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

Xiaofan Li, Qihan Wang, Jianfeng Li, Fangfang Tang, Xiaofeng Yang, Zhanglin Lin

Abstract read

In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Xiaofan LiSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.

Qihan WangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.

Jianfeng LiSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.

Fangfang TangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.

Xiaofeng YangSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.

Zhanglin LinSchool of Biology and Biological Engineering, South China University of Technology, Guangzhou, Guangdong 510006, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

Artificial intelligenceBiocomputational methodCellProteinStructural biology

Identifiers

PMID40927680

DOI10.1016/j.isci.2025.113324

PMCPMC12415074

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.