Harnessing AlphaFold: Applications in Disease Understanding, Drug Discovery, and Vaccine Design

Abstract

• AlphaFold accelerates drug discovery and repurposing. • Structural insights aid diagnosis and vaccine design. • Hybrid methods resolve dynamic and disorder limitations. Proteins are essential macromolecules involved in almost all biological processes, with their function depending on their three-dimensional structures. Understanding these architectures is crucial to elucidating the molecular mechanism of disease and developing treatments and vaccines. AlphaFold, a recently emerged artificial intelligence system, has revolutionized protein structure prediction by offering a faster, cost-effective alternative to experimental techniques. In light of this, this review aims to give a thorough overview of the potential role of AlphaFold in elucidating disease mechanism, investigating diagnostic markers, drug discovery, and vaccine design. In the pharmaceutical sector, AlphaFold accelerates the development of new therapeutics by aiding the process from target identification to toxicity prediction as well as repurposing existing drugs. In addition, it has transformative applications in deciphering disease mechanisms, analyzing structural consequences of genetic variants linked to disease susceptibility, facilitating research into early diagnosis, and understanding molecular determinants of prognosis and therapeutic responsiveness. Furthermore, AlphaFold is valuable for vaccine design by providing detailed structures of the pathogen proteins. Despite its groundbreaking capabilities, the tool faces challenges, including limitations in computational power, its ability to predict disordered proteins, complex structures, and conformational changes, as well as its integration with experimental methods. In conclusion, AlphaFold is a powerful tool with significant implications for biomedical research, but overcoming its limitations and integrating it with experimental approaches will be crucial for maximizing its potential for future clinical application.