375 results about "Peptide sequence" patented technology

Provided is an antibacterial peptide having an amino acid sequence X1KRFKKFFX2KLKKWV-NH2, wherein X1 is selected from any one or of amino acids A, C, D, E, F, G, H, K, L, N, M, P, Q, R, S, I, V, W, Y, and T or is absent; and X2 is an amino acid having an aromatic side chain or an amino acid having an alkaline side chain. In view of defects such as poor gastrointestinal fluid stability of antibacterial peptides in the prior art, a brand-new sequence design scheme and preparation method for an antibacterial peptide are provided. By means of a complete or partial D-amino acid substitution or structural derivatization of a peptide sequence, a series of antimicrobial peptides having stronger antibacterial activity and gastrointestinal stability as compared with the prior art are obtained, such that the antibacterial peptides have a wider application range and higher development potential value.

The invention relates to the field of preparation and application of biological peptides, in particular to a swelling hippocampal peptide and application thereof in preparation of uric acid reducing and gout resisting products. The amino acid sequence is shown as SEQ ID NO.1. The uric acid reducing effect of the compound is proved through in-vitro xanthine oxidase (XOD) and adenosine deaminase (ADA) inhibition experiments and cell experiments; on the basis, the uric acid reducing and gout resisting activity of the peptide sequence is further verified by means of a hyperuricemia animal model. The achievement provides a solid theoretical support for high-value development and utilization of the hippocampus japonicus and research and development of uric acid reducing and gout resisting related products.

An MHCl binding peptide design method based on evolutionary information and a Transform neural network algorithm relates to the field of protein design, and comprises the following steps: S1, extracting evolutionary information features of alleles of MHCII molecules and binding core sequences of binding peptides corresponding to the alleles, S2, establishing a neural network model based on fusion of a convolution module and a Transform module, and S3, establishing a neural network model based on fusion of the convolution module and the Transform module, the method comprises the following steps: S1, extracting two frequency characteristic tensors from S11 and S12, taking the two frequency characteristic tensors extracted in S11 and S12 as double inputs, and finally obtaining probability distribution of 20 amino acids at each position of each sequence, and S3, according to an output result of a neural network model, carrying out random sampling according to the probability, and generating a binding core sequence of MHCII-peptide meeting target distribution. According to the method, evolutionary information such as sequence position amino acid frequency (first-order conservative analysis) and combined frequency (second-order conservative analysis) of amino acid pairs is introduced to design a new short peptide sequence, the problem that short peptides cannot be designed based on structures is solved, and the reliability of short peptide sequence design based on evolutionary information is provided.

The invention relates to the technical field of antibacterial drugs, in particular to an antibacterial peptide which has an amino acid sequence X1KRFKKFFX2KLKKWV-NH2, in which X1 is selected from any one or deletion of amino acids A, C, D, E, F, G, H, K, L, N, M, P, Q, R, S, I, V, W, Y and T; and X2 represents an amino acid having an aromatic side chain or an amino acid having a basic side chain. Aiming at the defects of poor gastrointestinal fluid stability and the like of the antibacterial peptide in the prior art, the invention provides a brand new antibacterial peptide sequence design scheme and a preparation method thereof, and the peptide sequence is subjected to full D-type amino acid or partial D-type amino acid replacement or structure derivation; compared with the prior art, a series of antibacterial peptides with higher antibacterial activity and gastrointestinal fluid stability are obtained, so that the antibacterial peptides have wider application range and higher potential development value.

The invention discloses a deep learning-based umami peptide prediction method and system. The method comprises the following steps of S1, constructing a data set; s2, feature selection and processing; s3, constructing a Umami-Transform model: constructing the Umami-Transform model which comprises a feature processing and generating module, a sequence information processing module, a feature information processing module and a result output module; s4, model training; and S5, umami peptide prediction: inputting a peptide sequence to be detected into the trained Umami-Transformer model to obtain a probability prediction result that the peptide sequence is the umami peptide. According to the method, the Transform architecture and eight key physical and chemical characteristics are fused, so that peptide sequence information can be efficiently processed, and the prediction precision of the umami peptide is remarkably improved. All dipeptides to pentapeptides are screened by further combining with an exhaustion method, and an interaction mechanism between candidate peptides and the umami receptor T1R1 / T1R3 is analyzed through a molecular docking technology, so that the whole-process research from theoretical prediction to biological experimental verification is finally realized, and an innovative technical path is provided for efficient excavation and functional application of the umami peptides.

