Method for constructing a mass spectrometry model for diagnosing novel coronavirus infection

Abstract

The present invention provides a characteristic polypeptide composition for detecting novel coronavirus infection, including 29 characteristic polypeptides with specific mass-to-charge ratios. By analyzing the expression of the characteristic polypeptides, it can be determined whether the sample is from a novel coronavirus-infected patient. The present invention also provides uses such as a mass spectrometry model prepared according to the characteristic polypeptide composition, a product for diagnosing novel coronavirus infection, and the like. The present invention proposes for the first time to find a plurality of characteristic protein combinations with differences in controls of novel coronavirus-infected patients / normal persons, tuberculosis patients, and patients with novel coronavirus-infected type symptoms, breaking through the traditional research idea of ​​finding characteristic polypeptides limited to normal persons and novel coronavirus-infected patients, effectively avoiding false positive results similar to novel coronavirus infection symptoms, and having simple operation, low detection cost, and high accuracy, and is expected to be used for large-scale screening of novel coronavirus infection.

Description

Technical Field

[0001] The present invention belongs to the field of detection and relates to a technology for rapidly detecting novel coronavirus infection using time-of-flight mass spectrometry. Background Art

[0002] Coronavirus is a type of pathogen that mainly causes respiratory and intestinal diseases. The surface of this type of virus particle has many regularly arranged protrusions, and the entire virus particle is like an emperor's crown, hence the name "coronavirus". In addition to humans, coronavirus can also infect many mammals such as pigs, cattle, cats, dogs, minks, camels, bats, mice, hedgehogs, and many birds. The new coronavirus COVID-19 is a new strain of the new coronavirus that has never been found in the human body before. Its transmission pattern, infection mechanism, and evolution and mutation patterns are still unclear, which brings difficulties to prevention and control.

[0003] In order to prevent the occurrence and spread of novel coronavirus (COVID-19) infection, take prompt measures and effectively control the development and spread of the epidemic, rapid detection of novel coronavirus infection is particularly important. For a long time, the identification of coronavirus has adopted traditional microbiological detection methods, namely morphological, physiological and biochemical characteristics and serological identification. Although this method is highly accurate, it takes too long, and it takes at least more than ten hours to complete, which is difficult to meet the requirements of rapid detection. The nucleic acid detection method based on multiplex PCR is of great significance for the early diagnosis of coronavirus and the discovery of the source of infection. In addition, multiplex PCR detection targets multiple genes, and the false negative rate is lower than that of single-plex PCR. However, the PCR detection method also has the problems of cumbersome detection process, high cost and limited detection throughput.

[0004] Matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF MS) is a mass spectrometry analysis technology that was introduced in the late 1980s and developed rapidly. Its mass analyzer is an ion drift tube. The ions generated by the ion source are first collected. In the collector, the speed of all ions becomes 0. After being accelerated by a pulsed electric field, they enter the field-free drift tube and fly to the ion receiver at a constant speed. The larger the mass of the ion, the longer it takes to reach the receiver; the smaller the mass of the ion, the shorter the time it takes to reach the receiver. Based on this principle, ions of different masses can be separated according to the mass-to-charge ratio, and the molecular mass and purity of biological macromolecules such as polypeptides, proteins, nucleic acids, and polysaccharides can be accurately detected. It has the advantages of high accuracy, strong flexibility, large throughput, short detection cycle, and high cost performance.

[0005] In recent years, mass spectrometry technology has emerged to detect characteristic polypeptides or mass spectrometry technology of pathogenic microorganisms or viruses. For example, Chinese patent application CN102337223A, "Antifungal protein Pc-Arctin of Penicillium chrysogenum and its preparation method", discloses a MALDI-TOF identification method for detecting the antifungal protein Pc-Arctin of Penicillium chrysogenum, wherein spores of Penicillium chrysogenum A096 are picked from a plate and inoculated into SGY liquid culture medium for culture, the crude protein solution obtained by pretreatment is separated and purified on a chromatographic column, and separated and purified on a carboxymethyl cation exchange chromatographic column, each eluted component is collected, each component is concentrated to a desired volume by centrifugation ultrafiltration, and Paecilomyces variotii is used as a sensitive test indicator bacteria to track the antifungal active component, and the purity of the obtained protein is determined by the determined active component; a single band on the SDS-PAGE electrophoresis diagram is cut and MALDI-TOF identification is performed. This method is only applicable to specific microorganisms and requires multiple protein purification processes. Finally, MALDI-TOF is used to identify the characteristic polypeptide Pc-Arctin. The process is cumbersome and has a narrow scope of application, and it cannot achieve the purpose of mass spectrometry detection of viruses.

[0006] Chinese patent applications 201110154723, “MALDI TOF MS Assisted Identification Method for Listeria monocytogenes” and 201110154469, “MALDI TOF MS Assisted Identification Method for Vibrio cholerae” disclose a method for using MALDI TOF MS technology to assist in the identification of bacteria, including: pre-treating bacterial cultures, collecting MALDI TOF MS spectra of all strain samples, preparing bacterial standard spectra according to software, detecting and collecting spectra of the bacteria to be tested using the same method, and comparing the spectra of the two, and making a judgment based on the matching score. Since this method uses conventional processing (treatment with anhydrous ethanol, formic acid and acetonitrile, assisted by centrifugation, and finally aspirating the supernatant for detection), although it can characterize the characteristic spectrum of the bacteria to a certain extent, because the analyte contains proteins, lipids, lipopolysaccharides and lipolysaccharides, DNA, polypeptides and other molecules that can be ionized, the obtained spectrum is essentially a collection of spectra of the above-mentioned various molecules. Therefore, the amount of spectrum information that needs to be processed and compared is too large, and the spectrum characteristic is low because the molecules to be tested are too large. Therefore, it is only applicable to a specific bacterium and cannot be extended to the detection of a large number of other viruses.

[0007] Chinese patent application 200880121570, titled "Methods and biomarkers for diagnosing and monitoring mental illness", reported that nearly 100 biological peptides related to mental illness, including influenza virus, can be detected by MALDI-TOF mass spectrometry. However, the method simply summarizes various possible technologies, and does not report specific protocols or specific targets of coronavirus, making it difficult to teach researchers to detect influenza virus by MALDI-TOF mass spectrometry.

[0008] Therefore, there is a need for a characteristic peptide mass spectrometry model and use for detecting novel coronavirus infection by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Summary of the invention

[0009] The first object of the present invention is to provide a composition of characteristic polypeptides based on serum peptidome, which can detect the new coronavirus (COVID-19) by MALDI-TOF mass spectrometry, wherein the characteristic polypeptide composition includes 25 characteristic polypeptides with the following mass-to-charge ratios: 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z, or 29 characteristic polypeptides having the following mass-to-charge ratios: 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z.

[0010] In any of the above embodiments, when the peaks of the characteristic polypeptides 8986m / z and 28091m / z are upregulated, and the peaks of the characteristic polypeptides 6939m / z, 13886m / z, 14049m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient is determined to be a patient infected with the new coronavirus, and the ten-fold cross-validation accuracy is about 91%. In a preferred embodiment, the composition of the characteristic polypeptides only contains characteristic polypeptides with mass-to-composite ratios of 8986m / z, 28091m / z, and 6939m / z, 13886m / z, 14049m / z, and 14102m / z, respectively.

[0011] In another arbitrary embodiment, when the peaks of characteristic polypeptides 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are upregulated, and at the same time, the peaks of characteristic polypeptides 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is approximately 93.31%. In a preferred embodiment, the composition of the characteristic polypeptides only comprises characteristic polypeptides with mass ratios of 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, 28091m / z, and 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z, respectively.

[0012] In other embodiments, when the characteristic polypeptide 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, When the peaks of 11680m / z, 15123m / z, 15867m / z, and 28091m / z are upregulated, and the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 98.69%.

[0013] The second inventive object of the present invention is to provide a mass spectrometry model for detecting novel coronavirus infection, which is prepared by a characteristic polypeptide composition having a mass-to-charge ratio peak value of any of the above-mentioned schemes.

[0014] In one embodiment, the mass spectrum model consists of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, The peaks of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 15123m / z, 15867m / z and 28091m / z were up-regulated, while the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, When the peaks of 14102m / z and 28232m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross-validation is about 97.96%.

[0015] Alternatively, in another embodiment of the above, the mass spectrum model consists of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z, wherein the characteristic polypeptides are 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, 28091m / z When the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 98.69%.

[0016] In another embodiment, the mass spectrum model is prepared only by the following characteristic polypeptide composition with mass ratios of 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, 28091m / z, and 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, respectively. Among them, when the peaks of characteristic polypeptides 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are upregulated, and at the same time, the peaks of characteristic polypeptides 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 93.31%.

