12 results about "Protein surface" patented technology

The invention relates to the technical field of food freezing, in particular to an anti-freezing agent for aquatic products as well as a preparation method and application of the anti-freezing agent. According to the invention, a binary compound system is formed by carboxymethyl chitosan oligosaccharide and sucrose ester, and based on a polar hydrophilic-hydrophobic synergistic protection strategy of saccharides, while the protection effect of a hydrophilic component on a protein surface hydration layer is fully exerted, a hydrophobic component is introduced to be directionally combined with a hydrophobic plaque of the protein; and full-range synergistic protection of hydrophilic coverage and hydrophobic shielding is realized, so that the concentration bottleneck of the traditional antifreeze agent is broken through under the condition of extremely low addition amount.

The invention provides a docking method, device and equipment for a protein surface structure and a drug target, and the method comprises the steps: obtaining the three-dimensional structure data of a protein, generating a surface mesh model based on the three-dimensional structure data, and extracting the multi-modal surface features of the protein; based on the surface mesh model and the surface features of the protein, constructing a multi-scale 3D graph convolutional network, and generating feature codes of the protein; performing nonlinear dimensionality reduction on the feature code of the protein based on an adversarial auto-encoder to generate a low-dimensional feature of the protein; and performing iterative reinforcement learning docking based on the low-dimensional features and ligand positions of the proteins to obtain an optimal docking result. Through the method and the device, the surface features of the protein are extracted more comprehensively and accurately, the calculation amount is greatly reduced, and the problem of poor docking accuracy in the prior art is solved.

This invention discloses nanoparticles based on transcytosis-activating ligands, their preparation method, and applications, particularly in the treatment of fundus diseases. The nanoparticles comprise an enzyme protein core and a polymer shell grown in situ on the protein surface. The polymer shell contains positively charged monomers, transcytosis-activating ligands, and neutral monomers. The nanoparticles can enhance the hydrolytic resistance of enzyme proteins (e.g., antioxidant enzymes), protecting them from degradation during transport and improving delivery stability. Simultaneously, the nanoparticles can also promote the efficient delivery of proteases (especially to the posterior segment of the eye), exhibiting good safety and showing excellent application prospects in the medical field.

The invention belongs to the fields of biotechnology, protein engineering and plant protection science, and particularly relates to an antagonistic protein mutant for cultivating a sweet potato composite virus disease (SPVD) resistant plant and application of the antagonistic protein mutant. The key pathogenic factor of the SPVD is the RNase3 protein coded by the sweet potato chlorotic stunt virus (SPCSV), and the RNase3 protein is used as an RNA silence suppressor (RSS) to destroy host immunity. The invention provides an SPCSV-RNase3 antagonistic protein mutant, which is derived from a natural antagonistic protein, namely a sweet potato trypsin inhibitor (IbSPLTI-a). According to the present invention, a compound structure model (such as construction through AlphaFold2) of wild type IbSPLTI-a and RNase3 is analyzed, and the IbSPLTI-a is modified by using a protein directed evolution strategy, particularly by using a hotspot amino acid scanning and protein surface design strategy, such that the high-activity inhibition mutant (such as IbSPLTI-a-m0805) is successfully created and screened; in-vivo and in-vitro function verification shows that the binding affinity of the mutant (such as IbSPLTI-a-m0805) and SPCSV-RNase 3 is remarkably enhanced, and the mutant shows a virus accumulation inhibition effect superior to that of a wild type IbSPLTI-a. The invention also provides a nucleic acid sequence for coding the mutant, a plant expression vector containing the sequence, and a method for culturing an anti-SPVD transgenic plant (especially sweet potato) by using the mutant. The invention provides a core gene resource and a technical path for genetic improvement of SPVD and development of a novel antiviral protein preparation.

This application relates to the field of bioassay technology, and more particularly to a detection reagent, kit, and application for bioanalysis. The detection reagent includes a conditioning medium, which comprises a buffer system. Through innovative component combinations and formulation optimization, it aims to reduce non-specific adsorption, improve the signal-to-noise ratio and detection sensitivity. In particular, the introduction of specific amino acid-based buffers can interact with hydrophobic regions on protein surfaces, reducing non-specific adsorption and significantly improving the performance of bioanalytical detection.

This invention discloses an EC hydrolase mutant with enhanced enzyme activity and tolerance, and its applications, belonging to the field of enzyme engineering technology. First, this invention identifies the disulfide bond site Q149C-F157C in the flexible region, constructing disulfide bonds between the α-helical structures on the protein surface, successfully significantly improving the stability of multiple aspects of the ES8 mutant V129S / I229M. Experimental data show that the mutant exhibits improved stability under different environments; the stability at 80℃ half-life, pH 4.0, and 20% ethanol is 8.14 times, 1.49 times, and 2.72 times that of Est8-CE, respectively. Molecular dynamics simulation analysis of protein structural changes shows that the introduction of disulfide bonds significantly reduces the RMSD and RMSF of Est8-CE, demonstrating enhanced structural stability and promising suitability for use in extreme environments, thus possessing greater industrial application value.

The present application belongs to the technical field of computational biology, and particularly relates to a protein surface multi-level modular representation method and an interaction interface prediction method. First, a protein manifold triangle mesh containing multi-modal physical and chemical characteristics is constructed. Then, based on the geodesic distance and local density field, the continuous surface is discretized into geometrically compact virtual residues using a restricted region growing algorithm. Next, an improved soft subspace clustering is used to mine atom patterns with specific functions, and a graph-guided manifold autoencoder is introduced to map them into high-dimensional embedding vectors. On this basis, a compatibility network is constructed by fusing statistical, semantic and physicochemical characteristics, frequent sub-patterns are mined, and the surface is resolved into a structured sub-pattern sequence. Finally, a graph neural network model is constructed for PPI prediction, and the prediction area is resolved into core modules and variable modules. The present application uses a unique four-level hierarchical structure and manifold geometry calculation to solve the problems of weak geometric perception and lack of interpretability in the prior art.

The application aims to provide a method and device for constructing a protein sequence prediction model, which comprises: determining corresponding surface point cloud information and sample protein sequence information of a sample protein based on sample protein information, wherein the sample protein information comprises protein sequence information and protein structure information, and sequence information belonging to the surface of the sample protein in the sample protein sequence information is masked; and training a corresponding protein sequence prediction model based on the surface point cloud information and the sample protein sequence information of the sample protein, wherein the protein sequence prediction model comprises a structure encoder, a sequence encoder and a corresponding decoder. In the construction of the protein sequence prediction model, the application introduces information of a protein surface for sequence prediction, which can effectively improve the prediction accuracy of the model and improve the surface accuracy of the predicted protein sequence.

The invention discloses a protein allosteric site identification and determination method and device, and the method comprises the steps: obtaining allosteric site data of protein, the allosteric site data comprising protein 3D structure data and protein amino acid feature data; inputting the allosteric site data into a trained integrated learning model, and scoring and sorting the allosteric site data; and identifying and determining the corresponding position of the allosteric site data with the score higher than a preset value on the protein as the protein allosteric site. According to the method, the information of the amino acid near the cavity on the surface of the protein is introduced, and the integrated learning model is utilized to process the obtained allosteric site data, so that the allosteric site of the protein molecule can be determined more quickly and accurately, the application range is wider, and the efficiency is higher.