VirusMINT: a viral protein interaction database

Chatr‐aryamontri, Andrew; Céol, Arnaud; Peluso, Daniele; Nardozza, Aurelio Pio; Panni, Simona; Sacco, Francesca; Tinti, Michele; Smolyar, Alex; Castagnoli, Luisa; Vidal, Marc; Cusick, Michael E.

doi:10.1093/nar/gkn739

Cited by 185 publications

(176 citation statements)

References 10 publications

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“…On this point, an interesting finding was the observed lopinavir-induced down-regulation of GFAP which is known to be capable of inhibiting the proteasome [25]. Since p53 is a target of high-risk HPV E6, we searched the Virus Molecular INTeraction (VirusMINT) database [26] for any other known E6 and E7 targeted proteins which are affected by lopinavir treatment (Table 1). At this time, only Tp53 is a confirmed target of high-risk HPV.…”

Section: Discussionmentioning

confidence: 99%

Lopinavir Up-Regulates Expression of the Antiviral Protein Ribonuclease L in Human Papillomavirus-Positive Cervical Carcinoma Cells

et al. 2010

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Section: Discussionmentioning

confidence: 99%

Lopinavir Up-Regulates Expression of the Antiviral Protein Ribonuclease L in Human Papillomavirus-Positive Cervical Carcinoma Cells

et al. 2010

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“…In addition to the general interaction data, SDREM uses condition-specific time-series data and a small set of proteins that are known to sense the environmental stress, interact with the infecting agent, or play some other role in initiating the response as input. In many cases, such proteins are either known (Kanehisa and Goto 2000) or can be experimentally determined using mass spectrometry or yeast two-hybrid experiments (e.g., in the response to viral infection) (Chatr-aryamontri et al 2009;Fu et al 2009;Mukhtar et al 2011).…”

Section: Resultsmentioning

confidence: 99%

Linking the signaling cascades and dynamic regulatory networks controlling stress responses

et al. 2012

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Accurate models of the cross-talk between signaling pathways and transcriptional regulatory networks within cells are essential to understand complex response programs. We present a new computational method that combines condition-specific time-series expression data with general protein interaction data to reconstruct dynamic and causal stress response networks. These networks characterize the pathways involved in the response, their time of activation, and the affected genes. The signaling and regulatory components of our networks are linked via a set of common transcription factors that serve as targets in the signaling network and as regulators of the transcriptional response network. Detailed case studies of stress responses in budding yeast demonstrate the predictive power of our method. Our method correctly identifies the core signaling proteins and transcription factors of the response programs. It further predicts the involvement of additional transcription factors and other proteins not previously implicated in the response pathways. We experimentally verify several of these predictions for the osmotic stress response network. Our approach requires little condition-specific data: only a partial set of upstream initiators and time-series gene expression data, which are readily available for many conditions and species. Consequently, our method is widely applicable and can be used to derive accurate, dynamic response models in several species.

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“…Endogenous PPI data were assembled from the IntAct database [15]; there are a total of 35,303 endogenous interactions. Human-virus PPI data were taken principally from VirusMINT [16] and supplemented with additional reports from IntAct; this produced an initial set of 1,814 exogenous interactions. Exogenous interactions were filtered to remove redundancy (e.g., orthologous viral proteins from different strains with the same human protein target).…”

Section: Constructing Binary and Structural Human-virus Interaction Nmentioning

confidence: 99%

Structural Models for Host-Pathogen Protein-Protein Interactions: Assessing Coverage and Bias

Franzosa

Xia

2011

Biocomputing 2012

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Recently, we applied structural systems biology to host-pathogen interaction and constructed the humanvirus structural interaction network (SIN) based on a combination of solved structures and homology models. Subsequent analysis of the human-virus SIN revealed significant differences between antagonistic human-virus and cooperative within-human protein-protein interactions (PPIs). Although the SIN approach is advantageous due to the complementary nature of 3D structure and network data, integration of these data sources is associated with two potential issues: reduced coverage of the full human-virus PPI network, and the introduction of specific biases from structure determination. In this work, we evaluate the impact of these issues by comparing the growth and properties of human-virus and within-human PPI networks with and without structural models. We find that although the human-virus SIN is small in size, it is largely depleted for false positives, which are common in the full network. In addition, the SIN shows potential for major growth in the near future. Furthermore, compared to the full network, the coverage of viral species in the human-virus SIN is large relative to its size, suggesting that it may be a less biased sampling of the universe of human-virus PPIs. Next, we systematically compare structural versus full networks of human-virus and within-human PPIs in terms of functional, physicochemical, and network properties. We find that although there exist biases inherent to the structural approach, such biases tend to affect both human-virus and within-human PPIs equally. As a result, the significant differences between structured human-virus and within-human PPI networks are never contradicted by the full networks. Collectively, these results suggest that a structural approach to host-pathogen systems biology is not only justified, but also highly complementary to previous approaches. In particular, conclusions drawn from direct comparisons of host-virus and host-host PPIs within the SIN are minimally confounded by the inherent biases of the structural approach.

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VirusMINT: a viral protein interaction database

Cited by 185 publications

References 10 publications

Lopinavir Up-Regulates Expression of the Antiviral Protein Ribonuclease L in Human Papillomavirus-Positive Cervical Carcinoma Cells

Lopinavir Up-Regulates Expression of the Antiviral Protein Ribonuclease L in Human Papillomavirus-Positive Cervical Carcinoma Cells

Linking the signaling cascades and dynamic regulatory networks controlling stress responses

Structural Models for Host-Pathogen Protein-Protein Interactions: Assessing Coverage and Bias

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