finDr: A web server for in silico D-peptide ligand identification
| Autor: | Paulina Kaas, Alisa Zinina, Tahira Aslan, Daniel Georg Weis, Philipp Franke, Nicole Gensch, Salomé Hahne, Fabian Baezner, Klara L. Lesch, Karsten Voigt, Emir Bora Akmeriç, Niklas Vesper, Enoch B. Antwi, Joshua Weygant, Felix Guischard, Mehmet Ali Öztürk, Helena Engel, Maja Köhn, Nico Höfflin, Dennis Dombrovskij, Janina Nandy, Fabian Krause, Barbara Di Ventura, Carolin Ruckes, Steffen Wolf, Normann Kilb |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Phage display
QH301-705.5 In silico Biomedical Engineering Protein Data Bank (RCSB PDB) Peptide Computational biology Drug action Applied Microbiology and Biotechnology Protein structure Structural Biology Web server Evolutionary algorithm Genetics Peptide design Biology (General) chemistry.chemical_classification Mirror-image phage display computer.file_format Protein Data Bank Chemical space chemistry D-peptide Molecular docking computer TP248.13-248.65 Biotechnology |
| Zdroj: | Synthetic and Systems Biotechnology, Vol 6, Iss 4, Pp 402-413 (2021) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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