Computer-assisted drug screening method, system and equipment based on IAPP

Abstract

The invention discloses a computer-aided drug screening method, system and device based on an IAPP. The application proves that the SARS-CoV-2S / N protein is directly combined with the IAPP and promotes the pathological aggregation of the IAPP in a host body for the first time. It is found for the first time that new coronavirus infection induces activation of a pancreas islet microenvironment complement system, and pro-inflammatory factor release and endoplasmic reticulum stress synergistically aggravate beta cell function failure. Insulin secretion disorder caused by the fact that IAPP aggregation destroys the SNARE complex mediated insulin vesicle transportation process is found for the first time. The invention provides a brand new theory that the new coronavirus causes beta cell dysfunction by physically hijacking key functional proteins of a host, and discloses an age-dependent mechanism that the virus aggravates islet amyloid protein deposition in the elderly population. And a new theoretical basis and a treatment direction are provided for age-dependent management and viral metabolic disorder research of diabetes after epidemic.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of intelligent medical treatment, and particularly relates to a computer-aided drug screening method, system and device based on IAPP. BACKGROUND

[0002] Clinical data shows that the new coronavirus infection not only causes acute respiratory disease, but also is closely related to the onset and deterioration of various chronic metabolic diseases (such as diabetes). Global multicenter epidemiological data compared nearly 200,000 new coronavirus infected persons (survived for more than 1 month) with the medical records of two groups of non-infected persons (more than 4 million people in each group). The study showed that the risk of diabetes increased by 40% (48 cases / 1000 people vs. 35 cases / 1000 people in the control group) in the new coronavirus infected persons one year later, mainly type II diabetes; and the severe rate and mortality rate of the patients with diabetes significantly increased after infection (the mortality rate increased from 1.1% to 11%). In addition, the symptoms of diabetes in the recovered patients of the new coronavirus were observed to be aggravated, the blood sugar control was difficult, and the management difficulty was increased. Large-scale cohort studies confirmed that the new coronavirus infection was positively correlated with the long-term risk of diabetes.

[0003] With the development of big data, cloud computing, hardware acceleration and other technologies, computer-aided drug design (CADD) has gradually become an important means of new drug research and development. Based on computer operation and simulation technology, the prior knowledge contained in a large amount of drug data is learned, and the interaction relationship between the target and the candidate drug is mined, so that active drug molecules with drug-like properties can be quickly selected from millions of molecules. This greatly reduces the blindness of screening candidate drug molecules and improves the research and development and drug screening efficiency. SUMMARY

[0004] In order to make up for the shortcomings of the prior art, the present application provides a computer-aided drug screening method, system and device based on IAPP and complement system, and a method, system and device for predicting sequelae of old coronavirus infection based on computer-aided.

[0005] To achieve the above object, the present application adopts the following technical solutions:

[0006] The first aspect of the present application provides a computer-aided drug screening method based on IAPP, which comprises:

[0007] Obtaining IAPP protein and S protein and / or N protein data of SARS-CoV-2;

[0008] Selecting the spatial structure of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex, and determining the binding site of the complex as the binding site of the targeted drug;

[0009] obtaining a candidate drug targeting the binding site through computer-aided screening.

[0010] Further, the process of the computer-aided screening is as follows:

[0011] obtaining the binding site of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex;

[0012] screening candidate drugs based on the spatial structure of the binding site of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex;

[0013] molecular docking the screened candidate drugs with the binding site to obtain the score of the docked molecules;

[0014] ranking according to the score to obtain the candidate drugs.

[0015] Further, the process of the computer-aided screening is as follows:

[0016] obtaining the molecular structure of the IAPP protein, S protein or N protein and inputting the molecular structure into the pharmacophore module library for matching, clustering all the action sites based on the interaction mode with the IAPP protein, S protein or N protein to obtain a pharmacophore model;

[0017] inputting the pharmacophore model into a molecular compound database for high-throughput screening to obtain candidate drugs.

[0018] Further, the process of the computer-aided screening is as follows:

[0019] first obtaining the molecular structure of the site binding agent or inhibitor of the IAPP protein, S protein or N protein, then screening similar structure compounds based on the molecular structure of the site binding agent or inhibitor, then molecular docking the similar structure compounds with the binding site to obtain the score of the docked molecules, and finally ranking to obtain the candidate drugs.

[0020] Further, the binding site of the IAPP protein includes one or more of ASN36, ARG44, SER52 and GLY71.

[0021] Further, the binding site of the IAPP protein and the S protein complex includes any one or more of: the amino acid site ASN36 of the IAPP and the amino acid site ASN354 of the S protein, the amino acid site ASN36 of the IAPP and the amino acid site ARG355 of the S protein, the amino acid site ARG44 of the IAPP and the amino acid site GLU471 of the S protein, the amino acid site ARG44 of the IAPP and the amino acid site THR470 of the S protein, the amino acid site SER52 on the IAPP and the amino acid site GLU484 of the S protein, the amino acid site GLY71 on the IAPP and the amino acid site GLN493 of the S protein.

[0022] Further, the binding site of the IAPP protein and the N protein complex includes any one or more of: the amino acid site SER52 of the IAPP and the amino acid site PRO309 of the N protein, the amino acid site GLY71 on the IAPP and the amino acid site LEU331 of the N protein.

[0023] The second aspect of the present application provides a computer-aided drug screening method based on the complement system, the method comprising:

[0024] Obtaining complement system protein data;

[0025] According to the spatial structure of the protein, determine the targeted drug;

[0026] Through computer-aided screening, candidate drugs targeting the complement system are obtained.

[0027] Further, the process of computer-aided screening is:

[0028] First, obtain the molecular structure of the inhibitor of the complement system protein, then screen for compounds with similar structures based on the molecular structure of the inhibitor, then perform molecular docking of the compounds with similar structures and the binding site to obtain the score of the docked molecules, and finally sort to obtain candidate drugs.

[0029] The scores are sorted, and the top n small molecules are selected to obtain candidate compounds, n is a natural number greater than or equal to 1.

[0030] Further, the complement system protein includes one or more of MAC, C1, C3, C4, C6, C7, C8, C9, C4BPA, FB, FH, HPX, Ceruloplasmin, C1QC, SERPING1.

[0031] The third aspect of the present application provides a computer-aided method for predicting sequelae of coronavirus infection in the elderly, the method comprising:

[0032] receiving input data, the data comprising IAPP data and / or glycemic index data of a recovered elderly coronavirus infection population;

[0033] applying a machine learning model to the input data to generate a prediction result indicating whether the recovered elderly coronavirus infection population has sequelae.

[0034] Further, the criterion for determining whether the recovered elderly coronavirus infection population has sequelae is that if IAPP abnormal aggregation and / or glycemic index disorder exist, the recovered elderly coronavirus infection population is determined to be a population with sequelae.

[0035] The fourth aspect of the present application provides a computer-aided drug screening system based on IAPP, comprising:

[0036] An acquisition unit: acquiring IAPP protein and S protein and / or N protein data of SARS-CoV-2;

[0037] A site determination unit: selecting the spatial structure of the IAPP protein and S protein and / or N protein complex to determine the binding site of the complex as the binding site of the targeted drug;

[0038] A screening unit: obtaining a candidate drug targeting the binding site through computer-aided screening.

[0039] The fifth aspect of the present application provides a computer-aided drug screening system based on the complement system, comprising:

[0040] An acquisition unit: acquiring complement system protein data;

[0041] A determination unit: determining a targeted drug according to the spatial structure of the protein;

[0042] A screening unit: obtaining a candidate drug targeting the complement system through computer-aided screening.

[0043] Further, the complement system protein comprises one or more of MAC, C1, C3, C4, C6, C7, C8, C9, C4BPA, FB, FH, HPX, Ceruloplasmin, C1QC, SERPING1.

[0044] The sixth aspect of the present application provides a computer-aided system for predicting sequelae of elderly coronavirus infection, comprising:

[0045] An acquisition unit: acquiring data of a recovered elderly coronavirus infection population;

[0046] An extraction unit: extracting IAPP data and / or glycemic index data of a recovered elderly coronavirus infection population;

[0047] Identifying unit: judging whether the old coronavirus infection recovered people have sequelae based on IAPP aggregation and / or sugar metabolism indicators.

[0048] Further, the judgment standard of whether the old coronavirus infection recovered people have sequelae is: if IAPP abnormal aggregation and / or sugar metabolism indicators are disorder, it is judged that the old coronavirus infection recovered people are people with sequelae.

[0049] The seventh aspect of the present application provides a computer device, the device comprising:

[0050] a memory and a processor, the memory is used to store program instructions; the processor is used to call the program instructions, when the program instructions are executed, the IAPP-based computer-aided drug screening method of the first aspect of the present application, the complement system-based computer-aided drug screening method of the second aspect of the present application or the method of predicting sequelae of old coronavirus infection based on computer-aided of the third aspect of the present application is realized.

[0051] The eighth aspect of the present application provides a computer readable storage medium, which has a computer program thereon, the computer program is executed by a processor to realize the IAPP-based computer-aided drug screening method of the first aspect of the present application, the complement system-based computer-aided drug screening method of the second aspect of the present application or the method of predicting sequelae of old coronavirus infection based on computer-aided of the third aspect of the present application.

[0052] The ninth aspect of the present application provides a computer program product, the product comprising a computer program, the computer program is executed by a processor to realize the steps of the IAPP-based computer-aided drug screening method of the first aspect of the present application, the steps of the complement system-based computer-aided drug screening method of the second aspect of the present application or the steps of the method of predicting sequelae of old coronavirus infection based on computer-aided of the third aspect of the present application.

[0053] The tenth aspect of the present application provides any one of the following applications:

[0054] 1) The application of the complex formed by the combination of IAPP protein, S protein and / or N protein in screening drugs for treating metabolic diseases;

[0055] 2) The application of complement system inhibitors in screening drugs for treating metabolic diseases.

[0056] Further, the proteins of the complement system include one or more of MAC, C1, C3, C4, C6, C7, C8, C9, C4BPA, FB, FH, HPX, Ceruloplasmin, C1QC, SERPING1.

[0057] Further, the drug also includes a pharmaceutically acceptable excipient.

[0058] Further, the metabolic disease includes post-coronavirus infection sequelae or diabetes.

[0059] Advantages and beneficial effects of the present application:

[0060] The present application found in the study that, compared with the physiological state of adult and old rhesus monkey pancreas, the new coronavirus infection aggravates the pathological aggregation of islet amyloid polypeptide in old rhesus monkeys. Immunohistochemistry, immunofluorescence, special staining and electron microscopy confirmed that IAPP was mainly expressed in pancreatic beta cells, and after new coronavirus infection, IAPP aggregated in beta cells and intercellular matrix to form oligomers or fibers, resulting in islet cell death. Molecular docking simulation and in vitro overexpression and co-precipitation experiments showed that the S protein and N protein of the new coronavirus directly interacted with IAPP, including multiple key amino acid sites acting together. Using microdissection to collect ROI islets, spatial proteomics confirmed that after infection, the new coronavirus activated the islet complement system (C4BPA, HPX, etc.), complement activation promoted the release of pro-inflammatory factors (IL-6, TNF-alpha), further aggravating endoplasmic reticulum stress (GRP78 and CHOP). The SNARE protein complex-mediated vesicle transport system (VAMP4 and VAMP8) and endocytosis system (Rab5 and VPS35, etc.) were severely damaged, and vesicles accumulated in the intercellular space and the basement membrane of islet microvessels, directly affecting the transport, release and cleavage of IAPP and insulin, interfering with the homeostasis of material transport in beta cells, vicious cycle, and eventually leading to cell death, thus losing the ability to release insulin and regulate blood glucose, causing glucose metabolism disorder.

