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

doi:10.1093/bioinformatics/btu194

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 journal › Article › peer-review

27 Scopus citations

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/ .

Original language	English (US)
Pages (from-to)	2733-2740
Number of pages	8
Journal	Bioinformatics
Volume	30
Issue number	19
DOIs	https://doi.org/10.1093/bioinformatics/btu194
State	Published - Jun 13 2014
Externally published	Yes

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.

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10.1093/bioinformatics/btu194

https://repository.kaust.edu.sa/bitstream/10754/596794/1/PMC4173008.pdf

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title = "Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.",

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/ .",

author = "Messih, {Mario Abdel} and Rosalba Lepore and Paolo Marcatili and Anna Tramontano",

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.",

year = "2014",

month = jun,

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doi = "10.1093/bioinformatics/btu194",

language = "English (US)",

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AU - Messih, Mario Abdel

AU - Lepore, Rosalba

AU - Marcatili, Paolo

AU - Tramontano, Anna

N1 - 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.

PY - 2014/6/13

Y1 - 2014/6/13

N2 - 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/ .

AB - 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/ .

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JO - Bioinformatics

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ER -

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

Abstract

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