Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

From Levinthal to pathways to funnels

K A Dill1, H S Chan

  • 1Department of Pharmaceutical Chemistry, University of California, San Francisco 94143-1204, USA. dill@maxwell.ucsf.edu

Nature Structural Biology
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Driving forces in the origins of life.

Open biology·2021
Same author

Automated real-time detection of drug-resistant Mycobacterium tuberculosis on a lab-on-a-disc by Recombinase Polymerase Amplification.

Analytical biochemistry·2018
Same author

Severity of airflow limitation, co-morbidities and management of chronic obstructive pulmonary disease patients acutely admitted to hospital.

Hong Kong medical journal = Xianggang yi xue za zhi·2013
Same author

Production and characterization of polyclonal and monoclonal antibodies against Aflatoxin B1 oxime-BSA in an enzyme-linked immunosorbent assay.

Mycotoxin research·2013
Same author

Modeling stochastic dynamics in biochemical systems with feedback using maximum caliber.

The journal of physical chemistry. B·2011
Same author

Dynamical fluctuations in biochemical reactions and cycles.

Physical review. E, Statistical, nonlinear, and soft matter physics·2011
Same journal

Fingering nucleic acids: the RNA did it.

Nature structural biology·2003
Same journal

Histone H1.2 as a trigger for apoptosis.

Nature structural biology·2003
Same journal

Tom40: more than just a channel.

Nature structural biology·2003
Same journal

Announcing the worldwide Protein Data Bank.

Nature structural biology·2003
Same journal

Small RNAs come of age.

Nature structural biology·2003
Same journal

Recognition and processing of the origin of transfer DNA by conjugative relaxase TrwC.

Nature structural biology·2003
See all related articles

The new view of protein folding kinetics emphasizes an ensemble of conformations and multiple pathways, resolving the paradox of pathway independence. This energy landscape framework explains protein folding dynamics and awaits experimental validation.

Area of Science:

  • Biophysics
  • Structural Biology
  • Computational Biology

Background:

  • Classical protein folding models rely on phenomenological approaches and structural intermediates.
  • The concept of distinct folding pathways has been challenged by experimental observations like Anfinsen's experiments and Levinthal's paradox.

Purpose of the Study:

  • To present a new view of protein folding kinetics emphasizing conformational ensembles and multi-pathway diffusion.
  • To reconcile the apparent paradox between pathway-dependent models and pathway-independent folding.

Main Methods:

  • Conceptual framework based on the protein folding energy landscape.
  • Discussion of two-state and multi-state folding kinetics within the new paradigm.

Main Results:

Related Experiment Videos

  • The new view proposes folding as parallel, diffusion-like processes through an ensemble of conformations.
  • Protein folding funnels to a stable state via multiple routes in conformational space, eliminating the pathway paradox.
  • The energy landscape model provides a unified framework for understanding folding kinetics.

Conclusions:

  • The ensemble-based, multi-pathway view offers a more accurate representation of protein folding dynamics.
  • Future experimental measurements of fluctuation correlations are crucial for validating and refining energy landscape models.
  • Understanding protein folding landscapes is key to advancing structural biology and drug discovery.