18 results about "Protein recognition" patented technology

The invention discloses an IV-type secretory effect protein recognition method and system based on multi-modal information, and belongs to the technical field of bioinformatics and secretory protein recognition. The method comprises the following steps: firstly, extracting amino acid residue characteristics of protein by using a protein language model, and constructing a spatial adjacency graph of the protein by using a three-dimensional structure predicted by a protein structure prediction model; respectively extracting sequence features and structural features of the protein through a deep sequence module and a hierarchical graph module; meanwhile, a contrast learning module is introduced to realize cross-modal alignment of the same protein in a potential space; and performing bidirectional interaction on the sequence features and the structural features by using a cross attention module to obtain joint features, and finally outputting a classification result after nonlinear transformation by a gating linear unit for predicting the IV-type secretion effect protein. According to the invention, efficient coordination of sequence and structure bimodal information is realized, and the identification accuracy of the IV-type secretory effect protein is remarkably improved.

The invention discloses an RPA-CRISPR / Cas12a system for detecting a cowfish based on environmental DNA (Deoxyribonucleic Acid) and a specific primer, and belongs to the technical field of biological detection. Aiming at the sequence of a COI fragment of a mitochondrial genome of the Jiangsu, an RPA primer with the species specificity of the Jiangsu is designed so as to amplify a target fragment; designing a high-activity and specific crRNA sequence by searching a Cas12a protein recognition site in a corresponding amplification sequence so as to guide the Cas12a protein to recognize a target fragment and activate the trans-cleavage activity of the Cas12a protein; by designing a molecular probe with stable performance, the probe is stably and efficiently cut by a Cas12a-crRNA active compound, and a detection result can be observed in real time through a luminoscope. The detection method is easy to operate, high in sensitivity and good in specificity, detection can be completed only through constant-temperature heating equipment, and a new technology and method are provided for rapid detection and protection of the cowfish.

The invention belongs to the technical field of biological medicines, and particularly relates to sulfonium salt polypeptide or protein as well as a preparation method and application thereof. Polypeptide or protein containing cysteine and a sulfonium salt compound are used as raw materials, and the sulfonium salt compound is used for alkylation of cysteine at a specific site of the polypeptide or protein to prepare the sulfonium salt polypeptide or protein. The sulfonium salt polypeptide or protein is used as a probe for covalently labeling tryptophan in a lysine methylation reader protein recognition pocket. According to the method disclosed by the invention, different types of sulfonium salt polypeptides and protein probes can be simply, conveniently and efficiently prepared, and the designed annular sulfonium salt with a novel skeleton has an obvious improvement on the polypeptide or protein labeling yield of reader protein. The prepared sulfonium salt probe not only can be used for identifying lysine methylation modified reader protein and finding a therapeutic target for disease-related pathways, but also can be used for modifying tryptophan at a specific site of an antibody-coupled drug and provides a powerful tool for targeted therapy research of tumors.

This invention belongs to the field of in vitro detection technology, specifically relating to a chemiluminescent immunoassay kit and its application in immunoassay. The chemiluminescent immunoassay kit provided by this invention uses a first target protein recognition molecule and a second target protein recognition molecule to capture proteins. Horseradish peroxidase on the second target protein recognition molecule undergoes a polymerization reaction with a chemiluminescent reagent and norepinephrine, forming a product enriched with the chemiluminescent reagent. Under the action of an alkaline pH adjuster and hydrogen peroxide, a chemiluminescent signal is directly generated. This eliminates the need for covalent modification of the protein recognition molecule with the chemiluminescent reagent, reducing the loss of biomolecule activity, improving the utilization rate of the protein recognition molecule, facilitating the control of batch-to-batch variations of the chemiluminescent reagent, and effectively improving detection sensitivity. The results of the examples show that the chemiluminescent immunoassay kit provided by this invention improves the detection sensitivity of pTau217 to approximately 0.25 pg / mL.

The application discloses a fluorescent probe for adjacent thiol protein labeling and bio-imaging and application thereof, which is used for in-vitro labeling of adjacent thiol proteins and bio-imaging of endogenous adjacent thiol in living cells. Based on the active dimethyl sulfide site on cyclopentanone, a series of adjacent thiol protein recognition probes (IDAs) with high specificity, high stability and biocompatibility are constructed and screened, so that in-vitro fluorescent detection of adjacent thiol proteins and in-situ fluorescent labeling of adjacent thiol proteins in cells are realized.

