768 results about "TOLLIP" patented technology

In various embodiments, the disclosure provides compositions, such as polypeptides, polynucleotides, gene editing systems, small molecules, vectors, or host cells, comprising and / or modulating the expression or activity of an immunomodulatory-related protein. In various embodiments, the disclosure also provides for the treatment of aging, senescence, fibrosis, metabolic diseases, cardiovascular diseases, endocrine-related disorders, genetic diseases, cancer, infections, immune diseases, and the like using agents comprising and / or modulating the expression or activity of an immunomodulatory-related protein. Methods of replacing an adaptation of therapy or combinations thereof with hormones, growth factors and / or proteins, as well as methods of identifying the agents.

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.

Disclosed herein are compositions and methods for modulating T cells. For example, the compositions and methods may be used to increase memory T cells. The compositions and methods may increase the expression or protein level of a transcription factor selected from THAP6, DMRT3, MEF2B, PAX2, ATOH7, KLF2, KLF1, TWIST1, NKX6, and FEV, or a combination thereof. The compositions and method may be used in combination with Adoptive T Cell Therapy (ACT) to enhance the ACT.

The invention discloses a method and device for predicting protein-ligand binding affinity based on distance and atomic contact number, comprising: acquiring a data set of protein-ligand binding affinity, the data set comprising: atomic information of a protein receptor and atomic information of a ligand; classifying atoms of a protein-ligand compound in the data set according to a preset atom type, and calculating a distance and an atom contact number of an atom pair of a protein-ligand in the data set according to the atom type; training a preset model by taking the protein-ligand distance and the atom contact number as training data to obtain a protein-ligand binding affinity prediction model; and obtaining to-be-predicted protein-ligand data information, inputting the to-be-predicted protein-ligand data information into the protein-ligand binding affinity prediction model, and outputting the to-be-predicted protein-ligand binding affinity. The method is not limited to the power exponent of the distance in the traditional electrostatic interaction energy and Van der Waals interaction energy, and the prediction capability of the affinity is improved.

The invention discloses a construction method and application of a multi-scale and multi-level regulation and control network, and relates to the field of biological information.The construction method comprises the following steps that firstly, 20 databases about interaction among protein, transcription factors, enhancer super enhancers, ncRNA and metabolites are collected; 2, performing quality control on each database, and annotating a specific disease physiological state to each regulation and control relationship; 3, integrating molecular regulation and control relationships among different levels according to biological logic; 4, a bidirectional random walk algorithm is adopted to cooperate with a multi-level biological network to predict and mine potential drug treatment targets; the regulation and control network constructed by the method has a multi-level structure and a specific disease physiological state, the dynamic regulation and control network conforming to the real disease physiological state can be constructed, and the method can be applied to mining potential drug targets and exploring pharmacological mechanisms.

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.

The invention relates to application of an ACBD3 inhibitor in preparation of a medicine for treating lung cancer and gRNA for inhibiting ACBD3 gene expression, and belongs to the field of bioengineering.199 binding proteins of ACBD3 are obtained through co-immunoprecipitation and mass spectrum identification, new biological functions of ACBD3 are excavated through GO analysis and KEGG pathway analysis of the binding proteins of ACBD3, and the application of the ACBD3 inhibitor in preparation of the medicine for treating lung cancer is developed. The ACBD3 is used for regulating and controlling a KEGG pathway enrichment map of a differential gene, and the ACBD3 is found to participate in the occurrence and development of cancers; the ACBD3 is successfully knocked out by designing gRNA, and tests prove that the multiple effects of slowing down A549 lung cancer cell proliferation, inhibiting lung cancer cell migration, inhibiting A549 lung cancer cell invasion, retarding A549 cell cycle G1 phase and reducing tumorigenicity can be achieved by knocking out the ACBD3.

The present invention relates to the field of tumor treatment and immunology biology. Provided are a pharmaceutical combination containing an anti-PD-1-anti-VEGFA bispecific antibody, and the use thereof. Specifically, the pharmaceutical combination comprises at least one bispecific antibody and at least one PARP inhibitor, wherein the bispecific antibody comprises a first protein functional region for targeting PD-1 and a second protein functional region for targeting VEGFA; and according to the EU numbering system, the heavy chain constant region of an immunoglobulin contained in the bispecific antibody is mutated at two sites, i.e. site 234 and site 235, and after mutation, the affinity constant of the bispecific antibody to FcγRI, FcγRIIa, FcγRIIIa and / or C1q is reduced compared with the affinity constant of the bispecific antibody thereto before mutation. The combined administration of the PARPi and the bispecific antibody has a significantly better therapeutic effect on tumors than the individual use of the PARPi or the bispecific antibody; and the present invention has good application prospects.

