202 results about "Protein structure" patented technology

The application discloses a method for recovering active glycoprotein Patatin from potato starch processing waste juice, and belongs to the technical field of protein extraction. The method is characterized in that the active Patatin protein is efficiently separated by using the combination of ammonium sulfate fractionation precipitation, anion exchange chromatography, Con A affinity chromatography and gel chromatography, and the obtained Patatin protein retains the original protein structure and biological function activity. Compared with the prior art, the method has better separation purity and higher efficiency, and the obtained Patatin protein has complete protein higher structure and function activity, and has a better application scene.

The invention relates to the technical field of atlas generation, in particular to a children cross-allergen risk dynamic atlas generation system, which comprises the following steps: firstly, determining sensitivity indexes according to immune response conditions of each type of allergens in all allergy detection processes; according to the similarity of each allergen and other allergens in two dimensions of allergen protein structure and IgE level, combining the sensitivity index, and comprehensively representing the cross reaction risk index of each allergen in each allergy detection process; then comprehensively determining the cross-allergy risk degree of various allergens in each season period according to the increasing trend of the cross-allergy risk index curve in each season period and the overall size of the cross-allergy risk index in combination with the characteristic that the cross-allergy reaction possibly caused by the season change is dynamically changed; and the cross allergen risk dynamic map generated in real time according to the cross allergen risk degree is more accurate.

The invention relates to the technical field of biology, and particularly discloses a recombinant protein for improving the curative effect of an ADC drug and application of the recombinant protein. The recombinant protein comprises a tumor cell surface targeting structure, a cell transmembrane structure and toxin molecules, the toxin molecule is connected and fused with the tumor cell surface targeting structure and / or the cell transmembrane structure; the tumor cell surface targeting structure is a protein structure capable of being specifically combined with a tumor cell surface target spot; and the cell penetrating structure is a protein structure for mediating the recombinant protein to penetrate through a cell membrane to enter the cell. The tumor cell surface targeting structure is used for specifically recognizing a target cell surface target spot, the killing activity of a conventional antibody is brought into play, then toxin molecules are brought into tumor cells through the cell transmembrane structure, toxin is released, the tumor killing effect is achieved, the ADC drug curative effect is improved, and the ADC drug application prospect is wide. The transmembrane efficiency of the cell transmembrane structure can be improved to 50-90%, so that the traditional ADC drug treatment window is improved.

The invention provides a vaccine target screening system based on calculation model simulation. The vaccine target screening system comprises a multi-source heterogeneous database, wherein the multi-source heterogeneous database integrates and standardizes pathogenic genes, protein structures, literatures and experimental data; the feature calculation module calls a calculation biological model to carry out structural analysis, immunogenicity simulation and stability prediction; the intelligent screening and sorting module applies a multi-objective optimization algorithm to perform parallel evaluation and outputs optimal target spots; a structure iteration optimizer automatically iteratively corrects the optimized target spots to generate a high-potential variant library; and the process suitability simulation module couples the variants with the preparation formula and the process parameters to simulate production storage behaviors and feeds back an optimization target. According to the invention, efficient screening and optimization of vaccine targets can be realized, the accuracy and efficiency of target screening are improved, the research and development cost is reduced, and the research and development process of vaccines is accelerated.

The invention relates to the technical field of aquatic product preservation, and discloses a natural eutectic solvent for maintaining the quality of frozen penaeus vannamei boone and application, the natural eutectic solvent is composed of choline chloride, sorbitol and water, the molar ratio of choline chloride to sorbitol is (1-5): (1-5), and the mass of water is 15-25% of the total mass of choline chloride and sorbitol. The invention also provides an application of the solvent, and the application comprises the following steps: soaking the penaeus vannamei meat in the solvent, and then freezing and storing the penaeus vannamei meat. A supramolecular network system is constructed through hydrogen-bond interaction between choline chloride and sorbitol, growth of ice crystals in the freezing storage process is inhibited, a protein structure is stabilized, and loss of unfrozen juice is reduced, so that the quality of the penaeus vannamei, such as texture, color and flavor, is maintained. The natural deep-eutectic solvent is natural and safe in component, low in heat, simple to prepare and convenient to apply, the application limitation of a traditional anti-freezing agent is solved, and the natural deep-eutectic solvent has a good industrial prospect.

