Advances in DNA sequencing technology have led to an enormous growth in the amount of available genomic data. Interpreting this data to produce meaningful and actionable results remains a challenge. Tools currently in use for annotating discovered variants rely on a sequence conservation score and provide little mechanistic insight to explain why a particular variant may be deleterious. Tools that exist for predicting the effect of mutations on the structure and function of a protein are laborious to use and require a crystal structure of the protein, severely limiting their coverage. ELASPIC, a pipeline recently developed in our lab, uses homology models instead of crystal structures to accurately predict the effect of a mutation on the stability of a protein and the affinity of one protein for another. ELASPIC is available both as a standalone application and as a webserver (http://elaspic.kimlab.org), with the latter providing a user-friendly interface for examining the structural effect of mutations. In this talk, we present the results of Provean, FoldX, ELASPIC, Rosetta, and Amber TI on the Frataxin challenge of the CAGI 5 competition.