The DOI matches its Crossref record; 72 of the 72 references it cites were found in a registry or an index.
The Protein-Folding Problem, 50 Years On
Abstract
Protein Folding: Past and Future: Fifty years ago the Nobel Prize in chemistry was awarded to Max Perutz and John Kendrew for determining the structure of globular proteins. Since first viewing their structure of myoglobin, scientists have sought to understand protein folding. Dill and MacCallum (p. 1042 ) review the progress that has been made on three central questions: What is the code that relates sequence to structure? How do proteins fold so fast? Can protein structure be computationally predicted? While we have come some way toward answering these questions, new questions have been gene rated. It is no longer useful to talk about “solving the protein-folding problem”—protein folding has grown into a field of research where the next 50 years promise to be as exciting as the last.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 [52]
In the citing paper
“According to the funnel hypothesis of protein folding, the native structure of a protein is believed to the global minimum of its free energy [177,193], although many counterexamples exist.”
From The prospects of quantum computing in computational molecular biology (2021) · cites this paper as [177]
In the citing paper
“In fact, as has often been pointed out (for example43) this is a fallacious argument, since the free energy falls progressively as the protein folds, providing sufficient guidance44.”
From Critical assessment of methods of protein structure prediction (CASP)—Round XIV (2021) · cites this paper as [44]
Citations
Cites
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
Murzin, A. G., Brenner, S. E., Hubbard, T., & Chothia, C. (1995) · Journal of Molecular Biology · doi:10.1016/s0022-2836(05)80134-2 · Cited by 6,435
Prusiner, S. B. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.23.13363 · Cited by 5,373
Protein folding and misfolding
Dobson, C. M. (2003) · Nature · doi:10.1038/nature02261 · Cited by 4,791
Dominant forces in protein folding
Dill, K. A. (1990) · Biochemistry · doi:10.1021/bi00483a001 · Cited by 3,829
Self-citationThe HHpred interactive server for protein homology detection and structure prediction
Söding, J., Biegert, A., & Lupas, A. N. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki408 · Cited by 3,775
The structure of proteins: Two hydrogen-bonded helical configurations of the polypeptide chain
Pauling, L. C., Corey, R. B., & Branson, H. R. (1951) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.37.4.205 · Cited by 3,007
Funnels, pathways, and the energy landscape of protein folding: A synthesis
Bryngelson, J. D., Onuchic, J. N., Socci, N. D., & Wolynes, P. G. (1995) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.340210302 · Cited by 2,648
Gellman, S. H. (1998) · Accounts of Chemical Research · doi:10.1021/ar960298r · Cited by 2,550
From Levinthal to pathways to funnels
Dill, K. A., & Chan, H. S. (1997) · Nature Structural & Molecular Biology · doi:10.1038/nsb0197-10 · Cited by 2,293
Self-citationHHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288
How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980
McCammon, J., Gelin, B., & Karplus, M. (1977) · Nature · doi:10.1038/267585a0 · Cited by 1,972
Atomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., et al. (2010) · Science · doi:10.1126/science.1187409 · Cited by 1,923
Force Fields for Protein Simulations
Ponder, J. W., & Case, D. A. (2003) · Advances in protein chemistry · doi:10.1016/s0065-3233(03)66002-x · Cited by 1,914
A Three-Dimensional Model of the Myoglobin Molecule Obtained by X-Ray Analysis
Kendrew, J. C., Bodo, G., Dintzis, H. M., Parrish, R. G., Wyckoff, H. W., & Phillips, D. C. (1958) · Nature · doi:10.1038/181662a0 · Cited by 1,671
Design of a Novel Globular Protein Fold with Atomic-Level Accuracy
Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666
Spin glasses and the statistical mechanics of protein folding.
Bryngelson, J. D., & Wolynes, P. G. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.21.7524 · Cited by 1,647
Are there pathways for protein folding?