A varicella-zoster virus (VZV) nanoparticle protein, and a preparation method therefor and a use thereof. The VZV protein comprises a partial or full sequence of an amino acid sequence of an extracellular region of a VZV gE glycoprotein, with W at position 200 mutation to C and L at position 245 mutation to C in the amino acid sequence of the extracellular region of the VZV gE glycoprotein. By means of the rational optimization design of the amino acid sequence of the VZV gE protein by means of protein genetic engineering, the VZV gE recombinant protein modified with amino acid mutations has increased stability and immunogenicity compared with the VZV gE protein. Moreover, by further designing the protein structure, the VZV gE protein is repeatedly displayed on the surface of ferritin nanoparticles with the desired epitopes exposed, thereby further enhancing immunogenicity.

The invention relates to a protein solubility prediction method and system based on GCN and an improved attention network. The method comprises the following steps: constructing a protein sequence data set; the method comprises the following steps: acquiring a protein three-dimensional structure file through AlphaFold3, and constructing a node feature vector; extracting position embedding, bidirectional projection direction features, a cross-node atom pair distance and a rotation relation, and constructing an edge feature vector; extracting global physicochemical property feature vectors of the protein; and constructing protein graph structure data, inputting the data to the graph convolutional network module and the improved graph attention network module, and performing training to obtain a graph convolutional network and an improved attention network model to perform protein solubility prediction. By extracting node and edge features and protein global physicochemical property features, subsequent model training is facilitated; and the two-way projection and the rotation relation enable the model to accurately sense the spatial orientation between residues, so that the model has more quantitative understanding on the protein folding and gathering driving force, and the accuracy of protein solubility prediction is improved.

A protein cryoelectron microscope structure assembling method based on point cloud registration belongs to the field of bioinformatics, and comprises the following steps: firstly, obtaining a cryoelectron microscope experimental density map after redundancy elimination and a corresponding protein structure, generating a simulation density map, carrying out uniform sampling and density vector calculation, and converting into point cloud data; training and evaluating a registration network based on the point cloud data; secondly, predicting a single-chain structure of a to-be-assembled protein compound by utilizing AlphaFold3, processing in the same way according to the training data, firstly registering the longest chain, performing local optimization by using an LBFGS optimization algorithm, and if a correlation coefficient is lower than a threshold value and the chain comprises a plurality of structural domains, improving the precision by splitting the structural domains and performing independent registration; finally, the remaining chains are gradually fitted to the density map according to the chain length sequence. According to the method, point cloud registration and local optimization are combined, and the protein structure assembling precision and speed under the low-resolution density map condition are remarkably improved.

The invention discloses a pretreatment method for improving water-retaining property and tenderness of low-temperature slowly-cooked chicken, and relates to the technical field of food processing.The pretreatment method comprises the steps that a chicken raw material is prepared, and unfreezing, surface drying and finishing treatment are conducted; the method comprises the following steps: soaking chicken in a pre-cooled weak acid solution to preliminarily relax a protein structure; transferring the chicken into a pre-cooled weak alkaline solution, promoting protein to be fully hydrated, and establishing an electrostatic repulsion network; immersing chicken in a precooled polysaccharide solution, and permeating to form a thermostable hydrophilic protective layer; and draining the chicken, performing vacuum packaging, and performing low-temperature slow cooking with precise temperature control. According to the method, the low-temperature slowly-cooked chicken can keep a stable hydration structure in the cooking process through a method for regulating protein and constructing an interface protection layer in stages, so that the water-retaining property, tenderness and flavor fusion degree of a finished product are improved.

A target protein drug binding prediction method based on meta learning and subgraph matching, by constructing protein structure, drug small molecule structure and its binding energy value, the meta learning training task is established according to protein grouping, that is, as the main model of the meta model; the sub model and its loss function for the protein prediction task are obtained after fine tuning of the meta model; then the task self-adaptive self-attention model is established to balance the optimization contribution of the sub model of each protein prediction task to the meta model, the weighted average is used for the loss function of the sub model of each protein prediction task to obtain the meta model loss function; then the meta model loss function is used to calculate the gradient, the meta model parameters are updated and optimized based on the preset learning rate, and after the training is completed, the trained meta model is used for fine tuning of the test set of new protein samples, the prediction sub model of the new protein is obtained, the sub model is used for prediction of the protein test set, and the evaluation result is obtained. The training method combining meta learning and subgraph matching can effectively avoid shortcut learning, enhance the generalization effect, and also solve the problem that the previous prediction model is difficult to predict the newly discovered protein.

