2104 results about "Target protein" patented technology

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 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 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 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.

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 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 discloses an mRNA non-coding region sequence optimization method and system and electronic equipment, and the mRNA non-coding region sequence optimization method comprises the steps: constructing an initial candidate library according to a target protein; inputting the initial candidate library into a pre-trained mRNA sequence optimization model to obtain a prediction data set; performing multi-dimensional scoring and sequence optimization on the prediction data set to obtain a sequence recommendation group; performing biological verification on the sequence recommendation group to obtain an optimized mRNA sequence; wherein the prediction data set comprises a sequence ID, a sequence content, a prediction TE score and a confidence interval. According to the method, the translation efficiency of the mRNA sequence can be efficiently and accurately predicted, the candidate sequence with high expression potential is screened out, meanwhile, the consumption of computing resources is reduced, and the overall design cost is reduced.

The invention relates to the field of artificial intelligence assisted drug discovery, in particular to a protein large language model-based molecule generation and optimization method, which comprises the following steps of: acquiring amino acid sequence information and three-dimensional structure information of a target protein pocket; encoding the amino acid sequence of the protein pocket by using a protein encoder constructed based on a protein large language model to obtain a protein pocket feature vector; using a context encoder module to encode the context information according to a preset molecule generation mode (de novo generation or optimization based on a seed compound) to obtain a latent vector; and fusing the protein pocket feature vector with the latent vector. According to the method, accurate representation of the protein pocket is realized by utilizing the protein large language model, and a generation-screening-optimization iterative drug design strategy is developed by supporting a unified framework of two generation modes, so that the targeting specificity of generated molecules and the overall efficiency of drug design are improved.

The invention discloses an enzyme response type KRAS targeted protein degradation chimera as well as a preparation method and application thereof, and belongs to the technical field of tumor targeted therapy. The enzyme response type KRAS targeted protein degradation chimera disclosed by the invention is obtained by covalently linking three parts, namely a KRAS targeted protein degradation chimera, an enzyme response type amino acid sequence and a cell penetrating peptide amino acid sequence, wherein the KRAS targeted protein degradation chimera is obtained by linking a KRAS targeted peptide and a VHL E3 ligase ligand through succinic acid. The enzyme response type KRAS targeted protein degradation chimera releases the KRAS targeted protein degradation chimera under the action of ATG4 enzyme shearing, and shows a relatively strong lung cancer resisting effect in vitro and in vivo. The invention has significant advantages in the aspect of antitumor drugs, and opens up a new field for the development of targeted protein degradation chimera drugs. Enzyme response type KRAS targeted protein degradation chimera molecular structural formula as follows: # imgabs0 #

According to the GPX4 protein degradation agent and the application, the degradation agent serves as molecular glue to induce tumor cell ferroptosis and is different from an existing PROTAC technology, and the molecular glue can induce interaction between E3 ubiquitin ligase and target protein GPX4 and promote ubiquitination of the E3 ubiquitin ligase and the target protein GPX4. Compared with the existing GPX4 degradation agent, the molecular glue has the advantages of small molecular weight, high cell permeability and better druggability. The GPX4 molecular glue disclosed by the invention can be used for effectively degrading GPX4 and has a killing effect on various tumor cell lines. The preparation method is simple in synthesis route and mild in reaction condition, can be used for being developed into a new generation of GPX4 targeting drugs, and has great clinical application value and considerable market potential.

Provided are compounds that may bind a target protein, and result in modification of the target protein. The compounds may further bind a modifier protein. The modifier protein may carry out or enhance the modification of the target protein. The modification may activate or reactivate the target protein. Also provided are methods of using the compounds.

The invention provides a double-target degradation molecule based on a functionalized nucleic acid connexon and application thereof, and relates to the technical field of biological medicine, the double-target degradation molecule comprises an E3 ubiquitin ligase ligand at one end, a first target protein ligand at the other end, and the functionalized nucleic acid connexon located between the E3 ubiquitin ligase ligand and the first target protein ligand; the functionalized nucleic acid linker is a nucleotide sequence capable of specifically recognizing and combining a second target protein or a coding gene thereof, so that the protein level degradation of the first target protein and the nucleic acid level or expression level inhibition of the second target protein / gene are realized in the same molecule. Functionalized nucleic acid and a PROTAC strategy are organically combined, single-molecule double-target collaborative intervention is achieved, targeting efficiency and treatment potential are improved, higher flexibility and expandability are provided in synthesis and design, and a new molecular platform and technical route are provided for multi-target accurate treatment.

