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

A free-rotating and self-avoiding chain model for deriving statistical potentials based on protein structures.

Ji Cheng1, Jianfeng Pei, Luhua Lai

  • 1State Key Laboratory for Structural Chemistry of Stable and Unstable Species, College of Chemistry and Molecular Engineering, and Center for Theoretical Biology, Peking University, Beijing, China.

Biophysical Journal
|March 14, 2007
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

Kaempferitrin attenuates DSS-induced colitis by promoting ubiquitination-mediated degradation of the nuclear factor kappa B p65.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

Recent Advances in Natural Products for Colitis: Mechanisms and Translational Perspectives.

Veterinary sciences·2026
Same author

Modulating Host Lipid Metabolism via Gut Microbiota: Therapeutic Potential of Plant-Derived Compounds.

Phytotherapy research : PTR·2026
Same author

Maladaptive appearance perfectionism and psychosocial distress among cosmetic dermatology patients.

Frontiers in public health·2026
Same author

L-Theanine activates the mTOR signaling pathway through TAS1R1-FRMD6 to promote milk synthesis in bovine mammary epithelial cells.

The Journal of nutritional biochemistry·2026
Same author

Use and Influencing Factors of mHealth Services Among Adult Survivors of Cancer: Cross-Sectional Survey Study.

Journal of medical Internet research·2026
Same journal

Tau protein differentially affects Piezo1 and Kir2.1 channels in brain capillary endothelial cells.

Biophysical journal·2026
Same journal

Emergent Intercellular Junction Stability during Cyclic Tissue Loading.

Biophysical journal·2026
Same journal

Enhanced-Sampling Simulations Reveal Distinct Intermediates in SARS-CoV-2 FSE Pseudoknot Interconversion.

Biophysical journal·2026
Same journal

Structure-based simulations of the full Flock House virus capsid reveal pathways and energetics of an infection-critical peptide externalization event.

Biophysical journal·2026
Same journal

Quantifying the Peripheral Surface Information Entropy from Conformational Ensembles of Globular Protein-Peptide Complexes.

Biophysical journal·2026
Same journal

Anisotropic unbinding and location-dependent hovering of a kinesin motor head over microtubule.

Biophysical journal·2026
See all related articles

This study introduces two novel physical reference states for statistical potentials in protein studies, improving protein fold recognition accuracy. These new potentials offer enhanced performance over existing methods.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Statistical potentials are crucial in protein studies but face theoretical challenges.
  • Existing methods often lack a robust theoretical foundation for their reference states.

Purpose of the Study:

  • To develop and evaluate new statistical potentials derived from physical reference states.
  • To improve the accuracy and reliability of protein fold recognition.

Main Methods:

  • Applied two physical reference states: free-rotating chain and self-avoiding chain.
  • Generated artificial self-avoiding backbones using Monte Carlo simulation for one reference state.
  • Ensured reference states were independent of known protein structures.

Related Experiment Videos

Main Results:

  • The new potentials demonstrated superior performance with higher Z-scores and success rates.
  • The self-avoiding chain model's end-to-end distance distribution closely matched protein atom distributions.
  • Physical reference models enhance statistical potential performance in protein fold recognition.

Conclusions:

  • A more physically grounded reference model significantly improves statistical potentials for protein fold recognition.
  • These enhanced potentials have potential applications beyond fold recognition in other protein studies.