The invention provides a full-atom protein structure design method based on a unified multi-modal Bayesian flow, and relates to the technical field of computers, the method comprises the following steps: obtaining belief parameters of protein; inputting the belief parameters into a unified multi-modal Bayesian flow model to obtain protein modal information output by the unified multi-modal Bayesian flow model; wherein the protein modal information comprises protein sequence modal information, protein position modal information, protein orientation modal information, protein skeleton torsion angle modal information and protein side chain torsion angle modal information; according to the protein sequence modal information, the protein position modal information, the protein orientation modal information, the protein skeleton torsion angle modal information and the protein side chain torsion angle modal information, protein rationalization design is carried out, and the full-atom protein structure of the protein is generated. According to the technical scheme, the stability of the full-atom protein structure is improved, and the error of generating the full-atom protein structure is reduced.

The invention discloses a protein and small molecule binding structure prediction method, device, equipment and medium, and relates to the technical field of protein and small molecule binding prediction.The method comprises the steps that a first modeling graph structure and a second modeling graph structure after a protein block and a to-be-tested small molecule compound are modeled respectively are obtained; the first modeling graph structure and the second modeling graph structure are input into a TANKBind model, and a predicted protein small molecule compound is output through the TANKBind model; generating a first topological file and a second topological file corresponding to the protein structure file and the small molecule structure file of the protein small molecule compound; performing molecular dynamics simulation by using a preset molecular dynamics simulation tool according to a simulation file generated by the first topological file and the second topological file to obtain a molecular dynamics simulation track file; and analyzing the molecular dynamics simulation track file, the first topology file and the second topology file to obtain a simulation analysis result.

The invention discloses a decolorized chlorella protein, a preparation method thereof and application of the decolorized chlorella protein in preparation of foaming liquid, and relates to the technical field of biology. The preparation method comprises the following steps: extracting and decolorizing chlorella protein of chlorella pyrenoidosa, and drying to obtain the decolorized chlorella protein. According to the invention, an efficient chlorella protein extraction technology suitable for industrial production is established; the chlorella protein is treated by adopting an ethanol decolorizing method, the influence of two decolorizing methods and different decolorizing times on the decolorizing effect, the protein structure and the physicochemical property is systematically evaluated, the decolorized chlorella protein foaming liquid is prepared and represented, and the application effect of the decolorized chlorella protein foaming liquid in the foamed coffee is verified. Results show that the decolorized chlorella protein provided by the invention has excellent foaming characteristic, has the advantages of high protein purity and high sensory acceptance, and provides important technical support for efficient extraction of chlorella protein and widening of application range of chlorella.

The application relates to a mycobacterium tuberculosis drug resistance phenotype prediction method and device and a computer device. The method comprises the following steps: determining a to-be-predicted mutation site which does not exist in a mutation phenotype annotation database from at least one mutation site of a target gene fragment of to-be-processed mycobacterium tuberculosis according to gene sequencing data of the mycobacterium tuberculosis; converting protein structure change data of the to-be-predicted mutation site according to protein structure data corresponding to the to-be-predicted mutation site; inputting the protein structure change data of the to-be-predicted mutation site into a drug resistance phenotype prediction model to obtain a predicted drug resistance phenotype of the to-be-predicted mutation site; and determining a drug resistance phenotype prediction result of the to-be-processed mycobacterium tuberculosis based on the predicted drug resistance phenotype of the to-be-predicted mutation site and drug resistance phenotypes of the mutation sites except the to-be-predicted mutation site. The method can effectively improve the mycobacterium tuberculosis drug resistance phenotype prediction efficiency.

The invention discloses a ligand molecule generation method based on multi-modal information guidance, which fully captures high-precision interaction between a protein pocket and a ligand molecule, not only considers structural information of the ligand molecule, but also performs sufficient information guidance aiming at a protein target point molecule generation method. The generated molecules have diversity, and the requirements of subsequent biological docking experiment verification are met; on the other hand, the potential interaction relationship between the protein structure characteristics and the ligand molecules is utilized, different protein targets are fused in the diffusion process, and various modal information is balanced to guide the generation of the ligand molecules.