[0017] In other embodiments, the mass spectrometry model is prepared only by the following characteristic polypeptide compositions with mass-to-mass ratios of 8986m / z, 28091m / z, 6939m / z, 13886m / z, 14049m / z, and 14102m / z, respectively. When the peaks of characteristic polypeptides 8986m / z and 28091m / z are upregulated, and at the same time, the peaks of characteristic polypeptides 6939m / z, 13886m / z, 14049m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, i.e., the patient is determined to be a patient infected with the new coronavirus, and the accuracy of ten-fold cross validation is approximately 91%.

[0018] The third inventive object of the present invention is to provide a kit for detecting novel coronavirus infection, which comprises the above-mentioned characteristic polypeptide composition, or comprises the above-mentioned mass spectrometry model.

[0019] In one embodiment, the polypeptide composition or mass spectrum model consists of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, The peaks of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 15123m / z, 15867m / z and 28091m / z were upregulated, while the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, When the peaks of 14095m / z, 14102m / z, and 28232m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is a patient with new coronavirus pneumonia, and the accuracy of ten-fold cross-validation is about 97.96%.

[0020] Alternatively, in another embodiment, the polypeptide composition or mass spectrum model consists of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z, wherein the characteristic polypeptides are 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, When the peak of 28091m / z is upregulated and the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 98.69%.

[0021] In another embodiment, the polypeptide composition or mass spectrum model is prepared only by the following mass-to-charge ratios of 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, 28091m / z, and 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, wherein when the characteristic polypeptides 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, When the peaks of 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are upregulated, and the peaks of characteristic polypeptides 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 93.31%.

[0022] In other embodiments, the polypeptide composition or mass spectrometry model is prepared only by the following characteristic polypeptides with mass-to-charge ratios of 8986m / z, 28091m / z, 6939m / z, 13886m / z, 14049m / z, and 14102m / z, respectively. When the peaks of characteristic polypeptides 8986m / z and 28091m / z are upregulated and the peaks of characteristic polypeptides 6939m / z, 13886m / z, 14049m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient is determined to be a patient infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 91%.

[0023] In one embodiment, the kit includes a sample processing solution, which is developed by Beijing Yixin Bochuang Biotechnology Co., Ltd.

[0024] In another embodiment, the kit also includes a standard mass spectrometry sample tube to ensure the accuracy of the molecular weight measured by the mass spectrometer. The sample tube can be either multiple sample tubes containing a single characteristic polypeptide or one sample tube containing multiple characteristic polypeptides. The sample in the standard sample tube is used for parallel mass spectrometry testing when performing mass spectrometry on the sample to be tested to determine whether the molecular weight information of the sample to be tested is accurate and reliable.

[0025] In another embodiment, the kit may contain software or a chip of the standard database of the above-mentioned characteristic polypeptides, which can be used to provide standard data or curve comparison when the sample to be tested is subjected to mass spectrometry to determine the expression status of the characteristic polypeptide in the sample to be tested.

[0026] The fourth inventive object of the present invention is to provide the use of the characteristic polypeptide composition, or the mass spectrometry model, in the preparation of products for diagnosing new coronavirus infection.

[0027] In one embodiment, the polypeptide composition or mass spectrum model consists of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, The peaks of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 15123m / z, 15867m / z and 28091m / z were up-regulated, while the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, When the peaks of 14095m / z, 14102m / z, and 28232m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is a patient with new coronavirus pneumonia, and the accuracy of ten-fold cross-validation is about 97.96%.

[0028] Alternatively, in another embodiment, the polypeptide composition or mass spectrum model consists of characteristic polypeptides 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z, wherein the characteristic polypeptides are 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, When the peak of 28091m / z is upregulated and the peaks of characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 98.69%.

[0029] In another embodiment, the polypeptide composition or mass spectrum model is prepared only by the following characteristic polypeptides with mass ratios of 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, 28091m / z, and 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z, wherein when the characteristic polypeptides are 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, When the peaks of 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are upregulated, and the peaks of characteristic polypeptides 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 93.31%.

[0030] In other embodiments, the polypeptide composition or mass spectrometry model is prepared only by the following characteristic polypeptides with mass-to-mass ratios of 8986m / z, 28091m / z, 6939m / z, 13886m / z, 14049m / z, and 14102m / z, respectively. When the peaks of characteristic polypeptides 8986m / z and 28091m / z are upregulated and the peaks of characteristic polypeptides 6939m / z, 13886m / z, 14049m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient is determined to be a patient infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 91%.

[0031] In any of the above embodiments, the product for diagnosing novel coronavirus infection refers to any conventional product used to diagnose novel coronavirus infection, including: detection reagents, detection chips, detection carriers, and detection kits, etc.

[0032] A fifth object of the present invention is to provide a method for preparing the mass spectrometry model, comprising:

[0033] 1) Collect serum samples from multiple clinically confirmed COVID-19 infected persons and non-COVID-19 infected controls (including tuberculosis patients, patients with similar symptoms of fever and cough, and healthy people), and freeze them for future use;

[0034] 2) Pre-treatment of serum proteins before mass spectrometry;

[0035] 3) Performing mass spectrometry on the two groups of pretreated serum proteins to obtain fingerprints of the two groups of serum peptides;

[0036] 4) Standardize the fingerprints of serum peptides from all patients and normal subjects and collect data;

[0037] 5) The obtained data were quality controlled and the characteristic peptides with the following mass-to-charge ratio peaks were screened out: 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z, the characteristic peptides were identified by secondary mass spectrometry, and a mass spectrometry model for detecting new coronavirus infection was established based on these mass-to-charge ratio peaks.

[0038] In one embodiment, in step 5), the obtained data are subjected to quality control processing to screen out characteristic polypeptides having the following mass-to-charge ratio peaks: 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z, the characteristic peptides were identified by secondary mass spectrometry, and a mass spectrometry model for detecting new coronavirus infection was established based on these mass-to-charge ratio peaks.

[0039] In a preferred embodiment, the mass spectrum model of step 5) is prepared only by the following characteristic polypeptides with mass ratios of 7614m / z, 8034m / z, 8226m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, 28091m / z, and 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z, respectively. When the peaks of 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are upregulated, and the peaks of characteristic polypeptides 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, and 14102m / z are downregulated, it means that the serum sample is a positive sample, that is, the patient is infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 93.31%.

[0040] In another embodiment, the mass spectrometry model of step 5) is prepared only by the following characteristic polypeptides with mass-to-mass ratios of 8986m / z, 28091m / z, 6939m / z, 13886m / z, 14049m / z, and 14102m / z, respectively. When the peaks of characteristic polypeptides 8986m / z and 28091m / z are upregulated and the peaks of characteristic polypeptides 6939m / z, 13886m / z, 14049m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient is determined to be a patient infected with the new coronavirus, and the accuracy of ten-fold cross validation is about 91%.

[0041] In addition, in any embodiment of any of the above-mentioned invention objects, the characteristic polypeptide composition, mass spectrometry model, detection product, use, and construction method may involve only 19 characteristic polypeptides having the following mass-to-charge ratios and polypeptide sequences:

[0042] A characteristic polypeptide with a mass-to-charge ratio of 6939 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 1;

[0043] A characteristic polypeptide with a mass-to-charge ratio of 7614 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 2;

[0044] A characteristic polypeptide with a mass-to-charge ratio of 8034 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 3;

[0045] A characteristic polypeptide with a mass-to-charge ratio of 8226 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 4;

[0046] A characteristic polypeptide with a mass-to-charge ratio of 8986 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.5;

[0047] A characteristic polypeptide with a mass-to-charge ratio of 9626 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.6;

[0048] A characteristic polypeptide with a mass-to-charge ratio of 13719 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.7;

[0049] A characteristic polypeptide with a mass-to-charge ratio of 13765 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 8;

[0050] A characteristic polypeptide with a mass-to-charge ratio of 13886 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.9;

[0051] A characteristic polypeptide with a mass-to-charge ratio of 14049 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 10;

[0052] A characteristic polypeptide with a mass-to-charge ratio of 14095 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.11;

[0053] A characteristic polypeptide with a mass-to-charge ratio of 14102 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 12;

[0054] A characteristic polypeptide with a mass-to-charge ratio of 15123 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 13;

[0055] A characteristic polypeptide with a mass-to-charge ratio of 15867 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 14;

[0056] A characteristic polypeptide with a mass-to-charge ratio of 28091 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 15;

[0057] A characteristic polypeptide with a mass-to-charge ratio of 11435 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 16;

[0058] A characteristic polypeptide with a mass-to-charge ratio of 11495 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 17;

[0059] A characteristic polypeptide with a mass-to-charge ratio of 11523 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 18;

[0060] The characteristic polypeptide with a mass-to-charge ratio of 11680 m / z has a polypeptide sequence selected from the sequence shown in SEQ ID No.19.

