Protein engineering in the deep learning era  

作　　者：Bingxin Zhou Yang Tan Yutong Hu Lirong Zheng Bozitao Zhong Liang Hong 

机构地区：[1]Institute of Natural Sciences,Shanghai Jiao Tong University,Shanghai,China [2]Shanghai National Center for Applied Mathematics(SJTU center),Shanghai Jiao Tong University,Shanghai,China [3]School of Information Science and Engineering,East China University of Science and Technology,Shanghai,China [4]Shanghai Artificial Intelligence Laboratory,Shanghai,China [5]School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai,China [6]Department of Cell and Developmental Biology,University of Michigan Medical School,Ann Arbor,Michigan,USA [7]School of Physics and Astronomy,Shanghai Jiao Tong University,Shanghai,China [8]Zhangjiang Institute for Advanced Study,Shanghai Jiao Tong University,Shanghai,China

出　　处：《mLife》2024年第4期477-491,共15页微生物(英文)

基　　金：supported by the National Natural Science Foundation of China(11974239 and 62302291);Innovation Program of Shanghai Municipal Education Commission(2019‐01‐07‐00‐02‐E00076);Shanghai Jiao Tong University Scientific and Technological Innovation Funds(21×010200843)。

摘　　要：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.

关 键 词：artificial intelligence geometric deep learning protein engineering protein language model synthetic biology 

分 类 号：Q51[生物学—生物化学]