Missense mutations: Backbone structure positional effects

Human diversity often manifests through single nucleotide polymorphisms (SNPs). Among these, missense mutations, or SNPs that alter amino acids, can modify a proteins three-dimensional (3D) structure. This impacts its function and can potentially elicit diseases or affect drug interactions. Thus, understanding protein single point mutations is crucial for precision medicine, as it helps tailor treatments based on individual genetic variations. As atomic locations can be susceptible to any number of changes that might or might not affect function, we focus on the secondary structure to provide concrete results on possible protein structural deformation that may occur from missense mutations. We assess state-of-the-art structure prediction methods regarding backbone deformations caused by missense mutations. We categorize these deformations as local, distant, or global based on the proximity of structural changes to the mutation site. Our analysis utilizes a diverse dataset from the Protein Data Bank, comprising over 500 protein clusters with experimentally determined structures and documented mutations. Our findings indicate that missense mutations can significantly affect the accuracy of structure prediction methods. These mutations often lead to predicted structural changes even when the actual secondary structures remain unchanged, suggesting that current methods overestimate the impact of missense mutations. This issue is particularly evident in advanced prediction algorithms, which struggle to accurately model proteins with stable mutations. We also found that the addition of low-performing prediction methods during structural analysis can positively impact the results on some proteins, particularly those with low homology. Furthermore, proteins that form complexes or bind ligands--such as membrane and transport proteins--are inaccurately predicted due to the absence of extra-molecular interaction data in the models, highlighting how missense mutations can complicate accurate structure prediction. All code and data are available at https://github.com/ivanpmartell/pdb-sam.