[0061] The present application first confirmed in the host body that SARS-CoV-2 S / N protein directly binds to IAPP and promotes its pathological aggregation. It was first found that new coronavirus infection induced activation of the islet microenvironment complement system, release of pro-inflammatory factors, and endoplasmic reticulum stress synergistically aggravated beta cell dysfunction. It was first found that IAPP aggregation disrupted the SNARE complex-mediated insulin vesicle transport process, leading to insulin secretion disorder. A new theory is proposed that the new coronavirus hijacks key functional proteins in the host to cause beta cell dysfunction, revealing the age-dependent mechanism of viral aggravation of islet amyloid deposition in the elderly population.

[0062] Based on the research results of the present application, at least the following beneficial effects are achieved:

[0063] 1) Provide molecular targets for post-epidemic metabolic syndrome management: ① Suggest that IAPP aggregation, abnormal activation of the complement system, and vesicle transport disorders are potential intervention targets. ② Consider developing IAPP binding inhibitors, complement system modulators, and other new metabolic protection therapies.

[0064] 2) Provide evidence for diabetes management and elderly population stratified intervention in Long COVID: Support early screening of IAPP abnormal aggregation and glucose metabolism indicators in elderly COVID-19 convalescents and early intervention.

[0065] 3) Expand the boundaries of virus-host interaction: Consider host "metabolic homeostasis proteins" as potential viral targets, and provide new ideas for the study of other viruses (such as influenza and hepatitis B) mediated chronic disease pathogenesis. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a schematic diagram of the computer-aided drug screening method based on IAPP;

[0067] Figure 2 is a schematic diagram of the computer-aided drug screening method based on the complement system;

[0068] Figure 3 is a schematic diagram of the computer-aided prediction method for Long COVID in the elderly;

[0069] Figure 4 is a schematic diagram of the computer-aided drug screening system based on IAPP;

[0070] Figure 5 is a schematic diagram of the computer-aided drug screening system based on the complement system;

[0071] Figure 6 is a schematic diagram of the computer-aided prediction system for Long COVID in the elderly;

[0072] Figure 7 is a schematic diagram of the computer device;

[0073] Figure 8FIG. 8 is a series of images showing SARS-CoV-2 directly infects islets in non-human primates (NHPs), wherein 8A is a representative image of multiplexed immunofluorescence (IF) of pancreatic tissue from an aged control NHP sample, 8B is a co-localization section image of S protein with pancreatic polypeptide (P) in pancreatic tissue from a SARS-CoV-2 infected aged NHP sample, 8C and 8D are representative multiplexed IF images of the magnified area in 8B, 8E is a co-localization section image of S protein with various islet endocrine cell markers in pancreatic tissue from a SARS-CoV-2 infected aged NHP sample, 8F is a representative multiplexed IF image of the magnified area in 8E, and 8G-8J are quantification analysis images of the percentage of glucagon-positive alpha cells, insulin-positive beta cells, somatostatin-positive delta cells, and pancreatic polypeptide-positive PP cells, and SARS-CoV-2 S protein-positive cells that also express glucagon, insulin, and somatostatin in aged control NHPs and aged COVID-19 model NHPs;

[0074] Figure 9 FIG. 9 is a series of images showing age-dependent changes in IAPP aggregation and amyloid deposition in SARS-CoV-2 infected islets of aged rhesus macaques, wherein 9A is a representative image of pancreatic tissue sections from adult control (AC), aged control (EC), and SARS-CoV-2 infected aged model (EM) rhesus macaques, and 9B is a quantification analysis image of the proportion of IAPP-positive area in total islet area;

[0075] Figure 10 FIG. 10 is a series of images showing SARS-CoV-2 infection promotes IAPP aggregation, oligomer formation, and activation of necroptosis signaling in islets of aged rhesus macaques, wherein 10A is an image of immunofluorescence staining of viral RNA (red) and IAPP (white) by RNAscope detection in islets of adult control (AC), aged control (EC), and SARS-CoV-2 infected aged model (EM) groups, 10B is a co-staining image of IAPP (red), oligomeric IAPP (green), and SARS-CoV-2 spike protein (gray) showing their co-localization in islets of the EM group, 10C is an image of immunofluorescence staining of the necroptosis markers P-MLKL (green), P-RIP3 (yellow), and insulin (magenta), and 10D is a quantification analysis image of the number of P-MLKL-positive and P-RIP3-positive cells per islet field of view;

[0076] Figure 11 FIG. 11 is a KEGG enriched pathway image comparing in situ spatial proteomics of islets from aged control and aged COVID-19 infected groups;

[0077] Figure 12Figure 12 is a graph showing age-dependent islet amyloid polypeptide (IAPP) aggregation and amyloid deposition in pancreatic islets of aged SARS-CoV-2 infected rhesus macaques, wherein 12A is representative images of adult controls (AC), aged controls (EC), and SARS-CoV-2 infected aged model (EM) rhesus macaques, 12B is a graph of quantitative analysis of IAPP positive area as a percentage of total islet area (n = 3-4 sections per animal per group), 12C is a graph of multiplex immunofluorescence of IAPP (green) and major islet hormones (insulin - orange, glucagon - red, somatostatin - yellow, pancreatic polypeptide - cyan) showing islet architecture disruption and prominent IAPP accumulation in EM animals (inset shows magnified view), 12D is a graph of multiplex immunofluorescence of IAPP (green), fibril-specific IAPP conformation antibody (red), insulin (cyan), and glucagon (white) showing extensive amyloid fibrils within EM islet beta cells, 12E is a graph of the number of IAPP+ / insulin+ double positive beta cells per islet field of view (20x magnification; total of 10 images examined per group), and 12F is a graph of total IAPP+ area (left) and amyloid IAPP fibril+ area (right) as a percentage of islet area (20x magnification; total of 10 images examined per group);

[0078] Figure 13 Figure 13 is a graph showing ultrastructural features of islet amyloid polypeptide fibril formation and organelle disruption in SARS-CoV-2 infected aged rhesus macaque beta cells, wherein 13A is a transmission electron microscopy image of a beta cell from an aged control (EC) animal showing abundant insulin granules, intact mitochondria, and normal endoplasmic reticulum, 13B-F are images of beta cells under electron microscopy showing extensive fibrillar aggregation (red arrows), vesicle accumulation (blue arrows), and inner membrane damage, 13G is a graph of a beta cell from an aged control (EC) animal showing abundant insulin granules and intact endoplasmic reticulum, 13H-L are graphs showing amyloid-like deposits (yellow asterisks), disrupted organelles, and dense fibrils (red arrows) in infected beta cells at increasing magnification;

[0079] Figure 14Figure 14 is a location relationship diagram of viral RNA and S protein and IAPP, wherein 14A is a co-localization diagram of viral RNA (red) detected by RNA scope and IAPP (white) detected by immunofluorescence in AC, EC and EM islets, 14B is a co-localization diagram of IAPP (red), oligomeric IAPP (green) and SARS-CoV-2 spike protein (gray) detected in EM islets, 14C is a diagram revealing EM group beta cell necrosis by detecting necroptosis markers P-MLKL (green), P-RIP3 (yellow) and insulin (magenta) by immunofluorescence, and 14D is a diagram of the number of P-MLKL+ and P-RIP3+ cells in each islet region;

[0080] Figure 15 Figure 15 is a molecular docking simulation diagram of S protein, N protein and IAPP protein of the new coronavirus, wherein 15A is a crystal structure diagram of SARS-CoV-2 Spike protein (S protein), 15B is a crystal structure diagram of IAPP protein, 15C is a crystal structure diagram of SARS-CoV-2 Nucleoprotein protein (N protein), 15D and 15E are docking results diagrams of Spike protein and IAPP protein, 15F and 15G are docking results diagrams of Nucleoprotein protein and IAPP protein, 15H is an amino acid site diagram of the interaction of the Spike protein and IAPP protein complex, 151 is an amino acid site diagram of the interaction of the Nucleoprotein protein and IAPP protein complex, and 15J is a statistical diagram of the average binding free energy between the protein complexes;

[0081] Figure 16 Figure 16 is a diagram of overexpression of S protein, N protein and IAPP protein of the new coronavirus, wherein 16A is a diagram of expression of IAPP in PANC-1 cells, 16B is a diagram of co-localization of the new coronavirus and IAPP in vitro in cells, 16C is a diagram of results confirming successful co-expression of target proteins in HEK293T cells by Western blotting, 16D is a diagram of a gel stained with Coomassie blue confirming equal loading of samples before immunoprecipitation, 16E is a diagram of an immunoprecipitation experiment showing specific interaction between Flag-IAPP and S protein and N protein, and IgG control and empty vector control verifying its specificity, and 16F is a diagram of specific peptide segment mass spectrometry analysis of S protein (29) and N protein (20);

[0082] Figure 17 Figure 17 is a diagram of changes in islet protein expression characteristics caused by viral infection, wherein 17A is a flowchart of spatial proteomics analysis, 17B is a principal component analysis diagram, 17C is a volcano plot, 17D is a non-supervised hierarchical clustering analysis diagram, and 17E is a KEGG Pathway enrichment analysis diagram;

[0083] Figure 18 FIG. 18 is a core protein analysis diagram, wherein 18A is a protein interaction network analysis diagram, 18B is a closely connected subnetwork analysis diagram, 18C is a Cluster 2 analysis diagram, 18D is a Cluster 3 analysis diagram, 18E is a Cluster 4 analysis diagram, and 18F is a Cluster 4 protein function enrichment analysis diagram;

[0084] Figure 19 FIG. 19 is a diagram of the influence on the complement system after viral infection, wherein 19A is a complement system KEGG pathway enrichment diagram, 19B is a multi-label immunofluorescence diagram of C4BPA, HPX and Ceruloplasmin, 19C and 19D are cell analysis statistics diagrams of the adult group and the elderly group, and 19E is a multi-label immunofluorescence diagram of endoplasmic reticulum stress (CHOP and GRP78) and inflammatory (IL-6 and TNF-α) biomarkers;

[0085] Figure 20 FIG. 20 is an analysis diagram of IAPP aggregation leading to SNARE complex-mediated dysfunction of insulin vesicle transport, wherein 20A is a pathway differential protein diagram after new coronavirus infection in an elderly rhesus monkey, 20B is a pancreatic beta cell diagram of the infection group, 20C and 20D are enlarged diagrams of the pancreatic beta cell of the infection group, 20E is an expression and distribution diagram of VAMP4, VAMP8, VPS35 and Rab5 in situ in pancreatic tissue, and 20F is an expression and distribution statistics diagram of VAMP4, VAMP8, VPS35 and Rab5 in situ in pancreatic tissue;

[0086] Figure 21 FIG. 21 is an expression statistics diagram of endoplasmic reticulum stress (CHOP and GRP78) and pro-inflammatory factor (IL-6 and TNF-α) related biomarkers in pancreatic islet cells. DETAILED DESCRIPTION

[0087] In order to enable persons skilled in the art to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be described clearly and completely below in combination with the accompanying drawings in the embodiments of the present application.

