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Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design
Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal stru...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513101/ https://www.ncbi.nlm.nih.gov/pubmed/31042706 http://dx.doi.org/10.1371/journal.pcbi.1006980 |
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author | Cannon, Daniel A. Shan, Lu Du, Qun Shirinian, Lena Rickert, Keith W. Rosenthal, Kim L. Korade, Martin van Vlerken-Ysla, Lilian E. Buchanan, Andrew Vaughan, Tristan J. Damschroder, Melissa M. Popovic, Bojana |
author_facet | Cannon, Daniel A. Shan, Lu Du, Qun Shirinian, Lena Rickert, Keith W. Rosenthal, Kim L. Korade, Martin van Vlerken-Ysla, Lilian E. Buchanan, Andrew Vaughan, Tristan J. Damschroder, Melissa M. Popovic, Bojana |
author_sort | Cannon, Daniel A. |
collection | PubMed |
description | Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed. |
format | Online Article Text |
id | pubmed-6513101 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-65131012019-05-31 Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design Cannon, Daniel A. Shan, Lu Du, Qun Shirinian, Lena Rickert, Keith W. Rosenthal, Kim L. Korade, Martin van Vlerken-Ysla, Lilian E. Buchanan, Andrew Vaughan, Tristan J. Damschroder, Melissa M. Popovic, Bojana PLoS Comput Biol Research Article Antibodies are an important class of therapeutics that have significant clinical impact for the treatment of severe diseases. Computational tools to support antibody drug discovery have been developing at an increasing rate over the last decade and typically rely upon a predetermined co-crystal structure of the antibody bound to the antigen for structural predictions. Here, we show an example of successful in silico affinity maturation of a hybridoma derived antibody, AB1, using just a homology model of the antibody fragment variable region and a protein-protein docking model of the AB1 antibody bound to the antigen, murine CCL20 (muCCL20). In silico affinity maturation, together with alanine scanning, has allowed us to fine-tune the protein-protein docking model to subsequently enable the identification of two single-point mutations that increase the affinity of AB1 for muCCL20. To our knowledge, this is one of the first examples of the use of homology modelling and protein docking for affinity maturation and represents an approach that can be widely deployed. Public Library of Science 2019-05-01 /pmc/articles/PMC6513101/ /pubmed/31042706 http://dx.doi.org/10.1371/journal.pcbi.1006980 Text en © 2019 Cannon 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, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Cannon, Daniel A. Shan, Lu Du, Qun Shirinian, Lena Rickert, Keith W. Rosenthal, Kim L. Korade, Martin van Vlerken-Ysla, Lilian E. Buchanan, Andrew Vaughan, Tristan J. Damschroder, Melissa M. Popovic, Bojana Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
title | Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
title_full | Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
title_fullStr | Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
title_full_unstemmed | Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
title_short | Experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
title_sort | experimentally guided computational antibody affinity maturation with de novo docking, modelling and rational design |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513101/ https://www.ncbi.nlm.nih.gov/pubmed/31042706 http://dx.doi.org/10.1371/journal.pcbi.1006980 |
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