1180 results about "Binding site" patented technology

The invention relates to an RNA-protein binding site discrimination method based on a convolutional block attention mechanism dense network, and belongs to the field of bioinformatics. Firstly, a public RNA-protein binding site data set is acquired, then, a feature space is constructed by adopting four feature extraction methods, and finally, a dense connection convolutional network improved based on a convolutional block attention mechanism is used for learning a long-term dependency relationship in a combination capture sequence of advanced local features, a bidirectional gating circulation network and a self-attention mechanism. The RNA-protein binding site is discriminatively analyzed by creatively using the dense network improved based on the convolutional block attention mechanism in combination with the idea of the bidirectional gating loop network and the self-attention mechanism, richer feature information can be learned and extracted, and then the model discrimination accuracy is improved.

The present invention provides a combination of (i) an immune checkpoint modulator and (ii) a T-cell redirecting multifunctional antibody, or an antigen binding fragment thereof, for use in therapeutic treatment of a cancer disease. The T-cell redirecting multifunctional antibody comprises (a) a specificity against a T cell surface antigen; (b) a specificity against a cancer- and / or tumor-associated antigen; and (c) a binding site for human FcγRI, FcγRIIa and / or FcγRIII, wherein the antibody, or the antigen binding fragment thereof, binds with a higher affinity to human FcγRI, FcγRIIa and / or FcγRIII than to human FcγRIIb.

The invention provides a nano antibody and application thereof, a variable region in an amino acid sequence of the nano antibody comprises a complementarity determining region CDR and a framework region FR, the complementarity determining region CDR comprises a complementarity determining region CDR1, a complementarity determining region CDR2 and a complementarity determining region CDR3, the CDR3 is RWPDXY family, and binding sites of the CDR3 and AAV capsid are W99, D101 and Y103. The nano antibody provided by the invention has very high affinity and activity, and can specifically recognize and combine various serotypes of AAV, and an adsorbent prepared from the nano antibody has very strong adsorption capacity on AAV, can be applied to AAV affinity chromatography, and is beneficial to industrial application of an AAV affinity chromatography column. In addition, the method can also be applied to the field of AAV detection, and can detect empty capsid and virions together.

The invention provides a liposome preparation and a preparation method thereof, the liposome preparation comprises an ion pairing agent, phospholipid or a derivative thereof, sterol, a lipid molecule derivative and a drug compound, the ion pairing agent comprises at least one of a compound (I) and a compound (II), and the compound (II) comprises at least one of a compound (III) and a compound (III). Structures in the compound (I) and the compound (II) contain a plurality of functional groups (such as amide, carbonyl, thioether and the like), so that abundant binding sites are provided for drug molecules, and the compound (I) and the compound (II) can interact with different types of drug compounds (such as fat-soluble and water-soluble drugs); the liposome preparation is allowed to be combined with various drug molecules through hydrogen bonds, hydrophobic interaction or electrostatic interaction, and the liposome preparation adapts to physicochemical properties of different drugs. The compound (I) and the compound (II) further comprise different hydrophobic groups (such as alkyl chains) and hydrophilic groups (such as sulfonic acid, carboxylic acid and the like), so that the liposome preparation has the amphiphilic characteristic, not only can encapsulate hydrophobic drugs, but also can encapsulate hydrophilic drugs, and the variety and range of encapsulated drugs are improved.

