24835 results about "Protein" patented technology

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 present disclosure provides systems and methods for processing or analyzing a sample. Methods may include generating supports (e.g., beads) comprising barcode molecules coupled thereto. A support comprising barcode molecules may be useful for analyzing or processing one or more analytes such as nucleic acid molecules, proteins, and / or perturbation agents.

The present invention discloses substituted 2-amino-9-(3,3-difluoropiperidin-4-yl)-7,9-dihydro-8H-purin-8-one compounds compounds having the structural formula represented by formula (I)： The substituted 2-amino-9-(3,3-difluoropiperidin-4-yl)-7,9-dihydro-8H-purin-8-one compounds, derivatives and pharmaceutically acceptable salts thereof of the present invention are capable of acting as a drug characteristic of a protein kinase inhibitor, in particular for the expression of a protein through a DNA-dependent protein kinase (DNA-PK) and can be used to treat or prevent disorders associated with abnormal protein kinase activity, such as cancer, cancer with brain metastases, cancer with meningeal metastases, glioma, glioblastoma, DIPG, and the like either as monotherapy or combination with other treatments, such as radiation therapy or chemotherapy.

Provided herein are SOS1 protein degraders, e.g., a compound of Formula (I), and pharmaceutical compositions thereof. Also provided herein are methods of their use for treating, preventing, or ameliorating one or more symptoms of an SOS1-mediated disorder, disease, or condition.

The invention relates to the improvement of endonuclease-based antimicrobials by blocking DNA repair of double-strand break(s) (DSB(s)) in prokaryotic cells. In this respect, the invention especially concerns a method involving blocking DNA repair after a nucleic acid has been submitted to DSB, in particular by a Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) associated programmable double-strand endonuclease. The invention particularly relates to the use of an exogenous molecule that inhibits DNA repair, preferably a protein that binds to the ends of the double-stranded break to block DSB repair. The invention also relates to vectors, particularly phagemids and plasmids, comprising nucleic acids encoding nucleases and Gam proteins, and a pharmaceutical composition and a product containing these vectors and their application.

The invention discloses an identification method and system of a short antibacterial peptide sequence, a terminal and a storage medium. Relates to the technical field of bioinformatics. A short antibacterial peptide data set is obtained in advance, a training set is obtained through division according to the short antibacterial peptide data set, and a pre-trained protein language model is finely adjusted according to the training set; obtaining sequence features of a to-be-classified sequence through the fine-tuned protein language model; obtaining physicochemical characteristics of the to-be-classified sequences through an analysis method based on pseudo amino acid components; inputting the sequence features and the physicochemical features into an attention model for feature fusion to obtain fusion features of the to-be-classified sequence; and inputting the fusion features into a classifier to obtain a prediction category of the to-be-classified sequence. According to the method, the pre-training model is finely adjusted by establishing a more perfect short antibacterial peptide data set, the sequence features and the physicochemical features of the to-be-classified sequences are fused, and the sequences are classified through the fused features, so that the recognition accuracy of the short antibacterial peptides can be effectively improved.

The present invention relates to various molecular constructs having at least one cellular-targeting element, one protein-targeting element, and one ubiquitin ligase recruitment element, wherein these elements are conjugated in a manner that facilitates cellular uptake and targeted degradation of specific proteins within cells. The present invention further relates to multifunctional proteolysis-targeting chimera conjugates and uses thereof for the treatment of diseases or disorders such as immune and inflammatory diseases, infectious diseases, cardiovascular diseases, endocrine disorders, and various cancers that are otherwise resistant to traditional therapeutic regimens.

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 relates to the field of drug-target affinity prediction, in particular to a drug-target affinity prediction method based on a multi-scale mixed attention network. According to the method, global residue characteristics can be learned by protein pocket residues by utilizing a self-attention mechanism, so that the global characteristics of the protein are learned. In addition, a cross-modal feature fusion mechanism is utilized, protein and drug structural features are enhanced in sequence features, cross-modal feature fusion in entities is enhanced, meanwhile, a cross-entity interaction module is utilized to recognize key atoms of drug molecules or key residues of protein, and therefore the overall performance of the model is improved. Finally, multi-level protein feature extraction is further introduced to further extract protein features.

