数字农科院2.0

Rational protein engineering using an omni-directional multipoint mutagenesis generation pipeline

文献类型： 外文期刊

作者： Lingxi Fan;Hui Wang;Han Gao;Yekun Ding;Jintong Zhao;Huiying Luo;Tao Tu;Ningfeng Wu;Bin Yao;Feifei Guan;Jian Tian;Huoqing Huang

作者机构：

关键词： Artificial intelligence;Bioinformatics;Biological sciences;Biomolecular engineering;Computational bioinformatics;Protein

期刊名称： iScience

ISSN： 2589-0042

年卷期： 2025 年 28 卷 9 期

页码：

收录情况： SCIE（2025版）

摘要： Generative models have transformed protein design by enabling the generation of extensive datasets. However, accurate identification of biologically active sequences with specific functions within such data remains a significant challenge. In this study, we present a novel pipeline that integrates models for sequence generation, ranking, and selection to engineer proteins with enhanced properties. Our Omni-Directional Multipoint Mutagenesis (ODM) generation model was developed by refining a pre-trained protein BERT model to produce 100,000 mutant proteins. To evaluate the effects of mutations on protein activity, we utilized the lowest probability prediction across all masked positions as an indicator to rank the mutant sequences. Furthermore, we developed thermostability models to identify protease mutants with improved thermostability and utilized biological indicators to enhance lysozyme activity by introducing additional basic residues. Through two iterative design cycles, we observed that 62.5% of protease mutants exhibited enhanced thermostability, while 50% of lysozyme mutants displayed increased bacteriolytic activity.

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