This invention provides a method for preparing low-bitter rice protein peptides, relating to the fields of food processing and bioengineering. The method involves preparing rice protein slurry, followed by ultra-fine pulverization using an ultra-high pressure, high-energy fluid mill. Through the synergistic effect of extreme shearing, cavitation, and impaction, the dense protein structure is efficiently disrupted, exposing enzyme cleavage sites and directionally reconstructing hydrophobic ends. Subsequently, a combination of endopeptides and exopeptides is added for enzymatic hydrolysis. The endopeptides achieve efficient hydrolysis, while the exopeptides precisely remove terminal hydrophobic amino acids, controlling bitterness generation at its source. This invention overcomes the technical bottlenecks of low efficiency and high bitterness associated with traditional enzymatic hydrolysis. The resulting rice protein peptides have a degree of hydrolysis exceeding 11%, a protein recovery rate exceeding 55%, and a bitterness value far lower than commercially available products. Furthermore, the process is continuous and the conditions are mild, making it suitable for high-end fields such as infant formula, sports nutrition foods, and special medical foods.

The invention discloses a method for preparing a VA-ECMO lung injury treatment medicine by using ginkgolide A to regulate YARS1, and relates to the technical field of biological medicine, the method comprises the following steps: by using ginkgolide A as a YARS1 protein regulator, preparing the medicine for treating the VA-ECMO lung injury through virtual screening, binding affinity confirmation and cell efficacy confirmation; wherein the virtual screening is based on a protein structure model of YARS1, and bilobalide A with high affinity binding energy with YARS1 is screened out through molecular docking. It is proved that ginkgolide A can effectively improve the lung ventilation function by regulating YARS1, repair the alveolar epithelial barrier structure and inhibit the inflammatory oxidative stress reaction, and the lung injury treatment effect and clinical transformation safety under the support of VA-ECMO are improved.

The present disclosure provides a method for embedding watermarks into protein generative models, comprising pretraining an SE(3)-equivariant watermark encoder and decoder, wherein the encoder receives a watermark code and generates a watermark-conditioned structure, and the decoder receives the watermark-conditioned structure and predicts an embedded watermark, and using a watermark-conditioned adaptation to encode a desired watermark code and generate an updated protein generative model by merging the desired watermark code into model weights from an original protein generative model, wherein the protein generative model is fine-tuned with a message retrieval loss and a consistency loss. The watermark-conditioned adaptation includes a gating vector derived from the watermark code. The method enables copyright authentication and tracking of generated protein structures while preserving structural integrity and biological functionality.

This invention belongs to the field of enzyme engineering technology, specifically providing a nitrile hydratase mutant and its application. The nitrile hydratase mutant is derived from *Aurantimonas manganoxydans*. Am NHase)SI859A was modified using rational design methods based on its three-dimensional protein structure. The resulting nitrile hydratase mutant ultimately achieved an enzyme activity that was up to 6.74 times higher than that of the wild enzyme, and its stability did not decrease significantly within two hours under heating conditions at 50°C.

This invention belongs to the field of bioengineering and virus-like particle (VLP) construction technology, specifically relating to an amino acid sequence with steric hindrance effect and its preparation method, the VP2 protein of recombinant porcine parvovirus and its nucleotide sequence, and the virus-like particles assembled from it. An amino acid sequence with steric hindrance effect, SEQ ID NO: 1, is provided. This invention provides an amino acid sequence with steric hindrance effect, which is fused to the N-terminus of the wild-type VP2 protein to obtain a modified VP2 protein. During the critical phase of the assembly of the modified VP2 protein into VLPs, SEQ ID NO: 1 at the N-terminus, due to its steric hindrance effect, pre-occupies and physically seals the 5-fold pores of the capsid, ensuring that the host background nucleic acid cannot enter the particle interior. Through protein structure modification, nucleic acid entry into the particle interior is physically blocked from the source, resulting in a pure antigen with extremely high hollowing rate.

