Journal Article

LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction

Chris Kauffman and George Karypis

in Bioinformatics

Volume 25, issue 23, pages 3099-3107
Published in print December 2009 | ISSN: 1367-4803
Published online September 2009 | e-ISSN: 1460-2059 | DOI: http://dx.doi.org/10.1093/bioinformatics/btp561
LIBRUS: combined machine learning and homology information for sequence-based ligand-binding residue prediction

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Motivation: Identifying residues that interact with ligands is useful as a first step to understanding protein function and as an aid to designing small molecules that target the protein for interaction. Several studies have shown that sequence features are very informative for this type of prediction, while structure features have also been useful when structure is available. We develop a sequence-based method, called LIBRUS, that combines homology-based transfer and direct prediction using machine learning and compare it to previous sequence-based work and current structure-based methods.

Results: Our analysis shows that homology-based transfer is slightly more discriminating than a support vector machine learner using profiles and predicted secondary structure. We combine these two approaches in a method called LIBRUS. On a benchmark of 885 sequence-independent proteins, it achieves an area under the ROC curve (ROC) of 0.83 with 45% precision at 50% recall, a significant improvement over previous sequence-based efforts. On an independent benchmark set, a current method, FINDSITE, based on structure features achieves an ROC of 0.81 with 54% precision at 50% recall, while LIBRUS achieves an ROC of 0.82 with 39% precision at 50% recall at a smaller computational cost. When LIBRUS and FINDSITE predictions are combined, performance is increased beyond either reaching an ROC of 0.86 and 59% precision at 50% recall.

Availability: Software developed for this study is available at http://bioinfo.cs.umn.edu/supplements/binf2009 along with Supplementary data on the study.

Contact: kauffman@cs.umn.edu; karypis@cs.umn.edu

Journal Article.  6855 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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