ArticleJournal of molecular evolution2018
Ancestral and Compensatory Mutations that Promote Antiviral Resistance in Influenza N1 Neuraminidase Revealed by a Phylonumerics Approach.
Article in Journal of molecular evolution, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Structural Phylogenetics with Protein Mass Spectrometry: A Proof-of-Concept.The protein journal · 2024Article
- 25 Years Responding to Respiratory and Other Viruses with Mass Spectrometry.Mass spectrometry (Tokyo, Japan) · 2023Review
- Analysis of bacterial biotyping datasets with a mass-based phylonumerics approach.Analytical and bioanalytical chemistry · 2022Article
- Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.ChemRxiv : the preprint server for chemistry · 2020Article
- Why Glycosylation Matters in Building a Better Flu Vaccine.Molecular & cellular proteomics : MCP · 2019Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Implementation of a new phylonumerics approach to construct a mass tree representing over 6000 H1N1 human influenza strains has enabled ancestral and compensatory descendant mutations to be identified in N1 neuraminidase that promote antiviral resistance and restore viral fitness. Adjacent to the H275Y resistance mutation site, mutations S299A and S247N, respectively, lead the evolution of oseltamivir-resistant strains and restore viral fitness to those strains thereafter. Importantly the mass tree phylonumerics approach can identify such mutations globally, without any positional bias, so that functionally linked or compensatory mutations remote in the sequence or structure of the protein can be identified and interrogated. This is achieved using mass map datasets commonly employed for protein identification in proteomics applications, thus avoiding the need for either gene or protein sequences that are central to other phylogenetic methods.
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Registered trials
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