A method for in situ characterization of human blood protein structures and its application in identification

Abstract

The present application relates to a kind of human blood protein structure in-situ characterization method and its application in identification.Firstly, using polarization tuning spectrum and spectral dimensionality extraction method, the primary structure and high-order structure of highly homologous protein are in-situ characterized;Ordinary absorption spectrum is upgraded to high-dimensional space, and the characteristics of small changes in spectrum are enhanced.Fusion method based on feature similarity is carried out on high-dimensional spectrum, and a feature screening method with multi-similarity measurement criterion fusion is designed, which effectively screens the typical features in high-dimensional spectrum, realizes the in-situ characterization of the sample to be measured.Based on the in-situ characterization result, the source of the sample to be measured can be detected, which has good specificity and accuracy, and can also realize the in-situ characterization of primary and high-order structure, which is of great significance to ensure the safety, effectiveness and quality controllability of human blood albumin product quality.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of detection, and particularly relates to a method for in-situ characterization of human blood protein structure and application thereof in identification. BACKGROUND

[0002] Human serum albumin (HSA) can be used as a clinical drug, mainly for treating shock caused by blood loss trauma and burns, reducing intracranial hypertension caused by brain edema or injury, improving edema or ascites caused by cirrhosis and kidney disease, and can also be used for the prevention and treatment of hypoproteinemia. As a special drug needed for clinical first aid, human serum albumin is in short supply and expensive, and belongs to valuable drugs. Counterfeit cases are common on the market. In the pharmaceutical industry, bovine serum albumin (BSA) is highly homologous to human serum albumin. The molecular weight is close to that of human serum albumin, the physiological function is similar, and the price is very low. The two are highly homologous proteins with a sequence similarity of 67%. Although bovine serum albumin has important roles in biochemical research, genetic engineering and pharmaceutical research, it is also used as a pharmaceutical health food and a seasoning, but it cannot be used as a clinical drug. Therefore, the identification of highly homologous albumin is crucial for the safety, effectiveness and quality controllability of human serum albumin preparations. In-situ characterization of the structure of human serum albumin in solution state is not only important for the quality evaluation of HSA, but also for the research of drugs related to HSA.

[0003] Currently, the identification methods of human serum albumin mainly include microsequencing (N-terminal Edman degradation), mass spectrometry combined with other techniques, amino acid group analysis, immunodiffusion method and immunoelectrophoresis method. Among them, microsequencing has strong specificity and high accuracy for the identification of HSA and BSA, but it is time-consuming and costly. Liquid chromatography-mass spectrometry is difficult to identify highly homologous albumin, and the accuracy is limited. Amino acid group analysis may have insufficient acid hydrolysis or partial degradation leading to amino acid variation, and needs to be confirmed by combining other protein properties. The properties of HSA and BSA are highly similar, and accurate identification is difficult. Infrared spectroscopy and Raman spectroscopy can achieve rapid identification of human serum albumin and other proteins, but cannot meet the qualitative analysis of HSA and BSA with high homology. In addition, the immunodiffusion method and immunoelectrophoresis method can identify human serum albumin products and other animal serum albumins such as horse serum, bovine serum, and sheep serum, but cannot characterize the structure of human serum albumin products, nor can they further evaluate the quality of the products.

[0004] The quality management of human serum albumin in China is mainly based on Chinese Pharmacopoeia. The production and testing of human serum albumin are clearly specified in Chinese Pharmacopoeia 2020. Among them, the production of human serum albumin is required to control the process, which is divided into three levels: whole process quality control, batch consistency control, and target component and non-target component control, that is, effective control of the whole production process of human serum albumin from raw materials, intermediate products, and products to ensure the quality of the final product. In the field of drug research and drug supervision, a specific identification and in-situ characterization method for human serum albumin is needed. SUMMARY

[0005] To solve the problems of complex sample preparation process, long analysis time, limited detection parameters, low sensitivity and other shortcomings of the commonly used instrument analysis methods such as mass spectrometry and chromatography; the traditional spectral method has good detection effect for molecular bond absorption, but the characterization ability for protein and other macromolecular structures is poor, the analysis method has low sensitivity and large interference, etc. The present application provides a human serum protein structure in-situ characterization method and its application in identification.

[0006] The technical scheme adopted by the present application is: a protein structure in-situ characterization method, detecting the polarization tuning spectrum of protein solution under the condition of temperature and concentration double disturbance, processing the obtained original spectrum by two-dimensional correlation analysis method, and using multi-dimensional slice spectrum to in-situ characterize the primary structure and / or high-order structure of protein solution.

[0007] Preferably, the specific steps are as follows:

[0008] Step one: under the condition of temperature and concentration double disturbance, the near-infrared spectrum is collected by a polarization tuning spectrum multi-modal spectrum detection device to obtain a data cube of albumin;

[0009] Step two: forming an original spectrum from the data cube; extracting features of the original spectrum at different concentrations at each temperature point by principal component analysis, extracting k mutually unrelated orthogonal components to form a principal component spectrum;

[0010] Step three: performing two-dimensional correlation analysis on the principal component spectrum to separate protein structure signals;

[0011] Step four: in-situ characterization of the primary structure and / or high-order structure of the protein solution by combining the synchronous spectrum and / or asynchronous spectrum obtained by two-dimensional correlation analysis.

[0012] Preferably, in step one, the polarization tuning spectrum multi-modal spectrum detection device comprises a light source, a polarizer, an analyzer, a beam splitter, a fiber coupling mirror, an optical switch device and a spectrometer, and further comprises a reference cell and a sample cell; the polarizer and the beam splitter are sequentially arranged downstream of the light source, the wide spectrum light emitted by the light source passes through the polarizer and is split into two paths under the action of the beam splitter, one of the two paths passes through the reference cell and the first fiber coupling mirror in sequence and is transmitted into the optical switch device by an optical fiber, and the other path passes through the sample cell, the analyzer and the second fiber coupling mirror in sequence and is transmitted into the optical switch device by an optical fiber; the two light signals are controlled by the optical switch and are detected in the spectrometer in time.

