covid19-ace2-variants
Research code and data to interpret the effect of ACE2 variants on SARS-CoV-2 infection
Science Score: 62.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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✓DOI references
Found 6 DOI reference(s) in README -
✓Academic publication links
Links to: biorxiv.org, zenodo.org -
○Academic email domains
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✓Institutional organization owner
Organization bartongroup has institutional domain (www.compbio.dundee.ac.uk) -
○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (7.1%) to scientific vocabulary
Repository
Research code and data to interpret the effect of ACE2 variants on SARS-CoV-2 infection
Basic Info
- Host: GitHub
- Owner: bartongroup
- License: mit
- Language: Jupyter Notebook
- Default Branch: master
- Size: 4.15 MB
Statistics
- Stars: 1
- Watchers: 6
- Forks: 0
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
COVID-19 ACE2 Variants
Author: Stuart MacGowan (smacgowan@dundee.ac.uk)
Overview
This repository contains research code and data used to analyse the impact of ACE2 variants on SARS-CoV-2 infection. The primary focus is on exploring how missense variants in ACE2 influence interaction with the SARS-CoV-2 Spike protein and their potential contribution to genetic risk factors in COVID-19.
Research Paper
The detailed analysis and figures related to this study are available in the paper titled: - "Missense variants in ACE2 are predicted to encourage and inhibit interaction with SARS-CoV-2 Spike and contribute to genetic risk in COVID-19".
This work is available as a preprint on bioRxiv.
Published Work
The findings of this research have been published in the following journal: - MacGowan SA, Barton MI, Kutuzov M, Dushek O, van der Merwe PA, et al. (2022). "Missense variants in human ACE2 strongly affect binding to SARS-CoV-2 Spike providing a mechanism for ACE2 mediated genetic risk in Covid-19: A case study in affinity predictions of interface variants." PLOS Computational Biology 18(3): e1009922. Link to Publication
Repository Contents
ACE2-variants-structure-and-assays.ipynb: Jupyter notebook containing the analysis and figures related to the study.
Owner
- Name: Geoff Barton's Computational Biology Group
- Login: bartongroup
- Kind: organization
- Location: Dundee, Scotland, UK
- Website: https://www.compbio.dundee.ac.uk
- Twitter: bartongrp
- Repositories: 57
- Profile: https://github.com/bartongroup
Citation (CITATION.bib)
@ARTICLE{MacGowan2022-jy,
title = "Missense variants in human {ACE2} strongly affect binding to
{SARS-CoV-2} Spike providing a mechanism for {ACE2} mediated
genetic risk in Covid-19: A case study in affinity predictions
of interface variants",
author = "MacGowan, Stuart A and Barton, Michael I and Kutuzov, Mikhail
and Dushek, Omer and van der Merwe, P Anton and Barton,
Geoffrey J",
affiliation = "Division of Computational Biology, School of Life Sciences,
University of Dundee, Dow Street, Dundee, Scotland, United
Kingdom. Sir William Dunn School of Pathology, South Parks
Road, University of Oxford, Oxford, Oxfordshire, United
Kingdom.",
journal = "PLoS Comput. Biol.",
publisher = "Public Library of Science San Francisco, CA USA",
volume = 18,
number = 3,
pages = "e1009922",
month = mar,
year = 2022,
url = "http://dx.doi.org/10.1371/journal.pcbi.1009922",
language = "en",
issn = "1553-734X, 1553-7358",
pmid = "35235558",
doi = "10.1371/journal.pcbi.1009922",
pmc = "PMC8920257"
}