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| Theoretical Study on Raman Characteristic Peaks of Coronavirus Spike Protein Based on Deep Learning |
| NI Shuang, WEN Jia-xing, ZHOU Min-jie, HUANG Jing-lin, LE Wei, CHEN Guo, HE Zhi-bing, LI Bo, ZHAO Song-nan, ZHAO Zong-qing, DU Kai* |
Laser Fusion Research Center, China Academy of Engineering Physics, Mianyang 621900, China
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Abstract COVID-19, which has lasted for a year, has caused great damage to the global economy. In order to control COVID-19 effectively, rapid detection of COVID-19 (SARS-CoV-2) is an urgent problem. Spike protein is the detection point of Raman spectroscopy to detect SARS-CoV-2. The construction of spike protein Raman characteristic peaks plays an important role in the rapid detection of SARS-CoV-2 using Raman technology. In this paper, we used Deep Neural Networks to construct the amideⅠ and Ⅲ characteristic peak model of spike proteins based on simplified exciton model, and combined with the experimental structures of seven coronaviruses (HCoV-229E, HCoV-HKU1, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2) spike proteins, analyzed the differences of amideⅠ andⅢ characteristic peaks of seven coronaviruses. The results showed that seven coronaviruses could be divided into four groups according to the amideⅠ and Ⅲ characteristic peaks of spike proteins: SARS-CoV-2, SARS-CoV, MERS-CoV form a group; HCoV-HKU1, HCoV-NL63 form a group; HCoV-229E and HCoV-OC43 form a group independently. The frequency of amideⅠ and Ⅲ in the same group is relatively close，and it is difficult to distinguish spike proteins by the frequency of amideⅠ and Ⅲ; the characteristic peaks of amideⅠ and Ⅲ in different groups are quite different, and spike proteins can be distinguished by Raman spectroscopy. The results provide a theoretical basis for the development of Raman spectroscopy for rapid detection of SARS-CoV-2.
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Received: 2021-03-05
Accepted: 2021-05-26
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Corresponding Authors:
DU Kai
E-mail: dukai@caep.cn
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