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 Video

Updated: Jun 18, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

A Phase-Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease-Associated Protein

Jia Shen Sio1, Wei Xuan Wilson Loo1, Yan Shan Loo1,2

  • 1School of Science, Monash University Malaysia, Jalan Lagoon Selatan, Selangor, Malaysia.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|June 17, 2026
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

Programmable self-replicating JEV nanotherapeutics redefine RNA delivery in ALS.

Communications biology·2025
Same author

Linker Design for the Antibody Drug Conjugates: A Comprehensive Review.

ChemMedChem·2025
Same author

Integrated transcriptomic profiling reveals a STING-mediated Type II Interferon signature in SOD1-mutant amyotrophic lateral sclerosis models.

Communications biology·2025
Same author

Phytochemical-based nanosystems: recent advances and emerging application in antiviral photodynamic therapy.

Nanomedicine (London, England)·2025
Same author

Voices in <i>Molecular Pharmaceutics</i>: Meet Yan Shan Loo, a Graduate Student Exploring Nanoformulations of Natural Products.

Molecular pharmaceutics·2023
Same author

Recent Advances in the Development of Liquid Crystalline Nanoparticles as Drug Delivery Systems.

Pharmaceutics·2023

SKALE 2.0, a new deep learning framework, predicts protein aggregation phases. It identifies specific mutations that drive nucleation or elongation, offering insights into neurodegenerative diseases.

Area of Science:

  • Computational biology
  • Structural biology
  • Neuroscience

Background:

  • Protein aggregation is a key driver of neurodegenerative diseases.
  • Current computational methods often oversimplify aggregation into static risk scores, failing to distinguish between nucleation and elongation phases.
  • Understanding the structural basis of these distinct aggregation phases is crucial for developing targeted therapies.

Purpose of the Study:

  • To develop a phase-resolved computational framework (SKALE 2.0) that distinguishes between protein nucleation and elongation.
  • To identify mutation-specific structural determinants influencing these aggregation phases.
  • To link atomic-level protein topology to phase-specific aggregation kinetics.

Main Methods:

  • Developed SKALE 2.0, a geometric deep learning framework representing proteins as multimodal structural graphs.
Keywords:
artificial intelligencecomputational biologydeep learningenhancerfibrillanguage modelnucleationprnpprotein aggregationsuppressor

More Related Videos

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Optogenetic Phase Transition of TDP-43 in Spinal Motor Neurons of Zebrafish Larvae
07:14

Optogenetic Phase Transition of TDP-43 in Spinal Motor Neurons of Zebrafish Larvae

Published on: February 25, 2022

Related Experiment Videos

Last Updated: Jun 18, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
09:39

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Optogenetic Phase Transition of TDP-43 in Spinal Motor Neurons of Zebrafish Larvae
07:14

Optogenetic Phase Transition of TDP-43 in Spinal Motor Neurons of Zebrafish Larvae

Published on: February 25, 2022

  • Trained the model on three-dimensional protein topology to learn mutation-induced aggregation phenotypes.
  • Compared SKALE 2.0 with baseline models including protein language models and AlphaFold-derived features.
  • Utilized recombinant SOD1 experiments to validate predicted mutations.

    • Main Results:

    • SKALE 2.0 successfully recovered a conserved transition from nucleation to elongation across multiple proteins (SOD1, TDP-43, MAPT, PRNP).
    • The framework resolved distinct mutation-specific phase sensitivities, outperforming non-phase-aware baselines.
    • Learned geometric features indicated nucleation is linked to buried hydrophobic changes, while elongation involves solvent-accessible interfaces.
    • Experimental validation confirmed predicted mutations' effects on aggregation initiation and propagation.

    Conclusions:

    • Explicit phase conditioning is essential for accurately modeling protein aggregation.
    • SKALE 2.0 provides a mechanistic link between protein structure, phase-specific assembly kinetics, and disease.
    • The framework enables the constraint-aware design of aggregation suppressors for neurodegenerative diseases.