25 results about "TARP Protein" patented technology

According to the present invention, a technique which enables the production of an antimicrobial peptide Persulcatusin at reduced cost has been developed. A plant cell into which a polynucleotide sequence that encodes a gene for a protease to which a signal peptide localized in an intercellular organelle is added and a polynucleotide sequence that encodes a Persulcatusin fusion protein to which a signal peptide localized in an intracellular organelle different from the aforementioned intracellular organelle is added are introduced is produced, wherein, in the Persulcatusin fusion protein, a sequence that is cleavable with the protease is disposed between Persulcatusin and a protein that fuses to the Persulcatusin. Thus, Persulcatusin, which can serve as a therapeutic agent for bovine mastitis, can be produced at low cost.

A complementation system including two fragments of photoactive yellow protein (PYP), or truncated fragments thereof, and its use with a fluorogenic hydroxybenzylidene rhodanine (HBR) analog for detecting interactions between biological molecules of interest, in particular between proteins of interest. Especially, a complementation system including a first PYP fragment having an amino acid sequence having at least about 70% identity with the amino acid sequence of SEQ ID NO: 23, or a truncated fragment thereof including at least 89 consecutive amino acids from the C-terminal end of the amino acid sequence; and a second PYP fragment having an amino acid sequence having at least about 70% identity with the amino acid sequence of SEQ ID NO: 34, or a truncated fragment thereof including at least 8 consecutive amino acids of the amino acid sequence, preferably 8 consecutive amino acids from the N-terminal end of the amino acid sequence.

This disclosure provides a method, apparatus, medium, and electronic device for predicting RNA-protein interactions; relating to the field of artificial intelligence technology. The method includes: acquiring an RNA-protein pair to be predicted; extracting features from the RNA-protein pair to obtain sequence features; vectorizing the RNA-protein pair to obtain RNA sequence representation vectors and protein sequence representation vectors; based on the sequence features, RNA sequence representation vectors, and protein sequence representation vectors of the RNA-protein pair, using an interaction prediction model to obtain predicted interaction values ​​for the RNA-protein pair; and determining the interaction between the RNA and protein based on the predicted interaction values.

The invention discloses a drug target affinity prediction method, electronic equipment and a computer readable storage medium, the method comprises the following steps: feature extraction is carried out on input small molecule SMILES and protein sequences, and the small molecule adopts 10 molecular fingerprints of RDK, Topological, MACCS, AtomPair, ECFP4, FCFP4, FCFP6, Avalon, Layered and Pattern to construct mixed fingerprint features; compressing the high-dimensional molecular fingerprint features to vector dimensions consistent with protein features through linear mapping to realize feature balance, and splicing to form a fusion feature vector; and performing nonlinear interaction and expression enhancement on the fusion features by adopting a multi-expert hybrid module, and finally outputting an affinity prediction value between the small molecule and the protein through a linear regression layer. Through fusion of multi-source chemical fingerprints and deep protein pre-training representation, higher feature expression ability and stronger model generalization are realized; the complexity of the model is reduced through feature mapping and a lightweight multi-expert hybrid model, so that the model has better stability and expandability.

The application relates to the fields of artificial intelligence and digital medical technologies, and provides a protein affinity change prediction method and related equipment, a first initial graph network of a protein before mutation is constructed, a second initial graph network of a protein after mutation is constructed; the first initial graph network is updated in the graph at least once, and the first initial graph network and the second initial graph network are updated between graphs at least once until a first target graph network is obtained; the second initial graph network is updated in the graph at least once, and the second initial graph network and the first initial graph network are updated between graphs at least once until a second target graph network is obtained; according to the first target graph network and the second target graph network, the affinity change between the protein before mutation and the protein after mutation is predicted, so that the prediction accuracy of the protein affinity change is improved.

The invention discloses a drug-target affinity prediction method based on text guidance and a hybrid expert network, and belongs to the technical field of biological information. The method aims at solving the problems that an existing drug-target affinity prediction method is only limited to single-modal feature extraction, and complex nonlinear interaction relations between drugs and proteins are difficult to capture. Based on drug molecular structure data, protein sequence data, text description information and drug-target affinity data, unified input features are formed through standardization and multi-modal feature construction, a structure-text cross-modal alignment mechanism is introduced in comparative learning to carry out characterization learning on the multi-modal features of drugs and proteins, and the multi-modal features of the drugs and the proteins are obtained. Semantic alignment and feature decorrelation between different modal representations of the same entity are realized through joint optimization of InfoNCE loss and Barlow Twins loss; four heterogeneous expert networks are determined in combination with a gating network and a multi-head cross attention mechanism to capture drug-target interaction, and adaptive weighted fusion is performed to realize prediction.

