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

Abstract : 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. 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. The source code is freely available at http://www.biocomputing.it/H3Loopred/ .
Type de document :
Article dans une revue
Bioinformatics (Oxford, England), 2014, 30 (19), pp.2733-40. 〈10.1093/bioinformatics/btu194〉
Liste complète des métadonnées

Littérature citée [38 références]  Voir  Masquer  Télécharger

https://hal-riip.archives-ouvertes.fr/pasteur-01202592
Contributeur : Istituto Pasteur Fondazione Cenci Bolognetti <>
Soumis le : lundi 21 septembre 2015 - 13:47:28
Dernière modification le : lundi 8 octobre 2018 - 17:44:06
Document(s) archivé(s) le : mardi 29 décembre 2015 - 08:58:10

Fichier

btu194.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Licence


Distributed under a Creative Commons Paternité - Pas d'utilisation commerciale 4.0 International License

Identifiants

Collections

Citation

Mario Abdel Messih, Rosalba Lepore, Paolo Marcatili, Anna Tramontano. Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.. Bioinformatics (Oxford, England), 2014, 30 (19), pp.2733-40. 〈10.1093/bioinformatics/btu194〉. 〈pasteur-01202592〉

Partager

Métriques

Consultations de la notice

196

Téléchargements de fichiers

53