26 results about "Protein chain" patented technology

A complex bait data set construction method based on structure and sequence collaborative redundancy elimination belongs to the field of bioinformatics, and comprises the following steps: screening an initial protein complex structure set, removing entries containing nucleic acids, small molecules or non-protein chains, and selecting binary complexes meeting integrity and resolution requirements; secondly, structure clustering and sequence clustering are carried out based on three-dimensional structure similarity and sequence homology, combined comparison is carried out on the two results, and redundant compound entries which are highly similar in structure and sequence are removed; then, taking each cluster representative compound as a target, generating a plurality of groups of bait structures by using a molecular docking or prediction modeling method, and calculating a quality index; and finally, performing stratified sampling and proportion balance based on the score interval of the quality index, and constructing a high-quality protein complex bait data set with structure and sequence collaborative redundancy elimination and balanced quality distribution. The data set generated by the method has the advantages of low redundancy, high diversity and quality distribution controllability.

The invention discloses a prediction method for identifying a protein-protein interaction site. According to the method, a deep learning framework is constructed based on protein chain hierarchical graph construction and double-branch graph neural network learning. The method comprises the following steps: firstly, constructing a corresponding hierarchical graph for each protein chain, including a residue-level graph and an atomic-level graph; next, the method constructs an isotropic graph neural network (EGNN) module to extract residue-level embedding with isotropic features. After a GraphSAGE module is further used for extracting atomic-scale embedding, the method integrates layered graph features by using a comparative learning strategy so as to learn residue-scale and atomic-scale consistency embedding. Finally, an improved gated multi-head attention mechanism is used for fusion embedding weighting, and residue-level interaction classification prediction is performed through a multi-layer perceptron. A comprehensive evaluation result on a plurality of data sets shows that the method is remarkably superior to an existing method. In conclusion, according to the method, various advanced technologies such as graph structure denaturation, multi-scale modeling and comparative learning are fused, and an accurate and steady solution with expansion potential is provided for protein interaction prediction. The method can assist researchers in deeply recognizing key protein acting sites and promoting analysis work of cell signal channels and metabolic networks.

The invention discloses an amyloid-like protein mediated long-acting metal organic framework antifouling agent as well as a preparation method and application thereof. Belongs to the field of marine antifouling material preparation. The anti-fouling agent is based on MOF with an anti-fouling function, and amyloid-like protein is self-assembled on the surface of the MOF to form a compact protective shell layer. According to the preparation method, metal ions and organic ligands react and are connected through coordinate bonds to form an MOF structure, and MOF particles are obtained through centrifugation, collection, precipitation, washing and drying; under the action of a water-soluble organic reducing substance, the protein completely opens a disulfide bond to form an unfolded protein chain, and the protein chain is self-assembled to obtain an amyloid-like protein solution; and placing the MOF particles in an amyloid-like protein solution, and carrying out simple one-step incubation to obtain the amyloid-like protein mediated long-acting metal organic framework antifouling agent. The antifouling agent is prepared by adopting a novel green, high-stability, rapid and efficient mild modification method, and has the advantages of stability, durability, universality and high efficiency.

The invention provides a phosphorus-fixing lanthanum-based nano composite material as well as a preparation method and application thereof, and relates to the field of novel nano composite materials. According to the material, lysozyme protein fibers are taken as a structural template, a confinement space is constructed by utilizing the ultrahigh length-diameter ratio of the lysozyme protein fibers, and controllable synthesis of the sub-5nm high-dispersion lanthanum oxide nanoparticles is realized. Functional groups in a protein chain can be enriched and nucleation sites can be provided through electrostatic attraction and key coordination synergistic effect, so that the lanthanum-based protein fiber nano composite material is formed. When the material is used for treating phosphorus wastewater, efficient phosphorus removal and long-term phosphorus fixation are realized through an enrichment-conversion synergistic mechanism: phosphate ions are enriched through electrostatic adsorption, and thermodynamically stable lanthanum phosphate is generated in situ at lanthanum active sites. According to the method, gt is still kept in the presence of high-concentration equal competitive ions; the phosphorus removal rate is 90%, the generated lanthanum phosphate can be used as a slow-release phosphate fertilizer or an industrial phosphorus chemical raw material, and closed-loop conversion from pollution purification to resource utilization is realized.

