7 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 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.

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 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.