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

Información del proyecto

ProCovar

Identificador del acuerdo de subvención: 695558

DOI 10.3030/695558

Proyecto cerrado

Fecha de la firma de la CE 6 Septiembre 2016

Fecha de inicio 1 Noviembre 2016

Fecha de finalización 30 Abril 2022

Financiado con arreglo a

• EXCELLENT SCIENCE - European Research Council (ERC)

Coste total € 2 433 679,00

Aportación de la UE € 2 433 679,00

Coordinado por

UNIVERSITY COLLEGE LONDON
United Kingdom

Publicaciones

Peer reviewed articles (9)

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

Autores: Lewis Moffat; Lewis Moffat; David T. Jones; David T. Jones
Publicado en: Bioinformatics, Edición 3, 2021, ISSN 1367-4803
Editor: Oxford University Press
DOI: 10.1093/bioinformatics/btab491

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

Autores: David T Jones, Shaun M Kandathil
Publicado en: Bioinformatics, Edición Volume 34, Edición 19, 2018, Página(s) 3308-3315, ISSN 1367-4803
Editor: Oxford University Press
DOI: 10.1093/bioinformatics/bty341

Design of metalloproteins and novel protein folds using variational autoencoders

Autores: Joe G. Greener, Lewis Moffat, David T Jones
Publicado en: Scientific Reports, Edición 8/1, 2018, ISSN 2045-2322
Editor: 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

Autores: Joe G. Greener, Shaun M. Kandathil, David T. Jones
Publicado en: Nature Communications, Edición 10/1, 2019, ISSN 2041-1723
Editor: Nature Publishing Group
DOI: 10.1038/s41467-019-11994-0

Prediction of interresidue contacts with DeepMetaPSICOV in CASP13

Autores: Shaun M. Kandathil, Joe G. Greener, David T. Jones
Publicado en: Proteins: Structure, Function, and Bioinformatics, Edición 87/12, 2019, Página(s) 1092-1099, ISSN 0887-3585
Editor: Wiley-Liss Inc
DOI: 10.1002/prot.25779

Recent developments in deep learning applied to protein structure prediction

Autores: Shaun M. Kandathil, Joe G. Greener, David T. Jones
Publicado en: Proteins: Structure, Function, and Bioinformatics, Edición 87/12, 2019, Página(s) 1179-1189, ISSN 0887-3585
Editor: Wiley-Liss Inc
DOI: 10.1002/prot.25824

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

Autores: Joe G Greener; David T. Jones
Publicado en: PLoS ONE, Edición 4, 2021, ISSN 1932-6203
Editor: Public Library of Science
DOI: 10.1371/journal.pone.0256990

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

Autores: Shaun M. Kandathil, Joe G. Greener, Andy M. Lau, David T. Jones
Publicado en: PNAS, 2022, ISSN 1091-6490
Editor: National Academy of Sciences
DOI: 10.1073/pnas.2113348119

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

Autores: Cen Wan; Cen Wan; David T. Jones; David T. Jones
Publicado en: Nature Machine Intelligence, Edición 5, 2020, ISSN 2522-5839
Editor: Springer Nature
DOI: 10.1101/730143