37 results about "TARP Protein" patented technology

The invention discloses a drug target binding affinity prediction method based on collaborative attention, and belongs to the technical field of natural language processing, and the method comprises the steps: building a CLAT-DTA prediction model comprising an input data representation module, a feature extraction module, an information fusion module and a prediction module; converting drug molecules into fingerprint representation, and pre-training protein sequence data by using ESM; extracting drug data by using Encoder, and extracting protein data by using Bi-LSTM (Bidirectional Long Short-Term Memory); fusing the drug target data using a collaborative attention mechanism; three-layer full ligation is used to predict drug target binding affinity. According to the method, important information can be better polymerized, and the binding affinity between the drug and the protein can be predicted.

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.

The application provides a boron-nitrogen-carbon nanosheet reinforced protein composite hydrogel, a preparation method and application thereof, and the protein composite hydrogel comprises the following raw material components: bovine serum albumin, boron-nitrogen-carbon nanosheets and a coupling agent, the coupling agent is configured to trigger the bovine serum albumin to be chemically cross-linked by itself to form a cross-linked network, and trigger the bovine serum albumin and the boron-nitrogen-carbon nanosheets to be chemically cross-linked to form a cross-linked network. The boron-nitrogen-carbon nanosheet reinforced protein composite hydrogel has good mechanical properties and biological activity, and can repair bone defects, especially large defects of load-bearing bones. The preparation method of the boron-nitrogen-carbon nanosheet reinforced protein composite hydrogel is simple and safe, only a small amount of coupling agent is introduced, the chemical bond cross-linking between proteins can be triggered, and the prepared hydrogel improves the defects of insufficient mechanical strength and insufficient biological activity of traditional hydrogels.

The invention discloses a protein interaction prediction method and device and a storage medium, and relates to the technical field of biomedicine, and the method comprises the following steps: S1, identifying functional groups of a two-dimensional protein structure diagram, S2, capturing geometric structure information of proteins, and obtaining atomic characteristics of two proteins, S3, defining one-dimensional sequences of the two proteins as U and V respectively, s4, updating the three-dimensional coordinates of the atoms in the U and the V, and obtaining the feature representation of the U and the V; s5, performing feature fusion on the two proteins to obtain a fusion vector; s6, predicting and analyzing an interaction result between the two proteins; a double-view Gram matrix considering functional group information is used for capturing missing protein three-dimensional structure information, and an improved sliding attention mechanism is used for replacing a traditional data-driven attention mechanism, so that the problems that the sliding attention mechanism does not consider the influence of residues in the same sequence and is only suitable for a one-dimensional sequence are solved; and thus, the protein-protein interaction prediction precision is improved.

Structure-based drug design using a computer, includes: identifying a protein target of pharmacological interest; identifying at least three different ligands for binding to the protein; for each of the ligands, determining a relative strength of binding between the ligand and the protein to form a corresponding complex; ranking the different ligands identified as forming complexes with the protein based on the determined relative binding free energies; and identifying one or more of the ranked ligands as candidates for the drug based on the ranking. Determining the relative strength includes: simulating, using the computer, a set of pairs of different ligands forming at least one closed thermodynamic cycle comprising a plurality of legs linking at least two different ligand pairs to determine multiple relative binding free energy differences for the set of pairs of different ligands; and determining, using the computer, a non-zero hysteresis magnitude associated with each closed thermodynamic cycle by summing the relative binding free energy differences for each of the ligand pairs that form a closed thermodynamic cycle.

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.

Disclosed herein is the use of DNA aptamer assemblies of varying DNA length, structure, and sequence to both bind to collagen and other proteins, to then act as a biocompatible, degradable, reversible, or permanent 3D crosslinkers between proteins, and to service as a biologically functional material when using the appropriate aptamer sequence. Therefore, disclosed herein are compositions comprising collagen fibers crosslinked with DNA aptamers. Also disclosed are devices and implants made from or coated with collagen fibers crosslinked with DNA aptamers. Also disclosed are methods of making collagen fibers. Also disclosed are kits for producing collagen fibers. Also disclosed herein are compositions DNA aptamers in a collagen fiber matrix that stabilizes the DNA aptamer.

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.

