Funnels, pathways, and the energy landscape of protein folding: A synthesis
Open accessAbstract
The understanding, and even the description of protein folding is impeded by the complexity of the process. Much of this complexity can be described and understood by taking a statistical approach to the energetics of protein conformation, that is, to the energy landscape. The statistical energy landscape approach explains when and why unique behaviors, such as specific folding pathways, occur in some proteins and more generally explains the distinction between folding processes common to all sequences and those peculiar to individual sequences. This approach also gives new, quantitative insights into the interpretation of experiments and simulations of protein folding thermodynamics and kinetics. Specifically, the picture provides simple explanations for folding as a two-state first-order phase transition, for the origin of metastable collapsed unfolded states and for the curved Arrhenius plots observed in both laboratory experiments and discrete lattice simulations. The relation of these quantitative ideas to folding pathways, to uniexponential vs. multiexponential behavior in protein folding experiments and to the effect of mutations on folding is also discussed. The success of energy landscape ideas in protein structure prediction is also described. The use of the energy landscape approach for analyzing data is illustrated with a quantitative analysis of some recent simulations, and a qualitative analysis of experiments on the folding of three proteins. The work unifies several previously proposed ideas concerning the mechanism protein folding and delimits the regions of validity of these ideas under different thermodynamic conditions.
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Statistical energy landscape theory originated as a framework for studying protein folding.
“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 [129]
According to the minimum frustration principle, evolution has optimized folding, and mutations perturb the landscape much like temperature changes.
“The minimum frustration principle implies that nature has optimized protein folding,129,174 thus the effect of point mutations on the energy landscape is analogous to thermal perturbations.175”
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 [129]
Levinthal argued that there is no need to search the vast folding space because the energy landscape is funnel-like and can guide sampling toward the biological conformational basin.
“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 [153]
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Flagged first, then by citations
Cited by
Sorted by citations
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BackgroundFrom Levinthal to pathways to funnels
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- Authors
- Bryngelson, J. D., Onuchic, J. N., Socci, N. D., & Wolynes, P. G.
- Affiliations
- National Institutes of Health (US)
- University of California San Diego (US)
- University of Illinois Urbana-Champaign (US)
- Corresponding author
- Bryngelson, Joseph D.
- Venue
- Proteins: Structure, Function, and Bioinformatics
- Publisher
- Wiley
- Published
- 1995-03
- Volume
- 21
- Issue
- 3
- Pages
- 167-195
- ISSN
- 0887-3585 (ISSN-L)
- 1097-0134
- OpenAlex
- W2171755502
- Cited by
- 2,648
- References
- 130
- Citations by year
- 2026: 41 · 2025: 58 · 2024: 54 · 2023: 54 · 2022: 86 · 2021: 86 · 2020: 78 · 2019: 72 · 2018: 110 · 2017: 92 · 2016: 104 · 2015: 90 · 2014: 104 · 2013: 123 · 2012: 107
- FWCI
- 32.88
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proteins Structure Function and Bioinformatics (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of General Medical Sciences (1R01 GM44557, R01GM044557)
- 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 › Molecular Biology › Protein purification and stability
- Keywords
- protein folding, energy landscape, folding funnel, folding pathways, Arrhenius plot, folding kinetics, folding thermodynamics, protein structure prediction, lattice simulation
- MeSH terms
- Amino Acid Sequence, Chemistry, Physical, Computer Simulation, Data Interpretation, Statistical, Kinetics, Models, Chemical, Molecular Sequence Data, Protein Conformation, Proteins, Thermodynamics, Protein Biosynthesis, Protein Folding, Chemical Phenomena
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