The application discloses a CUT&Tag method applied to plant pollen. The CUT&Tag method of plant pollen protected by the application contains Percoll in a nuclear extraction solution, so as to remove starch in pollen cells. After the starch is removed, the method further comprises a step of removing polysaccharides and polyphenols in the pollen cells by using a buffer containing 1,6-hexanediol and glycerol. The concentration of the Percoll in the nuclear extraction solution can be 60%. Experiments prove that the CUT&Tag method of plant pollen established in the application can effectively remove polysaccharides, polyphenols, high starch and other impurities rich in the pollen, and can effectively identify the interaction between DNA and proteins in the plant pollen. The method of the application can also be popularized to the research on the interaction between DNA and proteins in other tissues of corn or other plants.

Systems and methods are disclosed for building a regression-based machine learning algorithm to predict native and near-native binding confirmations between two or more proteins. They include receiving training data, the training data representing a plurality of protein to protein docking poses based on one or more hotspots and a set of descriptors characterizing interaction interface features of one or more of the plurality of protein to protein docking poses. The systems and methods further specify a dedicated training data set based on a variety of single domain interactions. The training data set comprises an information space required to create dedicated regression and classification models in order to identify near native binding configurations. A specific and diverse set of interaction interface descriptors is calculated to provide the foundation for both, the regression and classification models.

Characterization of the binding dynamics at the interface between any two proteins that specifically interact plays a role in myriad biomedical applications. The methods disclosed herein provide for the high-throughput characterization of the specific interaction at the interface between two protein binding partners and the identification of functionally significant mutations of one or both protein binding partners. For example, the methods disclosed herein may be useful for epitope and paratope mapping of an antibody-antigen pair, which is useful for the discovery and development of novel therapies, vaccines, diagnostics, among other biomedical applications.

The application discloses a conformation prediction method. The method comprises the following steps: acquiring a conformation prediction model, and acquiring structure data of a complex to be predicted; wherein the structure data of the complex to be predicted comprises structure data of a ligand and structure data of a target protein; inputting the structure data of the ligand and the structure data of the target protein into the conformation prediction model to acquire ligand features and target protein features; performing position embedding, cross attention and connection operation on the ligand features and the target protein features to obtain a connection result matched with the ligand features and the target protein features; and predicting a target binding conformation of the structure data of the ligand and the structure data of the target protein according to the connection result. The technical scheme of the embodiment of the application provides a new conformation prediction method, learns important features such as shapes between ligands and target proteins, improves the prediction performance of the binding conformation, and helps to find the optimal binding conformation.

The present invention relates to novel bifunctional molecules capable of undergoing bioorthogonal reactions and inducing proximity between two proteins, methods of inducing proximity between two proteins employing said novel molecules and bioorthogonal reactions and genetic code expansion, methods of evaluating protein-protein proximity interactions employing the novel bifunctional molecules, medical uses of the novel molecules, and methods of treatment of a disease involving post-translational modifications of a protein employing the novel bifunctional molecules.

A dual expression vector and a method are provided. The dual expression vector has a first multiple cloning site and a second multiple cloning site. The genes of the different proteins could be inserted into the first multiple cloning site and the second multiple cloning site of the dual expression vector for testing the interaction between the different proteins.

Disclosed is a method performed by a computing device. The method may include a method for training an artificial intelligence model by using interaction data between a protein and a ligand. The method may include: converting a binding structure between a ligand and a protein into at least one binding word in text form which is processable in an artificial intelligence-based Large Language Model (LLM); generating training data using the at least one binding word; and training the LLM using the training data.

Provided are devices and methods featuring a nanoelectronic interface between graphene devices (for example, field effect transistors or FETs) and biomolecules such as proteins, which in turn provides a pathway for production of bioelectronic devices that combine functionalities of the biomolecular and inorganic components. In one exemplary application, one may functionalize graphene FETs with fluorescent proteins to yield hybrids that respond to light at wavelengths defined by the optical absorption spectrum of the protein. The devices may also include graphene in electronic communication with a bio-molecule that preferentially binds to a particular analyte.