[0088] In some of the descriptions in the specification and claims of the present application and the above-mentioned accompanying drawings, a plurality of operations appearing in a specific order are included, but it should be clearly understood that these operations can be executed in parallel or in parallel with the order in which they appear in this text. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes can include more or fewer operations, and the operations can be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this text are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence. Neither "first" nor "second" is of a different type.

[0089] Figure 1 is a flowchart of the computer-aided drug screening method based on IAPP provided by the present application, which specifically comprises:

[0090] 101: Obtain IAPP protein and S protein and / or N protein data of SARS-CoV-2.

[0091] 102: Select the spatial structure of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex, and determine the binding site of the complex as the binding site of the targeted drug.

[0092] 103: Obtain the candidate drug targeting the binding site through computer-aided screening.

[0093] Figure 2 is a flowchart of the computer-aided drug screening method based on the complement system provided by the present application, which specifically comprises:

[0094] 201: Obtain complement system protein data.

[0095] 202: Determine the targeted drug according to the spatial structure of the protein.

[0096] 203: Obtain the candidate drug targeting the complement system through computer-aided screening.

[0097] In some embodiments, the IAPP protein and S protein and / or N protein data of SARS-CoV-2 include its molecular structure, binding site.

[0098] In some embodiments, virtual screening is an important method in targeted drug design, and its process generally includes:

[0099] First, based on the known complex structure of the drug and the target protein, the binding mode of the drug and the target protein is studied, and the key amino acid residues are determined. This step helps to understand how the drug interacts with the target protein and provides a basis for subsequent virtual screening.

[0100] Next, a large number of small molecule compounds are screened using computer-aided drug design methods. In this process, the binding ability of small molecule compounds and target proteins is predicted and evaluated.

[0101] Finally, the candidate drugs screened are further experimentally verified to confirm their interaction with the target protein and biological activity.

[0102] In some embodiments, a binding site is a specific location in a molecule that can form a stable interaction with a ligand. The binding site of a protein is usually gathered together in space by some mutually isolated amino acid residues on the polypeptide chain through the folding of the peptide chain, forming a specific spatial arrangement.

[0103] Once the binding site is determined, the method of computer-aided drug design can be used to dock the compounds in the small molecule compound library with the target protein. During the docking process, the computer simulates the interaction between the compound and the binding site, and evaluates their binding ability. According to the docking results, small molecule compounds with strong binding ability to the target protein can be screened as potential drug candidates. The determination of the binding site is crucial to the success of drug design. Therefore, when determining the binding site, various factors need to be considered, such as the structural characteristics of the target protein, the binding mode of known ligands, etc. At the same time, in the process of virtual screening, the binding site also needs to be reasonably processed and optimized to improve the accuracy and efficiency of screening.

[0104] In some embodiments, the binding site of the IAPP protein includes one or more of ASN36, ARG44, SER52, GLY71.

[0105] In some embodiments, the binding site of the IAPP protein and the S protein complex includes any one or more of: amino acid site ASN36 of IAPP and amino acid site ASN354 of S protein, amino acid site ASN36 of IAPP and amino acid site ARG355 of S protein, amino acid site ARG44 of IAPP and amino acid site GLU471 of S protein, amino acid site ARG44 of IAPP and amino acid site THR470 of S protein, amino acid site SER52 on IAPP and amino acid site GLU484 of S protein, amino acid site GLY71 on IAPP and amino acid site GLN493 of S protein.

[0106] In some embodiments, the molecular library used in virtual screening includes but is not limited to: ZINC, PubChem, DrugBank, ChEMBL, ChemDB, HMDB, BindingDB, SMPDB, RCSB PDB.

[0107] In addition, there are also some commercial databases such as ChemDiv, Enamine, Lifechemicals, Specs, Chembridge, Maybridge, Microsource, Vitas-M and Interbioscreen, etc. These databases are also commonly used for virtual screening.

[0108] In some embodiments, the process of the computer-aided screening is:

[0109] Obtaining the binding site of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex.

[0110] Screening candidate drugs based on the spatial structure of the binding site of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex.

[0111] Molecular docking the screened candidate drugs with the binding site to obtain the score of the docked molecules.

[0112] Ranking according to the score to obtain the candidate drugs.

[0113] In other embodiments, the process of the computer-aided screening is: obtaining the molecular structure of the IAPP protein, S protein or N protein and inputting the molecular structure into the pharmacophore module library for matching, clustering all the action sites based on the interaction mode with the IAPP protein, S protein or N protein to obtain the pharmacophore model.

[0114] Inputting the pharmacophore model into the molecular compound database for high-throughput screening to obtain the candidate drugs.

[0115] In other embodiments, the process of the computer-aided screening is: first obtaining the molecular structure of the site binding agent or inhibitor of the IAPP protein, S protein or N protein, then screening similar structure compounds based on the molecular structure of the site binding agent or inhibitor, then molecular docking the similar structure compounds with the binding site to obtain the score of the docked molecules, and finally ranking to obtain the candidate drugs.

[0116] In some embodiments, computer-aided drug screening is a technology that uses computer-aided drug design methods to screen drugs. It can help researchers quickly screen out candidate drugs with strong binding ability to target proteins and potential drug effects from a large number of small molecule compounds.

[0117] In some embodiments, molecular docking is a method of drug design through the characteristics of the receptor and the interaction between the receptor and the drug molecule. It mainly studies the interaction between molecules (such as ligands and receptors) and predicts the binding mode and affinity of a theoretical simulation method. This method is widely used in the early stages of drug development and can help researchers quickly screen out compounds with potential efficacy.

[0118] Molecular docking methods mainly focus on spatial matching and energy matching. Spatial matching refers to the geometric complementarity between the drug molecule and the receptor protein, while energy matching refers to the minimization of the interaction between the drug molecule and the receptor protein. For geometric matching calculation, methods such as grid calculation and fragment growth are commonly used, while energy calculation uses methods such as simulated annealing and genetic algorithm.

[0119] According to the degree and method of simplification, molecular docking methods can be divided into rigid docking, semi-flexible docking and flexible docking. In rigid docking methods, the conformation of the molecules involved in docking does not change during calculation, only the spatial position and attitude of the molecules change. Semi-flexible docking allows some conformation changes during calculation. Flexible docking allows more conformation changes.

[0120] In some embodiments, in molecular docking, affinity refers to the tightness of the binding between the molecule and the receptor. High affinity means that the binding is more stable, while low affinity means that the binding is less stable. Affinity is usually calculated, such as by calculating the binding free energy (ΔG) or the binding constant (Kd).

[0121] In the process of molecular docking, affinity depends on the interaction between the molecule and the receptor, including hydrogen bonds, van der Waals forces, electrostatic interactions, etc. These interactions together determine the binding mode between the molecule and the receptor, thereby affecting the affinity.

[0122] In order to evaluate the affinity, some scoring systems or scoring methods are usually used to quantify the interaction between the molecule and the receptor. These scoring methods are based on different algorithms and physical models, and can reflect the binding energy, interaction type and affinity between the molecule and the receptor.

[0123] In some embodiments, the process and method of designing protein analogs, antibodies and other drugs based on proteins can be summarized as follows:

[0124] Determine the target protein: First, determine the target protein, i.e. the target, for which the drug is to be designed. The target can be a known disease-related protein, viral antigen or other biological molecule.

[0125] Protein structure analysis: Perform structural analysis on the target protein to understand its three-dimensional conformation, surface configuration, subdomain structure, etc. This can be obtained through techniques such as X-ray crystal diffraction and nuclear magnetic resonance.

[0126] Determine the interaction between the drug and the protein: Study the interaction mechanism between the drug and the target protein, including the binding site, binding mode and binding kinetics, etc. This can be done through computer simulation, laboratory experiments, etc.

[0127] Designing drugs: Based on the interaction mechanism between drugs and target proteins, design drug molecules that can specifically bind to target proteins. This includes selecting appropriate drug types, designing the chemical structure of the molecule, optimizing the pharmacodynamic and pharmacokinetic properties of the molecule, etc.

[0128] Synthesis and verification of drugs: Prepare drug molecules through chemical synthesis and other methods, and verify their biological activity, safety, and pharmacokinetic properties. This includes different stages such as cell experiments, animal experiments, and clinical trials.

[0129] In some embodiments, the candidate drugs include protein analogs, antibodies, RNA drugs.

[0130] In some embodiments, protein analogs refer to substances that have similar structure and function to certain proteins in the body. They can mimic the function of proteins and play an important role in disease treatment.

[0131] Antibodies are immunoglobulins produced by plasma cells differentiated from B lymphocytes in the body's immune system in response to antigen stimulation, which can specifically bind to corresponding antigens. The structure of antibodies is mainly divided into two parts: constant region and variable region. In a given species, the constant regions of different antibody molecules have the same or almost the same amino acid sequence. The variable region is located at the end of the two arms of the "Y".

[0132] RNA drugs are a class of drugs designed based on the properties and mechanisms of action of RNA in cells. RNA can act as messenger RNA (mRNA) to guide protein synthesis and as microRNA (miRNA) to regulate gene expression in cells. Therefore, designing a reasonable RNA drug can be used to regulate gene expression in cells, thereby achieving the purpose of treating diseases. For example, many studies are exploring the use of mRNA vaccines to prevent and treat various infectious diseases.

[0133] In some embodiments, the general steps for inhibitory activity experiments of small molecule compounds are as follows:

[0134] Selecting appropriate target proteins: According to the research purpose and disease target, select appropriate target proteins as experimental objects. Ensure that the target protein has potential interaction with the small molecule compound being studied.

[0135] Preparation of small molecule compounds: Synthesize or purchase the required small molecule compounds and ensure their purity and structural accuracy. According to the needs, the small molecule compounds can be modified or modified to optimize their inhibitory activity.

[0136] Enzyme activity assay: Design an appropriate enzyme activity assay method to evaluate the inhibitory activity of small molecule compounds on target proteins. This can be achieved through techniques such as fluorescence resonance energy transfer (FRET), radioisotope labeling, enzyme-linked immunosorbent assay (ELISA), etc. Ensure that the assay method is reliable, sensitive, and can accurately reflect the interaction between small molecule compounds and target proteins.

[0137] Experimental operation: Mix different concentrations of small molecule compounds with target proteins, incubate under appropriate conditions for a period of time, and then measure enzyme activity. According to the experimental design, set up control and experimental groups to compare the inhibitory activity of different small molecule compounds.

