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

Mario Abdel Messih, Rosalba Lepore, Paolo Marcatili, Anna Tramontano

Research output: Contribution to journalArticlepeer-review

27 Scopus citations


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 .
Original languageEnglish (US)
Pages (from-to)2733-2740
Number of pages8
Issue number19
StatePublished - Jun 13 2014
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUK-I1-012-43
Acknowledgements: Funding: KAUST Award No. KUK-I1-012-43 made by King Abdullah University of Science and Technology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.'. Together they form a unique fingerprint.

Cite this