[0061] In any of the above embodiments, the pretreatment method in step 2) comprises using a sample treatment solution to dilute the serum protein or polypeptide in the stabilized sample.

[0062] In any of the above embodiments, in step 3), the two groups of serum proteins are diluted and read using a universal pretreatment kit for peptide mass spectrometry to obtain fingerprints of the two groups of serum peptides.

[0063] In any of the above embodiments, in the quality control process described in step 5), for a blank matrix, the crystallization point of the blank matrix is ​​detected using the same mass spectrometry parameters, and if an obvious mass spectrometry peak appears, it is considered that the quality of the matrix solution is unqualified.

[0064] In any of the above embodiments, in the quality control treatment described in step 5), the following 8 characteristic peaks are selected as quality control peaks: 6426m / z, 6623m / z, 8753m / z, 8785m / z, 8904m / z, 9118m / z, 9409m / z, and 9700m / z.

[0065] In the process of time-of-flight mass spectrometry detection of biological samples, the quality of the mass spectrometry spectrum is affected by multiple conditions such as individual differences, sample quality, changes in ambient temperature and humidity, and the crystallization state of the sample and the matrix. In order to avoid the influence of abnormal spectra on the analysis results, the above 8 characteristic peaks common in human serum are introduced as quality control peaks, and the appearance of quality control peaks has nothing to do with whether the patient is infected with the new coronavirus. Among the 843 spectra collected, 683 spectra can detect all 8 quality control peaks (accounting for 81.0% of the total number of spectra), and 156 spectra can detect 7 of them (accounting for 18.5% of the total number of spectra). Among them, the following spectrum quality control conditions are set: in the spectrum of a single sample, when the number of quality control peaks appears 6 to 8 and the internal standard peak molecular weight deviation is less than 0.002 (or the deviation range does not exceed 2‰), it is considered to be qualified for quality control. Unqualified spectra need to be retested.

[0066] The present invention combines bioinformatics methods to screen out corresponding novel coronavirus infection markers and establish a detection model for analysis and detection. The bioinformatics method includes standardizing the fingerprint spectrum, performing experimental quality control on the obtained data, screening the desired serum characteristic polypeptides and establishing a mass spectrometry model, and optionally including using the LR algorithm to establish and verify the mass spectrometry model. Among them, the experimental quality control process retains the mass spectrum data with no less than 6 internal standard peaks, and uses the internal standard peaks for secondary calibration of the spectrum.

[0067] Terms and Definitions

[0068] Ten-fold cross-validation, whose English name is 10-fold cross-validation, is used to test the accuracy of the algorithm. It is a commonly used test method. The data set is divided into ten parts, and 9 of them are used as training data and 1 part as test data in turn for testing. Each test will result in the corresponding accuracy (or error rate). The average value of the accuracy (or error rate) of the 10 results is used as an estimate of the accuracy of the algorithm. Generally, multiple 10-fold cross-validations (such as 10 10-fold cross-validations) are required, and then the average is calculated as an estimate of the accuracy of the algorithm. It should be pointed out that the ten-fold cross-validation accuracy is correlated with the actual detection accuracy (or sensitivity) but is not equivalent. In the process of evaluating the effect of the test algorithm of the present invention, the effect meets the ten-fold cross-validation accuracy of the confidence interval. If the correlation changes with the number of characteristic polypeptides and reaches a feasible value for clinical diagnosis, it indicates that the mass spectrometry model constructed by these polypeptides meets the requirements of clinical diagnosis.

[0069] SAA protein (Serum amyloid A protein) is a member of the serum amyloid A family, an acute phase protein, and a heterogeneous protein in the apolipoprotein family. There are four types of serum amyloid A genes in the human body, namely SAA1-SAA4, of which SAA1 and SAA2 are two acute phase proteins called A-SAA.

[0070] Technical Effects

[0071] Compared with the prior art, the present invention has the following advantages:

[0072] 1. The present invention uses a combination of multiple characteristic proteins that are different between patients infected with the new coronavirus and normal people, tuberculosis patients, and control patients with symptoms of the new coronavirus infection type to detect serum samples, and uses a method combining traditional statistics and modern bioinformatics methods to process data, thereby obtaining a polypeptide fingerprint detection model for virus-infected patients, healthy people, and other control patients, and the series of protein mass-to-charge ratio peaks discovered provide a basis and resources for finding new and more ideal markers.

[0073] 2. Compared with previous detection methods, it has higher sensitivity and specificity, simple operation, low detection cost and high throughput, and is expected to be used for large-scale screening of new coronavirus infection.

[0074] 3. The construction method of the model of the present invention is reasonably designed and feasible, which provides a new screening method for the clinical cure rate of novel coronavirus infection, and also provides new ideas for exploring the mechanism of occurrence and development of novel coronavirus infection.

[0075] 4. The present invention proposes for the first time to find multiple characteristic protein combinations with differences among 146 confirmed patients with novel coronavirus infection and 46 normal persons, 33 tuberculosis patients, and 73 controls with novel coronavirus infection-type symptoms. This breaks through the traditional research idea of ​​only finding characteristic peptides in normal persons and patients infected with novel coronavirus, and effectively avoids infection with false positive results similar to symptoms of novel coronavirus infection.

[0076] 5. Through the mass spectrometry model of the present invention, the detection accuracy reached 99%, the sensitivity was 98%, and the specificity was 100%. The results show that the serum peptidomics characteristic polypeptide model of the present invention can be quickly used to screen patients infected with the new coronavirus in the population.

[0077] 6. Compared with the composition and mass spectrometry model constructed by 25 characteristic polypeptides, the newly introduced 4 characteristic polypeptides (i.e., SEQ ID NO: 16-19) belong to the SAA protein marker family, which can be used as biomarkers in clinical diagnosis of bacterial and viral infections by ELASA, immunoturbidimetry, colloidal gold method, immunofluorescence chromatography and other methods. However, based on the mass spectrometry model of 25 special polypeptides, the present invention proposes for the first time to use SAA protein markers for laser flight mass spectrometry detection of viruses, and accurately identifies specific SAA protein sequences (i.e., SEQ ID NO: 16-19) for the first time, which can effectively avoid misdiagnosis of normal samples in clinical practice. The results show that compared with the 10-fold cross-validation accuracy of the 25 characteristic polypeptide mass spectrometry model of about 97.96%, the 29 characteristic polypeptide mass spectrometry models introducing 4 SAA polypeptide markers have a 10-fold cross-validation accuracy of about 98.69%. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 : Comparison of serum peptide fingerprints of different groups (healthy group, tuberculosis group, similar symptoms group, and new crown patient group), from top to bottom, negative healthy person spectrum, negative tuberculosis spectrum, negative similar symptoms, positive new crown patient spectrum

[0079] Figure 2-1 : The 20 most frequent peaks in LASSO. Figure 2-2 : The 20 most important peaks of VIP changes in PLS-DA.

[0080] Figure 2-3 : The top 10 peaks with the highest cross-validation accuracy in RFECV.

[0081] Figure 3 : The intensity of each characteristic peak, the left column is the negative control group, and the right column is the positive control group.

[0082] Figure 4-1 : Comparison of ROC curves of various machine learning methods and training sets. Figure 4-2 : Comparison of ROC curves of test sets.

[0083] Figure 5 : The prediction results of the test set confusion matrix of the true grouping.

[0084] Figure 6 :A process for establishing a characteristic peptide mass spectrometry model for rapid screening of patients infected with the new coronavirus (COVID-19).

[0085] Figure 7 : Mass spectrum peak of the characteristic peptide m / z 5157.6. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0086] Figure 8 : Mass spectrum peak of the characteristic peptide m / z 5366.2. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0087] Fig. 9 : Mass spectrum peak of the characteristic peptide m / z 5892.9. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0088] Fig.10 : Mass spectrum peak of the characteristic peptide m / z 6357.4. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0089] Fig.11: Mass spectrum peak of the characteristic peptide m / z 6654.0. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0090] Fig.12 : Mass spectrum peak of the characteristic peptide m / z 6939.1. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0091] Fig.13 : Mass spectrum peak of the characteristic peptide m / z 7364.2. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0092] Fig.14 : Mass spectrum peak of the characteristic peptide m / z 7614.2. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0093] Fig.15 : Mass spectrum peak of the characteristic peptide m / z 8034.3. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0094] Fig.16 : Mass spectrum peak of the characteristic peptide m / z 8042.7. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0095] Fig.17 : Mass spectrum peak of the characteristic peptide m / z 8226.4. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0096] Fig.18 : Mass spectrum peak of the characteristic peptide m / z 8424.9. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0097] Fig.19 : Mass spectrum peak of characteristic peptide m / z 8559.8. The upper figure is the mass spectrum of non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0098] Fig. 20 : Mass spectrum peak of the characteristic peptide m / z 8986.1. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0099] Fig.21 : Mass spectrum peak of the characteristic peptide m / z 9626.4. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0100] Fig. 22: Mass spectrum peak of the characteristic peptide m / z 13719.2. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0101] Fig.23 : Mass spectrum peak of the characteristic peptide m / z 13765.2. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0102] Fig.24 : Mass spectrum peak of the characteristic peptide m / z 13886.1. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0103] Fig.25 : Mass spectrum peak of the characteristic peptide m / z 14049.4. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0104] Fig.26 : Mass spectrum peak of the characteristic peptide m / z 14094.7. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0105] Fig. 27 : Mass spectrum peak of the characteristic peptide m / z 14101.8. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0106] Fig.28 : Mass spectrum peak of the characteristic peptide m / z 15123.4. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0107] Fig.29 : Mass spectrum peak of the characteristic peptide m / z 15866.5. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0108] Fig.30 : Mass spectrum peak of the characteristic peptide m / z 28091.4. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0109] Fig.31 : Mass spectrum peak of the characteristic peptide m / z 28231.5. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0110] Fig.32 : Mass spectrum peak of the characteristic peptide m / z 11435.1. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0111] Fig.33: Mass spectrum peak of the characteristic peptide m / z 11495.3. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0112] Fig.34 : Mass spectrum peak of the characteristic peptide m / z 11522.8. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19.

[0113] Fig.35 : Mass spectrum peak of the characteristic peptide m / z 11680.3. The upper figure is the mass spectrum of the non-COVID control, and the lower figure is the mass spectrum of COVID-19. DETAILED DESCRIPTION

[0114] The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0115] Example 1 Sample processing

[0116] The serum samples of 146 confirmed patients came from a hospital in Chongqing in February 2020. All patients tested positive for nucleic acid and were strictly classified according to the guideline standards.

[0117] Classification is based on the following criteria:

[0118] (1) Mild: mild clinical symptoms, no pneumonia findings on imaging;

[0119] (2) Common type: fever, respiratory symptoms, and pneumonia findings on imaging;

[0120] (3) Severe: dyspnea, respiratory rate ≥ 30 times / min, static oxygen saturation ≤ 93%, arterial blood partial pressure (PaO2) / oxygen concentration (FiO2) ≤ 300 mmHg;

[0121] (4) Critical condition: Respiratory failure, need for ventilator, shock, or other organ failure should be sent to the ICU for emergency treatment.

[0122] The 152 serum samples of non-COVID-19 infected controls were collected from a hospital in Chongqing in March 2020, including 46 normal people, 33 tuberculosis patient controls, and 73 controls with COVID-19 infection type symptoms.

[0123] All samples were collected in the early morning before eating and placed in vacuum serum collection tubes without additives, centrifuged at 2,264 g for 10 min, and incubated at 56°C for 30 min. The serum samples were then aliquoted and frozen at -80°C.

[0124] Mass spectrometry pretreatment of serum samples: Before conducting mass spectrometry detection experiments, extract 1 tube of serum samples from the low-temperature refrigerator and place them on wet ice. Thaw for 60-90 minutes. Pipette 5uL of serum sample, add 45uL of sample treatment solution, and vortex at 1200rpm for 30s; Pipette 10uL of the treated sample solution and add 10uL of the prepared matrix solution, and vortex at 1200rpm for 30s; Spot 1uL of the mixed solution on the target plate. Each sample needs to be spotted for three experimental replicates, and dry naturally before mass spectrometry detection.

[0125] Example 2: Establishment of MALDI-TOF-MS mass spectrometry model

[0126] (I) Sample preparation

[0127] 5ul serum of each sample was diluted in 45ul sample treatment solution (Bioyong Technologies Inc.) Then 10ul of the diluted serum was taken out and mixed with 10ul matrix solution (Bioyong Technologies Inc.).

[0128] Take out 2ul of the mixed solution and drip it onto the stainless steel target plate. After drying at room temperature, the sample was injected into a MALDI-TOF MS mass spectrometer (Clin-TOF-II; Bioyong Technologies Inc.). Each sample was tested in parallel 3 times.

[0129] The matrix-assisted laser desorption time-of-flight mass spectrometry Clin-TOF and the peptide mass spectrometry universal pretreatment kit used in the experiment were developed by Bioyong Company in China. The MALDIquant program was used for data preprocessing, and the processed data were square root transformed, smoothed using the filter fitting method, and baseline correction was performed. The mass spectrometer was calibrated with a mixture of peptide proteins with known molecular weights. The mass drift of the calibrator should be within 500 ppm. 500 spectra were collected for each sample point. The molecular weight acquisition range was m / z 3000-30000.

[0130] Mass spectra of different groups of samples can be found in Figure 1 ( Figure 1: Comparison of serum peptide fingerprint maps of different groups, including negative healthy person map, negative tuberculosis map, negative similar symptoms, and positive new crown patient map from top to bottom). In the negative healthy person spectrum, the peak intensities of 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11523m / z, 15123m / z, 15867m / z, and 28091m / z are low, while the peak intensities of 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are high. In the negative pulmonary tuberculosis spectrum, 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, The peak intensities of 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are low, while the peak intensities of 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are high. In the spectrum similar to the negative symptom group, the peak intensities of 5158m / z, 5366m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 15123m / z, 15867m / z, and 28091m / z are low, while the peak intensities of 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are high.In the spectrum of positive COVID-19 patients, the peak intensities of 5158m / z, 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z are relatively high, while those of 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, The peak intensities of 14049m / z, 14095m / z, 14102m / z, and 28232m / z were relatively low.

[0131] (II) Mass spectrometry data acquisition

[0132] Use Clin-TOF mass spectrometer. Set appropriate laser energy to collect a certain point of sample crystallization. Select 50 laser bombardment positions for each sample point, bombard each position 10 times, that is, perform 500 laser bombardments on each sample crystallization point and collect spectra. Laser frequency: 30Hz. Data collection range: 3~30KDa. Use standard products for external standard calibration before collecting sample crystallization points each time, and the average molecular weight deviation is less than 500ppm.

[0133] Experimental quality control:

[0134] (1) Use the same mass spectrometry parameters to detect the crystallization point of the blank matrix. If an obvious mass spectrometry peak appears, it is considered that the quality of the matrix solution is unqualified and a new matrix needs to be replaced.

[0135] (2) When using standards for external calibration, it is necessary to ensure that the mass deviation of different calibration points does not exceed 500 ppm, and all five calibration peaks must meet the requirements at the same time.

[0136] (3) Select 8 existing peptide peaks in serum as internal standard quality control peaks. If 6 to 8 internal standard peaks can be detected and the molecular weight deviation range of the internal standard peak does not exceed 2‰, the spectrum is considered qualified. Otherwise, the spectrum needs to be re-collected. The internal standard peak m / z is as follows: 6426m / z, 6623m / z, 8753m / z, 8785m / z, 8904m / z, 9118m / z, 9409m / z, 9700m / z.

[0137] 3. Raw data preprocessing

[0138] The MALDI-TOF raw data was calibrated with internal standard calibration software and saved as a txt file. The internal standard peaks used were m / z: 6426m / z, 6623m / z, 8753m / z, 8785m / z, 8904m / z, 9118m / z, 9409m / z, 9700m / z. The spectrum was then processed using the MALDIquant program. The spectrum processing included smoothing, baseline correction, and molecular weight calibration. Peak detection was performed at a signal-to-noise ratio of 3. The binPeaks command was used to bin the peaks, with a tolerance of 0.002. Peaks with a peak frequency of not less than 25% within the group were retained. Finally, the resulting matrix was used for the following analysis.

[0139] After log2 transformation, the peak intensity matrix was quantile normalized with the R package limma. In all samples, missing values ​​were filled with the minimum value. The COVID-19 patient data and control sample data were randomly divided into training and testing groups with a distribution ratio of 2:1.

[0140] (IV) Selection of characteristic proteins

[0141] After intensity normalization and missing value normalization, the peak values ​​of the training group were analyzed using the following three machine learning methods: LASSO, PLS-DA, and RFECV. LASSO stands for Least absolute shrinkage and selection operator, which is a compression estimate. It constructs a penalty function to obtain a more refined model, which compresses some regression coefficients, that is, forces the sum of the absolute values ​​of the coefficients to be less than a fixed value; at the same time, it sets some regression coefficients to zero. Therefore, the advantages of subset shrinkage are retained, and it is a biased estimate for dealing with data with complex collinearity.

[0142] Figure 2-1 The 20 peaks with the highest repetition frequency in LASSO are shown. The vertical axis is the mass-to-nuclear ratio of each preferred characteristic peak. Partial least squares discriminant analysis (PLS-DA) is a multivariate statistical analysis method used for discriminant analysis. Discriminant analysis is a common statistical analysis method that determines how to classify the research object based on several observed or measured variable values. Its principle is to train the characteristics of different processed samples (such as observation samples and control samples) separately, generate training sets, and test the credibility of the training sets.

[0143] Figure 2-2The 20 peaks with the highest importance of VIP changes in PLS-DA are shown. The vertical axis is the mass-to-core ratio of each preferred feature peak. RFECV refers to finding the optimal number of features through cross-validation. RFE (Recursive feature elimination) refers to recursive feature elimination, which is used to rate the importance of features. CV (Cross Validation) refers to cross-validation, that is, after feature rating, the best number of features is selected through cross-validation. Figure 2-3 The 10 peaks with the highest cross-validation accuracy in RFECV are shown, where the vertical axis is the mass-to-core ratio of each preferred characteristic peak.

[0144] Through the empirical test of the original spectra of the selected peaks, 29 peaks that passed the quality control were screened out as features. The intensity of each characteristic peak is shown in Figure 3. Each row in the figure represents a characteristic peak, each column represents a spectrum data, and the color depth in the figure represents the intensity of the peak. The left column is the negative control group, and the right column is the positive group. It can be seen that the characteristic polypeptides 6357m / z, 6654m / z, 6939m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, and 28232m / z are generally expressed in higher levels in the negative group than in the positive group. At the same time, the characteristic polypeptides 5158m / z, The peaks of 5366m / z, 5893m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 15123m / z, 15867m / z, and 28091m / z were generally expressed at higher levels in the positive group than in the negative group. The intensities of these peaks were significantly different between the COVID-19 and control groups.

[0145] (V) Model Algorithm

[0146] We tried to use 8 machine learning methods to build models with 29 characteristic peaks of the training set data, and evaluate the model results by cross-validation accuracy. The 8 machine learning methods analyzed are as follows: logistic regression (LR), support vector machine (SVM), random forest (RF), naive Bayes (NB), gradient descent tree (GBDT), K-nearest neighbor (KNN), decision tree (DT) and adaptive boosting algorithm (Adaboost).

[0147] Figure 4-1 and Figure 4-2The model results of the training group and the test group were presented in the form of ROC curves. The ROC curve is a curve drawn based on a series of different binary classification methods (cutoff values ​​or decision thresholds), with the true positive rate (sensitivity) as the vertical axis and the false positive rate (1-specificity) as the horizontal axis. By calculating the area under the ROC curve (AUC) of each test for comparison, the test with the largest AUC has the best diagnostic value. In this study, the area under the ROC curve AUC of all models in the training group was greater than 0.99, among which the AUC of LR, SVM, GBDT, DT and Adaboost was 1( Figure 4-1 ). In the ROC curve analysis of the validation group data, it was found that the AUC of the 8 models obtained by the 8 machine learning methods in the test set exceeded 0.94. For the LR model, the AUC was 1 ( Figure 4-2 After evaluating the accuracy, recall, precision, F1, sensitivity, and specificity of the eight models, it was found that the LR model had the best classification performance (AUC = 1, sensitivity = 98%, specificity = 100%, accuracy = 99%, precision = 99%, recall = 99%, F1 = 99%), which can be further applied to the detection of COVID-19.

[0148] The confusion matrix of the LR model in the test set is as follows Figure 5 As shown in the figure, the vertical axis represents the actual grouping of samples, the upper row represents the number of negative samples, and the lower row represents the number of positive samples; the horizontal axis represents the model prediction results, the left column represents the number of samples judged as negative by the model, and the right column represents the number of samples judged as positive by the model. Among the 51 negative samples, all were judged as negative, and the accuracy rate of negative sample judgment (i.e., model specificity) was 100%; among the 49 positive samples, 1 was misjudged as negative, and 48 were judged as positive, and the accuracy rate of positive sample judgment (i.e., model sensitivity) was 98.0%.

[0149] Table 1. Mean and interquartile range of 29 characteristic peptides in the training set in each group

[0150]

[0151]

[0152] For a detailed process of establishing a characteristic peptide mass spectrometry model for rapid screening of patients infected with the novel coronavirus (COVID-19), see Figure 6The process includes: (1) collecting serum samples from patients infected with the new coronavirus and negative controls; (2) using a kit to pre-treat the serum samples for mass spectrometry; (3) MALDI-TOF MS mass spectrometry detection to obtain spectrum information; (4) spectrum processing and obtaining a peak list; (5) bioinformatics analysis; (6) determining the mass spectrometry model.

[0153] Example 3: Establishment of a screening model for patients infected with the novel coronavirus

[0154] 198 of 298 serum samples (146 from confirmed COVID-19 infected patients, 46 normal people, 33 pulmonary tuberculosis patients as controls, and 73 controls with COVID-19 similar symptoms (fever and cough)) were selected as training samples for model establishment, including 97 from COVID-19 infected patients, 34 from normal people, 19 from pulmonary tuberculosis patients as controls, and 48 from controls with COVID-19 similar symptoms. All serum samples were drawn in the early morning on an empty stomach, and the serum was separated and the virus was inactivated before being stored in a -80°C refrigerator.

[0155] The remaining samples (49 patients infected with the new coronavirus, 12 normal people, 14 tuberculosis patients, and 25 patients with symptoms similar to the new coronavirus infection) were used as validation samples for blind selection testing. The processing method was the same as above.

[0156] The mass spectrometry model of novel coronavirus-infected polypeptides was established using the characteristic polypeptide peaks in the serum of novel coronavirus-infected patients screened out in Example 1-2. The model is set to use 29 characteristic peaks, namely: 5158m / z, 5366m / z, 5893m / z, 6357m / z, 6654m / z, 6939m / z, 7364m / z, 7614m / z, 8034m / z, 8043m / z, 8226m / z, 8425m / z, 8560m / z, 8986m / z, 9626m / z, 11435m / z, 11495m / z, 11523m / z, 11680m / z, 13719m / z, 13765m / z, 13886m / z, 14049m / z, 14095m / z, 14102m / z, 15123m / z, 15867m / z, 28091m / z, 28232m / z.

[0157] The characteristic mass spectrometry peaks of the above-mentioned characteristic peptides are shown in Figure 7-35 shown.

[0158] The AUC of the LR model for both the training set and the validation set is greater than 0.99. The accuracy of the test set is 99%, the sensitivity is 98%, and the specificity is 100%. The model has good predictive ability.

[0159] Table 2. Model training results

[0160] sample Number of cases Predicted to be COVID-19 infection Predicted to be non-COVID-19 infection Prediction accuracy % Patient Group 97 97 0 100.00 Normal group 34 0 34 100.00 Tuberculosis Group 19 0 19 100.00 Symptom-like group 48 0 48 100.00 total 198 100.00

[0161] From the above table, we can see that the results for the training group samples are: 34 out of 34 normal cases were correctly judged, with a specificity of 100.00%; 97 out of 97 patients were correctly judged, with a sensitivity of 100.00%; 19 out of 19 tuberculosis patients were correctly judged, with a sensitivity of 100.00%; 48 out of 48 patients with similar symptoms were correctly judged, with a sensitivity of 100.00%.

[0162] Example 4: Identification of novel coronavirus infection characteristic peptides

[0163] After the peaks to be identified in Examples 2 and 3 were determined, seven serum samples with different intensities of the peaks to be identified in the pre-processed samples were searched. After the samples were reduced with DTT, proteins with a molecular weight greater than 50 kDa were removed by ultrafiltration centrifugation. The filtered small molecule proteins / peptides were separated by tricine-SDS-PAGE. Each band was identified by secondary mass spectrometry after in-gel digestion.

[0164] The peptide sequence was identified using a nano-LC-MS / MS platform, including nanoflow HPLC (Thermo Fisher Scientific, USA) and Q-Exactive mass spectrometer (Thermo Fisher Scientific, USA). The ion mode was positive ion mode, and the scanning range was 300-1400 m / z. The primary mass spectrometry resolution was 70,000, and the secondary mass spectrometry resolution was 17,500.

[0165] Liquid phase analysis column: Model: Exsil Pure 120 C18 (Dr. Maisch GmbH, USA); Specification: 360 μm × 12 cm; Inner diameter: 150 μm; Particle size: 1.9 um. Elution method: Mobile phase from 7% B solution (80% acetonitrile, 0.1% formic acid) to 45% B solution, linear elution. Flow rate: 600 nl / min; Total time: 38 minutes. The identification results are shown in Tables 3 and 4.

