595 results about "TOLLIP" patented technology

The present invention discloses a human serum albumin-targeting nanobody and its application. A variety of human serum albumin-targeting nanobodies that can be used to construct long-acting fusion protein drugs and their immune screening methods are disclosed. The antibody is composed only of the heavy chain variable region and can be fused and expressed with therapeutic proteins or polypeptide drugs such as interleukin, interferon, and tumor necrosis factor in an Escherichia coli expression system. The fusion protein has the activity of specifically binding to human serum albumin, has a relatively high affinity, can effectively extend the drug half-life, and does not affect the biological activity of the recombinant protein drug at the same time.

Translation modulating agents that modulate expression of one or more translation start sites for expanded repeat (e.g., DPR) protein synthesis are provided. Compositions and methods for treating translation start sites for expanded repeat (e.g., DPR) protein synthesis-associated disorders are also provided.

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).

The present invention relates generally to compositions, formulations, and methods for immunotherapy and drug delivery, including the treatment of eye diseases such as dry eye disease (DED). In particular, the present invention relates to methods of producing exosomes from mesenchymal stem cells (MSC-Exos) and, optionally, loading said exosomes with one or more bioactive substances. The exosomes may be loaded using electroporation with one or more bioactive substances such as proteins, miRNAs, and / or siRNA. In specific embodiments, the exosomes, and / or one or more biological compounds derived from the exosomes, may be provided to an individual in need thereof, including as part of one or more compositions containing mesenchymal stem cells (MSCs). The individual in need thereof may be an individual having a medical disorder such as DED, immune disorders, cancer, and other disorders. Therefore, the compositions containing MSCs can be used for topical application to the eye. The compositions can contain MSCs, MSC-Exos, one or more MSC-derived biological compounds (e.g., growth factors, proteins, etc.).

The present invention discloses a drug-target prediction method based on a graph-attributed neural network, comprising the following steps: S1, constructing a multi-source heterogeneous biological network and uniquely identifying drugs, proteins, and diseases; S2, calculating the similarity between any two diseases based on the disease module theory of the human protein-protein interaction network; S3, using each biological entity pair and the corresponding similarity value as a training sample for the graph attention neural network representation learning phase; S4, using the training samples to drive the graph attention neural network learning to obtain a representation vector for each entity; and S5, using the trained drug-target prediction model to predict drug-target interactions. This invention reduces the dependence of deep learning models for drug-target interaction prediction on training samples, thereby improving prediction performance.

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.

The invention relates to the field of preparation of materials for articular cartilage repair, in particular to collagen and chitosan cross-linked temperature-sensitive gel for articular cartilage repair and a preparation method of the collagen and chitosan cross-linked temperature-sensitive gel. The cartilage repair material prepared by the invention adopts the recombinant human collagen with a triple helix structure as a main raw material. The raw material is prepared by using a mammalian cell expression system, is close to protein folding and polymerization of natural protein, and has a spatial structure and modification necessary for active protein. The recombinant human collagen with the triple-helix structure is good in biocompatibility and free of rejection reaction and anaphylactic reaction, and the risk of disease transmission possibly caused by animal-derived collagen can be avoided. The injection type temperature-sensitive gel is liquid at low temperature and is converted into hydrogel at 37 DEG C, so that the injection type temperature-sensitive gel is suitable for being used as a transfer carrier for in-vivo injection treatment, secondary injury of an organism is effectively avoided, and the injection type temperature-sensitive gel has a wide clinical application prospect.

