ArticleInnovation (Cambridge (Mass.))2025
Structure-based self-supervised learning enables ultrafast protein stability prediction upon mutation.
Article in Innovation (Cambridge (Mass.)), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Constrained diffusion-enabled construction of a highly expressed and robust peptide asparaginyl ligase.Chemical science · 2026Article
- Integrative machine learning approaches for enzyme kinetic parameter prediction.Briefings in bioinformatics · 2026Review
- TcrDesign: de novo design of epitope-specific full-length T cell receptors.Science China. Life sciences · 2026Article
- Applications and limitations of AI tools in enzyme design.Protein science : a publication of the Protein Society · 2026Review
- Persistent sheaf Laplacian analysis of protein stability and solubility changes upon mutation.Protein science : a publication of the Protein Society · 2026Article
- PythiaStudio: a one-stop protein engineering platform powered by Pythia model suite.Nucleic acids research · 2026Article
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Article
- Engineering a robust IsPETase for energy-efficient PET depolymerization in natural seawater at ambient temperatures.Advanced biotechnology · 2026Article
- Small Molecule Regulation of CLOCK:BMAL1 DNA Binding Activity.bioRxiv : the preprint server for biology · 2026Article
- Article
- JanusDDG: a physics-informed neural network for sequence-based protein stability via two-fronts attention.Communications biology · 2026Article
- Protein design drives synthetic biology research of plant natural products.Biodesign research · 2025Review
- Multiscale metabolic engineering in biological lignin valorization.Innovation (Cambridge (Mass.)) · 2025Review
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
- Data-efficient protein mutational effect prediction with weak supervision by molecular simulation and protein language models.Briefings in bioinformatics · 2025Article
- Reliable prediction of protein-protein binding affinity changes upon mutations with Pythia-PPI.National science review · 2025Article
- Mass balance approximation of unfolding boosts potential-based protein stability predictions.Protein science : a publication of the Protein Society · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting free energy changes (ΔΔG) is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development. While traditional methods offer valuable insights, they are often constrained by computational speed and reliance on biased training datasets. These constraints become particularly evident when aiming for accurate ΔΔG predictions across a diverse array of protein sequences. Herein, we introduce Pythia, a self-supervised graph neural network specifically designed for zero-shot ΔΔG predictions. Our comparative benchmarks demonstrate that Pythia outperforms other self-supervised pretraining models and force field-based approaches while also exhibiting competitive performance with fully supervised models. Notably, Pythia shows strong correlations and achieves a remarkable increase in computational speed of up to 10
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