101 results about "Docking (molecular)" patented technology

The invention provides a method and a system for screening and constructing a small molecule polypeptide simulant based on interaction between proteins, relates to the technical field of biology, and comprises the following steps: predicting an interaction model of RECK protein and ADAM10 protein through a molecular docking platform, and analyzing interaction sites of the RECK protein and the ADAM10 protein. And then, determining a KAZAL region of the RECK protein according to the interaction site, and predicting a sequence structure of the KAZAL region. And then carrying out molecular docking on the KAZAL region and ADAM10 protein, designing a plurality of peptide fragments with the length of 30 amino acids, and screening the peptide fragment sequence with the highest score as candidate polypeptide. And finally, performing species homology comparison and quality control detection on the candidate polypeptide, and verifying the binding capacity and biological function of the candidate polypeptide to ADAM10 protein through cell experiments. The invention provides a method for screening to obtain a polypeptide simulant specifically bound with ADAM10 protein. The polypeptide simulant can be used for treating ADAM10 related diseases.

The invention relates to preparation and application of umami peptide from procambarus clarkii, and belongs to the technical field of food biology. A preparation method of umami peptides from procambarus clarkia is characterized by comprising the following steps: firstly, separating and purifying polypeptides in a shrimp meat taste extract, predicting peptide activity through a database, carrying out molecular docking and molecular dynamics simulation method characterization, and finally identifying two potential umami peptides, namely LAEPLTFN and YGGEFPARPDN; the umami peptide is artificially synthesized, the fresh property of the umami peptide is verified through sensory evaluation and an electronic tongue method, and the synergistic effect of the umami peptide and other fresh substances is further evaluated. The umami peptide obtained by the method has a composite freshness increasing effect, enriches a fresh water source umami peptide library, and provides a new umami peptide raw material for development of composite freshness increasing seasonings.

The invention relates to a diacylglycerol acyltransferase mutant and application thereof in synthesis of triacylglycerol from saccharomyces cerevisiae, and belongs to the technical field of enzyme engineering. Diacylglycerol acyltransferase is a rate-limiting step of a synthetic route of saccharomyces cerevisiae triacylglycerol (TAG), and at present, a research on a DGA1 mutant for efficiently synthesizing TAG is lacked, and a research on the aspect of a DGA1 catalytic mechanism is also lacked. The DGA1 mutant capable of effectively improving TAG synthesis is obtained by screening in combination with a directed evolution technology, and a result shows that 282-site mutation of diacylglycerol acyltransferase can significantly increase the accumulation amount of TAG. Meanwhile, the catalytic mechanism of the DGA1 is preliminarily explored by combining protein structure prediction, molecular docking and molecular dynamics simulation, and a foundation is laid for deeply analyzing the catalytic mechanism of the DGA1. The recombinant saccharomyces cerevisiae is also constructed based on the mutant, so that the TAG synthesis is improved, and meanwhile, the relative proportion of C18: 1 in total fatty acids is remarkably increased.

The invention relates to the technical field of drug research and development, in particular to a target AKT1 drug molecule discovery and delivery integrated system and method based on deep learning. Aiming at the problems of molecular design, optimization and delivery link separation and low research and development efficiency of drugs in the prior art, the system constructs a multi-module collaborative framework, and comprises a target analysis module for analyzing a target structure and formulating a generation strategy; the molecule generation and optimization module is used for generating and optimizing candidate molecules in combination with the generation model and reinforcement learning; the delivery scheme design module is used for matching a delivery carrier based on molecular physicochemical properties; and a verification module that predicts and evaluates the molecule-deliverer combination using molecular docking and ADMET. An evaluation result of the verification module is fed back to the molecule generation and optimization module to form a closed-loop optimization mechanism, so that an automatic process from target analysis to output of candidate drug molecules and matched delivery schemes thereof is realized. Compared with the prior art, the efficiency and success rate of early drug discovery can be improved.

A complex bait data set construction method based on structure and sequence collaborative redundancy elimination belongs to the field of bioinformatics, and comprises the following steps: screening an initial protein complex structure set, removing entries containing nucleic acids, small molecules or non-protein chains, and selecting binary complexes meeting integrity and resolution requirements; secondly, structure clustering and sequence clustering are carried out based on three-dimensional structure similarity and sequence homology, combined comparison is carried out on the two results, and redundant compound entries which are highly similar in structure and sequence are removed; then, taking each cluster representative compound as a target, generating a plurality of groups of bait structures by using a molecular docking or prediction modeling method, and calculating a quality index; and finally, performing stratified sampling and proportion balance based on the score interval of the quality index, and constructing a high-quality protein complex bait data set with structure and sequence collaborative redundancy elimination and balanced quality distribution. The data set generated by the method has the advantages of low redundancy, high diversity and quality distribution controllability.

