ArticleBioinformatics (Oxford, England)2014
Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Computational Modeling of Antibody and T-Cell Receptor (CDR3 Loops).Methods in molecular biology (Clifton, N.J.) · 2023Article
- Quality Assessment of Protein Docking Models Based on Graph Neural Network.Frontiers in bioinformatics · 2021Article
- Quantitative Description of Surface Complementarity of Antibody-Antigen Interfaces.Frontiers in molecular biosciences · 2021Article
- Computational approaches to therapeutic antibody design: established methods and emerging trends.Briefings in bioinformatics · 2020Review
- Characterizing Epitope Binding Regions of Entire Antibody Panels by Combining Experimental and Computational Analysis of Antibody: Antigen Binding Competition.Molecules (Basel, Switzerland) · 2020Article
- How repertoire data are changing antibody science.The Journal of biological chemistry · 2020Review
- Article
- Automated shape-based clustering of 3D immunoglobulin protein structures in chronic lymphocytic leukemia.BMC bioinformatics · 2018Article
- SWISS-MODEL: homology modelling of protein structures and complexes.Nucleic acids research · 2018Article
- Role of Non-local Interactions between CDR Loops in Binding Affinity of MR78 Antibody to Marburg Virus Glycoprotein.Structure (London, England : 1993) · 2017Article
- PIGSPro: prediction of immunoGlobulin structures v2.Nucleic acids research · 2017Article
- Sphinx: merging knowledge-based and ab initio approaches to improve protein loop prediction.Bioinformatics (Oxford, England) · 2017Article
- Superposition-free comparison and clustering of antibody binding sites: implications for the prediction of the nature of their antigen.Scientific reports · 2017Article
- Distance-Guided Forward and Backward Chain-Growth Monte Carlo Method for Conformational Sampling and Structural Prediction of Antibody CDR-H3 Loops.Journal of chemical theory and computation · 2017Article
- Antibody H3 Structure Prediction.Computational and structural biotechnology journal · 2017Review
- LoopIng: a template-based tool for predicting the structure of protein loops.Bioinformatics (Oxford, England) · 2015Article
- LYRA, a webserver for lymphocyte receptor structural modeling.Nucleic acids research · 2015Article
- Antibody modeling using the prediction of immunoglobulin structure (PIGS) web server [corrected].Nature protocols · 2014Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationAntibodies 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.
resultsWe 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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