Doctoral thesis
English

Darwinian Evolution and Self-Assembly of Synthetic Molecules for Next-Generation Therapeutics

Number of pages256
Imprimatur date2025-11-06
Defense date2025-11-06
Abstract

This thesis explores the design, application, and structural characterization of supramolecular assemblies for use in reversible therapeutics and combinatorial library platforms.

A key focus is the development of a reversible supramolecular anticoagulant targeting thrombin. This inhibitor was constructed from weak-binding fragments linked via a peptide nucleic acid (PNA) scaffold, enabling high-affinity binding through templated assembly. A toehold-mediated strand displacement mechanism allowed reversal of inhibition using an ‘antidote’ PNA strand, with efficacy demonstrated in vitro and in vivo.

Building on this, a trimeric combinatorial library of 125,000 supramolecular assemblies was created to target thrombin’s three binding pockets. A MALDI-based in-solution selection strategy identified a potent trivalent inhibitor with picomolar affinity and rapid reversibility.

Furthermore, a DNA-encoded library (DEL) system was developed, allowing selection, amplification, and retranslation via DNA-templated synthesis. This system enabled iterative selection cycles, mimicking Darwinian evolution, for the development of MDM2 targeting alpha helices.

Finally, a structural study using cryo-electron microscopy resolved the architecture of a previously reported self-assembling peptide (SAP) binder complexed with the trimeric SARS-CoV-2 Spike protein, revealing binding in a: one Receptor-Binding-Domain (RBD) closed, two RBD open conformation.

Together, these studies demonstrate the versatility of supramolecular assemblies as reversible therapeutics and as platforms for high-throughput ligand discovery, combining rational design with combinatorial evolution and structural validation.

Research groups
Citation (ISO format)
DOCKERILL, Millicent. Darwinian Evolution and Self-Assembly of Synthetic Molecules for Next-Generation Therapeutics. Thèse, 2025. doi: 10.13097/archive-ouverte/unige:191111
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Thesis
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Technical informations

Creation29/01/2026 11:16:44
First validation29/01/2026 12:23:18
Update04/05/2026 15:05:57
Status update04/05/2026 15:05:57
Last indexation01/07/2026 06:12:34
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