Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.
KAUST Grant NumberKUK-I1-012-43
Permanent link to this recordhttp://hdl.handle.net/10754/596794
MetadataShow full item record
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/ .
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.
SponsorsFunding: KAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST).
PublisherOxford University Press (OUP)
PubMed Central IDPMC4173008
CollectionsPublications Acknowledging KAUST Support
Except where otherwise noted, this item's license is described as This 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 email@example.com
- Ab initio structure prediction of the antibody hypervariable H3 loop.
- Authors: Zhu K, Day T
- Issue date: 2013 Jun
- Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction.
- Authors: Zhu K, Day T, Warshaviak D, Murrett C, Friesner R, Pearlman D
- Issue date: 2014 Aug
- Predicting antibody complementarity determining region structures without classification.
- Authors: Choi Y, Deane CM
- Issue date: 2011 Dec
- The H3 loop of antibodies shows unique structural characteristics.
- Authors: Regep C, Georges G, Shi J, Popovic B, Deane CM
- Issue date: 2017 Jul
- Automated classification of antibody complementarity determining region 3 of the heavy chain (H3) loops into canonical forms and its application to protein structure prediction.
- Authors: Oliva B, Bates PA, Querol E, Avilés FX, Sternberg MJ
- Issue date: 1998 Jun 26