The invention discloses a universal HLA antigen presentation prediction method based on a protein language model and a multi-modal neural network, which comprises the following steps: firstly, extracting verified HLA binding peptide fragment sequences from an immune epitope database, generating equivalent non-epitope peptide fragment sequences in combination with a protein database, and constructing an HLA-I class and HLA-II class balanced data set; then, extracting sequence embedding characteristics and contact graph structure information of the peptide fragment sequence by utilizing a protein language model; the method comprises the following steps: processing a protein map constructed by a contact map through a map neural network to obtain global structure features, and meanwhile, carrying out regional convolution and residual convolution processing on sequence embedding by adopting a one-dimensional convolutional neural network (1DCNN) to extract local context sequence features; the method effectively fuses sequence semantics and space structure information, improves the accuracy and generalization ability of HLA antigen presentation prediction, and is suitable for immune recognition modeling tasks under various HLA subtypes.

The invention provides a plug-and-play Mi3 protein nanocage fusion protein with a VNP6 tag and a preparation method and application of the plug-and-play Mi3 protein nanocage fusion protein, the fusion protein comprises the VNP6 tag, a SpyCatch003 component, a Mi3 protein nanocage and a functional protein sequence, and the VNP6 tag is located at the N end of the fusion protein. The VNP6 peptide sequence or the variant thereof is utilized to induce a vesicle or vesicle-like structure beneficial to protein folding and stabilization in escherichia coli, so that the correct folding rate and activity of recombinant protein are improved while the overall intracellular molecular crowding effect is relieved. The core idea of the invention lies in that the internal structure of cells is regulated at low temperature, and the internal microenvironment of an escherichia coli expression system is optimized, so that protein molecules under high expression load can obtain more reasonable spatial distribution, and the problems of misfolding and aggregation caused by molecular crowding are reduced.

The invention discloses an antibacterial peptide generation method, system, equipment and medium, and relates to the technical field of antibacterial peptide generation, the method comprises the following steps: obtaining protein sequence data; inputting the protein sequence data into an antibacterial peptide generation model for training, and performing autoregression sampling on the protein sequence data through an LSTM module to generate an amino acid sequence; inputting the amino acid sequence into a Transform module, performing word segmentation according to characters, converting the amino acid sequence after word segmentation into an id sequence, and mapping the id sequence into an embedded vector; encoding the embedded vector through an encoder, and inputting global features extracted by encoding into a decoder to generate a new amino acid sequence; identifying and screening the antibacterial characteristics of the new amino acid sequence to obtain an antibacterial peptide sequence; inputting to-be-generated protein sequence data into the trained antibacterial peptide generation model to generate an antibacterial peptide sequence; the method improves the novelty and antibacterial property of the new polypeptide sequence.

An engineered polypeptide for use in treating age-related macular degeneration (AMD) comprising FHL-1 engineered variant peptides, compositions including these engineered polypeptides and methods of using them. Further, wherein polypeptides include a linker domain separating the first peptide sequence from the second peptide sequence, a first junction region between the first peptide sequence and the linker domain and a second junction region between the second peptide sequence and the linker domain.

The invention provides a new antigen immunogenicity prediction method and system based on multi-feature fusion, and belongs to the technical field of machine learning, and the method comprises the steps: inputting a candidate peptide fragment sequence into an antigen intracellular processing prediction model to obtain a score representing the intracellular processing capability; inputting the candidate peptide fragment sequence into a pre-trained Transform model to obtain a sequence vector representation of the candidate peptide fragment; training an immunogenicity prediction model by taking the immunogenicity related characteristics, the sequence vector representation of the candidate peptide fragment and the score representing the intracellular processing capacity as a training sample; and screening the target candidate peptide fragment sequence by using the immunogenicity prediction model to obtain the tumor neoantigen. According to the method, the scoring model special for evaluating the intracellular processing and presentation capacity of the antigen peptide is constructed, the scoring model is output as a key feature, efficient fusion and learning are carried out on the key feature, multiple immunogenicity-related biological features and sequence vector representation of candidate peptide fragments, and the accuracy and reliability of tumor neoantigen immunogenicity prediction can be remarkably improved.