Levinthal, C. (1968) · Journal de Chimie Physique · doi:10.1051/jcp/1968650044 · Cited by 1,541
Morcos, F., Pagnani, A., Lunt, B., Bertolino, A., Marks, D. S., Sander, C., Zecchina, R., Onuchic, J. N., Hwa, T., & Weigt, M. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1111471108 · Cited by 1,434
Predicting protein structures with a multiplayer online game
Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., & players, F. (2010) · Nature · doi:10.1038/nature09304 · Cited by 1,368
Structure of Myoglobin: A Three-Dimensional Fourier Synthesis at 2 Å. Resolution
Kendrew, J. C., Dickerson, R. E., Strandberg, B., Hart, R. G., Davies, D. R., Phillips, D. C., & Shore, V. C. (1960) · Nature · doi:10.1038/185422a0 · Cited by 1,351
Perutz, M. F., Rossmann, M. G., Cullis, A. F., Muirhead, H., Will, G., & North, A. C. T. (1960) · Nature · doi:10.1038/185416a0 · Cited by 1,351
Theory for the folding and stability of globular proteins
Dill, K. A. (1985) · Biochemistry · doi:10.1021/bi00327a032 · Cited by 1,256
Self-citationUnderstanding protein non-folding
Uversky, V. N., & Dunker, A. K. (2010) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2010.01.017 · Cited by 1,221
Dill, K. A., Ozkan, S. B., Shell, M. S., & Weikl, T. R. (2008) · Annual Review of Biophysics · doi:10.1146/annurev.biophys.37.092707.153558 · Cited by 1,163
Self-citationProtein 3D Structure Computed from Evolutionary Sequence Variation
Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144
INTERMEDIATES IN THE FOLDING REACTIONS OF SMALL PROTEINS
Kim, P. S., & Baldwin, R. L. (1990) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.59.070190.003215 · Cited by 1,136
LGA: a method for finding 3D similarities in protein structures
Zemla, A. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg571 · Cited by 1,069
Protein folding funnels: a kinetic approach to the sequence-structure relationship.
Leopold, P., Montal, M., & Onuchic, J. N. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.18.8721 · Cited by 929
Computational Design of an Enzyme Catalyst for a Stereoselective Bimolecular Diels-Alder Reaction
Siegel, J. B., Zanghellini, A., Lovick, H. M., Kiss, G., Lambert, A. R., St.Clair, J. L., Gallaher, J. L., Hilvert, D., Gelb, M. H., Stoddard, B. L., et al. (2010) · Science · doi:10.1126/science.1190239 · Cited by 921
Toward High-Resolution de Novo Structure Prediction for Small Proteins
Bradley, P. H., Misura, K. M. S., & Baker, D. A. (2005) · Science · doi:10.1126/science.1113801 · Cited by 865
Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 787
Protein Design by Binary Patterning of Polar and Nonpolar Amino Acids
Kamtekar, S., Schiffer, J. M., Xiong, H., Babik, J. M., & Hecht, M. H. (1993) · Science · doi:10.1126/science.8259512 · Cited by 731
CAPRI: A Critical Assessment of PRedicted Interactions
Janin, J., Henrick, K., Moult, J., Eyck, L. T., Sternberg, M. J. E., Vajda, S., Vakser, I., & Wodak, S. J. (2003) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.10381 · Cited by 703
Foldamers with Heterogeneous Backbones
Horne, W. S., & Gellman, S. H. (2008) · Accounts of Chemical Research · doi:10.1021/ar800009n · Cited by 689
Anton, a special-purpose machine for molecular dynamics simulation
Shaw, D., Deneroff, M. M., Dror, R., Kuskin, J., Larson, R. H., Salmon, J., Young, C., Batson, B., Bowers, K., Chao, J. C., et al. (2008) · CACM · doi:10.1145/1364782.1364802 · Cited by 588
Computational Design of Proteins Targeting the Conserved Stem Region of Influenza Hemagglutinin
Fleishman, S. J., Whitehead, T. A., Ekiert, D. C., Dreyfus, C., Corn, J. E., Strauch, E.-M., Wilson, I. A., & Baker, D. (2011) · Science · doi:10.1126/science.1202617 · Cited by 585
Screen Savers of the World Unite!
