NETWORK MODULATION

Protein interaction interference: linking chemical biology to short linear motifs

 Coordinatore THE UNIVERSITY OF EDINBURGH 

 Organization address address: OLD COLLEGE, SOUTH BRIDGE
city: EDINBURGH
postcode: EH8 9YL

contact info
Titolo: Ms.
Nome: Angela
Cognome: Noble
Email: send email
Telefono: +44 131 650 9024
Fax: +44 131 650 9023

 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 178˙307 €
 EC contributo 178˙307 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2007-2-1-IEF
 Funding Scheme MC-IEF
 Anno di inizio 2009
 Periodo (anno-mese-giorno) 2009-05-26   -   2011-05-25

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE UNIVERSITY OF EDINBURGH

 Organization address address: OLD COLLEGE, SOUTH BRIDGE
city: EDINBURGH
postcode: EH8 9YL

contact info
Titolo: Ms.
Nome: Angela
Cognome: Noble
Email: send email
Telefono: +44 131 650 9024
Fax: +44 131 650 9023

UK (EDINBURGH) coordinator 0.00

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 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

interaction    motif    function    datasets    mediated    pathways    small    modulated    chemical    genetic    molecules    principles    protein    interactions   

 Obiettivo del progetto (Objective)

'Although our knowledge about the function of basic biological elements such as genes and proteins has grown considerably, we lack a comprehensive picture of how these elements act in coordination, and our ability to modulate their function is still limited. Here, I propose a global study of motif-mediated protein interactions that aims to find interactions that can be modulated by small molecules or peptides, and their roles in the organization of signaling pathways. I will use one of the best-understood eukaryotic model organism Saccharomyces cerevisiae, for which several large-scale functional, interaction and chemical-genetic datasets are available. In addition, the genome sequences for more than a dozen of yeast species are largely complete. I will identify motif-mediated interactions using these proteomic and comparative data, and integrate them with large-scale chemical-genetic datasets to explore the correlations between genetic interactions and small molecules. I will test my predictions and study cellular pathways cross-talk using modelling and combinatorial protein interaction interference. With such a systematic approach, I aim to reveal the fundamental principles of how protein function can be modulated, and how these principles might carry over to vertebrates.'

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