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

CATH-ddG: towards robust mutation effect prediction on protein-protein interactions out of CATH homologous superfamily.

Bioinformatics (Oxford, England) - 1 Jul 2025

Yu Guanglei, Bi Xuehua, Ma Teng, Li Yaohang, Wang Jianxin

Abstract excerpt

MOTIVATION: Protein-protein interactions (PPIs) are fundamental aspects in understanding biological processes. Accurately predicting the effects of mutations on PPIs remains a critical requirement for drug design and disease mechanistic studies. Recently, deep learning models using protein 3D structures have become predominant for predicting mutation effects. However, significant challenges remain in practical...

Read the complete abstract on PubMed

Topics

Mutation
Humans
SARS-CoV-2
Deep Learning
Databases, Protein
Protein Interaction Mapping

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

Protein Conformation

Protein Binding

Computational Biology

Proteins

Spike Glycoprotein, Coronavirus

COVID-19

Source

Open in PubMed Open full text

Identifiers

PMID: 40662779
DOI: 10.1093/bioinformatics/btaf228
PMCID: PMC12261453

Genes

ACE2

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 23 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

CATH-ddG: towards robust mutation effect prediction on protein-protein interactions out of CATH homologous superfamily.PMID 40662779Read ↗

USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Semantic proximity Read ↗

Modeling mutational effects on biochemical phenotypes using convolutional neural networks: application to SARS-CoV-2Semantic proximity Read ↗

Mutation effect estimation on protein-protein interactions using deep contextualized representation learningSemantic proximity Read ↗

Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 Based on Deep Mutational Scanning Data.Semantic proximity Read ↗

MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Semantic proximity Read ↗

Graph masked self-distillation learning for prediction of mutation impact on protein-protein interactions.Semantic proximity Read ↗

Macon: Enhance Protein Mutation Representation using Contrastive Learning with Effect Prediction on Protein–protein InteractionsSemantic proximity Read ↗

Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.Semantic proximity Read ↗

MuToN Quantifies Binding Affinity Changes upon Protein Mutations by Geometric Deep Learning.Semantic proximity Read ↗

Protein multi-level structure feature-integrated deep learning method for mutational effect prediction.Semantic proximity Read ↗

Sequence-aware Prediction of Point Mutation-induced Effects on Protein-Protein Binding Affinity using Deep LearningSemantic proximity Read ↗

Overcoming Extrapolation Challenges of Deep Learning by Incorporating Physics in Protein Sequence-Function ModelingSemantic proximity Read ↗

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