The invention relates to means and methods for analysis of analytes using nanopore-based sensors, for example, to methods, nanopore systems and devices for the stochastic detection of (label-free) analytes in complex samples, for example the specific detection of a protein biomarker in a bodily sample. Provided is a method for detecting the presence of at least one analyte in a sample using a nanopore system comprising a cis chamber comprising a first conductive liquid medium in liquid communication with a trans chamber comprising a second conductive liquid medium through a modified nanopore, comprising:(a) adding a sample to be analyzed for the presence of an analyte to the cis chamber; (b) optionally applying an electrical potential across the modified nanopore; (c) measuring ionic current passing through the modified nanopore, wherein said modified nanopore is a biological nanopore that is functionalized with a 5 to 50 kDa, preferably 10 to 40 kDa, recognition element (e.g., proteinaceous recognition element) R capable of specifically binding to the analyte.

The invention discloses a probe for detecting EBV infected tissue, a kit and a detection method, and belongs to the technical field of virus detection, the key point of the technical scheme is that the probe for detecting the EBV infected tissue is an EBER-PNA probe, the sequence number is GCGACACATGTCTGC, the EBER-specific target probe and EBER in cells are hybridized, flow cytometry is combined, and the EBER-PNA probe can be used for detecting the EBV infected tissue. The kit is high in specificity, low in background and high in signal-to-noise ratio, the detection method is simple and accurate, EBER RNA expression and protein of the same cell can be efficiently detected, infected and non-infected EBV cell subsets can be recognized, and typing and function comparison are carried out.

This invention belongs to the medical field and provides a calcium carbonate manganese nanoprobe for in situ imaging detection of vascular epidermal growth factor, its preparation method, and its application. In this strategy, a DNA strand (P1 and P2) containing a protein recognition antibody, a protein recognition sequence, and a deoxyribonuclease, along with a molecular beacon DNA strand (H1), is stirred in a DMEM cell culture medium containing calcium chloride and manganese chloride to prepare the calcium carbonate manganese nanoprobe. In acidic tumor cells, this nanoprobe releases P1, P2, H1, and manganese ions. P1 and P2 can simultaneously recognize proteins, forming a complete deoxyribonuclease center, which catalyzes the cleavage of H1 under the action of the cofactor manganese ions, generating a fluorescent signal. This strategy achieves specific in situ dynamic imaging of low-abundance proteins in vivo and has great potential in biomedical research.

This invention provides a CRISPR-Cas12a-based nanopore sensor and its application in IGF-1 protein detection, belonging to the fields of biosensing and molecular diagnostics. The nanopore sensor includes: a solid-state nanopore unit, which is a glass capillary nanoparticle with an opening at the tip and a conductive layer covering the inner wall of the nanoparticle; a reporter molecule chemically bonded to the conductive layer; a protein recognition and signal conversion unit, including a DNA aptamer capable of specifically binding to Atlantic salmon IGF-1 protein and a single-stranded DNA complementary strand partially complementary to the DNA aptamer; and a signal amplification unit containing the Cas12a protein and crRNA complementary to the target DNA strand. This sensor achieves efficient, sensitive, and specific conversion from protein to electrical signal through a cleverly designed molecular competition system.

This invention relates to a method for identifying secretory effector proteins based on representational information content. It assesses the identification difficulty of each protein class by measuring the dispersion of its distribution in the feature space, and estimates the information content using a diagonal approximation method. This generates a dynamic additive margin, transforming abstract feature complexity into a penalty term that can be directly applied to the loss function. This achieves a dynamic correlation between class difficulty and the decision boundary. A novel loss function is constructed, which subtracts the dynamic margin from the cosine similarity of the target class, amplifies it uniformly by a scaling factor to obtain a logical value, and then calculates the cross-entropy. This forces the model to reserve a larger classification margin for high-difficulty classes, effectively alleviating the bias problem of traditional cross-entropy loss on the majority class. The advantages of this invention are: significantly improved model recognition accuracy for high-difficulty, low-sample classes under long-tailed distributions.

Disclosed herein is a system for quantifying a low concentration of an analyte in a sample, such as a human embryo culture. The system allows for precise identification and quantification of analytes, such as proteins, at a resolution and sensitivity of less than 1 pg / ml. The disclosed system provides a non-invasive, fast, and accurate measurement platform to achieve better patient care through more intelligent medical decisions. Also disclosed herein is a method of quantifying an analyte in a sample using the disclosed system.

