229 results about "Protein targeting" patented technology

Provided are compounds that may bind a target protein, and result in modification of the target protein. The compounds may further bind a modifier protein. The modifier protein may carry out or enhance the modification of the target protein. The modification may activate or reactivate the target protein. Also provided are methods of using the compounds.

The invention discloses a shark source nano antibody targeting an FPV-VP2 protein and an application of the shark source nano antibody. The amino acid sequence of the complementarity determining region 3 of the shark source nano antibody is selected from any one of the sequences shown in SEQ ID NO.16 to SEQ ID NO.30, or a sequence having homology with the sequence shown in any one of the sequences shown in SEQ ID NO.16 to SEQ ID NO.30; the amino acid sequence of the shark source nano antibody is selected from any one of the sequences shown in SEQ ID NO.1-SEQ ID NO.15, or the amino acid sequence of the shark source nano antibody is selected from any one of the sequences shown in SEQ ID NO.1-SEQ ID NO.15, or a sequence having homology with the sequence shown in any one of the sequences shown in SEQ ID NO.1-SEQ ID NO.15. The shark source VNAR nano antibody has the advantages of small molecular weight, high affinity, high stability, easiness in genetic engineering modification, low production cost and the like.

Disclosed in the present invention is a novel substituted heterocyclic compound having VAV1 target degradation activity. Specifically disclosed is a compound serving as a VAV1 target degradation agent and having the structure of formula (I), or a pharmaceutically acceptable salt, solvate, hydrate, isotopic substituent or isomer of the compound. The compound can be used for preventing or treating diseases related to VAV1 targets or signaling pathways.

The invention discloses an oligonucleotide intracellular nano delivery system based on polypeptide modification and application thereof. The system is composed of a periostin targeting sequence (SDSSD), a matrix metalloproteinase 2 (MMP2) response sequence (GPAGLLG), a cell penetrating sequence (RRRRRRRR, R9), a reactive oxygen species (ROS) scavenging and adhesion enhancing group (Gly-DOPA)) and a terminal dibenzocyclooctyne (DBCO) modified engineered polypeptide SDSSD-PEG5-YGFGG-GPAGLLG-R9-(G-DOPA) 3-K4-C-DBCO, and a target oligonucleotide miRNA-26a-A5-Azido modified by 5-polyadenylic acid (AAAAA) and an azide group (Azido), and the target oligonucleotide miRNA-26a-A5-Azido, the target oligonucleotide and assembling through a click chemical reaction and a non-covalent interaction. The nano system has good bone targeting, enzyme responsiveness, intracellular delivery effect and biological safety, can realize stable and efficient delivery of therapeutic oligonucleotides in vivo, and significantly improves the utilization efficiency and therapeutic potential of oligonucleotides. The oligonucleotide intracellular nano delivery system has a wide application prospect in the fields of clinical transformation and precise treatment of oligonucleotide drugs.

The present disclosure relates to a novel class of bifunctional molecules that are useful in a targeted or selective degradation of a protein.

The invention discloses an LbCas12a protein mutant as well as a preparation method and application thereof, the LbCas12a protein mutant is obtained by performing K390A or K945A mutation on a wild type LbCas12a protein, and the amino acid sequence of the wild type LbCas12a protein is as shown in SEQ ID NO. 1. According to the present invention, the LbCas12a protein is subjected to directional modification, the alanine mutation occurs at the K390 / K945 site, the LbCas12a-K390A protein mutant and the LbCas12a-K945A protein mutant are prepared, the protein mutants obtained based on the method provide a series of significant advantages in function, and the solid foundation is laid for the application of the protein mutants in multiple fields. Through Michaelis-Menten kinetic analysis, it is observed that when the LbCas12a-K390A / K945A protein mutant prepared through the method and the wild type LbCas12a protein target the same dsDNA target, the catalytic efficiency of the LbCas12a-K390A / K945A protein mutant is 42.1 times that of the wild type LbCas12a, and the catalytic efficiency of the LbCas12a-K390A / K945A protein mutant is 707.9 times that of the wild type LbCas12a, and the catalytic efficiency of the LbCas12a-K390A / K945A protein mutant is 707.9 times that of the wild type LbCas12a. The results show that K390 and K945 are mutated into alanine, so that the affinity of the LbCas12a protein to a substrate can be increased to a certain extent, and the LbCas12a protein has higher trans-cleavage activity.

The invention belongs to the technical field of medicines, and particularly relates to a PARP-1 protein targeted degradation chimera compound as well as a preparation method and application thereof. The PARP-1 protein targeted degradation chimera compound is as shown in a formula (I), wherein P represents a ligand of PARP protein, Z represents a ligand of E3 ligase, and L represents a middle connecting chain part; the compound can be used for treating or preventing tumors. The PARP-1 protein targeted degradation chimera disclosed by the invention has a relatively good inhibition effect on proliferation of tumor cells, has a very high degradation effect on PARP-1 protein, and has concentration gradient dependence. (I).

Virtual screening methods are disclosed for predicting interacting compounds, from a chemical library, for proteins. The methods using an Extremely Randomized Trees (Extra Trees) machine learning model in which parent node(s) represent a decision in relation to a target-compound complex, based on a plurality of features corresponding to a protein target of the complex and a plurality of features corresponding to a compound of the complex, and leaf nodes classify the target-compound complex as either interacting or non- interacting. The Extra Trees model outputs a probability that target-compound complex is interacting, based on aggregated classifications from each decision tree. Applying the Extra Trees model to a further protein target and compounds produces a probability that each compound will form an interacting complex with the further protein target. The compounds are then ranked based on the respective probabilities, and highest probability compounds are then explored using a docking model.

The subject matter described herein is directed to antibody-CIDE conjugates (Ab-CIDEs), to pharmaceutical compositions containing them, and to their use in treating diseases and conditions where targeted protein degradation is beneficial.

The invention belongs to the technical field of medical chemistry, and particularly relates to a targeted protein degradation compound containing an SYVN1 binding compound and application of the targeted protein degradation compound. According to the invention, a series of compounds interacting with SYVN1 are found, and the compounds can be used as'warheads' to participate in ERAD and effectively degrade transmembrane protein targets. Based on the SYVN1 binding compound, a new TPD technology for hijacking ERAD is established, and a brand new platform is provided for processing TMSPs and other proteins which are difficult to target by the current TPD technology. A SYVN1 interaction compound is further connected with a known ligand interacting with a protein target through a chemical linker, a series of ERADEC molecules are generated, the molecules can significantly degrade target protein, and a new possibility is provided for targeted degradation of drugs.

The invention relates to the technical field of bioinformatics, and discloses an iterative feedback type protein targeting molecule intelligent generation method and device, and the method comprises the steps: obtaining molecular structure data, training based on the molecular structure data to obtain a molecular fragment language model, and the molecular fragment language model is used for predicting subsequent molecular fragments according to an input molecular fragment sequence; obtaining an attribute preference data set, and performing alignment fine tuning on the pre-trained molecular fragment language model based on the attribute preference data set to obtain an attribute preference alignment model; and obtaining target structure information of the target protein, taking the attribute preference alignment model as a strategy network, iteratively generating and evaluating candidate molecules through a tree search algorithm under the constraint of the target structure information until a termination condition is met, and outputting the optimized protein targeting molecules. According to the application, excellent pharmacological properties can be taken into account while the protein targeting molecule binding effect is ensured, and the molecule generation quality can be dynamically optimized.

The application provides a drug virtual screening method and device based on deep learning, wherein the method comprises the following steps: inputting all candidate compound small molecules in a candidate molecule database into a pre-trained molecular encoder respectively to obtain molecular vectorization representation of the candidate compound small molecules; constructing an index structure corresponding to the candidate compound small molecules based on the molecular vectorization representation of the candidate compound small molecules; inputting a protein target to be matched into a pre-trained protein target encoder to obtain protein target vectorization representation corresponding to the protein target; and matching the protein target vectorization representation based on the index structure to obtain a target compound small molecule corresponding to the protein target. The method establishes a full-amount mapping function of the target and the molecule through a vector calculation mode, realizes high-throughput virtual screening in a second-level calculation time, realizes rapid virtual screening of a full-amount candidate molecule library, improves the precision of the virtual screening, and increases the possibility of drug discovery.

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 discloses a bifunctional molecule compound for inducing SHP2 protein degradation based on a DCAF16 ligand as well as a preparation method and application of the bifunctional molecule compound, belongs to the technical field of medicines, and relates to a bifunctional molecule for inducing SHP2 protein degradation based on the DCAF16 ligand as shown in a general formula (I) or pharmaceutically acceptable salt of the bifunctional molecule, and a pharmaceutical composition containing an SHP2 protein targeted degradation agent. The invention also relates to a preparation method of the compound and application of the compound in preparation of medicines for treating SHP2-mediated tumors and / or other diseases.

The application relates to the field of medicinal chemistry and provides a use of a diphenylacetonitrile compound and a protein-targeting hydrolytic chimeric compound. The application finds that a diphenylacetonitrile compound shown in formula (I) directly interacts with FEM1B, thereby serving as an inhibitor of a human E3 ubiquitin ligase substrate recognition receptor FEM1B. The application also provides a compound shown in formula (II), and the compound shown in formula (II) is a protein-targeting hydrolytic chimeric (PROTAC) drug, wherein Q2 is a target protein ligand molecule, Q1 is a FEM1B inhibitor, Q2 is combined with a target protein, thereby inducing E3 ligase FEM1B combined with Q1 to approach the target protein, leading to ubiquitination and degradation of the target protein. Experimental results show that the PROTAC drug shown in formula (II) provided by the application can specifically degrade a target protein in cells. Formula (I); formula (II).

Provided here are IRE1 degrader compounds comprising a protein target moiety covalently attached by a linker to an E3 ubiquitin ligase-binding moiety. Also provided are intermediate compositions for the preparation of IRE 1 degrader compounds and methods of treating diseases and disorders such as cancer with the IRE1 degrader compounds.

A compound including formula (I) or formula (II) for use in promoting targeted protein degradation.

The invention discloses a ligand molecule generation method based on multi-modal information guidance, which fully captures high-precision interaction between a protein pocket and a ligand molecule, not only considers structural information of the ligand molecule, but also performs sufficient information guidance aiming at a protein target point molecule generation method. The generated molecules have diversity, and the requirements of subsequent biological docking experiment verification are met; on the other hand, the potential interaction relationship between the protein structure characteristics and the ligand molecules is utilized, different protein targets are fused in the diffusion process, and various modal information is balanced to guide the generation of the ligand molecules.

[0003] Methods for treating a cancer that co-expresses one or more placental alkaline phosphatase (ALPP) proteins and a cancer target antigen in a patient in need thereof are provided, comprising administering to the patient a therapeutic regimen comprising administering one or more standard of care inhibitors or antiproliferative agents that increase cell surface expression of one or more ALPP proteins and one or more ALPP protein targeting agents. Also provided are methods for selecting a patient with a cancer that co-expresses one or more placental alkaline phosphatase (ALPP) proteins and a cancer target antigen, comprising: assaying for baseline cell surface expression levels of one or more ALPP proteins in a biological sample obtained from the patient; and assaying for co-expression of one or more cancer cell surface oncogenic drivers.

The invention discloses an engineered protein and application thereof in mediating non-ubiquitination degradation, and belongs to the technical field of biological medicine. The structural domains CATCH1 and CATCH2 of the MIDN protein and the region between the structural domains CATCH1 and CATCH2 are modified for the first time, the modification mode comprises the steps that the structural domains CATCH1 and CATCH2 and the region between the structural domains CATCH1 and CATCH2 are replaced with antibodies or polypeptides, then structure or sequence optimization is conducted on the basis, and the obtained MIDN engineered protein not only retains the proteasome binding capacity of MIDN, but also has the advantages of being capable of improving the protein quality and the like. According to the present invention, the MIDN-based target protein degradation system has the MIDN-based target protein degradation system, can expand the target range to the membrane protein or the cytoplasm protein, can improve the degradation efficiency of the MIDN-based target protein degradation system, further has the antibody-mediated target protein specificity so as to achieve the pathological / physiological protein distinguishing effect, and does not have the obvious toxicity; therefore, the engineered protein provided by the invention has a good application prospect.

The invention relates to the technical field of biological medicine, and particularly discloses an NSD protein targeted inhibitor and a preparation method thereof.The NSD protein targeted inhibitor is formed by assembling RK-0080552, rosmarinic acid, ferulic acid and TAT cell-penetrating peptide through non-covalent bonds, amino of the RK-0080552, phenolic hydroxyl of the rosmarinic acid and phenolic hydroxyl of the ferulic acid form a hydrogen bond, and the TAT cell-penetrating peptide forms a non-covalent bond. Arginine residues of the TAT cell-penetrating peptide are combined with carboxyl groups of rosmarinic acid and ferulic acid through electrostatic interaction to form a stable three-dimensional supramolecular structure; the preparation method comprises the following steps: respectively dissolving the components, mixing and crystallizing in proportion, washing, homogenizing at high pressure, and freeze-drying to obtain the NSD protein targeted inhibitor. According to the invention, drug activity enhancement and targeted delivery are realized through a supramolecular co-crystal technology, and the drug can be used for treating NSD2 high-expression tumors such as multiple myeloma and acute myelogenous leukemia, and has significant clinical application value.

The invention relates to the technical field of computational biology and artificial intelligence assisted drug discovery, and discloses a drug-target effect prediction method and system based on multi-mode self-supervised learning. The method comprises the steps of drug side self-supervised pre-training, protein side self-supervised pre-training, multi-feature fusion, drug-protein interaction modeling and multi-task joint prediction. Drug-target effect prediction is carried out based on self-supervised pre-training and multi-modal information fusion, a self-supervised learning method is firstly used to train a deep learning model by using label-free data, and then the deep learning model is used to carry out multi-modal characterization, multi-modal fusion and interaction relationship modeling on drug molecules and protein targets. According to the method, combined prediction of drug-target interaction, affinity and action mechanism can be realized, and the prediction precision and model robustness of the model are effectively improved.

This invention provides pharmaceutical protein degraders and E3 ubiquitin ligase binders for therapeutic applications as described further herein.

The present invention relates to quinazolinones and related compounds which degrade PARP14 and are useful, for example, in the treatment of cancer and inflammatory diseases.

The present invention provides isolated polypeptides capable of specifically binding to a protein target selected from LGALS9, CRP, BTLA, SAA, EGF, or FGA, or to a peptide thereof, especially under urinary conditions, which may be used for predicting in a urine sample of a subject suspected of having an infection whether the infection is a bacterial infection or a viral infection. The present invention further provides methods of diagnostics and treatment using the isolated polypeptides, and suitable diagnostic kits.

This disclosure features chemical entities (e.g., a compound or a pharmaceutically acceptable salt thereof) that degrade and / or otherwise modulate (e.g., inhibit) NIMA Related Kinase 7 (NEK7). Said chemical entities are useful, e.g., for treating a subject (e.g., a human subject) having one or more disorders or diseases associated with NLRP3 inflammasome activation. Said disorders or diseases include but are not limited to, autoinflammatory and autoimmune disorders (e.g., gout, inflammatory bowel disease, rheumatoid arthritis, multiple sclerosis), neurodegenerative diseases (e.g., Alzheimer's disease, Parkinson's disease), cardiovascular and metabolic disorders (eg. pericarditis, atherosclerosis, Type 2 diabetes, obesity, and metabolic syndrome), fibrotic disorders (e.g. interstitial lung disease, chronic kidney disease), hematology (eg, anemia of inflammation) and eye disorders (eg. macular degeneration). In embodiments, and while not wishing to be bound by theory, it is believed that the chemical entities described herein directly target (e.g., directly bind to) NEK7, thereby altering (e.g., attenuating) the inflammatory response modulated by the NLRP3 inflammasome. This disclosure also features compositions containing the same as well as methods of using and making the same.

