394 results about "TOLLIP" patented technology

The invention provides a drug-disease association prediction method and system, computer equipment and a medium, and belongs to the technical field of computers. The method comprises the following steps: constructing a drug-protein-disease heterogeneous network, and extracting a plurality of element path sub-graphs; inputting the meta-path sub-graph into a multi-scale diffusion graph convolution module, executing learnable multi-step graph diffusion on the basis of graph convolution, synchronously capturing local adjacency and high-order topological information, and generating node embedding; and performing dynamic weighted fusion by utilizing meta-path attention to obtain unified representation. In order to relieve imbalance of positive and negative samples, implementing difficult negative sampling in the embedding space, and constructing a balance training set with the positive samples; medicine-disease features are spliced, a regularization XGBoost classifier is trained, and unknown correlation accurate prediction is achieved. By adopting the method, the drug-disease association prediction precision and efficiency are improved, multi-scale topology and priori knowledge are fused, and a powerful calculation tool is provided for drug relocation.

Methods and apparatus for determining a representation of a protein-protein complex, given a constituent target complex of the protein-protein complex are presented; where the constituent target complex is some subset of the protein-protein complex. A recursive transformer neural network is devised, wherein for each iteration of the recursion, a representation of the output constituent protein complexed with the input constituent target complex is passed into the transformer as input for the next iteration. Some embodiments of the invention include design and manufacturing of effective synthetic biologic drugs, monoclonal antibody (mAb) drug, Antibody Drug Conjugate (ADC), peptide ligand drug, and small molecule drugs (SMDs).

Methods for differentiating pluripotent stem cells to neuroectoderm in dynamic suspension culture using small molecule or protein inhibitors of TGFβ / Activin / Nodal signaling and BMP signaling are provided. Also provided are methoc and protocols for differentiating pluripotent stem cells such as human embryonic stem cells first to neuroectoderm, then further to glial progenitor cells, and further to oligodendrocyte progenitor cells (OPCs), and compositions obtained thereby. The methods of the present disclosure reproducibly produce neuroectoderm progenitor cells by day 7 of the differentiation process, glial progenitor cells by day 21 of the differentiation process and OPCs by day 42 of the differentiation process.

The invention discloses a disease risk assessment method and screening device based on a multi-group student physical collaborative digital network, and relates to the field of intelligent medical detection. In order to solve the defect that multi-omics-level system collaborative analysis and robust risk assessment are difficult to realize in the prior art, the technical scheme provided by the invention is as follows: acquiring a plasma sample, acquiring a spectral signal by adopting an attenuated total reflection Fourier transform infrared spectrum, and establishing a plasma spectrum digital information space; the method comprises the following steps: constructing a biological collaborative digital network containing four nodes of protein, lipid, saccharides and nucleic acid based on pathophysiology priori knowledge, and defining node strength, edge weight and network collaborative efficiency; a health baseline configuration file is established by using a health sample, a standardized deviation score of a to-be-tested sample is calculated, a comprehensive risk score is obtained, a disease screening result is output in combination with a machine learning model, and digital evaluation of multi-omics collaborative characteristics is realized. The method is suitable for non-invasive rapid screening and risk assessment work of neurodegenerative diseases and mental diseases.

A construction and use of an environmentally adaptive co-regulated mRNA nanodelivery system. By incorporating EACR molecules into mRNA nanoparticles, the microenvironment is remodeled to be suitable for robust and sustained mRNA expression, while tissue damage associated with the self-immunogenicity of mRNA drugs is avoided. The nanodelivery system is compatible with ionizable lipid nanoparticles, cationic liposomes, cationic nanoemulsions, and polymeric nanoparticles. The EACR molecules include: anti-inflammatory drugs, tyrosine kinases / adaptors, JAK / STAT and MAPK pathway inhibitors, nutrients and metabolites, membrane transporters / ion channels, phosphodiesterase and cellular stress inhibitors, and natural viral proteins. The therapeutic mRNAs may encode tumor, viral, or bacterial antigens, immunomodulatory factors, therapeutic antibodies, or functional proteins / enzymes, and can play a role in the fields of regenerative medicine, protein supplementation / replacement therapy, targeted gene editing, and immunotherapy.

