An Seonghyeon, Suh Yeongjoo, Kelich Payam, Lee Dakyeon, Vukovic Lela, Jeong Sanghwa
Department of Biomedical Convergence Engineering, Pusan National University, Yangsan 50612, Republic of Korea.
Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX 79968, USA.
Nanomaterials (Basel). 2024 Jan 23;14(3):247. doi: 10.3390/nano14030247.
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.
在本研究中，我们采用了一种新颖的方法来改进血清素响应性单链DNA包裹的单壁碳纳米管（ssDNA-SWCNT）纳米传感器，该方法结合了定向进化和基于机器学习的预测。我们的迭代优化过程旨在提高ssDNA-SWCNT纳米传感器的灵敏度和选择性。在提高血清素灵敏度的三轮实验中，我们大幅提高了灵敏度，与原始序列相比，荧光响应显著增强了2.5倍。在此之后，我们在两轮实验中致力于提高血清素相对于多巴胺的选择性。尽管这些神经递质在结构上相似，但我们实现了选择性提高1.6倍。这种创新方法能够对突变序列进行高通量筛选，标志着生物传感器开发取得了重大进展。表现最佳的纳米传感器N2-1（灵敏度）和L1-14（选择性）为未来涉及血清素检测的研究提供了有前景的参考序列。