The invention provides an anti-cancer active component optimization method for breast cancer treatment, which comprises the following steps: analyzing a potential interference path of an anti-cancer active component on immune system cell viability by adopting a computational chemistry simulation method according to a preliminary structural function mapping relationship, and determining a specific molecular mechanism range of immune system weakening; according to the synergistic effect evaluation result, optimizing the structural parameters of the active components through a molecular docking algorithm, adjusting the binding affinity of the active components with tumor cell targets and pathogenic bacteria targets, and determining a final structural optimization scheme; aiming at the final structure optimization scheme, verifying the expression of the modified active component on tumor inhibition and antibacterial ability by adopting simulation data of an in-vitro activity test, and obtaining a verification data set of comprehensive performance; and aiming at the updated active component design data, through a multi-objective optimization model, balancing the synergism of an anti-cancer effect and a health protection mechanism, and determining a final compound structure configuration suitable for complex requirements of a clinical environment.

According to the invention, glycosyl transferase UGTBL1 derived from Bacillus licheniformis ZSP01 is taken as a research object, AlphaFold 2 is used for modeling, molecular docking, molecular dynamics simulation and other technologies, amino acid in an active pocket region of glycosyl transferase is analyzed, amino acid in a substrate channel is analyzed by software CAVER3.0, site-directed mutagenesis is carried out, and the glycosyl transferase UGTBL1 is obtained. The regioselectivity and the activity of the mutant for catalytically synthesizing the icariside D2 glycosyltransferase are improved.

The invention relates to a molecular dynamics simulation method based on interaction of nucleic acid and protein, which comprises the following steps: carrying out molecular modeling on a target nucleic acid sequence to obtain a nucleic acid three-dimensional structure model; performing structure prediction on the target protein sequence according to the homologous template and a deep learning method to obtain a protein three-dimensional structure model; performing interaction site screening on the nucleic acid three-dimensional structure model and the protein three-dimensional structure model by using a molecular docking algorithm to obtain a compound initial conformation of nucleic acid and protein; based on multi-stage energy minimization and ensemble balance conditions, performing dynamic simulation on the initial conformation of the compound to obtain conformation trajectory data; and performing multi-dimensional analysis on the conformation trajectory data to obtain a quantitative index of the interaction between the nucleic acid and the protein. According to the method, the modeling precision is improved, manual intervention is reduced, and the simulation efficiency is improved.

According to the present invention, glycosyl transferase UGTBL1 derived from Bacillus licheniformis ZSP01 is adopted as a research object, AlphaFold 2 is adopted to carry out modeling, molecular docking, molecular dynamics simulation and other technologies, amino acids in an active pocket region of glycosyl transferase are analyzed, site-directed mutagenesis is performed on the amino acids to obtain a series of glycosyl transferase mutants, and salidroside can be highly selectively catalyzed and synthesized.

A method for operating a protein structure-based candidate discovery system may include: displaying a first screen for receiving input of a plurality of ligands to be docked to a target protein; calculating predicted binding energy by sequentially performing molecular docking on the plurality of ligands based on settings input by a user via the first screen; displaying a first list on a second screen, the first list containing rows including a user interface control for user selection, names of the plurality of ligands, and the calculated binding energy values; predicting binding affinity for the rows selected by the user via the user interface control from the first list displayed on the second screen; and displaying a second list on a third screen, the second list containing rows including the names of the plurality of ligands, values of the binding energy, and values of the predicted binding affinity.

According to the screening method for the potential protein biomarkers and the drug targets of the gastric cancer, Mendel randomization analysis in a proteome range is adopted, the genetic causal relationship between circulating plasma protein and the risk of the gastric cancer is evaluated, and finally the remarkably related protein is identified. According to the screening method of the potential protein biomarker and the drug target of the gastric cancer, provided by the invention, the potential association between circulating plasma protein and the gastric cancer is systematically revealed by integrating Mendel randomization, single-cell RNA sequencing analysis, space transcriptome analysis, virtual drug screening, molecular docking, molecular dynamics simulation and other methods.

