Ferritin is a highly conserved spherical protein that stores iron and possesses triple and quadruple symmetry input ports. Additionally, it is composed of light chains that can be affected by post-translational mutations, reducing the iron storage capacity in the brain and leading to neuroferritinopathy, which is a rare disease with limited bioinformatics data. In this study, we analyzed the biochemical mechanism of different ferritin mutations reported in the literature, through the characterization and determination of the in silico structural model by searching databases, implementing bioinformatics programs such as Jalview, NetNGlyc 1.0, NetOGlyc 3.1, and three-dimensional structure predictors with machine learning such as Alphafold, demonstrating the generation of hairpin and steric hindrances that hinder the aggregation of subunits and changes in the size and arrangement of quadruple and triple entry holes of the A96T mutation compared to the wild-type protein, since in the quadruple entry hole, a decrease in area is observed compared to the wild-type protein and the triple entry hole has a decrease in distance measurements of 6.504 Å. This possibly affects the functionality of the protein, thus releasing high concentrations of iron in the brain and causing neurodegeneration.
Keywords: FTL; bioinformatics; ferritin; ferritin light chain; neuroferritinopathy; steric hindrance; variants.