bioRxivBiophysicsPreprint
Targeting the Plasmodium falciparum UCHL3 ubiquitin hydrolase using chemically constrained peptides
H. R. King, M. Bycroft, T.-B. Nguyen, G. Kelly, A. A. Vinogradov, P. J. Rowling, K. Stott, D. B. Ascher, H. Suga, L. S. Itzhaki, K. Artavanis-Tsakonas
Abstract
The ubiquitin-proteasome system is essential to all eukaryotes and has been shown to be critical to parasite survival as well, including Plasmodium falciparum, the causative agent of the deadliest form of malarial disease. Despite the central role of the ubiquitin-proteasome pathway to parasite viability across its entire life-cycle, specific inhibitors targeting the individual enzymes mediating ubiquitin attachment and removal do not currently exist. The ability to disrupt P. falciparum growth at multiple developmental stages is particularly attractive as this could potentially prevent both disease pathology, caused by asexually dividing parasites, as well as transmission which is mediated by sexually differentiated parasites. The deubiquitinating enzyme PfUCHL3 is an essential protein, transcribed across both human and mosquito developmental stages. PfUCHL3 is considered hard to drug by conventional methods given the high level of homology of its active site to human UCHL3 as well as to other UCH domain enzymes. Here, we apply the RaPID mRNA display technology and identify constrained peptides capable of binding to PfUCHL3 with nanomolar affinities. The two lead peptides were found to selectively inhibit the deubiquitinase activity of PfUCHL3 versus HsUCHL3. NMR spectroscopy revealed that the peptides do not act by binding to the active site but instead block binding of the ubiquitin substrate. We demonstrate that this approach can be used to target essential protein-protein interactions within the Plasmodium ubiquitin pathway, enabling the application of chemically constrained peptides as a novel class of anti-malarial therapeutics.
- organism: plant (other)
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
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