2634 results about "Target protein" patented technology

The present disclosure relates to methods of co-administering PROTAC (proteolysis targeting chimera) compounds that can promote ubiquitination and degradation of a target protein. The present disclosure also relates to antibody drug conjugates of PROTAC compounds Also disclosed herein are pharmaceutical compositions that can include a compound of Formula (V), the use and preparation thereof.

Disclosed are a compound targeting a protein modulator and a use thereof. The compound is a compound represented by formula (I), or a pharmaceutically acceptable salt thereof. The compound of the present invention has a good inhibition effect or protein degradation effect on a KRAS mutant.

The invention relates to the field of biological medicine, in particular to a stapled peptide targeting RAS-Raf protein interaction and application of the stapled peptide, the stapled peptide is obtained in the mode that Ac-SLHDCLMKALKVR-NH2 and Ac-SLHDCLMKALKVRGL-NH2 serve as peptide chain templates, amino acid at the i site and amino acid at the (i + 4) site are replaced with S5 and cyclized, and the nitrogen terminal is further modified with stearic acid. The stapled peptide can effectively inhibit proliferation and migration of tumor cells; the serum stability is high, and meanwhile, the cell transmembrane capability is excellent, so that target protein in cells can be better targeted, and the biological function of the target protein can be exerted. Therefore, the alphaH helical conformation of the RBD of the RAF can be stabilized through the stapled peptide and a nitrogen-terminal fatty acid modification strategy, the membrane permeability and stability of the RAF are improved, the binding force of the RAF and RAS is further improved, and proliferation and migration of various tumor cells are inhibited.

The present invention relates to a bifunctional Kras modulator (K-T or K-L-T) and the use thereof, wherein K represents a targeting group and is a modulator of a Kras protein (target protein), T is a ligand group of an E3 ubiquitin ligase, and L is a divalent linking group that chemically links the targeting group (K) to the ligand group (T). The bifunctional modulator or compound and a composition thereof disclosed in the present invention have pharmacological activity of degrading or inhibiting the target protein, and can be used for treating, inhibiting or preventing Kras-related diseases or disorders.

The invention discloses a drug and target interaction prediction method based on multi-scale convolution feature fusion, which comprises the following steps: acquiring drug molecule data, target protein data and drug and target interaction data, constructing a drug molecule map according to the drug molecule data, and coding a target protein sequence according to the target protein data; inputting the drug molecular map into a model, and obtaining drug features through a multi-scale map convolutional network and a dynamic gating attention mechanism; inputting a target protein sequence into the model, and obtaining target features through hierarchical cavity convolution and a bidirectional gating cycle unit; through multi-head cross attention, the drug features are aligned with the target features, local and global cross-modal fusion is carried out, and drug target fusion features are obtained; based on the drug target fusion features, outputting a drug and target interaction prediction probability; and training the model according to the drug and target interaction data and the prediction probability, and applying the trained model to drug and target interaction prediction.

The invention relates to the technical field of computer-aided drug design, in particular to a drug molecule screening and optimizing method based on artificial intelligence prediction, which comprises the following steps: S1, obtaining a dynamic protein conformation set and molecular multi-dimensional characterization: obtaining a dynamic conformation set of a target protein and a physicochemical property spatial distribution diagram of a binding pocket of the dynamic conformation set, a two-dimensional molecular map topological structure and three-dimensional conformation coordinates of the drug molecules are obtained; s2, multi-modal fusion prediction is carried out; s3, generating interpretable optimization guidance; and S4, automatic iterative optimization: performing batch prediction and screening on the new candidate molecular structure, taking the screened optimal molecule as a new starting point, repeatedly executing the interpretability optimization guidance generation step and the step until an iteration termination condition is met, and outputting a final optimized molecule list. Through the multi-modal fusion deep learning model, the interaction strength of the drug molecules and the target protein can be quickly and accurately predicted, and the screening efficiency of the drug molecules is greatly improved.

The invention discloses a BamA-targeting antibacterial peptide as well as a preparation method and application thereof, and is characterized in that amino acids in a sequence are all D-type amino acids, and the amino acid sequence is as follows: w-r-r-w-r-r-p-w-k-r (dTrp-dArg-dArg-dTrp-dArg-dArg-dPro-dTrp-dLys-dArg). The antibacterial peptide provided by the invention has good antibacterial activity, can be synthesized by adopting an Fmoc solid-phase chemical method, is low in preparation difficulty, and has high affinity with target protein BamA.

