finDr: A web server for in silico D-peptide ligand identification
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 shor...
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KeAi Communications Co., Ltd.
2021
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oai:doaj.org-article:f3353052e4f044fb8e554847ea9f7e952021-11-26T04:35:36ZfinDr: A web server for in silico D-peptide ligand identification2405-805X10.1016/j.synbio.2021.11.004https://doaj.org/article/f3353052e4f044fb8e554847ea9f7e952021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2405805X21000727https://doaj.org/toc/2405-805XIn 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.Helena EngelFelix GuischardFabian KrauseJanina NandyPaulina KaasNico HöfflinMaja KöhnNormann KilbKarsten VoigtSteffen WolfTahira AslanFabian BaeznerSalomé HahneCarolin RuckesJoshua WeygantAlisa ZininaEmir Bora AkmeriçEnoch B. AntwiDennis DombrovskijPhilipp FrankeKlara L. LeschNiklas VesperDaniel WeisNicole GenschBarbara Di VenturaMehmet Ali ÖztürkKeAi Communications Co., Ltd.articleD-peptideWeb serverEvolutionary algorithmPeptide designMolecular dockingMirror-image phage displayBiotechnologyTP248.13-248.65Biology (General)QH301-705.5ENSynthetic and Systems Biotechnology, Vol 6, Iss 4, Pp 402-413 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
D-peptide Web server Evolutionary algorithm Peptide design Molecular docking Mirror-image phage display Biotechnology TP248.13-248.65 Biology (General) QH301-705.5 |
| spellingShingle |
D-peptide Web server Evolutionary algorithm Peptide design Molecular docking Mirror-image phage display Biotechnology TP248.13-248.65 Biology (General) QH301-705.5 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 finDr: A web server for in silico D-peptide ligand identification |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Helena Engel |
| title |
finDr: A web server for in silico D-peptide ligand identification |
| title_short |
finDr: A web server for in silico D-peptide ligand identification |
| title_full |
finDr: A web server for in silico D-peptide ligand identification |
| title_fullStr |
finDr: A web server for in silico D-peptide ligand identification |
| title_full_unstemmed |
finDr: A web server for in silico D-peptide ligand identification |
| title_sort |
findr: a web server for in silico d-peptide ligand identification |
| publisher |
KeAi Communications Co., Ltd. |
| publishDate |
2021 |
| url |
https://doaj.org/article/f3353052e4f044fb8e554847ea9f7e95 |
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