The present application relates to a kind of rapid in-situ detection technology based on the interaction between photocatalysis-based nanoparticles and protein, a method for separating and extracting protein interacting with nanoparticles, the method steps are as follows: preparation contains photocatalysis probe and labeled reaction substrate nanoparticles, or preparation contains photocatalysis probe nanoparticles and labeled reaction substrate is added to the sample containing protein;2) the nanoparticles of step 1 and the sample containing protein are contacted to form the interaction between nanoparticles and protein;the labeled reaction substrate in nanoparticles is activated by the laser irradiation of the wavelength corresponding to photocatalysis probe, and the protein interacting with nanoparticles is obtained;3) the labeled protein interacting with nanoparticles is separated using streptavidin-coated magnetic beads.

The present application relates to the technical field of drug screening, in particular to an effective mobility ratio screening method based on affinity capillary electrochromatography and application. A target protein is fixed by a MOF material, and a capillary electrochromatography column is prepared by in-situ growth method; a to-be-tested substance is passed through the column, and screening is realized according to the affinity between the to-be-screened component and the protein; the present application adopts effective mobility ratio as a quantitative screening strategy, and the binding affinity between the to-be-screened component and the target protein is directly and quantitatively represented by calculating the effective mobility ratio of the drug in the column containing the fixed phase and the empty capillary column, so that efficient, stable and sensitive screening of multiple active ingredients in a complex system is realized, the stability and reusability of the affinity capillary electrochromatography platform are improved, the drug screening strategy is improved, and the present application is expected to become an effective and reliable strategy for screening of thrombin inhibitors in natural products.

The present application belongs to the field of biotechnology, cell biology and microscopic imaging technology, and particularly relates to a method for observing the morphology of dinoflagellate chromosomes. The method comprises the following steps: performing molecular anchoring treatment on dinoflagellate cells, performing gelation treatment on the anchored dinoflagellate cells to form a cell-embedded hydrogel, performing denaturation and hydration expansion treatment on the cell-embedded hydrogel, and then performing DNA fluorescent staining to realize observation of the morphology of dinoflagellate chromosomes through optical microscopic imaging. The present application performs molecular anchoring treatment on the fixed dinoflagellate cells, so that the chromosomes can be connected with the expanded hydrogel network, and then denaturation treatment is used to eliminate the interaction between proteins, so as to realize isotropic expansion of the gel, thereby significantly enlarging the spatial scale of the chromosomes on the premise of maintaining the relative spatial configuration, and combining with the fluorescent dye, high-resolution visualization of the morphology of dinoflagellate chromosomes can be realized.

The present application relates to the technical field of cosmetic raw materials, and particularly relates to a preparation method, product and application of a tea flower extract with anti-photoaging effect. The preparation steps of the tea flower extract include: S1. pretreatment of tea flowers; S2. activation of biological enzymes; S3. first enzymolysis; S4. extraction; S5. filtration; S6. second enzymolysis; S7. purification, and the tea flower extract is obtained. The present application aims to make full use of tea flower resources, avoid the waste of tea flowers as waste, and develop a tea flower extract which can resist ultraviolet damage and maintain skin homeostasis by excavating the synergistic effect between polysaccharides and proteins in tea flowers, so as to realize more comprehensive and in-depth development and utilization of tea flower resources.

The invention belongs to the technical field of food processing, and discloses a salted egg white gel block and a preparation method thereof. The surface of the salted egg white gel block is in a compact net shape; the gel block salted egg white comprises salted egg white and a protein network formed by cross-linking glycosylated salted egg white peptide through glutamine transaminase; the glycosylated salted egg white peptide is a glycosylated compound formed by carrying out Maillard reaction on salted egg white peptide and lactose; the salted egg white gel block has the following texture characteristics: the hardness of the salted egg white gel block is 6.68 N to 11.37 N, the chewiness of the salted egg white gel block is 4.84 N to 8.00 N, and the elasticity of the salted egg white gel block is 89.68% to 100.76%. According to the method, macromolecular protein is cut into peptide fragments with proper molecular weight through trypsin enzymolysis, and a triple modification technology of lactose glycosylation modification and glutamine transaminase cross-linking is combined, so that the content of alpha-helix and beta-corner of the salted egg white peptide is reduced, the content of beta-fold is increased, amido bonds are promoted to be formed between proteins, and the salted egg white peptide is prepared. And a compact cross-linked network structure is constructed. The hardness, the elasticity and the chewiness of the salted egg white gel block are obviously improved.

