13835 results about "Protein" patented technology

The present disclosure provides methods and assay systems for use in spatially encoded biological assays, including assays to determine a spatial pattern of abundance, expression, and / or activity of one or more biological targets across multiple sites in a sample. In particular, the biological targets comprise proteins, and the methods and assay systems do not depend on imaging techniques for the spatial information of the targets. The present disclosure provides methods and assay systems capable of high levels of multiplexing where reagents are provided to a biological sample in order to address tag the sites to which reagents are delivered; instrumentation capable of controlled delivery of reagents; and a decoding scheme providing a readout that is digital in nature.

Provided herein are methods, compositions, and kits to spatially detect a polypeptide-nucleic acid complex or a protein-nucleic acid complex in a biological sample. For example, such methods can include contacting binding agents with a biological sample, wherein a binding agent specifically binds a polypeptide of a polypeptide-nucleic acid complex; aligning the biological sample with a substrate comprising a plurality of capture moieties, wherein the binding agent interacts with a capture moiety; releasing the nucleic acid of the binding agent-polypeptide-nucleic acid complex; hybridizing the released nucleic acid to a capture domain of a capture probe on an array; and using determined sequences of a spatial barcode in the capture probe and the released nucleic acid to determine the location of the released nucleic acid in the biological sample, thereby determining the location of the polypeptide-nucleic acid complex in the biological sample.

This disclosure provides methods for spatial profiling of biological analytes present in a biological sample. Methods include generating feature arrays using a master / copy format using recessed arrays, and methods for using such arrays. For example spatially-tagged analyte capture analytes can be used in spatial detection in methods to determine the location of analytes (e.g., proteins) in biological samples.

The invention discloses a drug target affinity prediction method and system based on a multi-scale protein attention mechanism, and belongs to the crossing field of bioinformatics and artificial intelligence. The method comprises the following steps: firstly, extracting protein sequence features through an ESM2 pre-training model, predicting that a three-dimensional structure is converted into a two-dimensional contact graph, and extracting spatial topological information in combination with a graph convolutional network; a two-dimensional attention mechanism is innovatively designed, structural features are taken as query vectors, sequence features are taken as key value pairs, and cross-modal feature fusion is realized by dynamically associating sequence semantics and spatial proximity relationships through multiple attention. Drug molecules are characterized by adopting MACCS fingerprints, are spliced with protein multi-modal features and then are optimized through a deep network, and finally an affinity value is output through a regression prediction module. According to the technology, the problem of protein heterogeneous data fusion is effectively solved, the generalization ability to unknown targets is remarkably improved, an efficient calculation tool is provided for new drug research and development and drug relocation, and the drug research and development cost can be reduced.

The invention discloses a drug target activation and inhibition relation prediction method based on a depth map neural network, and aims to improve the modeling precision and prediction performance of an activation or inhibition action mechanism between a drug and a target. According to the method, on the basis of a fine-grained graph interaction modeling mechanism, multi-scale structural characteristics of drug molecules and three-dimensional space structural information of protein residue levels are fused, and a heterogeneous interaction graph between drugs and proteins is constructed. The method comprises the following steps: firstly, acquiring a drug-target sample with an activation / inhibition tag through a public database, predicting a protein structure by utilizing AlphaFold2, and constructing a protein residue map and a drug molecular map; multi-scale structure semantic representation is obtained through sub-graph decomposition, atomic-scale feature extraction and graph neural network coding of drug graph features; protein graph node features are combined with context embedding generated by a pre-training language model, DSSP coding, secondary structure spectrum and atomic structure features are constructed, and edge features are designed based on the geometrical relationship between residues. Then, based on constraints such as spatial distance and biochemical similarity, a fine-grained mapping relation between drug atoms and protein residues is established, an interaction graph is constructed, and coding is carried out through a GraphSAGE network; and finally, fusing the interacted multi-source embedding, and completing the prediction of the activation / suppression relationship through a multi-layer perceptron. A cross entropy loss function, an Adam optimizer and hyper-parameter grid search are adopted in model training; in the evaluation stage, five-fold cross validation and an independent test set are adopted, and indexes such as the accuracy rate, the recall rate, the F1 score, the specificity and the Morse correlation coefficient are used for comprehensively evaluating the performance of the model. Experimental results show that compared with an existing method, the method has the advantages that the prediction accuracy and mechanism interpretability are remarkably improved, and the method has good generalization ability and application prospects and is suitable for multiple fields of drug action mechanism research, new drug discovery and the like.

The disclosure features macrocyclic compounds, and pharmaceutical compositions and protein complexes thereof, capable of inhibiting Ras proteins, and their uses in the treatment of cancers.

