Synthetic and Systems Biotechnology (Dec 2021)

finDr: A web server for in silico D-peptide ligand identification

  • Helena Engel,
  • Felix Guischard,
  • Fabian Krause,
  • Janina Nandy,
  • Paulina Kaas,
  • Nico Höfflin,
  • Maja Köhn,
  • Normann Kilb,
  • Karsten Voigt,
  • Steffen Wolf,
  • Tahira Aslan,
  • Fabian Baezner,
  • Salomé Hahne,
  • Carolin Ruckes,
  • Joshua Weygant,
  • Alisa Zinina,
  • Emir Bora Akmeriç,
  • Enoch B. Antwi,
  • Dennis Dombrovskij,
  • Philipp Franke,
  • Klara L. Lesch,
  • Niklas Vesper,
  • Daniel Weis,
  • Nicole Gensch,
  • Barbara Di Ventura,
  • Mehmet Ali Öztürk

Journal volume & issue
Vol. 6, no. 4
pp. 402 – 413

Abstract

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In the rapidly expanding field of peptide therapeutics, the short in vivo half-life of peptides represents a considerable limitation for drug action. D-peptides, consisting entirely of the dextrorotatory enantiomers of naturally occurring levorotatory amino acids (AAs), do not suffer from these shortcomings as they are intrinsically resistant to proteolytic degradation, resulting in a favourable pharmacokinetic profile. To experimentally identify D-peptide binders to interesting therapeutic targets, so-called mirror-image phage display is typically performed, whereby the target is synthesized in D-form and L-peptide binders are screened as in conventional phage display. This technique is extremely powerful, but it requires the synthesis of the target in D-form, which is challenging for large proteins. Here we present finDr, a novel web server for the computational identification and optimization of D-peptide ligands to any protein structure (https://findr.biologie.uni-freiburg.de/). finDr performs molecular docking to virtually screen a library of helical 12-mer peptides extracted from the RCSB Protein Data Bank (PDB) for their ability to bind to the target. In a separate, heuristic approach to search the chemical space of 12-mer peptides, finDr executes a customizable evolutionary algorithm (EA) for the de novo identification or optimization of D-peptide ligands. As a proof of principle, we demonstrate the validity of our approach to predict optimal binders to the pharmacologically relevant target phenol soluble modulin alpha 3 (PSMα3), a toxin of methicillin-resistant Staphylococcus aureus (MRSA). We validate the predictions using in vitro binding assays, supporting the success of this approach. Compared to conventional methods, finDr provides a low cost and easy-to-use alternative for the identification of D-peptide ligands against protein targets of choice without size limitation. We believe finDr will facilitate D-peptide discovery with implications in biotechnology and biomedicine.

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