This invention proposes a sperm staining kit based on a sequential staining strategy of tail pre-staining – rapid differentiation – enhanced nuclear staining. A gentle fixation with 4% paraformaldehyde and acetate buffer preserves the complete nucleic acid of the sperm head and the protein structure of the tail. The addition of PVP K30 and Tween-20 effectively inhibits non-specific adsorption and reduces staining background interference. Brilliant Green SF combined with sodium tartrate is used for specific staining of the sperm tail, followed by rapid differentiation to wash away non-specific staining. Nuclear staining is performed using toluidine blue O combined with sodium chloride and sodium tetraborate hydrate, specifically binding to DNA and enhancing color development stability, resulting in a blue sperm head and a light green tail with clear color contrast. This invention produces a clean staining background, accurately distinguishing the morphology of the sperm head, acrosome, and tail, significantly improving the accuracy of microscopic identification of abnormal sperm such as large-headed, small-headed, curled-tailed, broken-tailed, and short-tailed sperm. It is suitable for detecting sperm morphology and abnormality rates in breeding males.

A method may include identifying an input molecule. A structural representation indicative of a conformation of the input molecule may be generated. In some cases, the structural representation may be a canonical conformation signature or a multimodal representation encoding the three-dimensional structural features of the input molecule. An output molecule may be generated by applying a molecule design computation model to modify the amino acid residue sequence of the input molecule. The modifying of the ammino acid residue sequence may be guided by the structural representations of the input molecule in order to preserve the conformation of the input molecule. In some cases, one or more properties of the output molecule may be determined based on the structural representation of the output molecule. Related systems and computer program products are also provided.

The application relates to a protein completion method and device, a computer device and a storage medium. The method comprises the following steps: acquiring a training protein set; the training protein set comprises a plurality of training protein data; each training protein data comprises a protein structure with a missing functional domain, a complete protein structure, a protein sequence with a missing functional domain and a complete protein sequence; based on a preset initial model, the initial model is trained according to the training protein set to determine a target completion model; acquiring to-be-predicted protein data; the to-be-predicted protein data comprises a to-be-predicted missing protein structure, a to-be-predicted complete protein structure and a to-be-predicted missing protein sequence; based on the target completion model, protein sequence completion is performed on the to-be-predicted protein data to determine a complete protein sequence, so that the protein sequence is completely and accurately completed.

The invention discloses a non-phosphorus green composite water holding agent for improving the quality of chicken feet and an application method of the non-phosphorus green composite water holding agent, and belongs to the technical field of food processing. The raw materials of the non-phosphorus green composite water-holding agent comprise a dissociation water-holding agent and a crosslinking water-holding agent, and sodium bicarbonate provides an alkaline environment, so that protein is negatively charged, and electrostatic repulsion among carboxyl groups is increased. And collagen can be dissociated, the water binding capacity of meat is increased, the tenderness of the meat is improved, the bulkiness of the meat is increased, the protein network structure of the treated chicken feet is looser, and the water binding capacity is remarkably enhanced. Transglutaminase can catalyze cross-linking of glutamine and lysine in protein to form a tight three-dimensional network to physically wrap moisture; the protein structure can be stretched, hydrophilic groups are exposed, and the binding force with water is enhanced; meanwhile, combination of muscle fibers and collagen is strengthened, and the structure is stabilized. In addition, water loss caused by high temperature, freezing and the like in processing can be resisted, the succulent property and elasticity of meat are improved, and therefore the water-retaining property is improved.

The invention discloses a preparation method of egg white protein nanogel. The preparation method comprises the following steps: firstly, adding an ethanol solution with the volume fraction of 0.1-11.5% into egg white liquid, and diluting until the protein concentration is 1-2%; the egg white protein ethanol solution is subjected to microwave and ultrasonic combined treatment, the treatment time is set to be 3-7 min, ultrasonic is in an intermittent mode, the ultrasonic power is 200-800 W, and the microwave heating temperature is 65-75 DEG C. And finally, removing ethanol in the solution subjected to microwave ultrasonic treatment to obtain the egg white protein nanogel particles. The method is different from an existing'bottom-to-top 'preparation method and comprises the following steps: firstly, carrying out mild denaturation on protein by adopting low-concentration ethanol, changing a protein structure and an intermolecular acting force to expose a hydrophobic structure and expand the structure, and then moderately gathering and crushing the protein into microgel by utilizing an ultrasonic cavitation effect and a microwave heat effect; the nanogel has smaller particle size, higher system stability and interface stability, and the preparation method is simple and low in cost.

