Application of a tas1r2 molecular sensory artificial intelligence biosensor in sweet key quality attributes

Abstract

The application provides an application of a TAS1R2 molecular sensory artificial intelligence biosensor in a sweetness key quality attribute. Specific contents comprise: (1) construction and optimization of a TAS1R2 molecular sensory artificial intelligence biosensor; (2) application of the TAS1R2 molecular sensory artificial intelligence biosensor in the sweetness key quality attribute. The biosensor is integrated by a TAS1R2 protein functionalized modified biosensing chip, a probe platform, an electrochemical workstation and a data display, has the advantages of high sensitivity, good stability, good reproducibility, wide detection range and the like, and can quickly and efficiently identify the sweetness key quality attribute. The application provides the application of the TAS1R2 molecular sensory artificial intelligence biosensor in the sweetness key quality attribute, and based on the biosensor, provides a new method and a new technology for subsequent identification of the sweetness key quality attribute of traditional Chinese medicines and traditional Chinese medicine compounds.

Description

TECHNICAL FIELD

[0001] The application relates to the application of a biosensor in traditional Chinese medicine, and particularly relates to application of a TAS1R2 molecular sensory artificial intelligence biosensor in a sweet key quality attribute. BACKGROUND

[0002] Due to the characteristics of traditional Chinese medicine compound preparations, such as multiple flavors, complex chemical components and the like, there are some defects in the identification research on the sweet key quality attribute by using traditional detection techniques, such as high detection limit, rough separation means, many interference factors, relatively complex analysis and the like, and the sweet key quality attribute cannot be directly combined with clinical efficacy. The biosensor oriented to sweetness provides key technical support for the identification of the sweet key quality attribute of the traditional Chinese medicine compound preparation, by the biological recognition element in direct contact with the transducer as a leading analysis technology at present. The high electron mobility transistor (HEMT) prepared on the basis of semiconductor materials is widely applied in the field of molecular recognition, and the detection limit can reach the picogram or picomolar level, because the HEMT has high sensitivity, strong specificity, rapid detection, easy integration, batch production and excellent biological compatibility and can be modified by biological functions.

[0003] Sweet traditional Chinese medicines play the roles of assisting main drugs, harmonizing various drugs and the like in the compound. The sweet traditional Chinese medicines are mainly distributed in the drugs for tonifying deficiency, and have the effects of tonifying deficiency, relieving acute pain, relieving pain and relieving drug properties. The sweet traditional Chinese medicines and other traditional Chinese medicines with different properties can produce different effects, such as the combination of the sweet traditional Chinese medicines and the pungent traditional Chinese medicines can solidify and stop the leakage; the combination of the sweet traditional Chinese medicines and the bitter traditional Chinese medicines can clear heat; the combination of the sweet traditional Chinese medicines and the sour traditional Chinese medicines can nourish and stop pain; and the combination of the sweet traditional Chinese medicines and the cold traditional Chinese medicines can nourish stomach yin. Therefore, the sweet traditional Chinese medicines occupy an important position in the traditional Chinese medicine compound from the aspects of effects and compatibility. Based on the large amount of literature mining in the early stage, it is found that the TAS1R2 receptor can easily produce strong and specific binding with sweet substances, mainly participates in the taste conduction pathway and the intestinal carbohydrate digestion and absorption pathway, and has relatively strong correlation with cell proliferation disorders, gastrointestinal diseases and endocrine diseases. Therefore, the application intends to construct a TAS1R2 molecular sensory artificial intelligence biosensor, to study the correlation between the sweet receptor and the active ingredients of the sweet traditional Chinese medicine, and to provide a new method for the identification of the sweet key quality attribute of the traditional Chinese medicine compound preparation. SUMMARY

[0004] To solve the above problems, the application provides application of a TAS1R2 molecular sensory artificial intelligence biosensor in a sweet key quality attribute.

[0005] According to some specific embodiments of the application, a TAS1R2 molecular sensory artificial intelligence biosensor is provided, and the specific construction steps of the biosensor are as follows:

[0006] Step 1: 60 μL of thiol reagent was added to the sample cell of the sensor chip and reacted at room temperature for 17-24 h to generate Au-S bond on the surface of the sensor chip and form a self-assembled monolayer;

[0007] Step 2: After the self-assembled monolayer in step 1 was formed, the sample cell was washed with deionized water to remove the excess thiol reagent, and 60 μL of a mixed solution of 20 mmol·L -1 carbodiimide hydrochloride and 50 mmol·L -1 N-hydroxysuccinimide was added to the sample cell and reacted at room temperature for 15 min to activate the carboxyl group.

