Zhang Hong, Lan Jiajing, Wang Huijie, Lu Ruijie, Zhang Nanqi, He Xiaobai, Yang Jun, Chen Linjie
School of Laboratory Medicine, Hangzhou Medical College, Hangzhou, China.
Key Laboratory of Biomarkers and In Vitro Diagnosis Translation of Zhejiang Province, Hangzhou, China.
Front Mol Biosci. 2024 Jul 30;11:1414916. doi: 10.3389/fmolb.2024.1414916. eCollection 2024.
Proteins, as the primary executors of physiological activity, serve as a key factor in disease diagnosis and treatment. Research into their structures, functions, and interactions is essential to better understand disease mechanisms and potential therapies. DeepMind's AlphaFold2, a deep-learning protein structure prediction model, has proven to be remarkably accurate, and it is widely employed in various aspects of diagnostic research, such as the study of disease biomarkers, microorganism pathogenicity, antigen-antibody structures, and missense mutations. Thus, AlphaFold2 serves as an exceptional tool to bridge fundamental protein research with breakthroughs in disease diagnosis, developments in diagnostic strategies, and the design of novel therapeutic approaches and enhancements in precision medicine. This review outlines the architecture, highlights, and limitations of AlphaFold2, placing particular emphasis on its applications within diagnostic research grounded in disciplines such as immunology, biochemistry, molecular biology, and microbiology.
蛋白质作为生理活动的主要执行者，是疾病诊断和治疗的关键因素。对其结构、功能及相互作用的研究对于更好地理解疾病机制和潜在治疗方法至关重要。DeepMind公司的AlphaFold2是一种深度学习蛋白质结构预测模型，已被证明具有极高的准确性，广泛应用于诊断研究的各个方面，如疾病生物标志物研究、微生物致病性研究、抗原-抗体结构研究和错义突变研究。因此，AlphaFold2是连接基础蛋白质研究与疾病诊断突破、诊断策略发展以及新型治疗方法设计和精准医学进步的杰出工具。本综述概述了AlphaFold2的架构、亮点和局限性，特别强调了其在基于免疫学、生物化学、分子生物学和微生物学等学科的诊断研究中的应用。