The present invention relates to a novel regulatory element. The regulatory element according to one embodiment is capable of increasing RNA stability or mRNA translation of a transcription product of a target gene, thereby being capable of increasing the expression level of the target protein, and can be effectively used in systems requiring precise control of gene expression, such as gene therapy, vaccine development, and production of protein therapeutics. Furthermore, the regulatory element of the present application exhibits excellent stability-increasing ability and translation-regulating ability not only in unmodified RNA but also in RNA containing a modified base, and thus can be effectively used in therapeutic mRNA or vaccine platforms requiring base modification.

The invention belongs to the technical field of gene identification, and discloses a method for integrating multi-omics data to enhance genome prediction and candidate gene identification, candidate gene identification is verified through multi-layer evidence integration, and the verification comprises priority ordering based on gene contribution scores, function enrichment analysis, generic genome network verification and CRISPR / Cas9 experimental verification. Evaluation on a corn population (n = 174) containing complete genomics, transcriptomics, translational omics and proteomics maps shows that the framework is remarkably improved in grain character prediction and is improved by 2.9-12.3% compared with a genome selection baseline, and meanwhile candidate genes verified by experiments are recognized. The invention further verifies the universality of the framework to five traits on an arabidopsis thaliana population, and provides an open source software platform to promote the practical application of the framework in a breeding plan.

The invention provides a pathogenic driver gene prediction method based on biological network fusion and related equipment. The method comprises the following steps: acquiring data of various driver genes for training; constructing an initial gene relationship map based on protein interaction, gene sequence similarity, KEGG pathway co-occurrence, a gene co-expression mode and semantic similarity of a gene ontology, and embedding various human driven gene data for training into each node in the initial gene relationship map to obtain various gene relationship maps; performing dynamic adjustment on each gene relationship map through edge discarding, feature discarding and difficult sample recognition enhancement to obtain an adjusted gene relationship map for training the constructed pathogenic driving gene prediction model to obtain a trained pathogenic driving gene prediction model; inputting the target driver gene data into the trained pathogenic driver gene prediction model for prediction to obtain a prediction result; and the accuracy and robustness of pathogenic driver gene prediction are improved.

The present disclosure relates to a mammalian cell which is modified to express on the surface of its membrane a binding domain which binds to a target molecule. The disclosure also relates to protein constructs and nucleic acids for producing such modified mammalian cells, and to methods for using the mammalian cells to deliver therapeutic agents to target cells or tissues in vivo.

Several embodiments of the methods and compositions disclosed herein relate to immune cells that are engineered to express chimeric antigen receptors as well as genetically edited or otherwise engineered enhance the persistence the cells in immunotherapy. In several embodiments, the cells are edited to knock out a target gene that encodes a protein involved in antigen processing and presentation by major histocompatibility complex class I molecules. In several embodiments, a mixture of immune cell types is used, optionally in allogeneic therapy. The engineering and editing of the cells, such as NK cells and / or T cells exhibit enhanced cytotoxicity and / or persistence, as well as reduced risk of reduced graft versus host, host versus graft, and graft versus graft effects.

The invention discloses a self-adaptive multi-channel fusion lncRNA subcellular localization prediction method based on a reinforcement learning agent, and relates to a long-chain non-coding RNA subcellular localization prediction method. The method aims at solving the problems that sample heterogeneity is ignored and RNA-protein interaction characteristics are ignored due to the fact that a fixed feature fusion strategy is adopted in an existing method. According to the method, firstly, multi-modal feature extraction is carried out, original feature vectors are obtained through splicing, the original feature vectors are projected to a reinforcement learning state, actions are obtained through an intelligent agent obtained through PPO training based on near-end strategy optimization, and the actions comprise feature selection mask codes, fusion weight guidance and dynamic hyper-parameter configuration. A personalized fusion strategy is obtained through action vectors generated by the reinforcement learning agent, the fusion prediction network fuses the features in the multi-modal feature set based on the personalized fusion strategy, and finally the positioning probability of the lncRNA at each subcell position is obtained.

