bioRxivBiochemistryPreprint
Characterization of P. falciparum dipeptidyl aminopeptidase 3 specificity reveals structural factors responsible for differences in amino acid preferences between peptide-based substrates and covalent inhibitors.
Laura E. de Vries, Mateo I. Sanchez, Katarzyna Groborz, Laurie Kuppens, Marcin Poreba, Christine Lehmann, Fang Yuan, Shirin Arastu-Kapur, Martin Horn, Michael Mares, Matthew Bogyo, Marcin Drag, Edgar Deu
Abstract
Malarial dipeptidyl aminopeptidases (DPAPs) are cysteine proteases important for parasite development thus making them attractive drug targets. In order to develop inhibitors specific to the parasite enzymes it is necessary to map the determinants of substrate specificity of the parasite enzymes and its mammalian homologue cathepsin C (CatC). Here, we screened peptide-based libraries of substrates and covalent inhibitors to characterize the differences in specificity between parasite DPAPs and CatC, and used this information to develop highly selective DPAP1 and DPAP3 inhibitors. Interestingly, while the primary amino acid specificity of a protease is often used to develop potent inhibitors, we show that equally potent and highly specific inhibitors can be developed based on the sequences of non-optimal peptide substrates. Importantly, analysis of previously published data about the specificity of other proteases also unveiled significant discrepancies in the amino acid preference between substrates and inhibitors. In this article, we also discuss important structural and theoretical reasons that might account for these discrepancies. Overall, this study illustrates that focusing the development of protease inhibitors solely on substrate specificity might overlook important structural features that can be exploited to develop highly potent and selective compounds.
- organism: plant (other)
- assay: proteomics
- research focus: drug response / drug discovery
- research focus: protein folding & dynamics
- research focus: protein structure prediction
- approach: experimental assay
- approach: statistical model
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