The present application relates to a kind of biomarkers related to osteoarthritis, which is Pin1 protein, by detecting whether the expression level of Pin1 protein is abnormal, osteoarthritis can be diagnosed or osteoarthritis prognosis can be predicted, by testing the influence of drug on the expression level of Pin1 protein, osteoarthritis treating drug can be screened or prepared, the quality and efficacy of osteoarthritis treating or relieving drug can be evaluated.The present application uses TMT proteomics technology to complete the screening of biomarkers related to osteoarthritis, and finds the common intersection of 6 proteins in the results of osteoarthritis disease target, TMT proteomics and protein targeting verification through way analysis, wherein Pin1 is consistent with the trend of TMT proteomics and protein targeting verification, i.e.Pin1 is decreased in osteoarthritis.New biomarker related to osteoarthritis-Pin1 protein is found, which has important biological significance and pharmaceutical significance.

The disclosure provides a traditional Chinese medicine efficacy evaluation method and device based on a node-weighted network, equipment and a storage medium. The method determines target disease protein targets and corresponding target weights based on the comprehensive scoring results of candidate disease protein targets by a public database and a large language model. When determining the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, the prescription ratio, chemical component information, and the interaction probability between the chemical components and the protein targets of each medicinal material in the traditional Chinese medicine prescription to be evaluated are comprehensively considered. The target disease protein targets and the corresponding disease protein target weights, as well as the protein targets of medicinal materials and the corresponding protein target weights of medicinal materials, are added to a pre-constructed protein interaction network. The obtained node-weighted network focuses on the real pharmacological basis, effectively improves the network biological interpretation, and the multi-dimensional network index determined accordingly is used to evaluate the regulation effect of the traditional Chinese medicine prescription to be evaluated on the target disease, which is more accurate.

The invention discloses a machine learning-based inhibitor drug screening method for targeting KRAS G12D mutation, and the method is used for predicting the binding capacity of candidate small molecules and KRAS G12D protein targets by constructing an integrated graph neural network (GNN) and molecular feature embedded deep learning model. Compared with a traditional virtual screening method, the method has remarkable advantages in the aspects of improving the recognition capacity of KRAS G12D mutation specific small molecules and reducing the false positive rate and has high application potential and industrial transformation value, and molecular dynamics simulation results of screened compounds show that compared with existing KRAS G12D mutation targeted drugs in research, the method has the advantages that the application potential and industrial transformation value of the screened compounds are greatly improved, and the application prospect of the KRAS G12D mutation targeted drugs is widened. The screened compound has a more stable binding trend with targeting protein in MD simulation, is expected to become a new KRAS G12D targeting inhibitor, and provides new possibility for treating pancreatic cancer.

This disclosure features chemical entities (e.g., a compound or a pharmaceutically acceptable salt thereof) that degrade and / or otherwise modulate (e.g., inhibit) NIMA Related Kinase 7 (NEK7). Said chemical entities are useful, e.g., for treating a subject (e.g., a human subject) having one or more disorders or diseases associated with NLRP3 inflammasome activation. Said disorders or diseases include but are not limited to, autoinflammatory and autoimmune disorders (e.g., gout, inflammatory bowel disease, rheumatoid arthritis, multiple sclerosis), neurodegenerative diseases (e.g., Alzheimer's disease, Parkinson's disease), cardiovascular and metabolic disorders (eg. pericarditis, atherosclerosis, Type 2 diabetes, obesity, and metabolic syndrome), fibrotic disorders (e.g. interstitial lung disease, chronic kidney disease), hematology (eg. anemia of inflammation) and eye disorders (eg. macular degeneration). In embodiments, and while not wishing to be bound by theory, it is believed that the chemical entities described herein directly target (e.g., directly bind to) NEK7, thereby altering (e.g., attenuating) the inflammatory response modulated by the NLRP3 inflammasome. This disclosure also features compositions containing the same as well as methods of using and making the same.