Literature DB >> 22710167

Uncovering new aspects of protein interactions through analysis of specificity landscapes in peptide recognition domains.

David Gfeller1.   

Abstract

Protein interactions underlie all biological processes. An important class of protein interactions, often observed in signaling pathways, consists of peptide recognition domains binding short protein segments on the surface of their target proteins. Recent developments in experimental techniques have uncovered many such interactions and shed new lights on their specificity. To analyze these data, novel computational methods have been introduced that can accurately describe the specificity landscape of peptide recognition domains and predict new interactions. Combining large-scale analysis of binding specificity data with structure-based modeling can further reveal new biological insights into the molecular recognition events underlying signaling pathways.
Copyright © 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

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Year:  2012        PMID: 22710167     DOI: 10.1016/j.febslet.2012.03.054

Source DB:  PubMed          Journal:  FEBS Lett        ISSN: 0014-5793            Impact factor:   4.124


  4 in total

1.  Engineering selective competitors for the discrimination of highly conserved protein-protein interaction modules.

Authors:  Charlotte Rimbault; Kashyap Maruthi; Christelle Breillat; Camille Genuer; Sara Crespillo; Virginia Puente-Muñoz; Ingrid Chamma; Isabel Gauthereau; Ségolène Antoine; Coraline Thibaut; Fabienne Wong Jun Tai; Benjamin Dartigues; Dolors Grillo-Bosch; Stéphane Claverol; Christel Poujol; Daniel Choquet; Cameron D Mackereth; Matthieu Sainlos
Journal:  Nat Commun       Date:  2019-10-04       Impact factor: 14.919

2.  GibbsCluster: unsupervised clustering and alignment of peptide sequences.

Authors:  Massimo Andreatta; Bruno Alvarez; Morten Nielsen
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

Review 3.  Predicting Antigen Presentation-What Could We Learn From a Million Peptides?

Authors:  David Gfeller; Michal Bassani-Sternberg
Journal:  Front Immunol       Date:  2018-07-25       Impact factor: 7.561

4.  Prediction and experimental characterization of nsSNPs altering human PDZ-binding motifs.

Authors:  David Gfeller; Andreas Ernst; Nick Jarvik; Sachdev S Sidhu; Gary D Bader
Journal:  PLoS One       Date:  2014-04-10       Impact factor: 3.240

  4 in total

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