The invention relates to a computer-aided drug design technology, and provides a protein affinity prediction method and device, electronic equipment and a computer readable storage medium, the method comprises the following steps: obtaining a three-dimensional structure of a protein, the three-dimensional structure comprises a plurality of protein chains, and each protein chain comprises a plurality of amino acids; determining a feature vector of each amino acid according to a connection relationship between the amino acids, wherein the connection relationship comprises intra-chain connection between the amino acids belonging to the same protein chain and inter-chain connection between the amino acids belonging to different protein chains; and inputting the feature vectors of all amino acids into a pre-trained prediction model to obtain binding free energy of the protein, and calculating the affinity of the protein according to the binding free energy. According to the method, through intra-chain connection and inter-chain connection between the amino acids, the determined characteristics of the amino acids are more comprehensive, and accurate prediction of the protein-protein binding affinity is realized.

The present invention discloses a bread digestibility improver and its application in the production of enzymolysis-resistant whole-wheat bread, and the field of whole-wheat bread processing. The present invention disperses gluten under alkaline conditions so that the protein chain is fully unfolded and then β-glucan is added, and a highland barley gluten / β-glucan complex is formed by molecular self-assembly to obtain a bread digestibility improver. The obtained bread digestibility improver is mixed with whole-wheat flour and gluten, and the enzymolysis-resistant whole-wheat bread is obtained through the steps of kneading, fermentation, molding, proofing, baking, etc. The present invention regulates the digestibility of starch in whole-wheat bread by adding a bread digestibility improver, greatly improves the resistant starch content in whole-wheat bread, and the obtained bread has a good taste and high nutritional value.

The application discloses a kind of amyloid-like protein-mediated long-acting metal organic framework antifouling agent and preparation method and application thereof, and belongs to the field of marine antifouling agent material preparation.The antifouling agent is based on MOF with antifouling function, and amyloid-like protein self-assembles to form a dense protective shell layer on the surface of MOF.In the preparation method, metal ions and organic ligands are connected by coordination bond to form MOF structure, and MOF particles are obtained by centrifugation, collection, precipitation, washing and drying;under the action of water-soluble organic reducing substance, the disulfide bond of protein is completely opened to form unfolded protein chain, and amyloid-like protein solution is obtained by self-assembly;MOF particles are incubated in amyloid-like protein solution by simple one-step method to obtain amyloid-like protein-mediated long-acting metal organic framework antifouling agent.The application uses a new method of green, high stability, fast and efficient mild modification to prepare antifouling agent, which has stability, durability, universality and high efficiency.

Methods for determining whether a pair of protein chains comprising a first chain and a second chain is likely to form a functional antigen binding protein are described. The method comprises: providing a query sequence pair comprising a sequence of a first protein chain and a sequence of a second protein chain to a deep learning model as input, the deep learning model is configured to take a pair of protein chain sequences as an input and to generate as an output a score indicative of a probability that the pair of protein chains forms a functional antigen binding protein, where the deep learning model comprises an encoder module and a classifier module, and wherein the deep learning module has been trained using pairs of training sequences from known antigen binding proteins. The methods can be used to identify antibodies from non-paired or single chain sequences. Related methods and products are described.

The invention relates to the technical field of drilling fluid, in particular to a shale gas water-based drilling fluid formula and a preparation method. The shale gas water-based drilling fluid is prepared from the following raw materials: bentonite slurry, a fiber material, starch, white asphalt, a stabilizer, a lubricant, calcium carbonate, barite and water. In the preparation of the shale gas water-based drilling fluid, the raw material of the fiber material comprises ketoprofen, ketoprofen is taken as an organic acid, can be subjected to solvation after being mixed with methanol and is used for modifying the soybean protein isolate, methanol is taken as a solvent to help dispersion and reaction, and by utilizing the hydratability and viscidity of the soybean protein isolate, the drilling fluid can be used for preparing the shale gas water-based drilling fluid. The viscosity of the drilling fluid can be increased, the formation of a filter cake is assisted through crosslinking of protein chains, and the high length-diameter ratio and strength of the multi-walled carbon nanotubes are utilized, so that a network structure can be formed in the drilling fluid, the viscosity can be increased, meanwhile, pores of the filter cake are filled, the permeability is reduced, the filter loss is reduced, and the use performance of the drilling fluid is improved.

