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Mohammed, Ahmed

https://orcid.org/0000-0002-8448-1432

Mayo Clinic

Mayo Clinic

Citations 1

Personalized molecular modeling for pinpointing associations of protein dysfunction and variants associated with hereditary cancer syndromes
Macklin, Sarah , Mohammed, Ahmed , Jackson, Jessica L. , Hines, Stephanie L., Caulfield, Thomas R. , Jackson, Jessica, Caulfield, Thomas R , Atwal, Paldeep S

Molecular Genetics & Genomic Medicine | July 25, 2018 | Volume: 6 | Issue: 5 | Pages: 805-810

10.1002/mgg3.447 Get It

AbstractBackgroundAlthough 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.MethodsTwo 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.ResultsModeling 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.ConclusionThe 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.