[0013] Preferably, the concentration disturbance condition is that 3-6 concentration detection points with gradient increase are set in 3.5%-5.1%; and the temperature disturbance condition is that 3-6 temperature detection points with gradient increase are set in 57-61℃.

[0014] Preferably, in step two, the original data is subjected to second-order 15-point SG filtering denoising and baseline correction to form the original spectrum.

[0015] Preferably, in step three, the two-dimensional correlation analysis process is as follows:

[0016] Analysis of spectrum change function under the action of external disturbance variable , wherein t represents the external disturbance variable temperature (℃), is the wave number (cm -1 ). When t changes between and , the measured spectrum intensity y, i.e. the original spectrum set, is defined as :

[0017] (formula 1);

[0018] , wherein is the average spectrum:

[0019] (formula 2);

[0020] Two-dimensional correlation synchronous spectrum intensity is the vector product of the dynamic spectrum intensity y at different wave numbers :

[0021] (formula 3);

[0022] Two-dimensional correlation asynchronous spectrum intensity is the Hilbert-Noda matrix (H) vector product of the dynamic spectrum intensity y at different wave numbers :

[0023] ​(Formula 4);

[0024] wherein, As shown in Formula 5:

[0025] (Formula 5).

[0026] Preferably, the autocorrelation peak of the diagonal line in the synchronous spectrum is analyzed as a characteristic signal of the protein, for identification and structural characterization.

[0027] Preferably, in step one, the near-infrared spectrum and the Raman spectrum are collected by the polarization-tuned multi-modal spectral detection device under the condition of temperature-concentration double perturbation, to obtain the data cube of the albumin.

[0028] Preferably, the heterospectral two-dimensional correlation method is used to analyze the correlation between the sum spectrum region of the near-infrared spectrum and the characteristic spectrum region of the Raman spectrum, and the structural signal of the albumin is analyzed by the heterospectral two-dimensional correlation spectrum.

[0029] Preferably, the orthogonal cross-peak in the heterospectral two-dimensional correlation synchronous spectrum is used to calculate the correlation coefficient r of the orthogonal cross-peak (Formula 6), which represents the common response of the corresponding spectrum segments of the near-infrared spectrum and the Raman spectrum to the concentration perturbation and the temperature perturbation.

[0030] (Formula 6);

[0031] wherein is the peak value of the orthogonal cross-peak, max is the maximum value of all peak values of the orthogonal cross-peak, and min is the minimum value of all peak values of the orthogonal cross-peak; the larger the value of r is, the stronger the correlation between the near-infrared spectrum segment and the Raman spectrum segment is, and the greater the probability of coming from the same structure is;

[0032] Preferably, r<0.3 is defined as weak correlation, r<0.6 is defined as moderate correlation, and r≥0.6 is defined as strong correlation.

[0033] A human blood protein identification method, in which the protein structure in situ characterization method is used to characterize an unknown protein solution, and the obtained information is compared with the information of a standard human blood protein sample to identify the unknown protein solution.

[0034] Preferably, according to the two-dimensional correlation diagram, the structural information and the corresponding spectrum segment, the autocorrelation peak or the slice spectrum of the protein is extracted, a HSA qualitative discrimination model is established by using the partial least squares discrimination method (PLS-DA), and the unknown protein type is identified by the model.

[0035] The advantages and positive effects of this invention are: it organically combines principal component analysis, two-dimensional correlation spectroscopy, and partial least squares discrimination, and comprehensively solves the problem of in-situ characterization of human serum albumin structure (especially higher conformations) in physiological environment concentrations from three aspects: structural signal extraction, structural signal separation, and structural signal analysis, and provides an effective technical means for the study of human serum albumin-drug interaction.

[0036] A feature similarity-based fusion method for high-dimensional spectra is proposed. The fused features are used to establish a protein identification model. This method not only demonstrates excellent specificity and accuracy in identifying HSA (high-order serum albumin), but also enables in-situ characterization of the primary and higher-order structures of physiological concentration HSA solutions. This method can be applied to the identification of human serum albumin, significantly shortening the identification cycle and achieving efficient, rapid, and accurate identification. It is of great significance for ensuring the safety, effectiveness, and quality control of human serum albumin products. Attached Figure Description

[0037] Figure 1 A schematic diagram of a polarization-tuned multimodal spectroscopy detection device; wherein, 1, light source, 2, polarizer, 3, beam splitter, 4, sample cell, 5, analyzer, 6, fiber optic coupler, 7, reference cell, 8, optical switch, 9, spectrometer.

[0038] Figure 2 Flowchart of in-situ characterization method for human blood protein structure;

[0039] Figure 3 Average raw near-infrared spectra of HSA and BSA solutions under temperature perturbation; a: HSA solution; b: HSA solution 4900-4700 cm⁻¹ -1 Enlarged band image; c: BSA solution; d: BSA solution 4900-4700 cm⁻¹ -1 Enlarged band view;

[0040] Figure 4 Near-infrared principal component spectra of protein solutions of all concentrations under temperature perturbation (4900-4200 cm⁻¹) -1 Principal component analysis: First principal component spectrum; a: HSA solution; b: BSA solution;

[0041] Figure 5 Protein feature signals extracted at 4800-4300 cm under temperature perturbation -1 Two-dimensional correlation analysis results; a: synchronous spectrum of HSA solution; b: asynchronous spectrum of HSA solution; c: synchronous spectrum of BSA solution; d: asynchronous spectrum of BSA solution;

[0042] Figure 6 Two-dimensional correlation analysis of the autocorrelation peaks in the synchronous spectrum;

[0043] Figure 7 Results of near-infrared two-dimensional correlation analysis under the disturbance of polarization modulation spectrum; a: HSA solution synchronous spectrum; b: BSA solution synchronous spectrum;

[0044] Figure 8 Results of Raman two-dimensional correlation analysis under single temperature disturbance; a: 1700-1600 cm -1 HSA solution synchronous spectrum; b: 1500-1400 cm -1 HSA solution synchronous spectrum; c: 1700-1600 cm -1 BSA solution synchronous spectrum; d: 1500-1400 cm -1 BSA solution synchronous spectrum;

[0045] Figure 9 Results of Raman-near-infrared spectrum heterospectral correlation analysis under temperature disturbance; a-c: HSA solution; d-f: BSA solution;

[0046] Figure 10 Results of HSA solution identification model verification. DETAILED DESCRIPTION

[0047] The embodiments of the present application will be described below with reference to the accompanying drawings.