1. Name of the Design Product: Graphical User Interface for Protein Sequence Design for Electronic Devices. 2. Use of the Design Product: For interaction with electronic devices. 3. Design Key Points of the Design Product: Lies in the interface content of the graphical user interface. 4. Picture or Photograph that Best Illustrates the Design Key Points: Front View. 5. Use of the Graphical User Interface: This graphical user interface of the appearance is used for protein sequence design. 6. Description of the Changing States of the Graphical User Interface: The front view is the initial interface for protein sequence design. In the upper part on the left side of the interface, it is used to input PDB codes and upload protein structure files (pdb files). In the lower part on the left side of the interface, there are option buttons for highlighting protein chains. Above the right side of the interface, the three-dimensional structure of the protein is displayed. In the middle of the interface, the sequence information of the protein is displayed, including: the current design number of the protein, the sequence length of the protein, and the folding panels of each chain of the protein. In the front view, select the folding panel of one of the chains, switch to the interface changing state diagram 1, expand the folding panel of the corresponding chain, and display the amino acid sequence of the corresponding chain. Among them, the abbreviations of each amino acid are displayed in order, and there is a corresponding serial number below each amino acid abbreviation; in the interface changing state diagram 1, click on the abbreviation of the amino acid to be modified, and switch to the interface changing state diagram 2. Above the folding panel, the serial number and abbreviation of the selected amino acid are displayed. In the interface changing state diagram 2, click on the design button on the right side of the sequence length, and switch to the interface changing state diagram 3; in the interface changing state diagram 3, the design results of the amino acids are displayed on the right side of the interface. Specifically, in the upper part on the right side of the interface, there is a "Temperature" adjustment button for adjusting the distribution smoothness of the probability histogram. The blue font below the "Temperature" adjustment button is the abbreviations of each amino acid displayed in order; below the selected amino acid, the replacement result is displayed. By default, the best replacement result of the selected amino acid is displayed. In the middle part on the right side of the interface, 20 candidate buttons are displayed, corresponding to 20 amino acids respectively; below the candidate buttons for the 20 amino acids, there is a corresponding probability histogram showing the predicted probabilities of the corresponding 20 amino acids. Among them, the amino acid with the highest proportion is used as the best replacement result, and by default, the best replacement result is highlighted. If the user wants to select other amino acids as replacement results, click on the corresponding candidate button, and the proportion of the candidate button corresponding to the probability histogram is highlighted. Below the selected amino acid, the replacement result selected by the user will be displayed. The three-dimensional structure of the designed protein is displayed at the lower part on the right side of the interface. A download button is displayed at the bottom on the right side for downloading the designed protein structure file.

The invention discloses a natural spider silk-based self-adaptive surgical suture and a preparation method thereof, and belongs to the technical field of biomedical materials. According to the surgical suture, natural spider silk serves as a raw material, protein chains in the spider silk are rearranged in order by regulating and controlling the moisture state, applying axial pre-stretching strain and conducting drying, annealing and shaping under the limited condition, and the suture material with the high strength and the environmental response characteristic is obtained. The surgical suture is based on natural spider silk, has excellent biocompatibility and inherent biodegradability, becomes an ideal candidate material for biomedical application, and is successfully applied to suture of skin, stomach and other parts. According to the invention, the spider silk protein chains are rearranged into the superfine nanofibers, so that the engineered natural spider silk suture with remarkably enhanced mechanical and functional properties can be produced, and the monofilament can realize the breaking strength of 2.0 GPa, the toughness of 480 MJ m < 3 > and the driving stress of 92.6 MPa.

