160 results about "Protein ligand" patented technology

A method for using machine learning to predict protein-ligand complex structures in protein folding and / or protein-ligand co-folding is presented. The method includes receiving, at a processor, a predicted set of coordinates for (1) a plurality of protein atoms and (2) a plurality of ligand atoms, included in a protein-ligand structure. At least one adjustment to be applied to the predicted set of coordinates is determined based on a plurality of loss functions associated with the plurality of protein atoms and / or the plurality of ligand atoms. A machine learning model is trained to predict geometric information of an unseen protein-ligand structure based on the at least one adjustment.

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 present application relates to a fibroblast activation protein ligand and a use thereof, and in particular to a compound as shown in formula (I) or a pharmaceutically acceptable salt thereof, a pharmaceutical composition containing the compound, and a use thereof in diagnosis or treatment of diseases related to a fibroblast activation protein, such as tumors or cancers.

The invention discloses a method for constructing a protein-ligand docking scoring model based on a graph Transform, which comprises a graph neural network, a multilayer network based on an attention mechanism and a decoding module, and is characterized in that the decoding module comprises an MND decoding module and a pKd decoding module; the MND decoding module is used for calculating the distance probability distribution between the protein and the ligand; the pKd decoding module is used for predicting an affinity value when the protein is combined with the ligand; the loss function comprises MDN loss and pKd loss, a first layer of the multi-layer network has mask vectors which are updated along with training iteration and are used for indicating that features are reserved or shielded, and docking scoring prediction is carried out by taking the protein graph and the ligand graph as input. According to the scheme, the model simultaneously uses the MDN decoding module and the pKd decoding module, the MDN module optimizes model parameters from the perspective of probability distribution, the pKd module optimizes the model parameters from the perspective of binding affinity, the MDN decoding module and the pKd decoding module carry out docking scoring from the spatial relation and the interaction relation, and the comprehensive evaluation ability of the model can be effectively improved through cooperation of the MDN decoding module and the pKd decoding module.

The invention relates to a candidate nucleic acid aptamer generation method based on a Poisson flow condition generation model. The candidate nucleic acid aptamer generation method comprises the following steps: step 1, obtaining protein data P of a plurality of target proteins; 2, candidate aptamer nucleotide sequence data X corresponding to the target protein is screened out through the SELEX technology, and a training data set {X, P} is obtained; 3, obtaining a feature vector belonging to p of the protein data P; step 4, obtaining a trained protein-ligand comparison pre-training model; 5, obtaining a trained high-dimensional Poisson flow aptamer condition generation model; and 6, generating the aptamer corresponding to the protein needing to generate the aptamer. According to the method, the protein characterization is matched with the characterization of the aptamer sequence screened by the corresponding SELEX, and compared with the condition that the protein characterization is directly used as a condition, the method has better properties for the aptamer sequence generated by the protein not contained in the data set.

The invention discloses a molecular optimization method for protein pocket three-dimensional structure perception based on a diffusion model, and relates to the field of molecular optimization. The method comprises the following steps: enabling a diffusion model constructed based on an EGNN to perform forward diffusion on a supervised training set so as to learn correct data distribution of atomic coordinates, atomic types and key types in optimized molecules, and obtaining an initial model; constructing a protein-ligand graph on the basis of a k-nearest neighbor algorithm, and constructing a full-connection ligand graph on the basis of bond types between atoms; carrying out reverse diffusion on the initial model, carrying out information transmission by utilizing a protein-ligand graph and a full-connection ligand graph, and carrying out iterative updating to obtain a molecular optimization model; and carrying out fragment connection, skeleton modification or skeleton transition by utilizing the molecular optimization model. Compared with the prior art, the method is applicable to fragment connection, skeleton modification and skeleton transition, so that the small molecule optimization process is more convenient, the three-dimensional protein pocket sensing effect is achieved, and the problem that more invalid molecules and non-real structure compounds are generated by a conventional model is solved.

The invention relates to the technical field of computational biology, and discloses a high-precision drug screening method and system based on hierarchical multi-modal representation learning, and the method comprises the steps: carrying out the feature extraction of an amino acid sequence of a target protein through a plurality of pre-trained protein language models; based on the three-dimensional structure of the target protein, multi-scale graph representation and ligand molecular graph representation of the protein are constructed; performing multi-modal feature learning on the multi-scale graph representation and the ligand molecular graph representation of the constructed protein by using a neural network based on message passing; and based on the multi-modal features, constructing a binding affinity prediction model for drug screening, and completing a drug screening task. A complex interaction relationship in a protein and ligand graph structure is effectively captured by using a multilayer graph neural network, and efficient coding of protein-ligand pair features is realized. The surface, the structure and the ligand embedding vector of the protein can be extracted, and the accuracy and the efficiency of protein-ligand interaction prediction are improved.

