Unravelling the molecular mechanisms behind mutations and their link to phenotypes using graph-based signatures

The vast majority of coding variants are rare, and assessment of the contribution of rare variants is hampered by low statistical power and limited functional data. Missense mutations can be particularly challenging due to their subtle changes to protein sequence. Elucidating the molecular mechanisms linking a mutation’s impact with phenotype is very often non-trivial, and functional interpretation of mutation data has consequently lagged behind generation of the data from modern high-throughput techniques. This is complicated by the multitude of effects a mutation may have on a proteins function.

We have developed a comprehensive computational platform that uses graph-based signatures to represent the wild-type environment of a residue in order to predict the structural and functional effects of mutations. This platform has been used to explore the effects of genetic disease and drug resistance mutations on protein folding, stability, dynamics and interactions, and their links to mutational tolerance and phenotypes. Mutations leading to larger molecular consequences, tended to be rarer, and needed the presence of compensatory mutations balancing these fitness costs to become fixed in a population.

We have now successfully clinically translated methods that use insights on the 3D effects of mutations to guide patient risk management in genetic diseases (VHL Syndrome and Alkaptonuria), and in the pre-emptive detection of drug resistance mutations in tuberculosis (rifampicin and pyrazinamide resistance). It has also been applied as part of the HIT-TB and MM4TB programs to guide development of drugs less prone to resistance.

This work has highted that structural bioinformatics tools, when applied in a systematic, integrated way, can provide a powerful and scalable approach for predicting structural and functional consequences of mutations in order to reveal molecular mechanisms leading to clinical and experimental phenotypes. These computational tools are freely available (http://biosig.unimelb.edu.au/biosig/tools).