Structure-based protein design with deep learning
Open accessAbstract
Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information - but largely piece-by-piece - from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.
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The difficulty of efficiently representing and learning structure likely explains why protein structure design is relatively underexplored.
“likely due to the difficulty of efficiently representing and learning structure (50)”
From Scaffolding protein functional sites using deep learning (2022) · cites this paper as [50]
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- Authors
- Ovchinnikov, S., & Huang, P.-S.
- Affiliations
- Harvard University (US)
- Stanford University (US)
- Corresponding author
- Ovchinnikov, Sergey
- Huang, Po-Ssu
- Venue
- Current Opinion in Chemical Biology
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 65
- Pages
- 136-144
- ISSN
- 1367-5931 (ISSN-L)
- PMC
- PMC8671290
- OpenAlex
- W3201100749
- Cited by
- 93
- References
- 61
- Citations by year
- 2026: 9 · 2025: 17 · 2024: 27 · 2023: 24 · 2022: 16
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Current Opinion in Chemical Biology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (2032259)
- U.S. Department of Energy
- National Institutes of Health (DP5OD026389)
- Office of Science
- Advanced Scientific Computing Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- structure-based protein design, deep learning, protein structure prediction, protein engineering
- MeSH terms
- Deep Learning, Proteins
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