Herein is reported an anti-TREM2 / Abeta protein antibody comprising a first binding site binding to human TREM2 comprising a heavy chain variable domain comprising a HVR-H1 of SEQ ID NO: 305, a HVR-H2 of SEQ ID NO: 306 and a HVR-H3 of SEQ ID NO: 307, and a light chain variable domain comprising a HVR-L1 of SEQ ID NO: 309, a HVR-L2 of SEQ ID NO: 310 and a HVR-L3 of SEQ ID NO: 311 (TREM2 3295), or a heavy chain variable domain comprising a HVR-H1 of SEQ ID NO: 329, a HVR-H2 of SEQ ID NO: 330 and a HVR-H3 of SEQ ID NO: 331, and a light chain variable domain comprising a HVR-L1 of SEQ ID NO: 333, a HVR-L2 of SEQ ID NO: 334 and a HVR-L3 of SEQ ID NO: 335 (TREM2 3306), and a second binding site binding to human Abeta protein comprising a heavy chain variable domain comprising a HVR-H1 of SEQ ID NO: 09, a HVR-H2 of SEQ ID NO: 10 and a HVR-H3 of SEQ ID NO: 11, and in the light chain variable domain a HVR- L1 of SEQ ID NO: 13, a HVR-L2 of SEQ ID NO: 14 and a HVR-L3 of SEQ ID NO: 15 (Abeta 8675).

The invention belongs to the field of drug design, and particularly discloses a virtual screening method of a small-molecule inhibitor targeting YTHDF1 protein, which comprises the following steps: acquiring a YTHDF1 protein structure, and carrying out homologous modeling to generate various conformation proteins; small molecules combined with the YTHDF1 protein are obtained, bait molecules are generated, and ligand molecules are formed; a ligand molecule is in butt joint with a binding site on the YTHDF1, and the compound is characterized through PLEC fingerprints; the method comprises the following steps: constructing a specificity scoring function by using a machine learning algorithm, carrying out scoring screening on a compound to be screened and a compound butted with YTHDF1 molecules, carrying out affinity prediction, and screening out the small-molecule inhibitor targeting YTHDF1. According to the virtual screening method provided by the invention, the YTHDF1 targeting molecules with high affinity can be accurately screened on a large scale, candidate molecules are provided for research and development of medicines for treating YTHDF1 related diseases, and a foundation is laid for clinical tests and medicine optimization.

The invention provides a shikimic acid pathway artificial operon and a construction method and application thereof.According to the artificial operon, shikimic acid pathway related genes are integrated and connected in different modes, a trc promoter and an RBS ribose binding site sequence are optimized, and the artificial operon formed through combination has the advantages of being stable, efficient and wide in applicability; different from a relatively extensive shikimic acid way transformation mode in the past, the operon can obviously improve the accumulation capacity of aromatic amino acid, and when the operon is applied to an L-phenylalanine production strain, the yield of L-phenylalanine reaches 105 g / L, and the saccharic acid conversion rate reaches 26.5%.

The invention relates to the field of protein-polypeptide interaction prediction in bioinformatics, in particular to a protein-polypeptide binding site prediction method based on graph attention and multi-head attention. The method mainly comprises the following steps: (1) collecting protein and polypeptide compound PDB structure information from an RCSB PDB database, and extracting sequence information of the protein and polypeptide compound PDB structure information; (2) extracting protein sequence information by using IUPred2, ProtBERT and ESM-2 (Extensible Subscriber for Mobile Communications); a ProtBERT method and an Integer method are used for extracting polypeptide sequence information; the method comprises the following steps: extracting protein structure information through biopython; (3) establishing a GAT model with residual connection to analyze a protein structure, and extracting features between amino acid nodes; (4) constructing a Circulate Block module, and carrying out deep extraction and fusion on protein and polypeptide information through four layers of Mti-head Attention and Dual Attention; and (5) finally, through a Final Attention, a linear layer and a Softmax layer, mapping the features to two dimensions to represent the interaction probability of each residue site.

The present invention relates to antisense oligonucleotides which are complementary to conserved TDP-43 binding sites on pre-mRNA transcripts, which are capable of restoring RNA binding protein function in the processing of multiple independent mRNAs in TDP-43 depleted cells. The contiguous nucleotide sequence of the antisense oligonucleotides comprise 2′-O-methoxyethyl-RNA (2′-MOE) nucleosides and the antisense oligonucleotides are attached to cholesterol moieties.

