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 6,445
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
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
Items 901–1,000 of 1,001
Time‐Resolved Spectroscopy: Instrumentation and Applications
Ariese, F., Roy, K., Venkatraman, R. K., Sudeeksha, H. C., Kayal, S., & Umapathy, S. (2017) · Wiley eBooks · doi:10.1002/9780470027318.a9555 · Cited by 9
Decoding the effects of mutation on protein interactions using machine learning
Wang, X., Li, A., Zhao, Y., & Peng, Y. (2025) · Biophysics Reviews · doi:10.1063/5.0249920 · Cited by 7
Protein foundation models: a comprehensive survey
Xu, H., Li, L., Pan, S., Cheng, P., Wang, Y., Rong, Z., Feng, L., Huang, X., Wang, S., & Shu, W. (2026) · Science China Life Sciences · doi:10.1007/s11427-025-3147-2 · Cited by 8
Liu, W., Bergé‐Lefranc, D., Chaspoul, F., & Slaveykova, V. I. (2023) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2023.110647 · Cited by 6
Ho, C.-H., & Paesani, F. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c03245 · Cited by 6
Reconstituted Cell-free Translation Systems for Exploring Protein Folding and Aggregation
Taguchi, H., & Niwa, T. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168726 · Cited by 6
Allostery without Conformational Change: A Native Mass Spectrometry Perspective
Sun, H. M., Evans, K. A., Powers, M., Xi, Z., Lantz, C., Laganowsky, A. D., Rye, H. S., & Russell, D. H. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c03261 · Cited by 8
Todd, I. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105608 · Cited by 5
Horn, T. L., Ivanov, I., Venere, A. D., Kakularam, K. R., Reddanna, P., Conrad, M. L., Richter, C., Scheerer, P., & Kühn, H. (2013) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2013.08.004 · Cited by 6
MASTERS: A General Sequence-based MultiAgent System for Protein TERtiary Structure Prediction
Paes, T. L., & de Souza, O. N. (2014) · Electronic Notes in Theoretical Computer Science · doi:10.1016/j.entcs.2014.06.014 · Cited by 6
Slow and Bimolecular Folding of a De Novo Designed Monomeric Protein DS119
Zhu, C., Dai, Z., Liang, H., Zhang, T., Gai, F., & Lai, L. (2013) · Biophysical Journal · doi:10.1016/j.bpj.2013.09.014 · Cited by 6
Folding with a protein's native shortcut network
Khor, S. (2018) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25524 · Cited by 6
Шайтан, К. В. (2017) · BIOPHYSICS · doi:10.1134/s0006350917010183 · Cited by 6
Biophysical and Computational Studies of the vCCI:vMIP-II Complex
Nguyen, A., Kuo, N.-W., Showalter, L., Ramos, R. M., Dupureur, C. M., Colvin, M. E., & LiWang, P. J. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18081778 · Cited by 6
Stereochemistry of residues in turning regions of helical proteins
Kozak, J. J., & Gray, H. B. (2019) · JBIC Journal of Biological Inorganic Chemistry · doi:10.1007/s00775-019-01696-9 · Cited by 6
Becerra, D., Butyaev, A., & Waldispühl, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz743 · Cited by 6
Surface-enhanced Raman Scattering of Self-assembled Superstructures
Feng, E., & Tian, Y. (2021) · Chemical Research in Chinese Universities · doi:10.1007/s40242-021-1263-7 · Cited by 6
Moreira, M. H., Almeida, F. C. L., Domitrovic, T., & Palhano, F. L. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.11.015 · Cited by 6
Campini, J. C., Aguilar, M. A., & Martín, M. E. (2022) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2022.118557 · Cited by 6
König, H. F., Hausmann, H., & Schreiner, P. R. (2022) · Journal of the American Chemical Society · doi:10.1021/jacs.2c06141 · Cited by 6
Stochastic transitions: Paths over higher energy barriers can dominate in the early stages
Fitzgerald, S. P., Hass, A. B., Leines, G. D., & Archer, A. J. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0135880 · Cited by 6
Moore, C. C., Staroverov, V. N., & Konermann, L. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c01581 · Cited by 6
Entangled Motifs in Membrane Protein Structures
Salicari, L., & Trovato, A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24119193 · Cited by 6
Ali, H., Mahmood, M., Adnan, M., Afzal, G., Perveen, S., Akhtar, R. W., Jabeen, R., Asif, A. R., Faryal, S., Rahman, A., et al. (2023) · Journal of King Saud University - Science · doi:10.1016/j.jksus.2023.102772 · Cited by 6
Effects of Methyl Side Chains on the Microsolvation Structure of Protonated Tripeptides
Sherman, S. L., Fischer, K. C., & Garand, É. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c03711 · Cited by 6
Why Do Proteins Fold into Unique 3D Structures? And Other Questions...
