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, Nathan Guerin Department of Computer Science, Duke University , 308 Research Drive, Durham, NC 27708 , United States Search for other works by this author on: Oxford Academic Henry Childs Department of Chemistry, Duke University , 124 Science Drive, Durham, NC 27708 , United States Search for other works by this author on: Oxford Academic Pei Zhou Department of Biochemistry, Duke University School of Medicine , 307 Research Drive, Durham, NC 22710 , United States Search for other works by this author on: Oxford Academic Bruce R Donald Department of Computer Science, Duke University , 308 Research Drive, Durham, NC 27708 , United States Department of Chemistry, Duke University , 124 Science Drive, Durham, NC 27708 , United States Department of Biochemistry, Duke University School of Medicine , 307 Research Drive, Durham, NC 22710 , United States Department of Mathematics, Duke University , 120 Science Drive, Durham, NC 27708 , United States Corresponding author. Department of Computer Science, Duke University, Durham, NC 27708, United States. E-mail: brd+peds23@cs.duke.edu Search for other works by this author on: Oxford Academic
https://orcid.org/0000-0001-6884-4398 Protein Engineering, Design and Selection, Volume 37, 2024, gzae007, https://doi.org/10.1093/protein/gzae007
Published:
17 May 2024
Article history
Received:
03 September 2023
Revision received:
17 April 2024
Revision requested:
22 April 2024
Published:
17 May 2024
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Nathan Guerin, Henry Childs, Pei Zhou, Bruce R Donald, DexDesign: an OSPREY-based algorithm for designing de novo D-peptide inhibitors, Protein Engineering, Design and Selection, Volume 37, 2024, gzae007, https://doi.org/10.1093/protein/gzae007
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Abstract
With over 270 unique occurrences in the human genome, peptide-recognizing PDZ domains play a central role in modulating polarization, signaling, and trafficking pathways. Mutations in PDZ domains lead to diseases such as cancer and cystic fibrosis, making PDZ domains attractive targets for therapeutic intervention. D-peptide inhibitors offer unique advantages as therapeutics, including increased metabolic stability and low immunogenicity. Here, we introduce DexDesign, a novel OSPREY-based algorithm for computationally designing de novo D-peptide inhibitors. DexDesign leverages three novel techniques that are broadly applicable to computational protein design: the Minimum Flexible Set, K*-based Mutational Scan, and Inverse Alanine Scan. We apply these techniques and DexDesign to generate novel D-peptide inhibitors of two biomedically important PDZ domain targets: CAL and MAST2. We introduce a framework for analyzing de novo peptides—evaluation along a replication/restitution axis—and apply it to the DexDesign-generated D-peptides. Notably, the peptides we generated are predicted to bind their targets tighter than their targets' endogenous ligands, validating the peptides' potential as lead inhibitors. We also provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.
© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com
This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/pages/standard-publication-reuse-rights)
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