9 results about "Protein profiling" patented technology

The invention discloses a construction method and application of a protein fingerprint spectrum of Tilletia foetida teliospore. The method comprises the following steps: firstly, preparing immunomagnetic beads by using a monoclonal antibody for resisting Tilletia foetida teliospore, wherein the immunomagnetic beads are used for specifically enriching target teliospore in a wheat sample; then, the captured magnetic bead-teliospore compound is placed in a formic acid-acetonitrile solution and subjected to efficient cracking through steel ball grinding, and internal protein of the teliospore is released. The released protein is subjected to MALDI-TOF-MS analysis, so that a protein fingerprint spectrum of the protein is obtained, and a characteristic protein fingerprint spectrum database of the fungal spores is constructed. The rapid identification of the Tilletia foetida teliospore is realized by comparing and analyzing the mass spectrum of the sample to be detected and the self-established database. The detection time is shortened to be within 1 h, the sensitivity reaches up to 105 spores per gram of samples, and the method has the advantages of being simple, convenient, accurate and efficient and is particularly suitable for on-site rapid screening in the field of food quality safety.

The disclosure relates generally to compounds and methods for selective gas phase enrichment of modified peptides for reactive protein profiling.

This invention relates to the field of bioinformatics processing and medical laboratory data analysis, specifically a method for classifying hepatitis B antibody patterns based on plasma protein profiles. The method includes: acquiring host hardware information of the execution environment and extracting central processing unit (CPU) cache parameters; acquiring a high-dimensional sparse one-dimensional array and extracting non-zero feature indices; performing mapping calculations using a locality-sensitive hashing (LSH) algorithm to reconstruct the data into a locally dense two-dimensional matrix; dynamically segmenting the data into independent sub-blocks according to cache parameters and initial segmentation dimensions; performing low-rank tensor decomposition on the independent sub-blocks to extract local latent feature vectors; concatenating the sub-blocks, weighting them through a single-layer attention network, and inputting the result into a classifier function to output a target classification pattern vector; and generating dimension update instructions based on a preset dynamic adjustment mechanism to adjust subsequent segmentation dimensions. This invention achieves lower memory peaks, higher cache hit rates, and stable multi-label pattern output capabilities.

The application discloses a kind of inherent disordered region prediction methods based on protein atlas double-scale features, it is related to protein structure prediction technical field.The method steps are:S1, obtain the original amino acid sequence of protein to be predicted;S2, utilize protein encoder to carry out feature coding to the original amino acid sequence of protein, obtain residue level embedding matrix feature and three-dimensional space coordinate feature;S3, construct the protein hetero atlas containing scalar feature and vector feature;S4, the protein hetero atlas is input into joint feature learning module and carries out feature fusion and learning, obtains the fusion feature representation of each amino acid residue;S5, based on the fusion feature representation, the probability that each amino acid residue belongs to ordered region or disordered region is predicted by classifier.The application significantly improves the prediction accuracy and reliability of protein inherent disordered region, and provides a better quality of computational analysis tool for related biological mechanism research.

The present invention generally pertains to methods of detecting host cell proteins. In particular, the present invention pertains to the use of a novel activity-based protein profiling probe to identify host cell proteins with lipase activity in a pharmaceutical formulation.

The present application provides a kind of based on PBA data's microorganism structure proteomics analysis method, by inputting the PBA original data of microorganism structure, extract the encoding nucleotide fragment containing specific information from the PBA original data, obtain intermediate transmission data;Then extract the protein expression data of the microorganism structure from the intermediate transmission data.From protein expression data, the total protein expression data of sample, the protein combination data of multiple protein co-expression, the subgroup data obtained after clustering analysis based on the protein expression data of the microorganism structure are analyzed to obtain machine learning.In the multi-level of protein expression, protein combination, subgroup, potential biomarker screening can be carried out.The analysis method described in the present application analyzes the total protein expression, protein combination form, microorganism structure subgroup and its protein fingerprint characteristics and quantification on the level of single microorganism structure, fills the blank of bioinformatics analysis in the field of microorganism structure proteomics detection.

The present invention generally pertains to methods of detecting host cell proteins. In particular, the present invention pertains to the use of a novel activity-based protein profiling probe to identify host cell proteins with lipase activity in a pharmaceutical formulation.

Disclosed in the present application are a database construction method, a substance identification apparatus, system and method, and a computer device, a program product and a medium. The substance identification method comprises: performing first similarity comparison on a protein fingerprint spectrum of an object under test and a standard protein fingerprint spectrum of each reference object, so as to obtain a plurality of candidate reference objects similar to the object under test; and on the basis of protein difference information of the candidate reference objects, performing second similarity comparison on the object under test and the candidate reference objects, and on the basis of comparison results of the candidate reference objects, determining an identification result of the object under test. On the basis of protein difference information corresponding to each candidate reference object determined during primary comparison, secondary similarity comparison is performed on an object under test, so as to determine, on the basis of a secondary comparison result, an identification result of the object under test. Therefore, by means of the use of protein difference information, the problem of it not being possible to determine an identification result of an object under test due to the relatively high similarity between protein fingerprint spectra of a plurality of microorganisms of known categories or the existence of a plurality of microorganisms in the object under test is solved, thereby improving the accuracy of microorganism identification.

The invention discloses a hepatitis B virus infection rapid diagnosis system combining machine learning and peripheral serum MALDI-TOF MS (Matrix Assisted Laser Desorption / Ionization Time of Flight Mass Spectrometry). On the basis of an HBV peripheral blood serum MALDI-TOF MS proteomics database, a LightGBM algorithm is utilized to construct an HBV infection machine learning prediction model, the AUC of the model is 0.88, and the F1 Score is 0.79. The HBVpred performs addition value standardization on multiple pieces of protein fingerprint spectrum data of a single peripheral blood serum sample, calls the models respectively, performs intelligent statistics and outputs a prediction result. Compared with the prior art, the HBVpred system has the advantages of high flux, short time consumption, low cost, high specificity, high identification accuracy, simplicity in operation and the like, and has a huge application prospect.