Shirts, M., & Pande, V. (2000) · Science · doi:10.1126/science.290.5498.1903 · Cited by 550
Zwanzig, R., Szabo, A., & Bagchi, B. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.1.20 · Cited by 527
A decade of CASP: progress, bottlenecks and prognosis in protein structure prediction
Moult, J. (2005) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2005.05.011 · Cited by 485
Shea, J.-E., & Brooks III, C. L. (2001) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.52.1.499 · Cited by 481
Matthews, C. R. (1993) · Annual Review of Biochemistry · doi:10.1146/annurev.bi.62.070193.003253 · Cited by 437
Free-floating ultrathin two-dimensional crystals from sequence-specific peptoid polymers
Nam, K. T., Shelby, S. A., Choi, P. H., Marciel, A. B., Chen, R., Tan, L., Chu, T. K., Mesch, R. A., Lee, B.-C., Connolly, M. D., et al. (2010) · Nature Materials · doi:10.1038/nmat2742 · Cited by 418
Rosetta in CASP4: Progress in ab initio protein structure prediction
Bonneau, R., Tsai, J. W., Ruczinski, I., Chivian, D. C., Rohl, C. A., Strauss, C. E. M., & Baker, D. A. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.1170 · Cited by 313
Fowler, S. A., & Blackwell, H. E. (2009) · Organic & Biomolecular Chemistry · doi:10.1039/b817980h · Cited by 294
Community-wide assessment of GPCR structure modelling and ligand docking: GPCR Dock 2008
participants, G. D. 2., Michino, M., Abola, E. E., Brooks, C. L., Dixon, J. S., Moult, J., & Stevens, R. C. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2877 · Cited by 279
Refinement of protein structure homology models via long, all‐atom molecular dynamics simulations
Raval, A., Piana, S., Eastwood, M. P., Dror, R. O., & Shaw, D. E. (2012) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24098 · Cited by 255
Blue Gene: A vision for protein science using a petaflop supercomputer
Allen, F. E., Almasi, G. S., Andreoni, W., Beece, D. K., Berne, B. J., Bright, A. A., Brunheroto, J. R., Caşcaval, C., Castaños, J. G., Coteus, P., et al. (2001) · IBM Systems Journal · doi:10.1147/sj.402.0310 · Cited by 254
Computational analysis of membrane proteins: the largest class of drug targets
Arinaminpathy, Y., Khurana, E., Engelman, D. M., & Gerstein, M. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.08.006 · Cited by 246
Computation-Guided Backbone Grafting of a Discontinuous Motif onto a Protein Scaffold
Azoitei, M. L., Correia, B. E., Ban, Y.-E. A., Carrico, C., Kalyuzhniy, O., Chen, L., Schroeter, A., Huang, P.-S., McLellan, J. S., Kwong, P. D., et al. (2011) · Science · doi:10.1126/science.1209368 · Cited by 224
A Blind Challenge for Computational Solvation Free Energies: Introduction and Overview
Guthrie, J. P. (2009) · The Journal of Physical Chemistry B · doi:10.1021/jp806724u · Cited by 211
Critical assessment of methods of protein structure prediction (CASP)—round IX
Moult, J., Fidelis, K. A., Kryshtafovych, A., & Tramontano, A. L. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23200 · Cited by 211
Designing polymers that mimic biomolecules
Kirshenbaum, K., Zuckermann, R. N., & DILL, K. (1999) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(99)80075-x · Cited by 209
Self-citationProtein folding and misfolding: mechanism and principles
Englander, S. W., Mayne, L. C., & Mallela, K. M. G. (2007) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583508004654 · Cited by 209
Assessment of template based protein structure predictions in CASP9
Mariani, V., Kiefer, F., Schmidt, T., Haas, J., & Schwede, T. F. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23177 · Cited by 183
Fersht, A. R. (2008) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2446 · Cited by 180
Taming the complexity of protein folding
Bowman, G. R., Voelz, V. A., & Pande, V. S. (2010) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2010.10.006 · Cited by 170
Lee, B.-C., Chu, T. K., Dill, K. A., & Zuckermann, R. N. (2008) · Journal of the American Chemical Society · doi:10.1021/ja802125x · Cited by 170