The invention discloses a method, a system, equipment and a medium for predicting binding affinity of protein and ligand. The method for predicting the binding affinity of the protein and the ligand comprises the following steps: acquiring olfactory protein and odorant molecules; inputting the olfactory protein and the odorant molecules into a prediction model to obtain a combined prediction result of the olfactory protein and the odorant molecules, the prediction model being pre-trained based on the data sample, the data sample comprises olfactory protein-odorant molecule experimental data from a data source and protein-ligand binding biological experimental data which is screened from a protein-ligand binding affinity biological experimental database and meets the similarity requirement with an olfactory protein sequence; the artificial bait is generated according to a positive sample in olfactory protein-odorant molecule experimental data and protein-ligand combined biological experimental data. By adopting the embodiment of the invention, the prediction accuracy and prediction efficiency of the binding affinity of the protein and the ligand are effectively improved through data collection and amplification and setting and introduction of novel characteristics.

The present invention enables the prediction of both the ligand type for protein-ligand binding sites and their druggability scores. The invention provides a computational method to investigate the ligand type and druggability of protein-ligand binding sites. The invention has two distinct training sets for two different prediction methods. The method leverages attention-based deep learning models for both ligand type and druggability prediction tasks. Deep learning models incorporate both channel-based and spatial-based attention mechanisms. The training phase focuses on the coordinates of known ligand binding sites to enhance the accuracy of druggability prediction. The method also provides the capability to update the training set with new data, thereby ensuring continued improvement in the prediction performance. The invention also details a computer device with a memory and processor, storing a computer program to implement the method. The device processes input in the PDB format, performs the necessary cleaning steps, and utilizes the trained models to predict ligand binding site's types and their respective druggability scores. The results are integrated into a table representation and presented to the user, offering an efficient way to understand and apply the predictions.

The invention discloses a protein de novo reverse design method and system based on a spectrum structure-activity relationship, and the method comprises the steps: obtaining a data set which comprises protein structure feature data, protein simulation spectrum data and protein-ligand interaction data; establishing a multi-task neural network model, training the multi-task neural network model by using the data set, inputting protein simulation spectrum data into the trained multi-task neural network model, and simultaneously obtaining a distance matrix between protein skeleton atoms and the number of protein amino acids; integrating the distance matrix between protein skeleton atoms and the number of protein amino acids to obtain a final protein distance matrix; obtaining an initial protein PDB structure according to the final protein distance matrix; optimizing the initial protein PDB structure by using an SCUBA-D protein structure optimization model to obtain a full-atom protein structure; proteinMPNN is applied, a full-atom protein structure is used as input, and a plurality of amino acid sequences are obtained. The development period of the medicine can be accelerated.

The embodiment of the invention discloses a protein-ligand interaction prediction method and a related device. Determining a prediction interaction pair formed by atoms of the protein and atoms of the ligand molecule according to the data of the protein and the data of the ligand molecule, establishing a loss function according to the node characteristics and the edge characteristics of the real interaction pair and the node characteristics and the edge characteristics of the prediction interaction pair, and adjusting model parameters according to the loss function, the target neural network model can be used for predicting the interaction between any protein and ligand molecules. Physical priori knowledge of protein-ligand interaction is fused in model training and prediction, so that the model can learn characteristics with more biological significance, and the predicted biological correlation is improved. According to the method, the advantages of deep learning are utilized, high-dimensional features are automatically extracted, complex pattern recognition is carried out, the complex relation in protein-ligand interaction is captured, and the stability and accuracy of protein-ligand interaction prediction are improved.

The invention discloses a protein-ligand binding affinity prediction method and system based on structure perception, and belongs to the field of bioinformatics and drug research and development. In order to solve the problem of low accuracy of affinity prediction caused by neglect of structural modal information of a protein-ligand compound in the existing affinity prediction, the invention provides the affinity prediction method. The method comprises the following steps: performing integer coding on a protein sequence and a ligand SMILES character string to obtain a sequence predicted value; expressing the protein binding pocket-ligand compound as an isomeric graph, and encoding the protein-ligand compound isomeric graph to obtain predicted values corresponding to node features and edge features; on the basis of the isomerism graph, homographs are generated through two element paths of'protein atoms-ligand atoms-protein atoms' and'ligand atoms-protein atoms-ligand atoms', encoding is carried out according to the homographs of the element paths, fusion features corresponding to the two element paths are obtained, and then corresponding predicted values are obtained; and obtaining a final predicted value based on all predicted values.