To provide a medicine for treating a disease in a mammal.SOLUTION: Provided is a medicine for treating a disease in a mammal, comprising an expression cassette that is a first expression cassette comprising a chimeric gene operably linked to a first expression control element, the chimeric gene comprising a first portion comprising a minigene having an alternatively spliced exon and a second portion encoding a first RNA encoding a protein, the first portion being 5' to the second portion, the second portion lacking an initiation codon or start codon, the alternatively spliced exon comprising a translation initiation regulatory sequence, expression of the protein being controlled by inclusion of the alternatively spliced exon, and the medicine being used in combination with a small molecule splicing modifier that modulates splicing of the alternatively spliced exon.SELECTED DRAWING: Figure 1

To provide constructs, vectors, relative host cells and pharmaceutical compositions which allow an effective gene therapy, in particular of genes larger than 5Kb.SOLUTION: The present invention implements cellular large protein reconstitution by providing to a target cell two or more fragments of large protein fused to split inteins to promote intein-mediated trans-splicing and reconstitute the functional protein.SELECTED DRAWING: None

The invention relates to the field of drug target interaction, and discloses an application of a multi-modal feature fusion model in drug target binding affinity prediction, which comprises the following steps: S1, obtaining an original data set of drug target binding affinity, and taking a binding affinity index in the data set as a label of a training set and a test set; s2, acquiring representation information of other modals by using the drug sequence and target sequence representation provided in the original data set, preprocessing the acquired representation information of drugs and targets in different modals, and processing the representation information into a model acceptable feature vector form; and S3, respectively storing the multi-modal feature vectors of the drug and the target as well as the binding affinity tags in a form of pytorchsensor (. Pt). According to the method, shallow characterization of the drug and protein sequences is learned by applying the pre-training model, and the problem that a drug target binding affinity data set is insufficient is solved.

This disclosure describes proteins that specifically bind human sperm protein 17 (Sp17) with nanomolar affinity and high specificity. These proteins include a recombinant human anti-Sp17 IgG that is suitable for use as a therapeutic antibody to treat cancers that ectopically express Sp17. Other Sp17-binding proteins are described including antibody fragments, antibody conjugates, and fusion proteins.

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 relates to the technical field of protein enzymolysis, and discloses an intelligent control system and method for an animal protein enzymolysis production process, and the method comprises the steps: obtaining key attribute parameters of animal protein raw materials, building a raw material grading model, and obtaining initial grades of the raw materials; matching a preset grading corresponding relation library according to the initial grading to obtain an initial stage optimal production parameter range; collecting a first production parameter before enzymolysis to obtain an adjusted production parameter; starting enzymolysis according to the adjusted parameters, collecting process key parameters in real time, inputting an optimal production parameter prediction model, and predicting an optimal production parameter at the next moment; if the optimal production parameters exceed the safety range, correction is carried out; continuously performing enzymolysis according to the corrected parameters, and continuously predicting and correcting the parameters until the enzymolysis is finished; through raw material grading and parameter real-time optimization, the process parameters can be dynamically regulated and controlled according to different batches of raw materials, the production efficiency is improved to the maximum extent while the product quality is guaranteed, and remarkable intelligence and adaptability are achieved.

This disclosure describes proteins that specifically bind human sperm protein 17 (Sp17) with nanomolar affinity and high specificity. These proteins include a recombinant human anti-Sp17 IgG that is suitable for use as a therapeutic antibody to treat cancers that ectopically express Sp17. Other Sp17-binding proteins are described including antibody fragments, antibody conjugates, and fusion proteins.

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 invention discloses a DNA binding residue prediction method based on a multi-modal protein language model in the technical field of bioinformatics. The DNA binding residue prediction method based on the multi-modal protein language model comprises the following steps: sequentially using tools to respectively obtain feature files of a protein sequence P to be subjected to DNA binding residue prediction; inputting all the feature files into the ESM3 to obtain an embedded matrix M; the method comprises the following steps: processing a protein sequence into a residue sample, constructing an isovariant graph neural network model according to a protein structure diagram, constructing a data set by utilizing the protein sequence of known DNA binding residues, and training the constructed network; and inputting a residue sample of the protein sequence into the trained model to obtain a DNA binding residue of the protein sequence. According to the DNA binding residue prediction method based on the multi-modal protein language model, the efficiency and accuracy of DNA binding residue prediction are improved.