[0166] Table 3. Characteristic peak peptide identification results

[0167] m / z Gene name Protein name 5158 H2AJ Histone H2A.J 6357 S100A7 Protein S100-A7 6654 IGLL5 Immunoglobulin lambda-like polypeptide 5 6939 UBB Polyubiquitin-B 7364 IGKV3-7 Probable non-functional immunoglobulin kappa variable 3-7 7614 PF4V1 Platelet factor 4 variant 8034 IGKV3-15 Immunoglobulin kappa variable 3-15 8226 CFI Complement factor I 8986 RAB7A Ras-related protein Rab-7a 9626 ELANE Neutrophil elastase 13719 B2M Beta-2-microglobulin 13765 TTR Transthyretin 13886 PPBP Platelet basic protein 14049 DUSP14 Dual specificity protein phosphatase 14 14095 H2AC11 Histone H2A type 1 14102 H2AC6 Histone H2A type 1-C 15123 HBA1 Hemoglobin subunit alpha 15867 HBB Hemoglobin subunit beta 28091 WRAP73 WD repeat-containing protein WRAP73 11435 SAA1 Serum amyloid A-1 protein 11495 SAA2 Serum amyloid A-2 protein 11523 SAA1 Serum amyloid A-1 protein 11680 SAA1 Serum amyloid A-1 protein

[0168] Table 4. Peptide identification sequences

[0169]

[0170]

[0171] Example 5: Blind selection test of the novel coronavirus infection patient screening model

[0172] After the model training was completed, a model for the input variables of 25 characteristic polypeptide fragments of SEQ ID NO: 1-15 and a model for the input variables of 29 characteristic polypeptide fragments of SEQ ID NO: 1-19 were established. In addition, a model for the input variables of the 19 characteristic polypeptide fragments (i.e., sequences SEQ 1-19) that had been sequenced was established.

[0173] According to the method of Example 3, the above three models were used to blindly select and predict samples of 49 patients infected with the new coronavirus, 12 normal people, 14 tuberculosis patients, and 21 types of symptoms, and the categories of the samples were determined in the same way as described in the above examples. The results are shown in Table 5-1, Table 5-2, and Table 5-3, respectively.

[0174] Table 5-1. Prediction results of test samples using 25 variables

[0175]

[0176]

[0177] It can be seen from Table 5-1 that the results for the test group samples are: 12 out of 12 normal cases were correctly judged, with a specificity of 100.00%; 48 out of 49 patients were correctly judged, with a sensitivity of 97.96%; 14 out of 14 tuberculosis patients were correctly judged, with a sensitivity of 100.00%; 25 out of 25 patients with similar symptoms were correctly judged, with a sensitivity of 100.00%.

[0178] Table 5-2. Prediction results of test samples using 29 variables

[0179] sample Number of cases Predicted to be COVID-19 infection Predicted to be non-COVID-19 infection Prediction accuracy % Patient group 49 48 1 97.96 Normal group 12 0 12 100.00 Tuberculosis Group 14 0 14 100.00 Symptom-like group 25 0 25 100.00 total 100 99.00

[0180] It can be seen from Table 5-2 that the results for the test group samples are: 12 out of 12 normal cases were correctly judged, with a specificity of 100.00%; 48 out of 49 patients were correctly judged, with a sensitivity of 97.96%; 14 out of 14 tuberculosis patients were correctly judged, with a sensitivity of 100.00%; 25 out of 25 patients with similar symptoms were correctly judged, with a sensitivity of 100.00%.

[0181] As shown in Table 5-1 and Table 5-2, the prediction accuracy of both for 100 identical samples meets the clinical diagnosis standard. Although the accuracy is the same, the possible reason is that the number of patients to be tested in China is too small, resulting in no discrimination. However, according to the accuracy of the ten-fold cross validation, it can be predicted that as the number of patients to be tested increases, the mass spectrometry diagnosis model using 29 characteristic peptides will show a higher accuracy.

[0182] Table 5-3. Prediction results of test samples using 19 variables

[0183] sample Number of cases Predicted to be COVID-19 infection Predicted to be non-COVID-19 infection Prediction accuracy % Patient group 49 46 3 93.88 Normal group 12 0 12 100.00 Tuberculosis Group 14 0 14 100.00 Symptom-like group 25 4 21 84.00 total 100 93.00

[0184] From Table 5-3, we can see that the results of the test group samples are: 46 out of 49 COVID-19 patients were correctly judged, with a sensitivity of 93.88%; 12 out of 12 normal groups were correctly judged, with a specificity of 100.00%; 14 out of 14 tuberculosis patients were correctly judged, with a specificity of 100.00%; 21 out of 25 patients with similar symptoms were correctly judged, with a sensitivity of 84.00%. This shows that the model composed of 19 characteristic peptide input variables has the same specificity as the detection results of the complete variables for healthy people and tuberculosis patients, and there are very few misjudgments in the other two groups. The model has met the needs of rapid screening of confirmed patients in clinical practice.

[0185] In addition, it can be seen from the above table that the blind detection accuracy of the new coronavirus infection group using the full variables of 29 characteristic peptides in the present invention is basically the same as the model training, but the prediction result for the non-new coronavirus group reaches 100%, indicating that in the results after model training, the experimenters can completely eliminate false positive results through subtle optimization, which shows that the diagnostic results for positive results are true and reliable, and avoid missed diagnosis and / or misdiagnosis to the greatest extent, so it has positive significance.

