PDF(Pubmed)
Abstract：
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.
摘要：
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。研究它们的结构，功能,和相互作用对于更好地了解疾病机制和潜在的治疗方法至关重要。DeepMind的AlphaFold2，一种深度学习蛋白质结构预测模型，已经证明非常准确，它广泛应用于诊断研究的各个方面，比如疾病生物标志物的研究，微生物致病性，抗原-抗体结构，和错义突变。因此，AlphaFold2是一种特殊的工具，可以将基础蛋白质研究与疾病诊断的突破联系起来。诊断策略的发展，以及新型治疗方法的设计和精准医学的增强。这篇综述概述了建筑，亮点，和AlphaFold2的局限性，特别强调其在免疫学等学科的诊断研究中的应用，生物化学,分子生物学,和微生物学。