The present disclosure relates to methods for generating a plurality of compatible structures for a peptide-protein complex that can be used, for example, to identify surface fingerprint and interface features of the peptide-protein complex. The methods can comprise inputting peptide sequence data and protein sequence data into a trained machine learning model to determine: (i) predicted pairwise distance data for peptide amino acid residues and protein amino acid residue in the peptide-protein complex; and (ii) predicted dihedral angle data for peptide amino acid residues in the peptide-protein complex; identifying an initial structure for the peptide-protein complex based on the predicted pairwise distance and dihedral angle data; and generating the plurality of compatible structures for the peptide-protein complex based on the initial structure, the predicted pairwise distance data, and the predicted dihedral angle data.

The invention relates to isolated synthetic or recombinant peptides comprising an epitope of human tau 2N4R, wherein the tau peptide sequence comprising the epitope is not phosphorylated. The invention also relates to use of such peptides to generate binding molecules, such as antibodies, specific for the non-phosphorylated tau epitopes and to such peptides and binding molecules, such as antibodies, for use in investigation, diagnosis and treatment of tauopathies, such as Alzheimer's disease.

The invention belongs to the technical field of bioengineering and food safety, and particularly relates to a fusion enzyme for degrading aflatoxin B1 (AFB1) and / or zearalenone (ZEN) and application of the fusion enzyme. The fusion enzyme S1-AsDPP III disclosed by the invention can effectively degrade AFB1 and ZEN, the fusion enzyme is obtained by fusing a section of self-assembled amphiphilic oligopeptide S1 sequence at the N end of wild type AsDPP III, and the thermal stability of the fusion enzyme and the amphipathy of the fusion enzyme in an oil-water coexistence system can be remarkably improved. Under mild reaction conditions, the fusion enzyme can efficiently degrade AFB1 and ZEN in vegetable oil, and is especially suitable for detoxification treatment of common edible oil such as peanut oil and corn oil. The invention provides a green, safe and efficient scheme for removing fungaltoxin from grease food, and the method has a good industrial application prospect.

The invention is applicable to the technical field of biology, and provides an L7 / L12-PADRE sequence-multi-linked B cell epitope recombinant protein and a kit for detecting an anti-Brucella antibody. The recombinant protein is a multi-epitope tandem recombinant protein composed of Brucella ribosome L7 / L12 protein, a PADRE polypeptide sequence and multiple B cell epitopes, the amino acid sequence of the recombinant protein is as shown in SEQ ID No.1, and the recombinant protein is good in antigenicity. The anti-brucella antibody indirect ELISA (iELISA) detection method and kit established by taking the recombinant protein as the coating antigen have the characteristics of high sensitivity, strong specificity and good repeatability, are suitable for detecting the condition of generating the anti-brucella antibody by an organism, can clarify the immune background of brucella infection or brucellosis vaccine, and can be used for detecting the brucella infection or brucellosis vaccine. Basic data is provided for prevention and control of the Brucella disease of humans and animals, and meanwhile, the kit is conveniently applied to large-scale sample detection and epidemiological monitoring.

The invention discloses a prebiotic-loaded intestinal targeting exosome and a preparation method and application thereof.The preparation method comprises the steps that firstly, plasmids containing intestinal cell targeting peptide sequences are constructed through a gene editing technology, the plasmids are transfected and introduced into cells, the transfected cells are screened with antibiotics, and cell strains stably expressing intestinal cell targeting peptides are obtained; then culturing and adding prebiotics, and continuously culturing to enable the cells to take in the prebiotics and release the exosomes loaded with the prebiotics; and finally, collecting cell culture supernate, and centrifuging and resuspending for multiple times under a low-temperature condition to obtain the prebiotics-loaded intestinal targeting exosome. According to the preparation method, extraction of the exosome, loading of the prebiotics and an intestinal targeting modification process are optimized, so that the prepared intestinal targeting exosome loaded with the prebiotics can be used for remarkably improving the loading efficiency and intestinal targeting of the prebiotics and enhancing the stability of the exosome in gastrointestinal tracts; therefore, the delivery efficiency and the treatment effect of the prebiotics in the intestinal tract are improved.