The invention provides a protein methylation site prediction method fusing quantum and region perception, which comprises the following steps: step 1, acquiring a protein sequence as a data source, and respectively constructing a training set and an independent test set; 2, constructing a multi-modal feature for each protein sequence by adopting a three-way nested scattering network, and fusing the multi-modal features to obtain an optimized fusion feature tensor; and step 3, inputting the optimized fusion feature tensor into a RaQMeNet network model, and performing a methylation site prediction task. The performance indexes of the method are greatly superior to those of the prior art, and the method has higher adaptability, stability and interpretability, can be widely applied to a plurality of bioinformatics and biological medicine related fields such as protein function annotation, disease mechanism research and drug target discovery, and has good application prospects and commercial values.

The invention relates to the field of rice gene engineering, and particularly provides OsSULTR2; the invention also discloses application of the 2 protein and the coding gene thereof in regulating and controlling the salt tolerance of rice. The protein meets the following conditions: B1) a protein with an amino acid sequence of SEQ ID NO.1; and B2) a fusion protein with the same function obtained by connecting a tag to the N end and / or C end of B1). The method is used for detecting OsSULTR2; the salt stress phenotype identification in the seedling stage is carried out on the transgenic rice with the gene knockout 1, 2, and the result shows that when the gene segment is deleted, the salt stress tolerance of the rice is improved, and the function and the application way of the gene are proved. Therefore, the OsSULTR2 of the present invention; the 2 protein and the coding gene thereof can regulate and control the salt tolerance of rice, and have important significance for cultivating salt-tolerant transgenic rice.

The invention discloses a site for stably expressing protein in a CHO cell gene NW023276806.1 and application of the site, and belongs to the technical field of biological genes. The site belongs to a fixed position in a CHO cell genome, different protein genes are introduced based on a micro-homologous end connection mechanism through a CRISPR / Cas9 tool, and stable expression is carried out. By adopting a site-specific integration method, a target gene is integrated to a stable expression area in a site-specific manner, repeated high-expression monoclonal screening is effectively avoided, and an MMEJ mechanism is introduced to integrate a donor fragment, so that the research and development time for constructing a stable expression cell strain in biological pharmacy can be effectively shortened, and the cost is reduced.

The invention relates to the technical field of pharmaceutical knowledge maps, in particular to a pharmaceutical knowledge map construction method and system based on artificial intelligence, and the method comprises the following steps: querying and collecting a molecular structure of a drug and a corresponding target protein sequence through a database, and carrying out the numerical coding of the molecular structure data of the drug, molecular fingerprints and protein structural domain features are extracted, and a drug and protein feature set is formed by combining drug chemical attributes and protein sequence features. According to the invention, through accurate analysis of the molecular structure of the drug and the target protein sequence thereof, the innovative scheme significantly enhances the understanding of the interaction of the drug and the protein, so that researchers can directly extract key features from data and monitor the dynamic change of the drug effect, thereby not only accelerating the development process of the drug, but also improving the development efficiency of the drug. By dynamically tracking the interaction between the side effect of the medicine and the pathological characteristics, the scheme provides powerful data support for personalized medical treatment.

The invention relates to the technical field of biology, in particular to a beta-1, 4-galactosyl transferase mutant and a method for biologically synthesizing lactose-N-neotetraose by using the beta-1, 4-galactosyl transferase mutant. The invention discloses a protein, which is a protein HpgalT-186 or a protein HpgalT-146-186, a protein HpgalT-146 and a protein HpgalT-146. The protein HpgalT-186 is a protein obtained by mutating an amino acid residue at the 186th site of a wild type HpgalT; the protein HpgalT-146-186 is a protein obtained by carrying out mutation on an amino acid residue at the 186 site and an amino acid residue at the 146 site of wild type HpgalT. On the basis, a series of escherichia coli is constructed and optimized, the yield of the LNnT synthesized through shake flask fermentation of the finally obtained strain reaches 1.73-4.1 g / L, the highest yield in a 5L fermentation tank reaches 20-45.2 g / L, and efficient synthesis of the LNnT is achieved.

The invention discloses a small molecule ligand drug screening method and system based on affinity prediction, and belongs to the technical field of biological medicine. The invention aims to solve the technical problem of low drug screening precision caused by molecular expression limitation, geometric invariance deficiency and insufficient multi-modal information fusion when virtual drug screening is carried out by using protein-ligand affinity. Comprising the following steps: acquiring ligand and protein structure information, and preprocessing to obtain coordinates and a feature matrix of ligand / pocket / residue; performing comprehensive representation, multi-feature flow self-adaption, geometric algebraic multi-layer perception and feature alignment processing on the feature matrix to obtain corresponding feature space representation; performing cross attention fusion and multi-scale interactive learning processing on the feature space representation in sequence to obtain fusion features; inputting the fused features into a multi-scale interactive learning module, and outputting final features; and finally, predicting the binding affinity of the ligand and the protein according to the fusion characteristics to obtain a binding affinity value.

