An iterative deep learning-guided algorithm for directed protein evolution.

ABSTRACT: Deep learning has rapidly emerged as a promising toolkit for protein optimization, yet its success remains limited, particularly in the realm of activity. Moreover, most algorithms lack rigorous iterative evaluation, a crucial aspect of protein engineering exemplified by classical directed evolution. This study introduces DeepDE, a robust iterative deep learning-guided algorithm leveraging triple mutants as building blocks and a compact library of ∼1,000 mutants for training. Triple mutants allow for the exploration of a much greater sequence space compared to single or double mutants in each iteration. When applied to GFP from Aequorea victoria, DeepDE achieved a remarkable 74.3-fold increase in activity over four rounds of evolution, far surpassing the benchmark superfolder GFP. O

SUBMITTER: Li X 

PROVIDER: S-EPMC12415074 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature