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ProCovar SIGNED

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

Total Cost €

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EC-Contrib. €

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Partnership

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Project "ProCovar" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY COLLEGE LONDON 

Organization address
address: GOWER STREET
city: LONDON
postcode: WC1E 6BT
website: n.a.

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://bioinf.cs.ucl.ac.uk/procovar
 Total cost 2˙433˙679 €
 EC max contribution 2˙433˙679 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2015-AdG
 Funding Scheme ERC-ADG
 Starting year 2016
 Duration (year-month-day) from 2016-11-01   to  2021-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY COLLEGE LONDON UK (LONDON) coordinator 2˙433˙679.00

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 Project objective

As a result of the rapid development of next generation sequencing, we now have access to hundreds and often many thousands of sequences which belong to the same family. Such a large amount of sequence data for a particular protein family, along with recent developments in computational statistics, enables an entirely new kind of evolutionary analysis to be performed on sequences, where for the first time we can compute statistically significant networks of correlated mutations. The proposal describes an integrated programme of work to fully explore the potential applications of the new amino acid covariation techniques in predicting aspects of protein structure and function. A particular emphasis in this proposal are proteins which are difficult to study by experimental techniques i.e. disordered proteins, transmembrane proteins and large complexes. The first objective will be to explore key developments in the underpinning algorithms, tackling both the issue of needing very large numbers of homologous sequences and also the downstream 3-D embedding to produce viable models. The second objective will involve experimental work with a collaborator where the idea that de novo protein design techniques might be exploited to artificially expand the set of available sequences for a given proto-family will be explored. The third objective will focus specifically on transmembrane protein modelling, where covariation-based approaches have proven to be highly effective. Here the goal will be to extend our existing FILM3 method to encompass both beta-barrel type TM proteins, but also to try to handle the issue of homomultimers, which is a critical aspect of TM protein modelling as so many families are known to adopt higher orders of structure than the fold level alone. Finally, applications of covariation analysis to probing multiple conformations of disordered proteins will be developed, with a specific focus on interactions of disordered proteins with DNA and RNA.

 Publications

year authors and title journal last update
List of publications.
2019 Joe G. Greener, Shaun M. Kandathil, David T. Jones
Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-019-11994-0
Nature Communications 10/1 2020-03-05
2019 Shaun M. Kandathil, Joe G. Greener, David T. Jones
Prediction of interresidue contacts with DeepMetaPSICOV in CASP13
published pages: 1092-1099, ISSN: 0887-3585, DOI: 10.1002/prot.25779
Proteins: Structure, Function, and Bioinformatics 87/12 2020-03-05
2019 Shaun M. Kandathil, Joe G. Greener, David T. Jones
Recent developments in deep learning applied to protein structure prediction
published pages: 1179-1189, ISSN: 0887-3585, DOI: 10.1002/prot.25824
Proteins: Structure, Function, and Bioinformatics 87/12 2020-03-05
2018 David T Jones, Shaun M Kandathil
High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features
published pages: , ISSN: 1367-4803, DOI: 10.1093/bioinformatics/bty341
Bioinformatics 2019-06-13
2018 Joe G. Greener, Lewis Moffat, David T Jones
Design of metalloproteins and novel protein folds using variational autoencoders
published pages: , ISSN: 2045-2322, DOI: 10.1038/s41598-018-34533-1
Scientific Reports 8/1 2019-08-05

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