The invention discloses a functional protein screening method based on a large language model, and belongs to the technical field of protein screening, and the method specifically comprises the following steps: obtaining a protein three-dimensional structure and functional literature, converting the protein three-dimensional structure and functional literature into a multi-modal feature vector by using an encoder, and constructing a domain-specific vector database; after a screening instruction is received, related feature data of semantic matching is called through a vector retrieval algorithm; then, inputting the data into a large language model to execute biological mechanism reasoning, and outputting an initial candidate protein sequence containing predicted activity; further, physical binding energy is calculated by using a molecular docking program, a deviation coefficient between the physical binding energy and the predicted activity is generated, and the coefficient is used as a penalty term to construct a dynamic correction function to update the scoring weight; and finally, performing secondary sorting on the candidate set based on the updated weight, and outputting the sequence sorted for the first time as a target protein. According to the method, AI reasoning and physical verification are effectively combined, and the screening accuracy is improved.

The invention discloses a molecular simulation and flavor perception-based beer umami peptide efficient identification method, and relates to the technical field of food flavor analysis and bioinformatics. The method comprises the following steps: performing liquid-liquid extraction and mass spectrum analysis on a beer sample to obtain a polypeptide sequence; screening the umami active peptide based on a UMPred FRL model; the method comprises the following steps: constructing a T1R1 / T1R3 receptor homologous model, and screening a peptide fragment of which the binding energy is lower than-25kcal / mol by adopting a CDOCKER semi-flexible docking mode; screening a stable peptide of which the delta Gbind is lower than-50kcal / mol through molecular dynamics simulation and MM / GBSA calculation; and finally, the umami characteristic is verified by combining an electronic tongue and a sensory test. The screened peptide fragment can be used for improving umami perception and taste balance of beer.

The invention relates to the technical field of safe conveying of oil and gas pipelines, in particular to a polypeptide type hydrate kinetic inhibitor screening method based on molecular simulation, which comprises the following steps: selecting target protein related to hydrate growth, and designing a polypeptide sequence; performing polypeptide pre-screening; evaluating the binding affinity and binding mode of the polypeptide and the target protein; performing MD simulation on the screened high-affinity polypeptide; verifying the binding affinity of the polypeptide and the target protein; and collecting all experiment and simulation data for statistical analysis, and optimizing the polypeptide sequence. The structure and function of the target protein are systematically analyzed by using a bioinformatics database and tools, it is ensured that the selected target plays an important role in hydrate formation, and meanwhile, a polypeptide sequence designed based on the target protein structure can effectively improve the binding capacity; and in the pre-screening and docking analysis process of the polypeptide, dynamic parameters are simulated and extracted by molecular dynamics, so that the interaction between the polypeptide and the target protein can be deeply understood.

The invention relates to the technical field of biological medicines, in particular to a preparation method of an inhibitor targeting a MAX-PD-L1 promoter specific binding motif and a double-target inhibitor, through ChIP-seq and site-directed mutagenesis experiments, a core binding motif of MAX and a PD-L1 promoter, such as 5 '-CAC [GA] TG-3', is defined, it is ensured that the inhibitor only targets a key site of MAX-PD-L1 interaction, and the activity of the MAX-PD-L1 promoter specific binding motif is improved. The off-target effect is avoided, and a high-specificity target spot is provided for subsequent inhibitor design. The cell permeability of the DNA aptamer screened based on the specific binding motif is improved after cholesterol modification, and the affinity of the DNA aptamer is obviously higher than that of a traditional antibody. A small molecule compound virtually screened through a molecular docking model is optimized through hydrogen bond and hydrophobic interaction, and then the binding affinity with MAX is improved. After treatment with the inhibitor, the combination inhibition rate of MAX and the PD-L1 promoter is high, the transcriptional activity of PD-L1 is obviously reduced, the killing rate of T cells to tumor cells is also improved, and immune escape is effectively blocked.