The invention relates to the technical field of protein purification, in particular to a biological method for purifying fusion protein through two different affinity tags. Two affinity purification tags and a dissolution promoting tag are perfectly combined, so that soluble expression of recombinant protein is ensured, high-purity target protein can be obtained through a two-step mild and efficient affinity purification process, the function and activity of the protein are ensured, a complex'capture-medium purification-fine purification 'multi-step purification process is avoided, and the method is suitable for industrial production. The purification process is shortened, the purification time is shortened, the purification efficiency is improved, and the method can be used for production and purification of functional proteins.

The invention provides a drug target prediction method based on a 3D structure and a multilevel attention mechanism, and belongs to the technical field of drug target prediction. The technical problem to be solved is to provide a drug target prediction method based on a 3D structure and a multi-level attention mechanism. According to the technical scheme, the method comprises the following steps: collecting data information of an interaction tag among a related drug, a target sequence and a drug target protein, and processing and extracting SMILES character string characteristics of the drug; learning the numeralized protein amino acid sequence by using a convolutional neural network, completing feature coding of a target spot on the sequence through multi-layer convolution and pooling operation, and extracting target spot sequence feature data; a PDB file is read and modeled into a pocket graph, feature calculation is carried out on the pocket graph, a final protein structure representation code is generated, and structural feature data of target protein is extracted by modeling the PDB file; the method is applied to drug target prediction.

The present invention provides a class of small molecule compounds for targeted degradation of a pan-KRAS protein, a preparation method therefor, and use thereof as a therapeutic agent for preventing and / or treating cancer. The compound of the present invention can effectively degrade and / or inhibit the pan-KRAS protein in cells, and can be used for preparing a drug for treating and / or preventing related diseases or disorders caused by pan-KRAS mediation.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention belongs to the technical field of genetic engineering, and particularly relates to recombinant collagen based on engineering bacterium expression and a preparation method thereof, and the recombinant collagen is formed by connecting repetitive units derived from human III type collagen in series for three times; the invention also discloses a base sequence of the coding gene of the recombinant collagen, efficient expression and accurate folding of target protein are realized through genetic modification of engineering bacteria, a light-operated dynamic folding process and an orthogonal purification strategy, and cascade purification of a His6-SUMO tag and an intein-CBD module is utilized to obtain the recombinant collagen. The product purity and the native conformation retention rate are remarkably improved, and the technical problems that hydroxyproline depends on exogenous addition, the folding controllability is poor and the purification efficiency is low in a traditional process are solved.

The invention provides a drug target binding affinity prediction method based on multi-modal data fusion. The method comprises the following steps: firstly, extracting sequence feature information of drug SMILES and target FASTA, then constructing an affinity graph, modeling drug molecules and target protein molecules into an undirected graph, and extracting molecular-level features of atoms, bonds, residues and contact. And fusing the hierarchical graph structure information of the affinity graph and the molecular graph to obtain the graph structure feature representation of the drug-target spot. The sequence feature information and the graph structure feature representation are further fused by using intramolecular and intermolecular attention fusion mechanisms. And finally, performing affinity prediction by using the fused features, and outputting a drug-target binding affinity score. According to the method, sequence and structural information are effectively fused, the accuracy of drug target affinity prediction is improved, and the problems of insufficient information fusion and insufficient structural information utilization in an existing method are solved.

The invention is applicable to the technical field of drug research and development, and provides a lead compound discovery method based on a large language model, which comprises the following steps: determining a target spot and constructing an initial compound library; generating fragments and forming an initial fragment library; calculating the frequency of occurrence of the fragments and the affinity contribution degree of the fragments to the target protein, and updating the fragment library; fragment sampling weight calculation and candidate fragment selection; the large language model generates molecules according to the candidate fragments; evaluating the quality of the generated molecules and updating the molecule library; and repeating the cycle until a preset iteration round number is reached. A fragment-based drug design method is combined with the large language model, rich domain knowledge of the large language model is used for replacing human experts to guide drug design, fragments are used as prompts to stimulate the internal potential of the large language model, the advantages of the two methods complement each other, exploration of the whole chemical space is completed, and the drug design efficiency is improved. The lead compound reaching the human expert level is designed in actual use, and a new technical means is provided for drug research and development.

The invention provides a recombinant vector for expressing LPL, a recombinant bacterium and an LPL production method, and belongs to the technical field of gene engineering. The invention provides a recombinant expression vector of Pseudomonas aeruginosa lipoprotein lipase LPL, wherein an LPL coding gene is derived from Pseudomonas aeruginosa. The recombinant expression vector is introduced into a pseudomonas aeruginosa PAO1 cell, an expression strain is successfully constructed, the expression quantity of target protein is improved, efficient expression of LPL in PAO1 is achieved, and the produced LPL is high in enzyme activity and wider in application range.

