Human cytomegalovirus interactome analysis identifies degradation hubs, domain associations and viral protein functions
- University of Cambridge, United Kingdom
- MRC-University of Glasgow Centre for Virus Research, United Kingdom
- Cardiff University School of Medicine, United Kingdom
- Harvard Medical School, United States
Abstract
Human cytomegalovirus (HCMV) extensively modulates host cells, downregulating >900 human proteins during viral replication and degrading ≥133 proteins shortly after infection. The mechanism of degradation of most host proteins remains unresolved, and the functions of many viral proteins are incompletely characterised. We performed a mass spectrometry-based interactome analysis of 169 tagged, stably-expressed canonical strain Merlin HCMV proteins, and two non-canonical HCMV proteins, in infected cells. This identified a network of >3,400 virus-host and >150 virus-virus protein interactions, providing insights into functions for multiple viral genes. Domain analysis predicted binding of the viral UL25 protein to SH3 domains of NCK Adaptor Protein-1. Viral interacting proteins were identified for 31/133 degraded host targets. Finally, the uncharacterised, non-canonical ORFL147C protein was found to interact with elements of the mRNA splicing machinery, and a mutational study suggested its importance in viral replication. The interactome data will be important for future studies of herpesvirus infection.
Data availability
All data analysed during this study are included in the manuscript and supporting files. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://www.proteomexchange.org/) via the PRIDE (Vizcaino et al., 2016) partner repository with the dataset identifier PXD014845.
Article and author information
Author details
Gavin WG Wilkinson
Funding
Wellcome (108070/Z/15/Z)
- Michael P Weekes
Medical Research Council (MR/L018373/1)
- Eddie CY Wang
- Gavin WG Wilkinson
- Richard J Stanton
Medical Research Council (MR/P001602/1)
- Eddie CY Wang
- Gavin WG Wilkinson
- Richard J Stanton
Wellcome (WT090323MA)
- Eddie CY Wang
- Gavin WG Wilkinson
- Richard J Stanton
Medical Research Council (MC_UU_12014/3)
- Andrew J Davison
National Institutes of Health (U24 HG006673)
- Edward L Huttlin
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Piet Maes, KU Leuven, Rega Institute for Medical Research, Belgium
Version history
- Received: July 3, 2019
- Accepted: December 24, 2019
- Accepted Manuscript published: December 24, 2019 (version 1)
- Version of Record published: January 14, 2020 (version 2)
Copyright
© 2019, Nobre et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,946
- views
-
- 781
- downloads
-
- 78
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Human cytomegalovirus interactome analysis identifies degradation hubs, domain associations and viral protein functions
eLife 8:e49894.
https://doi.org/10.7554/eLife.49894
Further reading
-
- Biochemistry and Chemical Biology
- Computational and Systems Biology
Previously, Tuller et al. found that the first 30–50 codons of the genes of yeast and other eukaryotes are slightly enriched for rare codons. They argued that this slowed translation, and was adaptive because it queued ribosomes to prevent collisions. Today, the translational speeds of different codons are known, and indeed rare codons are translated slowly. We re-examined this 5’ slow translation ‘ramp.’ We confirm that 5’ regions are slightly enriched for rare codons; in addition, they are depleted for downstream Start codons (which are fast), with both effects contributing to slow 5’ translation. However, we also find that the 5’ (and 3’) ends of yeast genes are poorly conserved in evolution, suggesting that they are unstable and turnover relatively rapidly. When a new 5’ end forms de novo, it is likely to include codons that would otherwise be rare. Because evolution has had a relatively short time to select against these codons, 5’ ends are typically slightly enriched for rare, slow codons. Opposite to the expectation of Tuller et al., we show by direct experiment that genes with slowly translated codons at the 5’ end are expressed relatively poorly, and that substituting faster synonymous codons improves expression. Direct experiment shows that slow codons do not prevent downstream ribosome collisions. Further informatic studies suggest that for natural genes, slow 5’ ends are correlated with poor gene expression, opposite to the expectation of Tuller et al. Thus, we conclude that slow 5’ translation is a ‘spandrel’--a non-adaptive consequence of something else, in this case, the turnover of 5’ ends in evolution, and it does not improve translation.
-
- Computational and Systems Biology
Accurate prediction of the structurally diverse complementarity determining region heavy chain 3 (CDR-H3) loop structure remains a primary and long-standing challenge for antibody modeling. Here, we present the H3-OPT toolkit for predicting the 3D structures of monoclonal antibodies and nanobodies. H3-OPT combines the strengths of AlphaFold2 with a pre-trained protein language model and provides a 2.24 Å average RMSDCα between predicted and experimentally determined CDR-H3 loops, thus outperforming other current computational methods in our non-redundant high-quality dataset. The model was validated by experimentally solving three structures of anti-VEGF nanobodies predicted by H3-OPT. We examined the potential applications of H3-OPT through analyzing antibody surface properties and antibody–antigen interactions. This structural prediction tool can be used to optimize antibody–antigen binding and engineer therapeutic antibodies with biophysical properties for specialized drug administration route.