[0138] Data analysis: Through statistical analysis of experimental data, determine the IC50 value (i.e. the concentration of compounds required to inhibit 50% of enzyme activity) of small molecule compounds. The smaller the IC50 value, the stronger the inhibitory activity of small molecule compounds. In addition, a dose-effect curve can be drawn to visually display the inhibitory activity of small molecule compounds.

[0139] Result interpretation and discussion: According to the experimental results, analyze the inhibitory activity of small molecule compounds on target proteins, and discuss their structure and properties. Explore possible binding modes, mechanisms of action, and comparisons with known inhibitors. In addition, further study the effects of small molecule compounds on cells or organisms to evaluate their potential as drug candidates.

[0140] In some embodiments, the screening of drugs by computer-aided drug screening technology includes one or more of the following: protein-small molecule docking, protein-protein docking, protein-nucleic acid docking.

[0141] In some embodiments, protein-small molecule docking refers to a computational simulation process that uses certain algorithms and programs to dock the structures of proteins and small molecules (such as drug molecules) together. This process can be used to study the interaction between proteins and small molecules, as well as possible biological functions.

[0142] In protein-small molecule docking, software such as DOCK is commonly used for computational simulation. DOCK is a highly automated drug design software that can achieve docking between small molecule ligands and biological macromolecular receptors. It uses a fragment-based scoring method to achieve fast and accurate docking. The basic algorithm of DOCK software includes two stages. The first stage is the low-precision stage, which mainly searches for rough matching between small molecule ligands and biological macromolecular receptors. The second stage is the high-precision stage, which considers all side chain conformations and calculates more accurate interaction energies. In the low-precision stage, DOCK software randomly translates and rotates small molecule ligands and performs a certain number of rigid body movements, and then calculates the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program performs more optimization and adjustment to achieve more accurate docking.

[0143] In some embodiments, protein-protein docking refers to the process of docking two protein structures together through certain algorithms and programs. This process can be used to study the interaction between proteins and possible biological functions.

[0144] RosettaDock is a commonly used protein-protein docking software that uses a fragment-based scoring method to achieve fast and accurate protein docking. The software can achieve accurate adjustment of side chain conformations during docking and consider various complex interactions such as hydrogen bonds, ionic bonds, and hydrophobic interactions. The basic algorithm of RosettaDock includes two stages. The first stage is the low-precision stage, which mainly searches for the degree of skeleton shape adaptation between two proteins. The second stage is the high-precision stage, which considers all side chain conformations and calculates more accurate interaction energies. In the low-precision stage, the program randomly translates and rotates a certain component of one protein molecule and performs a certain number of rigid body movements, and then calculates the interaction energy. After outputting the lowest conformation, it enters the high-precision stage. In the high-precision stage, the program performs 50 MCMPCycles: rearranges the conformation and minimizes the interaction energy, which serves as the initial starting conformation.

[0145] In some embodiments, protein-nucleic acid docking refers to the process of docking protein and nucleic acid (such as DNA or RNA) structures together through certain algorithms and programs. This process can be used to study the interaction between proteins and nucleic acids and possible biological functions.

[0146] In protein-nucleic acid docking, software such as NAflex is commonly used for computational simulation. NAflex is a software specifically developed for nucleic acid structure prediction and design, which can achieve accurate modeling and docking of DNA or RNA molecules. NAflex software uses a fragment-based scoring method to achieve fast and accurate docking. It considers various complex interactions such as hydrogen bonds, ionic bonds, hydrophobic interactions, etc., and can achieve accurate adjustment of side chain conformation during docking. The basic algorithm of NAflex software includes two stages. The first stage is the low-precision stage, which mainly searches for rough matching between proteins and nucleic acids. The second stage is the high-precision stage, which considers all side chain conformations and calculates more accurate interaction energies. In the low-precision stage, NAflex software will randomly translate and rotate the nucleic acid molecule and perform a certain number of rigid body movements, then calculate the interaction energy. After outputting the lowest conformation, enter the high-precision stage. In the high-precision stage, the program will perform more optimization and adjustment to achieve more accurate docking.

[0147] Figure 3 is a flowchart of the method provided by the present application for computer-aided prediction of sequelae of coronavirus infection in the elderly, specifically comprising:

[0148] 301: receiving input data, the data including IAPP data and / or glycemic index data of coronavirus infection recovered population in the elderly.

[0149] 302: applying a machine learning model to the input data to generate a prediction result indicating whether the coronavirus infection recovered population in the elderly has sequelae.

[0150] In some embodiments, the machine learning model includes but is not limited to a support vector machine learning model, a linear discriminant analysis model, a recursive feature elimination model, a prediction analysis of a microarray model, a logistic regression model, a CART algorithm, a flextree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, and combinations thereof.

[0151] Figure 4 is a schematic diagram of the computer-aided drug screening system based on IAPP provided by the present application, specifically comprising:

[0152] 401 acquisition unit: acquiring IAPP protein and S protein and / or N protein data of SARS-CoV-2.

[0153] 402 site determination unit: selecting the spatial structure of the IAPP protein and S protein and / or N protein complex, and determining the binding site of the complex as the binding site of the targeted drug.

[0154] 403 screening unit: obtaining candidate drugs targeting the binding site through computer-aided screening.

[0155] Figure 5 is a schematic diagram of a computer-aided drug screening system based on the complement system provided by the present application, and specifically comprises:

[0156] 501 acquisition unit: acquiring complement system protein data.

[0157] 502 determination unit: determining a targeted drug according to the spatial structure of the protein.

[0158] 503 screening unit: obtaining candidate drugs targeting the complement system through computer-aided screening.

[0159] Figure 6 is a schematic diagram of a computer-aided prediction system for sequelae of elderly coronavirus infection provided by the present application, and specifically comprises:

[0160] 601 acquisition unit: acquiring data of elderly coronavirus infection recovery population.

[0161] 602 extraction unit: extracting IAPP data and / or sugar metabolism index data of the elderly coronavirus infection recovery population.

[0162] 603 identification unit: determining whether the elderly coronavirus infection recovery population has sequelae based on IAPP aggregation and / or sugar metabolism index.

[0163] Figure 7 is a schematic diagram of a computer device provided by the present application, and specifically comprises: a memory and a processor, the memory is used to store program instructions; the processor is used to call program instructions, when the program instructions are executed, the above-mentioned computer-aided drug screening method based on IAPP, the above-mentioned computer-aided drug screening method based on the complement system or the above-mentioned method based on computer-aided prediction of sequelae of elderly coronavirus infection.

[0164] In the embodiments of the present application, the screening and verification of IAPP protein binding sites with SARS-CoV-2 S protein and / or N protein, complement system activation, and prediction of sequelae of elderly coronavirus infection indicators include:

[0165] In some embodiments, the experimental materials and methods used include:

[0166] All animal experiments were performed in an ABSL3 laboratory using isolators with high efficiency particulate air filters. All experiments were conducted in compliance with the Animal Welfare Act and other relevant animal and experimental regulations. In this study, adult rhesus macaques (Macaca mulatta) were between 3-5 years of age, and older macaques were between 15-25 years of age. The study protocol, especially the part involving animals, was approved by the Animal Welfare and Use Committee of the Institute of Medical Laboratory Animal, Chinese Academy of Medical Sciences.

[0167] Rhesus monkey diabetes model:

[0168] Rhesus monkeys (SPF level), 3-5 years old, 15-25 years old, test rhesus monkey blood glucose and blood biochemistry, screen blood glucose abnormal animals for infection in old monkeys.

[0169] 1 x 10 6 TCID 50 Droplet nasal infection with 1 ml per animal. Monitor after infection every day, monitoring period 7 days.

[0170] Hematoxylin-eosin staining:

[0171] All collected organs were preserved in 10% buffered formalin solution and stored in the wet specimen library. Paraffin sections with a thickness of 3-4 pm were prepared according to standard procedures. H&E staining was performed on all tissue sections, and an Olympus microscope was used to observe the histopathological changes of various tissues.

[0172] Histopathological analysis and immunohistochemistry (IHC):

[0173] Collected organs were preserved in 10% buffered formalin solution and paraffin sections of 3-4 pm thickness were prepared. We have evaluated and confirmed various commercial antibodies to determine positive signals for viral antigens or whether different antibodies are specifically reactive. Briefly, for the preliminary experiments of immunohistochemical staining or manual detection of various antibody expression, dehydrated paraffin sections were treated with antigen retrieval kit (AR0022) at 37°C for 1 minute and quenched endogenous peroxidase in 3% methanol in 3% hydrogen peroxide for 10 minutes. After blocking with 1% normal goat serum for 1 hour at room temperature, the sections were treated with different antibodies overnight at 4°C, followed by incubation with horseradish peroxidase (HRP)-labeled goat anti-mouse IgG secondary antibody (ZDR-5307, 1:200), HRP-labeled goat anti-rabbit IgG secondary antibody (ZDR-5306, 1:200), or HRP-labeled goat anti-rat IgG secondary antibody (ZF-0312, 1:200). Finally, the sections were incubated with DAB and observed under an Olympus microscope. Consecutive sections taken from all collected tissues were directly treated with HRP-labeled goat anti-mouse or anti-rabbit IgG as blank controls for various antibody staining. Consecutive sections taken from all collected tissues were incubated with recombinant anti-rabbit IgG antibody [SP137] as a negative control for protein expression.

[0174] Multiplexed immunofluorescence staining:

[0175] Multiplexed immunofluorescence staining was performed using Opal Polaris 7-color manual IHC kit or Opal color automated IHC kit. Various primary antibodies were used sequentially to recognize specific cellular markers, followed by fluorescent reagents in Opal 7-color IHC kit, which included DAPI and Opal Polaris 480, 520, 540, 570, 620, 650, and 690. Secondary antibodies with HRP connection were incubated first, followed by TSA signal amplification. After each TSA cycle, the slides were subjected to high-temperature treatment. After antigen labeling, the nuclei were stained with DAPI for 10 minutes, followed by mounting with Prolong Gold antifade reagent.

[0176] In situ hybridization:

[0177] Using 2.5 The HDDAB detection kit and ISH Probe-V-nCoV2019-S (genomic RNA fragments 21631-23303, RefSeq#NC 045512.2) were used to analyze SARS-CoV-2 genomic RNA in formalin-fixed paraffin-embedded tissues via in situ hybridization. In short, tissue sections were first dewaxed with xylene, then subjected to a series of ethanol washes and peroxidase blocking treatments, followed by heating in antigen extraction buffer and digestion with protease. After adding the ISH target probe, the sections were incubated at 40°C for 2 hours. After washing, the ISH signal was amplified using a pre-amplifier and an amplification agent bound to alkaline phosphatase, and then mixed with fluorescein at room temperature for visualization. The sections were then mounted, air-dried, and stored at 4°C until analysis.

[0178] In some implementations, one result of this application is the construction of a rhesus monkey diabetes model.

[0179] Type 2 diabetes mellitus (T2DM) is a metabolic disease characterized by insulin resistance and pancreatic β-cell dysfunction. The main pathological changes in the pancreas include: ① damage to pancreatic β-cells and β-cell dysfunction, leading to decreased insulin secretion; ② deposition of islet amyloid polypeptide (IAPP) within the islets, causing inflammation and cytotoxicity.