The invention relates to an organic molecule for the application in optoelectronic devices. According to the invention, the organic molecule hasa first chemical moiety with a structure of Formula I:anda second chemical moiety with a structure of Formula II:wherein W is the binding site of a single bond linking the first chemical moiety to the second chemical moiety,L is selected from a direct bond and a linking group with a structure of Formula BN-I:wherein the dashed lines denote the binding sites as indicated in Formula I, and # represents the binding site of the first chemical moiety to the second chemical moiety.

Embodiments of the present invention are directed to compositions and methods for anti-HIV (anti-CD4 binding site) broadly neutralizing antibodies having improved potency and breadth for neutralizing a range of HIV strains. Combinations of broadly neutralizing antibodies can also improve potency over a single antibody composition.

The invention relates to the technical field of cancer risk prediction, in particular to a deep learning cancer risk prediction method and system based on a methylation sequence. The method comprises the following steps: acquiring DNA methylation sequencing data and reference genome annotation data in a blood sample of a cancer patient; performing functional region classification processing on the DNA methylation sequencing data according to the reference genome annotation data to obtain promoter subregion methylation data, enhancement subregion methylation data and transcription factor binding site methylation data; performing dynamic sequence similarity calculation processing on the methylation data of the promoter region to obtain a first feature vector containing importance weight of the promoter region of the cancer-related gene; the methylation sites are classified according to the functions of the methylation sites in the transcriptional regulation pathway, so that the biological interpretation of the characteristics is improved, and the synergistic effect among different functional regions can be captured.

The present disclosure provides mouse models comprising an endogenous Stathmin2 (Stmn2) gene that comprises a human exon 2a polynucleotide sequence.

Aspects of the application provide bispecific antibodies and methods of using the same for promoting barrier function and reducing inflammation, and treating conditions such as Netherton syndrome, eosinophilic esophagitis and atopic dermatitis. In some embodiments, the bispecific antibody comprises at least one antigen-specific binding site that specifically binds KLK5 and KLK7 and at least one antigen-specific binding site that specifically binds to a Th2 cytokine (e.g., IL-13) or receptor thereof.

The invention discloses a drug target binding affinity prediction method based on multi-scale feature fusion, and the method comprises the steps: introducing the multi-scale structural features of atoms, atomic groups and molecular levels in the expression of drug molecules, and combining a graph neural network and a sequence modeling module as a feature extractor; and deep interaction and fusion between different scale features of the protein and the drug are realized by using a multi-head bilinear cross attention mechanism, so that potential binding site information is effectively captured, and the accuracy and interpretation capability of affinity prediction are improved. The method not only overcomes the problems that a traditional machine learning method depends on manpower and is low in efficiency in feature construction, but also solves the technical bottlenecks that an existing deep learning method is only limited to local neighborhood information and cannot model global structural features, and the interaction relationship modeling capability is insufficient due to direct splicing of drugs and protein representation.

Reported herein is, inter alia, a method for producing a trivalent bispecific antibody, the method comprising the steps of culturing a mammalian cell comprising a deoxyribonucleic acid encoding the trivalent bispecific antibody, and recovering the trivalent bispecific antibody from the cell or culture medium, wherein the deoxyribonucleic acid encoding the trivalent bispecific antibody is stably integrated into the genome of the mammalian cell and comprises in the 5' to 3' direction a first expression cassette encoding a first heavy chain, a second expression cassette encoding the first heavy chain, a third expression cassette encoding a first light chain, a fourth expression cassette encoding the first light chain, a fifth expression cassette encoding the second heavy chain, a sixth expression cassette encoding the first light chain or the second heavy chain or the second light chain, and a seventh expression cassette encoding the second light chain, wherein the first heavy chain The chain comprises, from N-terminus to C-terminus, a first heavy chain variable domain, a CH1 domain, a hinge region, a CH2 domain, a CH3 domain, a peptide linker, a second heavy chain variable domain and a CL domain, the second heavy chain comprises, from N-terminus to C-terminus, a first heavy chain variable domain, a CH1 domain, a hinge region, a CH2 domain and a CH3 domain, the first light chain comprises, from N-terminus to C-terminus, a first light chain variable domain and a CH1 domain, and the second light chain comprises, from N-terminus to C-terminus, a second light chain variable domain and a CL domain, wherein the second heavy chain variable domain and the first light chain variable domain form a first binding site, and the first heavy chain variable domain and the second light chain variable domain form a second binding site.

