Bibliographic Details
| 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 |
