48 results about "Structural biology" patented technology

The invention provides a mutant of enterovirus 71 and virus-like particles thereof, and relates to the field of biological medicines. The enterovirus 71 type virus-like particle is obtained by modifying structural genes VP0, VP1 and VP3 of virus capsid protein by utilizing computational structure biology and completing self-assembly in vivo through a hansenula polymorpha expression system. Compared with the unmodified enterovirus 71 type mutant and the virus-like particles thereof, the enterovirus 71 type mutant and the virus-like particles thereof have the advantages that the immunogenicity can be obviously improved, and the clinical application prospect is relatively high.

The invention belongs to the technical field of immunodetection, and discloses a kit for detecting the ratio of proBDNF to maure BDNF based on structural dynamics guidance and a preparation method of the kit, the kit comprises a solid phase carrier and a detection reagent, the three antibodies are determined on the basis of full-length conformational kinetics analysis of brain-derived neurotrophic factors; the first capture antibody targets an innate disorder region (SEQ ID NO: 1) of a proBDNF propeptide region; the second capture antibody targets an innate disorder region (SEQ ID NO: 2) of a proBDNF propeptide region; the second capture antibody is a neo-epitope specific antibody, is specifically combined with an N terminal (SEQ ID NO: 2) exposed by maure BDNF enzyme digestion, and is combined with a free alpha-amino group strictly dependent on the first histidine; and the universal detection antibody is combined to a Loop 4 region (SEQ ID NO: 3) of the Mature structural domain. The problems of steric hindrance and cross reaction in traditional immunodetection are solved through a structural biology strategy, and accurate distinguishing and ratio quantification of proBDNF and maure BDNF are achieved.

The invention discloses a method for realizing high-efficiency selenium labeling of protein, which comprises the following steps of: in the process of expressing plasmids containing target protein through a prokaryote expression system, realizing high-efficiency replacement of cysteine sulfur atoms in the target protein by selenium atoms by regulating and controlling the ratio of selenium to sulfur elements in a culture medium; on the premise of keeping a good growth state of cells, the selenium labeling efficiency can reach 86%, and the protein expression quantity is stabilized at about 1mg / g (protein / thallus wet weight); structural analysis shows that the secondary structure of the obtained selenium-labeled protein is basically consistent with that of natural protein and is not obviously changed. The method is easy and convenient to operate, high in labeling efficiency, high in protein yield, good in repeatability and suitable for structural biology research such as nuclear magnetic resonance, an effective means is provided for research of the functional mechanism of sulfur atoms in biomacromolecules (including small peptides, polypeptides, peptide fragments, proteins and protein or peptide complexes) with selenium as a probe, and the method has a wide application prospect. Good scientific research application prospects and industrial popularization values are realized.

The invention discloses an antibody combined with interleukin 4 receptor (IL-4R) protein and a computer optimization method, a compound structure analysis method and application thereof, the antibody comprises a Fa-2 sequence, a Fa-3 sequence, a Fa-4 sequence, a Fa-5 sequence, a Fa-7 sequence, a Fa-13 sequence, a Fa-15 sequence, a Fa-16 sequence, a Fa-17 sequence, a Fa-19 sequence and a Fa-23 sequence, and the Fa-2 sequence, the Fa-3 sequence, the Fa-4 sequence, the Fa-5 sequence, the Fa-7 sequence, the Fa-13 sequence, the Fa-15 sequence, the Fa-16 sequence, the Fa-17 sequence, the Fa-19 sequence and the Fa-23 sequence comprise a heavy chain variable region and a light chain variable region. The amino acid sequences of the amino acid sequences are respectively shown as SEQ ID NO: 7, 8, 9, 10, 11, 12, 13, 14, 17, 18, 29, 30, 33, 34, 35, 36, 37, 38, 41, 42, 49 and 50. According to the invention, a brand-new IL-4R epitope is found, and an antibody with a'non-ligand blocking 'characteristic is obtained for the first time. The invention opens up a brand new path for developing a therapy for intervening the IL-4R pathway through a new mechanism of regulating signal transduction or interacting with other coreceptors and the like, and is expected to overcome the limitation of the existing therapy. According to the method, computational biology and experimental structure biology are seamlessly connected, a set of repeatable and efficient research and development process is formed, and the method has a wide application prospect.