The invention discloses a protein retrieval method and a related device, which are applied to the field of big data. In the application, a data set of a protein structure is constructed, and a pre-training model is finely adjusted according to the data set to obtain a target model. Converting the protein structure into an embedded vector based on the target model, wherein the embedded vector is used for representing the similarity of the protein structure; and establishing an inverted list according to the embedded vector, wherein the inverted list comprises the quantization code of the embedded vector and the protein ID corresponding to the protein structure. And finally, based on the inverted list, retrieving the protein of which the structure meets similar conditions with the to-be-retrieved protein structure. According to the method and the device, when similar protein structures of the protein structures are retrieved, the related embedded vectors can be quickly positioned, and the protein retrieval efficiency is improved.

The invention provides an outdoor wool trouser fabric and a preparation method thereof, and the outdoor wool trouser fabric comprises a first protection layer which is arranged on the outermost side of the fabric and is used for providing wear-resistant protection and an external barrier; the second elastic supporting layer is arranged below the first protective layer, is interwoven and connected with the first protective layer and is used for providing dimensional elasticity and deformation support of the fabric; according to the invention, a full-link color spinning mode is constructed by adopting the first stock solution color filaments and the wool blended color spun yarns, and a low-temperature setting process at 130-140 DEG C is matched, so that a high-temperature and high-pressure environment at 130 DEG C or above in a traditional embryo dyeing process is completely avoided. The whole-process low-temperature steady-state processing mode effectively protects the natural protein structure of the wool fiber from being damaged by thermal denaturation, so that the original soft and glutinous hand feeling and biological activity of the wool are kept. In combination with the physical smoothing effect of oxidation desquamation treatment on fiber scales, the common needling feeling of the wool fabric is eliminated from the source, and the physiological comfort degree of close-fitting wearing is remarkably improved.

A protein structure-density map fitting method based on feature point matching belongs to the field of bioinformatics and structural biology, constructs a protein complex structure dataset, converts the structure into a unified resolution point set through voxelization and uniform sampling, and adopts the farthest point sampling to construct a multi-resolution point set to depict scale geometry; a deep learning network is adopted to learn the rotation equivariant and invariant features of the structure and the density map under multi-resolution, and a geometric self-attention mechanism based on nearest neighbor layer-by-layer expansion is introduced at the neck to enhance the representation; a coarse-to-fine feature point matching strategy is adopted in the training stage, combined with a superpoint matching loss, a point-level matching loss and a contrast rotation loss optimization; in the inference stage, multi-scale and hybrid sampling are adopted, and candidate poses are generated through translation mask, the candidate poses are screened and optimized according to the structure-density map fitting score, and the final fitting result is output. The present application can realize high-precision and high-efficiency structure-density map fitting under complex conditions.

The present invention proposes a system and method of reducing a footprint of a predictive computational model, and a method of predicting a protein structure using such a method of reducing footprint of the predictive model. The method of reducing a footprint of a predictive computational model comprises compressing the predictive computational model to come up with a compressed model, using a model compressor based on advanced network structures, and retraining the compressed model to adjust the compressed model and thereby produce an optimised predictive computational model with a reduced footprint.

The present application relates to a drug protein binding rate prediction method; first, the influence of drug protein binding on protein structure change is characterized by two-dimensional near-infrared correlation spectroscopy, and then a quantitative model is established for predicting the drug protein binding rate by using the characteristic spectrum area related to the drug protein binding. The two-dimensional correlation spectroscopy and machine learning are organically combined to establish a prediction model for the drug protein binding rate in the simulated physiological environment from the aspects of structure characterization signal extraction, separation and quantitative analysis; in actual operation, the sample processing method is simple, the time consumption is less, the cost is low, and the technology is suitable for various related scientific research activities, which can not only realize the protein binding rate prediction of one kind of drug (including acid / alkali type, salt type), but also can distinguish the protein binding rate difference of different products of the same drug, and has important significance for drug in vitro evaluation, generic drug consistency evaluation, pharmacodynamics research and the like.