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Updated: Mar 10, 2026
04:52
Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
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Localized structural frustration for evaluating the impact of sequence variants.
Sushant Kumar1,2, Declan Clarke1,2,3, Mark Gerstein4,2,5
1Program in Computational Biology and Bioinformatics, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA.
Nucleic Acids Research
|December 5, 2016
Summary
Localized frustration analysis of protein structures helps evaluate rare genetic variants. This method distinguishes disease-related and somatic variants, revealing distinct functional impacts on oncogenes and tumor suppressor genes.
Area of Science:
- Computational Biology
- Structural Biology
- Genomics
Background:
- Population sequencing reveals numerous rare single-nucleotide variants (SNVs) in coding regions.
- Assessing the deleteriousness of rare SNVs is difficult using traditional phenotype-genotype correlations.
- Global protein stability metrics may not capture localized functional impacts of SNVs.
Purpose of the Study:
- To introduce and validate a workflow using localized frustration to assess SNV impact on protein functionality.
- To differentiate disease-related from non-disease-related SNVs based on frustration changes.
- To investigate distinct effects of oncogene and tumor suppressor gene SNVs on protein structure and function.
Main Methods:
- Utilized localized frustration as a metric for quantifying unfavorable local interactions in proteins.
- Applied the workflow to analyze SNVs within the Protein Data Bank.
- Compared frustration changes induced by disease-related vs. non-disease-related SNVs.
- Analyzed frustration alterations in somatic SNVs from oncogenes and tumor suppressor genes.
Main Results:
- Disease-related SNVs induced greater changes in localized frustration than non-disease-related variants.
- Rare SNVs showed a larger disruption of local interactions compared to common variants.
- Somatic SNVs in tumor suppressor genes caused more core-localized frustration changes (loss-of-function).
- Somatic SNVs in oncogenes induced more surface-localized frustration changes (gain-of-function).
Conclusions:
- Localized frustration is a sensitive metric for evaluating the functional impact of rare SNVs.
- The workflow can distinguish between different classes of genetic variants based on their structural effects.
- Distinct frustration patterns for oncogene and tumor suppressor gene SNVs offer insights into cancer mechanisms.
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