Provided herein are lentiviral vectors comprising a mutated, heterologous envelope protein, a targeting protein, and at least one transgene for delivery to and expression by a cell characterized by the targeting protein. Also provided are methods and materials for producing the lentiviral vectors described herein, methods for transducing target cells, and cells transduced by lentiviral vectors according to the present disclosure.

The invention discloses a drug target prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing dynamic gating fusion on graph structure features and sequence features of a target drug to obtain drug features; performing dynamic feature enhancement on the digitized sequence of the target protein, and further obtaining protein features through context sensing optimization; obtaining a target affinity score of the target drug and the target protein by using a prediction model based on the drug characteristics and the protein characteristics; wherein the prediction model is obtained through feature representation training marked with actual affinity scores on the basis of a neural network. According to the method, the prediction precision of drug target interaction is improved through deep fusion of drug multi-modal features and protein sequence context perception optimization. The method can realize high-precision prediction of the drug target, and can be widely applied to the technical field of drug target prediction.

The invention belongs to the technical field of biology, and particularly relates to a nano antibody-proximity marker enzyme fusion protein and application thereof. Specifically, the present disclosure provides a fusion protein for proximity labeling wherein the fusion protein is operably linked together by (a) a nanobody and (b) a proximity labeling enzyme; optionally, a peptide linker is also included between the nano antibody and the proximity marker enzyme. The nano antibody-proximity marker enzyme fusion protein disclosed by the invention is obtained through in-vitro expression and purification, target protein is targeted by using an antibody, and the nano antibody-proximity marker enzyme fusion protein does not depend on an overexpression system and has compatibility to various cell types, tissues and clinical fixed samples; proteins with post-translational modification and various organelles, such as membraneless organelles, can be targeted; and the positioning accuracy rate reaches 100%.

The invention discloses a lysosome targeted chimera based on Sortilin and application thereof. The lysosome targeted chimera comprises a ligand molecule of a lysosome targeted receptor and a ligand molecule of a targeted protein to be degraded, wherein the ligand molecule and the ligand molecule are connected through a linker; the lysosome targeting receptor is Sortilin, and the ligand molecule of the lysosome targeting receptor is a polypeptide targeting the Sortilin; the protein to be degraded is membrane protein or extracellular protein. Furthermore, the polypeptide of the targeted Sortilin is connected with an enzyme response sequence and an integrin receptor targeting sequence. According to the invention, the Sortilin-combined ligand neurotensin is functionally coupled with the neutralizing antibody or polypeptide of overexpression protein in targeted tumor or inflammatory diseases, and the Sortilin-combined ligand neurotensin has obvious treatment gain in malignant solid tumor and psoriasis models. The discovery promotes the transformation potential of the lysosome participating in the biological agent, and provides a dual strategy for realizing accurate degradation through receptor recruitment and microenvironment induction.

The invention belongs to the technical field of antibody preparation, and particularly relates to an anti-human carbohydrate antigen CA125 monoclonal antibody and application thereof. The amino acid sequences of CDR1-3 on a light chain variable region of the monoclonal antibody are respectively shown as SEQ ID NO.3-5, and the amino acid sequences of CDR1-3 on a heavy chain variable region of the monoclonal antibody are respectively shown as SEQ ID NO.8-10. The monoclonal antibody provided by the invention can specifically recognize and bind to a natural carbohydrate antigen CA125 expressed by human cells or tissues, has good affinity and relatively high sensitivity of binding of the antibody and the antigen, has strong anti-interference ability to complex non-target protein components in the cells / tissues, is beneficial to significantly improving the accuracy and reliability of immunodetection of human CA125 protein, and has good application prospects. The method is suitable for high-specificity, high-sensitivity and high-reliability detection of the human CA125 protein, and has good applicability in a plurality of detection systems such as immunoblotting, immunoprecipitation, immunohistochemistry and the like.

The invention belongs to the field of functional proteomics research, and particularly relates to a combined analysis method for analyzing interaction between a small molecule substance and a targeted protein, which is used for analyzing the small molecule substance and the protein by adopting LiP-MS (Limited Protein-Mass Spectrometry) technology and phosphorylated proteome combined analysis. According to the method, the optimized LiP-MS technology is adopted, the target protein can be directly recognized under the complex proteome background without enriching specific protein, and the problem that low-abundance protein is difficult to detect is solved; phosphorylated proteome abundance transformation protein is analyzed by combining high-resolution mass spectrometry, so that the recognition capability on a drug target and a shear variant thereof is enhanced, and the detection sensitivity is improved.

