6 results about "Substrate molecule" patented technology

The application discloses a nanowire-nanotube hybrid sensor and a preparation method and application thereof. The nanowire-nanotube hybrid sensor has two regions of a nanochannel and a nanowire electrode by using a conical double-channel microcapillary. A substrate solution is precisely released to a specific site of a single cell through the nanochannel. After a to-be-measured glycosidase recognizes the substrate molecules, the glycosidic bond is cut to release active substances of the electrode. The nanowire electrode is used for real-time quantitative detection of the active substances, so that quantitative detection of glycosidase substances at a single / subcellular level is realized. The application constructs an efficient and universal detection strategy of non-electrochemical active glycosidase by using the nanowire-nanotube hybrid sensor and an electrical signal conversion method of the reaction of specific glycosidase and a substrate, widens the detection range of electrochemical target objects, and provides a promising method for high-time-space-resolution quantitative monitoring of hydrolytic enzymes at a single / subcellular level.

The application discloses a kind of nano bubble regulated hydrogen bond organic framework immobilized enzyme, and hydrogen bond organic framework immobilized enzyme is prepared by in-situ self-assembly and embedding method in water environment containing nano bubble from four (4-amidinophenyl) methane and four (4-carboxyphenyl) methane.The hydrogen bond organic framework is used to realize the immobilization of enzyme by in-situ embedding method, and a multi-level pore immobilized enzyme system based on nano bubble is constructed.Nano bubble can expand the pore size and pore volume of hydrogen bond organic framework as pore-forming template, while maintaining the stability of enzyme structure, significantly improving the mass transfer rate of substrate molecules in the framework, thereby enhancing the catalytic activity and substrate affinity of immobilized enzyme.The method effectively improves the enzyme catalytic reaction efficiency while maintaining the system immobilization efficiency, pH stability, temperature stability and reusability.The process is simple and suitable for the construction of multi-level pore immobilized system for various enzymes.

This invention discloses an artificial intelligence-based method for targeted optimization of chitosanase, comprising: constructing a sequence-function-product standardized association dataset containing chitosanase amino acid sequence, substrate molecular structure, enzyme activity data, and product polymerization degree data; encoding the enzyme sequence using a protein language model and the substrate structure using a chemical language model to obtain enzyme sequence feature vectors and substrate structure feature vectors, and forming a multimodal fusion feature vector by concatenating the vectors; inputting the fusion features into an Extra Trees ensemble model for training to predict the probability of enzyme-substrate interaction, enzyme activity, and product polymerization degree; and performing targeted screening of target chitosanase and determining mutation sites based on the prediction results to obtain optimized mutants. This invention can significantly improve the efficiency and prediction accuracy of chitosanase modification, reduce experimental screening costs, and achieve efficient and accurate optimization of chitosanase.

The application provides an enzyme reaction feasibility evaluation method and system based on multi-task and molecular multi-modal features. The application uses a public data set and a bioengineering reaction rule template library to construct an enzyme reaction feasibility data set; takes the SMILES sequence features and the Morgan fingerprint spatial structure features of the reaction product molecules and the substrate molecules as the inputs of a neural network; constructs a double-branch network based on an attention mechanism and a convolutional neural network to extract molecular multi-modal features; takes a product SMILES sequence generation task as an auxiliary task to strengthen the model learning sequence features, provides more abundant features for the enzyme reaction feasibility evaluation task, and effectively enables the trained model to make accurate judgments on the reaction feasibility by comprehensively considering the molecular multi-modal features. The application significantly improves the calculation speed and reliability in the bio-molecular retrosynthesis pathway design process, avoids unnecessary search expansion in the design process, and improves the experimental efficiency.

This invention belongs to the field of drug metabolism prediction technology, specifically relating to a method and system for predicting drug metabolites based on conditional generative adversarial networks (GANs). The method includes: extracting initial features of drug molecules and masking them, inputting them into a shared encoder to obtain a first encoding result; incorporating atomic position information into the first encoding result, decoding it, and then predicting the structure of the drug molecule; inputting reactant molecules into the shared encoder, inputting the obtained reactant features into a shared decoder, and predicting chemical reaction products; inputting substrate molecules into the shared encoder for encoding; fusing the substrate encoding result with the corresponding enzyme's amino acid sequence and inputting it into the shared decoder to predict metabolites; constructing a generator using the shared encoder and shared decoder, building a multi-task discriminator, and performing adversarial training on the generator and discriminator to obtain a prediction model for predicting metabolites. This invention improves prediction accuracy by fusing information from drug structure and enzymes to construct a prediction model.

The present application relates to the technical fields of serum creatinine detection and nanozyme preparation, and particularly relates to a double-ligand regulated Fe-MOF nanozyme and a preparation method and application thereof. The method comprises the following steps: FeCl3.6H2O, 1,10-phenanthroline and monocarboxylic acid ligand are dissolved in DMF to form solution A by stirring; terephthalic acid is dissolved in DMF to form solution B; solution B is added to solution A, ultrasonic dispersion is performed, and then the solution is transferred to a stainless steel high-pressure reaction kettle for high-temperature reaction; after the reaction is completed and cooled to room temperature, centrifugal washing and vacuum drying are performed to obtain a double-ligand regulated Fe-MOF nanozyme sample. As a ligand, n-butyric acid enhances the affinity to the substrate molecule; as a ligand, 1,10-phenanthroline increases the enzyme activity; the synergy of 1,10-phenanthroline and n-butyric acid achieves high sensitivity and specificity.