The invention discloses a global function domain-introduced Cas protein classification method and system. The method comprises the following steps of: reading a Cas protein sequence file, preprocessing the Cas protein sequence file and constructing positive and negative sample pairs; a LoRA dynamic rank adjustment mechanism based on Cas protein sequence global functional domain priori is introduced on a pre-trained protein language model, the trained protein language model is used for Cas protein sequence classification, functional domain coverage frequency of each position in a Cas protein sequence is used for quantizing functional importance of each position to generate a global hotspot functional domain vector, and the global hotspot functional domain vector is used for classifying the global hotspot functional domain. And dynamically guiding different LoRA layer rank parameters in the LoRA model, and adjusting weight parameters of the protein language model. According to the technical scheme, on the basis of a prior LoRA dynamic rank adjustment mechanism of a global functional domain of a Cas protein sequence and through a layered dynamic strategy, rank distribution is highly consistent with functional domain evolution conservative property and structural characteristics, and the model is endowed with higher biological interpretability.

The invention discloses application of a TaLBD30 protein and a coding gene thereof in regulating and controlling a wheat plant type. The invention belongs to the technical field of biology, and particularly relates to application of TaLBD30 protein and a coding gene thereof to regulation and control of wheat plant types. The protein is any one of the following proteins: A1) a protein with an amino acid sequence as shown in SEQ ID No: 1; a2) a protein which is obtained by substitution and / or deletion and / or addition of amino acid residues on the protein of A1), has 80% or more of identity with the protein of A1) and has the same function as the protein of A1); a3) a fusion protein obtained by connecting a protein tag to the N terminal or / and the C terminal of A1) or A2). TaLBD30 is a transcription factor with transcription inhibition activity, and compared with a wild type, the TaLBD30 gene overexpression shows that after TaLBD30 overexpression, the wheat plant height and ear length are remarkably reduced.

Methods and apparatus for obtaining representations of proteins and small molecule drugs for synthesis; wherein pre-trained mixed modality protein and natural language fusion models are further trained by supervised fine tuning using reasoning-oriented query—chain-of-thought (CoT) response pairs. The resulting reasoning-oriented neural network is then used to obtain representations of output proteins or small molecule drugs, in response to mixed modality reasoning-oriented input queries specifying conditions on the output. In one embodiment, the neural network is an autoregressive multicapitate transformer whose decoder output heads correspond to the represented modalities. The method returns mixed modality output representations of proteins or small molecule drugs for synthesis or manufacture.

Provided is the use of a protein derived from wheat and a biomaterial related thereto in increasing the yield per plant and grain protein content of a crop. The protein is designated as SWEET11, and may be a protein having an amino acid sequence as shown in sequence 9, sequence 6 or sequence 3 in the sequence listing. The wheat SWEET11 protein (TaSWEET11) is over-expressed in the Zhengmai 7698 wheat variety (nationally approved), and it is found that the over-expression of TaSWEET11 can significantly improve the thousand-grain weight of wheat grains and increase the yield. In addition, the over-expression of TaSWEET11 also increases the protein content in the wheat grain, significantly improving the grain quality while increasing the yield. The TaSWEET11 protein can be used for breeding and quality improvement of crops.

The present disclosure provides compositions, methods, systems, and devices for polynucleotide processing and analyte characterization. Such polynucleotide processing may be useful for a variety of applications, including analyte characterization by polynucleotide sequencing. The compositions, methods, systems, and devices disclosed herein generally describe barcoded oligonucleotides, which can be bound to a bead, such as a gel bead, useful for characterizing one or more analytes including, for example, protein (e.g., cell surface or intracellular proteins), genomic DNA, and RNA (e.g., mRNA or CRISPR guide RNAs). Also described herein, are barcoded labelling agents and oligonucleotide molecules useful for “tagging” analytes for characterization.