The invention relates to low dose compositions and peptides or peptide sequences that induce an immune response in an animal or a mammal that is protective against infection by one or more pathogens, and the antibodies generated. In addition, the invention relates to immunogenic composition and vaccines comprising compositions and peptide sequences or antibodies, and to methods for treating and preventing an infection in animals and mammals such as humans.

Disclosed are compositions and methods useful for targeting and internalizing molecules into cells of interest and for penetration by molecules of tissues of interest. The compositions and methods are based on peptide sequences that are selectively internalized by a cell, penetrate tissue, or both. The disclosed internalization and tissue penetration is useful for delivering therapeutic and detectable agents to cells and tissues of interest.

The invention provides a milk protein peptide which takes milk protein as a raw material and has the effects of promoting calcium absorption and improving bone health and application of the milk protein peptide. Specifically, milk protein peptide sequences, including YGGVSL and SQRY, are screened by adopting enzymolysis, sequence identification and molecular docking methods. The milk protein peptide is proved to have high calcium transport promoting capacity in the in-vitro intervention of Caco-2 cells, and the milk protein peptide is proved to have the capacity of promoting osteogenesis and inhibiting bone resorption in the in-vitro intervention of MC3T3-E1 mouse embryo osteoblasts. In animal experiments, by increasing the content of blood calcium and bone calcium, reducing the content of excrement calcium, improving mRNA expression of calcium ion channel protein, calcium binding protein and peptide transporter in intestinal tracts, enhancing the efficiency of calcium entering blood, promoting osteogenesis and inhibiting bone resorption, the milk protein peptide is comprehensively proved to have the capabilities of improving calcium resorption of organisms and improving bone health. The milk protein peptide has both functionality and nutrition, and has a wide application prospect in the aspect of bone health.

The invention relates to the technical field of deep learning, and discloses a multifunctional bioactive peptide function classification method based on evolutionary deep learning. Comprising the following steps: acquiring a data sample set, and dividing the data sample set into a training set and a test set according to a preset proportion; preprocessing the training set and the test set to obtain a preprocessed training set and a preprocessed test set; constructing an initial deep learning model for active peptide function classification, searching parameters of the model by using an evolutionary algorithm, and constructing an optimized deep learning model based on the parameters; training an optimized deep learning model by using the preprocessed training set, and testing the trained optimized deep learning model by using the preprocessed test set to obtain a trained deep learning model; and preprocessing a to-be-classified peptide sequence, and inputting the preprocessed peptide sequence into the trained deep learning model to obtain a classification result. By adopting the method, the classification precision of the active peptide and the migration ability of the model to adapt to different data sets can be improved.

Provided herein is disclosure of a recombinant bifunctional protein or polypeptide capable of binding to a cell surface receptor for lysosome targeting that is made up of an N-glycosylated peptide comprising at least one N-glycan group and a protein of interest, or antibody or antibody fragment capable of binding to a protein of interest. Also provided herein are methods for producing said recombinant bifunctional protein. Also provided herein are methods for lysosomal degradation of a protein of interest comprising introducing to a cell the peptide sequence of the recombinant bifunctional protein.

The invention discloses an antibacterial peptide prediction method based on dual-channel sparse attention. The method comprises the following specific steps: extracting initial embedding by using a protein language model; adaptively weighting the channel by using the channel attention enhanced convolutional neural network; capturing short-range interactions between amino acid residues using local sparse attention based on a sliding window; global sparse attention containing a scoring function is used for obtaining long-range interaction between key amino acid residues and the sequence, and local and global information is integrated so as to realize complete characterization of the peptide sequence; and predicting the final feature matrix by using a full connection layer. According to the method, the sequence information of the peptide is comprehensively modeled by using a simple and efficient training process, and a dual-channel sparse attention mechanism is introduced to effectively relieve the problems of representation redundancy and calculation complexity.