The invention discloses a self-assembled triblock piezoelectric peptide, a preparation method thereof and application of the self-assembled triblock piezoelectric peptide in preparation of a high-performance piezoelectric material. The self-assembled triblock piezoelectric peptide is prepared from 3, 3-diphenylalanine (FF), a silk protein chain segment GAGAGS and tyrosine tripeptide YYY, and is characterized in that the self-assembled triblock piezoelectric peptide is prepared from the following raw materials: a polypeptide A and a polypeptide B; a 3, 3-diphenylalanine piezoelectric group is introduced to one end of the piezoelectric peptide, a GAGAGS silk protein chain segment structure is introduced to the middle of the piezoelectric peptide, and a hydrophilic chain segment of tyrosine tripeptide is introduced to the other end of the piezoelectric peptide; by regulating and controlling the temperature or pH and the like, self-assembly is carried out to form a highly-ordered nanofiber (tubular or linear nanofiber) structure, so that the piezoelectric peptide with remarkably enhanced piezoelectric property is obtained; furthermore, the piezoelectric peptide obtained by self-assembly is added into different medical polymer materials, so that a high-performance piezoelectric material is prepared, and the high-performance piezoelectric material has good biocompatibility and piezoelectric performance and has wide application prospects in the fields of flexibility and biomedicine.

The invention relates to the technical field of protein antigen epitope prediction, in particular to a deep learning method and system for protein antigen epitope prediction, and the method comprises the steps: collecting a protein chain with epitope annotation from a SabDab database, and constructing a data set for learning training; a three-dimensional structure of the protein is represented as a graph structure, nodes of the graph structure are amino acid residues, and edges are determined by spatial distances among the amino acid residues; taking the graph structure as input based on the data set, and pre-training to obtain a VQVAE model; and processing the graph structure according to an encoder, extracting node features as input to train a GT model, performing antigen epitope prediction based on the data set, and outputting the probability that each amino acid residue belongs to the antigen epitope. According to the method, key limitations in the aspects of feature characterization, long-range dependence modeling and data utilization are effectively solved, so that the prediction accuracy of the protein antigen epitope is remarkably improved on the whole.

The present disclosure provides a method, apparatus, and electronic device for obtaining the conformation of an antigen-antibody complex, relating to the field of artificial intelligence technology, and specifically to the field of bio-computation technology. The specific implementation scheme is as follows: obtaining an amino acid sequence, where the amino acid sequence includes an antigen sequence and a reference antibody sequence; performing a multiple sequence alignment (MSA) on the amino acid sequence to obtain the MSA sequence of each protein chain and the reference conformation corresponding to the protein chain; and obtaining the conformation of the antigen-antibody complex based on the amino acid sequence, the MSA sequence of the protein chain, and the reference conformation.

The present invention relates to novel heterodimeric multispecific forms of multiple antibody variable domains, which comprise a core of two separate pairs of variable domains, wherein all two variable light domains and two homologous variable heavy domains are tandemly located on two separate protein chains, respectively.

The invention belongs to the technical field of formula food for special medical purposes, and particularly relates to liquid special medical nutritional food containing whey protein and a preparation method of the liquid special medical nutritional food. The food is prepared from the following raw materials: maltodextrin, whey protein powder, phospholipid, citric acid fatty glyceride, stevioside, gellan gum, sodium carboxymethyl cellulose, microcrystalline cellulose, sunflower seed oil, rapeseed oil, medium chain triglyceride, macro minerals, a vitamin premix, a trace mineral premix, a functional premix, food essence and water. The pre-denatured whey protein powder accounting for 50%-100% of the total amount of protein is introduced, hydrophobic groups are exposed in advance, part of protein chains are effectively broken, a compact gel network formed by hydrophobic interaction and disulfide bonds among protein molecules in the subsequent high-temperature sterilization process is reduced, and thermal aggregation precipitation of the protein is inhibited from the source; the compounded stabilizer system and the pretreated protein have a synergistic effect to jointly construct a stable colloid system.