[0008] Step 3: After the carboxyl group in step 2 was activated, the sample cell was washed with 100 mmol·L -1 phosphate buffer solution, and the sweet key protein was added and modified at 4°C for 2-5 h to obtain a sweet protein functionalized biosensor chip.

[0009] Step 4: The sweet protein functionalized biosensor chip in step 3 was integrated with a probe platform, an electrochemical workstation, and a data display to form a sweet molecule sensory artificial intelligence biosensor.

[0010] According to some embodiments of the present application, the sensor chip in step 1 of the construction of a sweet molecule sensory artificial intelligence biosensor includes but is not limited to a high electron mobility field effect transistor (HEMT); the sweet key protein in step 3 is a purified TAS1R2 protein; the sweet protein functionalized biosensor chip in step 4 is a biological recognition element, the probe platform is a signal transmission system, the electrochemical workstation is a signal conversion system of the sensor, and the data display is a signal acquisition system.

[0011] According to some embodiments of the present application, the present application provides a detection method of a sweet molecule sensory artificial intelligence biosensor, which includes but is not limited to linear sweep voltammetry (LSV) and amperometric i-t curve (AC), the scanning voltage range is 0-2 V, the current sensitivity can reach the μA level, and the collected electrical signals are current-voltage curve (I-V) and current-time curve (I-T).

[0012] According to some embodiments of the present application, the construction results of the TAS1R2-HEMT biosensor are as follows: Figure 1As shown, the signals collected before and after TAS1R2 protein modification and after adding PBS solution were statistically analyzed, and the three groups of data did not obey normality; and the Kolmogorov-Smirnov non-parametric test was used on the three groups of data, showing that at the 0.001 level, there were significant differences before and after protein modification, indicating that the protein modification was successful. After TAS1R2 protein modification, the 2DEG concentration decreased, causing the resistance of the sensor to increase, so the current decreased; at the 0.05 level, there was no significant difference after adding PBS solution, so PBS solution did not affect the protein activity.

[0013] According to some specific embodiments of the present invention, the present invention provides a method for parameter optimization and performance evaluation of a TAS1R2-HEMT biosensor, characterized in that the biosensor parameters include: TAS1R2 protein modification time (h), sample reaction time (min), sampling interval (V), and scanning speed (V / s); and the performance includes: sensitivity, detection range, repeatability, specificity, and stability.

[0014] The specific optimization methods and results of the above biosensor parameters are as follows:

[0015] (1) Optimization of TAS1R2 protein modification time

[0016] TAS1R2 protein was added to the sample pool of the carboxyl-activated sensor chip for modification and expression. The characteristics of protein modification for 0.5 to 5 hours were collected using a CHI660E electrochemical workstation. DS -V DS The curve was tested three times every 30 minutes and the average value was obtained. The specific results are as follows:

[0017] Biosensor I DS -V DS The curve changes with protein modification time as shown in Figure 2 As shown in the figure, when TAS1R2 protein was added for 0.5h, I DS -V DS The curve remained essentially unchanged. When TAS1R2 protein was added for 2 hours, the absolute current offset fluctuated significantly. After 2.5 hours, the absolute current offset fluctuations became more stable. Therefore, the TAS1R2 protein functionalization time should be greater than or equal to 2.5 hours.

[0018] (2) Optimization of sample reaction time

[0019] Glucose standard solution (C = 1 mmol·L -1 ) as a carrier, and the electrochemical workstation was used to collect the characteristics of the sweet protein functionalized biosensor chip sample pool after adding the test solution to react for 0 to 10 minutes. DS -VDS The curve is shown in Figure 1, and the results are as follows:

[0020] Biosensor I DS -V DS The curve is shown in Figure 1, and the results are as follows: Figure 3 As shown in Figure 1, the current changed obviously after the sample was added and reacted for a period of time. DS -V DS The curve changed obviously; when the sample reacted for 5 min, the fluctuation range of the absolute offset value of the current was large; when the sample reacted for 6 min, the fluctuation range of the absolute offset value of the current tended to be stable. Therefore, the optimal detection time of the sweet protein reacting with the sample should be greater than or equal to 6 min.

