Mitochondrial diseases result from defects in oxidative phosphorylation caused by mutations in genes in the nuclear and mitochondrial DNA. These mutations produce a wide range of abnormalities and symptoms that can make an accurate diagnosis difficult.
Despite advances in sequencing technology, only a few of the thousands of pathogenic non-synonymous mutations that target the 13 proteins encoded in the mitochondrial genome have been identified and verified.
APOGEE, a machine-learning algorithm developed by my research group, has been recommended for use worldwide as a reference for the assessment of mtDNA variants but still struggles when dealing with the structural-chemophysical effects of missense variants targeting functional domains of mitochondrial proteins. The structural analysis of all known mitochondrial missense variants is thus a major challenge that can only be met by massively parallel simulation of all known missense variants.
In this project proposal, our aim is to describe the key conformations and kinetic properties of all these variants and explore their long-range effects, thereby speculating on the role of putative co-occurring variants. This latter point is especially important for variants with divergent pathogenicity assessments, which are primarily brought on by the presence of one or more co-inherited missense variants in the same protein or structurally/functionally related proteins that affect the functional role of a mutant residue.