70 results about "Protein-protein complex" patented technology

A complex bait data set construction method based on structure and sequence collaborative redundancy elimination belongs to the field of bioinformatics, and comprises the following steps: screening an initial protein complex structure set, removing entries containing nucleic acids, small molecules or non-protein chains, and selecting binary complexes meeting integrity and resolution requirements; secondly, structure clustering and sequence clustering are carried out based on three-dimensional structure similarity and sequence homology, combined comparison is carried out on the two results, and redundant compound entries which are highly similar in structure and sequence are removed; then, taking each cluster representative compound as a target, generating a plurality of groups of bait structures by using a molecular docking or prediction modeling method, and calculating a quality index; and finally, performing stratified sampling and proportion balance based on the score interval of the quality index, and constructing a high-quality protein complex bait data set with structure and sequence collaborative redundancy elimination and balanced quality distribution. The data set generated by the method has the advantages of low redundancy, high diversity and quality distribution controllability.

The invention belongs to the field of construction of a protein structure on a cryoelectron microscope density map, and relates to a method for constructing a protein structure from a cryoelectron microscope density map by combining de novo modeling and structure prediction, a computer device, a readable storage medium and a program product. According to the method, the atomic probability and the amino acid type are predicted through the three deep neural networks respectively, full-atom optimization is carried out, the output result is combined with the graph theory, optimization and geometric algorithms to jointly assist protein structure modeling, and protein structure information can be mined to the maximum extent. According to the method, de novo modeling can be carried out in the absence of full-length structure data of the protein monomer, integrated modeling can also be carried out under the condition of inputting the full-length structure data of the protein monomer, the application scene is wide, the structure of the protein compound can be automatically constructed in a cryoelectron microscope density map with middle and high resolution, and the method is suitable for popularization and application. And for a region with poor local resolution in a traditional method, the modeling precision can be remarkably improved.

The invention discloses a protein complex identification method based on knowledge enhancement graph comparative learning, and belongs to the technical field of bioinformatics. Edge trimming is performed by using a gene expression profile and subcellular localization data, so that a knowledge enhancement PPI network is constructed; and incorporating the molecular function and the biological process as network attributes as attributes for enhancing the network. Graph structure enhancement is applied to knowledge enhanced PPI attribute networks, and multiple perturbation views are generated with diverse random perturbation policies including edge addition, edge discarding, and feature masking to help mitigate data sparsity. A perturbation view generated by graph structure enhancement is coded through a randomized graph convolutional network layer, so that multi-scale features are extracted, key PPI interaction is reserved, and meanwhile prediction performance is improved. And finally, the original PPI network is weighted again, and the protein complex is identified by adopting a core-affiliated strategy.

The disclosure features macrocyclic compounds, and pharmaceutical compositions and protein complexes thereof, capable of inhibiting Ras proteins, and their uses in the treatment of cancers.

A protein cryoelectron microscope structure assembling method based on point cloud registration belongs to the field of bioinformatics, and comprises the following steps: firstly, obtaining a cryoelectron microscope experimental density map after redundancy elimination and a corresponding protein structure, generating a simulation density map, carrying out uniform sampling and density vector calculation, and converting into point cloud data; training and evaluating a registration network based on the point cloud data; secondly, predicting a single-chain structure of a to-be-assembled protein compound by utilizing AlphaFold3, processing in the same way according to the training data, firstly registering the longest chain, performing local optimization by using an LBFGS optimization algorithm, and if a correlation coefficient is lower than a threshold value and the chain comprises a plurality of structural domains, improving the precision by splitting the structural domains and performing independent registration; finally, the remaining chains are gradually fitted to the density map according to the chain length sequence. According to the method, point cloud registration and local optimization are combined, and the protein structure assembling precision and speed under the low-resolution density map condition are remarkably improved.

The disclosure features macrocyclic compounds, and pharmaceutical compositions and protein complexes thereof, capable of inhibiting Ras proteins, and their uses in the treatment of cancers.

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.

A dispersion solution contains a complex of cerium oxide nanoparticle with protein in which a hydrodynamic diameter and zeta potential of the protein are maintained. The dispersion solution is produced by mixing a solution containing the protein with a solution containing a cerium (III) ion or with a cerium (III) salt followed by adding an oxidizing agent thereto.

A system and method for determining a biological activity parameter of a ligand are disclosed, the system comprising a processor configured to: obtain ligand-protein complex structure data and reference biological activity data; determine ligand-protein complex interaction parameters indicative of intermolecular interaction data of the ligand-protein complex in a docking conformation; determine aggregated ligand-protein complex interaction parameters across a plurality of docking conformations; and determine the biological activity parameter based on the aggregated ligand-protein complex interaction parameters, wherein determining the ligand-protein complex interaction parameters comprises determining non-covalent parameters indicative of intermolecular interaction data of a ligand node and a protein node based on ligand and protein attributes and ligand-protein non-covalent parameters indicative of binding stability of a first non-covalent edge of a dispersion force type.

The present disclosure relates to a novel platform for the design of multi-specific antibodies, incorporating innovative splits of antibodies, cytokines, or other proteins at various positions within a single antibody framework. This design aims to reduce toxicity and enhance therapeutic efficacy, particularly in applications such as immunotherapy and antibody-drug conjugates (ADCs). Multi-specific antibodies—including monospecific, bispecific, and multi-specific forms—comprise one or more antigen-binding domains that engage one or more epitopes of the same or different antigens. This disclosure encompasses methods for producing antibodies with one, two, or multi antigen-binding domains, which may consist of immunoglobulin heavy chain variable domains alone or in combination with other functional domains.

Provided is a complex composition, of which positional isomers are minimized by using a N-terminus of an immunoglobulin Fc region as a binding site when the immunoglobulin Fc region is used as a carrier. Also provided are a protein complex which is prepared by N-terminal-specific binding of immunoglobulin Fc region, thereby prolonging blood half-life of the physiologically active polypeptide, maintaining in vivo potency at a high level, and having no risk of immune responses, a preparation method thereof, and a pharmaceutical composition including the same for improving in vivo duration and stability of the physiologically active polypeptide. The protein complex may be usefully applied to the development of long-acting formulations of various physiologically active polypeptide drugs.

The present invention relates to an immune cell comprising a protein complex, said protein complex comprising a kinase of the SRC family and at least one stimulus-activatable probe for use as a drug. The invention also relates to protein complexes.

Methods for determining a representation of a protein complex, given a constituent target complex of that protein complex are presented; where the constituent target complex is a single entity constituent or subcomplex of the protein complex; and wherein a protein complex is a complex of some combination of one or more of proteins, nucleic acids, metal ions, and small molecules. A recursive neural network is devised, wherein for each iteration of the recursion, a representation of the output constituent of the protein complex together with the input constituent target complex is passed into the neural network 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).

A protein structure-density map fitting method based on feature point matching belongs to the field of bioinformatics and structural biology, constructs a protein complex structure dataset, converts the structure into a unified resolution point set through voxelization and uniform sampling, and adopts the farthest point sampling to construct a multi-resolution point set to depict scale geometry; a deep learning network is adopted to learn the rotation equivariant and invariant features of the structure and the density map under multi-resolution, and a geometric self-attention mechanism based on nearest neighbor layer-by-layer expansion is introduced at the neck to enhance the representation; a coarse-to-fine feature point matching strategy is adopted in the training stage, combined with a superpoint matching loss, a point-level matching loss and a contrast rotation loss optimization; in the inference stage, multi-scale and hybrid sampling are adopted, and candidate poses are generated through translation mask, the candidate poses are screened and optimized according to the structure-density map fitting score, and the final fitting result is output. The present application can realize high-precision and high-efficiency structure-density map fitting under complex conditions.

The present application relates to RAS inhibitors. The invention features macrocyclic compounds capable of inhibiting Ras proteins and pharmaceutical compositions and protein complexes thereof, and their use in the treatment of cancer.

In alternative embodiments, provided are synthetic, or non-natural, protein heterodimers or homodimers whose protein / protein binding interactions are controlled through metal or small molecule binding, and methods of making and using them. In alternative embodiments, provided are metal-controlled or small molecule-controlled heterodimers or homodimers, wherein each dimer is fused or joined to half or a portion of a detectable entity, and the when the two dimers are joined or come in contact with each other because of each dimer's binding to a metal, the detectable entity's halves, now also joined or having come in contact with each other, only now can directly or indirectly generate or initiate a detectable signal. In alternative embodiments, provided are biosensors or microfluidic devices comprising synthetic, or non-natural, protein heterodimers or homodimers as provided herein.

The present disclosure provides macrocyclic compounds capable of inhibiting Ras proteins, as well as pharmaceutical compositions and protein complexes thereof, and their use in the treatment of cancer. The present invention relates to RAS inhibitors.

The present invention relates to an immune cell comprising a protein complex, said protein complex comprising an SRC family kinase and at least one probe activatable by stimuli, for use as a medicament. The invention also relates to protein complexes.

Provided herein are, inter alia, protein complexes having the functionality of a T cell engager (TCE) as well as an antibody-drug conjugate (ADC). These protein complexes provided surprisingly effective mechanism of action for cancer treatment as compared to traditional TCEs (e.g., bispecific antibodies) as they exhibit high specificity and effectivity while lacking undesirable adverse side effects.

Protein complexes including a cytidine deaminase and a helicase. In some embodiments, the cytidine deaminase is an altered cytidine deaminase. In some embodiments, the protein complex converts 5 methylcytosine to thymine. Kits, compositions, and methods of use for the protein complexes including a cytidine deaminase and a helicase are also described.

A protein structure-density map fitting method based on feature point matching belongs to the field of bioinformatics and structural biology, and comprises the following steps: constructing a protein compound structure data set, converting the structure into a uniform resolution point set through voxelization and uniform sampling, and constructing a multi-resolution point set by adopting farthest point sampling to depict scale geometry; a deep learning network is adopted to learn rotation equivariant and invariant features of a structure and a density map under multiple resolutions, and a geometric self-attention mechanism based on nearest neighbor layer-by-layer expansion is introduced to the neck to enhance characterization; in the training stage, a feature point matching strategy from coarse to fine is adopted, and optimization is carried out in combination with over-point matching loss, point-level matching loss and comparison rotation loss; in the reasoning stage, multi-scale and mixed sampling is adopted, candidate poses are generated through translation masks, the candidate poses are screened and optimized according to the structure-density map fitting score, and a final fitting result is output. According to the invention, high-precision and high-efficiency structure-density map fitting can be realized under complex conditions.

The application belongs to the technical field of life science and computer science, and provides a gene cluster and protein complex detection method and system based on multi-supergroup coupling, which comprises the following steps: obtaining gene and protein data of a species from a public biological database, constructing a gene supergroup and a protein supergroup based on the relationship between the gene data and the protein data of the species; constructing an inter-layer edge relationship between the gene supergroup and the protein supergroup; constructing a multi-supergroup coupling random block model with shared potential functional modules; obtaining a community membership matrix of proteins and genes in different relationship supergroups through joint inference, and then identifying the gene cluster and the protein complex. Through the method, the accuracy of gene cluster and protein complex detection is improved.

The invention discloses a two-step immobilization and step-by-step separation method of an RNA-protein compound, and belongs to the field of analytical chemistry. The protein is crosslinked by utilizing the characteristic that N-hydroxysuccinimide ester (NHS) structures at two ends of a DSP (Digital Signal Processor) structure react with amino groups of the protein, and the complete RPC is enriched and captured from living cells by utilizing the characteristic that 254nm crosslinked RNA and a Psoralen probe capture RNA and are compatible with M-280 magnetic beads. And respectively eluting the RNA indirect binding protein and the RNA direct binding protein which form the RNA-protein compound by utilizing the property that the TCEP breaks the disulfide bond, so as to carry out proteomics mass spectrometry analysis on the RNA binding protein in the RNA-protein compound.