[0048] The present application relates to a method for in-situ characterization of human blood protein structure and its application in identification. Firstly, the primary structure and the high-order structure of human blood albumin and bovine blood albumin with high homology are characterized in-situ by using polarization tuning spectrum and spectrum dimensionality extraction method. A multi-modal spectrum detection device based on near-infrared polarization tuning spectrum is designed, and the absorption spectrum of the sample in different polarization states is obtained by using the near-infrared polarization tuning spectrum detection device. On the basis of multi-modal spectrum, a spectrum dimensionality method is provided to enhance the small change characteristics in the spectrum by dimensionality of ordinary absorption spectrum to high-dimensional space. Then, a fusion method based on feature similarity is used for high-dimensional spectrum, a feature screening method with multi-similarity measurement criterion fusion is designed to effectively screen the typical features in the high-dimensional spectrum, and the in-situ characterization of the sample to be tested is realized. Based on the in-situ characterization result, the sample to be tested can be quickly identified, and a protein species identification model is established by using the fused features. The identification model uses PLS-DA method based on nonlinear kernel function to effectively extract the nonlinear features in the high-dimensional spectrum, thereby improving the identification ability of the model for isomeric proteins. The method can be used for identification of protein species or origin.

[0049] The structure of the polarization tuning spectrum multi-modal spectrum detection device is as follows Figure 1As shown, the wide spectrum light emitted by the light source is divided into two paths by the polarizer, one of which is the reference path, which passes through the reference cell and the fiber coupling mirror in turn, and then is transmitted into the optical switch by the fiber; the other path is the sample path, which passes through the sample cell and the fiber coupling mirror in turn, and then is transmitted into the optical switch by the fiber; the two light signals are controlled by the optical switch and enter the spectrometer at different times to detect the spectrum. When the positions of the polarizer and the analyzer are rotated, the polarization tuning spectrum can be obtained; by using the characteristics that polarized light is sensitive to the molecular bond structure, the polarization spectrum of the protein macromolecule under different polarization states is obtained, thereby providing rich information for protein structure analysis and classification. During detection, the reference spectrum is under the condition of no polarization modulation; the polarizer and the analyzer in the sample path are set to different angles, and N polarization states are selected for measurement; the angle difference a is set to one deflection unit, and the polarization states under a, 2a, 3a…Na are detected respectively, and multiple polarization tuning spectra can be obtained.

[0050] Ordinary absorption spectrum cannot effectively characterize the high homologous protein structure (especially the advanced conformation in solution). The polarization tuning spectrum multi-modal spectrum detection device utilizes the characteristics that polarized light is sensitive to the molecular bond structure, and obtains the polarization spectrum of the protein macromolecule under different polarization states, thereby providing rich information for protein structure analysis and classification.

[0051] Based on the polarization tuning spectrum multi-modal spectrum detection device, near-infrared spectrum data of the measured albumin solution is collected under the conditions of concentration disturbance and temperature disturbance, and a data cube is established. The obtained spectrum is pretreated. The original spectrum is subjected to second-order 15-point SG filtering denoising and baseline correction, and the original spectrum of each polarization state is subjected to feature extraction by principal component analysis, k orthogonal (mutually unrelated) principal component spectra are extracted, and the original spectrum containing a series of spectral characteristic signals is obtained by removing other interference signals by retaining the polarization information related to the protein structure. The structure information of the albumin is separated by two-dimensional correlation, and the principal component spectrum is used to replace the original spectrum for two-dimensional correlation analysis. The obtained synchronous spectrum and asynchronous spectrum are used for analyzing the characteristics of the kinetic process of the albumin structure with temperature change. Among them, the orthogonal cross peak of the synchronous spectrum represents the direction of the kinetic change of the albumin, and the asynchronous spectrum represents the rate of the kinetic change of the albumin with temperature.

[0052] In some embodiments of the present application, when the method is used for distinguishing human blood albumin and bovine blood albumin, the autocorrelation peak of the synchronous spectrum of the known type albumin solution is established, and then the autocorrelation peak of the synchronous spectrum is used as a control standard spectrum to detect the unknown sample, and the autocorrelation peak of the obtained synchronous spectrum is compared with the standard spectrum to determine whether it is human blood albumin.

[0053] When it is necessary to distinguish between highly homologous human blood albumin and bovine blood albumin, first, a data cube of human blood albumin and bovine blood albumin is established by a polarization tuning spectrum multi-modal spectrum detection device. In some embodiments of the present application, a concentration disturbance condition can be set, such as a normal physiological environment human serum albumin concentration range of 35-51 g / L, i.e. 3.5%-5.1% (w / v, g / 100ml). In a pathological condition, five concentration points of 1%, 2%, 3%, 4%, and 5% in a concentration range of 1%-5% are set as the concentration disturbance condition. Spectra are collected by the polarization tuning spectrum multi-modal spectrum detection device under the concentration disturbance condition to obtain a data cube of albumin. Near-infrared spectrum determination conditions: wavelength range: 10000-4000 cm -1 , transmission sampling mode, scanning number: 32 (background / sample), resolution: 4 cm -1 .