The invention discloses an EB (Epstein-Barr) virus antigen with chimeric design and application thereof. The EB virus chimeric antigen provided by the invention is reasonably designed on the basis of structural biology, gL, gH and gp42 proteins of the EB virus are connected through a linker, and the chimeric designed antigen stably presents antigen epitopes of the three proteins at the same time, so that the production amplification process and quality control can be simplified while the vaccine immunogenicity is improved. The EB virus chimeric antigen can be used independently or in combination with other antigens, has high immunogenicity when being used as a vaccine or a vaccine component, can induce an immunized animal to generate a high-level neutralizing antibody, can be used for preparing a vaccine for preventing or treating EB virus infection, and can also be used as a reagent for detecting EB virus.

The invention provides a molecular interaction network protection system based on structural biology and an optimization method thereof, and relates to the technical field of biotechnology and molecular engineering.The molecular interaction network protection system comprises a USAG-1 protein binding domain, and the USAG-1 protein binding domain comprises a Glu32-Arg58-Ser76 hydrogen bond network and a Phe41 / Trp65 / Leu83 hydrophobic core; the MMP-9 cleavage site mutant is used for carrying out 20 substitutions from D to N / V / A in the PLGLA sequence, and is combined with G52S mutation; the ellipticity of the CD spectrum of the RGD peptide at 222nm is less than or equal to-15 mdeg, and the RGD peptide comprises a salt bridge at the i / i + 4 position. The USAG-1 protein binding domain also comprises an Asn45 carbohydrate chain, and the molecular weight of the Asn45 carbohydrate chain is 500 to 2000 Da. The phi angle of the RGD peptide is-57 + / -5 degrees, and the psi angle of the RGD peptide is-47 + / -5 degrees. According to the invention, through a multi-dimensional structure locking strategy, while the functional activity of biomolecules is maintained, accurate control of alpha-helical conformation is realized, a resistance mutation library is constructed, and the fundamental contradiction of stability-activity imbalance, conformation control misalignment and single-point mutation drug resistance in the traditional technology is thoroughly solved.

The invention relates to the field of biological medicine, and provides a coxsackie virus A16 type mutant and a virus-like particle thereof, compared with a wild type coxsackie virus A16 type, the mutant has at least one amino acid mutation site in the following regions: (1) a canyon region of a virus capsid; (2) a channel area at a secondary axis; or (3) a channel region at a quasi-tertiary axis. The mutant and the virus-like particle are obtained by optimizing and modifying structural proteins VP0, VP1 and VP3 which form a virus capsid by using computational biology and structural biology methods, co-expressing and co-assembling the structural proteins in a hansenula polymorpha expression system to form the virus-like particle, and then carrying out series of chromatographic purification. Compared with a non-mutated coxsackie virus A16 type virus-like particle mutation scheme, the coxsackie virus A16 type virus-like particle mutation scheme disclosed by the invention has the advantages that the antigen immunogenicity can be obviously improved, and the clinical application value is realized.

The invention discloses a method for improving the water-resistant straw-like dwarf virus capability of rice based on D14 protein site editing and application of the method, according to the scheme, an interaction interface of RGSV P3 and rice D14 protein is analyzed through a structural biology means, and it is determined that the 102nd part, with the D14 binding site, of D14 and P3 is aspartic acid (Asp, D). Then, accurate editing of the D14 gene in the rice is realized by utilizing a cytidine base editor (CBE) system, and the site is mutated into asparagine (Asn, N), so that D14 (D102N) transgenic rice is obtained. Disease resistance identification confirms that the mutant has significant resistance to RGSV. Furthermore, a homozygous non-transgenic disease-resistant material which does not contain exogenous transgenic ingredients is obtained through genetic screening and has a good breeding application prospect.

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 invention provides a mammal protein efficient expression system based on a prokaryotic two-component signal transduction system (TCS, two-component system) and application of the mammal protein efficient expression system, and the mammal protein efficient expression system comprises the TCS capable of efficiently recruiting transcription factors and a carrier capable of being induced by the TCS to express a target gene. By transfecting mammalian cells with the system, a protein of interest can be produced in the cells or extracellular proteins can be produced in a secreted form. Based on the mammal protein high-efficiency expression system provided by the invention, stable and high-level expression of the target protein can be realized. The invention provides a new method for production of therapeutic proteins and antibodies, provides key technical support for preparation of mammalian protein samples in the research fields of structural biology and the like, and has important scientific significance for development of life science, diagnosis and treatment of diseases and research and development of medicines.

This application discloses a method for predicting the quantitative activity of endocrine disruptors, relating to the field of virtual screening of endocrine disruptors. The method includes: acquiring in vitro experimental data of nuclear receptors and removing duplicate data, as well as removing compound sets that do not contain the simplified molecular linear input canonical (SMILES) representation; using a molecular fingerprinting method to extract the primary, secondary, and tertiary structural features of the compounds. For each compound cluster, a quantitative prediction model based on machine learning or quantitative read-across is constructed to predict the quantitative activity value of the compound. Addressing the low efficiency of existing methods for predicting the quantitative activity of endocrine disruptors, this application extracts multi-level structural features of the compounds and constructs corresponding quantitative prediction models for compound structural clusters of different sizes. Through molecular docking and molecular dynamics simulations, the interaction mechanism between endocrine disruptors and nuclear receptors is studied from a structural biology perspective, thus improving efficiency.