The invention discloses a multi-layer heterogeneous network unicellular organism network inference method based on meta-path enhancement, which mainly comprises a gene regulation knowledge base enhanced multi-layer heterogeneous network construction module for integrating an external gene interaction network and multiple omics data such as scRNA-seq, scATAC-seq, ST and the like; constructing a single-cell multi-omics multilayer heterogeneous network containing cell-cell, cell-gene and gene-gene relationships, and fusing spatial constraints to consider cell positions and tissue structures; and the feature enhancement module based on the meta-path explores complex semantics of the network by designing a multi-hop meta-path mode, designs an adaptive multi-view learning framework and a multi-round enhancement mechanism, and optimizes feature representation by using cell-gene interaction and cross-modal attention fusion. The unicellular biological network can be effectively deduced, the deduction accuracy and biological interpretation are remarkably improved, the method plays an important role in understanding the cell biological process, developing and treating diseases and the like, has good expandability, and can further integrate multi-modal omics data such as proteomics and metabonomics.

Provided herein are engineered Epstein-Barr virus nuclear antigen 1 (EBNA1), coding molecules thereof, vectors and mammalian cell expression systems comprising the same, and polypeptide of interest recombinantly produced by the foregoing. Also provided are methods for the preparation of the engineered EBNAls, coding molecules thereof, vectors and mammalian cell expression systems and methods for using the same in recombinant expression.

The application provides a method for analyzing the synergistic mechanism of hepatotoxicity and nephrotoxicity of non-steroidal anti-inflammatory drugs. The method comprises the following steps: preliminary toxicity prediction of NSAIDs and collection of toxicity target points, collection of liver and kidney disease target points, then cross and screening of the target points to obtain core target points and common core target points of NSAIDs induced liver and kidney diseases, and then constructing a protein interaction network of the common core target points; enrichment analysis of the common core target points to obtain the common action pathway of NSAIDs induced liver and kidney diseases; finally, further screening of the common core target points to obtain the key target points of NSAIDs induced liver and kidney diseases, and verification by using molecular docking technology. Compared with the traditional method, the advantages of the method are: first, the method does not depend on large-scale patient clinical data and a large number of animal or cell experiments, avoiding the ethical controversy in animal experiments and human experiments; second, the method can identify the potential cross-pathway and synergistic toxicity mechanism when a compound triggers multiple diseases, which is helpful for more comprehensive evaluation of the toxicity risk of NSAIDs.

The embodiment of the invention provides a drug optimization method and system based on the overall effect of a protein network. The method comprises the following steps: constructing a protein interaction network related to a target disease, and dividing each protein target into a risk protein set and a protection protein set; respectively calculating first binding affinity data of the candidate drugs and each protein target in the risk protein set, and generating a first network comprehensive score based on the first binding affinity data; respectively calculating second binding affinity data of the candidate drugs and each protein target in the protection protein set, and generating a second network comprehensive score based on the second binding affinity data; calculating network confrontation scores of the candidate drugs according to the first network comprehensive score and the second network comprehensive score; and determining whether the candidate drug is a preferred drug based on the network adversarial score. The method can overcome the defect that a single-target drug is insufficient in curative effect due to a network compensation effect, so that safer and more effective candidate drugs are screened out.

The invention relates generally to CD19 antibodies and antigen-binding fragments thereof, to chimeric receptors comprising the same, and to cells configured to express such proteins. The invention also relates to methods of using such antibodies, chimeric receptors, and cells in the treatment of various diseases, including cancer and autoimmune diseases.

Disclosed is a novel class of fusion proteins to recruit the cell's innate chaperone machinery, specifically the Hsp70-mediated system, to specifically reduce TDP-43-mediated protein aggregation and associated protein conformational diseases.

The present invention relates to an immunogenic construct with the capability to activate the mammalian immune system. In particular, the present invention relates to an immunogenic construct comprising bacterial proteins or fragments thereof that can both target and activate immune cells through interaction with pattern recognition receptors (PRR) expressed on the immune cells.

