publications

Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy

Intracellular and intercellular signaling networks in cancer initiation, development and precision anti-cancer therapy

Structural host-microbiota interaction networks

Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host‐pathogen Interaction Network Parameters

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