The present disclosure provides in some embodiments, a multi -objective binder design framework by aligning autoregressive molecular foundation models (e.g., protein language models (pLMs)) to different objectives, such as binding and developability considerations. In some embodiments, direct preference optimization (DPO) can be utilized in the methods and systems described herein to encode multiple design objectives in the language model through direct optimization on expert curated preference sequence datasets comprising preferred and dispreferred distributions. In some embodiments, utilizing the described framework can enable molecular foundation models (e.g., protein language models), such as ProtGPT2, to effectively design binders conditioned on specified receptors and one or more drug developability criteria. Besides being multi -objective, in some embodiments, the methods and systems provided herein conceive online lab-in-the-loop design pipelines by actively incorporating experimental feedback.

The invention discloses a ruminant feed capable of replacing soybean meal as well as a preparation method and application of the ruminant feed. The ruminant animal feed is prepared from the following raw material matrixes in parts by weight through compound probiotic fermentation: 45-55 parts of corn sugar residues, 15-25 parts of corn steep liquor, 10-20 parts of corn germ meal, 3-8 parts of extruded soybeans, 3-8 parts of corn protein powder, 2-4 parts of urea and 0.5-1.5 parts of lysine. The compound probiotics are prepared from saccharomyces cerevisiae, lactobacillus plantarum and bacillus subtilis. The invention further discloses a preparation method of the ruminant feed and application of the ruminant feed in completely replacing soybean meal in daily ration of ruminants. The ruminant feed disclosed by the invention can completely replace soybean meal in daily ration of ruminants, and even if the ruminants are in a fattening period, the soybean meal does not need to be additionally added to maintain protein supply, so that the feed cost is effectively reduced, and multiple targets of saving cost, improving efficiency and protecting environment are achieved.

The invention discloses a drug target prediction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing dynamic gating fusion on graph structure features and sequence features of a target drug to obtain drug features; performing dynamic feature enhancement on the digitized sequence of the target protein, and further obtaining protein features through context sensing optimization; obtaining a target affinity score of the target drug and the target protein by using a prediction model based on the drug characteristics and the protein characteristics; wherein the prediction model is obtained through feature representation training marked with actual affinity scores on the basis of a neural network. According to the method, the prediction precision of drug target interaction is improved through deep fusion of drug multi-modal features and protein sequence context perception optimization. The method can realize high-precision prediction of the drug target, and can be widely applied to the technical field of drug target prediction.

The invention provides a recombinant XVII type collagen and application thereof, and relates to the technical field of protein engineering. The novel recombinant human XVII type collagen is obtained by optimizing and screening collagen structural domain sequences of the human XVII type collagen and then combining the sequences. The recombinant human XVII type collagen is consistent with the corresponding part of the amino acid sequence of the human XVII type collagen, does not generate immunological rejection when being applied to a human body, and can be widely applied to the industries of surgical medical treatment and medical beauty. In addition, the recombinant human XVII type collagen is high in stability, and the problem of protein degradation in the process of expressing recombinant protein by pichia pastoris can be avoided. In addition, the recombinant human XVII type collagen also has the effects of high biological activity, high biological safety, promotion of cell proliferation and differentiation and promotion of hair growth, and can be applied to the field of cosmetics or medicines as a functional component.

A protein complex model interface quality evaluation method based on a multi-scale isovariant graph neural network comprises the following steps: firstly, screening out a co-crystallized natural protein complex structure from a non-redundant protein interaction database PRISM, and generating a bait structure by using a HDock docking algorithm; the method comprises the following steps: firstly, extracting molecular surface interaction fingerprints, atomic-level features and residue-level features on the basis of each compound bait structure, obtaining graph representation of the compound bait structures, then fully capturing and fusing multi-scale information through a depth isotropic graph neural network, and finally obtaining an interface mass fraction through prototype comparison prediction. According to the method, the interface quality evaluation of the protein compound model can be accurately carried out, and the problems of low precision and poor generalization of the interface quality evaluation of the protein compound model are effectively solved.

The invention discloses a protein active site multi-classification identification method based on multi-modal deep learning. According to the method, protein sequence information, three-dimensional structure information and functional text information are fused, a pre-trained protein language model, an isotropic graph neural network and a biomedical language model are utilized, an innovative multi-modal feature extraction and fusion mechanism is designed, and the model performance is optimized through a self-adaptive weighted fusion strategy. According to the method provided by the invention, accurate prediction of protein active sites can be realized through acquisition of a high-quality data set, construction of a cross-modal feature fusion module, setting of a weighted fusion mechanism and design of a composite loss function.