[0021] (3) Scan speed optimization

[0022] The characteristic I-V curve was collected by using the electrochemical workstation at a scan speed of 0.1-10 V / s. DS -V DS The curve is shown in Figure 1, and the results are as follows:

[0023] Biosensor I DS -V DS The curve is shown in Figure 1, and the results are as follows: Figure 4 As shown in Figure 1, the current changed obviously after the sample was added and reacted for a period of time. DS -V DS The curve changed obviously; when the sample reacted for 5 min, the fluctuation range of the absolute offset value of the current was large; when the sample reacted for 6 min, the fluctuation range of the absolute offset value of the current tended to be stable. Therefore, the optimal detection time of the sweet protein reacting with the sample should be greater than or equal to 6 min.

[0024] (4) Sampling interval optimization

[0025] The characteristic I-V curve was collected by using the electrochemical workstation at a sampling interval of 0.001-0.01 V and 0.01-0.06 V. DS -V DS The curve is shown in Figure 1, and the results are as follows:

[0026] Biosensor I DS -V DS The curve is shown in Figure 1, and the results are as follows: Figure 5 As shown in Figure 1, the current changed obviously after the sample was added and reacted for a period of time. DS -V DSThe curve changes significantly in the range of 0 to 0.75 V, and does not change significantly in the range of 0.75 to 2 V. The data detected at each sampling interval do not obey the normal distribution, so the Kruskal-Wallis variance analysis test is used to show that at the 0.05 level, the I collected at different sampling intervals (V) DS -V DS There is no significant difference in the curves, so the effect of the sampling interval on the experiment can be ignored.

[0027] According to the above results, the current changes caused by the scanning speed (V / s) and sampling interval (V) are not significant. In order to consider the experimental efficiency and feasibility, the subsequent detection will directly set the scanning speed to 0.5V / s, the sampling interval to 0.01V, the TAS1R2 protein modification time to 2.5h, and the sample reaction time to 6min.

[0028] The specific inspection methods and results of the above biosensor performance are as follows:

[0029] (1) Specificity investigation

[0030] It is known that ginsenoside Rg1 belongs to the tetracyclic triterpenoid derivatives and has a sweet taste; glycyrrhizic acid is a triterpenoid compound that can be used as a sweetener; and quinine is a recognized bitter substance. The present invention is based on the above-mentioned optimized biosensor exposed to 1mmol·L -1 The specificity of the three drug solutions was investigated, and the sensor chip without TAS1R2 protein modification was used as a negative control. -1 PBS solution was used as blank control, and the drug solution was added for 6 minutes. At room temperature, I was collected using an electrochemical workstation in the voltage range of 0 to 2 V. DS -V DS The curve was repeated three times and the absolute current value was calculated. The specific results are as follows:

[0031] Depend on Figure 6 As shown, the biosensor chip modified with TAS1R2 protein exhibited a strong and specific response to ginsenoside Rg1, but weaker responses to glycyrrhizic acid and quinine. Furthermore, in the negative control group, a sensor chip not modified with TAS1R2 protein showed no significant response to any of the substances. These results demonstrate that the sweet molecule sensor AI biosensor can detect substances that bind to TAS1R2 protein with high specificity.

[0032] (2) Investigation of the temporal stability of functionalized biosensor chips

[0033] Glucose standard (C = 1 mmol·L -1) as the sample. After the sample was injected and tested, the solution remained in the sample cell. The functionalized biosensor chip was carefully placed in a sealed environment at 4°C for 2, 4, 6, 10, and 14 days. The chip's stability was then evaluated by comparing the response signal with the relative current offset from the initial test. The specific results are as follows:

[0034] Depend on Figure 7 As shown, when the functionalized biosensor chip was exposed to a glucose standard solution, the number of days it was stored affected its performance. Compared to the signal collected during the initial test, the relative change in current remained relatively stable within four days, indicating no change in performance. After six days, the signal detected by the biosensor weakened, and the relative change in current fluctuated widely, indicating a decrease in performance. These experimental results demonstrate that the chip's performance was stable within the first four days of construction, exhibiting a strong response to sweet substances. Testing is best completed within this period. Furthermore, storage conditions can also affect biosensor performance. It is recommended to store the chip in the dark at a temperature of 4°C and to add an appropriate amount of 100 mmol / L PBS solution to the sample tube.

[0035] (3) Detection limit and sensitivity investigation

[0036] The four standard solutions of arginine, lysine, glucose and sucrose were mixed from low to high concentration (1 nmol·L -1 ~1mmol·L -1 ) were loaded in the order of , the reaction time was 6 min, and after TAS1R2 protein was fully combined with each substance, the I at each concentration was recorded at room temperature. DS -V DS The voltage range is 0~2V, and each concentration is tested three times. Considering the significance of the data, the I DS To calculate the relative current change and examine the logarithm of the concentration (Lg[A g ])-the linear relationship between the relative value of the current change ((I-I0) / I0), following which the detection range and sensitivity of the biosensor are obtained.