The invention provides a hydrogen bond organic framework material, a preparation method and application of the hydrogen bond organic framework material in dopamine detection, the preparation method of the hydrogen bond organic framework material comprises the following steps: S1, dissolving 1, 3, 5-tri (4-carboxyphenyl) benzene in an organic solvent to obtain an organic solution; and S2, dropwise adding an alcohol solvent into the organic solution to react to obtain turbid liquid, and drying to obtain the hydrogen bond organic framework material. The hydrogen bond organic framework material HOF-BTB prepared by the invention is a porous material, has two-dimensional (2D) pi-pi stacked hexagonal lamellas and complex eight interpenetration, generates a one-dimensional channel in the framework, and is simple in preparation process, mild in synthesis condition, low in cost, good in biocompatibility and low in cytotoxicity; and due to rigid organic construction molecules, the compound shows excellent fluorescence performance. The HOF-BTB material has high selectivity due to differential recognition of binding sites, and can be effectively applied to detection of dopamine.

The invention provides a lysine artificial operon and application thereof. The gene sequence of the artificial operon comprises a promoter, a lysC gene for coding aspartate kinase, an asd gene for coding aspartate semialdehyde dehydrogenase, a dapA gene for coding dihydropicolinic acid synthetase and a dapB gene for coding dihydropicolinic acid reductase, the ddh gene is used for coding diaminopimelate dehydrogenase; the lysA gene is used for coding diaminopimelate decarboxylase; except for the first lysC gene, sequences of RBS ribose binding sites of other genes are RBS ribose binding sites translated after gene transcription by intercepting 20-26nt DNA sequences at the upstream of a coding region ATG (adenosine triphosphate); the RBS ribose binding site is connected in front of the corresponding gene.

The present disclosure provides T cell modulatory polypeptide-epitope conjugates (T-Cell-MP-epitope conjugates) comprising a chemical conjugation site at which a coronavirus peptide epitope is covalently attached and at least one immunomodulatory polypeptide sequence. The T-Cell-MP-epitope conjugates are useful for modulating the activity (e.g., increasing proliferation or cytotoxic activity) of T-cells specific to the coronavirus peptide epitope in an epitope selective / specific manner, and accordingly, for treating individuals with a coronavirus infection including long COVID. Alternatively, when the T-Cell-MP-epitope conjugate is redirected to a neoplastic cell by a targeting sequence, the redirected T-Cell-MP-epitope conjugate may be used to treat neoplasms including various cancers.

The invention relates to a genome regulatory element analysis method based on deep learning. The method comprises the following steps: acquiring a genome DNA sequence and DNA shape information; on the basis of a shared hybrid neural network, transcription factor binding site features are extracted from a genome sequence, and binding specificity prediction is carried out aiming at the sequence level; aiming at the specificity of transcription factor binding, accurately positioning a transcription factor binding site on a nucleotide level by adopting a full convolutional neural network coupled with different models, and identifying a motif; for a chromosome three-dimensional genome, a chromatin ring is recognized in the three-dimensional whole genome based on a deep learning model, the relationship between the chromatin ring and regulatory elements in all cell lines is analyzed, and the chromatin ring represents cell type specificity and co-enrichment of transcription factor motifs among different cell lines and species. Compared with the prior art, the method has the advantages that the regulatory elements of the regulatory mechanism can be identified and analyzed by utilizing multiple information sources, and the accuracy of predicting the whole genome chromatin ring is effectively improved.

