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 DEF data base of sequence based protein fold class predictions

M Reczko1, H Bohr

  • 1DKFZ-German Cancer Research Center, Heidelberg, Germany.

Nucleic Acids Research
|September 1, 1994
PubMed
Summary

A novel computational method predicts protein fold-classes and domains from amino acid sequences. This tool achieves high accuracy, even with low sequence identity, aiding protein structure prediction.

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

Neuroprotection requires the functions of the RNA-binding protein HuR.

Cell death and differentiation·2014
Same author

A novel approach to prediction of the 3-dimensional structures of protein backbones by neural networks.

FEBS letters·2009
Same author

DIANA-mirPath: Integrating human and mouse microRNAs in pathways.

Bioinformatics (Oxford, England)·2009
Same author

DIANA-microT web server: elucidating microRNA functions through target prediction.

Nucleic acids research·2009
Same author

Fiber ring laser with a feedback mirror.

Applied optics·2005
Same author

Enkephalins: Raman spectral analysis and comparison as function of pH 1-13.

Biopolymers·2003

Area of Science:

  • * Computational biology and bioinformatics.
  • * Structural bioinformatics and protein science.

Background:

  • * Predicting protein structure and function from amino acid sequences is a fundamental challenge in biology.
  • * Existing methods often struggle with accuracy, especially at low sequence identities.

Purpose of the Study:

  • * To develop and validate a new computational method for predicting protein fold-classes and domains.
  • * To create a database of protein fold-class assignments.
  • * To assess the accuracy and robustness of the prediction method.

Main Methods:

  • * Development of a novel algorithm for protein fold-class prediction from sequence data.
  • * Generation of a comprehensive database of protein fold-class assignments.
  • * Evaluation of prediction accuracy for both super fold-classes and specific fold-classes.

Main Results:

  • * The method assigns sequences to one of 45 specific protein fold-classes and one of 4 super fold-classes.
  • * Prediction accuracy reached approximately 91% for super fold-classes and 82% for specific fold-classes.
  • * High accuracy was maintained even with sequences sharing only a few percent identity.

Conclusions:

  • * The developed method provides accurate predictions of protein fold-classes and domains from sequence data.
  • * The generated database serves as a valuable resource for structural bioinformatics.
  • * The method's robustness at low sequence identities enhances its utility for diverse proteomic analyses.

Related Experiment Videos