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Understanding Protein-Protein Interactions Using Local Structural Features

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Authors:
  • Planas-Iglesias, Joan ;
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    unknown
  • Bonet, Jaume ;
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    unknown
  • García-García, Javier ;
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    unknown
  • Marín-López, Manuel A ;
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    unknown
  • Feliu, Elisenda ;
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    Orcid logo0000-0001-7205-6511
    Department of Mathematical Sciences, Faculty of Science, Københavns Universitet
  • Oliva, Baldo
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    unknown
DOI:
10.1016/j.jmb.2013.01.014
Abstract:
Protein-protein interactions (PPIs) play a relevant role among the different functions of a cell. Identifying the PPI network of a given organism (interactome) is useful to shed light on the key molecular mechanisms within a biological system. In this work, we show the role of structural features (loops and domains) to comprehend the molecular mechanisms of PPIs. A paradox in protein-protein binding is to explain how the unbound proteins of a binary complex recognize each other among a large population within a cell and how they find their best docking interface in a short timescale. We use interacting and non-interacting protein pairs to classify the structural features that sustain the binding (or non-binding) behavior. Our study indicates that not only the interacting region but also the rest of the protein surface are important for the interaction fate. The interpretation of this classification suggests that the balance between favoring and disfavoring structural features determines if a pair of proteins interacts or not. Our results are in agreement with previous works and support the funnel-like intermolecular energy landscape theory that explains PPIs. We have used these features to score the likelihood of the interaction between two proteins and to develop a method for the prediction of PPIs. We have tested our method on several sets with unbalanced ratios of interactions and non-interactions to simulate real conditions, obtaining accuracies higher than 25% in the most unfavorable circumstances.
Type:
Journal article
Language:
English
Published in:
Journal of Molecular Biology, 2013, Vol 425, Issue 7, p. 1210-1224
Main Research Area:
Science/technology
Publication Status:
Published
Review type:
Peer Review
Submission year:
2013
Scientific Level:
Scientific
ID:
239792283

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