ReviewFrontiers in molecular biosciences2022
Deep learning approaches for conformational flexibility and switching properties in protein design.
Review in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Geometric Deep Learning-Based Drug Design Models for Small-Molecule Drug Discovery.Molecular informatics · 2026Review
- Enhancing antibody-antigen interaction prediction with atomic flexibility.PLoS computational biology · 2025Article
- G-Computational and structural biotechnology journal · 2024Article
- Machine learning-aided design and screening of an emergent protein function in synthetic cells.Nature communications · 2024Article
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3 authors.
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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.
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