[0037] The above results show that the detection signal sensitivity of the biosensor is 1e-006A / V; the biosensor can detect -9 ~10 -3 mol·L -1 This indicates that the biosensor has high sensitivity, wide detection range and low detection limit.

[0038] (4) Repeatability study

[0039] Based on the electrical signals obtained in (3), the effect of voltage on the relative value of current change ((I-I0) / I0) at the same concentration is compared, as shown in Figure 3.Figure 8 As shown, with the increase of voltage, the relative change value of current gradually tends to be stable, the relative standard deviation (RSD, n=14) of the relative change value of current in the range of 0.5-1.9 voltage is 6.4%, and when the voltage is greater than 0.6, the relative standard deviation (RSD, n=13) of the relative change value of current is less than 5%, so the voltage in the range of 0.6-1.9 has less effect on the relative change value of current, and the reproducibility is good, and the subsequent experiment should fix the voltage in this range. The dissociation constant K of the interaction between ginsenoside Rg1 and TAS1R2 protein in different channels of the biosensor chip is calculated by using a fixed formula D , it is found that the dissociation constant K D values (P<0.01) of C1, C2 and C3 channels have no significant difference, and the relative standard deviation (RSD, n=3) of the K D values of the three is 2.4%, so the reproducibility of the sweet molecule sensory artificial intelligence biosensor is good.

[0040] According to some specific embodiments of the present application, the present application provides an application of a sweet molecule sensory artificial intelligence biosensor in identifying sweet key quality attributes, characterized in that the sweet key quality attributes include sugars, glycosides, amino acid compounds and the like, and specific embodiments are shown in

Example 4

[0041] The present application has the following advantages:

[0042] (1) A TAS1R2 sweet molecule sensory artificial intelligence biosensor is provided, which is composed of a sweet protein functionalized biosensor chip, a probe station, an electrochemical workstation and a data display.

[0043] (2) The biosensor has fast response speed, high sensitivity, wide detection range and good reproducibility.

[0044] (3) The chemical signal is converted into an electrical signal, which can be directly observed by naked eye, and the operation is simple.

[0045] (4) The specific binding ability of the quantified sweet key quality attributes and TAS1R2 protein is provided.

[0046] (5) A sweet molecule sensory artificial intelligence biosensor is provided to provide technical support for the identification of sweet key quality attributes of subsequent traditional Chinese medicine large varieties. BRIEF DESCRIPTION OF DRAWINGS

[0047] Figure 1 I-V curves of TAS1R2 protein before and after modification and after adding PBS solution DS -V DS curve

[0048] Figure 2Biosensor I under different modification times of TAS1R2 protein DS -V DS Curve change

[0049] Figure 3 Biosensor I under different reaction times DS -V DS Curve change

[0050] Figure 4 Biosensor I under different scanning speeds DS -V DS Curve change

[0051] Figure 5 Biosensor I under different sampling intervals DS -V DS Curve change

[0052] Figure 6 Response of biosensor chips modified with different sensitive elements (modified or unmodified TAS1R2 protein) to different substances (all relative current values are expressed as mean ± standard deviation of triplicate measurements; *** Student's t-test, p < 0.01)

[0053] Figure 7 I of the sensor after being exposed to the solution for different days DS -V DS Curve change

[0054] Figure 8 (a) Effect of voltage on relative change in current; (b) difference in relative change in current in different voltage ranges; (c) effect of different channels on dissociation constant of interaction between sweeteners and TAS1R2 protein.

[0055] Figure 9 Biosensor I of ginsenoside Rg1 under different concentration gradients DS -V DS Curve change DETAILED DESCRIPTION

[0056] The following provides a specific embodiment of application of a TAS1R2 molecular sensory artificial intelligence biosensor in sweet key quality attributes.

[0057]

Example 1

[0058] The present application provides a TAS1R2 molecular sensory artificial intelligence biosensor construction method, and the specific construction steps are as follows:

[0059] Step 1: 60 μL of thiol reagent was added into the sample pool set on the HEMT sensing chip, and Au-S bond was generated on the surface of the sensing chip to form a self-assembled monolayer after reaction at room temperature for 17-24 hours;

[0060] Step 2: After the self-assembled monolayer was formed, the excess thiol reagent in the sample pool was washed away with deionized water, and 60 μL of an equal volume mixture of 20 mmol·L -1 carbonyldiimidazole hydrochloride and 50 mmol·L -1 N-hydroxysuccinimide was added into the sample pool, and the carboxyl group was activated after reaction at room temperature for 15 min;

[0061] Step 3: The sample pool was washed with 100 mmol·L -1 phosphate buffer solution, and the purified TAS1R2 sweet key protein was added, and the TAS1R2-HEMT biosensing chip was obtained after incubation at 4°C for 2-4 h.

