10 results about "Functional prediction" patented technology

The application provides a method and system for generating and predicting the function of mRNA untranslated region sequence based on coding sequence. The method comprises: constructing a pre-training data set and a downstream task data set; using the pre-training data set to perform autoregressive training on the constructed conditional generation model to obtain a UTR sequence generation model; using the downstream task data set to fine-tune the UTR sequence generation model to obtain a downstream task function prediction model; generating candidate UTR sequences based on the UTR sequence generation model, and evaluating the generation ability of the UTR sequence generation model in the non-coding region sequence generation task; and predicting the function attribute of the UTR sequence based on the downstream task function prediction model, and evaluating the prediction ability of the downstream task function prediction model in multiple UTR related downstream tasks. The application considers the synergistic relationship between UTR and CDS in the UTR sequence generation process, and improves the efficiency and rationality of UTR sequence design.

The present disclosure provides a rational modification method of spermidine synthase (SpeE) in Bacillus subtilis, which relates to the technical fields of enzyme engineering and computational structural biology. The modification method comprises the following steps: constructing a high-confidence three-dimensional model of Bacillus subtilis SpeE, determining the key action scope by double-substrate (S-adenosyl methionine and putrescine) docking, performing virtual saturation mutation on the residues in the region, screening the optimal mutant by combining function prediction, stability analysis, molecular dynamics simulation and binding free energy calculation, and explaining the mutation mechanism from the atomic level. The SpeE mutant (such as G85S, G85A) obtained by the modification method has significantly improved catalytic efficiency and substrate binding affinity, and when applied to a whole-cell catalytic system, it can greatly improve the yield of spermidine, and is suitable for the industrialized green biosynthesis of spermidine.

This invention relates to a method for targeted design and optimization of the 5′ untranslated region (UTR) of mRNA based on generative language models and reinforcement learning. The method includes: constructing pre-training data for the 5′ UTR; obtaining a prediction task dataset; calculating the minimum free energy (MFE) corresponding to the pre-training data; constructing a generative language model and performing phased pre-training on the generative language model using the pre-training data and the MFE to obtain a generative model and a prediction task fine-tuning model; fine-tuning the prediction task fine-tuning model based on the prediction task dataset to obtain a functional prediction model; evaluating the generative model's generation capability in the 5′ UTR sequence generation task; and performing targeted optimization design of the 5′ UTR sequence based on a reinforcement learning framework, combining the generative model and the functional prediction model. This invention can efficiently generate 5′ UTR sequences with specific biological functions, providing strong support for functional mRNA design and showing broad application prospects.

The application discloses a kind of predictable phaseolus vulgaris response melatonin regulation SSR molecular markers and its application, it is related to biotechnology field.The application carries out the effectiveness identification of ordinary phaseolus vulgaris natural germplasm resources to melatonin treatment, the efficiency of melatonin treatment-cell wall-salt tolerance in ordinary phaseolus vulgaris germplasm resources is analyzed, and using the reference genome in Esembl plants database, a complete set of SSR molecular markers is developed, and according to transcriptome information, the SSR marker near response gene is mined, through polymorphism analysis and single marker analysis, two SSR markers with significant difference associated with melatonin traits are screened, can be used as the molecular marker for predicting phaseolus vulgaris response melatonin regulation, and provides technical support for the function prediction of melatonin treatment in the sprouting stage of ordinary phaseolus vulgaris.

The present application relates to the field of bioengineering and genetic engineering, and particularly provides a rapid mining method of isomerase genes. The method is based on big data analysis and bioinformatics technology, combined with sequence alignment, gene expression analysis and function prediction, etc. means, realizes the rapid and accurate mining of isomerase genes, and provides strong technical support for the development and application of enzyme engineering field.

This invention belongs to the field of plant rhizosphere research and discloses a method for determining the regulatory effect of exogenous selenium application on the rhizosphere microecology of Amorphophallus bulbifera. The method includes: experimental design and sample collection; soil microbial DNA extraction and amplicon sequencing; and quality control and analysis of the microbiome data. This invention utilizes Illumina MiSeq high-throughput sequencing technology to analyze the changes in the composition and diversity of rhizosphere soil bacteria communities in Amorphophallus bulbifera under exogenous selenium treatment, and further analyzes the functions of related communities through PICRUSt functional prediction analysis. The conclusions are as follows: exogenous selenium treatment induces significant changes in the rhizosphere soil bacterial community of Amorphophallus bulbifera, which is beneficial for creating a stable and more diverse rhizosphere microbial community structure.

The application relates to the technical field of quantum computing, in particular to a protein function prediction method based on a hybrid quantum-classical neural network, which comprises the following steps: obtaining a target protein sequence, extracting a high-dimensional feature vector of the target protein sequence by using a pre-trained classical protein language model and performing dimension reduction processing to obtain a low-dimensional input feature; inputting the low-dimensional input feature into a variational quantum circuit, performing adaptive quantum state coding by using frequency parameters and phase parameters that are optimized through training, and obtaining an initial quantum state; performing multi-scale feature entanglement on the initial quantum state in the variational quantum circuit to obtain an evolved quantum state; performing measurement on the evolved quantum state to obtain quantum feature expectation values, inputting the quantum feature expectation values into a classical classification network, and outputting a function prediction result of the target protein sequence. The scheme can improve the separability of protein function categories in a quantum feature space and reduce the parameter size of the classical classification network.

The present application relates to the technical field of molecular diagnosis, and specifically relates to application of female uterine cavity flora as a marker in endometriosis diagnosis. The present application collects uterine cavity flora of patients, extracts genomic DNA, performs bacterial 16s-rRNA sequencing, uses KEGG as a reference database for function prediction, determines that abnormal abundance of uterine cavity flora dominant bacteria has significant correlation with dysmenorrhea and high CA125 value of endometriosis patients, clarifies the correlation between abnormal uterine cavity flora and endometriosis, and provides a new method for evaluating the risk of endometriosis by detecting uterine cavity flora atlas.