Ergothioneine derivative with higher affinity with OCTN1 obtained by method for predicting OCTN1 protein structure and interaction relationship between OCTN1 protein structure and ergothioneine

Abstract

The invention discloses a method for predicting an OCTN1 protein structure and an interaction relationship between the OCTN1 protein structure and ergothioneine, an obtained ergothioneine derivative with higher affinity with the OCTN1, a method for optimizing the ergothioneine derivative based on the interaction relationship and application of the ergothioneine derivative. The three-dimensional structure of the OCTN1 protein is predicted through a HelixFold model, then the interaction mode of the ergothioneine and the OCTN1 protein is analyzed, rational design is carried out based on the interaction mode, and the ergothioneine derivative with higher OCTN1 binding affinity is obtained. The invention also discloses a pharmaceutical composition containing the derivative and application of the pharmaceutical composition in preparation of drugs for treating related diseases. The method and the system provided by the invention provide a new way for deeply researching the functions of the OCTN1 and developing efficient ergothioneine drugs.

Description

Technical Field

[0001] This invention relates to the fields of bioinformatics and drug design, specifically to a method for predicting the structure of the OCTN1 protein and its interaction with ergothioneine, as well as a method and application for optimizing ergothioneine derivatives based on this interaction. Background Technology

[0002] OCTN1 (organic cation / carnitine transporter 1) is an important membrane transport protein belonging to the SLC22A family. In the human body, it is primarily responsible for the transmembrane transport of organic cations and carnitine. Studies have shown that OCTN1 is closely related to various physiological and pathological processes, including drug metabolism, cell protection, and disease development.

[0003] Ergothioneine (EGT) is a naturally occurring sulfur-containing amino acid derivative with unique antioxidant, anti-inflammatory, and neuroprotective biological activities. Studies have found that ergothioneine mainly enters cells via OCTN1, but its specific transport mechanism and binding pattern remain unclear.

[0004] Traditional methods for determining protein structure (such as X-ray crystallography and nuclear magnetic resonance) are time-consuming, costly, and technically challenging, especially for membrane proteins like OCTN1, where structural determination is even more difficult. Therefore, using computational biology methods to predict the structure of OCTN1 and its interaction with ergothioneine is of great significance for a deeper understanding of ergothioneine transport mechanisms and the development of efficient ergothioneine derivatives.

[0005] In recent years, with the rapid development of artificial intelligence technology, especially the application of deep learning in the field of protein structure prediction, new approaches have been provided to solve the above problems. For example, large AI models such as AlphaFold and HelixFold have made breakthrough progress in protein structure prediction, and their prediction accuracy has approached the level of experimental determination.

[0006] However, there are currently no reports of systematic studies on the interaction between OCTN1 and ergothioneine and subsequent drug design. Summary of the Invention

[0007] The purpose of this invention is to provide a method for predicting the structure of the OCTN1 protein and its interaction with ergothionein to obtain ergothionein derivatives with higher affinity for OCTN1, as well as a method and application for optimizing ergothionein derivatives based on this interaction.

[0008] A method for predicting the structure of the OCTN1 protein and its interaction with ergothionein includes the following steps: Provides the amino acid sequence of the human OCTN1 protein, as shown in SEQ ID NO:1; The amino acid sequence was input into the HelixFold model for structure prediction. The three-dimensional structure prediction results of the OCTN1 protein were obtained from the HelixFold model.

[0009] The HelixFold model mentioned is a large propeller-shaped biological model provided by http: / / paddlehelix.baidu.com / app / all / helixfold3 / forecast.

[0010] A method for predicting the interaction structure between ergothionein and OCTN1 protein includes the following steps: Obtain the predicted three-dimensional structure of the OCTN1 protein obtained by the method described above; Provide a SMILE format representation of ergothioneine, wherein the SMILE format of ergothioneine is [O-]C(=O)C([N+](C)(C)C)CC1=CNC(N1)=C; The three-dimensional structure of the OCTN1 protein and the SMILE format of ergothionein were input into the HelixFold model for interaction structure prediction. The interaction structure prediction results between ergothionein and OCTN1 protein were obtained from the HelixFold model.

[0011] The interaction structure prediction includes determining the binding site of ergothionein to the OCTN1 protein.