Шайтан, К. В. (2023) · Russian Journal of Physical Chemistry B · doi:10.1134/s1990793123030259 · Cited by 6
A benchmark study of protein folding algorithms on nanobodies
Liang, S., Liang, Z., Wu, Z., Huang, F., Wang, X., Tan, Z., He, R., Lu, Z., Cai, Y., Huang, B., et al. (2023) · International Conference on Communications, Computing and Artificial Intelligence (CCCAI) · doi:10.1109/cccai59026.2023.00025 · Cited by 5
Recent Advances in Protein Folding Pathway Prediction through Computational Methods
Zhao, K., Liang, F., Xia, Y., Hou, M., & Zhang, G. (2023) · Current Medicinal Chemistry · doi:10.2174/0109298673265249231004193520 · Cited by 5
Essential dynamics of ubiquitin in water and in a natural deep eutectic solvent
Gomes, I., Martins, G. F., & Galamba, N. (2024) · Physical Chemistry Chemical Physics · doi:10.1039/d4cp01773k · Cited by 5
Ulloa‐Aguirre, A., & Conn, P. M. (2014) · Elsevier eBooks · doi:10.1016/b978-0-12-408134-5.00021-4 · Cited by 6
Rocha, G. K., Custódio, F. L., Barbosa, H. J. C., & Dardenne, L. E. (2015) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb.2015.7300284 · Cited by 6
Single-molecule spectroscopy of unfolded proteins and chaperonin action
Hofmann, H. (2014) · Biological Chemistry · doi:10.1515/hsz-2014-0132 · Cited by 6
Impact of a Reducing Agent on the Dynamic Surface Properties of Lysozyme Solutions
Tihonov, M. M., Kim, V. V., & Noskov, B. A. (2016) · Journal of Oleo Science · doi:10.5650/jos.ess15247 · Cited by 6
Robotic folding of 2D and 3D structures from a ribbon
Wang, L., Plecnik, M. M., & Fearing, R. S. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487550 · Cited by 6
Kineman, J. J. (2016) · Translational systems sciences · doi:10.1007/978-981-10-0263-2_2 · Cited by 6
Quantitative Study of the Oligomerization of Yeast Prion Sup35NM Proteins
Wang, Y., & Wu, C. (2017) · Biochemistry · doi:10.1021/acs.biochem.7b00966 · Cited by 6
Exploring the conformational space for protein folding with sequential Monte Carlo
Wong, S. W. K., Liu, J. S., & Kou, S. C. (2018) · The Annals of Applied Statistics · doi:10.1214/17-aoas1124 · Cited by 7
Filippis, V. D., Pozzi, N., Acquasaliente, L., Artusi, I., Pontarollo, G., & Peterle, D. (2018) · Peptide Science · doi:10.1002/pep2.24090 · Cited by 6
Protein Inter-Residue Contacts Prediction: Methods, Performances and Applications
Jing, X., Dong, Q., Lu, R., & Dong, Q. (2018) · Current Bioinformatics · doi:10.2174/1574893613666181109130430 · Cited by 6
Effect of Weakly Interacting Cosolutes on Lysozyme Conformations
Levartovsky, Y., Shemesh, A., Asor, R., & Raviv, U. (2018) · ACS Omega · doi:10.1021/acsomega.8b01289 · Cited by 6
Energetic Self-Folding Mechanism in α-Helices
Bastida, A., Zúñiga, J., Requena, A., & Cerezo, J. (2019) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b05860 · Cited by 6
Carrillo-Cabada, H., Benson, J., Razavi, A. M., Mulligan, B. S., Cuendet, M. A., Weinstein, H., Taufer, M., & Estrada, T. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2945291 · Cited by 6
Wang, D., & Marszałek, P. E. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00080 · Cited by 6
Cui, Y., Allmon, S. D., & Siegel, J. B. (2020) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.10547 · Cited by 6