The invention discloses a 5-bromoindole-2-carboxylic acid methyl ester derivative as well as a preparation method and application thereof in a protein inhibitor, and relates to the technical field of synthesis and preparation of inhibitors. According to the 5-bromoindole-2-carboxylic acid methyl ester derivative, compared with a contrast product, the synthetic route steps of the prepared 5-bromoindole-2-carboxylic acid methyl ester derivative are simpler and more convenient, structural modification and preparation are easier, the application range of a substrate is widened, the yield of the product is increased, and the yield of the product is increased. The differentiated requirements on the molecular structure novelty in the field of kinase inhibitors can be met. An epidermal growth factor receptor (EGFR) inhibitor prepared from the 5-bromoindole-2-carboxylic acid methyl ester derivative is higher in inhibition efficiency on biological activity, the physiological solubility is improved more remarkably, a more excellent and stable target binding result can be shown, and the EGFR inhibitor can be used for preparing an epidermal growth factor receptor (EGFR) inhibitor. The application effect of the compound in scenes such as antitumor drug development and the like is favorably improved.

Described herein are compounds that mediate the degradation of cyclin-dependent kinase 2 (CDK2) and are therefore useful in the treatment of various disorders, such as cancer.

This disclosure relates to an immunoglobulin single variable domain (ISVD)-containing modulator that allosterically binds to a three-dimensional (3D) epitope of a protein complex comprising at least one inhibitory immune receptor and at least one corresponding ligand. The modulator regulates cooperativity within such complexes, thereby affecting the binding affinity and downstream signaling pathways. Specifically, the allosteric modulator of this invention induces positive cooperativity in immunoinhibitory receptor-ligand complexes, thus suppressing immune responses in a spatiotemporally restricted manner. Accordingly, these allosteric modulators are useful as therapeutic agents for inflammatory diseases, such as autoimmune diseases, allergic diseases, or graft-versus-host disease (GVHD). Moreover, this disclosure pertains to methods for identifying, selecting, and producing said allosteric modulators.

To provide novel methods and uses for increasing a yield of recombinant proteins in host cells, which are simple, efficient and suitable for use in industrial methods.SOLUTION: Provided is a method of increasing a yield of a protein of interest (POI) in an eukaryotic host cell, preferably a yeast, by overexpressing at least one polynucleotide encoding at least one transcription factor of the present invention, preferably Msn4 / 2. Further provided are a recombinant eukaryotic host cell for manufacturing the POI, the host cell being engineered to overexpress at least one polynucleotide encoding at least one transcription factor, as well as the use of the host cell for manufacturing the POI.SELECTED DRAWING: None

The invention provides a method of preparing a vaccine composition. The method includes infecting gene-edited porcine endogenous retrovirus (PERV) negative swine cells with a microorganism which expresses at least one protein antigen capable of inducing protective immunity in an animal against an infectious agent; culturing the infected cells in culture medium to propagate the microorganism; and harvesting the propagated microorganism from the culture medium to obtain a fraction comprising a PERV free antigen for use in immunizing an animal against the infectious agent.

The application relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information, and the method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; constructing a cell-cell function correlation network and a cell-cell physical interaction network respectively; performing weighted fusion processing on the two networks to obtain a comprehensive cell relationship network; calculating a final cell infiltration score through a restart walk algorithm, and inferring the infiltration degree in a tumor microenvironment according to the final cell infiltration score. The application innovatively fuses GO function annotation information and PPI network data, comprehensively considers the functional similarity and physical or signal interaction between cells, enables the model to understand cell synergy from the biological pathway level and analyze cell direct interaction from the protein interaction level, avoids one-sidedness of a single perspective, and provides a more stereoscopic cognitive framework for tumor microenvironment analysis.

The invention belongs to the technical field of biological medicines, and particularly relates to lipid nanoparticles as well as a preparation method and application thereof. The invention discloses a lipid nanoparticle. The material of the lipid nanoparticle comprises phospholipid and cholesterol. According to the invention, a lipid nano-carrier delivery system entrapped with apoptotic protein Bax is prepared through a microfluidic technology, and high encapsulation rate and stable delivery of protein drugs are realized. The nano-carrier delivery system capable of actively encapsulating the lipidosome realizes targeted delivery to senescent cells in vivo, so that senescent cell apoptosis is effectively induced, the proportion of senescent cells in tissues is reduced, and a new thought and means are provided for anti-aging treatment.