[0012] A method for optimizing the binding affinity of ergothionein to OCTN1 protein includes the following steps: Based on the predicted interaction structure of claim 3 or 4, the binding mode of ergothionein to OCTN1 protein was analyzed. By rationally designing the aforementioned binding mode, ergothioneine derivatives were obtained. The ergothioneine derivative was converted to the SMILE format. The docking structure between the ergothioneine derivative and the OCTN1 protein was predicted using the HelixDock function; The binding affinity of the ergothioneine derivative to the OCTN1 protein was assessed using the small molecule affinity prediction-SIGN function. Based on the binding affinity assessment results, derivatives with binding affinity superior to the original ergothioneine were screened.

[0013] The rational design involves modifying the chemical structure of ergothionein to enhance its hydrogen bonding, hydrophobic interactions, or electrostatic interactions with the OCTN1 protein.

[0014] The ergothioneine derivatives include compounds obtained by chemically modifying the carboxyl, amino, or thiazole ring of ergothioneine.

[0015] The ergothioneine derivative obtained by the method has a higher affinity for OCTN1, characterized in that the binding affinity of the derivative to the OCTN1 protein is superior to that of the original ergothioneine.

[0016] A pharmaceutical composition comprising the ergothioneine derivative of claim 8 and a pharmaceutically acceptable carrier or excipient.

[0017] The application of the ergothioneine derivative in the preparation of drugs for improving OCTN1-mediated substance transport-related diseases.

[0018] The application of the ergothioneine derivatives in the preparation of antioxidants, anti-inflammatory agents or neuroprotective agents.

[0019] A system for predicting the structure of protein-small molecule interactions, comprising: The protein structure prediction module is used to predict the three-dimensional structure of proteins. The small molecule representation module is used to convert small molecules into SMILE format; The interaction prediction module is used to predict the interaction structure between the protein and small molecules based on the protein's three-dimensional structure and the small molecule SMILE format. An affinity assessment module is used to assess the binding affinity of the protein to small molecules; The protein structure prediction module, small molecule representation module, interaction prediction module, and affinity assessment module are all implemented based on a large AI model.

[0020] The AI ​​big model includes the HelixFold model and the small molecule affinity prediction-SIGN model.

[0021] The system also includes a rational design module, which is used to rationally design small molecules based on the interaction structure prediction results to obtain small molecule derivatives.

[0022] The rational design module includes a structure analysis unit and a chemical modification unit. The structure analysis unit is used to analyze the binding mode between proteins and small molecules, and the chemical modification unit is used to chemically modify small molecules based on the binding mode.

[0023] The beneficial effects of this invention are as follows: This invention is the first to predict the three-dimensional structure of the OCTN1 protein, providing an important structural basis for in-depth research on the function and mechanism of action of OCTN1.

[0024] This invention reveals the interaction pattern between ergothionein and the OCTN1 protein, providing a new perspective for understanding the transport mechanism of ergothionein.

[0025] Based on the results of interaction structure prediction, this invention optimizes the structure of ergothioneine through rational design methods, obtaining derivatives with higher OCTN1 binding affinity, laying the foundation for the development of highly effective ergothioneine drugs.

[0026] The method and system provided by this invention are universal and can be extended to the fields of research on other protein-small molecule interactions and drug design. Attached Figure Description

[0027] Figure 1 shows the predicted three-dimensional structure of the OCTN1 protein in an embodiment of the present invention.

[0028] Figure 2 shows the predicted interaction structure between ergothionein and OCTN1 protein in an embodiment of the present invention.

[0029] Figure 3 shows one of the design processes for ergothioneine derivatives in an embodiment of the present invention.

[0030] Figure 4 shows the second part of the design process for ergothioneine derivatives in an embodiment of the present invention.

[0031] Figure 5 shows the binding affinity assessment results of ergothioneine derivatives to OCTN1 protein in embodiments of the present invention.

[0032] Figure 6 shows the effect of ergothioneine derivatives on ROS in muscle cells under oxidative stress in an embodiment of the present invention. Detailed Implementation

[0033] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0034] A first aspect of the present invention provides a method for predicting the structure of the OCTN1 protein, comprising the following steps: Provided is the amino acid sequence of the human OCTN1 protein, as shown in SEQ ID NO:1; The amino acid sequence was input into the HelixFold model for structure prediction; The three-dimensional structure prediction results of the OCTN1 protein were obtained from the HelixFold model.

[0035] A second aspect of the present invention provides a method for predicting the interaction structure between ergothionein and OCTN1 protein, comprising the following steps: Obtain the predicted three-dimensional structure of the OCTN1 protein obtained by the method described in the first aspect; Provide a SMILE format representation of ergothioneine, wherein the SMILE format of ergothioneine is [O-] C (=O)C (N+(C)C)CC1=CNC(N1)=C; The three-dimensional structure of the OCTN1 protein and the SMILE format of ergothionein were input into the HelixFold model for interaction structure prediction. The interaction structure prediction results between ergothionein and OCTN1 protein were obtained from the HelixFold model.