The invention provides an AI-assisted network pharmacology exploration method for treating liver cancer by Qi-tonifying and Zhongzhong decoction, which is used for revealing a molecular mechanism of the Qi-tonifying and Zhongzhong decoction for treating liver cancer. The research belongs to the field of computer-aided drug design, and aims to systematically promote key links of drug development through a mode of combining multi-database resource integration with an AI technology. Firstly, a plurality of authoritative databases are comprehensively utilized to screen potential active components and target genes thereof, gene expression data in a GEO database are combined, a disease-related gene network is constructed through an AI algorithm, and core target genes for liver cancer treatment are accurately recognized. Secondly, on the basis of a ChEMBL database and an AI auxiliary screening method, candidate compounds highly related to core target genes are mined; afterwards, through a full-automatic molecular docking technology and molecular dynamics simulation, the binding performance and stability of the candidate compound and the core target spot on the molecular level are deeply evaluated, and therefore the stable compound with the excellent binding characteristic is screened out. Finally, the efficacy and safety of the Qi-tonifying and middle-jiao-strengthening decoction are verified through in-vitro experiments, and a theoretical basis and experimental support are further provided for a molecular mechanism of the Qi-tonifying and middle-jiao-strengthening decoction for treating liver cancer. According to the research, on the basis of multi-database resource integration, artificial intelligence and an advanced calculation simulation technology are combined, a systematic process from drug discovery to verification is established, and an innovative method and a practical path are provided for modernization and precise tumor treatment of traditional Chinese medicine.

This application provides a method for screening inhibitors targeting CYP7A1 and their use in the preparation of anti-liver cancer drugs, relating to the field of tumor targeted drug design technology. The method for screening inhibitors targeting CYP7A1 protein includes: using the crystal structure of human cholesterol 7α-hydroxylase as a receptor model, precise docking screening is performed using software to screen drug-like molecules with molecular weights of 250-500 Da and lipid-water partition coefficients of -1 to 5 from a compound database; a graph neural network active learning model is used to predict and rank the protein-small molecule binding affinity of compounds in the database; the ΔG between candidate molecules and CYP7A1 is calculated using molecular mechanics / generalized Born surface area methods, with molecules having ΔG ≤ -40 kcal / mol considered potential inhibitors. The inhibitors of this application do not rely on the enzymatic activity of CYP7A1 to achieve anti-tumor effects and do not significantly affect cholesterol and bile acid metabolism levels.

The invention relates to the technical field of biology, in particular to a method for improving affinity of a nano antibody based on computer-aided design. According to the method, a series of bioinformatics tools for nano antibody affinity maturation are investigated and researched in literatures, and part of databases, servers and software are optimized to construct a set of computer-aided design (CAD) nano antibody affinity improvement method by evaluating the operability and the improvement effect of the tools. In order to verify the effectiveness and universality of the method, based on the method, a high-affinity mutant nano antibody is virtually screened out of an Anti-Nectin-4 camel source nano antibody NBNT-1 and an Anti-PD-L1 shark source nano antibody NBNT4 screened in a synthetic library through model construction, model evaluation, site prediction, molecular docking, structural analysis, mutation prediction and mutation evaluation. Traditional in-vitro affinity maturation methods, such as error-prone PCR, are low in screening efficiency, large in randomness introduced by mutation, long in experimental period, large in workload and difficult to accurately optimize the affinity of target molecules. According to the method, various defects of a traditional in-vitro affinity maturation method are overcome, and the success rate and efficiency of affinity maturation are improved.

The invention discloses a construction method of a high-activity mutant of chondroitin 6-O sulfotransferase, and belongs to the field of biological enzyme engineering and synthetic biology. Interaction sites of MusC6 protein and a substrate PAPS are accurately analyzed through a computer-aided molecular docking technology, and key active sites are directionally modified by adopting a strategy of combining rational design and saturated mutation. Experimental results show that the catalytic performance of the obtained mutant S139T / S140L / Y364M / K412V is remarkably improved, and the enzyme activity of the mutant reaches 1182U / L and is improved by 4.4 times compared with the enzyme activity of a wild type 270U / L. According to the invention, the technical bottleneck of low catalytic efficiency of a wild type enzyme on a natural substrate is solved through a multi-site synergistic mutation strategy, and a biocatalyst with industrial application value is provided for efficiently synthesizing chondroitin sulfate C by a microbiological method.

The invention provides a virtual screening method based on scoring function optimization. The virtual screening method is used for efficiently screening a PAK4 inhibitor. According to the method, molecular docking, scoring function optimization, machine learning and molecular dynamics simulation are combined, and the screening efficiency and accuracy are improved through a multi-level screening strategy. The method comprises the following steps: firstly, screening candidate compounds in a database by utilizing a molecular similarity algorithm, and reserving molecules similar to target binding pocket characteristics; and then, screening and optimizing the compound by adopting rapid molecular docking and key residues. Thirdly, using scoring functions such as RF-VS and GNINA as well as a deep learning method to optimize screening, and evaluating the performance of the model through an ROC curve and an enrichment factor; and finally, verifying a screening result by utilizing high-precision docking and 150ns molecular dynamics simulation, and confirming the stability and targeted binding capacity of the candidate molecules. The method is successfully applied to PAK4 inhibitor screening, the screening efficiency is remarkably improved, and an efficient and reliable solution is provided for research and development of PAK4 targeted drugs.

