Kryshtafovych, A;
Moult, J;
Billings, WM;
Della Corte, D;
Fidelis, K;
Kwon, S;
Olechnovič, K;
... CASP-COVID participants; + view all
(2021)
Modeling SARS-CoV-2 proteins in the CASP-commons experiment.
Proteins
, 89
(12)
pp. 1987-1996.
10.1002/prot.26231.
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Abstract
Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).
Type: | Article |
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Title: | Modeling SARS-CoV-2 proteins in the CASP-commons experiment |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1002/prot.26231 |
Publisher version: | http://dx.doi.org/10.1002/prot.26231 |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | CASP, COVID, EMA, SARS-CoV-2, model accuracy, protein structure prediction |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
URI: | https://discovery.ucl.ac.uk/id/eprint/10138687 |
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