Mittal Sneha, Jena Milan Kumar, Pathak Biswarup
Department of Chemistry, Indian Institute of Technology (IIT) Indore, Indore, Madhya Pradesh, 453552, India.
Chemistry. 2023 Oct 23;29(59):e202301667. doi: 10.1002/chem.202301667. Epub 2023 Sep 21.
Achieving high throughput protein sequencing at single molecule resolution remains a daunting challenge. Herein, relying on a solid-state 2D phosphorene nanoslit device, an extraordinary biosensor to rapidly identify the key signatures of all twenty amino acids using an interpretable machine learning (ML) model is reported. The XGBoost regression algorithm allows the determination of the transmission function of all twenty amino acids with high accuracy. The resultant ML and DFT studies reveal that it is possible to identify individual amino acids through transmission and current signals readouts with high sensitivity and selectivity. Moreover, we thoroughly compared our results to those from graphene nanoslit and found that the phosphorene nanoslit device can be an ideal candidate for protein sequencing up to a 20-fold increase in transmission sensitivity. The present study facilitates high throughput screening of all twenty amino acids and can be further extended to other biomolecules for disease diagnosis and therapeutic decision making.
在单分子分辨率下实现高通量蛋白质测序仍然是一项艰巨的挑战。在此，依托固态二维磷烯纳米狭缝器件，报道了一种非凡的生物传感器，该传感器利用可解释的机器学习（ML）模型快速识别所有二十种氨基酸的关键特征。XGBoost回归算法能够高精度地确定所有二十种氨基酸的传输函数。由此产生的ML和DFT研究表明，通过传输和电流信号读数以高灵敏度和选择性识别单个氨基酸是可能的。此外，我们将我们的结果与石墨烯纳米狭缝的结果进行了全面比较，发现磷烯纳米狭缝器件可以成为蛋白质测序的理想候选者，其传输灵敏度提高了20倍。本研究有助于对所有二十种氨基酸进行高通量筛选，并且可以进一步扩展到其他生物分子用于疾病诊断和治疗决策。