Research progress of molecular structure and dynamics of biological water
Ye, S., Chuan-Zhao, L., Zhang, J., Jun-Jun, T., & Luo, Y. (2019) · Acta Physica Sinica · doi:10.7498/aps.68.20181273 · Cited by 6
Structure and Dynamics of Ribonuclease A during Thermal Unfolding: The Failure of the Zimm Model
Fischer, J., Rădulescu, A., Falus, P., Richter, D., & Biehl, R. (2021) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.0c09476 · Cited by 6
Hata, Y., Li, X., Chung, U., & Sakai, T. (2021) · Polymer Journal · doi:10.1038/s41428-021-00461-7 · Cited by 6
Effective screening of medium-assisted van der Waals interactions between embedded particles
Fiedler, J., Walter, M., & Buhmann, S. Y. (2021) · NORA - Norwegian Open Research Archives · Cited by 6
A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing
Sánchez-Hernández, J. P., Frausto–Solís, J., Barbosa, J. J. G., Soto-Monterrubio, D. A., Maldonado-Nava, F. G., & Valdez, G. C. (2021) · Mathematical and Computational Applications · doi:10.3390/mca26020039 · Cited by 6
Badasyan, A. V., Tonoyan, S. A., Valant, M., & Grdadolnik, J. (2021) · Communications Chemistry · doi:10.1038/s42004-021-00499-x · Cited by 6
Krishnamohan, A., Hamilton, G. L., Goutam, R., Sanabria, H., & Morcos, F. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c06720 · Cited by 6
van der Linden, M. G., Ferreira, D. C., de Oliveira, L. C., Onuchic, J. N., & de Araújo, A. F. P. (2013) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24483 · Cited by 6
Network measures for protein folding state discrimination
Menichetti, G., Fariselli, P., & Remondini, D. (2016) · Scientific Reports · doi:10.1038/srep30367 · Cited by 6
Ferreira, D. C., van der Linden, M. G., de Oliveira, L. C., Onuchic, J. N., & de Araújo, A. F. P. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.24998 · Cited by 6
Sauro Succi, A. (2019) · The European Physical Journal Plus · doi:10.1140/epjp/i2019-12576-3 · Cited by 6
Energy-dependent protein folding: modeling how a protein folding machine may work
Sahakyan, H., Nazaryan, K., Mushegian, A. R., & Sorokina, I. (2021) · F1000Research · doi:10.12688/f1000research.28175.1 · Cited by 6
Structural and functional characterization of a recombinant leucine aminopeptidase
Hernández-Moreno, A. V., Perdomo‐Abúndez, F. C., Martínez, V. P. M., Luna‐Bárcenas, G., Villaseñor‐Ortega, F., Pérez, N. O., López‐Morales, C. A., Flores-Ortiz, L. F., & Medina‐Rivero, E. (2015) · Journal of Molecular Catalysis B Enzymatic · doi:10.1016/j.molcatb.2014.12.013 · Cited by 6
Pathway regulation mechanism revealed by cotranslational folding of villin headpiece subdomain HP35
Tao, P., Wang, E., & Xiao, Y. (2020) · Physical review. E · doi:10.1103/physreve.101.052403 · Cited by 6
Driving Forces in Pressure-Induced Protein Transitions
Chalikian, T. V. (2015) · Sub-cellular biochemistry/Subcellular biochemistry · doi:10.1007/978-94-017-9918-8_3 · Cited by 6
MUFold-SS: Protein Secondary Structure Prediction Using Deep Inception-Inside-Inception NetworksPreprint
Fang, C., Shang, Y., & Xu, D. H. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.06165 · Cited by 6
Sequence‐Controlled Peptoid Polymers: Bridging the Gap between Biology and Synthetic Polymers