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).

Provided herein are methods for treating conditions and diseases characterized by SCN1A, SCN8A or SCN5A protein deficiency by targeting the alternative splicing events in SCN1A gene and modulating the expression level of functional proteins in Dravet Syndrome patients and / or inhibit aberrant protein expression.

The invention provides an immunogenicity prediction method and device based on multimode fusion and a storage medium. The method comprises the following steps: constructing a multimode prediction model; obtaining each data set; correspondingly training a multi-modal prediction model based on each data set to obtain a trained multi-modal prediction model; fusing the trained multi-modal prediction models, and constructing an immunogenicity comprehensive evaluation model; forming an intelligent immunogenicity prediction system based on the trained multi-modal prediction model and the immunogenicity comprehensive evaluation model; and performing immunogenicity prediction on the to-be-predicted protein based on the immunogenicity prediction intelligent system to obtain a test result, the test result comprising the immunogenicity risk score of the to-be-predicted protein and the potential immunogenicity T cell epitope on the to-be-predicted protein. According to the method, the effect of directionally optimizing the immunogenicity on the basis of the prediction result can be achieved, and the drug effect and safety of protein biological drugs can be improved.

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.

The present invention inter alia relates to a T-cell expressing a chimeric CD 95 receptor comprising a polypeptide, wherein said polypeptide comprises at least one CD 95-derived polypeptide region having at least 60% sequence identity with a polypeptide domain, a polypeptide region or a polypeptide motif of a human CD 95 wildtype receptor, wherein said human CD 95 polypeptide region comprises a CD 95 extracellular ligand binding domain; further wherein said polypeptide comprises at least one non-CD 95-derived co- stimulatory cytoplasmic polypeptide domain, region or motif of a tumor necrosis factor receptor superfamily protein comprising CD40, CD40L, CD27, HVEM, GITR, CD30, OX40, and / or LTBR, an immunoglobulin superfamily (IgSF) protein, a Toll-like-receptor, and / or an IL6-receptor family protein; wherein the T-cell further expresses an engineered T-cell receptor. The present invention also relates to a vector comprising nucleic acids encoding for the CD 95 receptor and the engineered T-cell receptor, as well as to a kit for preparing the T-cells of the present invention, and a pharmaceutical composition comprising the T-cells. The invention also relates to a method for preparing a T-cell for immunotherapy, and to a method for treating a patient having a disease comprising administering the pharmaceutical composition.

Novel systems and methods for drug design and screening exploiting dynamics of protein post-translational modifications are provided.

The invention provides a single-cell drug sensitivity prediction algorithm based on multi-modal reinforcement learning, and aims to solve the problem that the accuracy of a traditional drug sensitivity prediction method is limited. According to the system, multi-modal bioinformatics data including cell line gene expression data, proteome data, metabolome data, drug reaction data and single-cell RNA sequencing data are integrated, and feature extraction, fusion and selection are performed by using a deep reinforcement learning technology, so that high-precision prediction of single-cell drug sensitivity is realized. The algorithm mainly comprises five core modules: a multi-modal data integration and preprocessing module, a deep reinforcement learning feature extraction and fusion module, a dynamic feature selection and importance evaluation module, a cross-domain migration and adaptive learning module and a deep reinforcement learning prediction model and performance optimization module. The modules work cooperatively, potential features can be learned automatically from multi-modal data, a feature fusion strategy can be optimized, a key feature subset can be selected dynamically, and a model structure and parameters can be adjusted adaptively according to features of target domain data, so that prediction accuracy and stability are improved remarkably. Experimental results show that the algorithm shows significant advantages in multiple evaluation indexes, and compared with a traditional prediction method, the algorithm has higher prediction accuracy and wider applicability. Therefore, the invention provides a novel and efficient drug sensitivity prediction tool for the fields of personalized medical treatment and drug research and development, and has important practical application value.