The invention discloses an abdominal aortic aneurysm progress prediction method and system based on cross-modal knowledge distillation. The method comprises the following steps: collecting blood of a target object; the method comprises the following steps: detecting the concentration of blood protein fingerprint markers of blood of a target object, wherein the blood protein fingerprint markers comprise matrix metalloproteinase-12, calcitonin-related polypeptide-alpha, uromodulin, nerve injury induction protein-1, glycosylated hemoglobin A1c and fibroblast growth factor-9; carrying out abdominal aortic aneurysm risk calculation by calling Student-net of cross-modal knowledge distillation based on the blood protein fingerprint marker concentration; wherein the input of the Teaser-net of the cross-modal knowledge distillation comprises a CTA image feature and a pathological section feature, and a category probability vector output by the Student-net is aligned with a category probability vector output by the Teaser-net; and outputting the risk level of the abdominal aortic aneurysm and the probability of occurrence / progress / rupture of the abdominal aortic aneurysm in the preset year. And the development of the aortic aneurysm disease is accurately predicted.

The invention provides a method for carrying out corn quality seed selection by using a hyperspectral imaging technology, and belongs to the technical field of corn quality seed selection. Corn kernels are subjected to spectrum scanning in a wavelength range of 400nm to 2500nm, spectrum data are preprocessed through a continuous wavelet transform algorithm, baseline drift and noise interference are removed, and a high-quality seed selection result is obtained. And accurately extracting spectral characteristic peaks of protein, starch, grease and moisture. And establishing a characteristic peak distribution matrix to record peak intensity distribution, and calculating a characteristic peak intensity weight coefficient and a position offset. And constructing a characteristic peak quality incidence matrix, establishing a numerical mapping relationship between the spectral characteristics and the quality parameters, and obtaining a peak width parameter and a spectral noise level. The spectral quality fusion function is adopted to process the multi-dimensional characteristic parameters, and the comprehensive quality evaluation index and the single quality evaluation index are calculated, so that the technical problem that the multi-dimensional quality characteristics of the corn kernels cannot be accurately identified and accurately graded is solved.

According to the pre-training language model-based drug-target interaction prediction method designed by the invention, natural language processing and graph neural network technologies are fused, context semantic features are automatically extracted from drug molecule SMILES character strings and protein sequences, and by constructing a graph structure taking drug-target pairs as nodes, the drug-target interaction is predicted. The weight of an edge is defined according to the similarity between embedded vectors, and a simplified graph convolutional network is adopted to carry out graph structure modeling to realize complex relation learning, so that the accuracy, generalization and interpretability of prediction are improved, the limitation of a traditional method on the problems of sparse feature expression, mutual information loss and'words outside a vocabulary 'is overcome, and the prediction accuracy, generalization and interpretability are improved. And finally, the accuracy of predicting the drug-target interaction relationship is improved.

Methods and apparatus for obtaining representations of proteins and small molecule drugs for synthesis; wherein pre-trained mixed modality protein and natural language fusion models are further trained by supervised fine tuning using reasoning-oriented query—chain-of-thought (CoT) response pairs. The resulting reasoning-oriented neural network is then used to obtain representations of output proteins or small molecule drugs, in response to mixed modality reasoning-oriented input queries specifying conditions on the output. In one embodiment, the neural network is an autoregressive multicapitate transformer whose decoder output heads correspond to the represented modalities. The method returns mixed modality output representations of proteins or small molecule drugs for synthesis or manufacture.

This disclosure provides methods for spatial profiling of biological analytes present in a biological sample. Methods include generating feature arrays using a master / copy format using recessed arrays, and methods for using such arrays. For example spatially-tagged analyte capture analytes can be used in spatial detection in methods to determine the location of analytes (e.g., proteins) in biological samples.

Some aspects of this disclosure provide strategies, systems, reagents, methods, and kits that are useful for the targeted editing of nucleic acids or the modification of nucleic acids or proteins, including editing a single site within the genome of a cell or subject, e.g., within the human genome. In some embodiments, fusion proteins of nucleic acid programmable DNA binding proteins e.g., GeoCas9 or variants thereof, and effector domains, e.g., deaminase domains, are provided. In some embodiments, methods for targeted nucleic acid editing or protein modification are provided. In some embodiments, reagents and kits for the generation of targeted nucleic acid editing proteins, e.g., fusion proteins of a GeoCas9 and effector domains, are provided.

A specification of a binding target protein is received. A machine learning model is used to predict a plurality of candidates for a property of a selected amino acid position of a binder protein to bind to the binding target protein. For each selected property candidate of the plurality of property candidates, the selected property candidate is used as a model input to predict properties for one or more other amino acid positions into a corresponding candidate set of properties. The corresponding candidate sets are evaluated using a binding quality evaluation. Based on the evaluation, one of the plurality of property candidates is selected as a determined result property of the selected amino acid position. The determined result property is used as a model input to predict a plurality of candidates for a property of a different selected amino acid position included in the binder protein.