Minisymposium Presentation

Point mutations in the protein sequences are known to have potential to alter the protein’s native fold, stability, and functions, and may result in observable disease phenotypes. Currently, there are over 200,000 experimental protein structures deposited in the Protein Data Bank, enabling the training of deep learning models for structure prediction. However, these models often fail to accurately predict the structural effects of mutations. As a result, we lack a quantitative understanding of the effect of mutations on protein structures. Here, we curate a dataset of x-ray crystal structure duplicates and their corresponding single-point mutant structures, creating opportunities to leverage high-performance computing for large-scale analysis and the training of predictive models. We quantify the local structural deformation per residue between wildtype-mutant pairs and compare them to the baseline within wildtype duplicates. Our analysis shows that on average, the magnitude of structural perturbation decreases as the sequence and spatial distance from the mutation site increase. We aim to illustrate the key physical features that determine the mutational impact and develop predictive models using data-driven approach in future studies. These results could advance our understanding of genetic diseases and support the development of structure-based drug discovery and therapeutic design.