To provide a transcriptional regulator protein degradation-promoting composition that is capable of directly facilitating the induction of transcription regulator protein degradation and also capable of enhancing the degradation-inducing effect through the combined administration of multiple components, wherein the transcriptional regulator protein degradation-promoting composition serves as a highly effective cancer therapeutic agent, as well as to provide a therapeutic agent comprising the same.SOLUTION: The present invention provides a transcriptional regulator protein degradation-promoting composition that facilitates the degradation of the transcription regulator protein, comprising a PARG inhibitor, a PERK inhibitor, an HSP90 inhibitor, or a histone acetylation enzyme inhibitor, as well as a therapeutic agent comprising the same.SELECTED DRAWING: None

Provided herein are Toll-like receptor 2 (TLR2) agonists for use in enhancing human immune response and / or as adjuvants in vaccines. The TLR2 agonists include thiophenes, imidazoles, or phenyl-containing compounds, which may be compounds of Formulae (I), (II), (III), and pharmaceutically acceptable salts, solvates, hydrates, polymorphs, co-crystals, tautomers, stereoisomers, isotopically labeled derivatives, prodrugs, and compositions thereof. The compounds described herein are used as enhancers of an immune response (e.g., innate and / or adaptive immune response), and are useful in treating and / or preventing a disease, as adjuvants in a vaccine for the disease. (e.g., proliferative disease, inflammatory disease, autoimmune disease, infectious disease, or chronic disease). Also provided in the present disclosure are pharmaceutical compositions, kits, methods, and uses including or using a compound described herein.

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.

The invention discloses a novel single-cell CRISPR screening library. The invention provides a double-gRNA expression cassette, a double-gRNA expression cassette vector for single-cell CRISPR (clustered regularly interspaced short palindromic repeats) screening and a construction method of a single-cell CRISPR screening library. According to the CRISPR screening library constructed by the method, the single-cell CRISPR screening range can be expanded from a protein coding gene to ncRNA, and the ncRNA can be knocked out.

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 discloses a Mendel randomization-based mental disease drug targeted analysis method and system, and the method comprises the steps: carrying out the analysis processing through a Mendel randomization analysis process, and obtaining the causal effect estimation of each target gene corresponding to a plurality of phenotypes; performing multiple inspection correction on the causal effect estimation of each target gene corresponding to multiple phenotypes to obtain a significant target gene after correction; carrying out sensitivity analysis on the corrected significant target genes to obtain the target genes screened for the first time; performing co-localization analysis on the target genes screened for the first time to obtain target genes screened for the second time; carrying out pathway enrichment analysis on the target genes screened for the second time, then carrying out protein interaction network analysis, and finally carrying out drug matching on the target genes of the protein interaction network to obtain an old drug new use prediction result or taking the target genes as the target genes for guiding new drug preparation.

The invention discloses a construction method of a model for predicting an esophageal squamous cell carcinoma intervention effect. The construction method comprises the following steps: acquiring detection data of a predictive factor in a sample; the predictive factors at least comprise the content or the expression level of a depletion marker of a functional CD8 T cell in a sample and the content or the expression level of a C1QC + macrophage immune checkpoint molecule; and predicting the intervention effect of the esophageal squamous cell carcinoma according to the detection data of the predictive factor. Establishing a space proteomics map of a complex space tissue of a tumor microenvironment (TME) during treatment; key spatial features which are related to specific cell behaviors and influence treatment results are determined and layered; a powerful prediction model of drug response is developed by utilizing a machine learning technology, and the area under curve (AUC) can reach 97%.

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.

The invention belongs to the technical field of biological medicines, and relates to application of a PAPSS1 agonist and an endogenous protein sulfation modification up-regulating agent in preparation and screening of medicines for treating ischemic diseases. According to the application, the association between PAPSS1 up-regulation / excitation and histone sulfation modification mediated by PAPSS1 up-regulation / excitation and ischemic diseases is disclosed for the first time, and a new prevention and treatment thought is provided for ischemic cerebral apoplexy and other ischemic 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 discloses a targeted human serum albumin nano antibody and application thereof. The invention discloses a plurality of targeted human serum albumin nano antibodies capable of being used for constructing long-acting fusion protein drugs and an immune screening method thereof. The antibody is only composed of a heavy chain variable region and can be subjected to fusion expression with interleukin, interferon, tumor necrosis factors and other therapeutic proteins or polypeptide drugs in an escherichia coli expression system, the fusion protein has the activity of specific binding with human serum albumin, the affinity is high, the half-life period of the drugs can be effectively prolonged, and the anti-tumor effect is good. Meanwhile, the biological activity of the recombinant protein medicine is not influenced.