Self-citationFolding a Nonbiological Polymer into a Compact Multihelical Structure
Lee, B.-C., Zuckermann, R. N., & Dill, K. A. (2005) · Journal of the American Chemical Society · doi:10.1021/ja0514904 · Cited by 156
Self-citationPredicting novel protein folds by using FRAGFOLD
Jones, D. T. (2001) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.1171 · Cited by 146
CASP9 results compared to those of previous casp experiments
Kryshtafovych, A., Fidelis, K. A., & Moult, J. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23182 · Cited by 135
Hydrophobicity scales: a thermodynamic looking glass into lipid–protein interactions
MacCallum, J. L., & Tieleman, D. P. (2011) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2011.08.003 · Cited by 96
Self-citationPeptoids: Bio-Inspired Polymers as Potential Pharmaceuticals
Dohm, M. T., Kapoor, R., & Barron, A. E. (2011) · Current Pharmaceutical Design · doi:10.2174/138161211797416066 · Cited by 88
Assembling novel protein folds from super-secondary structural fragments
Jones, D. T., & McGuffin, L. J. (2003) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10542 · Cited by 80
Molecular mechanism of β‐sheet self‐organization at water‐hydrophobic interfaces
Nikolic, A., Baud, S., Rauscher, S., & Pomès, R. (2010) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22854 · Cited by 49
Service, R. F. (2008) · Science · doi:10.1126/science.321.5890.784 · Cited by 49
Routes are trees: The parsing perspective on protein folding
Hockenmaier, J., Joshi, A. K., & Dill, K. A. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21195 · Cited by 33
Self-citationExploring zipping and assembly as a protein folding principle
Voelz, V. A., & Dill, K. A. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21234 · Cited by 31
Self-citationPhotoresponsive peptoid oligomers bearing azobenzene side chains
Shah, N. H., & Kirshenbaum, K. (2008) · Organic & Biomolecular Chemistry · doi:10.1039/b804802a · Cited by 28
Flagged first, then by citations
Research works printed but not linked: 2
- Englander S. W., Mayne L., Krishna M. M. G., Q. Rev. Biophys. 40, 287 (2008).
- Snow C. D., Nguyen H., Pande V. S., Gruebele M., Nat. Struct. Mol. Biol. 420, 102 (2002).
Cited by
Sorted by citations
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
Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials
Boles, M. A., Engel, M., & Talapin, D. V. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00196 · Cited by 2,095
HH-suite3 for fast remote homology detection and deep protein annotation
Steinegger, M., Meier, M., Mirdita, M., Vöhringer, H., Haunsberger, S. J., & Söding, J. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3019-7 · Cited by 1,145
DNA Origami: Scaffolds for Creating Higher Order Structures
Fan, H. B., Zhang, F., Liu, Y., & Yan, H. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00825 · Cited by 1,108
Coarse-Grained Protein Models and Their Applications
Kmiecik, S., Gront, D., Koliński, M., Wieteska, Ł., Badaczewska-Dawid, A. E., & Koliński, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,113
Metadata
- Authors
- Dill, K. A., & MacCallum, J. L.
- Affiliations
- Stony Brook University (US)
- Corresponding author
- Dill, Ken A.
- Venue
- Science
- Published
- 2012-11-23
- Volume
- 338
- Issue
- 6110
- Pages
- 1042-1046
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2014050554
- Cited by
- 1,699
- References
- 72
- Citations by year
- 2026: 140 · 2025: 132 · 2024: 117 · 2023: 148 · 2022: 114 · 2021: 137 · 2020: 123 · 2019: 144 · 2018: 111 · 2017: 123 · 2016: 121 · 2015: 119 · 2014: 94 · 2013: 73 · 2012: 1
- FWCI
- 8.37
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- 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 kinetics, folding funnel, energy landscape, thermal fluctuations, protein structure prediction, Protein Data Bank
- MeSH terms
- Amino Acid Sequence, Computer Simulation, Pharmaceutical Preparations, Models, Chemical, Protein Conformation, Proteins, Protein Folding, Caspase 9
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
