Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening
In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Nanomaterials |
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| Online Access: | https://www.mdpi.com/2079-4991/14/3/247 |
| _version_ | 1827354620804988928 |
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| author | Seonghyeon An Yeongjoo Suh Payam Kelich Dakyeon Lee Lela Vukovic Sanghwa Jeong |
| author_facet | Seonghyeon An Yeongjoo Suh Payam Kelich Dakyeon Lee Lela Vukovic Sanghwa Jeong |
| author_sort | Seonghyeon An |
| collection | DOAJ |
| description | In this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of ssDNA-SWCNT nanosensors. In the three rounds for higher serotonin sensitivity, we substantially improved sensitivity, achieving a remarkable 2.5-fold enhancement in fluorescence response compared to the original sequence. Following this, we directed our efforts towards selectivity for serotonin over dopamine in the two rounds. Despite the structural similarity between these neurotransmitters, we achieved a 1.6-fold increase in selectivity. This innovative methodology, offering high-throughput screening of mutated sequences, marks a significant advancement in biosensor development. The top-performing nanosensors, N2-1 (sensitivity) and L1-14 (selectivity) present promising reference sequences for future studies involving serotonin detection. |
| first_indexed | 2024-03-08T03:51:19Z |
| format | Article |
| id | doaj.art-0016aa036e9f4d61b2c89c2a3c82289c |
| institution | Directory Open Access Journal |
| issn | 2079-4991 |
| language | English |
| last_indexed | 2024-03-08T03:51:19Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Nanomaterials |
| spelling | doaj.art-0016aa036e9f4d61b2c89c2a3c82289c2024-02-09T15:19:19ZengMDPI AGNanomaterials2079-49912024-01-0114324710.3390/nano14030247Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based ScreeningSeonghyeon An0Yeongjoo Suh1Payam Kelich2Dakyeon Lee3Lela Vukovic4Sanghwa Jeong5Department of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Republic of KoreaDepartment of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Republic of KoreaDepartment of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX 79968, USADepartment of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Republic of KoreaDepartment of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX 79968, USADepartment of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Republic of KoreaIn this study, we employed a novel approach to improve the serotonin-responsive ssDNA-wrapped single-walled carbon nanotube (ssDNA-SWCNT) nanosensors, combining directed evolution and machine learning-based prediction. Our iterative optimization process is aimed at the sensitivity and selectivity of ssDNA-SWCNT nanosensors. In the three rounds for higher serotonin sensitivity, we substantially improved sensitivity, achieving a remarkable 2.5-fold enhancement in fluorescence response compared to the original sequence. Following this, we directed our efforts towards selectivity for serotonin over dopamine in the two rounds. Despite the structural similarity between these neurotransmitters, we achieved a 1.6-fold increase in selectivity. This innovative methodology, offering high-throughput screening of mutated sequences, marks a significant advancement in biosensor development. The top-performing nanosensors, N2-1 (sensitivity) and L1-14 (selectivity) present promising reference sequences for future studies involving serotonin detection.https://www.mdpi.com/2079-4991/14/3/247directed evolutioncarbon nanotubeserotoninnanosensormachine learningfluorescence |
| spellingShingle | Seonghyeon An Yeongjoo Suh Payam Kelich Dakyeon Lee Lela Vukovic Sanghwa Jeong Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening Nanomaterials directed evolution carbon nanotube serotonin nanosensor machine learning fluorescence |
| title | Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening |
| title_full | Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening |
| title_fullStr | Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening |
| title_full_unstemmed | Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening |
| title_short | Directed Evolution of Near-Infrared Serotonin Nanosensors with Machine Learning-Based Screening |
| title_sort | directed evolution of near infrared serotonin nanosensors with machine learning based screening |
| topic | directed evolution carbon nanotube serotonin nanosensor machine learning fluorescence |
| url | https://www.mdpi.com/2079-4991/14/3/247 |
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