Structural Insights into Separase Architecture and Substrate Recognition through Computational Modelling of Caspase-Like and Death Domains

Winter, Anja; Schmid, Ralf; Bayliss, Richard

Abstract

Separases are large proteins that mediate sister chromatid disjunction in all eukaryotes. They belong to clan CD of cysteine peptidases and contain a well-conserved C-terminal catalytic protease domain similar to caspases and gingipains. However, unlike other well-characterized groups of clan CD peptidases, there are no high-resolution structures of separases and the details of their regulation and substrate recognition are poorly understood. Here we undertook an in-depth bioinformatical analysis of separases from different species with respect to their similarity in amino acid sequence and protein fold in comparison to caspases, MALT-1 proteins (mucosa-associated lymphoidtissue lymphoma translocation protein 1) and gingipain-R. A comparative model of the single C-terminal caspase-like domain in separase from C. elegans suggests similar binding modes of substrate peptides between these protein subfamilies, and enables differences in substrate specificity of separase proteins to be rationalised. We also modelled a newly identified putative death domain, located N-terminal to the caspase-like domain. The surface features of this domain identify potential sites of protein-protein interactions. Notably, we identified a novel conserved region with the consensus sequence WWxxRxxLD predicted to be exposed on the surface of the death domain, which we termed the WR motif. We envisage that findings from our study will guide structural and functional studies of this important protein family.

Citation

Winter, A., Schmid, R., & Bayliss, R. (2015). Structural Insights into Separase Architecture and Substrate Recognition through Computational Modelling of Caspase-Like and Death Domains. PLoS Computational Biology, e1004548 - ?. https://doi.org/10.1371/journal.pcbi.1004548

• Acceptance Date: Aug 31, 2015
• Publication Date: Oct 29, 2015
• Publicly Available Date: May 26, 2023
• Journal: PLoS Computer Biology
• Print ISSN: 1553-734X
• Publisher: Public Library of Science
• Pages: e1004548 - ?
• DOI: https://doi.org/10.1371/journal.pcbi.1004548
• Publisher URL: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004548