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

Evolutionary computer programming of protein folding and structure predictions.

Bengt Nölting1, Dennis Jülich, Winfried Vonau

  • 1Prussian Private Institute of Technology at Berlin, Am Schlosspark 30, Berlin D-13187, Germany. nolting@pitb.de

Journal of Theoretical Biology
|June 5, 2004
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

Lithium treatment for affective disorders: Exploring the potential of salivary therapeutic monitoring.

Neuroscience applied·2025
Same author

How general is the nucleation-condensation mechanism?

Proteins·2008
Same author

Protein folding forces.

Journal of theoretical biology·2008
Same author

Electric cell-substrate impedance sensing with screen printed electrode structures.

Lab on a chip·2006
Same author

Structural determinants of the rate of protein folding.

Journal of theoretical biology·2003

This study optimized protein folding simulations using evolutionary computer programming. The evolved program found protein folding energy minima over 10 times faster, aiding artificial enzyme design.

Area of Science:

  • Computational Biology
  • Biophysics
  • Bioinformatics

Background:

  • Understanding protein folding mechanisms is crucial for designing stable artificial enzymes.
  • Accurate simulation of protein folding reactions and structure prediction is essential for biomacromolecular research.

Purpose of the Study:

  • To enhance protein folding simulations using evolutionary computer programming.
  • To accelerate the identification of deep minima in protein folding energy landscapes.

Main Methods:

  • Applied evolutionary computer programming to a protein folding simulation program.
  • Introduced evolutionary pressure favoring faster discovery of energy landscape minima.

Main Results:

  • The evolved program demonstrated a significant speed increase in finding energy minima.

Related Experiment Videos

  • Achieved more than a 10-fold improvement in simulation speed after 20 evolution steps.

    • Conclusions:

    • Evolutionary computer programming effectively accelerates protein folding simulations.
    • This method holds promise for the rational de novo design of fast-folding, stable artificial enzymes.