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

Handle URI:
http://hdl.handle.net/10754/596794
Title:
Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.
Authors:
Messih, Mario Abdel; Lepore, Rosalba; Marcatili, Paolo; Tramontano, Anna
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/ .
Citation:
Messih MA, Lepore R, Marcatili P, Tramontano A (2014) Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies. Bioinformatics 30: 2733–2740. Available: http://dx.doi.org/10.1093/bioinformatics/btu194.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
KUK-I1-012-43
Issue Date:
13-Jun-2014
DOI:
10.1093/bioinformatics/btu194
PubMed ID:
24930144
PubMed Central ID:
PMC4173008
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
Funding: KAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST).
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorMessih, Mario Abdelen
dc.contributor.authorLepore, Rosalbaen
dc.contributor.authorMarcatili, Paoloen
dc.contributor.authorTramontano, Annaen
dc.date.accessioned2016-02-21T08:50:49Zen
dc.date.available2016-02-21T08:50:49Zen
dc.date.issued2014-06-13en
dc.identifier.citationMessih MA, Lepore R, Marcatili P, Tramontano A (2014) Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies. Bioinformatics 30: 2733–2740. Available: http://dx.doi.org/10.1093/bioinformatics/btu194.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.pmid24930144en
dc.identifier.doi10.1093/bioinformatics/btu194en
dc.identifier.urihttp://hdl.handle.net/10754/596794en
dc.description.abstractMOTIVATION: 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/ .en
dc.description.sponsorshipFunding: KAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST).en
dc.publisherOxford University Press (OUP)en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.comen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/3.0/en
dc.titleImproving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.en
dc.typeArticleen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC4173008en
dc.contributor.institutionDepartment 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.en
dc.contributor.institutionDepartment 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.en
kaust.grant.numberKUK-I1-012-43en

Related articles on PubMed

All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.