14 results about "Model protein" patented technology

The invention discloses a low-sodium myofibrillar protein flavor prediction method and a low-sodium myofibrillar protein flavor prediction device. The method comprises the following steps: firstly, constructing a protein-flavor interaction system containing myofibrillar protein, potassium chloride, kappa-carrageenan and representative aldehyde flavor compounds; then, by means of spectroscopy, rheometer combination, an electronic tongue and the like, protein conformational change, typical aldehyde molecule binding rate and taste features are represented in a multi-dimensional mode. And establishing a random forest prediction model, taking protein conformation parameters as input, and taking a flavor binding rate and effective taste intensity as output. The model can reveal cross correlation between protein conformations and functions and identify key conformation parameters that affect flavor. And in combination with quantification and prediction capabilities of the model, a conformation-flavor relationship can be systematically analyzed, a basis is provided for optimization of a low-sodium formula, flavor is enhanced while salty taste is increased and bitter taste is inhibited, and healthy and sustainable development of low-sodium meat products is promoted.

The invention discloses an application of a betaine-lactic acid deep eutectic solvent in promotion of protein intestinal absorption and an application in preparation of a protein intestinal absorption promoting agent. In the betaine-lactic acid eutectic solvent, the molar ratio of betaine to lactic acid is 1: (0.5-1.5). The invention further discloses a protein intestinal tract promoting absorbent which is composed of insulin, betaine and lactic acid, the concentration of the insulin is 2-5 mg / ml, and the molar ratio of the betaine to the lactic acid is 1: (0.5-1.5). According to the invention, model protein insulin is loaded by BL-DES, so that the intestinal absorption efficiency of insulin can be improved. The BL-DES stabilizes the insulin structure through the interaction of hydrogen bonds, Van der Waals' force and static electricity, reduces the viscosity of intestinal mucus, promotes model protein insulin bypass transport by reversibly opening epithelium tight connection, and significantly improves the transmembrane transport efficiency and oral bioavailability of protein. The research of the invention is of great significance to oral protein treatment.

This utility model relates to a quick-release adjustable explosion-proof protein separator, comprising a needle brush pump, a support base, a gas-water regulating valve, a gas-gathering and flow-slowing hood, a reaction cup, a collection cup connecting sleeve, and a collection cup. The needle brush pump is installed on the lower side of the support base, the gas-water regulating valve is located on the upper side of the support base, the gas-gathering and flow-slowing hood is installed on the gas-water regulating valve, the reaction cup is installed on the gas-water regulating valve and sleeved on the outside of the gas-gathering and flow-slowing hood, and the collection cup is connected to the upper side of the reaction cup through the collection cup connecting sleeve. A cylinder wall clamp is movably sleeved on the reaction cup, and an adjusting bracket is fixedly connected to the support base. The cylinder wall clamp is movably fitted on the adjusting bracket, and the adjusting bracket is provided with a fixing mechanism for fixing the cylinder wall clamp. This utility model protein separator achieves efficient water purification function by optimizing bubble generation and distribution, the bubble formation circulation mechanism, and an effective foam collection mechanism. It also allows users to quickly adjust the installation height as needed, improving the flexibility and adaptability of the equipment.

The invention discloses a method for simultaneously detecting protein phosphorylation and S-nitrosation, which comprises the following steps of: marking S-nitrosation protein by using a phosphoric acid affinity label, and converting S-nitrosation modification into a phosphorylation simulation type label; therefore, the conventional phosphorylated proteomics technology can be used for simultaneously enriching and analyzing two modifications of S-nitrosation and phosphorylation, and tests on model proteins and complex biological samples prove that the method is high in sensitivity and good in repeatability, has a wide protein coverage range, is equivalent to a traditional independent enrichment method, and has a wide application prospect. The technical challenge of analyzing modification of different chemical properties is overcome, and a reliable tool is provided for comprehensively researching PTM cross dialogue.

