Bacteria initiate complicated signaling cascades from the detection of intracellular metabolites or exogenous substances by hundreds of transcription factors, which have been widely investigated as genetically-encoded biosensors for molecular recognition. However, the limited number of transcription factors and their broad substrate specificity results in ambiguity in small molecule identification. This study presents a new small molecule fingerprinting technique using evolutionary biosensor arrays with a machine learning technique that can capture highly specific substrate signals. Employing multiple mutant transcription factors derived from a single transcription factor has effectively circumvented the limited availability of transcription factors induced by a small molecule of our interest. This method achieved up to 95.3% true positive rate for identifying small molecules, and the high-resolution protein engineering technique improved the limit of detection 75-fold. The signal trade-offs with background noises caused by the complex cellular biochemistry of mutant transcription factors enable the biosensor arrays to be more informative in terms of statistical variance. The machine-learning technology, coupled with the single transcription factor-driven evolutionary biosensor array, will open new avenues for molecular fingerprinting technologies.
Keywords : Genetically-encoded biosensors; Machine learning; Molecular identification; Protein engineering; Biosensor array; High-throughput screening system