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Bioinformatics. 2014 Oct;30(19):2733-40. doi: 10.1093/bioinformatics/btu194. Epub 2014 Jun 13.

Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.

Author information

1
Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy.
2
Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy Department of Physics, Sapienza University, 00185 Rome, Italy, Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Lyngby, Denmark and Istituto Pasteur-Fondazione Cenci Bolognetti, 00185 Rome, Italy.

Abstract

MOTIVATION:

Antibodies are able to recognize a wide range of antigens through their complementary determining regions formed by six hypervariable loops. Predicting the 3D structure of these loops is essential for the analysis and reengineering of novel antibodies with enhanced affinity and specificity. The canonical structure model allows high accuracy prediction for five of the loops. The third loop of the heavy chain, H3, is the hardest to predict because of its diversity in structure, length and sequence composition.

RESULTS:

We describe a method, based on the Random Forest automatic learning technique, to select structural templates for H3 loops among a dataset of candidates. These can be used to predict the structure of the loop with a higher accuracy than that achieved by any of the presently available methods. The method also has the advantage of being extremely fast and returning a reliable estimate of the model quality.

AVAILABILITY AND IMPLEMENTATION:

The source code is freely available at http://www.biocomputing.it/H3Loopred/ .

PMID:
24930144
PMCID:
PMC4173008
DOI:
10.1093/bioinformatics/btu194
[Indexed for MEDLINE]
Free PMC Article

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