[0180] The pathological manifestations of the novel coronavirus-infected rhesus monkey model constructed in this application: compared with the control group ( Figure 8 A) After infection with the novel coronavirus: ① Damage to pancreatic β cells, β cell dysfunction: decreased secretion of insulin (ins-β cells) in situ ( Figure 8 ); ② Deposition of islet amyloid polypeptide (IAPP) in pancreatic islets ( Figure 9 ), triggering cytotoxicity ( Figure 10 ③ In situ fibrosis of the islet + islet spatial proteomics showed that the differentially expressed proteins in the model were mainly enriched in insulin resistance, type II diabetes, and insulin secretion-related signaling pathways. Figure 11 This confirms that the model in this application is a rhesus monkey diabetes model.

[0181] In some implementations, one result of this application is that SARS-CoV-2 infection exacerbates IAPP deposition in the pancreatic islets of older rhesus monkeys.

[0182] Through observation of pancreatic HE staining and IAPP immunohistochemistry in adult rhesus monkeys (n=3, Adult control), aged rhesus monkeys (n=3, Elder control), and aged infected rhesus monkeys (n=4, Elder model), the number of islets with amyloid protein deposition / total islets in different tissue sections was statistically analyzed. HE staining showed that typical amyloid protein was an amorphous, homogeneous, eosinophilic substance. The results showed that: a. Under physiological conditions, compared with adult animals (0 / 185), a small number of punctate and focal amyloid protein deposits appeared in the islets as animals aged (22 / 167 = 9.58%). b. SARS-CoV-2 infection significantly promoted IAPP deposition in the islets of aged animals, with a significant increase in both the number of positive islets (441 / 496 = 88.91%) and the area of ​​amyloid plaques. c. Amyloid deposition in elderly infected individuals presents two pathological phenotypes: One phenotype involves swollen and degenerated β-cells, with remaining bundles of pancreatic β-cells and a small amount of light brown positive IAPP signal expression in the intercellular matrix surrounding IAPP-positive cell bundles. The other phenotype involves a sharp decrease in the number of pancreatic islet cells, with the formation of numerous dark brown IAPP-positive amyloid plaques in the intercellular matrix. Figure 12 (A and 12B). Thioflavin S staining is mainly used to detect and observe amyloid protein deposition in tissues. Compared with adult and elderly control groups, elderly rhesus monkeys infected with SARS-CoV-2 showed significant IAPP deposition in the islets of Langerhans and exhibited positive expression of thioflavin S staining, appearing as radiating, flower-like green patches. Figure 12 A). In summary, there was no significant difference in IAPP expression levels in adult rhesus monkeys regardless of whether they were infected with SARS-CoV-2. However, in older rhesus monkeys, there were significant differences in IAPP expression and distribution regardless of whether they were infected with SARS-CoV-2. SARS-CoV-2 infection promotes the accumulation of IAPP in the pancreatic islets of aged animals.

[0183] Further investigation was conducted using panoramic multi-label immunofluorescence to observe the differences in IAPP distribution within the pancreatic islets under physiological and pathological conditions, as well as its positional relationship with insulin+ β cells, glucose+ α cells, somatostatin+ δ cells, and polypeptide+ PP cells. Results showed that under physiological conditions, there was no significant difference in total IAPP protein expression within the pancreatic islets from adulthood to old age; however, the number of β cells co-localized with insulin+ decreased from 1.02% to 0.42%. Figure 12 CF). Under pathological conditions, after SARS-CoV-2 infection, the number of double-positive cells co-localized with IAPP and insulin+β cells significantly increased (3.61%), and they were abundantly clustered around β cells. The IAPP-positive areas lost their cell outlines and appeared as amorphous plaques. Figure 12C-E). Amyloid formation involves an abnormal aggregation process of proteins, in which IAPP transforms from its normal soluble state to insoluble amyloid fibrils. Further examination of whether fibrillar IAPP is expressed. The results observed that: in physiological state, compared with adult animals (0.19%), old animals have a small amount of IAPP fiber expression (0.64%), mainly distributed around the islets, not co-localized with beta cells. After SARS-CoV-2 infection, IAPP fibers are significantly expressed (5.59%), and are distributed throughout the islets, squeezing alpha cells and beta cells, as described above, the expression of insulin and glucagon in the islets is significantly reduced Figure 12 D and 12F). Therefore, SARS-CoV-2 infection promotes the formation of insoluble IAPP fibers in the islets of old animals.

[0184] Further transmission electron microscopy was used to observe whether the subcellular organelle structure of pancreatic beta cells changed, and to observe the effect of the phenotype with such obvious histological changes on the intracellular structure. Transmission electron microscopy (TEM) observation found that compared with uninfected pancreatic beta cells ( Figure 13 A and 13G), old infected NHP pancreatic islet cells showed two subcellular structure phenotypes: a. A large number of high-density disordered interwoven fibrous materials were scattered in the region, the intercellular space was widened, and the tight junction of the cells was loose ( Figure 13 B-D). A large number of vesicle structures were scattered in the pancreatic beta cells and intercellular space, which destroyed the integrity of the cell membrane ( Figure 13 C-F). b. The beta cells were severely swollen and degenerated, and the nuclei were marginated. The insulin granules in the cytoplasm of the cells were significantly reduced ( Figure 13 H). In some areas, the fibers were aggregated into higher density, parallel arrangement and compact fiber clusters ( Figure 13 H-K), the fiber clusters were aggregated between the endoplasmic reticulum, squeezed the adjacent endoplasmic reticulum, and the endoplasmic reticulum was expanded ( Figure 13 I), accumulated in the loose intercellular space, destroyed the integrity of the cell membrane ( Figure 13 J), accumulated around the nucleus ( Figure 13 K), and lysosomes were swollen in the cytoplasm, with undegraded electron-dense substances accumulated inside and fiber structures scattered around the periphery ( Figure 14 L). The pathological phenotypes of the above subcellular structures are consistent with the results observed by HE staining, and the fibers are accumulated in the beta cells and outside the cells.

[0185] In some embodiments, one result of the present application is that SARS-CoV-2 infection induces IAPP oligomer formation and beta cell necroptosis

[0186] FISH+protein co-staining and multi-label immunofluorescence were used to further track the location relationship between viral RNA and S protein and IAPP. FISH staining observed that compared with adult control group (AC) and elderly control group (EC), there were a small amount of new coronavirus RNA positive signals scattered around the nucleus in situ in the islet tissue of the elderly infected group (EM), which was co-located with IAPP amyloid deposits ( Figure 14 A). Because the oligomers of IAPP have high β-cell toxicity, multi-label immunofluorescence was used to observe whether IAPP oligomers were expressed and to observe their location relationship. The test results showed that only in the infected elderly group, scattered IAPP oligomers were observed to be expressed between IAPP proteins, co-located with part of the IAPP protein, and a small amount of scattered new coronavirus S protein was observed to be co-located with the IAPP protein in the islet ( Figure 14 B). After observing that it only exists in the infected group, further detection of cell necrosis (P-MLMK) and cell death (P-RIP3) related indicators was carried out, and insulin was co-stained to detect the activity of β cells in situ in the islet ( Figure 14 C). The test observed that no related indicators were observed in the adult control group, a small amount of cell necrosis indicators were observed in the elderly control group, and a small amount of P-RIP3 expression was observed, but the expression amount of P-MLMK and P-RIP3 in the infected group was significantly increased, P-MLMK was diffused in the interstitial space of atrophic necrotic β cells, and P-RIP3 was highly co-located with β cells ( Figure 15 D).

[0187] In some embodiments, one result of the present application is that viral S protein and N protein directly bind and promote IAPP aggregation

[0188] The co-location of SARS-CoV-2 and IAPP in situ prompted the inventors to continue to explore whether direct interaction between them would occur. The three-dimensional structures of S protein, N protein and IAPP were simulated using molecular docking experiments, and computer simulation was used to deduce whether direct interaction between proteins could occur. First, the crystal structures of SARS-CoV-2 Spike (QOS45029.1) ( Figure 15 A), IAPP (P10997) ( Figure 15 B) protein and N protein (P0DTC9) ( Figure 15 C) were downloaded from the RCSB PDB database, the largest protein crystal structure database in the world. The two groups of proteins were introduced into the docking software HDOCK, rigid molecular docking was performed, local docking was adopted, 100 conformations were searched, and other parameters were kept default. The lowest energy conformation was selected, and the interaction between proteins was visualized and analyzed using pymol. Figure 15 D and 15E are the docking results of Spike and IAPP, Figure 15F and 15G are the docking results of Nucleoprotein (N-CTD) and IAPP. The interacting amino acids of protein complex are labeled on Figure 15 H and Figure 15 I. It is worth noting that the amino acid sites ASN36 and ARG44 on IAPP can interact with two amino acids ASN354 and ARG355 on Spike protein, and GLU471 and THR470, respectively. The amino acid site SER52 on IAPP can bind to the amino acid site GLU484 on Spike and the amino acid site PRO309 on N-CTD at the same time. The amino acid site GLY71 on IAPP can bind to the amino acid site GLN493 on Spike and the amino acid site LEU331 on N-CTD at the same time. SER52 and GLY71 are common in the amino acids of IAPP binding sites with SARS-CoV-2 Spike and N-CTD, which may indicate that the interface of IAPP interacting with the two proteins of the virus is consistent. The above four amino acid sites of IAPP are potential key amino acid sites for subsequent targeted therapy. Using the GMX-MM-PBSA method, the conformations between 40-50 ns were extracted for calculating the average binding free energy between the complexes, and the average binding energy less than -75 represents strong binding force. The average binding energy of SARS-CoV-2 spike and IAPP docking is -103.5 kcal / mol. The average binding energy of N-CTD and IAPP docking is -151.5 kcal / mol( Figure 16 J). ΔG (unit: kcal / mol) represents the change of binding free energy (Gibbs Free Energy Change), which is a key thermodynamic parameter for evaluating the binding strength and stability of proteins. ΔG < 0 (negative value) indicates that the binding process is spontaneous, and the larger the negative value (more negative), the stronger the binding and the more stable. The prediction results show that the binding process of the two groups of proteins may be spontaneous. Kd is the steady-state and equilibrium constant. The smaller the Kd value, the stronger the affinity and the more stable the binding between proteins. Select the lowest energy conformation diagram, and then use the PPA-Pred server to calculate the Kd of the protein complex. The Kd of SARS-CoV-2 spike and IAPP is 1.27e-08M. The Kd of N-CTD and IAPP is 2.87e-08M( Figure 16 J). The results of molecular docking support that SARS-CoV-2 S protein and N protein can directly act on IAPP with high molecular bonds.

