410 results about "Protein structure" patented technology

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The invention discloses a small molecule ligand drug screening method and system based on affinity prediction, and belongs to the technical field of biological medicine. The invention aims to solve the technical problem of low drug screening precision caused by molecular expression limitation, geometric invariance deficiency and insufficient multi-modal information fusion when virtual drug screening is carried out by using protein-ligand affinity. Comprising the following steps: acquiring ligand and protein structure information, and preprocessing to obtain coordinates and a feature matrix of ligand / pocket / residue; performing comprehensive representation, multi-feature flow self-adaption, geometric algebraic multi-layer perception and feature alignment processing on the feature matrix to obtain corresponding feature space representation; performing cross attention fusion and multi-scale interactive learning processing on the feature space representation in sequence to obtain fusion features; inputting the fused features into a multi-scale interactive learning module, and outputting final features; and finally, predicting the binding affinity of the ligand and the protein according to the fusion characteristics to obtain a binding affinity value.

The invention relates to the field of protein-polypeptide interaction prediction in bioinformatics, in particular to a protein-polypeptide binding site prediction method based on graph attention and multi-head attention. The method mainly comprises the following steps: (1) collecting protein and polypeptide compound PDB structure information from an RCSB PDB database, and extracting sequence information of the protein and polypeptide compound PDB structure information; (2) extracting protein sequence information by using IUPred2, ProtBERT and ESM-2 (Extensible Subscriber for Mobile Communications); a ProtBERT method and an Integer method are used for extracting polypeptide sequence information; the method comprises the following steps: extracting protein structure information through biopython; (3) establishing a GAT model with residual connection to analyze a protein structure, and extracting features between amino acid nodes; (4) constructing a Circulate Block module, and carrying out deep extraction and fusion on protein and polypeptide information through four layers of Mti-head Attention and Dual Attention; and (5) finally, through a Final Attention, a linear layer and a Softmax layer, mapping the features to two dimensions to represent the interaction probability of each residue site.

The invention relates to the field of molecular targeted therapy, and discloses an application of an SLC16A5 (MCT6) small-molecule inhibitor MCT6-Ai7-2 in preparation of a medicine for treating acute myeloid leukemia (AML). The inhibitor takes an SLC16A5 protein structure predicted by Alphafold as a target spot, and is obtained through compound database screening, molecular docking and druggability optimization. An in-vitro experiment proves that MCT6-Ai7-2 can remarkably inhibit proliferation of AML cell lines such as U937 and MOLM-13, induce cell apoptosis and retard a cell cycle, has an inhibiting effect on a primary AML patient specimen and is relatively low in toxicity to normal cells; in-vivo experiments show that the compound is effective and has good safety in AML model mice. In addition, the MCT6-Ai7-2 and the vinca can be combined to synergistically enhance the inhibition effect on vinca drug-resistant cells, and by reducing the expression of anti-apoptotic protein MCL-1, the activation of pro-apoptotic factors BIM and tBID is promoted to play a role. The invention provides a novel targeting drug and strategy for treatment of AML (especially drug-resistant or recurrent patients).

The invention belongs to the field of construction of a protein structure on a cryoelectron microscope density map, and relates to a method for constructing a protein structure from a cryoelectron microscope density map by combining de novo modeling and structure prediction, a computer device, a readable storage medium and a program product. According to the method, the atomic probability and the amino acid type are predicted through the three deep neural networks respectively, full-atom optimization is carried out, the output result is combined with the graph theory, optimization and geometric algorithms to jointly assist protein structure modeling, and protein structure information can be mined to the maximum extent. According to the method, de novo modeling can be carried out in the absence of full-length structure data of the protein monomer, integrated modeling can also be carried out under the condition of inputting the full-length structure data of the protein monomer, the application scene is wide, the structure of the protein compound can be automatically constructed in a cryoelectron microscope density map with middle and high resolution, and the method is suitable for popularization and application. And for a region with poor local resolution in a traditional method, the modeling precision can be remarkably improved.

A method for functional antimicrobial peptide design is provided. The method includes running pretrained protein large language models as a generator and enhancing a sample candidate. The enhancing a sample candidate includes performing an automatic pipeline based on machine learning methods and a plurality of bioinformatics methods and is configured to perform protein inverse folding and computational protein sequence designing. The computational protein sequence designing includes running a pretrained deep learning-based protein structure model, an autoregressive pretrained protein sequence model, and a deep learning-based alignment model. The pretrained deep learning-based protein structure model is configured to learn three-dimensional structures of the proteins and output latent structure embeddings in a high dimensional space. The autoregressive pretrained protein sequence model includes a protein language model to automatically generate protein sequences and is trained to predict next amino acid in a protein sequence based on a preceding sequence(s) of amino acids.