[0054] Second-order 15-point SG filtering denoising and baseline correction are performed on the original data to realize spectrum pretreatment and form an original spectrum; a human blood albumin and bovine blood albumin characteristic spectral segment can be selected as 4900-4200 cm -1 . Principal component analysis is used to extract features from the original spectrum of each temperature point at five concentrations to extract k orthogonal (mutually unrelated) principal component spectra, and a series of spectral characteristic signals are obtained by removing other interference signals while retaining albumin information. The principal component number k is determined according to the cumulative representation rate and the principal component score. The principal component spectrum can represent the main structural information of albumin and is the basis for identifying albumin species.

[0055] Further, a temperature disturbance condition can be added to supplement the data cube of albumin for separating albumin structural information. The temperature disturbance condition: the denaturation temperature of albumin is usually about 65°C, and a temperature range of 57-61°C is set with a change gradient of 1°C. The temperature change process includes a warming process and a cooling process, and each temperature point is balanced for 5 minutes. Spectra are collected by the polarization tuning spectrum multi-modal spectrum detection device under the double disturbance condition to obtain a data cube of albumin. Near-infrared spectrum determination conditions: wavelength range: 10000-4000 cm -1 , transmission sampling mode, scanning number: 32 (background / sample), resolution: 4 cm -1 .

[0056] Based on the data cube, the albumin structural signal is separated by two-dimensional correlation spectroscopy. Two-dimensional correlation is used to separate albumin structural information, and principal component spectra are used instead of original spectra for two-dimensional correlation analysis. The calculation method and formula are as follows:

[0057] Analysis of the function of spectral changes under the action of an external disturbance variable , wherein t represents the external disturbance variable temperature (°C), is the wave number (cm-1 When t is and When the values ​​vary between these values, the measured spectral intensity y, i.e., the original spectral set, is defined as follows: :

[0058] (Formula 1);

[0059] in, It is the average spectrum:

[0060] (Formula 2);

[0061] Two-dimensional correlation synchronous spectral intensity For different wave numbers The vector product of the lower dynamic spectral intensity y:

[0062] (Formula 3);

[0063] Two-dimensional correlated asynchronous spectral intensity For different wave numbers The Hilbert-Noda matrix of the dynamic spectral intensity y ( Vector product:

[0064] (Formula 4);

[0065] in, As shown in Formula 5:

[0066] (Formula 5);

[0067] The dynamics of albumin structure under temperature change were analyzed using synchronous and asynchronous spectra in two-dimensional correlation to distinguish highly homologous HSA and BSA. In the synchronous spectrum, the positive cross-peaks represent the direction of albumin dynamic change, while the asynchronous spectrum represents the rate of albumin dynamic change with temperature.

[0068] The aforementioned dual-perturbation two-dimensional correlation spectroscopy method addresses the distinction between highly homologous HSA and BSA. Two-dimensional correlation spectroscopy typically uses a single perturbation condition; single-temperature perturbation provides limited information and is insufficient for effectively characterizing highly homologous protein structures (especially higher-order conformations in solution). Increasing the concentration perturbation significantly enhances the information content of albumin structures in the data cube, ultimately enabling the distinction between highly homologous HSA and BSA based on a wealth of effective information.

[0069] Further, the method can be further used for albumin structure signal analysis. Under the conditions of temperature and concentration perturbation, the molecular dynamics of HSA and BSA are obtained by combining near-infrared and Raman spectroscopy methods, and the correlation analysis of the near-infrared combination spectrum region and the characteristic spectrum region (amide I, II and III bands (1700-1200 cm -1 )) of Raman spectrum is performed by using the heterogeneous spectrum two-dimensional correlation method, so as to analyze the spectrum band difficult to be attributed to the near-infrared combination region and the primary structure or high-level conformation of the separated characteristic signal.

[0070] Raman spectrum determination conditions: excitation light source 785 nm, fiber probe sampling mode, long focal length (5 mm), spectral measurement range 3083-75 cm -1 , exposure time 1 min, accumulation number 4 times, resolution 4 cm -1 . Spectrum acquisition: the near-infrared spectrum and the Raman spectrum of each concentration point in the concentration perturbation condition are collected respectively under the temperature perturbation condition, and the optical path is 1 mm. The spectral intensity, signal-to-noise ratio, maximum and minimum noise in a certain spectral range are checked to ensure the accuracy and reliability of the spectrum for identification and structure characterization.

[0071] The original spectrum is pretreated, the characteristic extraction of the original spectrum is performed by using principal component analysis, the series of spectral characteristic signals are obtained by removing other interference signals by retaining the albumin information, and then the albumin structure signal is separated by using two-dimensional correlation spectrum method; the two-dimensional correlation is used for albumin structure information separation, the principal component spectrum is used to replace the original spectrum for two-dimensional correlation analysis, the calculation method is the same as that in the foregoing steps, and the synchronous spectrum and the asynchronous spectrum are obtained.

[0072] The albumin structure signal is analyzed by using the heterogeneous spectrum two-dimensional correlation method, the spectrum band difficult to be attributed to the near-infrared combination region and the primary structure or high-level conformation of the characteristic signal are analyzed, the corresponding relationship between the structure information and the corresponding spectrum segment is obtained. The orthogonal cross peak in the synchronous spectrum of the heterogeneous spectrum two-dimensional correlation represents the common response of the corresponding spectrum segments of the near-infrared spectrum and the Raman spectrum to the concentration perturbation and the temperature perturbation, and the correlation coefficient r (formula 6) of the orthogonal cross peak is calculated.

[0073] (Formula 6);

[0074] In the formula, the peak value of the orthogonal cross peak, max is the maximum value of all peak values of the orthogonal cross peaks, and min is the minimum value of all peak values of the orthogonal cross peaks. The larger the r value is, the stronger the correlation between the near-infrared spectrum segment and the Raman spectrum segment is, and the greater the probability of coming from the same structure is. The r value less than 0.3 is defined as weak correlation, the r value less than 0.6 is defined as medium correlation, and the r value greater than or equal to 0.6 is defined as strong correlation.