The present invention relates to molecular genetics, structural biology, immunology, and microbiology. The present application is directed to compositions and methods for preparing immunogenic compositions. More specifically, one embodiment of the present invention provides an immunogenic macrocomplex comprising a heteromeric protein complex to be used as a carrier for obtaining recombinant multiantigen vaccines with self-adjuvant capacity (SAMAP - Self Adjuvanted Multi Antigen Platform).

The invention relates to a protein conformation ensemble modeling method based on a diffusion model, which comprises the following steps of: firstly, extracting key features of protein on an atomic level, a residue level and an overall topological structure, and inputting the key features into a diffusion model based on a graph neural network GNN for learning; in the denoising process, a Modeler algorithm is introduced to carry out physical correction on the generated conformation so as to ensure the rationality and biological credibility of the conformation; and then, through loop iterative optimization, a protein conformation ensemble conforming to physical constraints is continuously generated, so that dynamic behaviors and functional conformation changes of the protein are accurately captured, and a more reliable modeling method is provided for structural biology, protein engineering and drug design. According to the method, dynamic information implied in a protein static database PDB is deeply excavated, and multiple conformational features contained in a protein amino acid sequence are analyzed in combination with a large language model, so that precise modeling of protein structure diversity is realized.

The invention relates to the technical field of bioengineering and structural biology, and provides a recombinant synechocystis pyruvate dehydrogenase E1 protein crystal which is prepared from a recombinant protein His6-SyPDCsE1, the space group of the crystal is H3, the cell parameters are as follows: a = 146.3, b = 146.3, c = 165.3, and the diffraction resolution is 1.8. The invention also provides a preparation method and application of the crystal. According to the invention, the recombinant protein His6-SyPDCsE1 with high purity and high uniformity is successfully prepared by adopting a double-gene co-expression strategy, and further E1 protein crystal and substrate binding state co-crystal are obtained; the crystal structure fills up the research blank of the prokaryotic synechocystis E1 structure, provides a key basis for revealing a prokaryotic PDHc catalytic mechanism and analyzing the specific structure and function relationship of species, and provides important support for screening drug targets of metabolism-related diseases and agricultural breeding research.

The invention provides a method for efficiently preparing high-purity NudCD3 protein in an in-vitro eukaryotic cell. The method comprises the following steps: synthesizing a target gene; carrying out enzyme digestion on the target gene and the pLEXmMBP vector, and connecting to obtain a recombinant vector; transforming the recombinant vector into competent cells, culturing, and extracting plasmids; transfecting an HEK 293F cell, culturing, and collecting the cell; resuspending the cells in a lysis buffer solution, carrying out ultrasonication and centrifugation, and taking a supernatant; and carrying out affinity chromatography column purification, Prescission protease digestion and molecular sieve purification to finally obtain the NudCD3 protein with the purity of more than 90%. The problems that in the prior art, NudCD3 protein is difficult to express in quantity and insufficient in purity are solved, high-purity protein is provided for structural biology research, drug screening and antibody preparation, and the method has the advantages of being easy to operate, high in yield, high in protein stability and the like.

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 present disclosure relates to the field of biological medicine, and provides a mutant of coxsackievirus A16 and a virus-like particle thereof, wherein the mutant has at least one amino acid mutation site in the following regions compared with wild-type coxsackievirus A16: (1) a canyon region of a virus capsid; (2) a channel region at a dihedral axis; or (3) a channel region at a quasi-trihedral axis. The mutant and the virus-like particle are obtained by optimizing and modifying structural proteins VP0, VP1 and VP3 constituting the virus capsid by using computational biology and structural biology methods, co-expressing and co-assembling the structural proteins into the virus-like particle in vivo in a Hansenula yeast expression system, and then purifying the virus-like particle by serial chromatography. The mutation scheme of the coxsackievirus A16 virus-like particle in the present disclosure can significantly improve the antigen immunogenicity compared with the unmutated virus-like particle, and has clinical application value.

