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Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines

Lise, S; Buchan, D; Pontil, M; Jones, DT; (2011) Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines. PLOS ONE , 6 (2) , Article e16774. 10.1371/journal.pone.0016774. Green and gold open access

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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.

Title:Predictions of Hot Spot Residues at Protein-Protein Interfaces Using Support Vector Machines
Open access status:An open access publication. A version is also available from UCL Discovery.
Publisher version:http://dx.doi.org/10.1371/journal.pone.0016774
Additional information:© 2011 Lise et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This work was funded by grant reference BB/E017452/1, from the Biotechnology and Biological Sciences Research Council (BBSRC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
UCL classification:UCL > School of BEAMS > Faculty of Engineering Science > Computer Science

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