[0062] Step 4: The TAS1R2-HEMT biosensing chip, the probe platform, the electrochemical workstation and the data display were integrated to form a TAS1R2-HEMT artificial intelligence biosensor. Among them, the TAS1R2-HEMT biosensing chip is a biological recognition element, the probe platform and the electrochemical workstation are a signal conversion and information acquisition system of the sensor, and the data display is a data processing system.

[0063] The detection method of the TAS1R2-HEMT biosensor is linear sweep voltammetry, the constant voltage range is 0-2 V, the current sensitivity can reach the μA level, and the collected electrical signal is a current-voltage curve.

[0064] The construction result of the TAS1R2-HEMT biosensor is shown in Figure 1 Table 1; and the three groups of data were subjected to Kolmogorov-Smirnov non-parametric test, and the results are shown in Table 2, which shows that there is a significant difference before and after the protein modification at the level of 0.001, indicating that the protein modification is successful, the 2DEG concentration is reduced after the TAS1R2 protein modification, which causes the increase of the resistance of the sensor, so the current is reduced; there is no significant difference after the addition of PBS solution at the level of 0.05, so the PBS solution does not affect the protein activity, and the results are shown in Table 3.

[0065] Table 1 Normal distribution results of signals collected before and after TAS1R2 protein modification and after the addition of PBS solution

[0066]

[0067]

[0068] Table 2 Kolmogorov-Smirnov non-parametric test results before and after TAS1R2 protein modification

[0069]

[0070] Table 3 Results of TAS1R2 protein activity before and after adding PBS solution

[0071]

[0072] Note: "-" in Table 2 and Table 3 represents the direction of current

[0073]

Example 2

[0074] The above biosensor parameter optimization includes: protein modification time (h), sample reaction time (min), sampling interval (V), scanning speed (V / s), and the specific optimization method is as follows:

[0075] (4) TAS1R2 protein modification time optimization

[0076] The TAS1R2 protein is added to the sample pool of the carboxyl-activated sensing chip for modification and expression, and the electrochemical workstation is used to collect the characteristic I after the protein modification for 0.5-5h DS -V DS curve, detect three times every 30 min, and calculate the average value, and the specific results are as follows:

[0077] Biosensor I DS -V DS curve changes with protein modification time as shown in Figure 2 The figure shows that when the TAS1R2 protein is added for 0.5h, the I DS -V DS curve basically has no change; when the TAS1R2 protein is added for 2h, the absolute offset value of the current fluctuates in a large range; after 2.5h of modification, the absolute offset value of the current fluctuates in a stable range. Therefore, the TAS1R2 protein functionalization modification time in the biosensor should be greater than or equal to 2.5h.

[0078] (4) Sample reaction time optimization

[0079] The glucose standard solution (C=1mmol·L -1 ) is used as a carrier, and the electrochemical workstation is used to collect the characteristic I after the addition of the test solution in the sample pool of the sweet protein functionalized biosensing chip for 0-10min DS -V DSThe curve, every 1 min scan three times, take the average value, the specific results are as follows:

[0080] Biosensor I DS -V DS The curve changes with the sample reaction time as shown in Figure 3 The figure shows that, just after adding the sample and reacting for a period of time, the I DS -V DS curve changes significantly; when the sample reacts for 5 min, the absolute offset value of the current fluctuates in a large range; after the sample reacts for 6 min, the absolute offset value of the current fluctuates in a stable range. Therefore, the optimal detection time of the sweet protein reacting with the sample should be greater than or equal to 6 min.

[0081] (4) Scan speed optimization

[0082] The glucose standard solution (C = 1 mmol·L -1 ) was used as a carrier, and the electrochemical workstation was used to collect the characteristic I DS -V DS curve, repeated scanning three times, take the average value, the specific results are as follows:

[0083] Biosensor I DS -V DS The curve changes with the different scan speeds as shown in Figure 4 The figure shows that, as the scan speed changes, the I DS -V DS curve changes are not obvious; within the voltage range of 0-2V and the scan speed of 0.1-10V / s, the current change difference caused by the scan speed is not significant, and the relative standard deviation (RSD, n = 14) is all below 1.8%. When the voltage is greater than 0.7V, the current change difference caused by the scan speed is the smallest, and the relative standard deviation (RSD, n = 14) is the lowest, reaching 0.2%. Therefore, the change of the scan speed has very little effect on the experiment and can be ignored.