A specification of a binding target protein is received. A machine learning model is used to predict a plurality of candidates for a property of a selected amino acid position of a binder protein to bind to the binding target protein. For each selected property candidate of the plurality of property candidates, the selected property candidate is used as a model input to predict properties for one or more other amino acid positions into a corresponding candidate set of properties. The corresponding candidate sets are evaluated using a binding quality evaluation. Based on the evaluation, one of the plurality of property candidates is selected as a determined result property of the selected amino acid position. The determined result property is used as a model input to predict a plurality of candidates for a property of a different selected amino acid position included in the binder protein.

The invention provides a membrane protein targeted degradation agent based on cell autophagy, the degradation agent has a first structure combined with membrane protein and also has a second structure capable of inducing cell autophagy, and the first structure is connected with the second structure. According to the membrane protein targeted degradation agent based on cell autophagy, the cell autophagy mechanism is utilized for membrane protein degradation for the first time, due to the fact that autophagy is an internal mechanism of various cells, universal induction can be achieved in various cells, and the autophagy does not depend on factors such as lysosome transporter protein and E3 ligase on cell membranes; the method has the characteristics of broad spectrum and higher universality; the design route of direct connection of the first structure and the second structure is simple and easy to realize.

The invention features compounds (e.g., macrocyclic compounds) capable of modulating biological processes, for example through binding to a presenter protein (e.g., a member of the FKBP family, a member of the cyclophilin family, or PIN1) and a target protein (e.g., a eukaryotic target protein such as a mammalian target protein or a fungal target protein or a prokaryotic target protein such as a bacterial target protein). These compounds bind endogenous intracellular presenter proteins, such as the FKBPs or cyclophilins, and the resulting binary complexes selectively bind and modulate the activity of intracellular target proteins. Formation of a tripartite complex among the presenter protein, the compound, and the target protein is driven by both protein-compound and protein-protein interactions, and both are required for modulation of the targeted protein's activity. In some embodiments, the compounds of the invention “re-program” the binding of the presenter proteins to protein targets that either do not normally bind to the presenter protein (e.g., do not show detectable binding in mammalian cells absent the compound). In some embodiments, provided compounds “re-program” presenter protein binding to greatly enhance interaction with a particular target with which it may have some interaction absent the compound. Interactions achieved through such reprogramming result in an ability to modulate the activity of these new targets.

The present invention discloses a method for separating and purifying recombinant human collagen, wherein the method first filters the recombinant human collagen fermentation broth, then uses cationic composite chromatography to crudely purify the recombinant human collagen to obtain a cationic composite chromatography eluent, and then uses a hydrophobic chromatography column to purify the purified recombinant human collagen, wherein the cationic composite chromatography column is filled with a high-flow rate agarose matrix containing a hydrophobic group of a phenyl group and an electrostatic group of a carboxyl group as a filler, and the hydrophobic chromatography column is filled with a hydrophilic polymethacrylate matrix containing a phenyl group as a filler. The final target protein purity of the present invention is greater than 99%, the recovery rate is greater than 85%, and the endotoxin content is less than 0.5EU / mg. The process of the present invention is simple and easy to operate, and the purification cost requirement is low. The recombinant human collagen obtained by the method has high purity and low endotoxin content, and meets the requirements of cosmetic raw materials, medical devices, and medical-grade high-purity collagen.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The invention discloses erianin PROTACs as well as a preparation method and application thereof, the structural formula of the erianin PROTACs is # imgabs0 #, E3 Ligand is pomalidomide, and Linker is an alkane chain or a PEG chain. The erianin PROTACs disclosed by the invention are superior to the prior art in the aspects of target protein degradation efficiency, anti-tumor activity, innovativeness, application prospect and the like, and show remarkable technical progress and practical value.

The invention relates to the field of prevention and treatment of respiratory diseases, and discloses a preparation method of a human respiratory syncytial virus preventive mRNA vaccine. The main components of the mRNA vaccine comprise mRNA for coding an antigen and lipid nanoparticles. According to the mRNA sequence provided by the invention, an independently researched and developed 5 'UTR / 3' UTR sequence and a codon optimization mode are adopted, and efficient translation of target protein can be realized. According to the mRNA vaccine provided by the invention, RSV F protein is selected as an antigen, and the RSV F protein sequence is optimally designed, so that stable pre-fusion conformation RSV F protein can be expressed, and after a mouse is immunized, the mouse can be induced to generate high-level RSV F protein specific binding antibody titer and neutralizing antibody titer.

The present disclosure relates to a novel class of bifunctional molecules that are useful in a targeted or selective degradation of a protein.