Exploring new applications of amino acid covariation analysis in modelling proteins and their complexes

Project Information

ProCovar

Grant agreement ID: 695558

DOI 10.3030/695558

Project closed

EC signature date 6 September 2016

Start date 1 November 2016

End date 30 April 2022

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost € 2 433 679,00

EU contribution € 2 433 679,00

Coordinated by

UNIVERSITY COLLEGE LONDON
United Kingdom

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Peer reviewed articles (9)

Increasing the Accuracy of Single Sequence Prediction Methods Using a Deep Semi-Supervised Learning Framework.

Author(s): Lewis Moffat; Lewis Moffat; David T. Jones; David T. Jones
Published in: Bioinformatics, Issue 3, 2021, ISSN 1367-4803
Publisher: Oxford University Press
DOI: 10.1093/bioinformatics/btab491

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

Author(s): David T Jones, Shaun M Kandathil
Published in: Bioinformatics, Issue Volume 34, Issue 19, 2018, Page(s) 3308-3315, ISSN 1367-4803
Publisher: Oxford University Press
DOI: 10.1093/bioinformatics/bty341

Design of metalloproteins and novel protein folds using variational autoencoders

Author(s): Joe G. Greener, Lewis Moffat, David T Jones
Published in: Scientific Reports, Issue 8/1, 2018, ISSN 2045-2322
Publisher: Nature Publishing Group
DOI: 10.1038/s41598-018-34533-1

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

Author(s): Joe G. Greener, Shaun M. Kandathil, David T. Jones
Published in: Nature Communications, Issue 10/1, 2019, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-019-11994-0

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

Author(s): Shaun M. Kandathil, Joe G. Greener, David T. Jones
Published in: Proteins: Structure, Function, and Bioinformatics, Issue 87/12, 2019, Page(s) 1092-1099, ISSN 0887-3585
Publisher: Wiley-Liss Inc
DOI: 10.1002/prot.25779

Recent developments in deep learning applied to protein structure prediction

Author(s): Shaun M. Kandathil, Joe G. Greener, David T. Jones
Published in: Proteins: Structure, Function, and Bioinformatics, Issue 87/12, 2019, Page(s) 1179-1189, ISSN 0887-3585
Publisher: Wiley-Liss Inc
DOI: 10.1002/prot.25824

Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins.

Author(s): Joe G Greener; David T. Jones
Published in: PLoS ONE, Issue 4, 2021, ISSN 1932-6203
Publisher: Public Library of Science
DOI: 10.1371/journal.pone.0256990

Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins

Author(s): Shaun M. Kandathil, Joe G. Greener, Andy M. Lau, David T. Jones
Published in: PNAS, 2022, ISSN 1091-6490
Publisher: National Academy of Sciences
DOI: 10.1073/pnas.2113348119

Improving protein function prediction with synthetic feature samples created by generative adversarial networks

Author(s): Cen Wan; Cen Wan; David T. Jones; David T. Jones
Published in: Nature Machine Intelligence, Issue 5, 2020, ISSN 2522-5839
Publisher: Springer Nature
DOI: 10.1101/730143