[0189] To further validate the above results, the SARS-CoV-2 (2019-nCoV) Spike Gene ORF cDNA clone expression plasmid (C-GFP Spark tag) and the SARS-CoV-2 (2019-nCoV) Nucleoprotein Gene ORF cDNA clone expression plasmid (C-GFP Spark tag) were co-transfected with the IAPP overexpression plasmid (Human IAPP / AmylinGene ORF cDNA clone expression plasmid (C-Flag tag) in PANC-1 pancreatic cells. This first confirmed that IAPP could be efficiently expressed in PANC-1 cells. Figure 16 A) Determining whether SARS-CoV-2 and IAPP co-localize in vitro and intracellularly using fluorescence confocal microscopy. Figure 16 B). To further improve transfection efficiency in 293T cells, immunoprecipitation was used to detect whether the virus directly interacted with IAPP. Four Western blot electrophoresis lanes: lane 1 for co-transfection of Spike protein and IAPP; lane 2 for co-transfection of the corresponding GFP and empty Flag vector; lane 3 for co-transfection of Nucleoprotein and IAPP; lane 4 for co-transfection of the corresponding OFP and empty Flag vector. Figure 16 C). 293T cells themselves do not express the above three proteins. The groups co-transfected with the target protein combination successfully overexpressed Spike protein and IAPP (lane 1), and Nucleoprotein and IAPP (lane 3), while the corresponding control groups showed no expression of the target protein. This detection preliminarily proves the successful construction of the in vitro overexpression system. Next, an immunoprecipitation experiment was used to detect whether Spike protein or Nucleoprotein interacts with IAPP. An IgG negative control was set up to exclude non-specific binding infections and ensure the reliability of the experimental results. To preliminarily verify the immunoprecipitation effect, help confirm whether the target protein and its binding proteins were successfully precipitated, and preliminarily detect the distribution of total protein, Coomassie Brilliant Blue stained gels were used (…). Figure 16 D). To ensure the reliability of the immunoprecipitation assay, it was confirmed that the IgG negative control did not express protein. In the immunoprecipitation assay, parallel control groups co-transfected with empty vector plasmids and IgG negative control groups were included to ensure the reliability of the system. The experimental results showed that IAPP not only specifically interacts with Spike protein but also specifically interacts with Nucleoprotein. Figure 17E) Further, to verify the reliability of IP results, the IP-down protein mixture was verified by mass spectrometry. Whether the protein mixture containing IAPP has SARS-CoV-2 spike (Accession: QOS45029.1) and Nucleoprotein (Accession: P0DTC9) was searched in the library. Generally, if there are more than two unique peptides of a certain protein in the mass spectrometry results, it can be determined that the protein exists. In the mass spectrometry detection results of the protein, 29 unique peptides of Spike protein and 20 unique peptides of Nucleoprotein were searched out. It is further confirmed that IAPP indeed interacts with the two key structural proteins spike and Nucleoprotein of SARS-CoV-2 respectively Figure 17 F).

[0190] In summary, the S protein and N protein of SARS-CoV-2 can directly interact with IAPP and induce pathological deposition of IAPP.

[0191] In some embodiments, one result of the present application is that microdissection + spatial proteomics reveals that viral infection changes the protein expression characteristics of pancreatic islets

[0192] Under electron microscopy, not only the aggregation of IAPP fibers, but also the expansion of endoplasmic reticulum and the accumulation of vesicle structures in pancreatic islet cells were observed. The interaction of the new crown virus with IAPP may affect the transport and degradation of IAPP through certain molecular mechanisms or cascades, and interfere with the homeostasis of pancreatic islets. Further, the pancreatic tissue sections of the elderly control group and the elderly infected group were stained using the fiber cutting technique, and the pancreatic islet tissue of interest was circled and cut, and the pancreatic islet tissue of 3 animals in each group was collected for spatial proteomics analysis. Figure 17 A) Principal component analysis showed that the characteristics of the samples in the elderly group were relatively consistent, while there were significant differences between the characteristics of the elderly infected group and the elderly group Figure 17 B) The two groups of samples with biological repeats were subjected to intergroup t test, and p-value and fold change values were obtained. Volcano plot was drawn using the two parameters to show the significance of the difference between the two groups of sample data, and to help find the most significantly changed proteins. The up-regulated differential proteins are represented by red dots, the down-regulated differential proteins are represented by blue dots, and the other proteins are represented by gray dots. It can be seen that there are 184 up-regulated proteins and 580 down-regulated proteins after infection Figure 17C) Further unsupervised hierarchical cluster analysis was performed on the differentially expressed proteins between groups. The distance matrix was calculated by the protein expression data between each pair of samples. Red represents the high expression of the differential protein in the grouped sample, and blue represents the low expression of the differential protein in the grouped sample. Helps to find proteins differentially expressed in different spatial locations Figure 14 D) It can be seen that the expression of most differential proteins on the islets as a whole is down-regulated or inhibited after infection, and a small part of the differential proteins is up-regulated or activated. The KEGG Pathway enrichment analysis was used to determine the signal pathway network in which the differential proteins were mainly enriched. The 20 items of interest with P<0.05 were selected to draw a Sankey diagram and a Dotplot diagram Figure 18 E) In each pathway, the first five common proteins were displayed as much as possible, and the common protein flow to different enriched pathways was intuitively represented by the width of the flow band, showing the key differential proteins in different differential pathways. The results show that: a. First of all, as expected, 19 proteins related to COVID-19 disease are indeed enriched

[0193] (ATF2 / C8B / PSMA1 / C1QC / NDUFB11 / VDAC2 / KLC1 / HSPA8 / PRKACG / CSNK2B / PIK3R2 / PIK3CB / C8G / C9 / LOC709412 / NDUFS6 / PSMD13 / RAC2 / ND2), demonstrating that pathological damages of islets are closely related to COVID-19 infection.b. The proteins associated with islet pathological phenotypes in aged monkeys are enriched in Type II diabetes mellitus (4), insulin signaling pathway (11), insulin secretion (6), glucagon signaling pathway (8), insulin resistance (6), and diabetic cardiomyopathy (13), lipid and atherosclerosis (16), demonstrating that COVID-19 infection indeed aggravates the significant differences in signals related to islet insulin secretion abnormalities in aged monkeys, interfering with the signaling pathways of insulin and glucagon secretion, causing glucose metabolism disorders. And the significant pathways of differential proteins are also associated with insulin resistance, type II diabetes, and related lipid metabolism disorders. Notably, the expression of insulin receptor (INSR) in the aged infected group decreased significantly (-7.447663533, P = 0.0037), because it has been proven that infection causes a decrease in insulin secretion in aged monkeys, and the inhibition of insulin receptor expression will double the blow to the ability to regulate blood glucose.c. The complement and coagulation cascades related to the immune system are significantly activated locally in the islets (14 proteins,

[0194] LOC721200 / C8B / VTN / C1QC / C4BPA / SERPING1 / F10 / CFB / C1S / C3 / C8G / C9 / LOC723314 / CFH).d. Endocrine hormones including IAPP and insulin require translation, processing, transport via secretory granules, and exocytosis mediated by SNARE protein complexes for their export from the beta cell. However, pathways related to protein synthesis and transport, vesicle formation, and vesicle transport were significantly enriched, including SNARE interactions invesicular transport (6) (GOSR1 / STX18 / VAMP7 / VAMP8 / STX2 / VAMP4), endocytosis (24), and protein processing in endoplasmic reticulum (15), suggesting that the endocrine hormone synthesis and transport system was disrupted first.e. Differential proteins of islet cells were also enriched in necroptosis (9) and efferocytosis (12) related pathways, suggesting that the fate of damaged islet cells was death, consistent with the results of immunofluorescence observation of significant increase of necrosis and death markers in islet cells Figure 18 C and 14D).f. Differential proteins tended to be enriched in signaling pathways related to chronic degenerative diseases, including Prion disease (17) (ATF2 / C8B / PSMA1 / C1QC / NDUFB11 / VDAC2 / KLC1 / HSPA8 / PRKACG / CSNK2B / PIK3R2 / PIK3CB / C8G / C9 / LOC709412 / NDUFS6 / PSMD13 / RAC2 / ND2), Parkinson disease (17) (UBE2J1 / PSMA1 / CAMK2B / MAOB / UBE2G2 / UBA1 / NDUFB11 / MAPT / VDAC2 / KLC1 / PRKACG / MFN1 / CALM1 / NDUFS6 / PSMD13 / GNAI2 / ND2), and Alzheimer disease (22) (DVL1 / CHUK / INSR / LRP1 / PSMA1 / PLCB4 / WNT4 / NDUFB11 / MAPT / VDAC2 / CSNK1E / KLC1 / AKT3 / CSNK2B / CALM1 / PIK3R2 / PIK3CB / CTNNB1 / NDUFS6 / PSMD13 / APOE / ND2), etc. Then, would the infection of the new coronavirus have a common effect on the “amyloid aggregation” of this series of misfolded proteins? Would the infection aggravate the pathological aggregation of the common proteins?

[0195] Further, in order to find the proteins in the core regulatory position in a large number of modulated proteins, protein interaction network analysis was carried out. Through calculation and analysis, it was found that the proteins in four clusters were closely related and gathered in a differential change of the population Figure 18 A). Further, the closely connected sub-networks were extracted, and the protein members were annotated by Pathway to intuitively understand which biological function proteins occurred in the differential change of the population. In Cluster 1, the main functions of the closely connected proteins were enriched in Ribosome, COVID-19 and antigen presentation Figure 18 B). The results showed that: a. Ribosome is a protein synthesis factory in cells, and the abnormal function of ribosome in pancreatic beta cells may lead to insufficient synthesis of insulin precursors, affecting blood glucose regulation. RPL26 is an important component of the large subunit of ribosome. Its function is not limited to the core structure of ribosome, but also plays a key role in protein translation. b. The enrichment of COVID-19 infection related proteins again proves the differential effect of infection on the islets of old monkeys. c. Antigen presentation presents pathogen antigen fragments to T cells through MHC-I / II molecules, and initiates adaptive immune response. It is proved that the islets try to make adaptive immune response during infection. In COVID-19 patients, the expression of CD74 is different in patients with different severity of the disease. In Cluster 2, the main functions of the closely connected proteins were enriched in Spliceosome and COVID-19 Figure 18 C). Spliceosome is responsible for removing introns in pre-mRNA and connecting exons to form mature mRNA. COVID-19 infection may interfere with host mRNA splicing, leading to abnormal expression of pro-inflammatory factors or anti-viral proteins. It plays an important role in RNA splicing, translation regulation and gene expression, and may be used by the virus to regulate host immune response in severe COVID-19. In Cluster 3, the main functions of the closely connected proteins were enriched in complement and coagulation cascades and COVID-19 Figure 18 D). The discovery in islets again verifies the above data and further extends to the changes in the microenvironment of islets. C1s is the core of the classical complement pathway, which can cleave C4 and C2 to start the complement cascade. C4BPA is a negative regulator of the classical pathway, which can inhibit complement activity to reduce inflammatory response. However, in COVID-19, this regulatory function may be insufficient, leading to overactivation of the complement system and exacerbation of lung injury. In Cluster 4, the main functions of the closely connected proteins were enriched in SNARE interactions in vesicular transport and synaptic vesicle cycle Figure 19E) SNARE proteins are core molecular machines that mediate vesicle fusion with target membranes and play a key role in endocrine hormone secretion. SNARE complex directly regulates the release of insulin vesicles. Abnormalities in this signaling pathway can directly lead to reduced insulin release.