This method for predicting a protein-ligand interaction executed by an information processing system including a processor and a memory comprises the steps of: predicting a protein-ligand interaction probability by a GNN-based first model, wherein a protein graph and a ligand graph in which a protein pocket and a ligand of a target protein-ligand pair are respectively structured as individual graphs are input to the first model to output the protein-ligand interaction probability; predicting protein-ligand binding free energy by a GNN-based second or third model wherein a protein-ligand graph in which the structure of a target protein-ligand complex is structured is inputted to the second model or third model to output a protein-ligand binding free energy prediction value; and generating a protein-ligand interaction prediction value by multiplying the protein-ligand interaction probability by the protein-ligand binding free energy prediction value.

The invention belongs to the technical field of biological medicines, and relates to a protein degradation system based on polyamide-amine dendrimers as well as a preparation method and application of the protein degradation system. The protein degradation system is of a nano-particle structure, and the nano-particle structure comprises silicon dioxide nano-particles and a polyamide-amine type dendritic polymer layer coating the surfaces of the silicon dioxide nano-particles. An MDM2 protein ligand, a GLUT1 protein ligand and an E3 ubiquitin enzyme ligand are connected to the polyamide-amine dendritic polymer layer through chemical bonds. The protein degradation system provided by the invention can be used for synergistically degrading the MDM2 protein and the GLUT1 protein. The synergistic degradation strategy not only can effectively inhibit proliferation and energy metabolism of tumor cells, but also can significantly enhance the stability of the p53 protein. By recovering the normal function of the p53 protein, the growth of tumor cells is further inhibited, and a new strategy is provided for tumor treatment.

The invention provides an engineered exosome for treating sepsis as well as a construction method and application of the engineered exosome, and belongs to the technical field of biological medicines. The invention provides an engineered exosome for treating sepsis. The engineered exosome comprises the following fusion proteins: a first fusion protein formed by a cryptochrome 2 protein and an exosome scaffold protein, a lysosome targeting peptide, and a second fusion protein formed by a Caspase 1 protein recruitment protein structural domain and a cryptochrome 2 protein ligand. The cell level and animal level prove that the engineered exosome can efficiently and accurately inhibit secretion of inflammatory factors, reduce inflammatory response and pathological conditions in a sepsis animal model, and restore animal functions of the sepsis model to a certain extent. Therefore, the engineered exosome provided by the invention provides a new means for treating sepsis, and has great market popularization and application values.

The present invention relates to ligands of fibroblast activating protein (FAP) for active delivery of various therapeutic payloads (e.g., cytotoxic drugs, therapeutic radionuclides, proteins, and immunomodulators) at disease sites. In particular, the present invention relates to the development of multivalent FAP ligands for therapeutic applications involving diseases or disorders such as cancer, inflammation or another disease characterized by FAP overexpression.

The invention belongs to the field of medicines, and discloses an SERPINE1 protein degradation agent and application thereof. The general formula of the SERPINE1 protein degradation agent is # imgabs0 # or # imgabs1 #. The research finds that a structural fragment for inhibiting SERPINE1 protein and a structural sheet or a hydrophobic label of an E3 protein ligand are coupled together through a linker, the length of the linker is adjusted, the obtained SERPINE1 protein degradation agent has an unexpected anti-tumor effect, the anti-tumor effect of the SERPINE1 protein degradation agent is superior to that of a single SERPINE1 inhibitor, and the SERPINE1 protein degradation agent has a good application prospect. The drug resistance of tumor cells can be overcome, and the drug-forming target property is good.

The invention belongs to the technical field of anti-cancer drugs, and particularly relates to an artesunate PROTAC derivative with GPX4 degradation activity, a composition and application of the artesunate PROTAC derivative with GPX4 degradation activity. R is a Cerebron protein ligand or a VHL protein ligand, and Linker is a linking group of artesunate and R; according to the invention, the anti-tumor effect is improved.

The present invention relates to the use of bifunctional compositions in methods of treating or preventing cancer. The compositions comprise a DNA ligand and a protein ligand for the treatment or prevention of cancer, wherein the DNA ligand interferes with DNA metabolism and the protein ligand interferes with signaling pathways. In particular, the present invention relates to the use of pyronaridine in methods of treating of preventing cancer. The invention also relates to compositions, compounds and medicaments comprising a DNA ligand and a protein ligand, for example, compositions, compounds and medicaments comprising pyronaridine.

To provide a compound comprising a cyclic peptide and a chelator, and its use.SOLUTION: The present invention provides a compound selected from the group consisting of compound Hex-[Cys(tMeBn(DOTA-AET))-Pro-Pro-Thr-Gln-Phe-Cys]-OH (3BP-3554) and compound Hex-[Cys(tMeBn(DOTA-PP))-Pro-Pro-Thr-Gln-Phe-Cys]-Asp-NH2 (3BP-3407).SELECTED DRAWING: None

The present invention discloses a method, device, terminal and storage medium for generating molecular motion trajectories, and relates to drug discovery technology. The method comprises: using a pre-trained generative model, generating the atomic coordinate information of several subsequent frames frame by frame for the atomic category sequence of the target protein-ligand complex and the atomic coordinate information of the first frame; the pre-trained generative model is trained based on the escape trajectory of an existing protein-ligand complex; and generating the molecular motion trajectory of the target protein-ligand complex according to the atomic category sequence of the target protein-ligand complex and the atomic coordinate information of all frames. The present invention simulates the molecular motion trajectory by adopting a paradigm of generating coordinates frame by frame by a generative model, and learns the inter-frame coordinate evolution law of the ligand in the process of escaping the protein pocket through the existing escape trajectory. In the inference stage, it is only necessary to input the atomic category sequence and the atomic coordinate information of the initial state of the first frame, and the atomic coordinate information of the subsequent frames can be iteratively generated quickly and accurately.

The application provides a target-specific virtual screening method and system based on machine learning. Active molecules and inactive molecules are docked with multiple conformations of a target, and protein-ligand interaction features of the docked molecules and related features of the ligands are extracted as input features of a machine learning model. A scoring function model is constructed using multiple machine learning models, and based on the advantages of multiple target-specific scoring function models, an integrated target-specific scoring function model is finally obtained by combining a model integration method. The integrated model can more effectively process complex protein-ligand interaction data, has excellent prediction performance and virtual screening capability, provides more reliable prediction results, and improves the virtual screening capability.

The present invention discloses a method and system for de novo protein reverse design based on spectrum structure-activity relationship. The method comprises: obtaining a data set including protein structural feature data, protein simulated spectral data, and protein-ligand interaction data; establishing a multi-task neural network model, training it using the data set, inputting the protein simulated spectral data into the trained multi-task neural network model, and simultaneously obtaining a distance matrix between protein backbone atoms and the number of protein amino acids; integrating the distance matrix between protein backbone atoms and the number of protein amino acids to obtain a final protein distance matrix; obtaining an initial protein PDB structure based on the final protein distance matrix; optimizing the initial protein PDB structure using the SCUBA-D protein structure optimization model to obtain an all-atom protein structure; and applying ProteinMPNN, using the all-atom protein structure as input, to obtain multiple amino acid sequences. The present invention can accelerate the drug development cycle.

The present disclosure relates to bifunctional compounds, which find utility as modulators of estrogen receptor (target protein). In particular, the present disclosure is directed to bifunctional compounds, which contain on one end at least one of a Von Hippel-Lindau ligand, a cereblon ligand, Inhibitors of Apoptosis Proteins ligand, mouse double-minute homolog 2 ligand, or a combination thereof, which binds to the respective E3 ubiquitin ligase, and on the other end a moiety which binds the target protein, such that the target protein is placed in proximity to the ubiquitin ligase to effect degradation (and inhibition) of target protein. The present disclosure exhibits a broad range of pharmacological activities associated with degradation / inhibition of target protein. Diseases or disorders that result from aggregation or accumulation of the target protein are treated or prevented with compounds and compositions of the present disclosure.

A carrier which may have excellent pressure resistance, and even when a protein ligand is not immobilized thereon, a high dynamic binding capacity to a target substance, and a high performance of separating a target substance from a biological sample. Such a carrier may include a polymer having a crosslinked structure containing a divalent group of formula (1):wherein R1 to R4 are independently a single bond or divalent hydrocarbon group, R5 and R6 are independently H or a hydrocarbon group, X is a thio group, sulfinyl group, sulfonyl group, oxy group, >N(—R31), >Si(—R32)2, >P(—R33), >P(═O)(—R34), >B(—R35), or >C(—R36)2 (R31 to R36 independently being H or hydrocarbon group), and * is a bond, provided that when both R1 and R3 or both R2 and R4 are a divalent hydrocarbon group, respectively, R1 and R3 or R2 and R4 may form a ring together with an adjacent carbon atom.

