Deep learning approaches for conformational flexibility and switching properties in protein design.
Front Mol Biosci
; 9: 928534, 2022.
Article
in En
| MEDLINE
| ID: mdl-36032687
ABSTRACT
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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Prognostic_studies
Language:
En
Journal:
Front Mol Biosci
Year:
2022
Document type:
Article
Affiliation country:
Switzerland