Tag Archives: protein-protein interaction

Journal club: Human enterovirus 71 protein interaction network prompts antiviral drug repositioning

Viruses are small infectious agents, which possess genetic code but have no independent metabolism. They propagate by infecting host cells and hijacking their machinery, often killing the cells in the process. One of the key challenges in developing effective antiviral therapies is the high mutation rate observed in viral genomes. A way to circumvent this issue is to target host proteins involved in virion assembly (also known as essential host factors, or EHFs), rather than the virion itself.

In their recent paper, Lu Han et al. [1] consider human virus protein-protein interactions in order to explore possible host drug targets, as well as drugs which could potentially be re-purposed as antivirals. Their study focuses on enterovirus 71 (EV71), one of the leading causes of hand, foot, and mouth disease.

Human virus protein-protein interactions and target identification

EHFs are typically detected by knocking out genes in the host organism and determining which of the knockouts result in virus control. Low repeat rates and high costs make this technique unsuitable for large scale studies. Instead, the authors use an extensive yeast two-hybrid screen to identify 37 unique protein-protein interactions between 7 of the 11 virus proteins and 29 human proteins. Pathway enrichment suggests that the human proteins interacting with EV71 are involved in a wide range of functions in the cell. Despite this range in functionality, as many as 17 are also associated with other viruses, either through known physical interactions, or as EHFs (Fig 1).

Fig. 1. Interactions between viral and human proteins (denoted as EIPs), and their connection to different viruses.

One of these is ATP6V0C, a subunit of vacuole ATP-ase. It interacts with the EV71 3A protein, and is a known essential host factor for five other viruses. The authors analyse the interaction further, and show that downregulating ATP6V0C gene expression inhibits EV71 propagation, while overexpressing it enhances virus propagation. Moreover, treating cells with bafilomycin A1, a selective inhibitor for vacuole ATP-ase, inhibits EV71 infection in a dose-dependent manner. The paper suggests that therefore ATP6V0C may be a suitable drug target, not only against EV71, but also perhaps even for a broad-spectrum antiviral. While this is encouraging, bafilomycin A1 is a toxic antibiotic used in research, but not suitable for human or drug use. Rather than exploring other compounds targeting ATP6V0C, the paper shifts focus to re-purposing known drugs as antivirals.

Drug prediction using CMap

A potential antiviral will ideally disturb most or all interactions between host cell and virion. One way to do this would be to inhibit the proteins known to interact with EV71. In order to check whether any known compounds already do so, the authors apply gene set enrichment analysis (GSEA) to data from the connectivity map (CMap). CMap is a database of gene expression profiles representing cellular response to a set of 1309 different compounds.  Enrichment analysis of the database reveals 27 potential EV71 drugs, of which the authors focus on the top ranking result, tanespimycin.

Tanespimycin is an orphan cancer drug, originally designed to target tumor cells by inhibiting HSP90. Its complex effects on the cell, however, may make it an effective antiviral. Following their CMap analysis, the authors show that tanespimycin reduces viral count and virus-induced cytopathic effects in a dose-dependent manner, without evidence of cytotoxicity.

Overall, the paper presents two different ways to think about target investigation and drug choice in antiviral therapeutics — by integrating different types of known host virus protein-protein interactions, and by analysing cell response to known compounds. While extensive further study is needed to determine whether the results are directly clinically relevant to the treatment of EV71, the paper shows how  interaction data analysis can be employed in drug discovery.


[1] Han, Lu, et al. “Human enterovirus 71 protein interaction network prompts antiviral drug repositioning.” Scientific Reports 7 (2017).


Is contacts-based protein-protein affinity prediction the way forward?

The binding affinity of protein interactions is useful information for a range of protein engineering and protein-protein interaction (PPI) network challenges. Obvious applications include the development of therapeutic antibodies to given drug targets or the engineering of novel interfaces for synthetic protein complexes. An accurate model would furthermore allow us to predict a large proportion of affinities in existing PPI networks, and enable the identification of new PPIs, which is critical for our ability to model protein network dynamics effectively.


“The design of an ideal scoring function for protein−protein docking that would also predict the binding affinity of a complex is one of the challenges in structural proteomics.” Adapted from Kastritis, Panagiotis L., and Alexandre MJJ Bonvin. Journal of proteome research 9.5 (2010): 2216-2225.

In last week’s paper a new binding-affinity prediction method based on interfacial contact information was described. Contacts have long been used to in docking methods but surprisingly this was the first time that binding affinity was predicted with them. Largely, this was due to the lack of a suitable benchmark data set that contained structural as well as affinity data . In 2011, however, Kastritis et al. presented a curated database of 144 non-redundant protein–protein complexes with experimentally determined Kd (ΔG) as well as x-ray structures.
Using this data set they trained and validated their method, compared it against others and concluded that interfacial contacts `can be considered the best structural property to describe binding strength`. This claim may be true but as we discussed in the meeting there is still some work to do before we take this model an run with it. A number of flags were raised:

  • Classification of experimental methods into reliable and non-reliable is based on what gives the best results with their method. Given that different types of protein complexes are often measured with different methods, some protein classes for which contact-based predictions are less effective may be excluded.
  • Number of parameters for model 6 is problematic without exact AIC information. As Lyuba righlty pointed out, the intercept in model 6 `explodes`. It is no surprise that the correlation improves with more parameters. Despite their AIC analysis, overfitting is still a worry due to the lack of details presented in the paper.


  • Comparison against other methods is biased in their favour; their method was trained on the same data set, the others were not. In order to ensure a fair comparison all methods should be trained on the same data set. Of course this is hard to do in practice, but the fact remains that a comparison of methods that has been trained on different data sets will be flawed.

Paper: Vangone, A., Bonvin, A. M. J. J., Alberts, B., Aloy, P., Russell, R., Andrusier, N., … Zhou, Y. (2015). Contacts-based prediction of binding affinity in protein-protein complexes. eLife, 4, e07454. http://doi.org/10.7554/eLife.07454