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Protein Folding Problem: A New Approach with Evolutionary Algorithms
(Alanya Alaaddin Keykubat Üniversitesi, 2025) Canatalay, Peren Jerfi; Namazova, Madina; Atalay, Vildan
AlphaFold is transforming the field of structural biology by predicting three-dimensional (3D) structures from protein sequences. This remarkable achievement has even led to claims that the protein folding problem has been 'solved.' However, the protein folding problem encompasses more than just structure prediction from sequences. Currently, it is unknown whether the AlphaFold revolution will help solve other issues related to protein folding. In this study, we evaluate AlphaFold's ability to predict the impact of single mutations on protein stability (??G) and function. To explore this question, we subtract the pLDDT and metrics from AlphaFold predictions before and after a single mutation in a protein, correlating the predicted changes with experimentally known ??G values. Additionally, we correlate the same AlphaFold pLDDT metrics with fluorescence levels experimentally tested using a large-scale GFP mutant data set, which indicates the effect of single mutations on the structure. We find very weak or no correlation between AlphaFold output metrics and protein stability or fluorescence change. Our results suggest that AlphaFold may not be immediately applicable to other protein folding problems or applications.