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<p>Protein engineering holds substantial promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited laboratory capacity constrains the discovery of optimal sequences. Here, to address this, we present the μProtein framework, which accelerates protein engineering by combining μFormer, a deep learning model for accurate mutational effect prediction, with μSearch, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape using μFormer as an oracle. μProtein leverages single-mutation data to predict optimal sequences with complex, multi-amino-acid mutations through its modelling of epistatic interactions and a multi-step search strategy. In addition to strong performance on benchmark datasets, μProtein identified high-gain-of-function multi-point mutants for the enzyme β-lactamase, surpassing one of the highest-known activity levels, in wet laboratory, trained solely on single-mutation data. These results demonstrate μProtein’s capability to discover impactful mutations across the vast protein sequence space, offering a robust, efficient approach for protein optimization.</p>

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Accelerating protein engineering with fitness landscape modelling and reinforcement learning

  • 期刊论文

  • 发表日期：

    2025年9月8日

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  • Haoran Sun,
  • Liang He,
  • Pan Deng,
  • Guoqing Liu,
  • Zhiyu Zhao,
  • Yuliang Jiang,
  • Chuan Cao,
  • Fusong Ju,
  • Lijun Wu,
  • Haiguang Liu,
  • Tao Qin,
  • Tie-Yan Liu

摘要

Protein engineering holds substantial promise for designing proteins with customized functions, yet the vast landscape of potential mutations versus limited laboratory capacity constrains the discovery of optimal sequences. Here, to address this, we present the μProtein framework, which accelerates protein engineering by combining μFormer, a deep learning model for accurate mutational effect prediction, with μSearch, a reinforcement learning algorithm designed to efficiently navigate the protein fitness landscape using μFormer as an oracle. μProtein leverages single-mutation data to predict optimal sequences with complex, multi-amino-acid mutations through its modelling of epistatic interactions and a multi-step search strategy. In addition to strong performance on benchmark datasets, μProtein identified high-gain-of-function multi-point mutants for the enzyme β-lactamase, surpassing one of the highest-known activity levels, in wet laboratory, trained solely on single-mutation data. These results demonstrate μProtein’s capability to discover impactful mutations across the vast protein sequence space, offering a robust, efficient approach for protein optimization.