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Publication Detail
Predictions of hot spot residues at protein-protein interfaces using support vector machines.
  • Publication Type:
    Journal article
  • Publication Sub Type:
    Evaluation Studies
  • Authors:
    Lise S, Buchan D, Pontil M, Jones DT
  • Publication date:
    28/02/2011
  • Pagination:
    e16774, ?
  • Journal:
    PLoS One
  • Volume:
    6
  • Issue:
    2
  • Status:
    Published online
  • Country:
    United States
  • Language:
    eng
  • Keywords:
    Amino Acid Motifs, Computational Biology, Databases, Protein, Forecasting, Humans, Interleukin-4, Models, Biological, Models, Molecular, Protein Binding, Protein Interaction Domains and Motifs, Protein Interaction Mapping, Receptors, Interleukin-4, Sequence Analysis, Protein, Software
Abstract
Protein-protein interactions are critically dependent on just a few 'hot spot' residues at the interface. Hot spots make a dominant contribution to the free energy of binding and they can disrupt the interaction if mutated to alanine. Here, we present HSPred, a support vector machine(SVM)-based method to predict hot spot residues, given the structure of a complex. HSPred represents an improvement over a previously described approach (Lise et al, BMC Bioinformatics 2009, 10:365). It achieves higher accuracy by treating separately predictions involving either an arginine or a glutamic acid residue. These are the amino acid types on which the original model did not perform well. We have therefore developed two additional SVM classifiers, specifically optimised for these cases. HSPred reaches an overall precision and recall respectively of 61% and 69%, which roughly corresponds to a 10% improvement. An implementation of the described method is available as a web server at http://bioinf.cs.ucl.ac.uk/hspred. It is free to non-commercial users.
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