Την Παρασκευή 10/07/2026 και ώρα 14.00 θα πραγματοποιηθεί στην αίθουσα διδασκαλίας του Εργαστηρίου Οργανικής Χημείας η δημόσια υποστήριξη της διδακτορικής διατριβής της υποψήφιας διδάκτορα Δέσποινας Κιούρη με θέμα:
« Ανακάλυψη νέων φαρμακευτικώνπροσεγγίσεων με την χρήση Δικτύων πρωτεϊνικώναλληλεπιδράσεων, Μοριακής Μοντελοποίησης καιΟργανικής Φασματοσκοπίας»
«Discovery of new pharmaceutical approaches using Protein Interaction Networks, Molecular Modeling and Organic Spectroscopy»
Τριμελής συμβουλευτική επιτροπή:
1. Χρήστος Χασάπης, Επίκουρος Καθηγητής, Τμήμα Χημείας ΕΚΠΑ (Κύριος Επιβλέπων)
2. Θωμάς Μαυρομούστακος, Καθηγητής, Τμήμα Χημείας ΕΚΠΑ
3. Αθανάσιος Γκιμήσης, Καθηγητής, Τμήμα Χημείας ΕΚΠΑ
Επταμελής εξεταστική επιτροπή:
1. Χρήστος Χασάπης, Επίκουρος Καθηγητής, Τμήμα Χημείας ΕΚΠΑ (Κύριος Επιβλέπων)
2. Θωμάς Μαυρομούστακος, Καθηγητής, Τμήμα Χημείας ΕΚΠΑ
3. Αθανάσιος Γκιμήσης, Καθηγητής, Τμήμα Χημείας ΕΚΠΑ
4. Δήμητρα Τζέλη, Καθηγητής, Τμήμα Χημείας ΕΚΠΑ
5. Γεωργία Μελαγράκη, Αναπληρώτρια Καθηγήτρια, Στρατιωτική Σχολή Ευελπίδων
6. ΝικόλαοςΠαπανικολάου, Καθηγητής, Department of Biochemistry & Medical Genetics, Touro College of Osteopathic Medicine, New York, USA
7. Ιωάννης Σουγκλάκος, Καθηγητής, Ιατρική Σχολή Πανεπιστημίου Κρήτης
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Abstact:
This thesis leverages advanced computational methodologies to predict and characterize the host–gut microbiome interplay through a structural lens, thereby addressing the inherent limitations of experimental interactomics and the scarcity of high-resolution structural data. The research is organized around three primary pillars: the development of predictive models, the biological characterization of the predicted interactome, and the translation of these findings into therapeutic insights.
The first phase of this work focuses on deciphering the molecular interplay between the microbial and host proteomes. Initial efforts involved the construction of a protein–protein interaction network (PPIN) using machine learning (ML) to extract statistically significant associations between Pfam domains. This model was trained on a large-scale, heterogeneous dataset comprising over one million experimentally validated interactions across multiple taxa. To enhance predictive fidelity, a novel hybrid deep learning (DL) architecture was subsequently developed. This framework integrates graph-based protein representations with Variational Autoencoders (VAEs) to generate latent structural embeddings, which are then processed by a bi-directional cross-attention module to predict interactions. To ensure robustness against class imbalance, the model employs a focal loss that prioritizes hard-to-classify samples.
The second objective involves the systematic biological and topological analysis of the predicted PPIN in order to identify disease associations. Through multi-parametric topological evaluations—including degree distributions, assortativity, and mean degree–abundance correlations—the study characterizes the resilience and organizational logic of the host–microbe interactome. This analysis identified critical protein hubs implicated in inflammation, cancer, mitochondrial dysfunction, and cardiometabolic disorders.
The final phase translates these computational findings into pharmacological insights. Focusing on the identified hubs, the study employed an orthological design strategy to develop small organic molecules targeting pathological interactions in obesity- and cardiometabolic-related pathways. Given the strong association uncovered between the interactome and colorectal cancer (CRC), the DL model was further applied to clinical metagenomic data to distinguish the molecular signatures of healthy individuals from those of CRC patients. Centrality analysis identified key host proteins, which were then targeted for the rational repurposing of natural products. Achillea millefolium was identified as a primary candidate, and molecular docking together with nuclear magnetic resonance (NMR) spectroscopy were subsequently employed to validate the binding affinity of its synthetic compounds against the identified host targets.
Overall, this thesis integrates computational modeling, structural biology, and clinical systems data to advance the understanding of the gut microbiome's influence on human health. By establishing a multi-scale pipeline—spanning from deep learning–based prediction to experimental validation—this work provides a foundational framework for discovering therapeutic targets and for elucidating the molecular mechanisms that underlie host–microbe cross-talk in both physiological and pathological states.