A system and method for determining a biological activity parameter of a ligand are disclosed, the system comprising a processor configured to: obtain ligand-protein complex structure data and reference biological activity data; determine ligand-protein complex interaction parameters indicative of intermolecular interaction data of the ligand-protein complex in a docking conformation; determine aggregated ligand-protein complex interaction parameters across a plurality of docking conformations; and determine the biological activity parameter based on the aggregated ligand-protein complex interaction parameters, wherein determining the ligand-protein complex interaction parameters comprises determining non-covalent parameters indicative of intermolecular interaction data of a ligand node and a protein node based on ligand and protein attributes and ligand-protein non-covalent parameters indicative of binding stability of a first non-covalent edge of a dispersion force type.

A system and method for structure-based, small molecule activity prediction using binding mode prediction information. Binding scores between ligands and target molecules, (e.g. proteins, RNA, DNA, lipids, sugars) are first generated using molecular docking. A first machine learned deep neural network (DNN) model is developed using data representing the molecular ligand-target pair 3D structures and docking features to predict binding modes. Using transfer learning, weights of layers learned in the first machine learned model are used as weights in layers of a second machine learned DNN model used to more accurately improve the performance of activity prediction of the second machine learned model. For a target newly paired ligand-target complex, the method further implements a binding mode selector for selecting one or more particular binding poses for input to the activity prediction model for use in activity mode prediction of an activity of the target paired ligand-protein complex.

The invention discloses a novel anti-tumor peptide and application thereof, and is characterized in that the novel peptide is a polypeptide with the lowest binding energy with c-FLIP DED1 obtained by screening three polypeptides in Procaspase-8DED2 split protein by using a molecular docking technology, and the amino acid sequence of the polypeptide is as shown in SEQ ID NO.2; the screened polypeptide acts on a human-derived cancer cell line to obtain that the polypeptide has a good inhibition effect on tumor proliferation, and the polypeptide is expected to become an anti-tumor small-molecule polypeptide and is applied to clinical tumor treatment.

The invention discloses a recombinant leech polypeptide with high anticoagulant activity as well as a preparation method and application thereof. A PelB-T2Y-N47K-N52Y mutant is constructed by carrying out structural optimization on a hirudin HV2 sequence, a polypeptide product with relatively high anticoagulant activity is obtained after induced expression and nickel affinity chromatography purification are carried out in a prokaryotic expression system, and the anticoagulant activity of the polypeptide product reaches 9135.8 ATU / mg and is remarkably superior to that of an initial sequence HV2. Meanwhile, the polypeptide shows a lower IC50 value in a hydroxyl radical scavenging experiment, and has the activity of inhibiting generation of AGEs in a non-enzymatic saccharification model. Molecular docking and binding free energy analysis show that the combination of the mutant and thrombin (PDB ID: 4HTC) is more stable. The leech polypeptide disclosed by the invention has the potential of being developed into a multifunctional bioactive peptide preparation, and is suitable for the related fields of anticoagulation, antioxidation and anti-saccharification.

This application discloses a method for predicting the quantitative activity of endocrine disruptors, relating to the field of virtual screening of endocrine disruptors. The method includes: acquiring in vitro experimental data of nuclear receptors and removing duplicate data, as well as removing compound sets that do not contain the simplified molecular linear input canonical (SMILES) representation; using a molecular fingerprinting method to extract the primary, secondary, and tertiary structural features of the compounds. For each compound cluster, a quantitative prediction model based on machine learning or quantitative read-across is constructed to predict the quantitative activity value of the compound. Addressing the low efficiency of existing methods for predicting the quantitative activity of endocrine disruptors, this application extracts multi-level structural features of the compounds and constructs corresponding quantitative prediction models for compound structural clusters of different sizes. Through molecular docking and molecular dynamics simulations, the interaction mechanism between endocrine disruptors and nuclear receptors is studied from a structural biology perspective, thus improving efficiency.