[0036] A third aspect of the present invention provides a method for optimizing the binding affinity of ergothionein to OCTN1 protein, comprising the following steps: Based on the interaction structure prediction results described in the second aspect, the binding mode of ergothionein to OCTN1 protein was analyzed. By rationally designing the aforementioned binding mode, ergothioneine derivatives were obtained. The ergothioneine derivative was converted to SMILE format; The docking structure between the ergothioneine derivative and the OCTN1 protein was predicted using the HelixDock function; The binding affinity of the ergothioneine derivative to the OCTN1 protein was assessed using small molecule affinity prediction-SIGN function. Based on the binding affinity assessment results, derivatives with binding affinity superior to the original ergothioneine were screened.

[0037] A fourth aspect of the present invention provides an ergothioneine derivative obtained by the method described in the third aspect, wherein the derivative has a better binding affinity to the OCTN1 protein than the original ergothioneine.

[0038] A fifth aspect of the invention provides a pharmaceutical composition comprising the ergothioneine derivative described in the fourth aspect and a pharmaceutically acceptable carrier or excipient.

[0039] The sixth aspect of the present invention provides the use of the ergothioneine derivative described in the fourth aspect in the preparation of a medicament for improving OCTN1-mediated substance transport-related diseases.

[0040] The seventh aspect of the invention provides the use of the ergothioneine derivatives described in the fourth aspect in the preparation of antioxidants, anti-inflammatory agents or neuroprotective agents.

[0041] An eighth aspect of the present invention provides a system for predicting the structure of protein-small molecule interactions, comprising: The protein structure prediction module is used to predict the three-dimensional structure of proteins. The small molecule representation module is used to convert small molecules into SMILE format; The interaction prediction module is used to predict the interaction structure between the protein and small molecules based on the protein's three-dimensional structure and the small molecule SMILE format. An affinity assessment module is used to assess the binding affinity of the protein to small molecules; The protein structure prediction module, small molecule representation module, interaction prediction module, and affinity assessment module are all implemented based on a large AI model.

[0042] Example 1: OCTN1 protein structure prediction 1. Obtain the amino acid sequence of the human OCTN1 protein, as follows: Mrdydeviaflgewgpfqrliffllsasiipngfngmsvvflagtpehrcrvpdaanlssawrnnsvplrlrdgrevphscsryrlatianfsalglepgrdvdlgqleqescldgwefsqdvylstvvtewnlvce dnwkvplttslffvgvllgsfvsgqlsdrfgrknvlfatmavqtgfsflqifsiswemftvlfvivgmgqisnyvvafilgteilgksvriifstlgvctffavgymllplfayfirdwrmlllaltvpgvlcvplww fipesprwlisqrrfreaediiqkaakmnntavpavifdsveelnplkqqkafildlfrtrniaimtimsllllwmltsvgyfalsldapnlhgdaylncflsalieipayitawlllrtlprryiiaavlfwgggvll fiqlvpvdyyflsiglvmlgkfgitsafsmlyvftaelyptlvrnmavgvtstasrvgsiiapyfvylgaynrmlpyivmgsltvligiftlffpeslgmtlpetleqmqkvkwfrsgkktrdsmeteenpkvlitaf 2. Input the above amino acid sequence into the HelixFold model for structure prediction; input OCTN1 into the propeller-shaped biological model. http: / / paddlehelix.baidu.com / app / all / helixfold3 / forecast The structure of OCTN1 was predicted using the HelixFold model.

[0043] 3. The three-dimensional structure prediction results of the OCTN1 protein were obtained from the HelixFold model, as shown in Figure 1.

[0044] Example 2: Prediction of the interaction structure between ergothionein and OCTN1 protein

[0045] 1. Obtain the predicted three-dimensional structure of the OCTN1 protein obtained in Example 1.

[0046] 2. In the HelixFold model, use the built-in "Draw Molecules" function to draw ergothioneine (oxidized form) and translate it into SMILE format: [O-] C (=O) C (N+(C)C)CC1=CNC(N1)=C.

[0047] 3. Input the 3D structure of OCTN1 protein and the SMILE format of ergothionein into the HelixFold model for interaction structure prediction.

[0048] 4. The predicted interaction structure between ergothionein and OCTN1 protein was obtained from the HelixFold model, as shown in Figure 2.