A method for preparing a functionalized two-dimensional metal-organic framework material for enriching glycopeptides belongs to the field of protein and polypeptide enrichment. The present invention comprises the following steps: first, synthesizing two-dimensional Zr-Fc MOF nanosheets; using an in-situ reduction method to load a large amount of Au nanoparticles on the surface of the nanosheets for subsequent modification; finally, modifying glutathione (GSH) and L-cysteine ​​(L-Cys) on the surface through Au-S interaction to further improve the hydrophilicity, thereby obtaining a two-dimensional metal-organic framework composite material with high hydrophilicity. The present invention overcomes the disadvantage of low glycopeptide enrichment efficiency of traditional MOFs. At the same time, the limited hydrophilicity of the two-dimensional metal-organic framework material is improved by post-synthetic modification of hydrophilic amino acids. The functionalized two-dimensional metal-organic framework material of the present invention is simple to prepare, has excellent sensitivity, selectivity, binding capacity and reusability, and exhibits excellent enrichment ability in both model proteins and actual samples.

The present invention discloses an oral colon-targeted silicon nanomedicine and its application, which belongs to the technical field of drug targeted delivery. The present invention is an oral colon-targeted delivery system based on biodegradable nano-silica, and the drug molecules can be simultaneously encapsulated during the preparation process, and hyaluronic acid is modified on the surface to enhance its targeting effect on inflammatory endothelial cells and macrophages. In order to ensure the stability of the delivery system in the gastrointestinal tract and avoid the influence of the strong acid environment and enzymes in the stomach on proteins, chitosan and sodium alginate are further modified on its surface through electrostatic effects. Hyaluronic acid, chitosan and sodium alginate are natural polymer materials with good biocompatibility. The present invention uses the new neurotrophic factor MANF as a model protein, and successfully delivers the MANF protein to the colon part by constructing an oral colon-targeted delivery system, and confirms at the animal level that it can improve ulcerative colitis damage through oral administration.

The invention discloses a tumor targeted self-assembly nano preparation based on a protein degradation technology and a preparation method and application thereof, and belongs to the technical field of pharmacy. The structure of the protein degradation agent is optimized on the basis of the tumor-targeted protein degradation agent, so that PROTAC molecules can be self-assembled into nanoparticles in a water solution, and other nano materials are not needed. Therefore, the invention provides a brand-new nano preparation of a protein degradation agent as well as a preparation method and application of the nano preparation, the nano preparation shows good target protein degradation activity, and the technology can realize inhibition on tumors by degrading model protein EGFR, has certain broad-spectrum antitumor activity, can obviously delay tumor growth, and can be applied to EGFR-mediated tumor diseases.

The invention discloses a method for evaluating iodine therapy response after total resection of papillary thyroid carcinoma by using a serum protein scoring system before radioiodine therapy, and develops a combined diagnosis model (FASLG, CXCL12 and HGF) for predicting iodine therapy response by measuring serum samples before radioiodine therapy after total resection of papillary thyroid carcinoma patients. The system is a scoring system for predicting curative effect response, and if the score is high, the treatment response is poor. By detecting the content of the combined diagnosis model protein marker in the serum sample of the patient, the patient with poor radioiodine treatment effect can be screened, and a patient layering method and an advanced intervention time window are provided, so that other intervention treatment is carried out before radioiodine treatment, and the life quality of the patient is improved.

The present disclosure describes techniques for generating protein sequences using machine learning models. A machine learning model is configured by implanting a structural adapter into a sequence decoder. The machine learning model is configured to generate a protein sequence from a specified structure. The machine learning model is endowed with protein structural awareness by the structural adapter. The machine learning model is equipped with protein sequential evolutionary knowledge by the sequence decoder. The machine learning model comprises the structural adapter, the sequence decoder, and a structure encoder. An initial sequence is generated based on the specified structure by the structure encoder. The protein sequence is optimized through an iterative process. The iterative process comprises progressively refining the protein sequence by iterative decoding. The structural adapter non-linearly imposes representations of the specified structure on a sequence predicted in the iterative process.

