ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation
Computational modelling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modelling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody&#...
| Main Authors: | , , , , |
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| Format: | Journal article |
| Language: | English |
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Taylor and Francis
2016
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| _version_ | 1797091564021874688 |
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| author | Leem, J Dunbar, J Georges, G Shi, J Deane, C |
| author_facet | Leem, J Dunbar, J Georges, G Shi, J Deane, C |
| author_sort | Leem, J |
| collection | OXFORD |
| description | Computational modelling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modelling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modelling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard four steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modelling, and side chain prediction. ABodyBuilder then annotates the 'confidence' of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modelled within a root-mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on four separate datasets, including the 11 antibodies from the Antibody Modelling Assessment-II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub-Angstrom predictions for the 'canonical' CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision-making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder . |
| first_indexed | 2024-03-07T03:34:53Z |
| format | Journal article |
| id | oxford-uuid:bbf22afe-335c-41a7-a485-32c4cffe3a2c |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T03:34:53Z |
| publishDate | 2016 |
| publisher | Taylor and Francis |
| record_format | dspace |
| spelling | oxford-uuid:bbf22afe-335c-41a7-a485-32c4cffe3a2c2022-03-27T05:20:52ZABodyBuilder: automated antibody structure prediction with data-driven accuracy estimationJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:bbf22afe-335c-41a7-a485-32c4cffe3a2cEnglishSymplectic Elements at OxfordTaylor and Francis2016Leem, JDunbar, JGeorges, GShi, JDeane, CComputational modelling of antibody structures plays a critical role in therapeutic antibody design. Several antibody modelling pipelines exist, but no freely available methods currently model nanobodies, provide estimates of expected model accuracy, or highlight potential issues with the antibody's experimental development. Here, we describe our automated antibody modelling pipeline, ABodyBuilder, designed to overcome these issues. The algorithm itself follows the standard four steps of template selection, orientation prediction, complementarity-determining region (CDR) loop modelling, and side chain prediction. ABodyBuilder then annotates the 'confidence' of the model as a probability that a component of the antibody (e.g., CDRL3 loop) will be modelled within a root-mean square deviation threshold. It also flags structural motifs on the model that are known to cause issues during in vitro development. ABodyBuilder was tested on four separate datasets, including the 11 antibodies from the Antibody Modelling Assessment-II competition. ABodyBuilder builds models that are of similar quality to other methodologies, with sub-Angstrom predictions for the 'canonical' CDR loops. Its ability to model nanobodies, and rapidly generate models (∼30 seconds per model) widens its potential usage. ABodyBuilder can also help users in decision-making for the development of novel antibodies because it provides model confidence and potential sequence liabilities. ABodyBuilder is freely available at http://opig.stats.ox.ac.uk/webapps/abodybuilder . |
| spellingShingle | Leem, J Dunbar, J Georges, G Shi, J Deane, C ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation |
| title | ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation |
| title_full | ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation |
| title_fullStr | ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation |
| title_full_unstemmed | ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation |
| title_short | ABodyBuilder: automated antibody structure prediction with data-driven accuracy estimation |
| title_sort | abodybuilder automated antibody structure prediction with data driven accuracy estimation |
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