Objectif 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. Champ scientifique natural sciencesbiological sciencesbiochemistrybiomoleculesproteinsproteomicsnatural sciencesbiological sciencesbiochemistrybiomoleculesnucleic acidsnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencesbiological sciencesgeneticsRNAnatural scienceschemical sciencesorganic chemistryamines Mots‑clés Protein co-evolution protein structure prediction transmembrane proteins natively disordered proteins protein design correlated mutations protein-protein interactions Programme(s) H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC) Main Programme Thème(s) ERC-ADG-2015 - ERC Advanced Grant Appel à propositions ERC-2015-AdG Voir d’autres projets de cet appel Régime de financement ERC-ADG - Advanced Grant Institution d’accueil UNIVERSITY COLLEGE LONDON Contribution nette de l'UE € 2 433 679,00 Adresse GOWER STREET WC1E 6BT London Royaume-Uni Voir sur la carte Région London Inner London — West Camden and City of London Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 2 433 679,00 Bénéficiaires (1) Trier par ordre alphabétique Trier par contribution nette de l'UE Tout développer Tout réduire UNIVERSITY COLLEGE LONDON Royaume-Uni Contribution nette de l'UE € 2 433 679,00 Adresse GOWER STREET WC1E 6BT London Voir sur la carte Région London Inner London — West Camden and City of London Type d’activité Higher or Secondary Education Establishments Liens Contacter l’organisation Opens in new window Site web Opens in new window Participation aux programmes de R&I de l'UE Opens in new window Réseau de collaboration HORIZON Opens in new window Coût total € 2 433 679,00