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 relates to the field of biological medicine, and discloses application of a GADD34 inhibitor in preparation of a medicine for enhancing CAR-T cell tumor treatment. The invention provides an innovative strategy for inhibiting CAR-T cell depletion through targeted regulation and control of an endoplasmic reticulum stress pathway to solve the problems that in an existing CAR-T therapy, the anti-tumor activity is reduced, and the solid tumor treatment effect is insufficient due to T cell depletion. Researches find that an IRE1-XBP1 pathway is used as a core signal axis of endoplasmic reticulum stress, and excessive activation of the IRE1-XBP1 pathway can drive CAR-T cell depletion related phenotypes (such as PD-1 / LAG-3 up regulation and cytokine secretion reduction). Screening experiments show that inhibiting the upstream regulatory factor GADD34 of the IRE1 not only can effectively reduce the IRE1-XBP1 signal intensity, but also can cooperatively relieve the endoplasmic reticulum stress pressure by regulating protein translation recovery, so that the CAR-T cell steady state is more comprehensively maintained. Therefore, the GADD34 inhibitor can provide a new way for enhancing CAR-T cell tumor treatment.

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.

Featured are the use of pharmaceutical compositions containing a CD40 antagonist or a CD154 antagonist for the treatment, reduction, and / or inhibition of an immune response, such as an anti-drug antibody response, following, e.g., protein replacement therapy or gene therapy.

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 methot 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 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.

The embodiment of the invention provides a human and virus protein interaction recognition method based on deep learning. The method is applied to the field of protein interaction recognition, and comprises the following steps: acquiring a human protein data set and a virus protein data set, and preprocessing the human protein data set and the virus protein data set; constructing a double-blind data set and a node degree balance data set according to the preprocessed human protein data set and virus protein data set; constructing a human and virus protein interaction prediction model based on a pre-trained protein language model, and performing training optimization on the human and virus protein interaction prediction model according to the double-blind data set, the node degree balance data set and a preset optimization target; and inputting to-be-processed human protein data and virus protein data into the trained and optimized human and virus protein interaction prediction model for analysis and processing to obtain a human and virus protein interaction recognition result, so that the accuracy and reliability of the recognition result are improved.

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 relates to a construction method and application of a retinal artery occlusion disease risk assessment model. Specifically, according to the retinal artery occlusion disease risk assessment model, a retinal artery occlusion disease risk assessment result of a subject is obtained based on the RAO protein marker level of a biological sample of the subject; wherein the RAO protein marker is a combination of integrin subunit alpha M, sex hormone binding globulin, S100 calcium binding protein A7, immune globulin heavy chain delta and synaptic-like protein 1. The prediction accuracy of the retinal artery occlusion disease risk prediction model can reach 88%, the area (AUC) under the working characteristic curve of a subject reaches 0.944, and extremely high distinguishing efficiency and clinical application value are shown.

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 cerebron degrading agent antibody conjugates (cDACs) comprising a cerebron degrading agent moiety covalently linked to an antibody. The cDACs target proteins for intracellular degradation and can be used to treat diseases and conditions.

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 present invention discloses a method for predicting drug-drug interactions based on secure multi-party computing, comprising the following steps: S1, obtaining a drug-drug interaction network, a drug-protein interaction network, a drug-disease association network, and a drug-side effect association network; S2, calculating Jaccard similarity based on different drug features to obtain similar features between all drugs, and using principal component analysis technology to reduce the dimensionality of all drug similarity features; S3, dividing each user's private feature data into four parts, encrypting them using secret sharing technology, and sending them to four servers using an additional secret sharing mechanism; S4, inputting the four parts of feature data into a preset private deep learning model to predict drug-drug interactions. The present invention enables high-quality collaboration between pharmaceutical companies and research institutions without leaking drug privacy information, thereby improving drug-drug interaction prediction.

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.

The present disclosure relates to a biomarker for distinguishing between depression and bipolar disorder and uses thereof, elucidating a composition for distinguishing between depression and bipolar disorders and a method for providing information for distinguishing between depression and bipolar disorder, using same, wherein the composition is based on a biomarker composition comprising eotaxin, nerve growth factors (NGF), tumor necrosis factor-α (TNF-α), glial cell derived neurotrophic factor (GDNF), interleukin-1β (IL-1β), and serum soluble CD40 Ligand (sCD40L) within a serum sample and includes an agent for measuring the expression level of each of the six biomarker proteins.

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.