DISRUPTOR: Computational identification of oncogenic mutants disrupting protein interactions

Kugler, Valentina; Lieb, Andreas; Guerin, Nathan; Donald, BR; Stefan, Eduard; Kaserer, Teresa

doi:10.1101/2022.11.02.514903

Cited by 1 publication

(1 citation statement)

References 29 publications

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“…In previous work 38,39,66–68 , a typical OSPREY-based computational protein redesign entailed: 1) selecting a starting molecular structure, 2) adding hydrogens, 3) specifying the design algorithm and its input parameters, 4) running the algorithm, and 5) analyzing the results. To enable Step 4, OSPREY included a default library of amino acid atom connectivity templates from Amber 69 and rotamers from Lovell et al 70 .…”

Section: Methodsmentioning

confidence: 99%

DexDesign: A new OSPREY-based algorithm for designingde novoD-peptide inhibitors

Guerin,

Childs,

Zhou

et al. 2024

Preprint

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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, which enable exponential reductions in the size of the peptide sequence search space. 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 new 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 therapeutic candidates. We provide an implementation of DexDesign in the free and open source computational protein design software OSPREY.

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