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

The victor/FRST function for model quality estimation.

Silvio C E Tosatto1

  • 1Department of Biology, University of Padova, Italy. silvio@cribi.unipd.it

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|December 29, 2005
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

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Tandem repeats matter for the functional versatility of giant proteins.

Trends in biochemical sciences·2026
Same author

Open and sustainable AI: challenges, opportunities and the road ahead in the life sciences.

Nature methods·2026
Same author

Exploring proteins and protein-ligand complexes through residue interaction networks.

Nature protocols·2026
Same author

Effects of Mutations on Tandem-Repeat Proteins Conformation Mechanisms. Application to the Phosphatase PP2A.

Journal of chemical information and modeling·2026
Same author

Toward a unified framework for determining conformational ensembles of disordered proteins.

Nature methods·2026
Same journal

GMSA: A Graph Matching and Point Cloud Registration-Based Method for Spatial Transcriptomics Data Alignment.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Investigations on Multiple Protein Scaffold Filling.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Cell Type Prediction for Single-Cell RNA Sequencing Utilizing Unsupervised Domain Adaptation and Semi-Supervised Learning.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

PPIGAN: Prediction of Protein-Protein Interactions Using Generative Adversarial Networks.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Deep Structure-Enhanced Cell Clustering Model for Single-Cell RNA Sequencing Data.

Journal of computational biology : a journal of computational molecular cell biology·2026
Same journal

Asymmetric Drug-Drug Interaction Prediction Based on Generative Adversarial Networks and Knowledge Graph.

Journal of computational biology : a journal of computational molecular cell biology·2026
See all related articles

A new scoring function combines four knowledge-based potentials to improve protein structure prediction accuracy. This energy function effectively distinguishes correct protein models from incorrect ones, enhancing model selection in prediction tasks.

Area of Science:

  • Computational Biology
  • Structural Bioinformatics
  • Biophysics

Background:

  • Scoring functions are crucial for selecting accurate protein structure models.
  • Distinguishing correct models from decoys is challenging in protein structure prediction.
  • Existing methods require robust scoring functions for comparative modeling and fold recognition.

Purpose of the Study:

  • To introduce and evaluate a novel scoring function for protein structure model selection.
  • To assess the performance of a combined knowledge-based potential in discriminating native-like structures.
  • To improve the accuracy of protein structure prediction through enhanced model selection.

Main Methods:

  • Developed a novel scoring function by combining four knowledge-based potentials: pairwise, solvation, hydrogen bond, and torsion angle.

Related Experiment Videos

  • Utilized a linear weighting function to integrate these potentials into a robust energy function.
  • Tested the scoring function on several benchmarking sets and a blind test (CAFASP-4 MQAP).

    • Main Results:

    • The torsion angle potential demonstrated the strongest correlation with protein model quality.
    • The combined energy function successfully discriminated native-like structures across various benchmarking sets.
    • In the CAFASP-4 MQAP blind test, the function reliably distinguished correct templates in 52 of 70 cases, including 33 of 34 for comparative modeling.

    Conclusions:

    • The novel scoring function, integrating orthogonal knowledge-based potentials, significantly enhances protein structure model selection.
    • The developed energy function is robust and effective in distinguishing native-like protein structures, outperforming previous methods.
    • An executable version of the Victor/FRST scoring function is available for researchers.