Deep learning approaches for conformational flexibility and switching properties in protein design
Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibilit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Molecular Biosciences |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2022.928534/full |
| _version_ | 1798037930630971392 |
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| author | Lucas S. P. Rudden Mahdi Hijazi Patrick Barth |
| author_facet | Lucas S. P. Rudden Mahdi Hijazi Patrick Barth |
| author_sort | Lucas S. P. Rudden |
| collection | DOAJ |
| description | Following the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future. |
| first_indexed | 2024-04-11T21:33:17Z |
| format | Article |
| id | doaj.art-ac0fb84c216840fa8c3e443a026d9a7e |
| institution | Directory Open Access Journal |
| issn | 2296-889X |
| language | English |
| last_indexed | 2024-04-11T21:33:17Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj.art-ac0fb84c216840fa8c3e443a026d9a7e2022-12-22T04:01:50ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2022-08-01910.3389/fmolb.2022.928534928534Deep learning approaches for conformational flexibility and switching properties in protein designLucas S. P. RuddenMahdi HijaziPatrick BarthFollowing the hugely successful application of deep learning methods to protein structure prediction, an increasing number of design methods seek to leverage generative models to design proteins with improved functionality over native proteins or novel structure and function. The inherent flexibility of proteins, from side-chain motion to larger conformational reshuffling, poses a challenge to design methods, where the ideal approach must consider both the spatial and temporal evolution of proteins in the context of their functional capacity. In this review, we highlight existing methods for protein design before discussing how methods at the forefront of deep learning-based design accommodate flexibility and where the field could evolve in the future.https://www.frontiersin.org/articles/10.3389/fmolb.2022.928534/fulldeep learningprotein designgenerative modelsprotein flexibilityprotein switches |
| spellingShingle | Lucas S. P. Rudden Mahdi Hijazi Patrick Barth Deep learning approaches for conformational flexibility and switching properties in protein design Frontiers in Molecular Biosciences deep learning protein design generative models protein flexibility protein switches |
| title | Deep learning approaches for conformational flexibility and switching properties in protein design |
| title_full | Deep learning approaches for conformational flexibility and switching properties in protein design |
| title_fullStr | Deep learning approaches for conformational flexibility and switching properties in protein design |
| title_full_unstemmed | Deep learning approaches for conformational flexibility and switching properties in protein design |
| title_short | Deep learning approaches for conformational flexibility and switching properties in protein design |
| title_sort | deep learning approaches for conformational flexibility and switching properties in protein design |
| topic | deep learning protein design generative models protein flexibility protein switches |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2022.928534/full |
| work_keys_str_mv | AT lucassprudden deeplearningapproachesforconformationalflexibilityandswitchingpropertiesinproteindesign AT mahdihijazi deeplearningapproachesforconformationalflexibilityandswitchingpropertiesinproteindesign AT patrickbarth deeplearningapproachesforconformationalflexibilityandswitchingpropertiesinproteindesign |