[0084] (4) Sampling interval optimization

[0085] The glucose standard solution (C = 1 mmol·L -1 ) was used as a carrier, and the electrochemical workstation was used to collect the characteristic I DS -V DS curve, repeated scanning three times, take the average value, the specific results are as follows:

[0086] Biosensor I DS -V DS The curve changes with the different sampling intervals as shown in Figure 5As shown in the figure, with the change of sampling interval, I DS -V DS The curve changes obviously in the range of 0-0.75V, and changes insignificantly in the range of 0.75-2V; the data detected at each sampling interval does not obey normal distribution, therefore Kruskal-Wallis variance analysis test is adopted, and the results are shown in Table 4, which shows that at the level of 0.05, there is no significant difference in I DS -V DS The curve has no significant difference, so the influence of sampling interval on the experiment can be ignored.

[0087] Table 4 I DS -V DS Kruskal-Wallis variance analysis results of the curve

[0088]

[0089]

[0090] Note: "-" represents the direction of current

[0091] According to the above results, the current changes caused by scanning speed (V / s) and sampling interval (V) are not significant, in order to consider the experimental efficiency and feasibility, the scanning speed is directly set to 0.5V / s in the subsequent detection, the sampling interval is 0.01V, the TAS1R2 protein modification time is 2.5h, and the sample reaction time is 6min.

[0092]

Example 3

[0093] The performance investigation of sweet molecule sensory artificial intelligence biosensor includes sensitivity, detection range, reproducibility, specificity, and stability. The specific investigation methods are as follows:

[0094] (1) Specificity investigation

[0095] It is known that ginsenoside Rg1 belongs to tetracyclic triterpenoid derivatives and has a sweet attribute; glycyrrhizic acid is a triterpenoid compound and can be used as a sweetener; and quinine is a recognized bitter substance. The biosensor based on the above-optimized biosensor is exposed to 1mmol·L -1 The specificity of the above three drug solutions is investigated, and the sensor chip without TAS1R2 protein modification is used as a negative control. 100mmol·L -1 PBS solution is used as a blank control, the drug solution is reacted for 6min, and the electrochemical workstation is used to collect I DS -V DSThe curve is measured three times each time and the absolute current offset value is calculated. The specific results are as follows:

[0096] Depend on Figure 6 As shown, the biosensor chip modified with TAS1R2 protein exhibited a strong and specific response to ginsenoside Rg1, but weaker responses to glycyrrhizic acid and quinine. Furthermore, in the negative control group, a sensor chip not modified with TAS1R2 protein showed no significant response to any of the substances. These results demonstrate that the sweet molecule sensor AI biosensor can detect substances that bind to TAS1R2 protein with high specificity.

[0097] (2) Investigation of the temporal stability of functionalized biosensor chips

[0098] Glucose standard solution (C = 1 mmol·L -1 ) as the sample. After the injection test, the solution was retained in the sample tube. The functionalized biosensor chip was carefully placed at 4°C for 2 days, 4 days, 6 days, 10 days, and 14 days before testing. This was repeated three times. The relative current offset at each time point compared to the initial test was calculated. The specific results are as follows:

[0099] Depend on Figure 7 As shown, when a functionalized biosensor chip is exposed to a glucose standard solution, the number of days it is stored affects its performance. Compared to the signal collected during the initial test, the relative change in current within four days is relatively stable, indicating no change in performance. After six days, the signal detected by the biosensor weakens, and the relative change in current fluctuates widely, indicating a decrease in performance. These experimental results demonstrate that the chip's performance is relatively stable within the first four days of construction, exhibiting a strong response to sweet substances. Testing is best completed within this period. Furthermore, the storage environment can also affect biosensor performance. It is recommended to store the chip in the dark, maintain a temperature of 4°C, and add an appropriate amount of PBS solution to the sample tube.

[0100] (3) Detection limit and sensitivity investigation

[0101] The four standard solutions of arginine, lysine, glucose and sucrose were mixed from low to high concentration (1 nmol·L -1 ~1mmol·L -1 ) were loaded in the order of , the reaction time was 6 min, and after TAS1R2 protein was fully combined with each substance, an electrochemical workstation was used to record the I DS -V DS The voltage range is 0~2V, and each concentration is tested three times. Considering the significance of the data, the I DS To calculate the relative current change and examine the logarithm of the concentration (Lg[Ag The linear relationship between the relative value of the current change ((I-I0) / I0) is obtained by following this rule to obtain the detection range and sensitivity of the biosensor. The specific results are shown in Table 5.