[0049] Example 3: Rational Design and Optimization of Ergothioneine Derivatives 1. Based on the interaction structure prediction results obtained in Example 2, the binding mode of ergothionein to OCTN1 protein was analyzed to determine the key binding sites and interaction types.

[0050] 2. By rationally designing the binding mode, the chemical structure of ergothioneine was modified to obtain a series of ergothioneine derivatives.

[0051] 3. Convert each ergothioneine derivative to SMILE format.

[0052] 4. Use HelixDock to predict the docking structure of each ergothioneine derivative with the OCTN1 protein.

[0053] 5. The binding affinity of each ergothioneine derivative to the OCTN1 protein was assessed using the small molecule affinity prediction-SIGN function.

[0054] 6. Based on the binding affinity assessment results, derivatives with binding affinity superior to the original ergothioneine were screened out, as shown in Figures 3 and 4.

[0055] Example 4: Preparation of the pharmaceutical composition

[0056] The ergothioneine derivatives screened in Example 3 were mixed with pharmaceutically acceptable carriers or excipients to prepare a pharmaceutical composition.

[0057] Example 5: A system for predicting the structure of protein-small molecule interactions

[0058] Construct a system for predicting the structure of protein-small molecule interactions, the system comprising: The protein structure prediction module is used to predict the three-dimensional structure of proteins. The small molecule representation module is used to convert small molecules into SMILE format; The interaction prediction module is used to predict the interaction structure between proteins and small molecules based on the three-dimensional structure of proteins and the SMILE format for small molecules. The affinity assessment module is used to evaluate the binding affinity between proteins and small molecules; Among them, the protein structure prediction module, small molecule representation module, interaction prediction module, and affinity assessment module are all implemented based on a large AI model.

[0059] The following are predictions of the affinity of eight rationally designed ergothioneine molecules for OCTN1.

[0060] Mod1 -- Smile: C[N+](C)(C)CC[C@@H](CC1=CNC(=S)N1)C(=O)[O-]; squeezed

[0061] Mod2 -- Smile: C[N+](C)(C)C(CC(=O)O)Cc1c[nH]c(=S)[nH]1; squeezed

[0062] Mod3 -- SMILE: O=C(O)C(Cc1c[nH]c(=S)[nH]1)c2ccccc2; overlap

[0063] Mod4 -- SMILE: C[N+](C)(C)C(CO)Cc1c[nH]c(=S)[nH]1; overlap

[0064] Mod5 -- SMILE: CC(C)COC(Cc1c[nH]c(=S)[nH]1)C(=O)O; overlap

[0065] Mod6 – SMILE: CC(C)COC(CC(=O)O)Cc1c[nH]c(=S)[nH]1

[0066] With overlapping atoms Mod7 – SMILE: CC(S)Cc1c[nH]c(=S)[nH]1

[0067] overlapping atoms Mod8 -- CC(C)(N)CCC(CC(=O)O)Cc1c[nH]c(=S)[nH]1 Table 1. Affinity scores of oxidized ergothioneine and different modified ergothioneine with OCTN1 serial number molecular SMILE format Affinity rating with OCTN1 1 Oxidized ergothioneine (EGT) S=c1[nH]c(c[nH]1)CC([N+](C)(C)C)C(O)=O 2.73 2 EGT-Mod1 C[N+](C)(C)[C@@H](Cc1c[nH]c(S)n1)C(=O)[O-] 4.26 3 EGT-Mod2 C[N+](C)(C)C(CC(=O)O)Cc1c[nH]c(=S)[nH]1 3.01 4 EGT-Mod3 O=C(O)C(Cc1c[nH]c(=S)[nH]1)c2ccccc2 3.40 5 EGT-Mod4 C[N+](C)(C)C(CO)Cc1c[nH]c(=S)[nH]1 2.79 6 EGT-Mod5 CC(C)COC(Cc1c[nH]c(=S)[nH]1)C(=O)O 3.42 7 EGT-Mod6 CC(C)COC(CC(=O)O)Cc1c[nH]c(=S)[nH]1 3.61 8 EGT-Mod7 CC(S)Cc1c[nH]c(=S)[nH]1 3.62 9 EGT-Mod8 CC(C)(N)CCC(CC(=O)O)Cc1c[nH]c(=S)[nH]1 3.23 The results show that the Mod1 molecule has a better binding affinity to OCTN1 than the original oxidized ergothioneine.