The invention relates to a tetrafunctional boron drug compound, a preparation method, application and a pharmaceutical composition. The tetrafunctional boron drug compound comprises a tetrafunctional compound and a racemate, a stereoisomer, a tautomer, a pharmaceutically acceptable salt or a solvate thereof. The tetrafunctional compound has an albumin binding agent for prolonging the blood half-life period of the drug, an isotope chelating agent, a 10B carrying agent for BNCT treatment, a targeted fibroblast activating protein ligand and a linker. The invention also provides a preparation method of the tetrafunctional boron drug compound. The invention also provides a tetrafunctional boron medicine compound composition for tumor nuclear medicine diagnosis and radioisotope therapy. The invention further provides a pharmaceutical composition for treating tumors through BNCT. The pharmaceutical composition comprises the tetrafunctional boron drug compound and at least one pharmaceutically acceptable auxiliary material. The problems of poor targeting property, poor water solubility, short blood half-life period, short tumor tissue drug residence time and the like of the existing boron drug are solved.

The application discloses a kind of bivalent ligand molecules for targeting BCL6 protein degradation and application thereof, it is related to the technical field of drug development;The structure general formula of the bivalent ligand molecules for targeting BCL6 protein degradation is as follows: M1-L-M2;Wherein, M1 and M2 are independent structure same or different BCL6 protein ligand;L is any chain or cyclic hydrocarbon fragment capable of forming covalent bond with BCL6 ligand.The application also provides the application of the bivalent ligand molecules in the preparation of anti-tumor and / or immune disease drugs.The bivalent ligand molecules can exert good anti-tumor activity on a variety of BCL6-dependent tumors by selectively inducing BCL6 protein degradation, and are also related to the occurrence of a variety of immune diseases, and can be used in related tumor and immune disease drugs.

The present invention relates to protein ligands for carbonic anhydrase IX (CAIX) as a biomedically relevant target. In particular, highly specific ligands can interact exclusively with an antigen, i.e., CAIX, expressed on the surface of tumor cells, sparing healthy target-expressing organs, thereby enabling efficient in vivo drug delivery applications. Ligands can exhibit particularly low dissociation constants and / or enzyme isoform specificity, making them suitable for targeted delivery of payloads, such as therapeutic and / or diagnostic agents, to sites affected by or at risk of diseases or disorders characterized by CAIX expression.

The present invention relates to a method and an apparatus for discovering an active substance through information about the three-dimensional binding structure of a protein and a ligand. An embodiment of the present invention provides a method comprising: a step for determining selected compounds among compounds to be analyzed, the selected compounds being determined on the basis of bond energy atom property information, generated from the three-dimensional binding structure of a protein to be analyzed and a ligand, and atom accessibility atom property information, generated from the three-dimensional structures of the compounds to be analyzed; a step for identifying active substances by screening the selected compounds on the basis of the ratio of key interaction property information, generated through three-dimensional docking (3D docking) of the protein to be analyzed and a positive control, to interaction property information, generated through three-dimensional docking (3D docking) of the protein to be analyzed and the selected compounds; a step for generating conformers on the basis of compound poses generated by docking the active substances with ligand derivatives; and a step for inputting the conformers to a deep learning model to determine the optimal pose and binding affinity of the compound with respect to the protein to be analyzed, wherein the bond energy atom property information and the atom accessibility atom property information are one-dimensional data compressed from the three-dimensional binding structure and the three-dimensional structure, respectively.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predicted property score of a protein and a ligand. In one aspect, a method comprises: obtaining a network input that characterizes a protein and a ligand; processing the network input characterizing the protein and the ligand using an embedding neural network to generate a protein-ligand embedding representing the protein and the ligand, wherein the embedding neural network has been jointly trained with a generative model that is configured to: receive an input protein-ligand embedding; and generate, while conditioned on the input protein-ligand embedding, a predicted joint three-dimensional (3D) structure of an input protein and an input ligand represented by the input protein-ligand embedding; and generating a property score that defines a predicted property of the protein and the ligand using the protein-ligand embedding.