[0102] Table 5 Detection range and sensitivity of T1R2-HEMT biosensor

[0103]

[0104] As can be seen from the table above, the detection signal sensitivity of the above biosensor is 1e-006A / V; the biosensor can detect -9 mol·L -1 -10 -3 mol·L -1 This indicates that the biosensor has high sensitivity, wide detection range and low detection limit.

[0105] (4) Repeatability study

[0106] Based on the electrical signals obtained in (3), the effect of voltage on the relative value of current change ((I-I0) / I0) at the same concentration is compared, as shown in Figure 3. Figure 8 As shown in Figures 8a and 8b, as the voltage increases, the relative change value of the current gradually tends to a stable state. The relative standard deviation (RSD, n = 15) of the relative change value of the current in the voltage range of 0.5 to 1.9 V is -6.4%, while when the voltage is greater than 0.6 V, the relative standard deviation (RSD, n = 14) of the relative change value of the current is less than 5%. Therefore, the voltage in the range of 0.6 to 1.9 V has little effect on the relative change value of the current and has good reproducibility. The voltage should be fixed in this range in subsequent experiments. The dissociation constant K of the interaction between ginsenoside Rg1 and TAS1R2 protein in different channels in the biosensor chip was calculated using a fixed formula D , the results are as follows Figure 8 As shown in c, the dissociation constants K corresponding to C1, C2, and C3 channels were found. D There was no significant difference in the values ​​(P<0.01), and the K D The relative standard deviation (RSD, n=3) of the value was 2.4%, which indicated that the sweet molecule sensory artificial intelligence biosensor had good reproducibility.

[0107] [Example 4] Application of a TAS1R2 molecular sensory artificial intelligence biosensor in identifying the key quality attribute of sweetness

[0108] Sweet key quality attributes include sugars, glycosides, amino acid compounds, etc. This case is mainly used to identify four sweet key quality attributes: glucose, ginsenoside Rg1, arginine, and lysine.

[0109] (1) Sample solution preparation

[0110] Preparation of glucose solution: Accurately weigh 0.9 mg of glucose standard (molecular weight 180.16) into a beaker and add an appropriate amount of deionized water to dissolve it. Pour the solution into a 5 mL volumetric flask along a glass rod. Rinse the beaker 2-3 times with deionized water and pour it into the volumetric flask. Shake to mix. Add deionized water to the volume until the concave liquid level is parallel to the scale line. Cover the bottle with a stopper, invert it upside down, and shake to obtain a concentration of approximately 1.0 mmol·L. -1 The glucose standard solution was diluted 6 times in a 10-fold gradient, with the concentrations being 0.1 mmol·L -1 to 1 nmol·L -1 Glucose standard solution, set aside.

[0111] Preparation of ginsenoside Rg1 solution: According to the above solution preparation method, the initial concentration of ginsenoside Rg1 standard solution was prepared to 0.3mmol·L -1 The solution was diluted 6 times in a 10-fold concentration gradient, with the concentrations being 30 μmol·L -1 to 0.3 nmol·L -1 Ginsenoside Rg1 standard solution, set aside.

[0112] Preparation of arginine solution: According to the above solution preparation method, the initial concentration of arginine standard solution is prepared to 1.0mmol·L -1 The solution was diluted 6 times in a 10-fold concentration gradient, with the concentrations being 0.1 mmol·L -1 to 1 nmol·L -1 Arginine standard solution, set aside.

[0113] Lysine solution preparation: According to the above solution preparation method, the initial concentration of lysine standard solution is prepared to 1.0 mmol·L -1 The solution was diluted 6 times in a 10-fold concentration gradient, with the concentrations being 0.1 mmol·L -1 to 1 nmol·L -1 Lysine standard solution, set aside.

[0114] (2) Sample testing

[0115] The solvent of each sample was selected as a blank control. 60 μL of each sample solution was added to the biosensor optimized in Example 2 from low to high concentrations. The reaction was carried out at 4°C for 6 minutes before the detection was started. The collected I DS -V DS Curve and analyze.

