VirusMentha: a new resource for virus-host protein interactions

Calderone, Alberto; Licata, Luana; Cesareni, Gianni

doi:10.1093/nar/gku830

Cited by 151 publications

(119 citation statements)

References 24 publications

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“…To date, these advances in proteomic-based approaches have paved the way for the identification of an impressive number of virus-host protein associations that otherwise may not have been uncovered using more traditional approaches. In fact, these studies have provided the foundation for publically accessible databases, which currently contain over 5000 curated and non-redundant protein interactions between components of the virus and of the permissive host cell [121][122][123][124][125][126][127]. These valuable repositories include the Database of Interacting Proteins [127], VirHostNet [124,125], VirusMentha [123], IntAct-MINT, and Uniprot [122].…”

Section: Expert Commentary and 5-year Viewmentioning

confidence: 99%

Proteomic approaches to uncovering virus–host protein interactions during the progression of viral infection

Lum

Cristea

2016

Expert Review of Proteomics

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The integration of proteomic methods to virology has facilitated a significant breadth of biological insight into mechanisms of virus replication, antiviral host responses and viral subversion of host defenses. Throughout the course of infection, these cellular mechanisms rely heavily on the formation of temporally and spatially regulated virus–host protein–protein interactions. Reviewed here are proteomic-based approaches that have been used to characterize this dynamic virus–host interplay. Specifically discussed are the contribution of integrative mass spectrometry, antibody-based affinity purification of protein complexes, cross-linking and protein array techniques for elucidating complex networks of virus–host protein associations during infection with a diverse range of RNA and DNA viruses. The benefits and limitations of applying proteomic methods to virology are explored, and the contribution of these approaches to important biological discoveries and to inspiring new tractable avenues for the design of antiviral therapeutics is highlighted.

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Section: Expert Commentary and 5-year Viewmentioning

confidence: 99%

Proteomic approaches to uncovering virus–host protein interactions during the progression of viral infection

Lum

Cristea

2016

Expert Review of Proteomics

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“…PHI databases that cover a wide range of viruses and hosts include VirHostNet [25], VirusMentha [27] and HPIDB [28] ( Table 3). In addition to data storage, VirusMentha and PHIDIAS [29] offer certain visualization and analysis tools.…”

Section: Virus-human Host Protein-protein Interaction Databasesmentioning

confidence: 99%

Protein-protein interactions of human viruses

Goodacre

Devkota

Bae

et al. 2020

Seminars in Cell & Developmental Biology

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Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.

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“…Reliable experimental methods are time-consuming and expensive, making it unjustifiable, thus for some special PHPPI, we might need to verify its result with duplicate experiments. Now owing to some earlier research efforts, there are several verified PHPPI datasets available, including HPIDB [4], PATRIC [24], PHISTO [25], VirHostNet [26] and VirusMentha [27]. These databases have provided some PPI pairs to build a "golden standard dataset", including positive PHPPI and negative PHPPI for verifying computational methods.…”

Section: Phppi Databasesmentioning

confidence: 99%

Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A Survey

Chen

Shen

Wang

et al. 2016

2016 IEEE International Congress on Big Data (BigData Congress)

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Abstract"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host protein-protein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host proteinprotein interactions (PHPPI), currently there is not a well structured database for research purposes, not even for infection mechanism studies for different species of pathogens. In this paper, we will survey the PHPPI researches and construct a public PHPPI dataset by ourselves for future research. It results in an utterly big and imbalanced data set associated with high dimension and large quantity. Several machine learning methodologies are also discussed in this paper to imply possible analytics solutions in near future. This paper contributes to a new, yet challenging, research area in applying data analytic technologies in bioinformatics, by learning and predicting pathogen-host protein-protein interactions. Abstract-"Big Data" is immersed in many disciplines, including computer vision, economics, online resources, bioinformatics and so on. Increasing researches are conducted on data mining and machine learning for uncovering and predicting related domain knowledge. Protein-protein interaction is one of the main areas in bioinformatics as it is the basis of the biological functions. However, most pathogen-host proteinprotein interactions, which would be able to reveal much more infectious mechanisms between pathogen and host, are still up for further investigation. Considering a decent feature representation of pathogen-host protein-protein interactions (PHPPI), currently there is not a well structured database for research purposes, not even for infection mechanism studies for different species of pathogens. In this paper, we will survey the PHPPI researches and construct a public PHPPI dataset by ourselves for future research. It results in an utterly big and imbalanced data set associated with high dimension and large quantity. Several machine learning methodologies are also discussed in this paper to imply possible analytics solutions in near future. This paper contributes to a new, yet challenging, research area in applying data analytic technologies in bioinformatics, by learning and predicting pathogen-host proteinprotein interactions.

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VirusMentha: a new resource for virus-host protein interactions

Cited by 151 publications

References 24 publications

Proteomic approaches to uncovering virus–host protein interactions during the progression of viral infection

Proteomic approaches to uncovering virus–host protein interactions during the progression of viral infection

Protein-protein interactions of human viruses

Towards Data Analytics of Pathogen-Host Protein-Protein Interaction: A Survey

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