2017
DOI: 10.1186/s13073-017-0509-y
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Mapping genetic variations to three-dimensional protein structures to enhance variant interpretation: a proposed framework

Abstract: The translation of personal genomics to precision medicine depends on the accurate interpretation of the multitude of genetic variants observed for each individual. However, even when genetic variants are predicted to modify a protein, their functional implications may be unclear. Many diseases are caused by genetic variants affecting important protein features, such as enzyme active sites or interaction interfaces. The scientific community has catalogued millions of genetic variants in genomic databases and t… Show more

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Cited by 54 publications
(49 citation statements)
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References 97 publications
(98 reference statements)
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“…However, predicting whether an amino acid change actually changes protein structure and function is extremely difficult. Diverse computational prediction models use both protein structure and conservation data (Glusman et al, 2017), but sparsity of experimental validation data remains a challenge (Raraigh et al, 2018). Splicing changes may dramatically alter protein structure or introduce a premature stop codon.…”
Section: Predicting Variant Effectsmentioning
confidence: 99%
“…However, predicting whether an amino acid change actually changes protein structure and function is extremely difficult. Diverse computational prediction models use both protein structure and conservation data (Glusman et al, 2017), but sparsity of experimental validation data remains a challenge (Raraigh et al, 2018). Splicing changes may dramatically alter protein structure or introduce a premature stop codon.…”
Section: Predicting Variant Effectsmentioning
confidence: 99%
“…Only 0.14% of the variants that VEP predicted to be highly likely of damaging the protein product based on the location in the coding region of the gene were predicted to be damaging by all of the other nine prediction algorithms evaluated. Fortunately, as the field of in silico variant prediction continues to develop novel methods, focused on advances like mapping variants to three-dimensional protein structures( 70 ), predictions should become more accurate and variant prioritization more efficient.…”
Section: Discussionmentioning
confidence: 99%
“…We expect that unbiased functional data from DMS experiments will be extremely useful in assessing and parameterizing prediction methods for a much wider set of amino acid changes. An important problem to tackle in the future is to map genetic variants on to accurate structural models for the entire human proteome [71], and to develop prediction methods that are robust towards structural noise in homology models. Finally, an important open question is how the different prediction methods are best combined, and how they can both provide accurate predictions of pathogenicity and aid in developing mechanistic hypotheses for the origin of disease.…”
Section: Discussionmentioning
confidence: 99%