The DOI matches its Crossref record; 249 of the 249 references it cites were found in a registry or an index.
The Protein Folding Problem
Open accessAbstract
The "protein folding problem" consists of three closely related puzzles: (a) What is the folding code? (b) What is the folding mechanism? (c) Can we predict the native structure of a protein from its amino acid sequence? Once regarded as a grand challenge, protein folding has seen great progress in recent years. Now, foldable proteins and nonbiological polymers are being designed routinely and moving toward successful applications. The structures of small proteins are now often well predicted by computer methods. And, there is now a testable explanation for how a protein can fold so quickly: A protein solves its large global optimization problem as a series of smaller local optimization problems, growing and assembling the native structure from peptide fragments, local structures first.
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“The statistical energy landscape theory was developed to study protein folding.1,128–131”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [130]
In the citing paper
“AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [51]
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“Levinthal argued that there is no need to search this vast space since the energy landscape is funnel-like, rather than flat, and thus can guide sampling toward the biological conformational basin.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [51]
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Cites
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Cited by
Sorted by citations
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
Improved protein structure prediction using potentials from deep learning
Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378
The Protein-Folding Problem, 50 Years On
Dill, K. A., & MacCallum, J. L. (2012) · Science · doi:10.1126/science.1219021 · Cited by 1,699
Self-citationBiological and Chemical Approaches to Diseases of Proteostasis Deficiency
Powers, E. T., Morimoto, R. I., Dillin, A., Kelly, J. W., & Balch, W. E. (2009) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.052308.114844 · Cited by 1,149
Multiblock Polymers: Panacea or Pandora’s Box?
Bates, F. S., Hillmyer, M. A., Lodge, T. P., Bates, C. M., Delaney, K. T., & Fredrickson, G. H. (2012) · Science · doi:10.1126/science.1215368 · Cited by 1,147
Metadata
- Authors
- Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R.
- Affiliations
- University of California, San Francisco (US)
- Arizona State University (US)
- University of California, Santa Barbara (US)
- Max Planck Institute of Colloids and Interfaces (DE)
- Venue
- Annual Review of Biophysics
- Publisher
- Annual Reviews
- Published
- 2008-06-01
- Volume
- 37
- Issue
- 1
- Pages
- 289-316
- ISSN
- 1936-122X (ISSN-L)
- 1936-1238
- PMC
- PMC2443096
- OpenAlex
- W2129020329
- Cited by
- 1,163
- References
- 248
- Citations by year
- 2026: 51 · 2025: 61 · 2024: 60 · 2023: 70 · 2022: 73 · 2021: 73 · 2020: 59 · 2019: 53 · 2018: 52 · 2017: 47 · 2016: 48 · 2015: 43 · 2014: 70 · 2013: 82 · 2012: 78
- FWCI
- 6.04
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Biophysics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institute of General Medical Sciences (GM34993, R01GM034993)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- protein folding, folding mechanism, protein structure prediction, de novo protein design, peptide fragments, local optimization, global optimization, folding rate
- MeSH terms
- Computer Simulation, Models, Chemical, Models, Molecular, Protein Conformation, Proteins, Protein Folding
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