Systems and methods are disclosed for building a regression-based machine learning algorithm to predict native and near-native binding confirmations between two or more proteins. They include receiving training data, the training data, representing a plurality of protein to protein docking poses based on one or more hotspots and a set of descriptors characterizing interaction interface features of one or more of the plurality of protein to protein docking poses. The systems and methods further specify a dedicated training data set based on a variety of single domain interactions. The training data, set comprises an information space required to create dedicated regression and classification models in order to identify near native binding configurations. A specific and diverse set of interaction interface descriptors is calculated to provide the foundation for both, the regression and classification models.

A method, apparatus, medium, and electronic device for predicting RNA-protein interactions are disclosed, relating to the field of artificial intelligence technology. The method includes: acquiring an RNA-protein pair to be predicted (S210); extracting features from the RNA-protein pair to obtain sequence features (S220); vectorizing the RNA-protein pair to obtain RNA sequence representation vectors and protein sequence representation vectors (S230); based on the sequence features, RNA sequence representation vectors, and protein sequence representation vectors of the RNA-protein pair, using multiple interaction prediction models to obtain multiple interaction prediction values ​​for the RNA-protein pair (S240); and determining the interaction between the RNA and protein based on the multiple interaction prediction values ​​(S250).

The application discloses magnetic nano titanium dioxide and a preparation method and application thereof in enrichment of low-abundance proteins. The application successfully prepares magnetic nano titanium dioxide (Fe3O4@SiO2@TiO2) by adopting a sol-gel method, which first pre-coats a layer of SiO2 on the surface of the iron core, and then coats TiO2; the pre-coating of SiO2 is more favorable to the formation of the TiO2 coating layer, and the surface properties of TiO2 are ensured from being destroyed, the protein adsorption performance of TiO2 does not decrease, and meanwhile, the magnetic property is endowed; and the application provides a more simple, rapid and efficient technical method for large-scale sample processing. The application realizes the enrichment of low-abundance proteins in various sample types by utilizing the coordination, electrostatic interaction, hydrogen bond interaction and Van der Waals force between the magnetic nano titanium dioxide and the proteins, effectively solves the interference caused by high-abundance proteins on the identification of low-abundance proteins during mass spectrometry detection, and the number of protein identifications can be increased by 100% to 700%.

A new class of molecular probes is provided for real, efficient, stable, and selective capture of protein-nucleic acid complexes inside cells. The molecular probes have a nucleic acid-binding functional group and a photo-reactive diazirine based functional group, separated by a linker of a selected length or with a multi-arm core, thereby generating a photocrosslink between nucleic acids and proteins in close proximity. This is useful in various chromatin research, including the study of interactions between transcription factors and DNA.

A method, a device, and a computer program for predicting the interaction between a compound and a protein are provided. A method for predicting the interaction between a compound and a protein, according to some embodiments of the present disclosure, may include: acquiring compound data for training, protein data for training, and training data including interaction scores; constructing a deep-learning model by using the acquired training data; and predicting the interaction between the given compound and protein by using the constructed deep-learning model. The interaction between the given compound and protein in an in vivo environment can be accurately predicted by training the deep-learning model, while excluding, from an amino acid sequence of the protein for training, amino acid sequences associated with a protein domain having a negative influence on the interaction.

The present disclosure relates to a method and device for training a relationship prediction model and predicting an interaction relationship, and relates to the technical field of machine learning. The method comprises: constructing a first data set according to first historical proteins and actual correlation relationship values between the first historical proteins; training a local feature extraction model according to the first data set to obtain a first interaction relationship prediction model, inputting a second historical protein into the first interaction relationship prediction model, and obtaining a first predicted correlation relationship value; constructing a second data set according to the second historical protein and the first predicted correlation relationship value, and training a global feature extraction model according to the first data set and the second data set to obtain a second interaction relationship prediction model; and constructing a target relationship prediction model according to the first interaction relationship prediction model and the second interaction relationship prediction model. The present disclosure improves the accuracy of the target relationship prediction model.