DeepSTABp

DeepSTABp predicts protein melting temperature (Tm) from amino acid sequences and growth conditions to assess protein thermal stability.

Key Features:

• Tm prediction: Predicts protein melting temperature (Tm) from amino acid sequences and specified growth conditions.
• Transformer-based embedding: Uses a transformer-based protein language model to compute sequence embeddings.
• Advanced feature extraction: Applies advanced feature extraction techniques to capture sequence-derived signals relevant to stability.
• Deep learning end-to-end model: Employs other deep learning methodologies in an end-to-end predictive framework for thermal stability.
• Structural and biological determinants: Captures structural and biological properties that influence protein stability and can identify key structural features contributing to stability.
• Extended coverage: Provides broader proteome and species coverage compared with thermal proteome profiling experimental methods.
• Scalability: Enables large-scale prediction across broad protein sets.

Scientific Applications:

• Proteome-wide stability profiling: Enables prediction of protein thermal stability (Tm) across proteomes and varied growth conditions.
• Structural determinants analysis: Facilitates identification of structural features that contribute to protein stability for structure–function studies.
• Complementing experimental data: Complements thermal proteome profiling by providing predicted Tm values to expand species and proteome coverage.
• Assessment of protein suitability: Informs evaluation of protein suitability across experimental and industrial conditions.

Methodology:

Sequence embedding using a transformer-based protein language model, advanced feature extraction, and other deep learning methodologies are used to perform end-to-end prediction of protein melting temperature (Tm).