AI Insight
Researchers developed StableProt, an AI system that predicts protein thermal stability and the optimal growth temperatures of organisms by combining amino acid sequence data with 3D structural information. The model addresses key limitations of existing approaches by incorporating structural features, eliminating bias toward moderate-temperature organisms through strategic data sampling, and providing calibrated confidence intervals rather than single-point estimates. StableProt achieved state-of-the-art accuracy in predicting melting temperatures and successfully identified 92% of heat-loving organisms, with validation showing 75-78% coverage of real-world temperature ranges.
Why it matters
This tool could accelerate the design of heat-stable enzymes for industrial processes and help identify promising proteins from environmental samples, particularly extremophiles living in harsh conditions. The calibrated uncertainty estimates make the predictions more reliable for practical applications in biotechnology and enzyme engineering.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
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Predicting protein melting temperature (Tm) and organismal optimal growth temperature (OGT) is essential for enzyme engineering, biocatalyst design, and metagenomic bioprospecting, yet computational prediction faces several fundamental bottlenecks: sequence-only representations miss tertiary packing; dual-task coupling induces destructive gradient conflict; natural database skew (>87% mesophiles) drives mesophilic collapse; and existing tools produce uncalibrated point predictions. To address these challenges, we present StableProt, a structure-aware framework that couples amino acid sequences with Foldseek 3Di structural tokens via a frozen SaProt backbone to capture 3D contacts. Disjoint heads condition thermodynamic stability on ecological context without gradient conflict, while targeted 14% mesophilic subsampling expands thermophile representation to eliminate mesophilic collapse. Calibrated ensemble modeling reduces calibration error to 1.92%, providing dependable 95% confidence intervals. Evaluating models across 10{degrees}C temperature intervals reveals severe baseline collapse at thermal extremes, whereas StableProt maintains robust accuracy across regimes. On ProThermDB and zero-shot FireProtDB, StableProt achieves state-of-the-art probabilistic accuracy (CRPS = 4.52{degrees}C and 8.71{degrees}C) while recovering 92.0% of hyperthermophiles ([≥]80{degrees}C), and in prospective validation, its calibrated intervals capture both enzyme stability (75.3% coverage) and host environmental growth temperatures (78.1% coverage). StableProt is freely accessible as an interactive web server at https://stableprot.dixitlab.org/.