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 and structural information to predict DNA-binding sites on DNA-binding proteins.

Igor B Kuznetsov1, Zhenkun Gou, Run Li

  • 1Gen*NY*sis Center for Excellence in Cancer Genomics, Department of Epidemiology and Biostatistics, University at Albany, Rensselaer, NewYork 12144, USA. Ikuznetsov@albany.edu

Proteins
|March 29, 2006
PubMed
Summary

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

Diagnostic value of systemic immune-inflammation composite index combined with triglyceride-glucose index in type 2 diabetes patients with coronary heart disease: a retrospective diagnostic model study.

BMC cardiovascular disorders·2026
Same author

Disruption of Histidine Biosynthesis Impairs Outer Membrane Stability and Intracellular Survival of <i>Brucella melitensis</i>, Resulting in Attenuated Virulence.

Microorganisms·2026
Same author

Thiaheptapyrrin and Thiatetrapyrrin Armed p-Phenylene-Bridged Norrole as NIR-II Dyes: Photothermal Behavior Effectively Enhanced by Protonation and Deprotonation.

Chemistry, an Asian journal·2026
Same author

Are diffusion models ready for materials discovery in unexplored chemical space?

Patterns (New York, N.Y.)·2026
Same author

Association Analysis Between HEI-2020 Index and Maternal Pregnancy Behaviors on ADHD in Adolescent Populations: A NHANES Cross-Sectional Study.

Developmental neurobiology·2026
Same author

Intratumor <i>Lactobacillus</i> drives ferroptosis resistance via D-lactate-STAT3 K631 lactylation in esophageal squamous cell carcinoma.

Gut microbes·2026

Predicting DNA-binding sites in proteins is crucial for understanding biological functions. Support Vector Machine (SVM) models using evolutionary conservation and structural data offer highly accurate predictions, outperforming other methods.

Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Computational Biology

Background:

  • DNA-binding proteins are essential for fundamental biological processes like DNA replication, transcription, and repair.
  • Accurate identification of DNA-binding sites is vital for protein function annotation, site-directed mutagenesis, and modeling protein-DNA interactions.

Purpose of the Study:

  • To develop and evaluate computational methods for predicting DNA-binding sites in proteins.
  • To assess the impact of different features, including amino acid sequence, evolutionary conservation, and structural information, on prediction accuracy.

Main Methods:

  • Application of Support Vector Machine (SVM), a supervised pattern recognition method.
  • Utilizing features such as amino acid sequence, position-specific scoring matrices (PSSM) derived from evolutionary conservation profiles, and low-resolution structural information.

Related Experiment Videos

  • Rigorous statistical analysis to evaluate predictor performance with various feature combinations.

    • Main Results:

    • SVM predictors utilizing evolutionary conservation profiles (PSSM) significantly outperformed PSSM-based neural network predictors.
    • The highest prediction accuracy was achieved by an SVM model combining evolutionary conservation profiles with low-resolution structural information.
    • Predictor performance was notably better for proteins in the mainly-alpha structural class and correlated with specific protein sequence and structural properties.

    Conclusions:

    • SVM models incorporating evolutionary conservation and structural data provide a robust approach for DNA-binding site prediction.
    • The findings suggest the potential for developing a reliability index for DNA-binding site predictions based on protein sequence and structural characteristics.
    • A web server implementing these predictors is available online for public use.