Computational Design of Novel Insulin Degrading Enzyme Inhibitors

Human insulin degrading enzyme (IDE) plays a role in the proteolytic cleavage of insulin, glucagon, and other short, hydrophobic peptides with roles in glucose and cellular metabolism. Because of IDE’s role in insulin clearance, IDE inhibitors may hold promise as therapies for potentiating insulin signaling in patients suffering from type 2 diabetes mellitus. IDE is a large (~100 kDa) chambered protease of the conserved M16A subfamily of zinc metalloproteases. The enzyme adopts a structure that is analogous to a clamshell formed by the joining of the N terminal and C terminal domains. The characteristic zinc binding and catalytic motif (HXXEH) is positioned within the enzyme’s N terminus, while C terminal residues also play important roles in substrate binding and catalysis. Here, we describe the use of a computational work-flow for identifying novel IDE inhibitors. The work flow integrates mutation-based active site structural analysis, virtual screening, docking and fragment-based design. Initial computational results appear promising and should lead to assay testing in the near future

Prioritisation of structural variant calls in cancer genomes

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants

Distributed under Creative Commons CC-BY 4.0 Prioritisation of structural variant calls in cancer genomes

ABSTRACT Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants