Research on mutant proteins based on AlphaFold model

Protein structure prediction is a crucial research field in life sciences and medicine, as well as a significant application area of artificial intelligence in scientific research. This study explores the structural characteristics and functional implications of mutation forms in the key protein causing cystic fibrosis—cystic fibrosis transmembrane conductance regulator (CFTR)—by employing the advanced protein structure prediction tool AlphaFold. The analysis particularly focuses on how mutated CFTR manifests in cystic fibrosis and predicts its three-dimensional structure using the AlphaFold model, providing a new perspective for understanding its pathological mechanisms. This paper highlights the feasibility of AlphaFold in predicting unique protein structures in the absence of homologous structural information and utilizes this technology to assess the effects of Trikafta therapy (comprising Elexacaftor, Tezacaftor, and Ivacaftor) on the structure and function of mutated CFTR. By comparing the structures of normal, mutated, and Trikafta-treated CFTR models, the study reveals the impact of mutations on the protein's function and stability and how Trikafta effectively improves the folding and channel function of mutated CFTR. This research not only demonstrates the critical application of the AlphaFold model in modern drug design and disease understanding but also provides a scientific basis for the treatment strategies of cystic fibrosis, advancing the development of personalized medicine and precision treatment for such diseases.