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 b

SUBMITTER: Messih MA 

PROVIDER: S-EPMC4173008 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature