109 results about "Protein-protein complex" patented technology

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 complex model interface quality evaluation method based on a multi-scale isovariant graph neural network comprises the following steps: firstly, screening out a co-crystallized natural protein complex structure from a non-redundant protein interaction database PRISM, and generating a bait structure by using a HDock docking algorithm; the method comprises the following steps: firstly, extracting molecular surface interaction fingerprints, atomic-level features and residue-level features on the basis of each compound bait structure, obtaining graph representation of the compound bait structures, then fully capturing and fusing multi-scale information through a depth isotropic graph neural network, and finally obtaining an interface mass fraction through prototype comparison prediction. According to the method, the interface quality evaluation of the protein compound model can be accurately carried out, and the problems of low precision and poor generalization of the interface quality evaluation of the protein compound model are effectively solved.

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 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 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 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 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 present disclosure relates to methods for generating a plurality of compatible structures for a peptide-protein complex that can be used, for example, to identify surface fingerprint and interface features of the peptide-protein complex. The methods can comprise inputting peptide sequence data and protein sequence data into a trained machine learning model to determine: (i) predicted pairwise distance data for peptide amino acid residues and protein amino acid residue in the peptide-protein complex; and (ii) predicted dihedral angle data for peptide amino acid residues in the peptide-protein complex; identifying an initial structure for the peptide-protein complex based on the predicted pairwise distance and dihedral angle data; and generating the plurality of compatible structures for the peptide-protein complex based on the initial structure, the predicted pairwise distance data, and the predicted dihedral angle data.

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 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 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 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.

A protein complex model global quality evaluation method based on a deep fusion network belongs to the field of bioinformatics and computer application, and comprises the following steps: firstly, setting specific thresholds for maximum sequence redundancy, resolution and residue length in a protein database RCSB PDB to screen out a natural protein structure; for each protein structure, generating a disturbance structure model by using an HDck and xTrimoMultimer docking algorithm and combining a chain orientation disturbance strategy; then, extracting evolutionary features, physical features and geometric features based on each protein structure model, and constructing a deep fusion network fusing a deep convolutional neural network, an axial attention mechanism network and a graph attention network so as to fully capture local and global topological information of a protein compound; and finally, a global quality score is obtained through prediction of a decoding module. According to the method, the global topological information of the protein complex model can be effectively captured, so that the quality of the protein complex model is evaluated.

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 invention relates to a method for purifying viruses, virus-like particles, extracellular vesicles, proteins having a diameter of 5 nm or more, protein complexes having a diameter of 5 nm or more, nucleic acids having a diameter of 5 nm or more, protein-nucleic acid complexes having a diameter of 5 nm or more, and / or molecules having a diameter of 5 nm or more by filtration of supported polyalkylene glycol precipitation, and a filtration module that can be used in the method.

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.

Provided herein are immunoglobulin M (IgM) scaffolds as well as protein complexes comprising both an IgM scaffold and one or more single-domain antigen-binding fragment (sdAb) conjugates. Also provided are methods of using the protein complexes disclosed herein for the treat of disease, including for the treatment of infection or cancer.

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.

A system and method for structure-based, small molecule activity prediction using binding mode prediction information. Binding scores between ligands and target molecules, (e.g. proteins, RNA, DNA, lipids, sugars) are first generated using molecular docking. A first machine learned deep neural network (DNN) model is developed using data representing the molecular ligand-target pair 3D structures and docking features to predict binding modes. Using transfer learning, weights of layers learned in the first machine learned model are used as weights in layers of a second machine learned DNN model used to more accurately improve the performance of activity prediction of the second machine learned model. For a target newly paired ligand-target complex, the method further implements a binding mode selector for selecting one or more particular binding poses for input to the activity prediction model for use in activity mode prediction of an activity of the target paired ligand-protein complex.

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 invention discloses a method of determining the glycoprofile of a protein, comprising (a) contacting a sample comprising said protein with first beads having coupled thereto an antibody directed against said protein, to form an antibody-protein complex, (b) contacting said antibody-protein complex with one or more further beads, each further bead having coupled thereto (i) a label which amplifies a signal being generated and (ii) a lectin, to form an antibody-protein-lectin complex; and (c) determining the glycoprofile of said protein. Further disclosed are methods for diagnosing cancer, autoimmune diseases and inflammatory diseases as well as kits for performing the methods disclosed herein.