The invention relates to the technical field of protein prediction, in particular to a protein binding site prediction method based on geometric deep learning. According to the method, protein geometric description and a corresponding geometric diagram learning scheme are introduced, and a feasible way is provided for modeling three-dimensional structural features; in addition, the invention also provides an RSA-guided two-stage hybrid transfer learning strategy, firstly, a model is trained on a larger data set to learn general representation, and then fine tuning is carried out for binding site prediction, so that the overfitting problem in limited data training is relieved; according to the invention, a prediction post-processing module and an integrated learning module based on density clustering are designed, and the high variance of the model in multiple times of training operation is significantly reduced.

According to the hydrogen-bond organic framework material and the preparation method and application thereof, the preparation process is simple, mild and low in cost, the prepared hydrogen-bond organic framework material HOF-BTB-NH2 is one of a few of HOF materials with permanent pore channels, and the porous material has great development potential; the BTB-NH2 linker is connected with carboxyl on an adjacent linker through a hydrogen bond on the carboxyl, a firm 3D porous material is further formed through face-to-face pi-pi stacking interaction between benzene rings, and the 3D porous material is provided with one-dimensional porous channels in the a-axis direction; according to the HOF-BTB-NH2 fluorescent probe, the rigid organic construction molecules enable the HOF-BTB-NH2 fluorescent probe to show excellent fluorescence performance, the porosity and large specific surface area of the HOF-BTB-NH2 material are beneficial to full contact with an analyte and promote interaction with guest molecules, so that the detection sensitivity is improved, and the HOF-BTB-NH2 fluorescent probe has high selectivity due to difference recognition of binding sites. By adopting the fluorescence characteristic of the new material HOF-BTB-NH2, the sensitive and specific detection on the metal ion Cu < 2 + > can be realized.

The invention relates method of treatment of cancer using novel bispecific antibodies carrying a different specificity for each binding site of the immunoglobulin molecule, where one of the binding sites is specific for CD47 and the second is specific for mesothelin (MSLN). In addition, the disclosure contains methods of treatment using novel bispecific antibodies in combination with anti-PD-1 antibody.

The invention provides an antibody drug conjugate property prediction method based on multi-modal fusion, and belongs to the field of bioinformatics. The method comprises the following steps: firstly, explicitly modeling sequence position information through sine position coding; secondly, introducing a bidirectional cross attention mechanism to establish interaction between a light chain and a heavy chain and alignment between an antigen and an antibody; thirdly, the integrated graph neural network reconstructs the adjacency relation according to the attention weight, and topological features are extracted; and finally, in combination with a double-stage self-adaptive refining module, two-stage treatment of alignment and refining is realized, and each modal feature contribution is adjusted in a self-adaptive manner. And meanwhile, the sequence robustness is improved through Mask perception feature extraction, and the interpretability analysis of the key binding sites is realized through the attention weight. The method can significantly improve the prediction accuracy, and can be widely applied to cancer targeted therapy and drug design optimization.

The invention discloses a preparation method of Fe3O4 magnetic nanoparticles rich in carboxyl groups on the surface, which comprises the following steps: modifying Fe3O4 nanoparticles by using polyethylene glycol and silicon dioxide to enhance the dispersity and biocompatibility of the magnetic nanoparticles, modifying the surfaces of PEG-Fe3O4 coated SiO2 nanoparticles by using a silane coupling agent containing silane groups and unsaturated double bonds to obtain the magnetic nanoparticles rich in carboxyl groups on the surface, and modifying the magnetic nanoparticles rich in carboxyl groups on the surface by using a silane coupling agent containing silane groups and unsaturated double bonds to obtain the magnetic nanoparticles rich in carboxyl groups on the surface. Then octavinyl-POSS and double bonds on the surfaces of the nano particles are subjected to click modification to form an octahedral cage, 4-mercaptobenzoic acid and double bonds are further subjected to click modification, the surfaces of the nano particles are rich in carboxyl, and a polymer brush rich in carboxyl is constructed. Compared with a conventional streptavidin coupling method, a high-density polymer chain provides a large number of carboxyl active binding sites, so that the streptavidin coupling amount is increased, the antibody coupling rate is increased, and a solution is provided for applications such as high-sensitivity biological detection, efficient targeted drug delivery and stable immunoassay.

