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Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery

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Title: Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery
Authors: Igor Jaszczyszyn, Weronika Bielska, Tomasz Gawlowski, Pawel Dudzic, Tadeusz Satława, Jarosław Kończak, Wiktoria Wilman, Bartosz Janusz, Sonia Wróbel, Dawid Chomicz, Jacob D. Galson, Jinwoo Leem, Sebastian Kelm, Konrad Krawczyk
Source: Frontiers in Molecular Biosciences, Vol 10 (2023)
Publisher Information: Frontiers Media S.A., 2023.
Publication Year: 2023
Collection: LCC:Biology (General)
Subject Terms: deep learning, structural modeling, drug discovery, antibody therapeutics, antibody structure prediction, Biology (General), QH301-705.5
More Details: AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.
Document Type: article
File Description: electronic resource
Language: English
ISSN: 2296-889X
Relation: https://www.frontiersin.org/articles/10.3389/fmolb.2023.1214424/full; https://doaj.org/toc/2296-889X
DOI: 10.3389/fmolb.2023.1214424
Access URL: https://doaj.org/article/b9bf830784aa46f6b4415c0e2d6b8f50
Accession Number: edsdoj.b9bf830784aa46f6b4415c0e2d6b8f50
Database: Directory of Open Access Journals
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  Data: Structural modeling of antibody variable regions using deep learning—progress and perspectives on drug discovery
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  Data: Frontiers in Molecular Biosciences, Vol 10 (2023)
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  Data: <searchLink fieldCode="DE" term="%22deep+learning%22">deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22structural+modeling%22">structural modeling</searchLink><br /><searchLink fieldCode="DE" term="%22drug+discovery%22">drug discovery</searchLink><br /><searchLink fieldCode="DE" term="%22antibody+therapeutics%22">antibody therapeutics</searchLink><br /><searchLink fieldCode="DE" term="%22antibody+structure+prediction%22">antibody structure prediction</searchLink><br /><searchLink fieldCode="DE" term="%22Biology+%28General%29%22">Biology (General)</searchLink><br /><searchLink fieldCode="DE" term="%22QH301-705%2E5%22">QH301-705.5</searchLink>
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  Data: AlphaFold2 has hallmarked a generational improvement in protein structure prediction. In particular, advances in antibody structure prediction have provided a highly translatable impact on drug discovery. Though AlphaFold2 laid the groundwork for all proteins, antibody-specific applications require adjustments tailored to these molecules, which has resulted in a handful of deep learning antibody structure predictors. Herein, we review the recent advances in antibody structure prediction and relate them to their role in advancing biologics discovery.
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  Data: https://www.frontiersin.org/articles/10.3389/fmolb.2023.1214424/full; https://doaj.org/toc/2296-889X
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      – SubjectFull: drug discovery
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      – SubjectFull: antibody structure prediction
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