During the last decade, novel technological developments in high-throughput DNA sequencing have brought in new information about benign and disease-associated genetic mutations. Over the past years, different studies have looked at the functional impact of missense variants, single nucleotide mutations that cause an amino acid substitution in a protein-coding region of the genome, based on conservation through evolution, sequence or structural information, however, there is still very limited understanding about their outcome.
Studying the structural and functional effects of missense variants is still a challenge, but thanks to the development of structural biology methods it represents a rising research area.
We have developed an automatic tool that allows us to characterise and annotate missense variants considering all public 3D protein structure information. Our tool searches for available data and builds a molecular model of the mutated structure to calculate multiple features such as increase or decrease of interactions or changes in solvent accessible surface area, among others.
We have validated the pipeline with data from the literature and applied it on larger datasets from HGMD and ClinVar, where we have identified some examples of, to date, unknown variant effect that might be explained with structural changes.
We have also identified crucial structural features that, when altered, can help in the explanation for disease onset.
Studying the impact of disease-causing variants and identifying new examples may give us not only a better understanding of the underlying molecular mechanisms of different disorders, but also higher chances of finding better treatments as well as helping to overcome drug resistance or selectivity in cancer, diabetes or other metabolic disorders.