674 results about "Peptide sequence" patented technology

The invention relates to the field of preparation and application of biological peptides, in particular to a hippocampal swelling peptide and application thereof in preparation of functional products for repairing male reproductive system injury. A green controllable enzymolysis technology is utilized to obtain protein polypeptide with nutritional, health-care and medicinal values, five active peptide sequences with high biological activity are obtained through further research on the basis, the high-purity active peptide is obtained through a chemical synthesis mode, and the biological activity of the peptide is verified through mouse spermatogonium.

The invention discloses two brand-new bioactive peptides with antioxidant and alpha-glucosidase inhibitory activity in traditional Chinese medicine hippocampus. The sequences of the novel bioactive peptides provided by the invention are respectively YLPPNW and SVWLGGSLL. According to the method, purity and structure identification is carried out on the new bioactive peptide obtained through separation by adopting liquid phase, primary mass spectrum and secondary mass spectrum, and a material basis is provided for further quality control and drug effect research of the traditional Chinese medicine hippocampus.

The invention provides a method and a system for screening and constructing a small molecule polypeptide simulant based on interaction between proteins, relates to the technical field of biology, and comprises the following steps: predicting an interaction model of RECK protein and ADAM10 protein through a molecular docking platform, and analyzing interaction sites of the RECK protein and the ADAM10 protein. And then, determining a KAZAL region of the RECK protein according to the interaction site, and predicting a sequence structure of the KAZAL region. And then carrying out molecular docking on the KAZAL region and ADAM10 protein, designing a plurality of peptide fragments with the length of 30 amino acids, and screening the peptide fragment sequence with the highest score as candidate polypeptide. And finally, performing species homology comparison and quality control detection on the candidate polypeptide, and verifying the binding capacity and biological function of the candidate polypeptide to ADAM10 protein through cell experiments. The invention provides a method for screening to obtain a polypeptide simulant specifically bound with ADAM10 protein. The polypeptide simulant can be used for treating ADAM10 related diseases.

The invention relates to the field of preparation and application of biological peptides, in particular to a swelling hippocampal peptide and application of the swelling hippocampal peptide in preparation of a product for improving memory ability and cognitive impairment. According to the invention, a green controllable enzymolysis technology is utilized to obtain protein polypeptide which has the main molecular weight of less than 1000 Da and has nutritional, health-care and medicinal values, and four active peptide sequences with high biological activity are obtained by further research on the basis of the protein polypeptide.

The invention provides a micromolecular multifunctional information peptide and application thereof, and belongs to the technical field of active peptide raw material development. The sequence of the small-molecule multifunctional information peptide is VHPYVPR, the small-molecule multifunctional information peptide can be effectively combined with a downstream receptor of EGF, the EGF function is simulated, and meanwhile the situation that use of existing cell growth factor raw materials in the field of cosmetics is limited due to the molecular weight problem is avoided. Experiments prove that the micromolecular multifunctional information peptide provided by the invention can promote the repair of skin damage phenomena such as scratches, inhibit the release of inflammatory factors, inhibit the activity of beta-galactosidase and promote the generation of I-type, III-type and XVII-type collagen, shows excellent repair, soothing and anti-aging functions, is high in permeability and has a wide application prospect.

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 discloses sturgeon ovary active peptide with ovary aging resistance and a preparation method of the sturgeon ovary active peptide. According to the invention, sturgeon ovary is taken as a raw material, and peptide with the highest ovarian aging resistance activity is selected from different enzymolysis combinations through an in-vitro anti-oxidation test and an ovarian granular cell and caenorhabditis elegans test; three sturgeon ovary active peptide sequences with ovary senescence resistance activity are screened by virtue of bioinformatics technologies such as mass spectrum identification, molecular docking and the like, and the amino acid sequences of the sturgeon ovary active peptide sequences are Phe-Met-Ile (FMI), Phe-Asp-Pro-Phe (FDPF) and Ser-Trp-Pro-Trp-Gln (SWPWQ). The sturgeon ovarian peptide prepared by the method not only has remarkable anti-premature ovarian failure activity and successfully solves the problem of high-valued utilization of sturgeon byproducts, but also can be used as a natural and efficient functional factor, is widely applied to functional products in the related fields of health care products, cosmetics, medicines and the like, and has a wide application prospect. And the method shows extremely wide market application prospects and huge economic values.