The invention relates to the technical field of gargle preparation, in particular to an oral gargle based on synthetic biological materials. Comprising the following steps: preparing a pre-crosslinked mussel protein adhesion network precursor solution, preparing a temperature-sensitive gel matrix, preparing a liquid main agent, preparing a nano-emulsion and preparing the oral gargle based on the synthetic biological material. Through sequential combination of tea polyphenol pretreatment and immobilized tyrosinase catalysis, the recombinant mussel byssus protein is subjected to directional chemical modification, in a weakly alkaline buffer solution, a catechol structure of tea polyphenol can be combined with a protein chain to play a role in pre-organization and stabilization, and the tea polyphenol plays a role in improving the stability of the mussel byssus protein while participating in network construction. The inherent antibacterial and antioxidant functions are integrated into a material matrix in situ, a mechanical stability foundation is created for the subsequent compounding of a protein network and temperature-sensitive hydrogel, the uniform coexistence of a protein phase and a hydrogel phase is ensured, and the wet adhesion strength of the protein material is essentially improved.

The invention belongs to the technical field of structural biology, and provides a cryoelectron microscope structure database building method and system and a storage medium, and the method comprises the steps: obtaining an input structure of each density map based on EMDB density maps; adjusting a protein chain in the input structure based on the density map to obtain a template structure; based on the density map, a protein sequence and unmodeled annotations in an input structure are adjusted, and the protein sequence and the unmodeled annotations matched with the template structure are obtained; performing structure prediction and optimization based on the density map, the protein sequence and the template structure to obtain a candidate structure; calculating a model-density map correlation coefficient of the candidate structure and the density map, and adjusting the candidate structure to obtain a final structure; and performing structure evaluation on the final structure, generating a structure annotation in combination with a protein sequence and an unmodeled annotation, and importing the final structure and the structure annotation into a database. According to the construction method of the cryoelectron microscope structure database, the structure quality in the database is improved.

The invention relates to a method for improving the quality of boneless chicken feet by adopting 60Co gamma ray irradiation, which comprises the following steps: pretreating the boneless chicken feet, and constructing a conformational prestress area with elastic residual tension in the tissue structure of the chicken feet in a manner of combining micro traction with low-temperature standing, so as to provide a structural tension guide basis for the irradiation process; on the basis of the conformational pre-stress area, 60Co gamma rays are applied, the irradiation dose is controlled between 2 kGy and 10 kGy, and a dynamic staggered cracking area is formed by inducing non-uniform energy distribution, so that directional cracking and reaction sites are exposed and reentangled in collagen and elastin chain segments, the tissue structure is remodeled, and the chewing elasticity is enhanced; according to the method, a protein chain segment directional reconstruction mechanism is constructed, a microscopic conformation forming path is controlled, and the interface structure regulation and control and flavor progressive release system is combined, so that multiple improvements on the tissue elasticity, the taste resilience, the flavor expressive force and the storage and transportation stability of the chicken feet are realized.

The present invention discloses a low-hysteresis and high-toughness soy protein adhesive and its preparation method and application. The metal coordination effect of calcium sulfoaluminate hydrate is used to induce entanglement of polyacrylamide macromolecular chains, so as to construct a soy protein-polyacrylamide chain entanglement polymer, and transmit external stress and dissipate energy through the sliding of entanglement sites, so as to improve the toughness of the adhesive layer under low hysteresis; at the same time, the polyacrylamide chain entanglement formed at the wood interface forms a large number of hydrogen bonds with the wood and the adhesive layer, respectively, to generate strong binding energy, thereby improving the interfacial force between the adhesive and the wood. The present invention introduces a chain entanglement structure into the soy protein adhesive system to construct a network structure in which protein and chain entanglement polymer are integrated, which helps to solve the problem that the adhesive cured adhesive layer is brittle and the interfacial force with the wood is weak, and provides technical support for improving the quality of artificial board products and promoting the large-scale application of soy protein adhesives.

