(661.9) Integrative Modeling, Molecular Mechanics, and Molecular Dynamics Evaluation of Genomics Variants in KMT2C (MLL3), a Gene Involved in Kleefstra Syndrome Type 2

Poster Board Number: A244

Salomao Jorge (Medical College of Wisconsin), Young-In Chi (Medical College of Wisconsin), Thiago Milech de Assuncao (Medical College of Wisconsin), Angela Mathison (Medical College of Wisconsin), Brian Volkman (Medical College of Wisconsin), Brian Smith (Medical College of Wisconsin), Gwen Lomberk (Medical College of Wisconsin), Michael Zimmermann (Medical College of Wisconsin), Raul Urrutia (Medical College of Wisconsin)

Kleefstra Syndrome (KS) is a genetic, neurodevelopmental disorder characterized by intellectual disability, infantile hypotonia, severe expressive language delay, and characteristic facial appearance, with a spectrum of other distinct clinical manifestations. Pathogenic mutations in the epigenetic modifier, type 2 lysine methyltransferase (KMT2C), are attributed to cause KS core features in individuals who are Euchromatic Histone Lysine Methyltransferase 1, EHMT1, mutation negative. These individuals are designated as having KS type 2. In this study, we made use of multidimensional approaches, including conventional genomic bioinformatics, molecular modeling, molecular mechanics, and molecular dynamics simulations, to understand and enhance the annotation and potential mechanisms by which disease-associated missense variants affect KMT2C function. In addition, we report a scoring system based on statistical integration and modeling of data derived from the structure and dynamics of KMT2C, which allows us to classify germline variants into SV (Structural Variants), DV (Dynamic Variants), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). Moreover, these scores of disease-associated variants reflect alterations in molecular fitness when compared with tolerated, benign variants, used as controls.  Thus, these studies provide data that is more refined, both structurally and functionally for each variant, compared to current annotation tools used in human genomics databases and reveal mechanisms for their dysfunction not predictable from clinical genomic methods alone. This new knowledge extends our understanding of molecular mechanisms underlying the dysfunction of KS type 2-associated genomic mutations. Lastly, by identifying KMT2C mutation-specific druggable conformations, this study builds the trajectory toward the future development of small molecules to ameliorate the symptoms of this disease.

This work was supported by NIH Grant R01DK52913, Advancing a Healthier Wisconsin Endowment, Harmony4Hope, Phoebe R. and John D. Lewis Foundation, and The Linda T. and John A. Mellowes Endowed Innovation and Discovery Fund.