Sensbio

Sensbio identifies potential allosteric transcription factor (aTF)–ligand pairs by comparing query molecules against an extensive TF–ligand reference database to support biosensor discovery and genetic circuit engineering.

Key Features:

• TF–ligand reference database: An extensive collection of documented transcription factor–ligand interactions used as the comparison reference.
• Similarity comparison: Molecular similarity comparisons are performed to match query molecules to known TF ligands.
• Predictive modeling: Computational algorithms are applied to infer putative TF–ligand associations from similarity results.
• Machine learning: Machine learning models are incorporated to enhance predictive capabilities for identifying candidate TFs activated by specific molecules.

Scientific Applications:

• Biosensor design: Identification of candidate aTF–ligand pairs to enable development of small-molecule biosensors.
• Genetic circuit engineering: Selection of regulatory elements (aTFs) responsive to target molecules for incorporation into genetic circuits.
• Synthetic biology research: Expansion of known molecule–TF interaction space to support pathway engineering and regulation studies.
• Biotechnology and therapeutic development: Discovery of regulatory interactions that can be harnessed for biotechnological applications and therapeutic strategies.

Methodology:

Sensbio performs molecular similarity comparisons against an extensive TF–ligand reference database and applies computational algorithms and machine learning models to predict putative TF–ligand pairs.