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

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.

Nature communications - 23 Jul 2024

Diaz Daniel J, Gong Chengyue, Ouyang-Zhang Jeffrey, Loy James M, Wells Jordan, Yang David, Ellington Andrew D, Dimakis Alexandros G, Klivans Adam R

Abstract excerpt

Engineering stabilized proteins is a fundamental challenge in the development of industrial and pharmaceutical biotechnologies. We present Stability Oracle: a structure-based graph-transformer framework that achieves SOTA performance on accurately identifying thermodynamically stabilizing mutations. Our framework introduces several innovations to overcome well-known challenges in data scarcity and bias,...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Neural Networks, Computer

Share to Protein Conformation

Share to Protein Engineering

Share to Protein Stability

Share to Proteins

Share to Thermodynamics

Source

Open in PubMed Open full text

Identifiers

PMID: 39043654
DOI: 10.1038/s41467-024-49780-2
PMCID: PMC11266546

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Stability Oracle: a structure-based graph-transformer framework for identifying stabilizing mutations.PMID 39043654Read ↗

StabLyzeGraph: High-throughput screening of combinatorial mutations using graph neural networks.Semantic proximity Read ↗

GraphESMStable: A Deep Learning Framework for Protein Stability Prediction Fusing Pre-trained Sequence Models and Graph Neural NetworksSemantic proximity Read ↗

ProSTAGE: Predicting Effects of Mutations on Protein Stability by Using Protein Embeddings and Graph Convolutional Networks.Semantic proximity Read ↗

Leveraging Unified Sequence-Structure Representations for Enhanced Protein Stability PredictionSemantic proximity Read ↗

DDMut: predicting effects of mutations on protein stability using deep learning.Semantic proximity Read ↗

Protein Engineering with Lightweight Graph Denoising Neural Networks.Semantic proximity Read ↗

Improving the prediction of protein stability changes upon mutations by geometric learning and a pre-training strategy.Semantic proximity Read ↗

An ensemble learning framework for protein stability prediction with enhanced recognition of stabilizing mutations.Semantic proximity Read ↗

Rapid protein stability prediction using deep learning representationsSemantic proximity Read ↗

JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Semantic proximity Read ↗

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

Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interfaceSemantic proximity Read ↗

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