This invention belongs to the field of food biotechnology and bioactive peptides, and specifically relates to a cottonseed protein-derived ACE-inhibiting peptide, its screening, modification methods, and applications. The screening method of this application includes four core steps: virtual enzymatic library construction, multi-stage computer filtering, molecular docking screening, and in vitro activity verification, obtaining 194 high-confidence candidate peptides. Four cottonseed protein-derived peptides (SYF, DIF, IAF, and RFY) with in vitro ACE-inhibiting activity were verified, all exhibiting mixed ACE-inhibiting activity, with DIF showing the strongest inhibitory activity. Molecular dynamics simulations confirmed the structural stability of the ACE-DIF complex. A modified peptide FIF was obtained through a dipeptide module substitution strategy, with an IC50 value of [missing information]. 50 The activity was significantly reduced, approximately 3.7 times that of the parent peptide DIF, and the inhibition type changed from mixed to competitive. This invention provides a new approach for the development of natural antihypertensive functional peptides and the high-value utilization of plant protein resources.

The invention discloses a method for analyzing an action mechanism of acanthopanax for treating non-small cell lung cancer based on network pharmacology, molecular docking and molecular dynamics simulation, and relates to a pharmacology mechanism research method, and the method comprises the following steps: S1, screening active ingredients; s2, acquiring a disease target; s3, constructing and analyzing a compound-target spot-disease network; s4, constructing a protein interaction network; s5, performing KEGG pathway enrichment analysis and GO function enrichment analysis to explore a specific anti-non-small cell lung cancer mechanism; s6, construction and analysis of a chemical component-target-pathway-disease network of the acanthopanax senticosus; s7, exploring the mechanism by molecular docking; and S8, evaluating the binding stability of the protein-ligand complex through molecular dynamics simulation. According to the method, the enrollment threshold of network pharmacology is simplified; according to the used method, data is directly mined in each database, and network pharmacology, molecular docking and molecular dynamics simulation are combined, so that the content is more layered, and the action mechanism of the chemical components of the acanthopanax senticosus for treating the non-small cell lung cancer can be accurately analyzed.

The invention belongs to the technical field of molecular biology and drug screening, and provides a screening method of capsaicin targeting SOCS5-RBMX protein interaction and application of the capsaicin, an SOCS5-RBMX protein complex structure is analyzed, and SOCS5-RBMX binding structural domains and SOCS5-RBMX binding key sites are determined; the effect of inhibiting protein binding is verified through point mutation of the key sites; determining a binding pocket, taking a drug in the ZINC22 small molecule drug database and a drug approved by FDA as ligand molecules, and taking the binding pocket as a docking region for virtual screening to obtain a compound; the obtained compound is screened through AMDET, and the screened medicine for inhibiting SOCS5-RBMX binding is capsaicin; capsaicin is used for carrying out in-vivo and in-vitro experiments for further screening verification; according to the method, the structure of the SOCS5-RBMX protein complex is analyzed, the binding structural domain and the key site are determined, virtual screening and experimental verification are carried out on the basis of the binding structural domain and the key site, the medicine for inhibiting SOCS5-RBMX binding can be accurately screened out, and the accuracy and efficiency of medicine screening are improved.

The present disclosure provides a rational modification method of spermidine synthase (SpeE) in Bacillus subtilis, which relates to the technical fields of enzyme engineering and computational structural biology. The modification method comprises the following steps: constructing a high-confidence three-dimensional model of Bacillus subtilis SpeE, determining the key action scope by double-substrate (S-adenosyl methionine and putrescine) docking, performing virtual saturation mutation on the residues in the region, screening the optimal mutant by combining function prediction, stability analysis, molecular dynamics simulation and binding free energy calculation, and explaining the mutation mechanism from the atomic level. The SpeE mutant (such as G85S, G85A) obtained by the modification method has significantly improved catalytic efficiency and substrate binding affinity, and when applied to a whole-cell catalytic system, it can greatly improve the yield of spermidine, and is suitable for the industrialized green biosynthesis of spermidine.

The application discloses a TYK2 inhibitor and a screening method and application thereof based on fusion of deep learning scoring and traditional molecular docking scoring. The application adopts a method of combining a deep learning scoring result CNNscore with an empirical scoring result obtained by Watvina, and improves the screening efficiency by multiplying the deep learning docking scoring and the empirical docking scoring, thereby relieving the problem of excessively high false positive rate of the traditional screening method, and saving time and cost. The application also applies the method to 12 million compounds, and verifies the compounds by in-vitro activity test, and obtains two small molecules with TYK2 inhibitory activity, which have application prospects in preventing or / and treating TYK2-mediated diseases. Therefore, the method is helpful to quickly identifying potential effective compounds from a huge compound library.