Personalized molecular modeling for pinpointing associations of protein dysfunction and variants associated with hereditary cancer syndromes

Abstract

Background: Although the process of reclassification of a variant of uncertain significance can be complex, they are commonly detected through molecular testing. It often takes years before enough clinical data are acquired, and it can be costly and time-consuming to perform functional analysis of a single variant. It is important that other tools are developed to aid in clarifying how a specific genetic variant impacts a protein's function, and ultimately the health of the patient.

Methods: Two more newly characterized, suspected pathogenic variants in NBN and PTEN were analyzed through personalized protein modeling. Comparisons between the wild-type and altered protein were studied using simulations, genomic exome analysis, and clinic study.

Results: Modeling of the new NBN and PTEN protein structures suggested loss of essential domains important for normal enzymatic function for these personalized genomic examples which matched the clinical findings.

Conclusion: The defects detected through modeling were consistent with the expected clinical effect. Personalized protein modeling is another tool for determination of correct variant classification, which can become further useful through construction of deposition archive.

Keywords: computational screening; hereditary cancer; protein modeling; variant classification.