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

Computing motif correlations in proteins.

Jorng-Tzong Horng1, Hsien-Da Huang, Shih-Hsien Wang

  • 1Department of Computer Science and Information Engineering, National Central University, Taiwan. horng@db.csie.ncu.edu.tw

Journal of Computational Chemistry
|October 8, 2003
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

DeepKbhb: Context-Aware Prediction of Human Lysine β-Hydroxybutyrylation Sites.

Journal of chemical information and modeling·2026
Same author

Arecoline as a Novel Scaffold Targeting the ATAD2 Bromodomain for Cell Cycle Modulation.

Pharmaceutics·2026
Same author

Laparoscopic identification and management of a pediatric direct inguinal hernia in a high-risk infant: A case report.

Urology case reports·2026
Same author

SiCmiR Atlas: Single-Cell miRNA Landscape Reveals Hub-miRNA and Network Signatures in Human Cancers.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

AI-powered rapid detection of multidrug-resistant <i>Klebsiella pneumoniae</i> with informative peaks of MALDI-TOF MS.

Bioinformatics advances·2026
Same author

DeepADR: multimodal prediction of adverse drug reaction frequency by integrating early-stage drug discovery information via Kolmogorov-Arnold networks.

Briefings in bioinformatics·2026

This study identifies correlations between protein motifs, conserved regions crucial for protein function and structure. These findings aid in understanding protein evolution and predicting protein structures.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein motifs are conserved sequence regions vital for protein function, folding, binding, and enzymatic activities.
  • Understanding motif sharing provides insights into protein biological functions and evolutionary processes across genomes.

Purpose of the Study:

  • To discover and analyze the occurrence correlations of protein motifs within protein sequences.
  • To explore the utility of identified motif correlations in protein structure prediction and evolutionary analyses.

Main Methods:

  • Utilized protein sequences from the PIR-NREF database.
  • Retrieved protein motifs from the PROSITE database.
  • Applied a data mining approach to identify motif occurrence correlations.

Related Experiment Videos

Main Results:

  • Discovered significant correlations in the co-occurrence of specific protein motifs.
  • Established a database storing mined motif correlations for public access.
  • Demonstrated the potential application of these correlations in protein structure prediction.

Conclusions:

  • The identified protein motif correlations offer a foundation for advancing protein structure prediction.
  • This approach provides valuable data for evolutionary analyses of protein families and genomes.