This invention discloses a key protein identification method based on two-stream supergraphs and multi-level gating in the field of multi-source bioinformatics. The method includes the following steps: S1, acquiring the target PPI network and multi-source biological heterogeneous attribute data, extracting network interaction edge data of the target protein, and constructing a basic PPI network view; S2, based on the PPI network view constructed in step 1, performing high-order topological information extraction based on local closed-loop subgraphs, thereby overcoming topological noise interference caused by inherent false positive edges in the basic interactive network, and deeply mining the synergistic interaction patterns of macromolecular complexes in spatial conformation. The algorithm of this invention exhibits good performance advantages in key protein identification, demonstrating that the dynamic fusion effect of high-order topology and multi-dimensional attributes is superior to single feature or homogeneous splicing strategies, providing a new approach and algorithm for solving the problem of accurate screening of key targets in complex biological networks.

The invention discloses an exosome protein recognition method based on multi-kernel learning and a fuzzy hypergraph model. The method comprises the following steps: 1, constructing and preprocessing a data set; 2, extracting and screening the optimal characteristics of the PSSM-DWT, the PSSM-AB and the PsePSSM; 3, fusing features by adopting HSIC multi-kernel learning to generate an optimal kernel matrix; 4, constructing a fuzzy hypergraph Laplacian support vector machine (H-FLapSVM) model, using kernel entropy component analysis reconstruction errors as fuzzy membership to suppress noise, and introducing a hypergraph Laplacian matrix to capture a sample high-order relation to complete training and prediction; and 5, performance evaluation. According to the method, multi-dimensional feature information is fused, the anti-interference capability of the model and the relation representation comprehensiveness are enhanced, and the exosome protein recognition precision is remarkably improved.

The invention belongs to the field of bioinformatics and proteomics, and particularly relates to a targeting lightweight spectrogram library construction system for high-sensitivity single-cell proteome research and application of the targeting lightweight spectrogram library construction system. According to the system, on the basis of single cell DIA data, accurate construction and redundancy control of a spectrogram library are achieved through high-confidence-coefficient peptide fragment extraction, functional protein priority import, target-oriented spectrogram deduction, expression score-driven entry compression and structure noise reduction optimization. According to the method, the protein recognition rate and repeatability of single-cell DIA data are remarkably improved, and particularly, the method is outstanding in recognition of functional proteins such as membrane receptors, kinases, transcription factors and ubiquitin regulatory proteins. The method can also be applied to key functional protein identification of various immune cells and tumor-related macrophage subpopulation identification, provides efficient and reliable technical support for functional analysis and subpopulation classification of a single-cell proteome level, and has good universality and popularization value.

The application provides a key protein identification method and device based on a dynamic graph neural network, the method comprising: obtaining first original protein data, and constructing a dynamic protein interaction network with a time attribute; performing random walk sampling on the dynamic protein interaction network, and constructing a training corpus; using a trained graph convolutional neural network model to extract structural features of protein nodes; inputting the structural feature data into a pre-trained long short-term memory network model, and outputting time features of the protein nodes; and identifying key proteins through pattern classification according to the structural features and the time features. The application models a protein interaction graph as a dynamic graph, and learns structural features and time features of protein nodes on the dynamic graph by combining a graph convolutional neural network model and a long short-term memory network model, so that key proteins can be identified more efficiently.

The invention discloses a method for constructing a mussel mucoprotein recognition model, which belongs to the technical field of biological information, and specifically comprises the following steps: inputting an amino acid sequence of a target protein, acquiring standard electromagnetic response waveforms of each amino acid residue in various non-aqueous solvent environments, and sequencing to form an initial electromagnetic fingerprint; polypeptide fragment fingerprints are generated through sliding window cutting, and shared frequency components of waveform of each fragment are extracted and sequenced according to intensity to form a feature sequence; extracting a local mode and context dependence of the sequence through an encoder network, and outputting a context vector; the decoder network maps the vector into an embedded representation of a fixed dimension; a known protein sequence with an adhesion function is used as a training sample, the above processing is performed on each sample to obtain an embedded representation set, and a binary classification model is trained to obtain an identification model. According to the invention, function identification is realized by using electromagnetic response characteristics of proteins in a non-water environment.

The application discloses a type IV secretory effector protein recognition method and system based on multi-modal information, and belongs to the technical field of bioinformatics and secretory protein recognition. The amino acid residue features of the protein are extracted by using a protein language model, and a spatial adjacency graph of the protein is constructed by using a three-dimensional structure predicted by a protein structure prediction model; then, sequence features and structure features of the protein are extracted by a deep sequence module and a hierarchical graph module respectively; meanwhile, a contrast learning module is introduced to realize cross-modal alignment of the same protein in a latent space; the sequence features and the structure features are bidirectionally interacted by using a cross attention module to obtain joint features, and finally, a classification result is output after a nonlinear transformation of a gate linear unit, and is used for prediction of the type IV secretory effector protein. The application realizes efficient cooperation of sequence and structure double modal information, and significantly improves the accuracy of type IV secretory effector protein recognition.