The invention provides a recombinant humanized III-type collagen with a triple helix structure and preparation and application thereof, and belongs to the technical field of protein engineering, the amino acid sequence of the recombinant humanized III-type collagen comprises n core units, n is an integer of 1 or greater than 1, and the amino acid sequence of each core unit is as shown in SEQ ID NO: 1; the invention also provides a construction method of the expression system and a preparation method of the recombinant humanized III-type collagen. The invention also provides practical industrial application according to the characteristics of the protein. The recombinant humanized III-type collagen provided by the invention is high in purity, good in stability and high in biological activity, and experiments show that the recombinant humanized III-type collagen has various biological activities of promoting cell proliferation and migration 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 present disclosure provides proteins useful for preventing, treating, inhibiting and / or eliminating infestation and infection of plants caused by various phytopathogenic pests, as well as formulations comprising such proteins, polynucleotides encoding such proteins, organisms expressing such proteins, and methods for preparing such proteins. And methods of using such proteins, formulations, polynucleotides, and organisms in agricultural and other effort areas.

Systems, methods, and apparatus for generating a DNA / gene sequence output, given a protein sequence input. In one aspect, the protein-to-DNA mapping system includes a protein language model (embedder) that generates a high-dimensional protein embedding from a low-dimensional protein sequence input; and the system includes a protein-to-DNA translator that processes the protein embedding and generates a DNA sequence output, based on the protein embedding. The protein-to-DNA translator is a neural network (e.g., a Seq2Seq model) and is configured with translation coefficients that may be trained with a training dataset, comprising the training protein and DNA embeddings. Training the translation coefficients can include supervised learning that maps the protein embedding (input) to a DNA sequence (target). The systems, methods, and apparatus may increase protein yield / expression, improve protein functionality, enhance gene therapy efficacy, streamline synthetic biology design, and / or broaden functional genomics studies, all leading to reduced costs, better performance, and increased accessibility.

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.

Compositions of retroviruses and methods of using the same for gene delivery are disclosed, wherein the retroviruses comprise a viral envelope protein comprising at least one mutation that diminishes its native function, a non-viral membrane-bound protein comprising a membrane-bound domain and an extracellular targeting domain.

The invention discloses a drug target interaction prediction method based on a protein sequence. The method comprises the following steps: constructing a drug target database; constructing a target feature extractor based on a pre-trained protein language model; constructing a drug feature extractor based on a pre-trained molecular model; constructing a feature learning method by adopting a self-attention mechanism, constructing a drug target interaction learning method by adopting a cross attention mechanism, constructing a predictor in combination with a linear layer, and constructing a prediction drug target interaction model; training and optimizing parameters of the drug target interaction prediction model to obtain an optimized drug target prediction model; inputting a drug target into the optimized prediction model, outputting a prediction result, and finally marking potential active sites and important amino acid residues of the drug and the target through attention score visualization. According to the method, the precision of predicting the interaction between the drug and the target spot based on the protein sequence is improved, and the interpretability of the deep learning model is realized.

The present disclosure relates to compositions of matter, methods, and systems for analyzing polymeric macromolecules, including polymeric macromolecules such as peptides, polypeptides, and proteins.

Provided are a protein language model training method, a method for extracting an amino acid sequence representation by using a protein language model, a method for obtaining a new amino acid sequence by using a protein language model, a method for obtaining a related amino acid sequence by using a protein language model, a method for predicting an amino acid sequence perplexity by using a protein language model, an electronic device, a computer-readable medium, and a program product. The protein language model training method comprises: during a training process, executing at least one training sub-task amongst two training sub-tasks included in a first type of training task and a second type of training task; and, on the basis of a first loss value corresponding to the first type of training task and a loss value corresponding to the second type of training task, adjusting parameters of a protein language model to obtain a trained protein language model.

The invention discloses a polypeptide and application thereof in preparation of a medicine for preventing and / or treating cancers. The amino acid sequence of the polypeptide is shown as SEQ ID No. 1 or SEQ ID No. 2. The cancer is at least one of breast cancer, colorectal cancer, leukemia, esophageal cancer, pancreatic cancer, brain glioma, kidney cancer, skin cancer, osteosarcoma, ovarian cancer and prostate cancer. The invention designs and synthesizes the polypeptide of a targeted staphylococcus tumor protein compound SND1-MTDH containing a nuclease structural domain 1 (SND1 and a heteromucin (MTDH), finds that the polypeptide has excellent inhibitory activity on various different types of human cancer cells for the first time, and provides an optional scheme for broad-spectrum anti-cancer drugs. The polypeptide applied in the invention is simple in synthesis process and low in production cost.