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

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