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Ratnasinghe, B. D.

Publications and source records attributed to Ratnasinghe, B. D..

2 recordsLinked to original sources

A Multi-State Structural Genomics Approach Enables Large-Scale, Mechanistic, and Context-Specific Classification of ABCC6 Genetic Variants Implicated in Calcification Diseases

PurposeGenetic variation in ATP Binding Cassette Subfamily C Member 6 (ABCC6) can cause both pseudoxanthoma elasticum (PXE) and generalized arterial calcification of infancy (GACI). Despite both diseases being rare, there are already 930 distinct missense variants in ABCC6 reported, 87% of which are of uncertain clinical significance (VUS). New approaches are needed to interpret and classify these VUS mechanistically. MethodsWe developed 3D protein models of ABCC6 in three functionally relevant conformations to calculate the structural effects of variants and identify 3D mutational hotspots. With this and additional functional information, we categorized variants in a mechanistic ontology based on which critical functions of ABCC6 they impact. We then compared PXE and GACI -associated variants. ResultsWe identified two three-dimensional hotspots of pathogenic variants and six specific functions of ABCC6 which variants impact. From this, we propose a mechanism for pathogenicity for 41% of VUS according to their impacted function, 30 of which could be reclassified as Likely Pathogenic from our non-clinical data. Finally, we found slight differences between PXE and GACI-associated variants. ConclusionThe mechanistic information we present will guide future research to better address calcification disorders and understand genetic variants. Further, our VUS reclassification will improve the diagnosis of ABCC6-driven diseases, shortening diagnostic odysseys. We believe that computational structural genomics approaches will soon take prominence in genomics data interpretation. HighlightsVariant 3D hotspot detection and effect clustering, GACI and PXE Variant Comparison, consistent ability to (re)categorize known pathogenic variants, and population genetics differences.

genetics↗

Beyond Structural Bioinformatics for Genomics with Dynamics Characterization of an Expanded KRAS Mutational Landscape

ABSTRACTCurrent capabilities in genomic sequencing outpace functional interpretations. Our previous work showed that 3D protein structure calculations enhance mechanistic understanding of genetic variation in sequenced tumors and patients with rare diseases. The KRAS GTPase is among the critical genetic factors driving cancer and germline conditions. Because KRAS-altered tumors frequently harbor one of three classic hotspot mutations, nearly all studies have focused on these mutations, leaving significant functional ambiguity across the broader KRAS genomic landscape observed in cancer and non-cancer diseases. Herein, we extend structural bioinformatics with molecular simulations to study an expanded landscape of 86 KRAS mutations. We identify multiple coordinated changes strongly associated with experimentally established KRAS biophysical and biochemical properties. The patterns we observe span hotspot and non-hotspot alterations, which can all dysregulate Switch regions, producing mutation-restricted conformations with different effector binding propensities. We experimentally measured mutation thermostability and identified shared and distinct patterns with simulations. Our results indicate mutation-specific conformations which show potential for future research into how these alterations reverberate into different molecular and cellular functions. The data we present is not predictable using current genomic tools, demonstrating the added functional information derived from molecular simulations for interpreting human genetic variation. Key PointsPlease provide 3 bullet points summarizing the manuscripts contribution to the field (100 characters max per point) 1) We functionally grouped 86 distinct KRAS mutations using MD scores, demonstrating scalability for genomics 2) MD-based groups explain experimental differences and mechanistic information about mutant proteins 3) Demonstrated added functional information from simulations for interpreting human genetic variation

genomics↗