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

Using evolutionary trees in protein secondary structure prediction and other comparative sequence analyses

N Goldman1, J L Thorne, D T Jones

  • 1Department of Genetics, University of Cambridge, UK.

Journal of Molecular Biology
|October 25, 1996
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

Circular Dichroism on the Edge of Quantum Hall Systems: From Many-Body Chern Number to Anisotropy Measurements.

Physical review letters·2026
Same author

Recent Developments in DFTB+, a Software Package for Efficient Atomistic Quantum Mechanical Simulations.

The journal of physical chemistry. A·2025
Same author

Thouless Pumping in a Driven-Dissipative Kerr Resonator Array.

Physical review letters·2025
Same author

Publisher Correction: Brain charts for the human lifespan.

Nature·2022
Same author

Brain charts for the human lifespan.

Nature·2022
Same author

Changes in Ventricular and Cortical Volumes following Shunt Placement in Patients with Idiopathic Normal Pressure Hydrocephalus.

AJNR. American journal of neuroradiology·2021

This study introduces a hidden Markov model for protein secondary structure prediction, improving accuracy by leveraging evolutionary information from sequence alignments. The method explicitly considers the phylogenetic tree, enhancing evolutionary insights for better predictions.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Structural Biology

Background:

  • Existing protein secondary structure prediction methods inadequately utilize evolutionary information present in multiple sequence alignments.
  • Failure to incorporate phylogenetic relationships limits the accuracy of current prediction models.

Purpose of the Study:

  • To develop a novel hidden Markov model approach for enhanced protein secondary structure prediction.
  • To improve the extraction and application of evolutionary information from protein sequence alignments.

Main Methods:

  • Development and application of a hidden Markov model (HMM) for secondary structure prediction.
  • Explicit incorporation of phylogenetic tree information into the HMM framework.
  • Comparative analysis of prediction accuracy with and without phylogenetic considerations.

Related Experiment Videos

Main Results:

  • The hidden Markov model approach demonstrated improved accuracy in protein secondary structure prediction compared to previous methods.
  • Experiments confirmed that explicitly representing evolutionary relatedness enhances prediction inferences.
  • The study provides a representative example and experimental validation of the proposed method.

Conclusions:

  • The novel HMM approach effectively utilizes evolutionary information for more accurate protein secondary structure prediction.
  • Incorporating phylogenetic relationships is crucial for maximizing the utility of evolutionary data in sequence analysis.
  • The findings suggest broader applicability to other comparative sequence analysis methods.