[0186] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention. 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Ambystoma laterale x Ambystoma jeffersonianum <400> 3 Met Glu Ala Pro Ala Gln Leu Leu Phe Leu Leu Leu Leu Trp Leu Pro 1 5 10 15 Asp Thr Thr Gly Glu Ile Val Met Thr Gln Ser Pro Ala Thr Leu Ser 20 25 30 Val Ser Pro Gly Glu Arg Ala Thr Leu Ser Cys Arg Ala Ser Gln Ser 35 40 45 Val Ser Ser Asn Leu Ala Trp Tyr Gln Gln Lys Pro Gly Gln Ala Pro 50 55 60 Arg Leu Leu Ile Tyr Gly Ala Ser Thr Arg 65 70 <210> 4 <211> 69 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 4 Met Lys Leu Leu His Val Phe Leu Leu Phe Leu Cys Phe His Leu Arg 1 5 10 15 Phe Cys Lys Val Thr Tyr Thr Ser Gln Glu Asp Leu Val Glu Lys Lys 20 25 30 Cys Leu Ala Lys Lys Tyr Thr His Leu Ser Cys Asp Lys Val Phe Cys 35 40 45 Gln Pro Trp Gln Arg Cys Ile Glu Gly Thr Cys Val Cys Lys Leu Pro 50 55 60 Tyr Gln Cys Pro Lys 65 <210> 5 <211> 79 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 5 Met Thr Ser Arg Lys Lys Val Leu Leu Lys Val Ile Ile Leu Gly Asp 1 5 10 15 Ser Gly Val Gly Lys Thr Ser Leu Met Asn Gln Tyr Val Asn Lys Lys 20 25 30 Phe Ser Asn Gln Tyr Lys Ala Thr Ile Gly Ala Asp Phe Leu Thr Lys 35 40 45 Glu Val Met Val Asp Asp Arg Leu Val Thr Met Gln Ile Trp Asp Thr 50 55 60 Ala Gly Gln Glu Arg Phe Gln Ser Leu Gly Val Ala Phe Tyr Arg 65 70 75 <210> 6 <211> 91 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 6 Met Thr Leu Gly Arg Arg Leu Ala Cys Leu Phe Leu Ala Cys Val Leu 1 5 10 15 Pro Ala Leu Leu Leu Gly Gly Thr Ala Leu Ala Ser Glu Ile Val Gly 20 25 30 Gly Arg Arg Ala Arg Pro His Ala Trp Pro Phe Met Val Ser Leu Gln 35 40 45 Leu Arg Gly Gly His Phe Cys Gly Ala Thr Leu Ile Ala Pro Asn Phe 50 55 60 Val Met Ser Ala Ala His Cys Val Ala Asn Val Asn Val Arg Ala Val 65 70 75 80 Arg Val Val Leu Gly Ala His Asn Leu Ser Arg 85 90 <210> 7 <211> 119 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 7 Met Ser Arg Ser Val Ala Leu Ala Val Leu Ala Leu Leu Ser Leu Ser 1 5 10 15 Gly Leu Glu Ala Ile Gln Arg Thr Pro Lys Ile Gln Val Tyr Ser Arg 20 25 30 His Pro Ala Glu Asn Gly Lys Ser Asn Phe Leu Asn Cys Tyr Val Ser 35 40 45 Gly Phe His Pro Ser Asp Ile Glu Val Asp Leu Leu Lys Asn Gly Glu 50 55 60 Arg Ile Glu Lys Val Glu His Ser Asp Leu Ser Phe Ser Lys Asp Trp 65 70 75 80 Ser Phe Tyr Leu Leu Tyr Tyr Thr Glu Phe Thr Pro Thr Glu Lys Asp 85 90 95 Glu Tyr Ala Cys Arg Val Asn His Val Thr Leu Ser Gln Pro Lys Ile 100 105 110 Val Lys Trp Asp Arg Asp Met 115 <210> 8 <211> 127 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 8 Gly Pro Thr Gly Thr Gly Glu Ser Lys Cys Pro Leu Met Val Lys Val 1 5 10 15 Leu Asp Ala Val Arg Gly Ser Pro Ala Ile Asn Val Ala Val His Val 20 25 30 Phe Arg Lys Ala Ala Asp Asp Thr Trp Glu Pro Phe Ala Ser Gly Lys 35 40 45 Thr Ser Glu Ser Gly Glu Leu His Gly Leu Thr Thr Glu Glu Glu Phe 50 55 60 Val Glu Gly Ile Tyr Lys Val Glu Ile Asp Thr Lys Ser Tyr Trp Lys 65 70 75 80 Ala Leu Gly Ile Ser Pro Phe His Glu His Ala Glu Val Val Phe Thr 85 90 95 Ala Asn Asp Ser Gly Pro Arg Arg Tyr Thr Ile Ala Ala Leu Leu Ser 100 105 110 Pro Tyr Ser Tyr Ser Thr Thr Ala Val Val Thr Asn Pro Lys Glu 115 120 125 <210> 9 <211> 128 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 9 Met Ser Leu Arg Leu Asp Thr Thr Pro Ser Cys Asn Ser Ala Arg Pro 1 5 10 15 Leu His Ala Leu Gln Val Leu Leu Leu Leu Ser Leu Leu Leu Thr Ala 20 25 30 Leu Ala Ser Ser Thr Lys Gly Gln Thr Lys Arg Asn Leu Ala Lys Gly 35 40 45 Lys Glu Glu Ser Leu Asp Ser Asp Leu Tyr Ala Glu Leu Arg Cys Met 50 55 60 Cys Ile Lys Thr Thr Ser Gly Ile His Pro Lys Asn Ile Gln Ser Leu 65 70 75 80 Glu Val Ile Gly Lys Gly Thr His Cys Asn Gln Val Glu Val Ile Ala 85 90 95 Thr Leu Lys Asp Gly Arg Lys Ile Cys Leu Asp Pro Asp Ala Pro Arg 100 105 110 Ile Lys Lys Ile Val Gln Lys Lys Leu Ala Gly Asp Glu Ser Ala Asp 115 120 125 <210> 10 <211> 123 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 10 Val Pro Leu Ala Asp Met Pro His Ala Pro Ile Gly Leu Tyr Phe Asp 1 5 10 15 Thr Val Ala Asp Lys Ile His Ser Val Ser Arg Lys His Gly Ala Thr 20 25 30 Leu Val His Cys Ala Ala Gly Val Ser Arg Ser Ala Thr Leu Cys Ile 35 40 45 Ala Tyr Leu Met Lys Phe His Asn Val Cys Leu Leu Glu Ala Tyr Asn 50 55 60 Trp Val Lys Ala Arg Arg Pro Val Ile Arg Pro Asn Val Gly Phe Trp 65 70 75 80 Arg Gln Leu Ile Asp Tyr Glu Arg Gln Leu Phe Gly Lys Ser Thr Val 85 90 95 Lys Met Val Gln Thr Pro Tyr Gly Ile Val Pro Asp Val Tyr Glu Lys 100 105 110 Glu Ser Arg His Leu Met Pro Tyr Trp Gly Ile 115 120 <210> 11 <211> 130 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 11 Met Ser Gly Arg Gly Lys Gln Gly Gly Lys Ala Arg Ala Lys Ala Lys 1 5 10 15 Ser Arg Ser Ser Arg Ala Gly Leu Gln Phe Pro Val Gly Arg Val His 20 25 30 Arg Leu Leu Arg Lys Gly Asn Tyr Ala Glu Arg Val Gly Ala Gly Ala 35 40 45 Pro Val Tyr Met Ala Ala Val Leu Glu Tyr Leu Thr Ala Glu Ile Leu 50 55 60 Glu Leu Ala Gly Asn Ala Ala Arg Asp Asn Lys Lys Thr Arg Ile Ile 65 70 75 80 Pro Arg His Leu Gln Leu Ala Ile Arg Asn Asp Glu Glu Leu Asn Lys 85 90 95 Leu Leu Gly Lys Val Thr Ile Ala Gln Gly Gly Val Leu Pro Asn Ile 100 105 110 Gln Ala Val Leu Leu Pro Lys Lys Thr Glu Ser His His Lys Ala Lys 115 120 125 Gly Lys 130 <210> 12 <211> 130 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 12 Met Ser Gly Arg Gly Lys Gln Gly Gly Lys Ala Arg Ala Lys Ala Lys 1 5 10 15 Ser Arg Ser Ser Arg Ala Gly Leu Gln Phe Pro Val Gly Arg Val His 20 25 30 Arg Leu Leu Arg Lys Gly Asn Tyr Ala Glu Arg Val Gly Ala Gly Ala 35 40 45 Pro Val Tyr Leu Ala Ala Val Leu Glu Tyr Leu Thr Ala Glu Ile Leu 50 55 60 Glu Leu Ala Gly Asn Ala Ala Arg Asp Asn Lys Lys Thr Arg Ile Ile 65 70 75 80 Pro Arg His Leu Gln Leu Ala Ile Arg Asn Asp Glu Glu Leu Asn Lys 85 90 95 Leu Leu Gly Arg Val Thr Ile Ala Gln Gly Gly Val Leu Pro Asn Ile 100 105 110 Gln Ala Val Leu Leu Pro Lys Lys Thr Glu Ser His His Lys Ala Lys 115 120 125 Gly Lys 130 <210> 13 <211> 141 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 13 Val Leu Ser Pro Ala Asp Lys Thr Asn Val Lys Ala Ala Trp Gly Lys 1 5 10 15 Val Gly Ala His Ala Gly Glu Tyr Gly Ala Glu Ala Leu Glu Arg Met 20 25 30 Phe Leu Ser Phe Pro Thr Thr Lys Thr Tyr Phe Pro His Phe Asp Leu 35 40 45 Ser His Gly Ser Ala Gln Val Lys Gly His Gly Lys Lys Val Ala Asp 50 55 60 Ala Leu Thr Asn Ala Val Ala His Val Asp Asp Met Pro Asn Ala Leu 65 70 75 80 Ser Ala Leu Ser Asp Leu His Ala His Lys Leu Arg Val Asp Pro Val 85 90 95 Asn Phe Lys Leu Leu Ser His Cys Leu Leu Val Thr Leu Ala Ala His 100 105 110 Leu Pro Ala Glu Phe Thr Pro Ala Val His Ala Ser Leu Asp Lys Phe 115 120 125 Leu Ala Ser Val Ser Thr Val Leu Thr Ser Lys Tyr Arg 130 135 140 <210> 14 <211> 146 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 14 Val His Leu Thr Pro Glu Glu Lys Ser Ala Val Thr Ala Leu Trp Gly 1 5 10 15 Lys Val Asn Val Asp Glu Val Gly Gly Glu Ala Leu Gly Arg Leu Leu 20 25 30 Val Val Tyr Pro Trp Thr Gln Arg Phe Phe Glu Ser Phe Gly Asp Leu 35 40 45 Ser Thr Pro Asp Ala Val Met Gly Asn Pro Lys Val Lys Ala His Gly 50 55 60 Lys Lys Val Leu Gly Ala Phe Ser Asp Gly Leu Ala His Leu Asp Asn 65 70 75 80 Leu Lys Gly Thr Phe Ala Thr Leu Ser Glu Leu His Cys Asp Lys Leu 85 90 95 His Val Asp Pro Glu Asn Phe Arg Leu Leu Gly Asn Val Leu Val Cys 100 105 110 Val Leu Ala His His Phe Gly Lys Glu Phe Thr Pro Pro Val Gln Ala 115 120 125 Ala Tyr Gln Lys Val Val Ala Gly Val Ala Asn Ala Leu Ala His Lys 130 135 140 Tyr His 145 <210> 15 <211> 257 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 15 Ile Leu Leu Tyr Ser Leu Asp Gly Arg Leu Leu Ser Thr Tyr Ser Ala 1 5 10 15 Tyr Glu Trp Ser Leu Gly Ile Lys Ser Val Ala Trp Ser Pro Ser Ser 20 25 30 Gln Phe Leu Ala Val Gly Ser Tyr Asp Gly Lys Val Arg Ile Leu Asn 35 40 45 His Val Thr Trp Lys Met Ile Thr Glu Phe Gly His Pro Ala Ala Ile 50 55 60 Asn Asp Pro Lys Ile Val Val Tyr Lys Glu Ala Glu Lys Ser Pro Gln 65 70 75 80 Leu Gly Leu Gly Cys Leu Ser Phe Pro Pro Pro Arg Ala Gly Ala Gly 85 90 95 Pro Leu Pro Ser Ser Glu Ser Lys Tyr Glu Ile Ala Ser Val Pro Val 100 105 110 Ser Leu Gln Thr Leu Lys Pro Val Thr Asp Arg Ala Asn Pro Lys Ile 115 120 125 Gly Ile Gly Met Leu Ala Phe Ser Pro Asp Ser Tyr Phe Leu Ala Thr 130 135 140 Arg Asn Asp Asn Ile Pro Asn Ala Val Trp Val Trp Asp Ile Gln Lys 145 150 155 160 Leu Arg Leu Phe Ala Val Leu Glu Gln Leu Ser Pro Val Arg Ala Phe 165 170 175 Gln Trp Asp Pro Gln Gln Pro Arg Leu Ala Ile Cys Thr Gly Gly Ser 180 185 190 Arg Leu Tyr Leu Trp Ser Pro Ala Gly Cys Met Ser Val Gln Val Pro 195 200 205 Gly Glu Gly Asp Phe Ala Val Leu Ser Leu Cys Trp His Leu Ser Gly 210 215 220 Asp Ser Met Ala Leu Leu Ser Lys Asp His Phe Cys Leu Cys Phe Leu 225 230 235 240 Glu Thr Glu Ala Val Val Gly Thr Ala Cys Arg Gln Leu Gly Gly His 245 250 255 Thr <210> 16 <211> 102 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 16 Phe Phe Ser Phe Leu Gly Glu Ala Phe Asp Gly Ala Arg Asp Met Trp 1 5 10 15 Arg Ala Tyr Ser Asp Met Arg Glu Ala Asn Tyr Ile Gly Ser Asp Lys 20 25 30 Tyr Phe His Ala Arg Gly Asn Tyr Asp Ala Ala Lys Arg Gly Pro Gly 35 40 45 Gly Val Trp Ala Ala Glu Ala Ile Ser Asp Ala Arg Glu Asn Ile Gln 50 55 60 Arg Phe Phe Gly His Gly Ala Glu Asp Ser Leu Ala Asp Gln Ala Ala 65 70 75 80 Asn Glu Trp Gly Arg Ser Gly Lys Asp Pro Asn His Phe Arg Pro Ala 85 90 95 Gly Leu Pro Glu Lys Tyr 100 <210> 17 <211> 103 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 17 Ser Phe Phe Ser Phe Leu Gly Glu Ala Phe Asp Gly Ala Arg Asp Met 1 5 10 15 Trp Arg Ala Tyr Ser Asp Met Arg Glu Ala Asn Tyr Ile Gly Ser Asp 20 25 30 Lys Tyr Phe His Ala Arg Gly Asn Tyr Asp Ala Ala Lys Arg Gly Pro 35 40 45 Gly Gly Ala Trp Ala Ala Glu Val Ile Ser Asn Ala Arg Glu Asn Ile 50 55 60 Gln Arg Leu Thr Gly Arg Gly Ala Glu Asp Ser Leu Ala Asp Gln Ala 65 70 75 80 Ala Asn Lys Trp Gly Arg Ser Gly Arg Asp Pro Asn His Phe Arg Pro 85 90 95 Ala Gly Leu Pro Glu Lys Tyr 100 <210> 18 <211> 103 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 18 Ser Phe Phe Ser Phe Leu Gly Glu Ala Phe Asp Gly Ala Arg Asp Met 1 5 10 15 Trp Arg Ala Tyr Ser Asp Met Arg Glu Ala Asn Tyr Ile Gly Ser Asp 20 25 30 Lys Tyr Phe His Ala Arg Gly Asn Tyr Asp Ala Ala Lys Arg Gly Pro 35 40 45 Gly Gly Val Trp Ala Ala Glu Ala Ile Ser Asp Ala Arg Glu Asn Ile 50 55 60 Gln Arg Phe Phe Gly His Gly Ala Glu Asp Ser Leu Ala Asp Gln Ala 65 70 75 80 Ala Asn Glu Trp Gly Arg Ser Gly Lys Asp Pro Asn His Phe Arg Pro 85 90 95 Ala Gly Leu Pro Glu Lys Tyr 100 <210> 19 <211> 104 <212> PRT <213> 2 Ambystoma laterale x Ambystoma jeffersonianum <400> 19 Arg Ser Phe Phe Ser Phe Leu Gly Glu Ala Phe Asp Gly Ala Arg Asp 1 5 10 15 Met Trp Arg Ala Tyr Ser Asp Met Arg Glu Ala Asn Tyr Ile Gly Ser 20 25 30 Asp Lys Tyr Phe His Ala Arg Gly Asn Tyr Asp Ala Ala Lys Arg Gly 35 40 45 Pro Gly Gly Val Trp Ala Ala Glu Ala Ile Ser Asp Ala Arg Glu Asn 50 55 60 Ile Gln Arg Phe Phe Gly His Gly Ala Glu Asp Ser Leu Ala Asp Gln 65 70 75 80 Ala Ala Asn Glu Trp Gly Arg Ser Gly Lys Asp Pro Asn His Phe Arg 85 90 95 Pro Ala Gly Leu Pro Glu Lys Tyr 100