[0196] Further analysis of the differentially expressed proteins from the previously detected serum proteomics of the elderly group and the elderly infected group and the islet spatial proteomics found that 13 proteins (CP (Ceruloplasmin) / CABPA / HPX / CFH / CFB / LBP / C2 / C9 / CRTAC1, etc. Up-regulated trend; ACO2, down-regulated trend) were consistent in both difference and trend Figure 19 F) Changes in peripheral blood affect the homeostasis of the islet microenvironment to some extent, proving its consistency and accuracy in the differential changes of the body in the elderly infected group. These proteins also have the potential to be developed as biomarkers for detecting islet damage caused by the new coronavirus using peripheral blood. Moreover, these up-regulated proteins are mostly closely related to the activation of the complement system, proving that the activation of the complement system is not only a characteristic change in the host infected with the new coronavirus, but also affects the microenvironment of the islet, further confirming the previous data analysis that the complement system is significantly enriched in the islet.

[0197] In some embodiments, one result of the present application is that the virus activates the complement system of the islet microenvironment, releases a large amount of pro-inflammatory factors, and exacerbates endoplasmic reticulum stress.

[0198] Further verification of the bioinformatics analysis results of spatial proteomics on the activation of the complement system in the islet microenvironment. First, KEGG pathway enrichment map was drawn using Pathview. By mapping the quantitative data of the differentially expressed proteins to the topological structure of the KEGG signaling pathway, the expression changes of the differentially expressed proteins in the complement and coagulation cascades were visually displayed. Red indicates up-regulation of expression, and green indicates down-regulation of expression. It can be visually seen that after the elderly monkeys were infected with the new coronavirus, the trend of the differential proteins of the entire complement system was significantly activated Figure 19 A) MAC, C1, C3, C4, C6 / 7 / 8 / 9, C4BPA, FB, FH were mainly activated, and only F10 was significantly reduced. Based on the common findings of spatial proteomics and serum proteomics, three representative common proteins activated in situ in the islet tissue were further verified using multi-label immunofluorescence: C4BPA, HPX, and Ceruloplasmin Figure 21B), and analyze their trends qualitatively and quantitatively, and their relationship with IAPP and insulin. C4BPA is a negative regulator of the classical pathway, which can inhibit complement activity to reduce inflammatory response. HPX (Hemopexin) is a plasma protein related to the complement system, and its main function is to bind free hemoglobin to reduce oxidative stress and inflammatory response. Ceruloplasmin is a copper-containing a2-glycoprotein, mainly involved in iron metabolism and antioxidant defense, and also has the function of regulating inflammatory and immune responses. These three proteins all have protective effects on host cells in normal physiological functions, mainly anti-inflammatory and antioxidant effects. Multi-label immunofluorescence quantitative analysis data shows that: a. The expression of C4BPA, HPX and Ceruloplasmin in the pancreas of adult rhesus monkeys is at a very low level, which is 0.056%, 1.256% and 0.250% respectively. b. Compared with adulthood, the old group significantly increased C4BPA (0.247%, P=0.0102) and Ceruloplasmin (1.96%, P=0.00886), and HPX tended to increase but had no significant difference (2.455%, P=0.0647), but there was no significant difference in the number of double-positive cells containing C4BPA+IAPP+, HPX+IAPP+ (0.31%, P=0.375) and Ceruloplasmin+IAPP+ (0.01%, P=0.336) co-localization proteins between the two groups. It is proved that for old rhesus monkeys, the local pancreas needs to secrete more protective proteins to maintain local homeostasis under physiological conditions, and these proteins are not affected by IAPP and normally play a physiological function. c. However, for the old model group, although the expression of the three proteins is significantly higher than that of the adult group and the old group (0.502%, 19.608%, 14.46), the number of double-positive cells of C4BPA+IAPP+ (0.01%), HPX+IAPP+ (2.023%) and Ceruloplasmin+IAPP+ (0.76%) co-localization proteins all increased, and the number of HPX+IAPP+ (P=0.000245, P=0.000462) and Ceruloplasmin+IAPP+ (P=0.00270, P=0.00282) double-positive cells was also significantly higher than that of the corresponding cells in the adult group and the old group Figure 20 C and 19D). It is proved that when infected, the local pancreas secretes a large amount of protective proteins to resist local inflammation, but the pathologically aggregated IAPP surrounds these protective proteins, making them unable to play a protective role, and compensatory secretion is difficult to really work.

[0199] Further, the expression of endoplasmic reticulum stress (CHOP and GRP78) and proinflammatory cytokines (IL-6 and TNF-a) related biomarkers in pancreatic islet cells were detected by multi-label immunofluorescence in situ. The results showed that: a. Under physiological conditions, the expression of CHOP (0.02%), GRP78 (0.00%), IL-6 (0.33%) and TNF-a (1.07%) in the adult group were at very low levels. b. Compared with the adult group, the two proteins CHOP (0.05%, P = 0.0589) and GRP78 (0.004%, P = 0.0998) in the old group increased slightly but not significantly. IL-6 (0.900%, P = 0.00275) and TNF-a (2.92%, P = 0.00464) were significantly increased, but there was no significant difference in the number of double-positive cells containing CHOP+IAPP+, GRP78+IAPP+and IL-6+IAPP+co-localized proteins between the two groups. Only TNF-a+IAPP+(0.107%, P = 0.0187) cells increased significantly, which proved that the local pancreatic islet of the old rhesus monkey was in a mild chronic inflammatory state under physiological conditions. c. However, for the old model group, the expression of GRP78 (0.06%), IL-6 (10.18%) and TNF-a (8.71%) were significantly higher than the adult group and the old group, and the expression of CHOP (0.040%) was not significantly different from the old group, but was significantly different from the adult group. And the number of double-positive cells containing CHOP+IAPP+(0.055%), GRP78+IAPP+(0.020%), IL-6+IAPP+(1.128%) and TNF-a+IAPP+(2.380%) co-localized proteins were significantly increased Figure 20

[0200] In diabetes, CHOP mediates pancreatic beta cell apoptosis and promotes insulin secretion disorder. GRP78 is involved in the regulation of pancreatic beta cell function and insulin resistance. IL-6 and TNF-a, as core inflammatory mediators, drive the inflammatory storm and immune disorder in COVID-19, leading to severe illness; in diabetes, they accelerate pancreatic beta cell damage and insulin secretion disorder through chronic inflammation and metabolic disorder. Moreover, in line with the previous spatial proteomics analysis results, endoplasmic reticulum stress in beta cells can lead to ribosome stalling, exacerbating beta cell death. In summary, the core proteins of the complement system (such as C1S, C2, C3, C4BPA, SERPING1 and C1QC) are overactivated in the pancreatic islets of old animals after COVID-19 infection by regulating the classical and terminal complement pathways. Excessive complement activation further leads to the occurrence of endoplasmic reticulum stress and proinflammatory response in beta cells.

[0201] ​In some embodiments, one result of the present application is that IAPP aggregation leads to dysfunction of SNARE complex-mediated insulin vesicle trafficking.

[0202] The interaction of SARS-CoV-2 with IAPP can affect the transport of IAPP through some molecular mechanism or cascade, causing vesicle accumulation in beta cells, inhibiting the normal work of the vesicle transport system, interfering with the transport and release of secretory endocrine hormones, and further affecting the transport and release of insulin, which is essential for blood glucose regulation. Further, Pathview is used to visually display the expression changes of differential proteins in SNARE interactions in vesicular transport. Red indicates up-regulation of expression, and green indicates down-regulation of expression. It can be seen intuitively that after SARS-CoV-2 infection in old monkeys, the trend of the whole pathway differential proteins is significantly inhibited (A), mainly VAMP4, VAMP7, VAMP8, and other key proteins related to vesicle transport. Figure 20 TEM observation shows that compared with the uninfected group, the number of insulin granules wrapped by single-layer membrane in the beta cells of the infected group is greatly reduced, and many only remain single-layer membrane structure (B). Enlarged observation of details can find that many vesicles are "captured" in the area where high-density fibrous material accumulates in the basement membrane of the islet microvessels next to the beta cells. Cell debris such as membrane structure disintegration products can be seen between the fibers (B-D). ​ ​

[0203] ​​Rab5 and VPS35 are key regulators in endocytosis and membrane trafficking system, maintaining the homeostasis of material transport in β cells. VAMP4 regulates insulin secretion by assembling SNARE complex and maintains insulin homeostasis in pancreatic β cells. VAMP4 regulates lysosomal targeting of insulin secretory granules by assembling SNARE complex with STX7, STX8 and VTI1B, thereby maintaining insulin homeostasis in β cells, which may be related to preventing abnormal aggregation of IAPP. VAMP8 participates in the fusion of insulin secretory granules by assembling SNARE complex with Munc18b and Syntaxin-3, and is an important SNARE protein regulating insulin granule secretion. Its dysfunction may affect insulin secretion and aggregation of IAPP in diabetes. Decreased expression of VAMP8 leads to insulin secretion disorder in type II diabetes. Further detection of the expression and distribution of VAMP4, VAMP8, VPS35 and Rab5 in situ in pancreatic tissue. The results showed that: a. Under physiological conditions, the expression of Rab5 in adult rhesus monkeys (18.57%) was relatively uniform in islets, and the expression in old monkeys was significantly increased (36.96%). The increase in Rab5 expression directly regulates the efficiency and specificity of the endocytosis pathway and assists intracellular recycling. However, compared with the old group, the expression of Rab5 in the old group after infection was significantly inhibited (7.62%), and the number of Rab5+IAPP+ cells in the old infected group (2.96%) was significantly higher than that in the adult group (0.473%, P=0.00181) and the old group (1.600%, P=0.0430) of double positive cells, suggesting that the aggregation of IAPP interferes with the endocytosis function of Rab5, directly reduces the endocytosis efficiency, and inhibits the transport and recycling of insulin raw materials. b. Under physiological conditions, the expression of VPS35 (2.45%) in the islets of adult rhesus monkeys was at a very low level, and the expression in old monkeys was significantly increased (34.71%, P=2.3229E-06). VPS35 is a core component of the retromer complex, and its significant increase in the old group is mainly because it is responsible for recycling specific cargo proteins from endosomes to the Golgi or cell membrane, maintaining the homeostasis of material transport in cells. However, compared with the old group, the VPS35 in the old group after infection was significantly inhibited (16.79%), and was distributed in an aggregated manner. More importantly, the number of VPS35+IAPP+ cells in the old infected group (2.89%) was significantly higher than that in the adult group (0.08%, P=0.000841) and the old group (0.998%, P=0.00753) of double positive cells, suggesting that the aggregation of IAPP interferes with the function of VPS35 in recycling proteins, leading to the disorder of material transport in cells. c. Under physiological conditions, the expression level of VAMP4 in the adult group was relatively low (1.71%), and the expression in the old group was slightly increased (5.09%). The expression of VAMP4 was significantly inhibited after infection (1.51%).Similarly, the number of VAMP4+IAPP+ cells (1.84%) was significantly higher than the double positive cells in the adult group (0.033%, P=0.000145) and the elderly group (0.026%, P=0.000141).d. Under physiological conditions, the expression level of VAMP8 in the adult group was very low (0.20%), and the expression of VAMP8 in the elderly group was significantly increased (2.90%, P=0.0102), which was used to help regulate the lysosomal targeting of insulin secretion granules, thereby maintaining the insulin homeostasis of β cells. However, the expression of VAMP8 was significantly inhibited after infection, which was significantly lower than that in the elderly group (0.17%, P=0.000469), directly affecting the transport of insulin.