The present disclosure relates generally to polyelectrolyte complexes of non-animal proteins and their use in various comestible products, such as food products, beverage products, and nutritional products, including meat and dairy analogue products. In some embodiments, the polyelectrolyte complexes comprise a plant protein, such as pea protein, soy protein, canola protein, potato protein, and bean proteins, such as fava bean protein. In some embodiments, the polyelectrolyte complexes mask certain undesirable tastes of one or more of the proteins and improve the mouthfeel of comestible compositions that include the protein complexes. In some embodiments, the polyelectrolyte complexes are combined with certain fibers, which allows the blend to be suitable for addition to an ingestible composition useful for making meat or dairy analogue products. In some embodiments, flavorings or flavor-modifiers are combined with the polyelectrolyte complexes as well.

The disclosure features macrocyclic compounds, and pharmaceutical compositions and protein complexes thereof, capable of inhibiting Ras proteins, and their uses in the treatment of cancers.

The present disclosure relates to systems and methods for spatial proteomics in cells or tissues. Particularly, the methods include labeling proteins in a cell or tissue with one or more mass tags at one or more target locations within the cell or tissue, and determining the identity and / or abundance of any or all of the proteins, protein modifications, or protein complexes (e.g., the proteome) in the one or more target locations by mass spectrometry analysis.

The present application relates to the field of life science and computer technology, and provides a multi-network protein complex detection method and system based on test-time training, which comprises the following steps: constructing a multi-network data model containing proteins and genes based on an existing database, and constructing a known complex set based on an existing protein complex database; constructing an agent based on the idea of reinforcement learning, introducing a multi-network enhanced total loss function, and completing the training of the agent; outputting a preliminary predicted complex containing a query protein; constructing a similar complex set as a new training set, and completing the secondary training of the agent; and finally outputting a complex containing the query protein. The method improves the detection accuracy of the protein complex containing the given protein.

The present disclosure features macrocyclic compounds capable of inhibiting Ras proteins, as well as pharmaceutical compositions and protein complexes thereof, and their use in the treatment of cancer.

The invention is applicable to the technical field of cell engineering, and provides a preparation method and application of a multifunctional glycoprotein photo-crosslinking agent, and according to the method, a biotin group type crosslinking agent containing a trifluorobis (acridine) photo-reaction active group, an oxamine sialic acid specific recognition group, a chain length-adjustable polyethylene glycol chain and a disulfide bond cleavable is prepared. The prepared multifunctional glycoprotein photo-crosslinking agent can realize efficient photo-induced protein crosslinking, and the structural design of the multifunctional glycoprotein photo-crosslinking agent is beneficial to subsequent protein decrosslinking and protein compound identification. By utilizing a trifluoro-bis-acridine photoreaction active group, the trifluoro-bis-acridine photo-crosslinking polymer has excellent photo-crosslinking activity; specific recognition and reaction on a sialic acid-containing sugar chain are realized by using an oxygen amine group; a polyethylene glycol chain with adjustable chain length is utilized to improve the solubility and biocompatibility, and cytoplasm pollution is avoided as far as possible; by utilizing biotin groups of disulfide bonds, efficient enrichment is realized, and the biotin groups can be selectively cut under a reduction condition.

Protein complexes targeting CD47, PD-L1, and / or TIGIT are provided, as well as methods for using the same. In one embodiment, the protein complex comprises a CD47-binding domain having all or part of the SIRPα extracellular domain, a PD-L1-binding domain having all or part of the PD-1 extracellular domain, and a TIGIT ligand-binding domain having all or part of the TIGIT extracellular domain.