Claims

1. A method for constructing a mass spectrometry model for diagnosing novel coronavirus infection, comprising: 1) Collect serum samples from multiple clinically confirmed COVID-19 infected persons and non-COVID-19 infected controls, freeze them for later use. The non-COVID-19 infected controls are selected from tuberculosis patients, patients with similar symptoms of fever and cough, and healthy people; 2) Pre-treatment of serum proteins before mass spectrometry; 3) Performing mass spectrometry on the two groups of pretreated serum proteins to obtain fingerprints of the two groups of serum peptides; 4) Standardize the fingerprints of serum peptides from all patients and normal subjects and collect data; 5) Perform quality control on the obtained data, screen out characteristic peptides with the following mass-to-charge ratio peaks: 6939m / z, 8986m / z, 13886m / z, 14049m / z, 14102m / z, 28091m / z, perform secondary mass spectrometry identification on the characteristic peptides, and establish a mass spectrometry model for detecting novel coronavirus infection based on these mass-to-charge ratio peaks, wherein: A characteristic polypeptide with a mass-to-charge ratio of 6939 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 1; A characteristic polypeptide with a mass-to-charge ratio of 8986 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.5; A characteristic polypeptide with a mass-to-charge ratio of 13886 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No.9; A characteristic polypeptide with a mass-to-charge ratio of 14049 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 10; A characteristic polypeptide with a mass-to-charge ratio of 14102 m / z, whose polypeptide sequence is selected from the sequence shown in SEQ ID No. 12; The characteristic polypeptide with a mass-to-charge ratio of 28091m / z has a polypeptide sequence selected from the sequence shown in SEQ ID No.

15.

2. The construction method of claim 1, wherein when the peaks of the characteristic polypeptides 8986m / z and 28091m / z of the serum sample of the patient to be tested are upregulated, and at the same time, the peaks of the characteristic polypeptides 6939m / z, 13886m / z, 14049m / z, and 14102m / z are downregulated, it indicates that the serum sample is a positive sample, that is, the patient to be tested is determined to be a patient infected with the new coronavirus, and the accuracy rate of ten-fold cross validation is about 91%. The person to be tested who does not have the above-mentioned characteristic polypeptide upregulation and downregulation is a control person who is not infected with the new coronavirus.

3. The construction method according to claim 1 or 2, wherein the pretreatment method in step 2) comprises using a sample treatment solution to dilute the serum protein or polypeptide in the stabilized sample.

4. The construction method according to claim 1 or 2, wherein in step 3), a universal pretreatment kit for polypeptide mass spectrometry is used to dilute and read the two groups of serum proteins to obtain fingerprints of the two groups of serum polypeptides.

5. The construction method according to claim 1 or 2, wherein the quality control treatment described in step 5) is to detect the crystallization point of the blank matrix using the same mass spectrometry parameters for the blank matrix, and if an obvious mass spectrometry peak appears, it is considered that the matrix solution is of unqualified quality.

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