[0204] The application provides use of a complex formed by IAPP protein combined with S protein and / or N protein in screening drugs for treating metabolic diseases.

[0205] The drug also includes a pharmaceutically acceptable excipient.

[0206] In some embodiments, the pharmaceutically acceptable excipient is used to refer to a material that is compatible with the recipient, preferably a mammal, more preferably a human, and suitable for delivering the active agent to the target site without terminating the activity of the agent. The toxicity or side effects associated with the pharmaceutically acceptable excipient, if any, are preferably commensurate with a reasonable risk / benefit ratio for the intended use of the active agent.

[0207] The pharmaceutically acceptable excipient includes but is not limited to diluent, binder, surface active agent, wetting agent, adsorption carrier, lubricant, filler, disintegrant. These excipients are used as needed to help the stability of the formula or to help improve the activity or its bioavailability or to produce an acceptable mouth feel or odor in the case of oral administration, the formulation that can be used in such drugs can be in the form of the original compound itself or optionally in the form of its pharmaceutically acceptable salt, the thus formulated pharmaceutical composition can be administered in any appropriate manner known to those skilled in the art as needed.

[0208] Among them, the diluent includes but is not limited to lactose, sodium chloride, glucose, urea, starch, water.

[0209] The binder includes but is not limited to starch, pregelatinized starch, dextrin, maltodextrin, sucrose, gum arabic, gelatin, methyl cellulose, carboxymethyl cellulose, ethyl cellulose, polyvinyl alcohol, polyethylene glycol, polyvinyl pyrrolidone, alginic acid and alginic acid salt, xanthan gum, hydroxypropyl cellulose and hydroxypropyl methyl cellulose.

[0210] The surface active agent includes but is not limited to polyoxyethylene sorbitan fatty acid ester, sodium dodecyl sulfate, stearic acid monoglyceride, cetyl alcohol.

[0211] Wetting agents include, but are not limited to, glycerol.

[0212] Adsorbent carriers include, but are not limited to, bentonite, silica gel, kaolin, and soap clay.

[0213] Lubricants include, but are not limited to, zinc stearate, glycerol monostearate, polyethylene glycol, talc, calcium and magnesium stearate, polyethylene glycol, boric acid powder, hydrogenated vegetable oil, sodium stearyl fumarate, polyoxyethylene monostearate, monolauric sucrose ester, sodium lauryl sulfate, magnesium lauryl sulfate, magnesium lauryl sulfate.

[0214] Fillers include, but are not limited to, mannitol (granular or powdered), xylitol, sorbitol, maltose, erythrose, microcrystalline cellulose, polymeric sugar, coupled sugar, glucose, lactose, sucrose, dextrin, starch, sodium alginate, laminarin powder, agar powder, calcium carbonate, and sodium bicarbonate.

[0215] Disintegrants include, but are not limited to, cross-linked vinyl pyrrolidone, sodium carboxymethyl starch, low-substituted hydroxypropyl methyl, cross-linked sodium carboxymethyl cellulose, soybean polysaccharide.

[0216] Those skilled in the art can clearly understand that, for the convenience and brevity of the description, the specific working processes of the system, the device and the unit described above can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here. In several embodiments provided by the present application, it should be understood that the disclosed system, device and method can be implemented by other ways. For example, the device embodiments described above are merely schematic, for example, the division of the units is merely a logical function division, and actual implementation can have another division mode, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms. The units described as separate components can be or can not be physically separated, and the components shown as units can be or can not be physical units, that is, can be located in one place, or can be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment scheme. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit. Those skilled in the art can understand that all or part of the steps in the above embodiments can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium, and the storage medium can include read only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0217] Those skilled in the art can understand that all or part of the steps in the above embodiment methods can be completed by programs instructing relevant hardware, and the programs can be stored in a computer readable storage medium, and the storage medium mentioned above can be read only memory, magnetic disk or optical disk, etc.

[0218] The computer device provided by the present application is introduced in detail above. For those skilled in the art, according to the idea of the embodiment of the present application, there will be changes in specific implementation modes and application ranges. In view of the above, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A computer-aided drug screening method based on IAPP, characterized in that, The method comprises: obtaining IAPP protein and S protein and / or N protein data of SARS-CoV-2; selecting the spatial structure of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex to determine the binding site of the complex as the binding site of the targeted drug; obtaining the binding site of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex; screening candidate drugs based on the spatial structure of the binding site of the IAPP protein and S protein complex and / or the IAPP protein and N protein complex; molecular docking of the screened candidate drugs and the binding site to obtain the score of the docked molecules; sorting according to the score to obtain the candidate drugs; preferably, the process of computer-aided screening is: obtaining the molecular structure of IAPP protein, S protein or N protein and inputting the molecular structure into the pharmacophore module library for matching, clustering all action sites based on the interaction mode with IAPP protein, S protein or N protein to obtain a pharmacophore model; inputting the pharmacophore model into the molecular compound database for high-throughput screening to obtain candidate drugs; preferably, the process of computer-aided screening is: first, obtaining the molecular structure of the site binding agent or inhibitor of IAPP protein, S protein or N protein, then screening compounds with similar structures based on the molecular structure of the site binding agent or inhibitor, then performing molecular docking of the compounds with similar structures and the binding site to obtain the score of the docked molecules, and finally sorting to obtain candidate drugs; preferably, the binding site of IAPP protein includes one or more of ASN36, ARG44, SER52, GLY71; preferably, the binding site of the IAPP protein and S protein complex includes any one or more of: the amino acid site ASN36 of IAPP and the amino acid site ASN354 of S protein, the amino acid site ASN36 of IAPP and the amino acid site ARG355 of S protein, the amino acid site ARG44 of IAPP and the amino acid site GLU471 of S protein, the amino acid site ARG44 of IAPP and the amino acid site THR470 of S protein, the amino acid site SER52 of IAPP and the amino acid site GLU484 of S protein, and the amino acid site GLY71 of IAPP and the amino acid site GLN493 of S protein; preferably, the binding site of the IAPP protein and N protein complex includes any one or more of: the amino acid site SER52 of IAPP and the amino acid site PRO309 of N protein, and the amino acid site GLY71 of IAPP and the amino acid site LEU331 of N protein. The method comprises: obtaining complement system protein data; 2. A computer-aided drug screening method based on the complement system, characterized in that, determining a targeted drug according to the spatial structure of the protein; obtaining a candidate drug targeting the complement system through computer-aided screening; preferably, the process of computer-aided screening is: ​ ​ The method comprises the following steps: obtaining a molecular structure of an inhibitor of a complement system protein; screening a compound with a similar structure based on the molecular structure of the inhibitor; performing molecular docking of the compound with a similar structure and a binding site to obtain a score of a docked molecule; and performing sorting to obtain a candidate drug. Preferably, the complement system protein comprises one or more of MAC, C1, C3, C4, C6, C7, C8, C9, C4BPA, FB, FH, HPX, Ceruloplasmin, C1QC, and SERPING1.

3. A computer-based method for predicting sequelae of coronavirus infection in the elderly, characterized in that, The method comprises the following steps: receiving input data, the data comprising IAPP data and / or sugar metabolism index data of an elderly coronavirus infection recovery population; applying a machine learning model to the input data to generate a prediction result indicating whether the elderly coronavirus infection recovery population has sequelae; Preferably, the criteria for determining whether the elderly coronavirus infection recovery population has sequelae are that if IAPP abnormally aggregates and / or the sugar metabolism index is disordered, the elderly coronavirus infection recovery population is determined to be a population with sequelae.

4. A computer-aided drug screening system based on IAPP, characterized in that The system comprises: an acquisition unit configured to acquire IAPP protein and S protein and / or N protein data of SARS-CoV-2; a site determination unit configured to select a spatial structure of the IAPP protein and S protein and / or N protein complex, and determine a binding site of the complex as a binding site of a targeted drug; a screening unit configured to screen a candidate drug targeting the binding site by computer-aided screening.

5. A computer-aided drug screening system based on the complement system, characterized in that The system comprises: an acquisition unit configured to acquire complement system protein data; a determination unit configured to determine a targeted drug based on the spatial structure of the protein; a screening unit configured to screen a candidate drug targeting the complement system by computer-aided screening; Preferably, the complement system protein comprises one or more of MAC, C1, C3, C4, C6, C7, C8, C9, C4BPA, FB, FH, HPX, Ceruloplasmin, C1QC, and SERPING1.

6. A system for computer-aided prediction of coronavirus infection sequelae in the elderly, characterized by The system comprises: an acquisition unit configured to acquire data of an elderly coronavirus infection recovery population; an extraction unit configured to extract IAPP data and / or sugar metabolism index data of the elderly coronavirus infection recovery population; an identification unit configured to determine whether the elderly coronavirus infection recovery population has sequelae based on IAPP aggregation and / or sugar metabolism index; Preferably, the criteria for determining whether the elderly coronavirus infection recovery population has sequelae are that if IAPP abnormally aggregates and / or the sugar metabolism index is disordered, the elderly coronavirus infection recovery population is determined to be a population with sequelae.

7. A computer device, comprising: The device comprises: a memory and a processor, the memory being configured to store program instructions; the processor being configured to invoke the program instructions, when the program instructions are executed, to implement the IAPP-based computer-aided drug screening method of claim 1, the complement system-based computer-aided drug screening method of claim 2, or the computer-aided prediction method for sequelae of elderly coronavirus infection of claim 3.

8. A computer readable storage medium having a computer program thereon, characterized in that, The computer program, when executed by the processor, implements the IAPP-based computer-aided drug screening method of claim 1, the complement system-based computer-aided drug screening method of claim 2, or the computer-aided method for predicting sequelae of coronavirus infection in the elderly of claim 3.

9. A computer program product, characterised in that, The product comprises a computer program which, when executed by the processor, implements the steps of the IAPP-based computer-aided drug screening method of claim 1, the steps of the complement system-based computer-aided drug screening method of claim 2, or the steps of the computer-aided method for predicting sequelae of coronavirus infection in the elderly of claim 3.

10. Any of the following uses: 1) use of a complex formed by the binding of an IAPP protein to an S protein and / or an N protein in the screening of drugs for the treatment of metabolic diseases; 2) use of a complement system inhibitor in the screening of drugs for the treatment of metabolic diseases; Preferably, the proteins of the complement system comprise one or more of MAC, C1, C3, C4, C6, C7, C8, C9, C4BPA, FB, FH, HPX, Ceruloplasmin, C1QC, SERPING1; Preferably, the drug further comprises a pharmaceutically acceptable excipient; Preferably, the metabolic disease comprises sequelae of coronavirus infection or diabetes.

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