Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification

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Title
Machine learning linked evolutionary biosensor array for highly sensitive and specific molecular identification
Author(s)
Haseong Kim; Wonjae Seong; Eugene RhaHyewon LeeSeong Keun KimKil Koang KwonKwang Hyun ParkDae-Hee LeeSeung Goo Lee
Bibliographic Citation
Biosensors & Bioelectronics, vol. 170, pp. 112670-112670
Publication Year
2020
Abstract
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 result 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.
Keyword
Genetically-encoded biosensorsMachine learningMolecular identificationProtein engineeringBiosensor arrayHigh-throughput screening system
ISSN
0956-5663
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.bios.2020.112670
Type
Article
Appears in Collections:
Division of Biomaterials Research > Synthetic Biology and Bioengineering Research Center > 1. Journal Articles
Division of Biomedical Research > Disease Target Structure Research Center > 1. Journal Articles
Division of Biomaterials Research > 1. Journal Articles
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