207 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.

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 polypeptide and application thereof in preparation of a medicine for preventing and / or treating cancers. The amino acid sequence of the polypeptide is shown as SEQ ID No. 1 or SEQ ID No. 2. The cancer is at least one of breast cancer, colorectal cancer, leukemia, esophageal cancer, pancreatic cancer, brain glioma, kidney cancer, skin cancer, osteosarcoma, ovarian cancer and prostate cancer. The invention designs and synthesizes the polypeptide of a targeted staphylococcus tumor protein compound SND1-MTDH containing a nuclease structural domain 1 (SND1 and a heteromucin (MTDH), finds that the polypeptide has excellent inhibitory activity on various different types of human cancer cells for the first time, and provides an optional scheme for broad-spectrum anti-cancer drugs. The polypeptide applied in the invention is simple in synthesis process and low in production cost.

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 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 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 invention relates to an analysis method for characterizing structures and interaction of non-denatured proteins and compounds thereof based on pulse extreme ultraviolet laser dissociation and mass spectrometry detection. Protein interacts with large and small molecules such as metal ions, drug small molecules, protein, nucleic acid or polysaccharide to form a protein compound. Natural protein and protein compound samples in a biocompatible neutral solution enter a gas phase through a non-denaturing electrospray ionization technology, then radiation excitation is performed on the natural protein and protein compound samples through pulse extreme ultraviolet laser, dissociation is induced to generate protein structure characteristic fragment ions, and finally detection and spectrogram recording are performed on the fragment ions through mass spectrum. According to the invention, the technology for characterizing the structure and interaction of the non-denatured protein and the compound of the non-denatured protein by the extreme ultraviolet band laser is realized for the first time internationally, and the method has the remarkable advantages of high sensitivity, high flux, single amino acid site resolution and the like in the aspect of researching fine structures and conformation dynamic changes of protein-protein and protein-micromolecule interaction interfaces.

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 invention relates to a training method and device of a protein complex structure prediction model, electronic equipment, a computer readable medium and a program product. The training method comprises the steps of obtaining a real distance map corresponding to current training data; obtaining an input feature corresponding to the current training data; model prediction; determining an optimal docking posture of the first protein and the second protein under the current parameters; updating the current parameter according to the outer layer loss function; updating the current training data; and repeatedly executing the steps of acquiring a real distance map corresponding to the current training data, acquiring input features corresponding to the current training data, predicting a model, determining an optimal docking posture of the first protein and the second protein under the current parameter, updating the current parameter according to the outer layer loss, and updating the current training data. And obtaining a trained protein complex structure prediction model until the cycle index reaches a cycle index threshold value or the protein complex structure prediction model converges.

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 invention discloses a protein complex model quality evaluation method based on a Voronoi diagram. The method comprises the following steps: firstly, screening 7099 structures in a PDB structure database through a PISCES server; and then generating about 150 structural models for each protein in the data set by adopting three methods of structural disturbance, template modeling and deep learning modeling. Extracting features based on a Voronoi diagram, such as a contact surface area, a solvent accessible area, an energy item based on a contact surface, a residue-level contact surface direction and the like, from an amino acid sequence and a protein model structure by each structure model, and extracting a Roselle feature and a language model feature; the invariant point attention mechanism neural network module captures feature sets of different scales, and the residual attention neural network decodes and outputs residue-by-residue lDDT scores. According to the method, the quality evaluation of the protein compound model can be quickly and specifically carried out, and the problems of low precision and poor reliability of the quality evaluation of the protein compound model are effectively solved.

The invention aims to provide a method and equipment for predicting protein complex affinity change caused by mutation, and the method comprises the following steps: utilizing a first sub-network in a twin network to predict protein complex affinity change caused by mutation on the basis of wild type protein complex information and mutation information; determining wild type multi-modal coding information comprising wild type sequence coding information and discretely coded wild type geometric feature coding information; determining affinity information of the wild type through a decoder; determining mutant affinity information through the corresponding second sub-network; and determining affinity change information. By using discrete coding and multi-modal information, the model is helped to cope with the condition that the local structure is not well optimized, so that the model can obtain a more accurate affinity change prediction result by using complex information with low precision. And protein surface information is introduced, so that the model is helped to better learn the influence of a cavity formed by a protein interaction interface on the affinity change, and the affinity change prediction accuracy is further improved.

This disclosure provides a method of preventing, alleviating, or treating a condition (i.e., neutropenia) in a patient in need thereof, the condition characterized by compromised white blood cell production in the patient. The method includes administering to the patient a therapeutically effective amount of a protein complex comprising a modified human granulocyte-colony stimulating factor (hG-CSF) covalently linked to an immunoglobulin Fc region via a non-peptidyl polymer. The non-peptidyl polymer is site-specifically linked to an N-terminus of the immunoglobulin Fc region, and the modified hG-CSF comprises substitutions in at least one of Cys17 and Pro65.

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

This disclosure provides a method of preventing, alleviating, or treating a condition (i.e., neutropenia) in a patient in need thereof, the condition characterized by compromised white blood cell production in the patient. The method includes administering to the patient a therapeutically effective amount of a protein complex comprising a modified human granulocyte-colony stimulating factor (hG-CSF) covalently linked to an immunoglobulin Fc region via a non-peptidyl polymer at a fixed dose regardless of the patient's weight.

A preparation method of a black corn anthocyanin-protein compound relates to the field of preparation of anthocyanin-protein composites.The method comprises the steps that black corn flour is degreased through petroleum ether, then an ethanol water solution is added into the degreased black corn flour, stirring and suction filtration are conducted, supernate is taken, a NaCl solution is added, centrifugation is conducted, and precipitates are dialyzed and freeze-dried to obtain the black corn anthocyanin-protein compound. And the black corn anthocyanin-protein compound is prepared. According to the preparation method disclosed by the invention, the anthocyanin-protein compound is directly obtained from a raw material (black corn), a green technology is combined with the use of a green solvent, a novel effective method is provided for preparing the anthocyanin-protein compound, and moreover, the anthocyanin-protein compound has a good application prospect. According to the method, anthocyanin decomposition caused when anthocyanin is independently extracted or anthocyanin and protein are compounded to prepare the anthocyanin-protein compound is avoided, the prepared anthocyanin-protein compound is stable, the conformation of protein can be influenced by the compound, and the quality and the nutritional value of the protein are further influenced.

A protein complex structure similar template searching method comprises the following steps: firstly, constructing a protein complex template database and a fusion template database; inputting a single-chain structure of the target protein, searching a global template and a local template in the compound template database according to the structure, and searching the global template and the local template again in the fusion template database if the quality of the searched templates is poor; and finally, selecting an optimal template obtained by searching in the two databases as a structural similar template of the target protein. The invention provides the protein complex structure similar template searching method with relatively high precision.

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.