Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

A Phase-Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease-Associated Protein Aggregation and Guides Suppressor Design.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) - 1 Sept 2026

Sio Jia Shen, Loo Wei Xuan Wilson, Loo Yan Shan, Tan Wen Xin, Lim Hui Xuan, Liu Huitao, Ng Chen Seng

Abstract excerpt

Protein aggregation drives major neurodegenerative diseases, yet most computational predictors collapse assembly into static risk scores and do not resolve the distinct structural determinants of nucleation and elongation. Here, we present SKALE 2.0, a phase-resolved geometric deep learning framework that represents proteins as multimodal structural graphs and learns mutation-induced aggregation phenotypes...

Read the complete abstract on PubMed

Topics

Deep Learning
Humans
Superoxide Dismutase-1
Protein Aggregation, Pathological
Protein Aggregates

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Neurodegenerative Diseases

Mutation

DNA-Binding Proteins

tau Proteins

Source

Open in PubMed Open full text

Identifiers

PMID: 42307331
DOI: 10.1002/advs.76118
PMCID: PMC13336538

Genes

SOD1
MAPT
PRNP

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 17 Oct 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A Phase-Resolved Geometric Deep Learning Framework Maps Structural Determinants of Disease-Associated Protein Aggregation and Guides Suppressor Design.PMID 42307331Read ↗

A machine learning framework for predicting and modulating condition-dependent protein phase separationSemantic proximity Read ↗

A machine learning framework for predicting and modulating condition-dependent protein phase separation.Semantic proximity Read ↗

Massive experimental quantification of amyloid nucleation allows interpretable deep learning of protein aggregationSemantic proximity Read ↗

PSTP: Decoding Latent Sequence Grammar for Protein Phase Separation through Transfer Learning and AttentionSemantic proximity Read ↗

Towards mechanistic models of mutational effects: Deep Learning on Alzheimer’s Aβ peptideSemantic proximity Read ↗

Interpretable Prediction of Phase Separation and Disease Variant Effects in Intrinsically Disordered RegionsSemantic proximity Read ↗

Dynamics-aware geometric learning predicts disease-associated molecular perturbationsSemantic proximity Read ↗

Interpretable and generative deep learning models explicate phase separating intrinsically disordered motifs.Semantic proximity Read ↗

SSPSPredictor: A Sequence and Structure based Deep Learning Model for Predicting Phase-Separating ProteinsSemantic proximity Read ↗

Leveraging a large language model to predict protein phase transition: a physical, multiscale and interpretable approachSemantic proximity Read ↗

Leveraging AlphaFold 3 for Structural Modeling of Neurological Disorder-Associated Proteins: A Pathway to Precision MedicineSemantic proximity Read ↗

SETH predicts nuances of residue disorder from protein embeddingsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.