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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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“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]
In the citing paper
“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]
In the citing paper
“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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BackgroundComputer Simulations of Globular Protein Folding and Tertiary Structure
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Background[6] Protein engineering in analysis of protein folding pathways and stability
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“4) implies that an Arrhenius plot of folding time versus inverse temperature would be curved, and in fact parabolic. Such curved Arrhenius plots are frequently observed in protein folding experiments [73]. Unfortunately, these plots can not be used to derive values for F‡ kin and ∆E(n‡ kin ) directly. First, our discu…”
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BackgroundEquilibrium folding and unfolding pathways for a model protein
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Sorted by citations
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Statistical potential for assessment and prediction of protein structures
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“Therefore, an accurate free energy function would enable the is to also prediction and assessment of protein structures (Dill 1985, 1997; Bryngelson et al. 1995; Dobson et al. 1998; Shakhnovich 2006).”
BackgroundFrom Levinthal to pathways to funnels
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THEORY OF PROTEIN FOLDING: The Energy Landscape Perspective
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Metadata
- 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
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 ›
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