Fully automated antibody structure prediction using BIOVIA tools: Validation study.
We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2017-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5436848?pdf=render |
| _version_ | 1819058770758598656 |
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| author | Helen Kemmish Marc Fasnacht Lisa Yan |
| author_facet | Helen Kemmish Marc Fasnacht Lisa Yan |
| author_sort | Helen Kemmish |
| collection | DOAJ |
| description | We describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements. |
| first_indexed | 2024-12-21T14:00:29Z |
| format | Article |
| id | doaj.art-c0fdc92d5f61451b98b9928c81078b8a |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T14:00:29Z |
| publishDate | 2017-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-c0fdc92d5f61451b98b9928c81078b8a2022-12-21T19:01:23ZengPublic Library of Science (PLoS)PLoS ONE1932-62032017-01-01125e017792310.1371/journal.pone.0177923Fully automated antibody structure prediction using BIOVIA tools: Validation study.Helen KemmishMarc FasnachtLisa YanWe describe the methodology and results from our validation study of the fully automated antibody structure prediction tool available in the BIOVIA (formerly Accelrys) protein modeling suite. Extending our previous study, we have validated the automated approach using a larger and more diverse data set (157 unique antibody Fv domains versus 11 in the previous study). In the current study, we explore the effect of varying several parameter settings in order to better understand their influence on the resulting model quality. Specifically, we investigated the dependence on different methods of framework model construction, antibody numbering schemes (Chothia, IMGT, Honegger and Kabat), the influence of compatibility of loop templates using canonical type filtering, wider exploration of model solution space, and others. Our results show that our recently introduced Top5 framework modeling method results in a small but significant improvement in model quality whereas the effect of other parameters is not significant. Our analysis provides improved guidelines of best practices for using our protocol to build antibody structures. We also identify some limitations of the current computational model which will enhance proper evaluation of model quality by users and suggests possible future enhancements.http://europepmc.org/articles/PMC5436848?pdf=render |
| spellingShingle | Helen Kemmish Marc Fasnacht Lisa Yan Fully automated antibody structure prediction using BIOVIA tools: Validation study. PLoS ONE |
| title | Fully automated antibody structure prediction using BIOVIA tools: Validation study. |
| title_full | Fully automated antibody structure prediction using BIOVIA tools: Validation study. |
| title_fullStr | Fully automated antibody structure prediction using BIOVIA tools: Validation study. |
| title_full_unstemmed | Fully automated antibody structure prediction using BIOVIA tools: Validation study. |
| title_short | Fully automated antibody structure prediction using BIOVIA tools: Validation study. |
| title_sort | fully automated antibody structure prediction using biovia tools validation study |
| url | http://europepmc.org/articles/PMC5436848?pdf=render |
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