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Updated: Apr 28, 2026
Assessment of Immunologically Relevant Dynamic Tertiary Structural Features of the HIV-1 V3 Loop Crown R2 Sequence by ab initio Folding
Published on: September 15, 2010
Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies
Mario Abdel Messih1, Rosalba Lepore1, Paolo Marcatili1
1Department 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.
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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