Oral Presentation The 45th Lorne Conference on Protein Structure and Function 2020

Var2Prot: A new tool to unravel the 3D structural effect of genetic variants (#27)

Sergio Ruiz-Carmona 1 2 , David B Ascher 1 3 4 , Michael Inouye 1 2 5 6 7
  1. Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
  2. Cambridge Baker Systems Genomics Initiative, Melbourne, Cambridge
  3. Department of Biochemistry and Molecular Biology, University of Melbourne, Melbourne, Victoria, Australia
  4. Department of Biochemistry, University of Cambridge, Cambridge, UK
  5. Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
  6. The Alan Turing Institute, London, UK
  7. Department of Clinical Pathology, The University of Melbourne, Parkville, Victoria, Australia

During the last decade, novel technological developments in high-throughput DNA sequencing have brought in new information about benign and disease-associated genetic mutations. One type of those are missense variants, which cause an amino acid substitution upon a single nucleotide change in a protein-coding region of the genome [1]. Over the past years, different studies have looked at the functional impact of missense variants based on conservation through evolution, sequence or structural information, however, there is still very limited understanding about their outcome and, in particular, how disease-associated missense variants impair their corresponding protein and trigger their associated phenotype [2,3].

Studying the structural and functional effects of missense variants is still a challenge, but thanks to the development of structural biology methods —with more than 100,000 new structures in the last 10 years and growing as a direct product of the evolution of new techniques— it represents a rising research area.

We assess the disease-causing missense variants by analysing the changes that they cause on the 3D protein structure, thanks to their atomic level insight. In this regard, we have developed a novel tool that allows us to characterize and annotate missense variants considering all public structural information. Our tool searches for available structural data and builds a molecular model of the mutated structure and, then, calculates multiple features such as increase or decrease of interactions, changes in solvent accessible surface area or proximity to a protein-protein interface, 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.

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 a better treatment for current and future patients as well as helping to overcome drug resistance, selectivity or toxicity in a plethora of diseases, such as cancer, diabetes or other metabolic disorders [4,5].

  1. Sumaiya I, et al. Insights into Protein Structural, Physicochemical, and Functional Consequences of Missense Variants in 1,330 Disease-associated Human Genes. bioRxiv. 2019; doi: 10.1101/69325.
  2. Zhou Y, et al. Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front. Pharmacol. 2018, 9:1437.
  3. Bhattacharya R, Rose PW, Burley SK, Prlić A. Impact of genetic variation on three dimensional structure and function of proteins. PLoS One. 2017; 12: e0171355.
  4. Gao M, et al. Insights into Disease-Associated Mutations in the Human Proteome through Protein Structural Analysis. Structure. 2015, Jul 7;23(7):1362-9.
  5. Hicks M, et al. Functional characterization of 3D protein structures informed by human genetic diversity. PNAS. 2019, 116 (18) 8960-8965.