[0075] Based on the above, a qualitative discrimination model for human serum albumin can be established based on the characteristics in the human serum albumin spectrum information, which can be used to determine the qualitative nature of unknown samples.

[0076] Based on the two-dimensional correlation spectrum and the correspondence between albumin structure and spectral segments, autocorrelation peaks or slice spectra that can distinguish highly homologous proteins are extracted. A qualitative HSA discrimination model is established using partial least squares discriminant analysis (PLS-DA). The model is validated using forward and reverse validation.

[0077] The forward validation uses legally qualified human serum albumin injection solution, while the reverse validation uses highly homologous bovine serum albumin solution, other protein products, and compound amino acids.

[0078] Using the true positive rate ( (Formula 17), True Negative Rate ( (Formula 18) evaluates the model's sensitivity and specificity, and the model's accuracy. See Formula 19.

[0079] (Formula 17);

[0080] (Formula 18);

[0081] (Formula 19);

[0082] In the formula, The number of true positives It is a true negative number. The total number of positive cases. The total number of negative cases. This represents the number of true negative samples. This represents the number of false negative samples.

[0083] The aforementioned characterization and identification methods address the problems of high cost, cumbersome operation, and inability to characterize the structure in existing protein-based biomolecule analysis and identification techniques. By establishing a discriminant model, two proteins can be rapidly, batch-wise, and accurately identified and classified. Tuned polarization spectroscopy is employed to obtain spectra in different polarization states. Furthermore, spectral dimensionality enhancement methods are used to perform correlation analysis on the absorption spectra of different polarization states, resolving the primary structure or higher-order conformations of spectral bands and characteristic signals in the near-infrared combination region that are difficult to assign.

[0084] The polarization tuning spectrum, principal component analysis, spectral dimensionality, and feature fusion discrimination are combined to comprehensively solve the in-situ characterization problem of the structure (especially the high-level conformation) of human serum albumin in a physiological environment concentration from three aspects of structure signal extraction, structure signal separation, and structure signal analysis, and provide an effective technical means for the research on the interaction between human serum albumin and drugs. The PLS-DA qualitative identification model established on the effective characterization result has good specificity, good sensitivity, and good accuracy, and has important significance for the quality evaluation of human serum albumin. In some embodiments of the present application, the characterization and identification method can also be extended to the characterization and analysis of other high-homologous protein structures.

[0085] The present application will be described below in conjunction with the accompanying drawings, wherein the experimental methods of the operation steps not specifically described are performed according to the corresponding product instructions. The instruments, reagents, and consumables used in the examples can be purchased from commercial companies if not specifically stated.

[0086] Example 1: Construction of a polarization tuning spectrum measurement device

[0087] As shown in Figure 1 , a polarization tuning spectrum measurement device is constructed. It includes a light source 1, a polarizer 2, an analyzer 5, a spectroscope 3, a fiber coupling mirror 6, an optical switch 8, and a spectrometer 9. A reference cell 7 and a sample cell 4 are provided. The sample cell 4 is used to store the sample to be measured, and the reference cell 7 can store samples in different states to improve the accuracy of spectral measurement. The light source 1 is sequentially provided with a polarizer 2 and a spectroscope 3 downstream. The wide-spectrum light emitted by the light source 1 passes through the polarizer 2 and is divided into two paths under the action of the spectroscope 3. One of the two paths sequentially passes through the reference cell 7, the first fiber coupling mirror, and is transmitted by the optical fiber into the optical switch 8 device. The other path sequentially passes through the sample cell 4, the analyzer 5, the second fiber coupling mirror, and is transmitted by the optical fiber into the optical switch 8 device. The two light signals are controlled by the optical switch 8 and are detected by the spectrometer 9 at different times.

[0088] When in use, the sample is a normal physiological environment human serum albumin solution, and the concentration range is 35-51 g / L, i.e. 3.5%-5.1% (w / v, g / 100ml). The reference spectrum is a non-polarization modulation condition, and the polarization state is adjusted by the angle difference between the polarizer and the analyzer. The polarization tuning spectrum is detected under different polarization states, and the wavelength range is 10000-4000 cm -1 , and the resolution is 4 cm -1 .

[0089] Comparative Example 1

[0090] The near-infrared spectra of human serum albumin HSA and bovine serum albumin BSA solution samples under different temperature conditions are detected, respectively, and the results are shown in Figure 3As shown in Figs. 3a and 3c, after simple spectral pretreatment, HSA and BSA have similar peak patterns and cannot be distinguished from each other.

[0091] As shown in Figs. 4a and 4c, the absorbance of the spectra in the 4900-4700 cm -1 band of HSA and BSA decreases with the increase of temperature. Figure 3 b and Figure 3 d, the two samples have the same temperature variation law, and the absorbance of the spectra in the 4900-4700 cm -1 band decreases with the increase of temperature.

[0092] Example 2: Characterization of human serum albumin and bovine serum albumin by near-infrared two-dimensional correlation spectroscopy

[0093] 2.1 Establishment of data cubes of human serum albumin and bovine serum albumin

[0094] The polarization tuning spectral measurement device constructed in Example 1 was used to detect human serum albumin and bovine serum albumin solution samples. Double disturbance conditions were set, including concentration disturbance and temperature disturbance. The concentration disturbance condition: the normal physiological environment human serum albumin concentration range is 35-51 g / L, i.e. 3.5%-5.1% (w / v, g / 100 ml), and in the case of pathological conditions, the concentration range of 1%-5% is set as the concentration disturbance condition, including 1%, 2%, 3%, 4% and 5% concentration points. The temperature disturbance condition: the denaturation temperature of albumin is usually around 65°C, and the temperature range of 57-61°C is set, with a change gradient of 1°C. The temperature change process includes the heating process and the cooling process, and each temperature point is balanced for 5 minutes. Under the double disturbance conditions, the spectral data cubes of the albumin were obtained.

[0095] Near-infrared spectral measurement conditions: wavelength range: 10000-4000 cm -1 , transmission sampling mode, scanning times: 32 (background / sample), resolution: 4 cm -1 .

