ArticleNature communications2025
Generalizable and scalable protein stability prediction with rewired protein generative models.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- A unified predictor of protein stability changes across all mutation typesChemical science · 2026Article
- Deep Contrastive Learning for High-Throughput Prediction of Drug Resistance Mutations from Sequences.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Artificial intelligence in the assessment of epilepsy-related genetic mutations: Learned from GABAEpilepsia open · 2026Review
- SynFit: Synergistic Contrastive Learning for Multi-Objective Protein Fitness Prediction and Optimization.bioRxiv : the preprint server for biology · 2026Article
- StrucNS reveals interaction-weighted network topology as the driving predictor of absolute stability of natural and de novo proteins.bioRxiv : the preprint server for biology · 2026Article
- Accurate protein stability prediction for small domains using mega-scale experiments.bioRxiv : the preprint server for biology · 2026Article
- Deconvolving mutation effects on protein stability and function with disentangled protein language models.bioRxiv : the preprint server for biology · 2026Article
- Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.Computational and structural biotechnology journal · 2026Article
- Generalizable and scalable protein stability prediction with rewired protein generative models.Nature communications · 2025Article
Corrections and comments
- Erratum issued
- Update of
Authors and funding
2 authors.
Funding
Abstract
Predicting changes in protein thermostability caused by amino acid substitutions is essential for understanding human diseases and engineering proteins for practical applications. While recent protein generative models demonstrate impressive zero-shot performance in predicting various protein properties without task-specific training, their strong unsupervised prediction ability remains underexploited to improve protein stability prediction. We present SPURS, a deep learning framework that rewires and integrates two complementary protein generative models-a protein language model and an inverse folding model-and reprograms this unified framework for stability prediction through supervised fine-tuning on mega-scale thermostability data. SPURS delivers accurate, efficient, and scalable stability predictions and generalizes to unseen proteins and mutations. Beyond stability prediction, SPURS enables broad applications in protein informatics, including zero-shot identification of functional residues, improved low-N protein fitness prediction, and systematic dissection of stability-pathogenicity for human diseases. Together, these capabilities establish SPURS as a versatile tool for advancing protein stability prediction and protein engineering at scale.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.