This invention relates to a recombinant type V collagen, its encoding gene, and its applications, belonging to the fields of genetic engineering and protein engineering technology. Based on the amino acid sequence of the α1 chain of human type V collagen, this invention utilizes bioinformatics and other methods to design the amino acid sequence, enabling the recombinant collagen to achieve higher expression efficiency and high stability while maintaining high biological activity. A novel collagen sequence containing 178 amino acids was designed. The nucleotide sequence of this recombinant collagen was optimized using E. coli codon preference to construct a high-expression recombinant strain. The recombinant collagen produced by fermentation of this strain contains no exogenous amino acids such as tags, making it a recombinant humanized type V collagen with good safety. Compared with commercially available type V collagen products, the recombinant type V collagen prepared using the above technical solution has better antioxidant activity and barrier repair capabilities, thus possessing good practical application value.

The present invention relates to inhibitors of protein fucosylation. More specifically, the present invention relates to carbocyclic compounds of formula (I) useful as inhibitors of protein fucosylation, or for the treatment of cancer, autoimmune diseases, infectious diseases, inflammatory diseases, or sickle cell diseases.

The invention relates to a cell infiltration inference method and system fusing GO function annotation and PPI network information. The method comprises the following steps: collecting gene expression data, GO function annotation data and PPI network data; respectively constructing a cell * cell function association network and a cell * cell physical interaction network; carrying out weighted fusion processing on the two to obtain a comprehensive cell relation network; and calculating a final cell infiltration fraction through a restart migration algorithm, and deducing the infiltration degree in the tumor microenvironment according to the final cell infiltration fraction. According to the method, GO function annotation information and PPI network data are creatively fused, functional similarity and physical or signal interaction between cells are comprehensively considered, the model can understand cell synergy from the biological pathway level and can analyze cell direct interaction from the protein interaction level, one-sidedness of a single view angle is avoided, and the method has the advantages of being simple in structure and convenient to operate. And a more three-dimensional cognitive framework is provided for tumor microenvironment analysis.

The invention relates to the technical field of biomedicine detection, and discloses a tumor cell marker detection system for predicting the activation state of intracellular protein kinase. Comprising a marker detection module used for qualitatively detecting epithelial cell markers, mesenchymal cell markers, cell polarity markers and extracellular matrix related markers in tumor cells; the data processing module is used for performing cross validation on an epithelial cell marker, a mesenchymal cell marker, a cell polarity marker and an extracellular matrix related marker; the dynamic weighting module is used for performing dynamic weighting on different markers based on a complex system theory, and endowing epithelial cadherin with a higher weight; and the judgment module is used for determining whether epithelial intercellular substance transformation occurs or not according to the dynamically weighted marker data, and predicting the activation state of intracellular phosphoinositide 3-kinase alpha and the invasiveness of tumor cells based on the determination result of the epithelial intercellular substance transformation.

The application discloses an antibody glycosylation modification regulation method, device and application, relates to the technical field of biopharmaceuticals and protein engineering, and realizes directional regulation of modification types, modification sites and modification proportions of N-glycan and O-glycan of the antibody through three core processes of constructing a glycosyltransferase engineering strain, optimizing a fermentation culture system and precisely regulating modification reaction conditions; solves the technical problems of low modification efficiency, poor specificity and insufficient product uniformity in the existing modification method, can improve the target glycan modification proportion to more than 90%, the modification product purity is greater than or equal to 98%, is suitable for glycosylation modification optimization of therapeutic monoclonal antibodies, bispecific antibodies and antibody drug conjugates, significantly improves the biological activity and pharmacokinetic performance of the antibody, wherein the ADCC activity is improved by 3-5 times, and the CDC activity is improved by 2-4 times, and has the advantages of strong controllability, good adaptability to large-scale production and high cost-effectiveness.