[0116] (3) Analysis of the specific binding ability of the sweet key quality attribute and TAS1R2 protein

[0117] Integration of the collected I in step (2) DS -V DS The specific binding strength of each substance to TAS1R2 protein was calculated using formulas 1 to 3, and K D The calculation formula is as follows:

[0118]

[0119]

[0120]

[0121] Among them [A b ] is the concentration of TAS1R2 protein, [A g ] is the concentration of the sample [C], K and K A is the binding constant, K D is the dissociation constant, ΔI is the current change value, ΔI μAx is the maximum change value of current.

[0122] The above biosensor was used to detect ginsenoside Rg1 as a demonstration analysis. Figure 9 It can be seen that the current response signal gradually decreases with the decrease of the concentration of ginsenoside Rg1. Considering the significance of the data, the voltage of I = 1V was selected. DS The relative current change was calculated by adding 20% ​​Tween-80 aqueous solution and reacting for 6 minutes. DS -V DS The curve was used as blank control, and the concentration of ginsenoside Rg1 was 3×10 -10 mol·L -1 -3×10 -7 mol·L -1 In the range of g ]) and the relative value of current change ((I-I0) / I0) show a good linear relationship, and the linear equation is y=0.0012x+0.0072, R 2 =0.9853; Ginsenoside Rg1 concentration ([A g ]) and its corresponding concentration / current change (I-I0, ΔI) also show a good linear relationship, and the linear equation is y=101665x-5×10 -5 , R 2 =0.9995. The dissociation constant K of the interaction between ginsenoside Rg1 and TAS1R2 protein was calculated according to the formula D =8.595×10 -9 M.

[0123] The dissociation constant of glucose interacting with TAS1R2 protein is 7.679 x 10 -8 M; the dissociation constant of lysine interacting with TAS1R2 protein is 7.852 x 10 -6 M; the dissociation constant of arginine interacting with TAS1R2 protein is 5.221 x 10 -7 M.

[0124] According to the analysis of the research results, K D values in the range of 2 x 10 -2 ~ 100 M indicate that the binding force is very weak or even nonexistent; in the range of 10 -3 ~ 10 -2 M, the binding force is weak; in the range of 10 -5 ~ 10 -3 M, the binding force is in an intermediate state; in the range of 10 -8 ~ 10 -5 M, the binding force is very strong; and in the range of 10 -13 ~ 10 -8 M, the binding force is very strong. Therefore, the specific binding ability of lysine and arginine with TAS1R2 protein is very strong; and the specific binding ability of glucose and ginsenoside Rg1 with TAS1R2 protein is very strong.

Claims

1. Application of a TAS1R2 molecular sensory artificial intelligence biosensor in identifying the key quality attribute of sweetness, characterized by: The specific steps are as follows: Step 1: Expression and purification of TAS1R2 protein; Step 2: The purified TAS1R2 protein was coupled to the high electron mobility field effect transistor on the biosensor chip, and integrated with the probe platform, electrochemical workstation, and data display to construct a TAS1R2 molecular sensor artificial intelligence biosensor. The steps for constructing the biosensor chip are as follows: Step 2.1: Add 60 μL of thiol reagent to the sample cell set in the sensor chip and react at room temperature for 17-24 hours to form Au-S bonds on the sensor chip surface to form a self-assembled monolayer; Step 2.2: After the self-assembled monolayer described in step 2.1 is formed, the excess thiol reagent in the sample pool is washed with deionized water, and 60 μL of 20 mmol·L -1 Carbodiimide hydrochloride and 50mmol·L -1 An equal volume mixture of N-hydroxysuccinimide was reacted at room temperature for 15 min to activate the carboxyl group; Step 2.3: After the carboxyl group activation described in step 2.2, 100 mmol·L -1 The sample pool was cleaned with phosphate buffer solution, TAS1R2 protein was added, and the sample was modified at 4°C for 2-5 hours to obtain a sweet taste protein functionalized biosensor chip. Step 3: Using the molecular sensory artificial intelligence biosensor constructed in step 2, the key quality attributes of sweetness are identified through the affinity interaction between the gradient concentration of the test substance and the sweet receptor TAS1R2. The application is the identification of four key quality attributes of sweetness: glucose, ginsenoside Rg1, arginine, and lysine.

2. The use according to claim 1, characterized in that The detection method of the biosensor is linear sweep voltammetry, the constant voltage range is 0-2V, the current sensitivity is μA level, and the collected electrical signal is a current-voltage curve.

3. The use according to claim 1, characterized in that The TAS1R2 protein specifically binds to the key quality attribute and is characterized by the dissociation constant Kd value, thereby identifying the sweetness characteristics of the quality attribute. The sensor is suitable for detecting trace sweetness key quality attributes.

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