Phosphocholine cytidylyltransferase mining method based on deep learning model and application thereof

By combining deep learning models with structural prediction and rational design, the problem of insufficient catalytic activity and expression efficiency of existing CCT enzymes in the synthesis of citicoline was solved. This enabled the efficient identification and modification of highly active, low-homology CCT enzymes, thereby improving the efficiency of citicoline biosynthesis.

CN121768473BActive Publication Date: 2026-07-24BEIJING LIFEWE BIOTECHNOLOGY INSTITUTE CO LTD
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Patent Information

Application Number
CN202610249550.X
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-03-03
Publication Date
2026-07-24
Estimated Expiration
2046-03-03

AI Technical Summary

Technical Problem

Existing CCT enzymes lack sufficient catalytic activity, thermal stability, and heterologous host expression efficiency in the industrial production of citicoline. Traditional enzyme mining methods struggle to identify high-activity, low-homology enzymes from massive metagenomic data, and the screening results are highly homogeneous.

Method used

Using a deep learning model-based approach that combines protein language models and structure prediction models, we screened for highly catalytically active and soluble CCT candidate enzymes through Seq-to-Seq search, Hidden Markov Model alignment, transmembrane segment and signal peptide prediction, structure prediction, and ligand molecule docking. We then obtained mutants with improved performance through rational design.

Benefits of technology

This method efficiently identifies highly active CCT enzymes with low homology within a sequence space of hundreds of millions of sequences, overcoming the limitations of traditional methods, significantly improving the screening success rate, obtaining novel CCT enzymes and mutants, overcoming the problem of poor expression adaptability, and improving the efficiency of cytidine diphosphate choline biosynthesis.

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Abstract

The application discloses a cephalin cytidylyltransferase mining method based on a deep learning model. The method breaks through the limitation of traditional homologous comparison, and can identify CCT candidate enzymes with high catalytic activity, excellent solubility and suitable for engineering application in a protein sequence space of hundreds of millions of levels with higher screening efficiency and prediction accuracy. On this basis, rational design and zero-sample mutation prediction strategies are combined to further obtain CCT mutants with significantly improved performance, so as to provide a core catalytic element for efficient biosynthesis of cytidine diphosphate choline.
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