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Advances in protein structure prediction and design
Open accessAbstract
The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.
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“The folding paradigm stipulated that unfolded molecules will always spontaneously fold into the same shape; that is, the linear amino acid sequence specifies a protein’s folded native state.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [58]
In the citing paper
“Quantum annealing is also suitable to the problem of protein design [43, 77, 78], i.e. finding the amino acid sequence that minimizes the energy of a chosen main-chain configuration.”
From Quantum computing algorithms: getting closer to critical problems in computational biology (2022) · cites this paper as [43]
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Metadata
- Authors
- Kuhlman, B., & Bradley, P.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- University of Washington (US)
- Corresponding author
- Kuhlman, Brian
- Bradley, Philip
- Venue
- Nature Reviews Molecular Cell Biology
- Published
- 2019-08-15
- Volume
- 20
- Issue
- 11
- Pages
- 681-697
- ISSN
- 1471-0072 (ISSN-L)
- 1471-0080
- PMC
- PMC7032036
- OpenAlex
- W2968494487
- Cited by
- 806
- References
- 175
- Citations by year
- 2026: 53 · 2025: 106 · 2024: 153 · 2023: 156 · 2022: 137 · 2021: 136 · 2020: 60 · 2019: 5
- FWCI
- 7.46
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Reviews Molecular Cell Biology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM117968, R01GM121487, R01GM123378, R35GM131923)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, protein design, protein folding, protein structure modeling, protein-protein interfaces, fluorescent protein engineering, signalling proteins, rational protein design, protein function prediction
- MeSH terms
- Algorithms, Animals, Humans, Models, Molecular, Protein Conformation, Proteins, Sequence Analysis, Protein, Databases, Protein
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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