The invention belongs to the technical field of biomedical engineering, and particularly relates to a polypeptide preparation method for preparing a GLP-1 analogue through tandem expression. According to the method, a coding gene sequence of the GLP-1 analogue is repeatedly connected in series for a plurality of times, and then expression preparation is carried out on the basis of a genetic engineering technology. The inventor optimizes a series-connected polypeptide structure by adding a section of key peptide sequence. The key peptide serving as a leading peptide can greatly improve the expression quantity of the strain in the fermentation expression process; as a linked peptide, the key peptide sequence can effectively improve the recognition capability of enzyme digestion sites on one hand and can adjust the overall isoelectric point of the sequence on the other hand, so that the solubility, enzyme digestion yield and purity of a target product can be improved. Preliminary experiment results show that the expression quantity of the polypeptide prepared through expression is extremely high, the enzyme digestion efficiency is good, the good enzyme digestion efficiency can be achieved under the condition that the enzyme dosage is low, and the yield and purity of the final GLP-1 analogue are high.

The invention discloses a paralichthys olivaceus rhabdovirus G protein tandem antigen epitope peptide and application thereof, and belongs to the field of fish molecular immunology. The amino acid sequence of the tandem antigen epitope peptide is shown as SEQ ID NO: 1. The preparation method comprises the following steps: (1) firstly, analyzing structural characteristics of HIRRV-G protein, predicting B cell antigen epitopes of the HIRRV-G protein, screening the antigen epitopes with advantages on the basis of a prediction result, and synthesizing the antigen epitopes; (2) screening a candidate peptide fragment with high affinity through an enzyme-linked immunosorbent assay; and (3) sequentially connecting the high-affinity peptide fragment sequences meeting the requirements by using a GPGPG connexon, cloning the connected sequences into a pET-28a prokaryotic expression vector, and performing induced expression to obtain the tandem antigen epitope peptide. Compared with a full-length G protein, the tandem antigen epitope peptide is smaller in molecular weight, higher in stability and higher in hydrophilicity; a large number of specific antibodies can be induced in fish bodies, and the death rate of the paralichthys olivaceus infected by viruses is remarkably reduced. The method can be used for HIRRV diagnosis detection reagent and subunit vaccine development.

The invention discloses sturgeon cartilage active peptide capable of promoting growth and improving bone development as well as a preparation method and application of the sturgeon cartilage active peptide, and belongs to the technical field of active peptide. According to the invention, sturgeon cartilage is taken as a raw material, polypeptide with the highest activity of promoting bone growth and development is selected from different enzymolysis combinations through MC3T3-E1 cell tests, and the positive effect of the polypeptide is verified through zebra fish tests; then, two potential sturgeon cartilage active peptide sequences with the function of promoting bone growth and development are screened out through bioinformatics technologies such as mass spectrum identification, molecular docking and the like, and the amino acid sequences of the sturgeon cartilage active peptide fragments are shown as SEQ ID NO.2 and SEQ ID NO.5. The sturgeon cartilage peptide prepared by the method disclosed by the invention has the activity of promoting bone growth and development, can solve the problem of high-value utilization of sturgeons, can also be applied to functional products as a functional factor, and has a good application prospect.

The invention relates to the technical field of gene engineering, in particular to a transgenic zebrafish system pTo12-efla-tjp1a-P2A-mCherry based on a Tol2 transposition subsystem and a construction method of the transgenic zebrafish system pTo12-efla-tjp1a-P2A-mCherry. The preparation method comprises the following steps: S1, constructing a Tol2 transposon expression vector pTol2-ef1a-tjp1a-P2A-mCherry which contains an ef1a promoter, a tjp1a gene, a P2A peptide sequence and an mCherry reporter gene; s2, carrying out in-vitro transcription to prepare Tol2 transposase mRNA; s3, mixing the expression vector with Tol2 transposase mRNA, and microinjecting the mixture into the single-cell stage embryo of the zebra fish; s4, performing fluorescence screening on the F0 generation embryos surviving after injection to obtain the Founder fish with positive transgenosis; s5, after the F0-generation positive fish is bred to be sexually mature, an F1 generation is obtained through mating, transgenic positive individuals with stable inheritance are screened out after identification, and a transgenic zebrafish strain is established. According to the transgenic zebrafish system pTo12-efla-tjp1a-P2A-mCherry based on the Tol2 transposon system and the construction method of the transgenic zebrafish system pTo12-efla-tjp1a-P2A-mCherry based on the Tol2 transposon system, efficient integration of exogenous genes is achieved by utilizing the Tol2 transposon system, and the transgenic zebrafish line capable of being stably inherited to the F1 generation is successfully obtained.

