Dokument: Computational Workflows to Capture Molecular Flexibility in the Design of Peptide-Based Therapeutics

Titel:	Computational Workflows to Capture Molecular Flexibility in the Design of Peptide-Based Therapeutics
URL für Lesezeichen:	https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=74579
URN (NBN):	urn:nbn:de:hbz:061-20261022-160725-6
Kollektion:	Dissertationen
Sprache:	Englisch
Dokumententyp:	Wissenschaftliche Abschlussarbeiten » Dissertation
Medientyp:	Text
Autor:	Scharbert, Lara [Autor]
Dateien:	[Dateien anzeigen] Adobe PDF [Details] 211,98 MB in einer Datei [ZIP-Datei erzeugen] Dateien vom 29.09.2026 / geändert 29.09.2026
Beitragende:	Prof. Dr. Strodel, Birgit [Gutachter] Jun.-Prof. Dr. Meisner, Jan [Gutachter]
Stichwörter:	Drug Design, Therapeutic Peptides, Molecular Dynamics
Dewey Dezimal-Klassifikation:	500 Naturwissenschaften und Mathematik » 570 Biowissenschaften; Biologie
Beschreibung:	Peptides have emerged as a highly promising class of therapeutics, offering unique advantages such as high specificity, versatility, and low toxicity. Experimental advances in modification strategies have addressed key limitations of poor proteolytic stability and rapid clearance, facilitating the clinical translation of peptide-based therapeutics. However, computational peptide design remains challenging despite rapidly developing computational methods, including artificial intelligence tools such as AlphaFold. The intrinsic flexibility of peptides complicates reliable prediction of peptide protein structures and interactions, which is essential for structure-based drug design. To address this challenge, we developed computational workflows combining diverse strategies, including docking and molecular dynamics (MD) simulations, to accurately model and design peptide-based therapeutics. We focused on the following target systems: the main protease (Mpro) and papain-like protease (PLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), modeled with natural substrates and peptide inhibitors, and the prostate specific membrane antigen (PSMA) in complex with peptide-based radiopharmaceuticals. We developed the computational dynamic saturation mutagenesis (CDSM) protocol for automated saturation mutagenesis at each peptide position using MD simulation snapshots to capture the flexibility of peptide–protein interactions. The resulting binding free energy deviations from parent residues served as design suggestions, enabling the generation of an 8-amino acid peptide inhibitor with high binding affinity for Mpro, validated by in vitro inhibition assays (IC50 = 4.8 ± 0.5 μM). To elucidate the structure-activity relationship of PSMA radioligands, we investigated the binding modes of eight clinically relevant radioligands using a workflow that combines docking at varying hydration levels, re-scoring of docking poses, and MD simulations to generate accurate and stable ligand poses. To date, this represents the most comprehensive computational modeling study of PSMA radioligands. The computational modeling and design strategies presented in this thesis—especially the CDSM protocol, which is broadly applicable to various peptide-protein systems, as well as the workflows for elucidating PSMA-radioligand interactions—lay the groundwork for future development of peptide-based therapeutics.
Lizenz:	Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz
Fachbereich / Einrichtung:	Mathematisch- Naturwissenschaftliche Fakultät » WE Chemie » Theoretische Chemie und Computerchemie
Dokument erstellt am:	22.10.2026
Dateien geändert am:	22.10.2026
Promotionsantrag am:	07.07.2026
Datum der Promotion:	04.09.2026