Methods and apparatus for determining protein and ligand sequence, structure, and docking site given a target protein sequence and structure are presented. A multicapitate transformer architecture with a number of heads including a sequence head and a structure head is introduced, wherein given a target protein sequence and structure, a candidate ligand is generated, wherein the transformer's sequence head yields the ligand sequence and the structure head yields the ligand structure and docking site. Non-capitate weights are shared between the output heads. In one embodiment, a discriminative feature localization method is used to optimize the target protein's input structure representation towards the desired ligand effect class. The methods and apparatus presented enable design and synthesis of both peptide ligands and small molecule drugs each with specified ligand effect categories.

The invention relates to a bifunctional compound and application thereof in targeted degradation of protein, the compound comprises a target protein binding compound and a cell-penetrating peptide which are connected, and the target protein binding compound is a compound capable of binding with cell membrane protein or extracellular protein or a derivative thereof; the target protein binding compound and the cell-penetrating peptide are connected through a linker or directly connected. According to the invention, it is found for the first time that specific extracellular proteins or cell membrane proteins can enter cells in an endocytosis manner and are degraded through lysosomes by using cell-penetrating peptides and small molecule conjugates. The method disclosed by the invention has the characteristic of delivering bioactive macromolecules through endosome wrapping without depending on a single receptor, and the effect of degrading the target protein by using the cells can be realized as long as specific binding micromolecules of the protein to be degraded can be found and coupled with the cell-penetrating peptide; a widely applicable strategy is provided for degradation of extracellular proteins and cell membrane proteins.

The invention discloses a recombinant human IV-type collagen as well as a preparation method and application thereof, and belongs to the field of bioengineering and protein engineering. According to the invention, three recombinant human IV type collagen new molecules are designed and screened on the basis of a part of sequences in a human IV type collagen alpha1 chain, soluble expression of a target protein is realized by using an escherichia coli system, and the recombinant collagen with the purity of more than 98% can be obtained through nickel column purification and acid regulation treatment. The recombinant collagen provided by the invention has good cell migration promotion activity and cell adhesion activity, and can be widely applied to the fields of tissue engineering, medical equipment, cosmetics, health care products and the like.

A system and method for generating thermostable variants of a protein is disclosed. The system receives a three-dimensional structure of a target protein and identifies mutable regions, including solvent-exposed residues and loop regions. Conserved and active site residues are excluded from mutation through a fixed-position mask. A message-passing neural network (MPNN) generates mutant sequences at unmasked positions, executed under multiple temperature parameters. Design scores based on Shannon entropy and log probability are computed, and high-confidence variants are selected. Predicted structures for selected variants are evaluated using structural and sequence-based features to compute stability scores. A ranked list of thermostable variants is generated. Top candidates undergo molecular dynamics simulations to compute dynamic metrics such as RMSD, radius of gyration, SASA, and ddG, and are re-ranked accordingly. The system enables accurate, constraint-driven protein design with high structural and functional fidelity, suitable for industrial and therapeutic applications.

The embodiment of the invention discloses an mRNA sequence generation method and device, a model training method and device and electronic equipment. The method comprises the steps that a target protein sequence, a target species text corresponding to the target protein sequence and target translation efficiency corresponding to the target protein sequence are obtained; determining a first target embedding corresponding to the target species text, determining a second target embedding corresponding to the target protein sequence, and determining a third target embedding corresponding to the target translation efficiency; splicing the first target embedding, the second target embedding and the third target embedding to obtain a fourth target embedding; a sequence generation model is called to map the fourth target embedding, a target mRNA sequence is generated, and the target mRNA sequence comprises a first coding region, a first untranslated region and a second untranslated region; according to the embodiment of the invention, the accurate and complete target mRNA sequence can be obtained, the application effect of the target mRNA sequence can be improved, and the method can be widely applied to scenes such as cloud technology, artificial intelligence and smart medical treatment.

The invention relates to the technical field of agricultural product detection, in particular to an agricultural product pesticide residue detection method and system. According to the method, the spatial orientation characteristics of molecular three-dimensional conformation parameters are introduced, a symmetry index set is established, matching calculation is carried out in combination with temperature distribution in chromatographic column operation parameters and the sample injection direction, accurate linkage of structural information and a chromatographic path is achieved, and through coupling analysis of response starting time and chromatographic regulation and control parameters, the accuracy of the chromatographic regulation and control parameters is improved. A detection time window taking a target protein signal as a reference is set, a response enhancement fragment with a significant rising rate is screened out in combination with a signal change trend in the window, and a target signal interval is obtained through weighted fitting, so that the specificity and pertinence of detection data are effectively improved, and the detection accuracy is improved through comparison of peak position time and response data. According to the scheme, accurate labeling of pesticide types in a complex sample is achieved, and the method has high stability and judgment accuracy under the scene that the sample heterogeneity is high and signal interference is complex.

