Date: 22 September 2026
Time: 15:00 CET
This special edition of the BioExcel webinar series features student speakers who were awarded poster prizes at the BioExcel Summer School 2026. Below you can find out more about our speakers and their research.
Marc El Hasbany
Marc is a first-year PhD student at the Institute of Chemistry of Nice (ICN), Université Côte d’Azur in France. He holds a Bachelor of Science in Bioinformatics from the Lebanese American University in Beirut, Lebanon, and completed a Master’s degree in In Silico Drug Design and Macromolecular Modelling at Université Paris Cité in Paris, where he was first introduced to molecular dynamics simulations. Under the supervision of Dr. Jérémie Topin, his doctoral research focuses on deciphering the molecular mechanisms of olfactory receptor activation and inhibition using molecular dynamics simulations, as well as identifying potential non-agonist ligands of ectopically expressed olfactory receptors. His work is conducted in collaboration with Seoul National University in South Korea, where computational predictions are experimentally validated
Title: Deciphering the molecular basis for activation and inhibition of olfactory receptors
Olfactory receptors (ORs) are part of the largest family of G protein-coupled receptors (GPCR) and constitute the molecular basis for the detection and discrimination of a vast chemical space of volatile compounds. ORs function through a combinatorial mechanism, meaning that a single odorant can activate multiple receptors, while a single receptor can respond to structurally diverse ligands. Beyond their sensory role, select ORs are ectopically expressed in non-olfactory tissues; Olfr412, for instance, is expressed in spermatozoa and participates in chemotaxis, highlighting the broader physiological relevance of this receptor family. Despite significant advances in the field regarding the molecular mechanisms of odorant recognition and receptor activation, receptor inhibition remains poorly understood.
This study integrates computational modeling with experimental validation to decipher and further understand the molecular determinants of OR activation and inhibition. Olfr412, the ectopically expressed OR in mice, bound with different types of odorants served as the selected model systems. We combine molecular docking and simulations to characterize agonist and non agonist binding at the atomic level. Comparative analysis of OR-agonist and OR-antagonist complexes reveals differences in receptor-ligand interactions highlighting the differences between activation and inhibition. Additionally, virtual screening was performed on a large dataset of odorants collected from the M2OR database to identify other potential agonists and non agonists. The selected odorants were subsequently tested experimentally to validate their effect on OR activity. Furthermore, Umbrella Sampling was employed to map the translocation pathways of odorants through the Olfr412.
Collectively, this work deepens our understanding of OR activation and inhibition at the molecular level, with implications for both their sensory and ectopic function, enabling the design of modulators.
José Luis Muñoz Reyes
José trained as a Chemical and Bioprocess Engineer at the Karlsruhe Institute of Technology (KIT), where he completed his Bachelor’s and Master’s degrees. After an internship and Master’s thesis at Boehringer Ingelheim, focusing on Raman spectroscopic quantification of protein mixtures, he joined the lab of Dr. Thales Kronenberger at the University of Tübingen for a PhD in computational drug discovery. His research focuses on molecular dynamics simulations, particularly enhanced-sampling methods, to understand the relationship between protein dynamics and function, and uncover allosteric mechanisms across protein families, including GPCRs, kinases, and membrane transporters.
Title: Understanding the activation dynamics of the CXCR4 receptor using adaptive sampling simulations
CXCR4 is a class A G protein-coupled receptor (GPCR) that plays a pivotal role in regulating the migration, positioning, and survival of diverse cell types. Aberrant CXCR4 signalling is implicated in numerous pathological conditions, including cancer, inflammation, autoimmune disorders, and HIV infection. Despite the availability of many experimental structures, the molecular determinants governing CXCR4 conformational transitions remain poorly understood, limiting the development of novel therapeutic strategies such as allosteric modulation. To characterize the dynamic landscape of CXCR4 and identify potential allosteric regulatory sites, we performed extensive molecular dynamics simulations spanning transitions between active and inactive receptor states. Our simulations reveal state-specific, highly persistent lipid insertion sites that represent prospective druggable pockets on the receptor surface. While conventional microsecond-scale simulations remained trapped in local free-energy minima, an adaptive sampling workflow based on the FAST algorithm efficiently promoted transitions between conformational states and enhanced exploration of the receptor’s conformational landscape. Using Markov state models, we show that CXCR4 activation proceeds through four metastable intermediates, providing a mechanistic framework for understanding receptor activation and identifying opportunities for allosteric drug design.
Gianluca Santini
Gianluca is a first-year Ph.D. student in Chemistry at the University of Milano-Bicocca, working within the molecular modeling research group. His doctoral project focuses on advancing Computer-Aided Drug Design by integrating Machine Learning (ML) with physics-based methods, particularly Molecular Dynamics (MD) simulations. The primary targets of his research are Nuclear Receptors, a crucial protein family involved in different pathophysiological conditions. By applying deep learning architectures to analyze MD trajectories, his work aims to decode the highly dynamic behavior of these receptors, isolating structural and functional signatures to guide the rational discovery of novel therapeutic modulators.
Title: Isolating Functional Signatures in Protein Dynamics with an Explainable Spatiotemporal Graph Neural Network
Biomolecular function is fundamentally driven by conformational dynamics, with proteins constantly adapting their structures in response to binding events or mutations. To understand these mechanisms, computational chemists frequently compare molecular dynamics (MD) simulations of proteins in distinct states (such as ligand-bound and unbound). The primary challenge in this comparative MD analysis lies in separating the true functional signatures from the stochastic thermal noise and simulation-specific variance. Standard geometric or dimensionality reduction methods often struggle to isolate these subtle, mechanistically relevant differences across independent simulation replicas.
In this talk, I will introduce an explainable deep learning approach (GISTnet-MD) designed to overcome these limitations and learn state-specific dynamic signatures directly from raw MD trajectories [1]. Specifically, I will illustrate how GISTnet-MD filters out trajectory-specific correlations and random fluctuations to capture the invariant biophysical patterns defining a specific functional state. By translating raw trajectory differences into interpretable, residue-level determinants, I will demonstrate how the model can successfully isolate fundamental mechanisms such as distributed allostery and specific activation signatures [1].
Finally, I will discuss the application of this framework to the nuclear receptor superfamily. To this end, I am constructing a comprehensive MD simulation dataset to systematically compare their active (agonist-bound) and inactive (apo) states, leveraging this AI-driven methodology to decode their allosteric regulation.
- Motta, S. et al. Spatiotemporal Graph Neural Networks Reveal Conformational Binding Signature in Protein Dynamics. bioRxiv Preprint at https://doi.org/10.64898/2026.05.19.726195 (2026).