The invention relates to a protein implicit binding site prediction method based on deep learning. The method comprises four stages of data acquisition, feature engineering, model training and verification application. The method comprises the following steps: firstly, acquiring initial data from a protein database, performing data screening and sequence redundancy elimination, performing standardization processing on a PDB structure file, and generating a sample; then, constructing a composite feature space containing basic protein features and evolutionary conservative features; thirdly, feature dimension reduction is achieved through a hierarchical feature conversion and integrated feature selection method, and a model is constructed; finally, verification and application show that the method obtains 99.44% prediction accuracy on 954 non-redundant protein structures, ROC-AUC and PR-AUC both reach 0.9998, and the overlapping rate with a BioLiP database is 44.4%. The method can accurately identify potential drug binding sites, especially shows good generalization ability for drug targets difficult to prepare, and has important application prospects.

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 provides a system for designing and optimizing D-type biomolecules and a method and application thereof. The system comprises a stereoisomerization conversion module, a conjugate skeleton generation module, a sequence design module and an optimization module. The method comprises the following steps: firstly, carrying out stereoisomerization conversion on a target protein structure to generate a stereoisomer structure, and generating a constrained conjugate by utilizing a modeling algorithm under the structural constraint of the stereoisomer structure; on the basis of the generated conjugate skeleton, predicting an adaptive amino acid sequence, and then performing stereoisomerization conversion to obtain the corresponding D-type biomolecule conjugate. And meanwhile, an evolutionary Bayesian optimization algorithm is adopted to carry out iterative optimization on the biomolecule sequence. According to the method, structural constraint and an advanced optimization algorithm are fully utilized, efficient design and optimization of the D-type biomolecules are achieved, high universality and expandability are achieved, and a powerful tool is provided for drug development and biotechnology research.

The invention relates to the technical field of gene identification, in particular to a method for identifying a horizontal transfer gene based on a model error, which comprises the following steps of: acquiring a class group protein data set from a public database, and performing data cleaning on the class group protein data set to obtain a class group protein data set; the class group protein data set comprises a plurality of protein sequences of different class groups; performing structure prediction on each protein in the cleaned class group protein data set, and extracting a multi-dimensional feature vector based on a prediction result; a classification prediction model is constructed, the multi-dimensional feature vectors are adopted for training, and the output of the classification prediction model is the class group label probability; and predicting a protein structure in a to-be-detected protein sequence, extracting a multi-dimensional feature vector with the same dimension as the training stage based on a prediction result, inputting the multi-dimensional feature vector into the trained classification prediction model, and identifying a potential HGT gene according to a prediction error of the classification prediction model.

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 discloses a green preparation method of hilsa herring roe-derived peptide, the hilsa herring roe-derived peptide and application of the hilsa herring roe-derived peptide, the hilsa herring roe-derived peptide is prepared by the following steps: cleaning and dehydrating hilsa herring roes with 10% saline water to reduce the water content of the roes, then freeze-drying and crushing to prepare freeze-dried hilsa herring roe powder, and degreasing the protein of the hilsa herring roes by a supercritical CO2 extractor to obtain the hilsa herring roe-derived peptide. Rancidity of the hilsa herring roes caused by lipid oxidation is effectively prevented; a hilsa herring egg solution is subjected to ultrasonic treatment, the protein structure of the hilsa herring eggs is uniformly damaged, and specific protease is adopted for enzymolysis, so that the enzymolysis time can be effectively shortened, the enzymolysis efficiency can be improved, and the hilsa herring egg source peptide with antioxidant activity and ACE inhibitory activity is obtained. The anti-aging test of the hilsa herring roe-derived peptide in a nematode animal model shows that the hilsa herring roe-derived bioactive peptide can obviously prolong the life of nematodes, and the average life of the nematodes can be prolonged by 33.33%. The method disclosed by the invention is of great significance in fully utilizing shad roe resources, developing functional foods and anti-aging products and realizing high-valued utilization of characteristic aquatic resources.

The invention relates to the technical field of molecular docking, in particular to an automatic molecular docking and screening analysis method which comprises the following steps: S1, aligning protein structures; s2, processing the receptor protein file; s3, carrying out batch molecular docking; s4, calculating the distance between the substrate and the catalytic site; s5, performing combined screening on the combination energy and the spatial relationship; and S6, summarizing and visualizing results. According to the method, a large-scale molecular docking task can be efficiently and accurately completed, and powerful technical support is provided for the fields of drug design, enzymology research and the like.