[0096] The sample path polarizer and analyzer were set to be different by 15° for one polarization state. In this example, 15°, 30°, 45°, 60°, 75°, 90°, 105°, 120°, 135°, 150°, 165° and 180° polarization states were selected, and the polarization tuning spectra were recorded as S n (λ), wavelength range: 10000-4000 cm -1 , resolution: 4 cm -1 .

[0097] 2.2 Extraction of albumin characteristic structure information from data cubes

[0098] The original spectrum was subjected to second-order 15-point SG filtering for noise reduction and baseline correction, targeting the 4900-4200 cm⁻¹ range. -1 Albumin features were extracted from the spectral bands. Principal component analysis was used to extract features from the original spectra of five concentrations at each temperature point, extracting k orthogonal (uncorrelated) principal component spectra. By retaining albumin information and removing other interfering signals, a series of spectral feature signals were obtained. The number of principal components k was determined based on the cumulative representativeness and the principal component scores.

[0099] The original near-infrared spectrum was replaced with the first principal component spectrum, and a two-dimensional correlation analysis of the temperature perturbation was performed. The results are as follows: Figure 4 As shown, the spectral resolution is relatively high. Figure 3 The levels of albumin information were significantly increased, and there were obvious differences in the autocorrelation peaks and cross-peaks of HSA and BSA. This indicates that the kinetic processes of structural changes of highly homologous HSA and BSA with increasing temperature are different, which can be used as the main basis for distinguishing the two.

[0100] 2.3 Two-dimensional correlation spectroscopy for separating albumin structural signals

[0101] Two-dimensional correlation was used to separate albumin structural information, and the obtained principal component spectra were used to replace the original spectra for spectral upsizing. The principal component spectra of different polarization states were cross-correlated with the reference spectrum. The calculation method and formula for dimensionality upsizing are as follows:

[0102] Function of spectral change Where t represents the spectral polarization state, Wave number (cm) -1 When t is and When the values ​​vary between these values, the measured spectral intensity y, i.e., the principal component spectral set, is defined as follows: :

[0103] (Formula 1);

[0104] in, It is the average spectrum:

[0105] (Formula 2);

[0106] Two-dimensional correlation synchronous spectral intensity For different wave numbers The vector product of the lower dynamic spectral intensity y:

[0107] (Formula 3);

[0108] Two-dimensional correlated asynchronous spectral intensity For different wave numbers The Hilbert-Noda matrix of the dynamic spectral intensity y ( Vector product:

[0109] (Formula 4);

[0110] in, As shown in Formula 5:

[0111] (Formula 5);

[0112] By employing the aforementioned dimensionality-upgrading method, the original one-dimensional spectrum can be transformed into two two-dimensional spectra, defined as synchronous and asynchronous spectra, respectively. This allows for the analysis of the kinetics of albumin structure changes with temperature, distinguishing highly homologous HSA and BSA. Specifically, the positive cross-peaks in the synchronous spectrum represent the direction of albumin kinetic change, while the asynchronous spectrum represents the rate of albumin kinetic change with temperature.

[0113] Will Figure 4 After content dimensionality enhancement processing, the following results are obtained: Figure 5 The synchronous and asynchronous spectra are shown. For example... Figure 5 As shown in the synchronous spectra (a) and (c), the positive crossover peak positions of HSA and BSA differ significantly: the positive crossover peak of human serum albumin is at (4320 cm⁻¹). -1 4436 cm -1 (4512 cm) -1 4752 cm -1 The peak of bovine serum albumin crossover is at (4344 cm⁻¹). -1 4592 cm -1 (4344 cm) -1 4436 cm -1 (4436 cm) -1 4592 cm -1 ( ) place. For example Figure 5 As shown in the asynchronous spectra (b) and (d), human serum albumin is at (4356 cm⁻¹) -1 4436 cm -1 (4356 cm) -1 4512 cm -1 A negative cross peak appears at (4436 cm⁻¹). -1 4512 cm -1 (4512cm) -1 4752 cm -1 A positive crossover peak appears at (4392 cm⁻¹), while bovine serum albumin shows a peak at (4392 cm⁻¹). -1 4592 cm -1 (4436 cm) -1 4592 cm-1 (4548 cm) -1 4592 cm -1 A negative cross peak appears at (4592 cm⁻¹). -1 4776 cm -1 The presence of a positive cross peak at point () indicates that the kinetic change rates of HSA and BSA also differ.

[0114] extract Figure 5 The autocorrelation peaks along the diagonal of the mid-synchronous spectrum serve as characteristic signals for distinguishing highly homologous albumin proteins, and are used for identification and structural characterization. For example... Figure 6 As shown, HSA and BSA differ significantly. The characteristic signal of HSA is 4752 cm⁻¹. -1 4708 cm -1 4512 cm -1 4436 cm -1 4356 cm -1 4320 cm -1 The BSA characteristic signal is 4776 cm⁻¹. -1 4592 cm -1 4548cm -1 4436 cm -1 4392 cm -1 4344 cm -1 .

[0115] Comparative Example 2:

[0116] Human serum albumin and bovine serum albumin samples were detected according to the method in Example 2, and near-infrared two-dimensional correlation spectra were obtained. The difference is that in step 2.1, only a single polarization-tuned spectrum was used to obtain the near-infrared two-dimensional correlation spectra. The results are as follows. Figure 7 As shown. Alternatively, Raman two-dimensional correlation analysis was performed using only a single temperature perturbation, and the results are as follows. Figure 8 As shown.

[0117] Depend on Figure 7 and Figure 8 It is evident that due to the broad spectral peaks and low resolution, this spectrum cannot be used for identification and structural characterization. Furthermore, the spectrum is covered by strong absorption by water and cannot be used directly.

[0118] Example 3: Analysis of albumin structural signals using heterogeneous two-dimensional correlation method

[0119] Near-infrared-Raman spectroscopy was used in conjunction with other methods to simultaneously obtain the molecular dynamics of HSA and BSA under conditions of dual temperature and concentration perturbations. The combined near-infrared spectrum and the characteristic Raman spectrum (amide I, II, and III bands, 1700-1200 cm⁻¹) were then analyzed using a two-dimensional correlation method.-1 ) correlation analysis was performed to analyze the bands in the near-infrared combination region and the primary structure or higher-order conformation of the separated characteristic signals.

[0120] Raman spectrum measurement conditions: excitation light source 785 nm, fiber probe sampling mode, long focal length (5 mm), spectral measurement range 3083-75 cm -1 , exposure time 1 min, number of accumulations 4 times, resolution 4 cm -1 . Spectrum collection: the near-infrared spectrum and the Raman spectrum of each concentration point in the concentration disturbance condition were collected according to the temperature disturbance condition, with an optical path of 1 mm. The spectral intensity, signal-to-noise ratio, maximum and minimum noise in a certain spectral range were checked to ensure the accuracy and reliability of the spectrum for identification and structural characterization.

[0121] The Raman-near-infrared heterospectrum is shown in Figure 9 . The orthogonal cross-peak in the two-dimensional correlation synchronous spectrum of the heterospectrum represents the common response of the corresponding spectral segments of the near-infrared spectrum and the Raman spectrum to concentration disturbance and temperature disturbance. The correlation coefficient r of the orthogonal cross-peak was calculated (formula 6).

[0122] (Formula 6);

[0123] In the formula, is the peak value of the orthogonal cross-peak, max is the maximum value of all peak values of the orthogonal cross-peak, and min is the minimum value of all peak values of the orthogonal cross-peak. The larger the r value, the stronger the correlation between the near-infrared spectral segment and the Raman spectral segment, and the greater the probability of coming from the same structure. Define r < 0.3 as weak correlation, r < 0.6 as moderate correlation, and r ≥ 0.6 as strong correlation.

[0124] The structural analysis results of the orthogonal cross-peak and its peak correlation coefficient are shown in Tables 1 and 2. The experimental results show that although there is some overlap in the distribution of the heterospectrum cross-peak of HSA and BSA, the structural analysis results have significant differences. For example, the -helix signal in the secondary structure of HSA accounts for a high proportion, and the near-infrared characteristic signals at 4356 cm -1 and 4664 cm -1 are strongly correlated; while the -fold and -turn signals account for a small proportion, and are moderately correlated at 4512 cm -1 and 4708 cm -1 ; the -helix signal in the secondary structure of BSA also accounts for a high proportion, and is moderately correlated at 4548 cm -1 and 4664 cm -1 , - The folding signal is small, with a medium correlation at 4392 cm -1 and a weak correlation at 4548 cm -1 .

[0125] Table 1 Structure and conformation assignment of near-infrared characteristic spectral regions of human blood albumin solution

[0126]

[0127] Table 2 Structure and conformation assignment of near-infrared characteristic spectral regions of bovine blood albumin solution

[0128]

[0129] Example 4: HSA qualitative discrimination model

[0130] 4.1 Establishment of HSA qualitative discrimination model

[0131] According to the two-dimensional correlation diagram shown in Figure 5 , Figure 6 and Figure 9 , and the structure information and corresponding spectral bands in Table 1, the autocorrelation peaks or slice spectra that can distinguish high homologous proteins are extracted. The partial least squares discrimination method (PLS-DA) is used to establish the HSA qualitative discrimination model.

[0132] In order to extract high-dimensional spectral features from synchronous and asynchronous spectra, high-dimensional spectral feature parameters are calculated. Among them, homogeneity reflects the closeness of gray value and diagonal line; energy reflects the uniformity of image gray distribution and the fineness of texture; contrast reflects the sharpness of image and the depth of texture; correlation reflects the similarity of image in row or column. The specific calculation formula is as follows:

[0133] (Formula 7);

[0134] (Formula 8);

[0135] (Formula 9);

[0136] (Formula 10);

[0137] Among them, , , , indicates the probability statistics of the pixels with gray levels i and j appearing simultaneously in the corresponding texture image.

[0138] In order to further screen effective features, from different angles of distance, angle, shape, information entropy, etc., six similarity measurement criteria of Euclidean distance, Mahalanobis distance, cosine distance, spectral gradient angle, Pearson correlation coefficient and spectral information divergence are selected for weighted fusion, and the fusion formula is shown as formula 11.

[0139] (Formula 11);

[0140] In the formula, is the fusion similarity value, ,,,, and respectively represent the similarity measurement values of Euclidean distance, Mahalanobis distance, cosine distance, spectral gradient angle, Pearson correlation coefficient and spectral information divergence, is the fusion weighted coefficient of each similarity measurement value, and the fusion weighted similarity measurement method of distance, angle, shape, information entropy and the like is fused, so that the correlation between the to-be-measured data sample and the historical data sample features is more comprehensively measured, effective feature variables are screened out, and the discrimination accuracy is improved.

[0141] A qualitative discrimination model of human blood albumin is established. According to the characteristics of high-dimensional spectrum, a fusion similarity weighted kernel partial least squares discrimination method is designed. First, the input data is mapped to a high-dimensional feature space by a kernel function, and then a kernel feature matrix is obtained. Then, according to the fusion similarity measurement of the corresponding sample weight of the to-be-measured sample and each training sample in the high-dimensional feature space, the weight matrix is formed by the corresponding weight of each training sample. After weighting the samples in the high-dimensional feature space by the weight matrix, the PLS-DA regression algorithm is used to establish a discrimination model to estimate the category of the current to-be-measured sample.

