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11 results about "Protein ligand" patented technology
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In biochemistry, a protein ligand is an atom, a molecule or an ion which can bind to a specific site on a protein. Alternative names used to mean a protein ligand are affinity reagents or protein binders. To date, antibodies are the most widely used protein ligands in life-science investigations, however, other molecules such as protein scaffolds, nucleic acids, peptides are also being used. Main methods to study protein–ligand interactions are principal hydrodynamic and calorimetric techniques, and principal spectroscopic and structural methods such as Fourier transform spectroscopy Raman spectroscopy Fluorescence spectroscopy Circular dichroism Nuclear magnetic resonance Mass spectrometry Atomic force microscope Paramagnetic probes Dual Polarisation Interferometry Other techniques include: fluorescence intensity, bimolecular fluorescence complementation, FRET / FRET quenching surface plasmon resonance, Bio-Layer Interferometry, Coimmunopreciptation indirect ELIS, equilibrium dialysis, gel electrophoresis, far western blot, fluorescence polarization anisotropy, electron paramagnetic resonance, Microscale Thermophoresis
Predicting joint three-dimensional (3D) structures of proteins and ligands by cofolding
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a predicted joint 3D structure of a protein and one or more ligands. In one aspect, a method comprises: obtaining a network input that characterizes the protein and the one or more ligands; processing the network input using an embedding neural network to generate a protein-ligand embedding of the protein and the one or more ligands; and generating, using a generative model and while the generative model is conditioned on the protein-ligand embedding, the predicted joint three-dimensional (3D) structure of the protein and the one or more ligands.
Owner:ISOMORPHIC LABS LTD
Predicting the joint three-dimensional (3D) structure of a protein and a ligand by co-folding
PendingCN122139223ABiostatisticsNeural learning methodsTheoretical computer scienceNetwork processing
Methods, systems, and apparatus for generating predicted joint 3D structures of proteins and one or more ligands, comprising computer programs encoded on a computer storage medium. In one aspect, a method includes: obtaining network input characterizing the protein and the one or more ligands; processing the network input using an embedded neural network to generate protein-ligand embeddings of the protein and the one or more ligands; and using a generative model and, when the generative model is conditioned on the protein-ligand embeddings, generating the predicted joint three-dimensional (3D) structure of the protein and the one or more ligands.
Owner:ISOMOFICO LABORATORIES LTD
Protein degraders developed on basis of bcl-2 family proteins ligand compounds and uses thereof
Owner:SHANGHAI TECH UNIV +1
High-throughput parallel synthesis of small molecule degraders
Disclosed herein are high-throughput synthetic methods for deliberately and prospectively discovering molecular glues that can be used to form complex protein-ligand surfaces that facilitate interfacial binding to other proteins on a dispersed surface. Specifically, the present application discloses a high-throughput method that uses sulfur(VI) fluoride exchange (SuFEx) transformation and N-hydroxysuccinimide (NHS)-ester derivatized amide coupling to prospectively repurpose known ligands of a protein of interest as degraders and compounds capable of inducing proximity to other proteins. Disclosed herein are methods of developing known ligands of a target protein into degraders of the target protein. Also disclosed are methods of developing a novel small molecule chromatin-competitive inhibitor of the 11-19 Leukemia (ENL) YEATS domain into an effective degrader of ENL.
Owner:THE SCRIPPS RES INST
Bivalent ligand molecules targeting egfr and uses thereof
This invention discloses a bivalent ligand molecule targeting EGFR and its applications, belonging to the field of drug development technology. Its general structural formula is: [Formula omitted for brevity], where L is a linking group, and M1 and M2 are EGFR protein ligands. This invention forms a bivalent EGFR ligand molecule by covalently linking two EGFR ligands through a linking group. This bivalent ligand molecule can induce additional protein-protein interactions between EGFR monomers, which greatly enhances the binding strength and stability of the drug to EGFR, thereby overcoming the drug resistance problem of traditional EGFR inhibitors and providing a new treatment strategy for cancer patients carrying EGFR mutations and other patients with other diseases.
Owner:SOUTHWEST JIAOTONG UNIV
A method and system for predicting ligand-target dissociation kinetics parameters
This application relates to the field of computer-aided drug design technology, and in particular to a method and system for predicting ligand-target dissociation kinetic parameters. The method includes the following steps: constructing and parameterizing a protein-ligand system; applying constraints to the system to maintain structural stability; setting multiple sets of simulation condition pairs; globally scaling the non-bonded interaction potential energy of the system or the force derived from that potential energy under each set of simulation condition pairs; performing molecular dynamics simulations and detecting ligand dissociation events under each set of simulation condition pairs; estimating the dissociation rate under each simulation condition pair; and extrapolating to the target condition based on the relationship between the dissociation rate and a unified extrapolation independent variable to obtain the dissociation rate and residence time under the target condition. This invention solves the problem in existing technologies where it is difficult to reliably obtain the dissociation rate of ligands and corresponding target receptors at an affordable computational cost, and it also features controllable parameters and ease of parallelization.
