Key result
All-atom computational modeling of the cardiac thin filament, corroborated by biophysical metrics, enabled the reclassification of 9 variants of unknown significance into pathogenic, likely pathogenic, benign, or likely benign categories.
Why the study?
Establishing the pathogenic potential of individual sarcomeric protein mutations remains difficult, often limiting the use of genotype in the management of affected families.
Does computational modeling of structural and dynamic changes predict the pathogenicity of variants of unknown significance in the cardiac thin filament?
Does computational modeling of structural and dynamic changes predict the pathogenicity of variants of unknown significance in the cardiac thin filament?
An all-atom computational model combined with biophysical experiments can predict the pathogenicity of variants of unknown significance in the cardiac thin filament, aiding in the classification of indeterminate genetic variants in cardiomyopathies.
Point mutations within sarcomeric proteins have been associated with altered function and cardiomyopathy development. Difficulties remain, however, in establishing the pathogenic potential of individual mutations, often limiting the use of genotype in management of affected families. To directly address this challenge, we utilized our all-atom computational model of the human full cardiac thin filament (CTF) to predict how sequence substitutions in CTF proteins might affect structure and dynamics on an atomistic level. Utilizing molecular dynamics calculations, we simulated 21 well-defined genetic pathogenic cardiac troponin T and tropomyosin variants to establish a baseline of pathogenic changes induced in computational observables. Computational results were verified via differential scanning calorimetry on a subset of variants to develop an experimental correlation. Calculations were performed on 9 independent variants of unknown significance (VUS), and results were compared with pathogenic variants to identify high-resolution pathogenic signatures. Results for VUS were compared with the baseline set to determine induced structural and dynamic changes, and potential variant reclassifications were proposed. This unbiased, high-resolution computational methodology can provide unique structural and dynamic information that can be incorporated into existing analyses to facilitate classification both for de novo variants and those where established approaches have provided conflicting information.
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Mason et al. (2021) studied Cardiomyopathy (HCM/DCM) (n=30). Variants of unknown significance in cardiac thin filament vs. Wild-type and established pathogenic variants was evaluated on Pathogenicity classification based on structural and dynamic changes (Euclidean distance from pathogenic average). All-atom computational modeling of the cardiac thin filament, corroborated by biophysical metrics, enabled the reclassification of 9 variants of unknown significance into pathogenic, likely pathogenic, benign, or likely benign categories.
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