The DOI matches its Crossref record; 75 of the 75 references it cites were found in a registry or an index.
Folding funnels and binding mechanisms
Abstract
The long-held views on lock-and-key versus induced fit in binding arose from the notion that a protein exists in a single, most stable conformation, dictated by its sequence. However, in solution proteins exist in a range of conformations, which may be described by statistical mechanical laws and their populations follow statistical distributions. Upon binding, the equilibrium will shift in favor of the bound conformation from the ensemble of conformations around the bottom of the folding funnel. Hence here we extend the implications and the usefulness of the folding funnel concept to explain fundamental binding mechanisms.
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In the citing paper
“The ‘conformational selection and population shift’ model87–89,134 provides a more realistic description of the molecular mechanism considering the ensemble nature of protein molecules.”
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 [87]
In the citing paper
“During binding, higher energy (lower population) conformers which are most complementary to some pre-existing ligand conformations can be selected and the equilibrium shifts toward these conformers.87–89,134”
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 [87]
In the citing paper
“biomolecules are dynamical objects that are always interconverting between a variety of structures with varying energies,166 and that this is the origin of allosteric mechanisms.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [168]
In the citing paper
“In contrast, the conformational selection mechanism167−169,185 theorizes that the energy surface hosts a very large number of conformations, and the one that fits best is selected, with subsequent minor induced fit optimization, largely by side chains.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [168]
Citations
Cites
The Energy Landscapes and Motions of Proteins
Frauenfelder, H., Sligar, S. G., & Wolynes, P. G. (1991) · Science · doi:10.1126/science.1749933 · Cited by 3,250
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
Jarrett, J. T., & Lansbury, P. T. (1993) · Cell · doi:10.1016/0092-8674(93)90635-4 · Cited by 2,178
Application of a Theory of Enzyme Specificity to Protein Synthesis.
Koshland, D. (1958) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.44.2.98 · Cited by 2,140
Molecular Biology of Prion Diseases
Prusiner, S. B. (1991) · Science · doi:10.1126/science.1675487 · Cited by 1,975
Einfluss der Configuration auf die Wirkung der Enzyme
Fischer, E. (1894) · Berichte der deutschen chemischen Gesellschaft · doi:10.1002/cber.18940270364 · Cited by 1,841
Creighton, T. E. (1992) · Cited by 1,583
Principles of protein folding — A perspective from simple exact models
Dill, K. A., Bromberg, S., Yue, K., Chan, H. S., Fiebig, K. M., Yee, D. P., & Thomas, P. D. (1995) · Protein Science · doi:10.1002/pro.5560040401 · Cited by 1,529
Three-Dimensional Structure of an Antigen-Antibody Complex at 2.8 Å Resolution
Amit, A. G., Mariuzza, R. A., Phillips, S. E. V., & Poljak, R. J. (1986) · Science · doi:10.1126/science.2426778 · Cited by 1,183
Wolynes, P., Onuchic, J., & Thirumalai, D. (1995) · Science · doi:10.1126/science.7886447 · Cited by 1,080
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Metadata
- Authors
- Ma, B., Kumar, S., Tsai, C.-J., & Nussinov, R.
- Affiliations
- National Institutes of Health (US)
- National Cancer Institute (US)
- Venue
- Protein Engineering, Design and Selection
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-09
- Volume
- 12
- Issue
- 9
- Pages
- 713-720
- ISSN
- 0269-2139 (ISSN-L)
- 1741-0134
- 1741-0126
- OpenAlex
- W2123529526
- Cited by
- 594
- References
- 75
- Citations by year
- 2026: 12 · 2025: 15 · 2024: 16 · 2023: 27 · 2022: 21 · 2021: 30 · 2020: 29 · 2019: 27 · 2018: 21 · 2017: 35 · 2016: 33 · 2015: 19 · 2014: 34 · 2013: 43 · 2012: 26
- FWCI
- 0.70
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Protein Engineering Design and Selection (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services
- Israel Science Foundation
- Tel Aviv University
- National Institutes of Health (n01co056000-057, NO1-CO-56000, CO-56000)
- National Cancer Institute (N01 CO 56000, NO1CO56000)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Interaction Studies and Fluorescence Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
- Keywords
- folding funnel, protein binding mechanism, induced fit, lock-and-key model, protein conformational ensembles, conformational statistics, binding equilibrium, protein folding
- MeSH terms
- Peptides, Protein Binding, Protein Conformation, Proteins, Protein Folding
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