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

Amino acid composition predicts prion activity.

Fayyaz Ul Amir Afsar Minhas1, Eric D Ross2, Asa Ben-Hur3

  • 1Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.

Plos Computational Biology
|April 11, 2017
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

On the state of protein function prediction: a report on the fourth CAFA challenge.

bioRxiv : the preprint server for biology·2026
Same author

Accuracy of non-invasive electrocardiographic imaging in scar-dependent ventricular tachycardia: relationship to arrhythmogenic substrate and imaging defined scar.

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology·2026
Same author

A composition-matching algorithm, MatchIDR, identifies prion-like domains that localize to stress granules.

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

Confounding factors and biases abound when predicting molecular biomarkers from histological images.

Nature biomedical engineering·2026
Same author

Impact of Various Inactivation Approaches on Surrogate Proteinaceous Particles for Sample Return Missions.

Astrobiology·2026
Same author

A comprehensive evaluation of self-attention for detecting regulatory feature interactions.

NAR genomics and bioinformatics·2026
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Prion protein conversion is primarily driven by amino acid composition, not specific short sequences. A novel machine learning method using sequence composition alone accurately predicts prion-forming proteins.

Area of Science:

  • Biochemistry
  • Computational Biology
  • Molecular Biology

Background:

  • Prion-forming proteins often feature glutamine/asparagine-rich domains.
  • The precise role of primary protein sequence in prion formation is debated.

Purpose of the Study:

  • To investigate whether prion formation is driven by amino acid composition or specific short sequence elements.
  • To develop a more accurate computational method for predicting prion-forming proteins.

Main Methods:

  • Analysis of sequence element permutations to assess their impact on prion prediction accuracy.
  • Development of a novel multiple-instance machine learning model utilizing sequence composition.
  • Comparison of the new model's accuracy against existing prion prediction approaches.

Related Experiment Videos

Main Results:

  • Permuting known amyloid-forming sequence elements unexpectedly increased prediction accuracy.
  • The proposed machine learning method, based solely on sequence composition, outperformed all previous methods.
  • Experimental findings suggest sequence composition is sufficient for predicting prion-forming potential.

Conclusions:

  • Protein sequence composition, rather than specific short motifs, appears to be the primary driver of prion formation.
  • The developed computational tool offers a significant advancement in predicting prionogenic proteins.
  • This research provides a new perspective on the fundamental mechanisms underlying prion disease pathogenesis.