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

Function prediction of uncharacterized proteins.

Troy Hawkins1, Daisuke Kihara

  • 1Department of Biological Sciences, Purdue University, West Lafayette, IN, USA. thawkins@purdue.edu

Journal of Bioinformatics and Computational Biology
|May 5, 2007
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

DAQplugin: Deep Learning based Real-time Model Evaluation Plugin for ChimeraX.

bioRxiv : the preprint server for biology·2026
Same author

Direct Detection and Atomic Modeling of Ligands in Cryo-EM Maps Using Deep Learning.

bioRxiv : the preprint server for biology·2026
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

PL-PatchSurfer3: improved structure-based virtual screening for structure variation using 3D Zernike descriptors.

Journal of cheminformatics·2026
Same author

Multivalent recognition of ferritin by full-length NCOA4 enables robust ferritinophagy.

Protein science : a publication of the Protein Society·2026
Same author

MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.

Knowledge-based systems·2026
Same journal

CNV-ECOD: A copy number variation detection method based on ECOD algorithm using next-generation sequencing data.

Journal of bioinformatics and computational biology·2026
Same journal

ReinVar: A model-free paradigm-based reinforcement learning approach to detect copy number variation.

Journal of bioinformatics and computational biology·2026
Same journal

When pipelines run but coordinates fail: A simple spatial specificity check for false locality in post-GWAS analysis.

Journal of bioinformatics and computational biology·2026
Same journal

Comparative benchmarking of template-based, evolutionary-diffusion, and generative language models for IsPETase structure prediction.

Journal of bioinformatics and computational biology·2026
Same journal

Trap spaces as labelled ideals of SCC posets: A structural-functional theory of reachability in asynchronous boolean networks.

Journal of bioinformatics and computational biology·2026
Same journal

Erratum - DDINet: Drug-drug interaction prediction network based on multi-molecular fingerprint features and multi-head attention centered weighted autoencoder.

Journal of bioinformatics and computational biology·2026
See all related articles

Computational biology advances protein function prediction beyond sequence similarity. New methods using structure, genomics, and proteomics data offer greater accuracy for uncharacterized proteins.

Area of Science:

  • Computational biology
  • Bioinformatics
  • Genomics
  • Proteomics

Background:

  • Genome projects generate vast numbers of uncharacterized protein sequences.
  • Traditional sequence similarity methods have limitations in predicting protein function.
  • Integrating diverse data types is crucial for accurate function prediction.

Purpose of the Study:

  • To review and categorize computational approaches for protein function prediction beyond sequence similarity.
  • To highlight the advantages of structure-, association-, interaction-, process-, and experiment-based methods.
  • To emphasize the need for integrated systems biology techniques.

Main Methods:

  • Categorization of computational function prediction methods.

Related Experiment Videos

  • Review of structure-based approaches.
  • Analysis of genomic context (association), cellular context (interaction), metabolic context (process), and proteomics experiment-based methods.

    • Main Results:

    • Several non-sequence-based methods enhance protein function prediction accuracy and reliability.
    • These methods leverage structural and experimental data.
    • Online resource tables are provided for each category.

    Conclusions:

    • Advanced computational methods significantly improve protein function prediction for uncharacterized proteins.
    • Further development of comprehensive systems biology approaches is essential.
    • Collaboration between computational and experimental biology is vital for future research.