The invention relates to an HIV (Human Immunodeficiency Virus) nanoparticle vaccine. According to the invention, through a structural biology strategy and a nanoparticle vaccine platform, immunogens with stable conformations are respectively designed for MPER and CD4bs, and are respectively displayed through virus-like particles (VLP), and finally, a combined double-epitope vaccine (Cocktail) is constructed. The immune effect of the vaccine in a mouse model finds that the vaccine can induce MPER and CD4bs double-target specific antibodies and stimulate the neutralizing activity on multi-subtype HIV pseudoviruses, and the broad spectrum is good.

The invention discloses a pancreatic cancer organoid with tumor microenvironment characteristics and a construction method and application thereof, and belongs to the technical field of organoid. According to the invention, KPC transgenic mouse in-situ pancreatic tumor tissue is adopted as a cell source, and an organ with a pancreatic cancer tissue structure, biological characteristics and a complex tumor microenvironment is successfully constructed in combination with a culture medium formula with optimized culture conditions and characteristics and a multi-component co-culture mode; the obtained organoid is composed of multiple cell types such as tumor cells, cancer-related fibroblasts (CAFs), tumor-related macrophages (TAMs), myeloid-derived suppressor cells (MDSCs), T cells and the like, and a complex pancreatic cancer immune microenvironment can be rebuilt truly. The organ can be widely applied to efficacy evaluation of pancreatic cancer immunotherapy drugs, an efficient and repeatable in-vitro experimental platform is provided for early-stage research and drug development of pancreatic cancer immunotherapy, the research and development process of related drugs is expected to be accelerated, and the clinical conversion efficiency is improved.

The invention relates to a deep learning cryoelectron microscope image noise removal system and method, and belongs to the technical field of cryoelectron microscope single particle analysis. According to the method, through an innovative denoising algorithm, the signal-to-noise ratio of the image is effectively improved, meanwhile, key molecular structure details are reserved, high-frequency details are reserved to the maximum extent while denoising is conducted, and therefore higher resolution and classification precision are provided in fine structure analysis of protein. The system adopts a modular design, is easy to be integrated into the existing structural biology working process, and can efficiently run on a computer equipped with a GPU (Graphics Processing Unit). The invention also provides a detailed user course and a pre-training model, so that the diffusion denoising model can be widely applied to structural biology research to reveal high-resolution three-dimensional structures of proteins and macromolecular compounds.

The application discloses a method for efficiently labeling proteins with selenium, which comprises the following steps: adjusting the ratio of selenium and sulfur in the culture medium during the expression of a plasmid containing a target protein by a prokaryotic expression system, and realizing the efficient replacement of selenium atoms for the cysteine sulfur atoms in the target protein; under the premise of maintaining the good growth state of cells, the selenium labeling efficiency can reach 86%, and the protein expression amount is stably about 1 mg / g (protein / biomass wet weight); structure analysis shows that the obtained selenium labeled protein is basically consistent with the natural protein in the secondary structure and does not change obviously. The method is simple in operation, high in labeling efficiency and protein yield, good in repeatability, and suitable for structural biology research such as nuclear magnetic resonance, provides an effective means for studying the function mechanism of sulfur atoms in biological macromolecules (including small peptides, polypeptides, peptide segments, proteins, and complexes of proteins or peptides) by using selenium as a probe, and has good scientific research application prospect and industrialization popularization value.

Systems and methods that integrate structural biology, artificial intelligence, and psychology / psychiatry to explore the physical boundaries of self-consciousness are described. Parallels between protein interactions and psychological self-structures are drawn to analyze and visualize patterns in self-report questionnaire data. Variation in self-report response patterns across individuals can be leveraged to map the topology of self-consciousness boundary, akin to genetic linkage mapping. It is thus possible to conduct whole psyche analysis and leverage variations in the response patterns across individuals to solve the physical topology of the self-boundary.

This invention discloses a monkeypox virus protein B4R nuclease domain protein, its crystal, and its preparation and application, belonging to the field of protein crystal culture technology. This invention efficiently expresses and purifies B4R-CTD in *E. coli*, and confirms its endonuclease activity through biochemical experiments. Protein crystals are cultured using a gas-phase diffusion method, and the crystal structure of the B4R-CTD protein is resolved. It was found that the wild-type B4R-CTD exists in solution as monomers and tetramers, and the polymerization morphology affects enzyme activity. The B4R-CTD protein prepared by this invention has high purity, stable properties, and excellent crystal quality, and can be widely used in the structural biology research of monkeypox virus, possessing significant scientific research and application value.

Devices and methods that can detect and control an individual polymer in a mixture is acted upon by another compound, for example, an enzyme, in a nanopore are provided. The devices and methods also determine (˜>50 Hz) the nucleotide base sequence of a polynucleotide under feedback control or using signals generated by the interactions between the polynucleotide and the nanopore. The invention is of particular use in the fields of molecular biology, structural biology, cell biology, molecular switches, molecular circuits, and molecular computational devices, and the manufacture thereof.