The present invention provides compounds, compositions thereof, and methods of using the same for the targeted degradation of proteins, and the treatment of target protein-mediated disorders.

The invention discloses a lung squamous cell carcinoma drug discovery method and system based on disturbance response scanning analysis, and belongs to the field of biological medicines.The method comprises the steps that lung squamous cell carcinoma protein expression profile data is downloaded and preprocessed, a robust network module is constructed and multi-scale analysis and evaluation are conducted, and drug genomic data are obtained based on drug genomic data. Predicting the drug sensitivity of the patient by using machine learning, and generating a DRN through difference analysis of the drug sensitivity; predicting drug-target protein binding affinity in combination with deep learning, quantifying node sensitivity through a PRS technology, calculating a DPI disturbance score and ranking drug priorities; high-ranking drugs are screened through comprehensive sensitivity analysis and literature investigation; a drug for a lung squamous carcinoma cell line is screened out through a pre-experiment and is relocated. According to the framework developed by the invention, by integrating proteomics, pharmacogenomics and dynamic network analysis, systematic analysis is provided for relocation drug identification of lung squamous cell carcinoma, and good news is brought to treatment of lung squamous cell carcinoma.

The invention provides a tumor cell vaccine as well as a preparation method and application thereof. The preparation method comprises the following steps: acquiring and culturing tumor cells; enabling the cell membrane of the tumor cell to express a targeting antibody by utilizing a gene vector infection or gene editing technology, and enabling the targeting antibody to be used for enabling the tumor vaccine to be combined with the dendritic cell; performing overexpression of tumor specific protein on the cell membrane surface of the tumor cell by using a gene vector infection or gene editing technology; a nucleic acid substance of a target protein is wrapped by lipid nanoparticles to form a vaccine framework, and the nucleic acid substance can enable target cells to express directional chemotactic molecules; wherein the directed chemotactic molecule is used for transferring the dendritic cells to lymph nodes; the target vaccine is obtained by wrapping the vaccine framework with the cell membranes of the tumor cells, lymph node homing of the dendritic cells can be promoted, and the antigen presentation efficiency is increased and the immune effect is enhanced through tumor specific protein carried by the target vaccine and assisted by an immunologic adjuvant.

The invention belongs to the technical field of preparation of polypeptides, and particularly relates to a self-assembled polypeptide capable of responding to alkaline phosphatase and degrading Neuropilin-1 as well as a preparation method and application of the self-assembled polypeptide. The self-assembled polypeptide (formula I) provided by the invention has a self-assembly starting unit 'GNNQQNY (SEQ ID NO: 1)', can be self-assembled in vitro, and can form nanofibers after responding to alkaline phosphatase, so that not only can the critical self-assembly concentration of the polypeptide be reduced, but also the binding capacity with target protein can be improved. The material can degrade NRP1 on the surfaces of tumor cells and Treg cells, and has huge potential in promoting immunotherapy of tumors.

The invention discloses PROTAC capable of efficiently degrading a target protein and a preparation and delivery method of the PROTAC, a phase separation sequence and a designed double-targeting molecule are fused to respectively target the target protein and a proteasome, and phase separation of the PROTAC and the target protein is realized, so that efficient degradation of the target protein is realized. In addition, the delivery of PROTAC is also realized by encapsulating mRNA with LNP, and the target protein can also be efficiently degraded in cells, so that a way is also provided for the delivery of peptide or protein PROTAC into the human body.

The invention provides an automatic construction method and system of an active polypeptide, and the method comprises the following steps: firstly, determining a target metabolite, and obtaining a target protein having binding activity with the target metabolite; then, obtaining interaction information of the target protein and the target metabolite and secondary structure information of target protein residues; then, inputting the interaction information into a large language model LLM to obtain information of key residues of the target protein; and finally, inputting the structural information of the target protein, the information of the key residues, the secondary structural information and the constraint information into LLM to obtain an active polypeptide capable of being specifically combined with the target metabolite, namely the potential peptide aptamer. According to the method, by combining a biological simulation tool and a large language model, automatic batch generation of the active polypeptide is realized, the acquisition process of the polypeptide is greatly simplified, and a new strategy and a new way are provided for metabolite peptide aptamer design.