The invention provides a multifunctional self-assembly peptide recognition method and system, and relates to the technical field of biological peptide recognition. The method comprises the following steps: firstly, obtaining peptide sequences of a self-assembled peptide and a plurality of biological functional peptides, and inputting the peptide sequences into an embedded layer of a polypeptide function prediction model for 21-dimensional vectorization; and learning combination modes and sequence features of amino acids through an amino acid association feature extraction layer of the LSTM network. Then, acquiring direct contact information among different amino acids on the basis of a fine-grained feature learning layer of a multi-head self-attention mechanism; and finally, designing a Transform multi-task classifier which comprises five sub-task classifiers which are used for respectively predicting the self-assembly function, the anti-cancer function, the anti-inflammatory function, the antibacterial function and the antioxidant function. According to the method, a multi-layer structure is adopted to carry out layer-by-layer deep learning and optimization on polypeptide sequence features, efficient recognition and classification of multifunctional biological peptides are achieved, and finally the model performance is comprehensively evaluated through the accuracy rate, the precision rate, the recall rate and the F1 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 discloses sturgeon roe peptide for preventing alcoholic liver injury as well as a preparation method and application of the sturgeon roe peptide. The peptide sequence is ILKPTDAGTL, ALPLDPK, LGGL, VNRDL, LLGQ, VFVLN or LAGL. The preparation method specifically comprises the following steps: (1) adding deionized water into the sturgeon roes, and homogenizing; (2) adjusting the pH value, adding alkaline protease, carrying out enzymolysis, carrying out enzyme inactivation, and cooling; and (3) performing centrifugal treatment, and taking supernate to obtain the traditional Chinese medicine composition. The sturgeon roe peptide disclosed by the invention has the efficacy of preventing or treating alcoholic liver injury, and is remarkable in hangover alleviating effect, simple to prepare and low in cost.

The present invention discloses small molecule active peptides, compositions and their use in preparing products with antioxidant and hypoglycemic effects, belonging to the technical field of bioactive peptides. The amino acid sequence of the small molecule active peptide of the present invention is at least one of DQFPR, LDQFPR and SYGLPR. The present invention separates and purifies a plurality of small molecule active peptides with antioxidant and hypoglycemic effects from peanut protein, chemically synthesizes the identified peptide sequences, and detects the antioxidant activity and hypoglycemic activity of the single peptide segment and the composite peptide segment of the synthetic peptide segment. The small molecule active peptide and its composite peptide segment both have good free radical scavenging and ɑ-glucosidase and ɑ-amylase inhibitory effects, and have good application prospects in the preparation of products with antioxidant and hypoglycemic effects, laying a foundation for the high added value utilization of peanut protein and the promotion of the research and development and application of functional active peptides.

The invention belongs to the technical field of active peptides, and particularly relates to spirulina phycocyanin polypeptide capable of relieving inflammatory response and a preparation method and application thereof.The sequence of the spirulina phycocyanin polypeptide is shown as SEQ ID NO.1, a polypeptide sequence structure is obtained through enzymolysis, separation and structural identification, and the spirulina phycocyanin polypeptide is obtained through enzymolysis, separation and structural identification. A macrophage in-vitro anti-inflammatory experiment verifies that the active peptide has anti-inflammatory ability.

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 provides a salty peptide classification screening method and system based on peptiomics and machine learning. The method comprises the following specific steps: (1) obtaining a peptide sequence to be analyzed; (2) calculating basic physicochemical properties and sequence characteristics of the peptide sequence, and extracting high-dimensional embedding characteristics of the peptide sequence based on an evolution scale modeling model; (3) carrying out feature fusion on the basic physicochemical properties, the sequence features and the high-dimensional embedded features obtained in the step (2) to obtain a fused feature vector; (4) performing feature screening and importance sorting on the fused feature vector by using a random forest and an XGBoost and Extra Tres ensemble learning model to obtain a key feature subset; and (5) inputting the key feature subset into the constructed salty peptide classification prediction model, and outputting a salty peptide prediction result. According to the method, a multi-dimensional feature extraction algorithm is combined with a deep learning model, a salty peptide prediction model is constructed, and accurate identification and functional characteristic analysis of salty peptides are realized.

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 belongs to the technical field of gene editing, and discloses a base editor system and application. According to the invention, a heterologous peptide sequence is inserted into IscB protein, the obtained peptide embedded IscB is fused with deaminase or glycosylase to construct a base editor, and DNA base editing is carried out. According to the base editor system, the size limitation is overcome, and single AAV delivery is achieved; the editing efficiency is obviously improved; the broad-spectrum applicability is realized; the system optimization is more flexible; the method has efficient editing potential in vivo, and the off-target effect is reduced. The invention provides an important new strategy for hyperlipemia treatment and provides a basis for gene therapy of other hereditary diseases.

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 relates to the technical field of bioactive peptides, and particularly discloses a peptide oxidation resistance prediction method and system based on a pre-training model and descriptors, and the method comprises the following steps: obtaining an amino acid sequence of a to-be-predicted polypeptide and a physicochemical characteristic description index of each amino acid; enabling the coded amino acid sequence to pass through a BERT embedding layer to form high-dimensional vector representation, inputting the high-dimensional vector representation into a pre-trained deep neural network model to obtain features of a peptide sequence, and inputting the features of the peptide sequence into a pre-trained artificial neural network to obtain deep representation of the peptide sequence; according to the physicochemical characteristic description index of each amino acid, generating a characteristic vector of each amino acid, and constructing the characteristic of the polypeptide; and inputting the depth representation of the peptide sequence and the characteristics of the polypeptide into a predictor to obtain a prediction result of the oxidation resistance of the peptide. According to the method, the fine-tuned model characterization is combined with the traditional peptide sequence descriptor, so that the prediction accuracy of the antioxidant peptide and the stability of the model are successfully improved.

