한빛사논문
- 조회721
Payam Kelich1,†, Sanghwa Jeong2,†, Nicole Navarro3,†, Jaquesta Adams3, Xiaoqi Sun3, Huanhuan Zhao1, Markita P. Landry3,4,5,6,*, Lela Vuković1,*
1Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX, 79968 USA
2School of Convergence Engineering, Pusan National University, Yangsan, 50612 South Korea
3Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, 94720 USA
4California Institute for Quantitative Biosciences, QB3, University of California, Berkeley, CA, 94720 USA
5Innovative Genomics Institute, Berkeley, CA, 94702 USA
6Chan‐Zuckerberg Biohub, San Francisco, CA, 94158 USA
*Corresponding Authors
†These authors contributed equally
Abstract
DNA-wrapped single walled carbon nanotube (SWNT) conjugates have distinct optical properties leading to their use in biosensing and imaging applications. A critical limitation in the development of DNA-SWNT sensors is the current inability to predict unique DNA sequences that confer a strong analyte-specific optical response to these sensors. Here, near-infrared (nIR) fluorescence response data sets for ∼100 DNA-SWNT conjugates, narrowed down by a selective evolution protocol starting from a pool of ∼1010 unique DNA-SWNT candidates, are used to train machine learning (ML) models to predict DNA sequences with strong optical response to neurotransmitter serotonin. First, classifier models based on convolutional neural networks (CNN) are trained on sequence features to classify DNA ligands as either high response or low response to serotonin. Second, support vector machine (SVM) regression models are trained to predict relative optical response values for DNA sequences. Finally, we demonstrate with validation experiments that integrating the predictions of ensembles of the highest quality neural network classifiers (convolutional or artificial) and SVM regression models leads to the best predictions of both high and low response sequences. With our ML approaches, we discovered five DNA-SWNT sensors with higher fluorescence intensity response to serotonin than obtained previously. Overall, the explored ML approaches, shown to predict useful DNA sequences, can be used for discovery of DNA-based sensors and nanobiotechnologies.
논문정보
- Research article
- 2021년 12월 (BRIC 등록일 2021-12-24)
- 국내+국외 연구진
- 바이오·의료융합 > 바이오센싱 및 나노바이오물질
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