This application proposes a method, device, electronic device, and storage medium for constructing a mirror protein interaction map. The method for constructing a mirror protein interaction map includes: constructing a first graph neural network based on the original protein, and inputting the amino acid sequence of the original protein into the first graph neural network to predict the properties of the original protein and the interactions between the original protein and other original proteins; constructing a second graph neural network based on the interactions between the original proteins; inputting any amino acid sequence into the second graph neural network to predict the properties, three-dimensional structure, and interactions between the mirror protein and other proteins corresponding to the amino acid sequence; and constructing a mirror protein interaction map using the protein properties and three-dimensional structure as node information and the interactions between any two proteins as edges. This application can predict the structure and properties of mirror proteins and construct a mirror protein interaction map.

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.

This invention discloses a protein interaction prediction method based on a graph neural network, belonging to the technical field of protein interaction prediction. The method comprises the following steps: S1: amino acid sequence preprocessing; S2: extraction of protein structure and sequence information; S3: fusion of structure information and feature information; S4: construction of a graph neural network; S5: model network training; and S6: protein-protein interaction prediction. This method uses a fusion of two features as a protein feature expression; employs the ESM‑2 module to extract protein structural features from the amino acid sequence of the protein, eliminating the need for separate structural features; and employs an asymmetric loss function to effectively address the sample label imbalance problem in protein-protein interactions.

The present disclosure provides methods of determining association rates or dissociation rates between affinity reagents and proteins. The methods can be configured to monitor a large number of proteins in parallel, for example, using arrays of proteins that are contacted with solutions containing affinity reagents. The methods can be further configured to detect the arrayed proteins at single-molecule resolution. Accordingly the methods allow a large population of proteins to be monitored on an individual basis. As such binding kinetics and thermodynamics can be determined on a population level while allowing individual interactions to be evaluated.

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 disclosure provides methods of determining association rates or dissociation rates between affinity reagents and proteins. The methods can be configured to monitor a large number of proteins in parallel, for example, using arrays of proteins that are contacted with solutions containing affinity reagents. The methods can be further configured to detect the arrayed proteins at single-molecule resolution. Accordingly the methods allow a large population of proteins to be monitored on an individual basis. As such binding kinetics and thermodynamics can be determined on a population level while allowing individual interactions to be evaluated.

The present disclosure relates, in part, to improved therapeutic cells (e.g., comprising immune cells, such as T cells, e.g., human T cells) that encode or express: a collagen-binding payload; and a target-binding protein (e.g., a CAR, a TCR, a scTCR, a TruC, a TCR / CAR, a synNotch receptor, an IFP, or a multispecific T cell engager). In some embodiments, a collagen-binding payload molecule comprises a cytokine, a toxin, a polypeptide that inhibits an interaction between two or more proteins, an antibody or antigen-binding portion thereof, or a combination of any two or more of the foregoing. In certain embodiments, a collagen-binding payload comprises a cytokine (e.g., a human cytokine), such as a proinflammatory cytokine. In some embodiments, the cytokine is an IL-12. In some embodiments (e.g., in a T cell), expression of the collagen-binding payload is driven by binding of NFAT (nuclear factor of activated T cells) to a NFAT binding site (also referred to as NFAT binding motif). In some embodiments, one or more NFAT binding sites are present and can function as a promoter. In some embodiments, an inducible promoter is responsive to NFAT binding to the one or more NFAT binding sites, and the inducible promoter is operably linked to a sequence encoding the collagen-binding payload.

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 invention relates to a nanometer material and protein interaction data set construction method and device and a medium, and the method comprises the steps: processing a literature corpus, and carrying out the extraction to obtain one or more first information groups; performing semantic alignment to obtain unfilled structured feature data; filling the structural feature data in all the first information groups with missing features to obtain filled structural feature data; respectively calculating the interaction strength between the nano material in the first information group and each detected protein, and generating a first type of samples; according to all the first information groups from the same literature corpus, performing local filling to obtain a second type of samples; performing global filling according to all the first information groups from different literature corpora to obtain a third type of samples; and marking the first type of samples according to the interaction strength of the first type of samples, taking the second type of samples and the third type of samples as negative samples, and constructing a data set. Compared with the prior art, the method has the advantages that the data construction speed is increased, and the quality and number of samples are improved.

Embodiments of the present disclosure generally relate to compositions, methods, and systems for desiccation tolerance. Embodiments described herein also generally relate to compositions and methods for stabilizing a biological material. In an embodiment is provided a method that includes introducing an exogenous cosolute with an intrinsically disordered protein to induce aggregation or oligomerization of the intrinsically disordered protein.

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.