[0068] Example 6: Effects of different types of ergothioneine molecules on ROS in muscle cells under oxidative stress

[0069] Based on the predicted affinity of ergothioneine molecules for OCTN1 in Example 5, different types of ergothioneine molecules (Mod1, Mod5, Mod6, Mod7) were synthesized in vitro for subsequent experiments. The experiment was divided into 7 groups: a blank control group (Control), an oxidative stress model group (Model), the original oxidized ergothioneine (EGT), and treatment groups with different types of ergothioneine molecules (EGT-Mod1, EGT-Mod3, EGT-Mod6, EGT-Mod7). C2C12 skeletal muscle cells were cultured in vitro in high-glucose DMEM medium containing 10% fetal bovine serum (FBS). After the cells reached the logarithmic growth phase, they were treated with cell digestion solution, centrifuged, and the supernatant was discarded. The cells were then resuspended in high-glucose DMEM medium containing 10% fetal bovine serum (FBS). Subsequently, C2C12 cells were cultured at 1 × 10⁶ cells per well. 4Cells were seeded at a density of 100 μL per well into 96-well plates. When cells adhered and reached 80% confluence, each well was replaced with high-glucose DMEM medium containing 2% horse serum to induce differentiation for 5 days. Subsequently, except for the blank control group and the oxidative stress model group, each treatment group was given 100 μM of various types of ergothioneine solution, and the plates were incubated at 37°C and 5% CO2 for 24 hours to ensure sufficient contact time between ergothioneine molecules and cells. After incubation, the supernatant was discarded, and each group was incubated with 100 μL of H2DCF-DA (25 μM) for 1 hour. Then, the supernatant was discarded, and except for the blank group, each group was incubated with 100 μL of H2O2 (150 μM) for 30 minutes. The fluorescence intensity in each well was then measured using a microplate reader at 485 / 535 nm wavelengths.

[0070] The above embodiments of the present invention are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the structure of OCTN1 protein and its interaction with ergothionein, characterized in that, Includes the following steps: Provides the amino acid sequence of the human OCTN1 protein, as shown in SEQ ID NO:1; The amino acid sequence was input into the HelixFold model for structure prediction. The three-dimensional structure prediction results of the OCTN1 protein were obtained from the HelixFold model.

2. The method according to claim 1, characterized in that, The HelixFold model mentioned is a large propeller-shaped biological model provided by http: / / paddlehelix.baidu.com / app / all / helixfold3 / forecast.

3. A method for predicting the interaction structure between ergothionein and OCTN1 protein, characterized in that, Includes the following steps: Obtain the predicted three-dimensional structure of the OCTN1 protein obtained by the method described in claim 1 or 2; Provide a SMILE format representation of ergothioneine, wherein the SMILE format of ergothioneine is [O-]C(=O)C([N+](C)(C)C)CC1=CNC(N1)=C; The three-dimensional structure of the OCTN1 protein and the SMILE format of ergothionein were input into the HelixFold model for interaction structure prediction. The interaction structure prediction results between ergothionein and OCTN1 protein were obtained from the HelixFold model.

4. The method according to claim 3, characterized in that, The interaction structure prediction includes determining the binding site of ergothionein to the OCTN1 protein.

5. A method for optimizing the binding affinity of ergothionein to OCTN1 protein, characterized in that, Includes the following steps: Based on the predicted interaction structure of claim 3 or 4, the binding mode of ergothionein to OCTN1 protein was analyzed. By rationally designing the aforementioned binding mode, ergothioneine derivatives were obtained. The ergothioneine derivative was converted to the SMILE format. The docking structure between the ergothioneine derivative and the OCTN1 protein was predicted using the HelixDock function; The binding affinity of the ergothioneine derivative to the OCTN1 protein was assessed using the small molecule affinity prediction-SIGN function. Based on the binding affinity assessment results, derivatives with binding affinity superior to the original ergothioneine were screened.

6. The method according to claim 5, characterized in that, The rational design involves modifying the chemical structure of ergothionein to enhance its hydrogen bonding, hydrophobic interactions, or electrostatic interactions with the OCTN1 protein.

7. The method according to claim 5, characterized in that, The ergothioneine derivatives include compounds obtained by chemically modifying the carboxyl, amino, or thiazole ring of ergothioneine.

8. An ergothioneine derivative with higher affinity for OCTN1 obtained by the method according to any one of claims 5-7, characterized in that, The derivative exhibits a superior affinity for the OCTN1 protein compared to the original ergothionein.

9. A pharmaceutical composition, characterized in that, It includes the ergothioneine derivative of claim 8 and a pharmaceutically acceptable carrier or excipient.

10. The use of the ergothioneine derivative of claim 8 in the preparation of a medicament for improving OCTN1-mediated substance transport-related diseases.

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