3645 results about "Protein target" patented technology

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 various molecular constructs having at least one cellular-targeting element, one protein-targeting element, and one ubiquitin ligase recruitment element, wherein these elements are conjugated in a manner that facilitates cellular uptake and targeted degradation of specific proteins within cells. The present invention further relates to multifunctional proteolysis-targeting chimera conjugates and uses thereof for the treatment of diseases or disorders such as immune and inflammatory diseases, infectious diseases, cardiovascular diseases, endocrine disorders, and various cancers that are otherwise resistant to traditional therapeutic regimens.

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 modified Trim 21 (delta Trim 21)-based target protein degradation control system, which takes a modified protein delta Trim 21 degradation system as a basis, takes blue light, abscisic acid, Danoprevir and / or MK-5172 as inductors, controls the combination of target protein and delta Trim 21 by introducing and modifying a controllable protein dimerization system on the basis of the modified protein delta Trim 21 degradation system, and improves the target protein degradation control system. The protein targeted degradation capable of being modularly regulated and controlled is carried out. Two regulation and control modules, namely a target protein recognition module and a degradation module based on E3 ubiquitinase, are constructed by splitting and recombining a controllable protein dimerization system and a delta Trim 21 structural domain. The invention also discloses a degradation device comprising the system. The invention also discloses a method for accurately regulating endogenous proteins (degradation of proteins related to tumorigenesis) by using the programmable regulation type modular protein degradation device, so as to achieve the effect of specifically killing tumors. The invention also provides a eukaryotic expression vector and a regulation method of the programmable regulation type protein targeted degradation device.

The present invention relates to a method for the production and purification of a protein of interest, and more particularly, the present invention relates to a fusion protein comprising: a self-aggregating peptide, a linker peptide comprising an amino acid sequence selected from an elastin-like polypeptide (ELP) or a protease propeptide (ProP) and the spacer comprising a cleavage site, and a protein portion of interest, and a method for column-free one-step purification of a target protein by expressing the fusion protein.

The invention discloses a mutant protein screening method and device and related equipment thereof, and relates to the technical field of material determination and analysis. The method comprises the following steps: respectively carrying out simulated single mutation on a plurality of amino acid sites in a protein sequence of a target protein to obtain a plurality of simulated mutant proteins, and determining adaptability evaluation values F of the plurality of simulated mutant proteins based on a target protein language model; determining structural stability values S of the plurality of simulated mutant proteins based on a protein folding energy calculation tool; performing normalization processing on the adaptability evaluation value F and the structural stability value S, and performing single-index sorting and comprehensive-index sorting on normalization processing results; and screening the plurality of simulated mutant proteins according to the target screening number, the single index sorting result and the comprehensive index sorting result to obtain a target mutant protein. According to the method, the protein design transformation difficulty can be reduced, the mutant protein screening effect is improved, the calculation amount required by protein transformation and screening is reduced, and the consumption cost is reduced.

The invention provides a preparation method of an electrochemical biosensor based on a PLL-coated VPAu modified nuclear track membrane, and belongs to the field of electrochemical biosensing analysis. The electrochemical biosensor for detecting troponin is constructed by adsorbing a PLL (at) VPAu composite nanomaterial synthesized by an ultrasonic liquid phase assisted method into a nuclear track membrane pore channel through electrostatic interaction and connecting a specific recognition molecule of a target to the surface of the pore channel modified by the PLL (at) VPAu by utilizing a bonding reaction mechanism. After a target object is captured by the recognition molecules connected to the nuclear track membrane, the effective aperture of the nuclear track membrane nanopores is reduced, and the electrochemical workstation can convert the biological behavior into an electric signal, so that high-sensitivity and rapid detection of the target protein is realized. The method has very important theoretical and practical significance in the field of medical diagnosis, and a new direction is provided for application of the nuclear track membrane in the field of analysis and detection.

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 present disclosure provides methods and reagents useful for analyzing protein-protein interfaces such as interfaces between a presenter protein (e.g., a member of the FKBP family, a member of the cyclophilin family, or PIN1) and a target protein. In some embodiments, the target and / or presenter proteins are intracellular proteins. In some embodiments, the target and / or presenter proteins are mammalian proteins.

The invention discloses a traditional Chinese medicine multi-target interaction prediction method based on a Transform architecture. The method comprises the following steps: creating an interaction label of a medicine and a target; respectively carrying out feature extraction on the traditional Chinese medicine component data and the target protein data; fusing the traditional Chinese medicine component feature vector and the target feature vector through a cross attention mechanism; creating a data set by using the feature vector F and an interaction label of the drug and the target; the method comprises the following steps of: constructing and training a Transform encoder; inputting a to-be-detected medicine-target data set into the trained Transformer encoder, outputting a probability value of interaction between each target and the traditional Chinese medicine component, and when the probability value of interaction between the traditional Chinese medicine component and the target is greater than a preset threshold value, determining that interaction exists; the invention provides an efficient and accurate traditional Chinese medicine multi-target interaction prediction means, and is beneficial to acceleration of traditional Chinese medicine research and new medicine development processes.

The invention relates to the technical field of pharmacology and artificial intelligence, and provides a compound-target interaction prediction method and system, and the method comprises the steps: constructing a knowledge graph containing compounds, target entries and disease entities, and the correlation of the compounds, the target entries and the disease entities; inputting the knowledge graph into a multi-modal feature extraction model, and extracting topological information and internal semantic information in the knowledge graph; wherein the multi-modal feature extraction model comprises an ICD2Vec layer, a graph Transformer, a gene ontology embedding layer and an attention mechanism-based graph neural network, and the ICD2Vec layer, the graph Transformer, the gene ontology embedding layer and the attention mechanism-based graph neural network are respectively used for extracting disease semantic features, compound semantic features, protein target semantic features and topological features of a knowledge graph; performing knowledge fusion on the extracted semantic features and topological features of the knowledge graph through a feature fusion module, and obtaining a compound-target prediction probability matrix through a knowledge graph embedding method; and according to the compound-target prediction probability, performing descending sorting on the input compound-target interaction, and outputting a target recommendation list.