Owner:LINGNAN INST OF TECH
Protein potential mutation site identification method and related device
The embodiment of the invention discloses a protein potential mutation site identification method and a related device. Molecular dynamics simulation of protein-ligand compound structure data is operated based on the simulation system, an equilibrium stage trajectory of molecular dynamics simulation is extracted, a dynamic association network is constructed according to the equilibrium stage trajectory and the compound structure data, a center node of the dynamic association network is determined according to correlation between nodes, and the dynamic association network is constructed. And taking the central node and the associated nodes as pre-selected residue sites, carrying out alanine scanning on the pre-selected residue sites according to the equilibrium stage trajectory, and determining that the pre-selected residue sites with free energy greater than a preset difference value are potential mutation sites. Through residue correlation analysis, the influence mechanism of the far-end site in the protein structure on the activity can be deeply understood, so that the burden of screening work in the enzyme modification process is remarkably relieved, the design and optimization process in enzyme engineering is simplified, and reliable guidance is provided for improving the enzyme catalysis efficiency and specificity.
Owner:SHENZHEN READLINE BIOTECH CO LTD
A method for drug structure design integrating chemical priori and physical law
This invention relates to a drug structure design method integrating chemical priors and physical laws. Specifically, it relates to a drug structure design method integrating chemical priors and physical laws. The purpose of this invention is to address the problems of chemical ineffectiveness and physical inconsistencies in drug molecules generated in existing technologies, and to propose a drug structure design method integrating chemical priors and physical laws. The process is as follows: Step 1: Convert the protein-ligand complex into graph structure data; convert the ligands in the protein-ligand complex into graph structure data; Step 2: Train the DrugRPG model based on the graph structure data until the total loss function converges, obtaining a trained DrugRPG model; Step 3: Input the protein-ligand complex to be tested into the trained DrugRPG model, and the trained DrugRPG model outputs the structure of the ligands in the protein-ligand complex.
Owner:HARBIN INST OF TECH
Process for the manufacture of chromatographic supports
This invention provides a method for easily manufacturing a chromatographic carrier with a large dynamic binding capacity to antibodies or their fragments, minimal leakage of protein ligands during separation, and minimal aggregation of carriers. The method for manufacturing this chromatographic carrier includes the following steps: ligand binding step, ligand binding carrier bed formation step, ligand binding carrier liquid flushing step, and ligand binding carrier stirring and cleaning step. The ligand binding step involves binding a protein ligand to a solid-phase carrier; the ligand binding carrier bed formation step involves filling the ligand binding carrier obtained in the ligand binding step into a container to form a ligand binding carrier bed; the ligand binding carrier liquid flushing step involves flushing the ligand binding carrier bed formed in the ligand binding carrier bed formation step with a cleaning solution at least once; and the ligand binding carrier stirring and cleaning step involves stirring the ligand binding carrier after liquid flushing at least once in a cleaning solution.
Owner:JSR CORPORATION
A protein ligand affinity prediction method based on multi-source feature fusion
The application discloses a protein ligand affinity prediction method based on multi-source feature fusion, and belongs to the technical field of protein and ligand binding affinity prediction. The SMILES representation and the SELFIES representation of the ligand are fused, so that the representation ability of the ligand chemical structure information is enhanced; the global feature of the protein is extracted through a cavity convolution, different inflation rates are set to capture multi-scale context information, and the calculation complexity and information redundancy problems caused by the multi-layer convolution stacking are avoided; the cross Transformer is adopted to fuse the local pocket feature and the ligand feature, so that the interaction and alignment of the key information between the protein and the ligand are realized; the one-dimensional convolution is adopted to extract the local pocket feature, so that the model is more focused on the binding site region, and the description ability of the protein-ligand binding mode and the stability of the prediction result are improved.
Owner:ANQING NORMAL UNIV
Protein ligand pocket prediction method based on spatial gate and local feature enhancement
This invention belongs to the field of intelligent cell biometrics, specifically involving a protein ligand pocket prediction method based on spatial gating and local feature enhancement. The method comprises four stages: data preprocessing and initial feature extraction, multi-scale feature encoding, spatially gating cross-layer feature fusion, and segmentation prediction based on depthwise separable convolution. This method integrates a Mamba module and a local feature enhancement module in the multi-scale feature encoding stage to achieve global modeling of protein features and adaptive enhancement of local features. Simultaneously, in the spatially gating cross-layer feature fusion skip connection stage, a spatially enhanced Mamba gating module is constructed to filter redundant information and enhance the fusion of multi-scale features. This invention effectively captures the long-range semantic information of proteins and intelligently filters and enhances cross-layer features, ultimately achieving protein ligand binding pocket prediction by generating a voxel-level probability map through depthwise separable convolution.
Owner:JIANGNAN UNIV