scholarly journals Computational approaches for prediction of pathogen-host protein-protein interactions

2015 ◽  
Vol 6 ◽  
Author(s):  
Esmaeil Nourani ◽  
Farshad Khunjush ◽  
Saliha DurmuÅŸ
Keyword(s):  
Protein Interactions ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Computational Approaches

Elucidating Protein-protein Interactions Through Computational Approaches and Designing Small Molecule Inhibitors Against them for Various Diseases

2018 ◽  
Vol 18 (20) ◽  
pp. 1719-1736 ◽  
Author(s):  
Sharanya Sarkar ◽  
Khushboo Gulati ◽  
Manikyaprabhu Kairamkonda ◽  
Amit Mishra ◽  
Krishna Mohan Poluri
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Small Molecule Inhibitors ◽  
Computational Techniques ◽  
Protein Protein Interactions ◽  
Computational Tools ◽  
Computational Approaches ◽  
Protein Protein Interaction ◽  
Wide Range ◽  
Therapeutic Measures

Background: To carry out wide range of cellular functionalities, proteins often associate with one or more proteins in a phenomenon known as Protein-Protein Interaction (PPI). Experimental and computational approaches were applied on PPIs in order to determine the interacting partners, and also to understand how an abnormality in such interactions can become the principle cause of a disease. Objective: This review aims to elucidate the case studies where PPIs involved in various human diseases have been proven or validated with computational techniques, and also to elucidate how small molecule inhibitors of PPIs have been designed computationally to act as effective therapeutic measures against certain diseases. Results: Computational techniques to predict PPIs are emerging rapidly in the modern day. They not only help in predicting new PPIs, but also generate outputs that substantiate the experimentally determined results. Moreover, computation has aided in the designing of novel inhibitor molecules disrupting the PPIs. Some of them are already being tested in the clinical trials. Conclusion: This review delineated the classification of computational tools that are essential to investigate PPIs. Furthermore, the review shed light on how indispensable computational tools have become in the field of medicine to analyze the interaction networks and to design novel inhibitors efficiently against dreadful diseases in a shorter time span.

scholarly journals A Field-Proven Yeast Two-Hybrid Protocol Used to Identify Coronavirus–Host Protein–Protein Interactions

2015 ◽  
pp. 213-229 ◽  
Author(s):  
Pierre-Olivier Vidalain ◽  
Yves Jacob ◽  
Marne C. Hagemeijer ◽  
Louis M. Jones ◽  
Grégory Neveu ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Two Hybrid

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches

2019 ◽  
Vol 20 (3) ◽  
pp. 177-184 ◽  
Author(s):  
Nantao Zheng ◽  
Kairou Wang ◽  
Weihua Zhan ◽  
Lei Deng
Keyword(s):  
Machine Learning ◽  
Computational Methods ◽  
Protein Interactions ◽  
Prediction Models ◽  
Learning Algorithms ◽  
Biological Data ◽  
Machine Learning Algorithms ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Protein Motifs

Background:Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions.Methods:In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods.Results:We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions.Conclusion:The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

scholarly journals Analysis of Vaccinia Virus−Host Protein−Protein Interactions: Validations of Yeast Two-Hybrid Screenings

2009 ◽  
Vol 8 (9) ◽  
pp. 4311-4318 ◽  
Author(s):  
Leiliang Zhang ◽  
Nancy Y. Villa ◽  
Masmudur M. Rahman ◽  
Sherin Smallwood ◽  
Donna Shattuck ◽  
...  
Keyword(s):  
Vaccinia Virus ◽  
Protein Interactions ◽  
Host Protein ◽  
Protein Protein Interactions ◽  
Yeast Two Hybrid ◽  
Two Hybrid

scholarly journals Virus–Host Protein–Protein Interactions between Human Papillomavirus 16 E6 A1 and D2/D3 Sub-Lineages: Variances and Similarities

2020 ◽  
Vol 21 (21) ◽  
pp. 7980
Author(s):  
Guillem Dayer ◽  
Mehran L. Masoom ◽  
Melissa Togtema ◽  
Ingeborg Zehbe
Keyword(s):  
Human Papillomavirus ◽  
Asian American ◽  
Protein Interactions ◽  
Host Protein ◽  
Cellular Proteins ◽  
Loss Of Function ◽  
Protein Protein Interactions ◽  
Human Papillomavirus 16 ◽  
Novel Approach ◽  
Causative Agents

High-risk strains of human papillomavirus are causative agents for cervical and other mucosal cancers, with type 16 being the most frequent. Compared to the European Prototype (EP; A1), the Asian-American (AA; D2/D3) sub-lineage seems to have increased abilities to promote carcinogenesis. Here, we studied protein–protein interactions (PPIs) between host proteins and sub-lineages of the key transforming E6 protein. We transduced human keratinocyte with EP or AA E6 genes and co-immunoprecipitated E6 proteins along with interacting cellular proteins to detect virus–host binding partners. AAE6 and EPE6 may have unique PPIs with host cellular proteins, conferring gain or loss of function and resulting in varied abilities to promote carcinogenesis. Using liquid chromatography-mass spectrometry and stringent interactor selection criteria based on the number of peptides, we identified 25 candidates: 6 unique to AAE6 and EPE6, along with 13 E6 targets common to both. A novel approach based on pathway selection discovered 171 target proteins: 90 unique AAE6 and 61 unique EPE6 along with 20 common E6 targets. Interpretations were made using databases, such as UniProt, BioGRID, and Reactome. Detected E6 targets were differentially implicated in important hallmarks of cancer: deregulating Notch signaling, energetics and hypoxia, DNA replication and repair, and immune response.

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