The invention discloses a biotin ligase mutant and application thereof, and belongs to the technical field of gene engineering. According to the invention, a series of biotin ligase mutants are prepared by docking biotin ligase and substrate molecules thereof and carrying out multiple mutations on screened 10 amino acids with binding force acting sites. On the basis, a biotin ligase mutant is connected to escherichia coli through a homologous recombination technology, a series of escherichia coli recombinant bacteria with high biotin yield are obtained, and the biotin yield reaches 0.873 mg / L to the maximum after 24-hour fermentation.

The invention relates to a UGT enzyme subtype substrate specificity prediction method and model. On the basis of a directional message passing neural network, graph structure characterization of a small molecule compound and features of specific protein binding sites of UGT enzyme are deeply fused, a bimodal prediction normal form of'molecule + protein binding sites' is designed, a deep learning model is constructed, conversion from compound center prediction to molecule-enzyme binding site comprehensive prediction is achieved, and the prediction accuracy is improved. And accurate classification prediction can be carried out on UGT enzyme substrates and non-substrates.

In one aspect, a method is provided for identifying a residue in a particular conformation of a protein molecule that is capable of forming a stable bond between a compound and the protein molecule. The method can include identifying one or more residues from the conformation of the protein molecule to include in a perturbation set, the one or more residues exhibiting a protection factor that satisfies one or more thresholds. One or more residues may be excluded from the perturbation set based at least on the spatial arrangement and / or orientation of the one or more residues. One or more rounds of molecular dynamics simulations may be performed, the results of which are indicative of interactions between the compound and the residues in the perturbation set. The residues in the perturbation set may be identified as forming a stable binding between the compound and the protein molecule based at least on the results of the molecular dynamics simulation.

The present invention provides multispecific antibodies comprising a Fab region and an Fc region, wherein the Fab region comprises a binding site specific for an epitope of the variable delta 1 (Vδ1) chain of a γδ T cell receptor (TCR); and the Fc region comprises an EGFR binding site. The present invention also provides compositions and pharmaceutical compositions comprising such multispecific antibodies, and method of making such multispecific antibodies. The present invention also provides methods of treatment and medical uses involving the multispecific antibodies.

The invention discloses a deep learning prediction method fused with multi-modal features, which can accurately identify protein hidden binding sites in a ligand-free (apoo) state. The method comprises the following steps of: firstly, constructing a protein graph by taking residues as nodes and taking C alpha distance less than or equal to 14 as edges, wherein node feature sets comprise amino acid one-hot, secondary structures, atomic attributes, protein language model embedding and BLOSUM62 evolutionary information, and edge features comprise distance and angle similarity; then capturing three-dimensional geometric equivariant features by adopting an equivariant graph neural network (EGNN), and modeling a chemical topological relation by using a graph isomorphic network (GINE) with edge features; eGNN and GINE double-branch feature fusion and global dependence integration are realized through gating cross attention and gating multi-head attention; and finally, inputting the fusion features into a Kolmogorov-Arnold network (KAN) classifier, and predicting whether each residue belongs to a hidden binding site or not. The method can adapt to large-scale conformation change without coordinate alignment, AUC and F1 on a standard data set are remarkably superior to those of an existing method, high robustness and generalization are kept for multi-chain protein and complex conformation, and the method can be widely applied to drug target discovery and structure-driven drug design.