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 relates to an artificial intelligence-based multi-omics sequence data integration analysis method, which comprises the following steps of: acquiring multi-omics data samples, preprocessing the multi-omics data samples, and constructing training data and test data; constructing a network architecture of a multi-omics calculation model, and performing training and test fine tuning on the multi-omics calculation model by using the training data and the test data to obtain a Unii-Life calculation model; the method comprises the following steps: preprocessing to-be-processed multi-omics data, then inputting the to-be-processed multi-omics data into a Unii-Life calculation model, and outputting to obtain a nucleotide sequence, gene labeling information, a protein structure and species category information. Compared with the prior art, the method has the advantages that the multi-omics data is uniformly modeled into three levels of nucleotide, gene and function, so that the function, structure and other information of the single omics can be predicted, the complex relationship among the gene, metabolism, protein structure and phenotype can be accurately revealed, and the integrated analysis of single omics function prediction and multi-group physical data interaction is realized.

A protein complex model global quality evaluation method based on a deep fusion network belongs to the field of bioinformatics and computer application, and comprises the following steps: firstly, setting specific thresholds for maximum sequence redundancy, resolution and residue length in a protein database RCSB PDB to screen out a natural protein structure; for each protein structure, generating a disturbance structure model by using an HDck and xTrimoMultimer docking algorithm and combining a chain orientation disturbance strategy; then, extracting evolutionary features, physical features and geometric features based on each protein structure model, and constructing a deep fusion network fusing a deep convolutional neural network, an axial attention mechanism network and a graph attention network so as to fully capture local and global topological information of a protein compound; and finally, a global quality score is obtained through prediction of a decoding module. According to the method, the global topological information of the protein complex model can be effectively captured, so that the quality of the protein complex model is evaluated.

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 application discloses cytosine deaminase and related biological materials and applications thereof, and belongs to the technical field of proteins. The technical problem to be solved by the application is to mine natural sequence-unpreferred cytosine deaminase, and to improve base editing efficiency and range. The application provides a protein with amino acid sequence of SEQ ID NO. 2 at positions 15-143. The application also provides application of the above protein and related biological materials thereof in single base editing. The application uses macro-genome big data, performs comprehensive and systematic bioinformatics mining analysis on novel cytosine deaminase, and selects a protein L85 highly similar to known DddA protein structure but with low sequence similarity for function verification. The protein L85 has high catalytic activity as cytosine deaminase, and has no target sequence preference restriction, and can be widely applied to in-vitro and in-cell / tissue gene editing, and has good application potential and development value.

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.

Disclosed are systems, methods, and computer software for determining a conformational change in a structure of a protein. One method includes delivering a magnetic nanoparticle-nanobody (MNP-NB) complex to a sample containing a protein, where the MNP-NB complex will bind to the protein in the sample. An external magnetic field is applied to the sample with a magnetic field generation system. Signals are detected from the MNP-NB complex that reflect a response to the external magnetic field and a conformational change in a structure of the protein in the sample is determined from the signals.

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 relates to the technical field of molecular biology and the technical field of gene editing, in particular to pegRNA of a specific target EGFR gene tyrosine 1068 site, a pilot editing system and application. The pegRNA comprises sgRNA (small guide RNA), sgRNA scaffold, an RT (reverse transcription) template and PBS (phosphate buffer solution); wherein the sequence of the pegRNA is as shown in SEQ ID NO. 1; the sequence of the sgRNA is as shown in SEQ ID NO. 2; the sequence of the sgRNA scaffold is as shown in SEQ ID NO. 3; the sequence of the RT template is as shown in SEQ ID NO. 4; the sequence of the PBS is as shown in SEQ ID NO. 5. The tyrosine 1068 site of the EGFR gene is mutated into phenylalanine, so that phosphorylation of the site is blocked, and a protein structure is maintained. The tyrosine 1068 site point mutant with the EGFR gene can be applied to research on preparation of drugs for treating cancers, research on drug resistance and research on screening of compounds of targeted EGFR signal channels.

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 invention provides high-whiteness and low-isoflavone-content soybean protein isolate for infant food, and belongs to the technical field of infant complementary food. Comprising the following components in percentage by mass: 92.5%-6% of soybean protein isolate, 6.0% of maltodextrin, 0.8% of sodium carboxymethyl cellulose, 0.1% of sucralose and 0.6% of ethyl vanillin, the risk of residual chemical bleaching agents (hydrogen peroxide and sodium hypochlorite) is avoided, the content of isoflavone is reduced to be less than or equal to 10mg / kg, the hidden danger of endocrine interference is reduced, the damage of high temperature and strong acid / alkali to a protein structure is reduced, and the quality of the protein is improved. The energy consumption is reduced through low-temperature sterilization and vacuum freezing dehydration, the whiteness is greater than or equal to 85%, the sensory requirements are met, the protein content is greater than or equal to 90%, the purity is high, and the protein is suitable for being used as a high-quality protein source of infant food; the green solvent and physical / biological decoloration reduce chemical pollution, and the process is more environment-friendly.