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.

A high-throughput screening method for an aptamer, and a use. An implementation route is divided into three parts, a first part is construction of a library of a pre-enriched aptamer and sequencing same, a second part is generation of an aptamer cluster on a slide, and a third part is combination of a target protein and the aptamer cluster. Provided is a method for high-throughput rapid detection of an aptamer. The method has the beneficial effects: sequencing time, sequence synthesis and affinity re-verification time are saved, the affinity verification costs are greatly saved, rapid screening of aptamers can be promoted, and the actual use of aptamers is promoted along with the increase in the types and numbers of aptamers. The existing platform BGISEQ 500 of Beijing Genomics Institute can be used to grow an aptamer cluster on a chip by using DNB as a template, and the use of sequencers is expanded by integrating sequencing, sequence synthesis and affinity identification.

The invention discloses a recombinant human-derived II-type collagen, a nucleic acid molecule, a carrier, a cell, a composition and application thereof, and relates to the technical fields of genetic engineering and synthetic biology. The recombinant human-derived II-type collagen comprises a calcium ion binding domain and has an effect of promoting osteoblast proliferation, so that the recombinant human-derived II-type collagen has a good application prospect in preparation of medical instruments and compositions. The recombinant human II-type collagen provided by the invention can be expressed in a common expression system, and has the advantages of high expression quantity and no endotoxin hidden danger. Moreover, the recombinant human II-type collagen does not carry purification labels such as histidine labels and the like, and can be purified through a molecular sieve, so that the target protein can be directly obtained finally, and the purification labels such as the histidine labels and the like do not need to be additionally cut off. The production method of the recombinant human-derived II-type collagen is suitable for industrial large-scale production, and the prepared product does not have an animal-derived infection source, so that the biological safety is higher.

The invention relates to the field of biological medicines, and particularly provides a purification method of an anti-IL-11 monoclonal antibody, which comprises the following steps: affinity chromatography: preliminarily purifying and concentrating a target protein containing the anti-IL-11 monoclonal antibody, and carrying out virus inactivation on a protein product in an acid solution to obtain a protein sample; anion exchange chromatography: carrying out secondary purification on the protein sample, and collecting flow-through liquid; and cation exchange chromatography: carrying out refined purification on the flow-through liquid to obtain a protein solution of the anti-IL-11 monoclonal antibody. A proper purification process is provided for the anti-IL-11 monoclonal antibody, the purification efficiency and the recovery rate of the anti-IL-11 monoclonal antibody are improved, flow-through liquid collected by anion exchange chromatography directly enters cation exchange chromatography without treatment, sample treatment or solution replacement is not needed in the process, the purification time is saved, and the purification efficiency and the recovery rate of the anti-IL-11 monoclonal antibody are improved. Impurities are prevented from being introduced in the purification process, the purity of purified protein is improved, and high practicability is achieved.

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 provides recombinant IV-type collagen as well as a preparation method and application thereof, and relates to the technical field of biochemical engineering. The preparation method of the recombinant IV-type collagen comprises the following steps: obtaining a codon from amino acid of the recombinant IV-type collagen, further obtaining a target gene, and synthesizing the target gene onto an expression vector to obtain a plasmid; transferring the plasmid into an expression host, and screening a positive transformant to obtain an expression strain; and culturing the expression strain, and expressing the target protein to obtain the crude recombinant IV-type collagen. The high-purity recombinant IV-type collagen is prepared through a genetic engineering technology, and the recombinant IV-type collagen can significantly down-regulate inflammatory factors and reduce inflammation; heat injury can be reduced by up-regulating heat shock protein; after heat injury, the recombinant IV-type collagen can obviously lower metal matrix protease of heat injury cells, and also can obviously promote gene expression of type I collagen and type III collagen, improve skin elasticity and moisture content and delay skin aging.

The invention discloses a molecule generation method and device, a storage medium, equipment and a program product, which are applied to a molecule generation scene. The method comprises the following steps: obtaining known modal protein information corresponding to a target protein; performing feature processing on the known modal protein information based on a feature processing module of the trained multi-modal language model to obtain multi-modal standardized features corresponding to the target protein; based on a molecule generation module of the trained multi-modal language model, performing molecule generation processing on the multi-modal standardized features to obtain a target molecule aiming at the target protein; the trained multi-modal language model is obtained by training based on a multi-modal data set, and the multi-modal data set comprises a sequence modal sample, a structure modal sample and a pocket modal sample of a sample protein corresponding to a sample molecule. According to the method, high-quality drug molecules can be generated under the condition that the target protein lacks certain modal information, and the drug design efficiency is improved.

The invention relates to the technical field of biological medicine, and provides a targeting exosome capable of delivering nucleic acid drugs and a preparation method of the targeting exosome. The exosome comprises a fusion polypeptide, the fusion polypeptide comprises a targeting protein molecule, an exosome transmembrane protein and a nucleic acid binding domain, and the exosome can load a nucleic acid molecule through binding of the nucleic acid binding domain. According to the invention, engineering modification is carried out on the exosome, so that nucleic acid molecules can be efficiently loaded into the exosome, and meanwhile, the exosome is endowed with the capability of targeting to specific target cells, tissues and organs.