The system and methods of the present invention involve estimating model parameters for non-linear systems of Differential Equations (DEs). This includes receiving data from these systems and applying Weak-form Estimation of Nonlinear Dynamics methods to the data. These methods are robust to instances of large measurement noise. The system and methods also involve converting strong form representations of a model to weak forms, solving regression problems to perform parameter inference, and using Errors-In-Variables frameworks and iteratively reweighted least squares algorithms. The system and methods of the present invention can be applied to various models from different fields such as population biology, neuroscience, and biochemistry. The system and methods are highly robust and exhibit computational efficiency when used to estimate parameters in some common models including logistic growth, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, a Protein Transduction Benchmark model and Kuramoto-Sivashinsky.

Training a protein diffusion model includes receiving a representation of a protein as training data, the representation comprising at least three dimensions. It further includes training a protein diffusion model at least in part by performing rotational diffusion based at least in part on the representation of the protein.Generating proteins includes receiving protein conditioning information. It further includes, based at least in part on the protein conditioning information, performing conditional sampling of a protein diffusion model. The protein diffusion model is trained at least in part by performing rotational diffusion. Based at least in part on the conditional sampling of the protein diffusion model, the protein diffusion model generates one or more of a protein structure or a protein sequence.

The present invention discloses a whole crystal gel medium embedded with bacterial cellulose and a preparation method thereof, wherein the crystal gel medium has an internal pore size of 10 to 300 microns, a porosity of 70 to 88%, and a permeability of 10 ‑12 ~10 ‑13 m 2 ; After chemical modification, the crystal gel has a higher adsorption capacity with bovine serum albumin as a model protein. The crystal gel has hydroxyethyl methacrylate with embedded bacterial cellulose particles as a skeleton and grafted anionic functional groups. The crystal gel medium provided by the present invention has good biocompatibility, wherein the bacterial cellulose is biodegradable, and due to the embedded bacterial cellulose particles, more functional groups are obtained, the specific surface area is increased, and the mechanical strength of the crystal gel medium is further enhanced, which is conducive to repeated use and has a good application prospect in the field of biological separation.

The invention relates to the technical field of biology and artificial intelligence, and discloses a protein-micromolecule interaction prediction method, device and equipment and a storage medium, and the method comprises the steps: obtaining the three-dimensional structure data of a target protein and a micromolecule compound, respectively constructing protein map structure data and micromolecule map structure data, and calculating the three-dimensional structure data of the target protein and the micromolecule compound; carrying out feature extraction to obtain protein map features and small molecule map features; fusing the small molecule pre-training features with the small molecule map features to obtain fused small molecule features, and fusing the protein pre-training features with the protein map features to obtain fused protein features; and the binding affinity and the root mean square deviation are respectively predicted through the multi-layer perceptron. According to the method, 3D information of protein and small molecules is added, the information can be well utilized in subsequent prediction, and the prediction effect is improved. The introduction of large model protein and molecular characteristics with strong generalization performance can improve the prediction effect of the model on brand new protein and small molecules.

This invention discloses a method for simultaneously detecting protein phosphorylation and S-nitrosylation. It utilizes phosphate affinity tags to label S-nitrosylated proteins, transforming the S-nitrosylation modification into a phosphorylation-mimicking tag. This allows for the simultaneous enrichment and analysis of both S-nitrosylation and phosphorylation using conventional phosphorylated proteomics techniques. Through testing on model proteins and complex biological samples, we have verified that this method exhibits high sensitivity, good reproducibility, and broad protein coverage, comparable to traditional single-method enrichment. It overcomes the technical challenges of analyzing modifications with different chemical properties and provides a reliable tool for comprehensive research on cross-functional PTM (phosphorylated protein-to-protein) interactions.