SDS, a structural disruption score for assessment of missense variant deleteriousness

Abstract

We have developed a novel structure-based evaluation for missense variants that explicitly models protein structure and amino acid properties to predict the likelihood that a variant disrupts protein function. A structural disruption score (SDS) is introduced as a measure to depict the likelihood that a case variant is functional. The score is constructed using characteristics that distinguish between causal and neutral variants within a group of proteins. The SDS score is correlated with standard sequence-based deleteriousness, but shows promise for improving discrimination between neutral and causal variants at less conserved sites. The prediction was performed on 3-dimentional structures of 57 gene products whose homozygous SNPs were identified as case-exclusive variants in an exome sequencing study of epilepsy disorders. We contrasted the candidate epilepsy variants with scores for likely benign variants found in the EVS database, and for positive control variants in the same genes that are suspected to promote a range of diseases. To derive a characteristic profile of damaging SNPs, we transformed continuous scores into categorical variables based on the score distribution of each measurement, collected from all possible SNPs in this protein set, where extreme measures were assumed to be deleterious. A second epilepsy dataset was used to replicate the findings. Causal variants tend to receive higher sequence-based deleterious scores, induce larger physico-chemical changes between amino acid pairs, locate in protein domains, buried sites or on conserved protein surface clusters, and cause protein destabilization, relative to negative controls. These measures were agglomerated for each variant. A list of nine high-priority putative functional variants for epilepsy was generated. Our newly developed SDS protocol facilitates SNP prioritization for experimental validation.

Single nucleotide variations: Biological impact and theoretical interpretation

“…Later methods used this information to help calculate basic potentials, low-order and high-order coupling terms, volume terms, and solvent accessibility terms for comprehensive scoring functions that can be weighted through training with machine learning techniques61 or direct fitting to empirical data 63,65. Other structural components that are taken into account include small-molecule binding sites, protein–protein interactions, entropy optimization, and Van der Waals and torsional clashes 63,66…”

“…For example, in a recent study on epilepsy disorders66 only 18/68 of the proteins of interest had partial structures. For the remaining proteins, only 22% of the mutations could be mapped onto a predicted structure from theoretical models based on homology of known structures 66. For a larger perspective, only 7.6% of 57,525 nsSNVs from the Humsavar database could be mapped to structures 74.…”

“…63,65 Other structural components that are taken into account include smallmolecule binding sites, protein-protein interactions, entropy optimization, and Van der Waals and torsional clashes. 63,66 These structure-based methods give insight about the local environment of the mutation. Variants on the surface are, in general, more likely to be neutral than variants in the core, 67 indicating that disease-associated mutations often affect intrinsic structural features of proteins.…”

The challenge of comprehensive and consistent sequence variant interpretation between clinical laboratories

“…Although in silico analysis may be a helpful tool to guide and improve variant interpretation, more specific communication of information used in these programs, such as conservation and the nature of the substituting residue, can be informative because the programs are not vetted for clinical use and have recognized short-comings. 19,20 The next generation of such tools, like Structural Disruption Score (SDS), 21 that predict the protein consequence of a particular nucleotide variation may provide more generalizable outcomes but do need to be validated against existing variant and clinical databases. When our understanding of the molecular blueprint of unique gene families is translated into a computational algorithm that includes the extant knowledge from large, accurate genotype-phenotype databases, the proportion of true VUSs will diminish.…”

Insights into the Genetic Foundations of Human Communication

“…However, these tools have moderate sensitivity and low specificity -one study found that 31-32 % of pathogenic variants were predicted to be benign, while 84-87 % of benign variants were predicted to be pathogenic (Flanagan et al 2010;Dorfman et al 2010). More accurate assessments may be gained by considering each variant in the context of the three-dimensional structure of the protein (Venselaar et al 2013;Preeprem and Gibson 2014).…”