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Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence
The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103075/ https://www.ncbi.nlm.nih.gov/pubmed/25071985 http://dx.doi.org/10.7717/peerj.455 |
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author | Nikoloudis, Dimitris Pitts, Jim E. Saldanha, José W. |
author_facet | Nikoloudis, Dimitris Pitts, Jim E. Saldanha, José W. |
author_sort | Nikoloudis, Dimitris |
collection | PubMed |
description | The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak. |
format | Online Article Text |
id | pubmed-4103075 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-41030752014-07-28 Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence Nikoloudis, Dimitris Pitts, Jim E. Saldanha, José W. PeerJ Bioinformatics The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak. PeerJ Inc. 2014-07-01 /pmc/articles/PMC4103075/ /pubmed/25071985 http://dx.doi.org/10.7717/peerj.455 Text en © 2014 Nikoloudis et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Nikoloudis, Dimitris Pitts, Jim E. Saldanha, José W. Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence |
title | Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence |
title_full | Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence |
title_fullStr | Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence |
title_full_unstemmed | Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence |
title_short | Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence |
title_sort | disjoint combinations profiling (dcp): a new method for the prediction of antibody cdr conformation from sequence |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4103075/ https://www.ncbi.nlm.nih.gov/pubmed/25071985 http://dx.doi.org/10.7717/peerj.455 |
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