[0142] According to the PLS algorithm of the nonlinear kernel function, the local weighted regression model of the to-be-measured sample is established according to the following steps:

[0143] Let , , initialize , the number of principal components , the convergence condition and the maximum iteration number (set equal to any one column in the output variable , and the concentration value is taken as the initial value of the two);

[0144] Calculate the score vector of , and unitize it:

[0145] (Formula 12);

[0146] If the convergence condition is met Or the maximum number of iterations has been reached. Then calculate the residual, let , And calculate the residuals:

[0147] (Formula 13);

[0148] (Formula 14);

[0149] judge Is it equal to If so, then stop iterating, the algorithm ends, and we obtain information about the input. Principal component score matrix Then the regression coefficient matrix is:

[0150] (Formula 15);

[0151] The predicted output value of the sample to be tested is calculated using Formula 16:

[0152] (Equation 16).

[0153] 4.2 Validation of the HSA qualitative discriminant model

[0154] Using the true positive rate ( (Formula 17), True Negative Rate ( (Formula 18) evaluates the model's sensitivity and specificity, and the model's accuracy. See Formula 19.

[0155] (Formula 17);

[0156] (Formula 18);

[0157] (Formula 19);

[0158] In the formula, The number of true positives It is a true negative number. The total number of positive cases. The total number of negative cases. This represents the number of true negative samples. This represents the number of false negative samples.

[0159] The model is verified by forward verification and reverse verification. The forward verification adopts the statutory inspection qualified human blood albumin injection finished product, the reverse verification adopts the highly homologous bovine blood albumin solution, other protein products and compound amino acid, etc., specifically adopts the egg white solution, amino acid solution, placental polypeptide solution, interleukin injection, interferon spray and immunoglobulin injection. The data cube is measured according to the steps of example 2 and example 3, and the category is determined, so as to detect the sensitivity and specificity of the HSA qualitative discrimination model. The model verification result is as shown in Figure 10 and table 3, it can be seen that for the human blood albumin solution sample, the model can accurately classify, which fully proves the sensitivity, specificity and accuracy of the identification method.

[0160] Table 3 HSA solution discrimination model analysis result

[0161]

[0162] Example 5: Application of human blood protein structure in-situ characterization method in source identification

[0163] The unknown solution sample is characterized according to the method of example 2 and example 3, and the near-infrared spectrum under different temperature and different polarization state disturbance conditions is measured, and the spectrum is analyzed by principal component analysis, and the signal of albumin in the solution is extracted, and compared with the autocorrelation peak of the two-dimensional correlation analysis synchronous spectrum shown in Figure 6 The HSA qualitative discrimination model is used to determine whether the unknown solution sample is human blood albumin.

[0164] The above embodiments of the present application are described in detail, but the content described is only the preferred embodiment of the present application, and cannot be considered as limiting the scope of the present application. Any equivalent changes and improvements made according to the scope of the present application should still belong to the scope of the present application.

Claims

1. A method for in-situ characterization of protein structure, characterized in that: Detecting the polarization tuning spectrum of protein solution under the condition of temperature and concentration double perturbation, and processing the obtained original spectrum by two-dimensional correlation analysis method, and using multi-dimensional slice spectrum to in-situ characterize the primary structure and / or high order structure of protein solution; The specific steps are as follows: Step one: under the condition of temperature and concentration double perturbation, the near-infrared spectrum is collected by a polarization tuning spectrum multi-modal spectrum detection device to obtain the data cube of albumin; the polarization tuning spectrum multi-modal spectrum detection device comprises a light source, a polarizer, an analyzer, a spectroscope, a fiber coupling mirror, an optical switch device and a spectrometer, and further comprises a reference cell and a sample cell; the polarizer and the spectroscope are sequentially arranged downstream of the light source, the broadband light emitted by the light source passes through the polarizer, and under the action of the spectroscope, the broadband light is divided into two paths, one of which passes through the reference cell and the first fiber coupling mirror in sequence, and is transmitted into the optical switch device by the optical fiber; the other path passes through the sample cell, the analyzer and the second fiber coupling mirror in sequence, and is transmitted into the optical switch device by the optical fiber; the two light signals are controlled by the optical switch and are detected by the spectrometer in time; the polarizer and the analyzer in the sample path are set to different angles, and N polarization states are selected for measurement; the angle difference a is set as one deflection unit, and the polarization states under a, 2a, 3a…Na states are detected respectively, and a plurality of polarization tuning spectra can be obtained; Step two: forming the original spectrum from the data cube; extracting k orthogonal principal components from the original spectrum of different concentrations at each temperature point by principal component analysis; Step three: performing two-dimensional correlation analysis on the principal component spectrum to separate the protein structure signal; Step four: combining the in-situ characterization of the primary structure of the protein solution obtained by the two-dimensional correlation analysis to analyze the autocorrelation peak on the diagonal line in the synchronous spectrum as the characteristic signal of the protein; Step five: under the condition of temperature and concentration double perturbation, near-infrared spectrum and Raman spectrum are collected respectively to obtain the data cube of albumin; the heterospectral two-dimensional correlation method is used to analyze the correlation between the sum frequency spectrum region of near-infrared and the characteristic spectrum region of Raman spectrum, and the structure signal of albumin is analyzed by the heterospectral two-dimensional correlation spectrum.

2. The method of in situ characterization of protein structure according to claim 1, wherein: The concentration perturbation condition is that 3-6 concentration detection points are set in 3.5%-5.1% with gradient increase; the temperature perturbation condition is that 3-6 temperature detection points are set in 57-61℃ with gradient increase.

3. The method of in situ characterization of protein structure of claim 1, wherein: In step two, the original data is subjected to second-order 15-point SG filtering and baseline correction to form the original spectrum.

4. A method for identifying human blood proteins, characterized by: The method is used for distinguishing high homologous HSA and BSA, and unknown protein solution is characterized by the in-situ characterization method of protein structure in any one of claims 1-3, and the obtained information is compared with the information of standard human blood protein sample to identify the unknown protein solution; further comprising step six; Step six: extracting the autocorrelation peak or slice spectrum of the protein according to the two-dimensional correlation diagram, the structure information and the corresponding spectrum region, and establishing an HSA qualitative discrimination model by using the partial least squares discrimination method to identify the unknown protein type by the model.

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