Unbiased Protein Interface Prediction Based on Ligand Diversity Quantification

Authors Reyhaneh Esmaielbeiki, Jean-Christophe Nebel

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Reyhaneh Esmaielbeiki
Jean-Christophe Nebel

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Reyhaneh Esmaielbeiki and Jean-Christophe Nebel. Unbiased Protein Interface Prediction Based on Ligand Diversity Quantification. In German Conference on Bioinformatics 2012. Open Access Series in Informatics (OASIcs), Volume 26, pp. 119-130, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)


Proteins interact with each other to perform essential functions in cells. Consequently, identification of their binding interfaces can provide key information for drug design. Here, we introduce Weighted Protein Interface Prediction (WePIP), an original framework which predicts protein interfaces from homologous complexes. WePIP takes advantage of a novel weighted score which is not only based on structural neighbours' information but, unlike current state-of-the-art methods, also takes into consideration the nature of their interaction partners. Experimental validation demonstrates that our weighted schema significantly improves prediction performance. In particular, we have established a major contribution to ligand diversity quantification. Moreover, application of our framework on a standard dataset shows WePIP performance compares favourably with other state of the art methods.
  • Protein-protein interaction
  • protein interface prediction
  • homology modeling


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