The application provides a novel trimerization element library and a construction method and application thereof, relates to the fields of recombinant protein engineering, protein structure biology and biomedical application. The application designs and screens a universal amino acid sequence element through bioinformatics analysis of a trimer protein structure, and constructs an element library containing 10 unique trimerization sequences. The element library can be fused with a target protein to realize efficient trimerization of the recombinant protein, and does not affect the biological activity of the target protein. The application solves four core technical bottlenecks in the construction of a recombinant protein trimer, provides a high-efficiency and reliable core module support for the research and development of a trimer protein drug, and has important theoretical innovation significance and industrial application prospect.

The present invention provides a method for predicting the change in the ratio of monomers and dimers after a single-point mutation of a PSI protein and a mutant of the PSI protein, belonging to the technical fields of computational biology and structural biology. The present invention calculates the change in the Gibbs free energy of the monomer conformation and dimer conformation of the protein after a single-point mutation of the wild-type PSI protein, screens out three mutant proteins with significant changes in monomer and dimer stability: G14A-PSI, W18A-PSI and V22R-PSI, and obtains the two-dimensional stability of each mutant at pH 3.0, pH 5.0 and pH 7.4, respectively, through liquid nuclear magnetic resonance experiments. 1 H‑ 15 N HSQC spectrum, the results showed that the dimer ratio of the G14A‑PSI mutant protein was significantly increased compared with the wild-type PSI.

The present invention provides methods for controllably forming a layer of amorphous ice and other amorphous solids on a substrate, and also provides cryo-electron microscopy (cryo-EM) sample preparation methods and systems that utilize in vacuo formation of amorphous ice and other solids. Formation of the amorphous solid layer can be independent of the deposition of sample molecules to be analyzed using electron microscopy, and allows for the generation of a uniformly thick layer. Optionally, mass spectrometry instruments are used to generate and purify molecules deposited on the generated amorphous solid layer. The techniques and systems described herein can deliver near ideal cryo-EM sample preparation to greatly increase resolution, sensitivity, scope, and throughput of cryo-EM protein imaging, and therefore greatly impact the field of structural biology.

The invention discloses a drug target prediction method and system fusing structural features and system features, and belongs to the technical field of computer-aided drug design, and the system comprises a data preprocessing module which is used for preprocessing input data information and constructing a dynamic interaction network; the structural biological feature extraction module is used for extracting structural biological features of the medicine and the target spot; the dynamic system biological feature extraction module is used for extracting dynamic system biological features of the drug-target interaction network; and the prediction module is used for acquiring an interaction prediction score of an input drug and a target spot based on the structural biological characteristics and the dynamic system biological characteristics. According to the method, any input drug target pairs can be predicted, the predictable range is expanded, and particularly, the robustness of the method is enhanced on the prediction of new drugs and new targets.

The invention belongs to the fields of biochemistry, genetic engineering, structural biology and protein engineering, and particularly relates to a low-density lipoprotein receptor-like protein NvLRP expressed in Nacoia vitripennis as well as a nucleotide sequence coded by the low-density lipoprotein receptor-like protein NvLRP and a function of the low-density lipoprotein receptor-like protein NvLRP. The invention provides a low-density lipoprotein receptor-like protein (NvLRP) of chrysalis rivularis. The NvLRP has an amino acid sequence as shown in SEQ ID NO: 2. The NvLRP protein provided by the invention has a remarkable agglutination effect on blood cells of boettcherisca peregrina, affects the immune response of the boettcherisca peregrina, and has insecticidal potential.

The invention provides a disease marker subtype classification method based on structural biology, and belongs to the technical field of disease markers.The method comprises the steps that firstly, protein three-dimensional structure data is obtained, and rigid and flexible structure areas are distinguished through stable dynamic decomposition calculation; then, constructing an interaction network taking a structural domain as a node, and establishing a structural parameter matrix containing parameters such as a spatial distance and a contact area; a low-dimensional structure feature space is obtained through tensor decomposition and deep neural network dimension reduction processing, and community division is performed in the low-dimensional structure feature space to obtain protein subgroups. And calculating the structural feature uncertainty distribution of each subgroup by using a random inactivation neural network, and screening out the subgroup with high confidence coefficient. And finally, a machine learning classification model is established in combination with clinical sample data, and structural subtype classification is performed on to-be-classified patient samples after parameter iterative optimization, so that the problem that the classification accuracy of disease markers is not high enough due to the fact that dynamic characteristics of protein structures are difficult to effectively characterize and analyze is solved.