The present disclosure relates to compounds, complexes, compositions, kits and methods for determining interacting and / or binding sites between e.g., proteins, proteins and nucleic acids, proteins and small molecules, or intrachain protein domains. The disclosure provides rapid and direct positive identification of the contact interface region between such molecules, and can be applied to individual interacting pairs, as well as large-scale or global interactions.

The invention discloses a protein electrostatic spinning fibrous membrane dressing for promoting scar-free healing of wounds. The protein electrostatic spinning fibrous membrane dressing comprises water-soluble globular protein, a disulfide bond reducing agent and a stabilizer (trifluoroethanol, a urea water solution, trifluoroacetic acid and the like). According to the dressing, disulfide bonds of water-soluble globular protein are broken through a disulfide bond reducing agent to form an expanded protein chain, and then the expanded protein chain is stabilized through a stabilizer; electrostatic spinning is carried out by controlling the direct-current voltage between the spray head and the roller receiver rotating at a high speed, the receiving distance and the rotating speed of the roller receiver, and the protein nanofiber membrane dressing with the oriented arrangement structure is prepared. The protein nanofiber membrane dressing inhibits excessive wound contraction by reducing expression of alpha-SMA, promotes wound healing at the same time, and has a remarkable anti-scar effect. The dressing has a wide application prospect in wound treatment, especially in clinical application of pursuing scar-free healing.

This invention discloses a biotechnology-based process and product for preparing deodorized mushroom concentrate, relating to the field of mushroom concentrate technology. The invention involves taking finely filtered mushroom broth, concentrating it, and controlling the solid content to obtain a mushroom concentrate. The mushroom concentrate is then adjusted to pH using sodium hydroxide or hydrochloric acid, heated, and immobilized bromelain and papain are added, stirred, and then immobilized flavor protease is added, stirred, and filtered to obtain the deodorized mushroom concentrate. Bromelain, papain, and flavor protease are selected. Bromelain and papain are endopeptidases that cleave long protein chains in mushrooms at different sites, providing more ends for the flavor protease. The flavor protease is an exopeptidase that hydrolyzes the protein fragments cleaved by bromelain and papain to form flavor amino acids. During the enzymatic hydrolysis process, the immobilized carriers of the three enzymes remove off-odors through adsorption.

This invention discloses a system, technology, and material composition for correcting eye condition. The system includes a processing device and an etching device. The processing device includes a pattern correction module configured to provide a selected two-dimensional pattern based on visually non-permanent input data instructing a user, and configured to generate operating instructions to form the selected two-dimensional pattern on the surface of the cornea via the etching device. The etching device is configured to etch the selected pattern onto the surface of a user's cornea. The material composition includes an aqueous solution containing a plurality of nanoparticles, the nanoparticles comprising magnetite nanoparticles encapsulated by biocompatible protein chains. The material composition can be used as eye drops, thereby allowing the nanoparticles to occupy the etched area on the cornea, thus maintaining the correction of eye condition.

A fermented protein-based meat substitute comprising:protein strands having a dimensionless aspect ratio ASP defined by:ASP=L2 / Axwhere:L is the average length of the long dimension of said strands; andAx is the average cross-sectional area of ​​the strand dimensions transverse to said long dimension;and wherein ASP is at least 35; anda mycelium of a fermenting microorganism;wherein the protein strands are held or connected together by the mycelium to form the fermented meat substitute, andwherein the protein in the protein strands comprises extruded vegetable protein.

A method is described for determining whether a protein chain pair comprising a first chain and a second chain is likely to form a functional antigen-binding protein. The method includes providing a query sequence pair comprising a first protein chain sequence and a second protein chain sequence as input to a deep learning model configured to receive the protein chain sequence pair as input and generate a score as output indicative of the likelihood that the protein chain pair will form a functional antigen-binding protein, the deep learning model including an encoder module and a classifier module, and the deep learning module has been trained using paired training sequences from known antigen-binding proteins. The method finds related use in identifying antibodies from amplicon or single chain sequences. Related methods and products are described.