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Advances in protein structure prediction and design
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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]
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“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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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gapped BLAST and PSI-BLAST: a new generation of protein database search programs
Altschul, S. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.17.3389 · Cited by 73,302
Berman, H. M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.235 · Cited by 40,196
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Crystallography & NMR System: A New Software Suite for Macromolecular Structure Determination
Brünger, A. T., Adams, P. D., Clore, G. M., DeLano, W. L., Gros, P., Grosse‐Kunstleve, R. W., Jiang, J., Kuszewski, J. J., Nilges, M., Pannu, N., et al. (1998) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s0907444998003254 · Cited by 15,723
SWISS-MODEL: homology modelling of protein structures and complexes
Waterhouse, A. M., Bertoni, M., Bienert, S., Studer, G., Tauriello, G., Gumienny, R., Heer, F. T., de Beer, T., Rempfer, C., Bordoli, L., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky427 · Cited by 13,248
CHARMM36m: an improved force field for folded and intrinsically disordered proteins
Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. D. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 7,334
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096
The I-TASSER Suite: protein structure and function prediction
Yang, J., Yan, R., Roy, A., Xu, D., Poisson, J., & Zhang, Y. (2015) · Nature Methods · doi:10.1038/nmeth.3213 · Cited by 5,596
Research works printed but not linked: 1
- Levinthal, C. How to fold graciously. Mossbauer Spectrosc. Biol. Syst. 67, 22–24 (1969).
Cited by
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Zhang, Z., Zhou, L., Xie, N., Nice, E. C., Zhang, T., Cui, Y., & Huang, C. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00213-8 · Cited by 544
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You, Y., Lai, X., Pan, Y. F., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-022-00994-0 · Cited by 499
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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
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