The present invention constructs a method and system for identifying anticancer peptides based on an attention mechanism and multi-granularity hierarchical features. This method is of great significance in the field of anticancer peptide identification technology. First, atomic-level features are learned through transfer learning, revealing potential features that were difficult to discover in previous work. Secondly, the ChouFasman algorithm is used to represent the secondary structure in the amino acid sequence layer extraction process, which increases the richness of information. Next, the problem that the existing technology cannot capture the high-order structural similarity of anticancer peptide sequences is solved by constructing a hypergraph, and the importance of subsequences to the overall sequence is learned through the attention mechanism. Finally, the multi-granularity hierarchical features are fused, so that the model can fully understand and describe the characteristics of the anticancer peptide sequence. This invention provides a more accurate and comprehensive analysis tool for the discovery and design of anticancer peptides, and can contribute to the development of anticancer drugs.

The invention discloses an antibacterial peptide generating and screening method based on a conditional potential diffusion model, which comprises the following steps: modeling antibacterial peptide sequence distribution in a continuous submerged space, realizing controllable generation of antibacterial peptide sequences through conditional constraints, and improving the efficiency and reliability of an antibacterial peptide discovery process by combining a multi-stage calculation screening and experimental verification strategy; therefore, the problem that in an existing antibacterial peptide design method, due to the factors that the sequence space scale is huge, the generation process is difficult to control, the candidate sequence redundancy is high, and the screening cost is high, the antibacterial peptide discovery efficiency is limited is solved. According to the method, a conditional diffusion generation mechanism is introduced into a continuous submerged space, and a multi-stage screening process formed by calculation screening and experimental verification is combined, so that systematic design and optimization of an antibacterial peptide sequence from data driven generation to performance evaluation are realized.

The application discloses a method for transforming any polypeptide sequence into an antibacterial peptide, and belongs to the technical field of bioinformatics. The application develops a hypergraph neural network predictor for predicting the antibacterial activity score of a polypeptide, performs fine-tuning training on a pre-trained protein language model to capture antibacterial peptide sequence features, and trains a strategy network for selecting a to-be-mutated amino acid site in a polypeptide sequence. For any starting polypeptide sequence, the strategy network is first used to select a to-be-mutated amino acid site, then the fine-tuned protein language model is used to give the amino acid type after mutation of the site, and then the hypergraph neural network predictor is used to predict the antibacterial activity score of the sequence after mutation. The process is iteratively executed until an ideal antibacterial peptide sequence output is obtained. The method has the ability to transform any polypeptide sequence into an antibacterial peptide, and the antibacterial activity score of the transformed polypeptide is greatly improved, and can be applied to the actual development of antibacterial drugs, thereby laying a foundation for solving drug-resistant bacterial infections.

A peptide sequence mimicking endogenous cofilin-1 incorporates transmembrane amino acid sequences to enhance cellular uptake of the peptide drug. When cofilin peptide drugs are introduced into cancer cells, they affect cancer cell migration and invasion abilities. Furthermore, the peptide sequence can be conjugated with a chelator for cancer diagnosis.

Provided herein are computer-implemented methods of generating a disease map of a population. In some embodiments, the methods include applying a clustering algorithm to a set of weight and bias values of a trained electronic neural network to generate the disease map of the population. In some embodiments, the electronic neural network has been trained on training data that comprises representations of peptide sequence and binding value pair data sets obtained from reference subjects in the population in which a given peptide sequence and binding value pair data set comprises peptide sequence information and peptide binding values of antibodies to peptides that comprises the peptide sequence information. In some embodiments, the antibodies are from a sample obtained from a given reference subject in the population and are indicative of one or more disease states. Related systems, computer readable media, and additional methods are also provided.