The present invention provides a monkey serum amyloid A recombinant protein, a monoclonal antibody and a preparation method and application thereof, wherein the amino acid sequence of the recombinant protein is shown in SEQ ID No. 1, and the nucleotide sequence encoding the recombinant protein is shown in SEQ ID No. 2. By optimizing the antigen peptide sequence and the expression vector, the efficient and soluble expression of monkey serum amyloid A in Escherichia coli is achieved, and a mouse monoclonal antibody specifically binding to monkey serum amyloid A is screened based on the monkey serum amyloid A recombinant protein, and the above mouse monoclonal antibody is used to screen a pairable monoclonal antibody, and further based on the monoclonal antibody combination, a double antibody sandwich immune ELISA detection method or kit applied to monkey serum amyloid A recombinant protein is obtained, which has high detection sensitivity and can be well applied to the detection of monkey serum.

The invention discloses a composite double-protein hypoglycemic peptide as well as a preparation method and application thereof, and belongs to the technical field of animal and plant source double-protein active peptides. The method comprises the following steps: performing computer simulation enzyme digestion and virtual screening by utilizing bioinformatics to determine a protein raw material and protease, performing enzymolysis on animal and plant double proteins (soybean protein and casein) serving as raw materials by adopting neutral protease, and performing separation, purification and structural identification, thereby obtaining the protein. The protein peptide fragment with high alpha-glucosidase inhibitory activity is obtained by virtually screening molecular docking, and the amino acid sequences of the protein peptide fragment are shown as SEQ ID No: 6, SEQ ID No: 9 and SEQ ID No: 24. The compound double-protein peptide sequence provided by the invention has an inhibiting effect on the activity of alpha-glucosidase, can be used for preventing and treating diabetes mellitus, and can be used for long-term health care or treatment of people with impaired glucose tolerance or diabetics as a functional food ingredient.

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 invention discloses an HLA and antigen peptide combination prediction method based on interactive attention, and belongs to the technical field of computational biology. The method comprises the following steps: 1, collecting HLA allele and unique peptide fragment data, and constructing an HLA and antigen peptide binding data set; 2, constructing an HLA and antigen peptide binding prediction model, and training the prediction model by using the HLA and antigen peptide binding data set to obtain a trained prediction model; and step 3, inputting the HLA sequence and the antigen peptide sequence into the prediction model to obtain a prediction result of combination of the HLA and the antigen peptide. According to the method, prediction tasks based on a sequence and a structure are integrated into a unified model, and the potential of the pHLA about the sequence and the structure is analyzed and predicted. Compared with an existing method, the two-dimensional evaluation provides a more comprehensive antigen immunogenicity view angle, and a new insight is provided for the quality of a new antigen for triggering an immunoreaction.

Collagen peptide compositions and methods for producing these collagen peptide compositions from fish skin or fish scale are provided. The collagen peptide compositions are characterized by high protein content, minimal impurities, enriched hydroxyproline, and specific peptide sequences, making them suitable for use as supplements or additives in food and beverages.

The invention discloses a bioactive peptide with antioxidant and hypoglycemic effects, a preparation and application thereof, and belongs to the technical field of bioactive peptides. The bioactive peptide provided by the invention is as shown in at least one of SEQ ID NO: 1 to SEQ ID NO: 4. Degreased shiny-leaved yellowhorn meal is used as a raw material, after high-temperature heat treatment and ultrahigh-pressure pretreatment are performed, the bioactive peptide with antioxidant and hypoglycemic effects is separated through enzymolysis, ultrafiltration and a liquid chromatography separation technology in combination with a bioinformatics technology, and components and sequences of the bioactive peptide are preliminarily identified through a liquid chromatography-mass spectrometry technology. And carrying out chemical synthesis according to the identified peptide fragment sequence to obtain four bioactive peptides with antioxidant and hypoglycemic effects. Through detection, the peptide fragments and the compound peptide fragments thereof have good alpha-glucosidase and alpha-amylase inhibitory activity, and have good application prospects in preparation of products with antioxidant and hypoglycemic effects.

PEPTIDES The peptide of the present invention presents 3 short peptide sequences which are spaced apart by a linker sequence and include either a specific N Terminal sequence (SEQ ID No: 1) or a specific C-Terminal sequence (SEQ ID No: 2), or both. The linker provides a consistent spacing between the short peptide sequences and structural integrity and together with the specified N-Terminal and / or C-Terminal sequences surprisingly improves solubility and film forming ability. The short peptide sequence can have a sequence which comprises or consists of any one of SEQ ID Nos: 3 to 26. The peptides are very soluble and are able to form films and gels, making them particularly attractive for personal care products.

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.