Protein engineering in the deep learning era

2024/12/01 by Bingxin Zhou, Yang Tan, Yutong Hu +3 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Machine Learning in Bioinformatics #vaccines and immunoinformatics approaches #RNA and protein synthesis mechanisms

paper · pdf · doi:10.1002/mlf2.12157

openalex publication_date 2024/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Advances in deep learning have significantly aided protein engineering in addressing challenges in industrial production, healthcare, and environmental sustainability. This review frames frequently researched problems in protein understanding and engineering from the perspective of deep learning. It provides a thorough discussion of representation methods for protein sequences and structures, along with general encoding pipelines that support both pre-training and supervised learning tasks. We summarize state-of-the-art protein language models, geometric deep learning techniques, and the combination of distinct approaches to learning from multi-modal biological data. Additionally, we outline common downstream tasks and relevant benchmark datasets for training and evaluating deep learning models, focusing on satisfying the particular needs of protein engineering applications, such as identifying mutation sites and predicting properties for candidates' virtual screening. This review offers biologists the latest tools for assisting their engineering projects while providing a clear and comprehensive guide for computer scientists to develop more powerful solutions by standardizing problem formulation and consolidating data resources. Future research can foresee a deeper integration of the communities of biology and computer science, unleashing the full potential of deep learning in protein engineering and driving new scientific breakthroughs.

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Discussions

• 2. Protein engineering in the deep learning era This review highlights how AI and deep learning are transforming protein design—enabling structure prediction, function optimization, and de novo enzyme [bsky, 0 points, 1 comments]

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