AUTHOR=Zhang Hong , Lan Jiajing , Wang Huijie , Lu Ruijie , Zhang Nanqi , He Xiaobai , Yang Jun , Chen Linjie TITLE=AlphaFold2 in biomedical research: facilitating the development of diagnostic strategies for disease JOURNAL=Frontiers in Molecular Biosciences VOLUME=11 YEAR=2024 URL=https://www.frontiersin.org/journals/molecular-biosciences/articles/10.3389/fmolb.2024.1414916 DOI=10.3389/fmolb.2024.1414916 ISSN=2296-889X 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.