Molecular modification method for glycosyltransferase based on artificial intelligence

Through multi-dimensional analysis and enzyme activity prediction model based on artificial intelligence, the problem of cumbersome screening and difficult mutation control in glycosyltransferase molecular transformation is solved, and efficient and accurate molecular transformation and enzyme activity prediction are achieved.

CN119724342APending Publication Date: 2025-03-28BEIJING QINGKE BIOTECHNOLOGY CO LTD
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Patent Information

Application Number
CN202411792332.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-06
Publication Date
2025-03-28

AI Technical Summary

Technical Problem

The prior art has problems in the transformation of glycosyltransferase molecules that are complicated in the screening process, inefficient efficiency and difficulty in precise control of mutation direction and effect.

Method used

Using an artificial intelligence-based method, an enzyme activity prediction model is constructed to guide molecular transformation by obtaining the description information of glycosyltransferase mutants, including physical and chemical properties analysis of mutation sites, similar sequence search, position-specific scoring matrix construction, functional segment determination and three-dimensional structure prediction.

Benefits of technology

It improves the efficiency and accuracy of glycosyltransferase molecular transformation, avoids blind screening, achieves more precise control of mutation direction and effect, and improves the accuracy of enzyme activity prediction.

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Abstract

The invention provides a molecular modification method for glycosyltransferase based on artificial intelligence. The method comprises the following steps: acquiring description information of enzyme mutants to form a basic data set; performing physicochemical property analysis on the basic data set to obtain an analysis result set; calculating the quantized value of the conservative degree of the mutation site and the quantized value of the influence degree of different mutations on the sequence so as to construct a position specificity scoring matrix; extracting a position specificity scoring value and a frequency from the scoring matrix, determining a functional section, and performing weighting processing; performing three-dimensional structure prediction and energy calculation based on the three-dimensional structure reconstruction model of the mutant to obtain an energy value; calculating an energy change value between the mutant and the wild type; performing molecular docking and dynamic simulation analysis on the mutant protein to obtain quantitative description of an interaction mode and strength and dynamic simulation characteristic data; and constructing a model training set to train a machine learning model for predicting the change of the enzyme activity of the mutant.
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