Kline, M. A., Guo, L. Q., & Zuckermann, R. N. (2017) · doi:10.1002/9783527806096.ch7 · Cited by 6
Evolving Drug Design Methodology: from QSAR to AIDDPreprint
Xu, J. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-9fwmg · Cited by 6
Mapping cryptic phosphorylation sites in the human proteome
Gasparotto, D., Zanon, A., Bonaldo, V., Marchiori, E., Casagranda, M., Di Domenico, E., Copat, L., Asquini, T. F., Rigoli, M., Feltrin, S. V., et al. (2025) · The EMBO Journal · doi:10.1038/s44318-025-00567-1 · Cited by 9
Ma, T., Mu, B., & Tian, W. (2025) · Angewandte Chemie International Edition · doi:10.1002/anie.202500354 · Cited by 5
Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01198-9 · Cited by 5
Adaptive patch grid strategy for parallel protein folding using atomic burials with NAMD
Macedo, E. D. A., & de Melo, A. C. M. A. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104868 · Cited by 5
Structure-Based Protein Assembly Simulations Including Various Binding Sites and Conformations
Walter, L. J., Quoika, P. K., & Zacharias, M. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00212 · Cited by 5
Annealed fractional Lévy–Itō diffusion models for protein generation
Paquet, E., Soleymani, F., Viktor, H. L., & Michalowski, W. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.009 · Cited by 7
iMAX FRET (Information Maximized FRET) for Multipoint Single-Molecule Structural Analysis
Joshi, B. S., de Lannoy, C. V., Howarth, M. R., Kim, S. H., & Joo, C. (2024) · Nano Letters · doi:10.1021/acs.nanolett.4c00447 · Cited by 5
Cai, F., Wei, Y., Kirchhofer, D., Chang, A. C., & Zhang, Y. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012609 · Cited by 5
Benchmarking recent computational tools for DNA-binding protein identification
Luo, X., Yi, A. S., HuiKai, A. L., Ong, T. J., Wong, L., & Rahman, C. R. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae634 · Cited by 6
Chameleon sequences—Structural effects
Słupina, M., Stąpor, K., Konieczny, L., Gądek, K., Nowakowski, P., & Roterman, I. (2025) · PLoS ONE · doi:10.1371/journal.pone.0315901 · Cited by 5
Huang, Y., Xu, X., Guo, X., Kong, B., Guo, Z., Liu, H., & Wang, Z. (2025) · Food Chemistry · doi:10.1016/j.foodchem.2025.145895 · Cited by 5
Mesoscopic Self-Assembly: A Shift to Complexity
Mastrangeli, M. (2015) · Frontiers in Mechanical Engineering · doi:10.3389/fmech.2015.00006 · Cited by 5
Human aldose reductase unfolds through an intermediate
Sekhon, G., & Singh, R. (2019) · F1000Research · doi:10.12688/f1000research.18963.1 · Cited by 5
Wang, Y., Tian, P., Boomsma, W., & Lindorff‐Larsen, K. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b06335 · Cited by 5
Non-Markov bond model for dynamic force spectroscopy
Bullerjahn, J. T., Sturm, S., & Kroy, K. (2020) · The Journal of Chemical Physics · doi:10.1063/1.5134742 · Cited by 5
Causation and cognition: an epistemic approach
Taylor, S. D. (2021) · Synthese · doi:10.1007/s11229-021-03197-2 · Cited by 5
Schennach, M., & Breuker, K. (2013) · Angewandte Chemie · doi:10.1002/ange.201306838 · Cited by 5
Meinhold, D. W., Felitsky, D. J., Dyson, H. J., & Wright, P. E. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c05592 · Cited by 5
A cavity formation energy formula for hard spheres in simple electrolyte solutions
Xiao, T., & Zhou, Y. (2023) · Physical Chemistry Chemical Physics · doi:10.1039/d3cp00623a · Cited by 5
Vergara, R., Berrocal, T., Mejía, E. I. J., Romero‐Romero, S., Velázquez‐López, I., Pulido, N. O., Sánchez, H. A. L., Silva, D., Costas, M., Rodríguez‐Romero, A., et al. (2023) · FEBS Journal · doi:10.1111/febs.16819 · Cited by 5
Choi, S., Son, S. H., Kim, M. Y., Na, I., Uversky, V. N., & Kim, C. G. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.126526 · Cited by 5
Santhouse, J. R., Leung, J. M. G., Chong, L. T., & Horne, W. S. (2023) · Chemical Science · doi:10.1039/d3sc03976e · Cited by 5
Nouri, B., & Chen, H. (2024) · Journal of Polymer Research · doi:10.1007/s10965-024-03958-w · Cited by 5
Louros, N. N., Rousseau, F., & Schymkowitz, J. W. H. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae279 · Cited by 5
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-Training
Liu, Y., Li, M., Li, X., Huang, L., Giunchiglia, F., Liang, Y., Feng, X., & Guan, R. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3679018 · Cited by 5
Direct coupling analysis and the attention mechanism
(2025) · doi:10.1186/s12859-025-06062-y · Cited by 5
Conformational ensembles for protein structure prediction
Yang, J., Cheng, W. X., Zhang, P., Wu, G., Sheng, S. T., Yang, J., Zhao, S., Shi, Q., Ji, W., & Shi, Q. (2025) · Scientific Reports · doi:10.1038/s41598-024-84066-z · Cited by 7
Applications of Computational Methods to Simulations of Proteins Dynamics
Nowak, W. (2017) · Springer eBooks · doi:10.1007/978-3-319-27282-5_31 · Cited by 5
Wang, J., & Wang, W. (2016) · Advances in Physics X · doi:10.1080/23746149.2016.1216329 · Cited by 5
Fleming, L. (2017) · Philosophy Theory and Practice in Biology · doi:10.3998/ptb.6959004.0009.002 · Cited by 5
Hosseini, A. S., Pace, C. J., Esposito, A. A., & Gao, J. (2017) · Protein Science · doi:10.1002/pro.3242 · Cited by 5
Itoh, Y., & Aida, T. (2017) · Nature Chemistry · doi:10.1038/nchem.2870 · Cited by 5
Paixão-Côrtes, V. S. M., Tanus, M. D. S. D. S., Paixão-Côrtes, W. R., de Souza, O. N., Campos, M. D. B., & Silveira, M. S. (2018) · Information · doi:10.3390/info9010020 · Cited by 5
Dynamic ACO-based Fault Tolerance in Grid Computing
Bukhari, S., Ku‐Mahamud, K. R., & Morino, H. (2017) · International Journal of Grid and Distributed Computing · doi:10.14257/ijgdc.2017.10.12.11 · Cited by 5
Incorporating Polarizability of Backbone Hydrogen Bonds Improved Folding of Short α-Helical Peptides
Zhang, D., Lazim, R., & Yip, Y. M. (2019) · Biophysical Journal · doi:10.1016/j.bpj.2019.10.020 · Cited by 5
GRSA Enhanced for Protein Folding Problem in the Case of Peptides
Frausto–Solís, J., Sánchez-Hernández, J. P., Maldonado-Nava, F. G., & Barbosa, J. J. G. (2019) · Axioms · doi:10.3390/axioms8040136 · Cited by 5
Investigating the potential for a limited quantum speedup on protein lattice problems
Outeiral, C., Morris, G. M., Shi, J., Strahm, M., Benjamin, S. C., & Deane, C. M. (2021) · New Journal of Physics · doi:10.1088/1367-2630/ac29ff · Cited by 8
Michaelis, M., Cupellini, L., Mensch, C., Perry, C. C., Delle Piane, M., & Colombi Ciacchi, L. (2023) · Chemical Science · doi:10.1039/d3sc02202a · Cited by 6
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