Events

Speaker:

Prof. Francesco Zamponi

Title:

 Emergent time scale of epistasis in protein evolution

Time:

14:00

Location:

Room 132

Abstract:

I will discuss a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. This in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. I will show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. The model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. It also suggests that evolutionary dynamics can be extremely heterogeneous depending on its starting point.

Speaker:

Prof. Bernd Ensing

Title:

The happy marriage between molecular modeling and machine learning

Time:

14:00

Location:

Room 132

Abstract:

Machine learning has quickly entered all corners of science and society. But there is a special synergy between machine learning and molecular modeling. In this two-lecture series, we will examine the inner workings of modern machine learning approaches that are especially well suited for molecular and materials systems, for example for predicting properties, sampling equilibrium and nonequilibrium distributions, and generating new molecular structures. We will also highlight striking analogies between several machine learning algorithms and concepts from statistical mechanics and discuss how these connections have inspired advances in both fields. Topics that will be addressed include graph neural networks, energy-based models, denoising diffusion models, and Bayesian optimization.

Speaker:

Prof. Alex Evilevitch

Title:

Unveiling the Role of DNA Bending Stress in Herpesvirus Capsid Pressurization and Genome Organization

Time:

16:00

Location:

Room 132

Abstract:

Herpesviruses, such as HSV-1, encapsidate their genomes within a proteinaceous capsid under significant internal pressure. This pressure arises from a balance of DNA-DNA electrostatic repulsion and bending stress imposed by the capsid’s confined space. While DNA-DNA interactions have been extensively studied, the contribution of bending stress to the total genome pressure remains underexplored. We present an innovative approach combining osmotic suppression assays with small-angle X-ray scattering (SAXS) to empirically derive the equation of state (EOS) for encapsidated DNA. This EOS quantifies the relative contributions of DNA-DNA interaction energy and bending stress to the total capsid pressure.

Our results reveal that bending stress contributes ~50% of the total genome pressure in HSV-1 capsids, challenging previous assumptions that bending stress is negligible. Importantly, this finding extends to other herpesviruses, suggesting that bending stress is a conserved feature of genome organization in viruses with varying capsid sizes and DNA densities. Using our SAXS-osmometer platform, we predict internal pressures for multiple herpesvirus family members, including HSV-2, EBV, and CMV, ranging from 8 to 20 atm, with higher pressures correlating to tighter genome packing.

This work underscores the critical role of bending stress in viral genome mechanics, influencing capsid stability, genome ejection, and infection dynamics. Our findings not only expand the understanding of herpesvirus biophysics but also open new avenues for antiviral strategies targeting genome stress in the capsid.

Speaker:

Alex Klotz

Title:

Kinetoplast DNA: physics, topology, and biology

Time:

14:00

Location:

Room 005

Abstract:

Kinetoplast DNA is a complex structure found in the mitochondria of certain parasites, consisting of thousands of topology linked circular DNA molecules. In recent years, the complex molecular architecture of kDNA has lead to interest in its use as a model experimental system to study thermal effects in two-dimensional materials and the elastic response of catenanes. In this talk I will discuss some of the work characterizing the physics of kDNA as well as ongoing work to understand the relation between the topology of the network and its physical properties.

Speaker:

Gasper Tkacik

Title:

Invariances and the molecular complexity in gene regulation

Time:

14:00

Location:

Room 004

Abstract:

Eukaryotic gene regulation is based on stochastic yet controlled promoter switching, during which genes transition between transcriptionally active and inactive states. Despite the molecular complexity of this process, recent studies reveal a surprising invariance of the “switching correlation time”, which characterizes promoter activity fluctuations. This quantity is constant across gene expression levels in diverse genes and organisms. A biophysically plausible explanation for this invariance remains missing. I will present recent work in collaboration with our experimental colleagues (Gregor lab), which provides a mechanistic and functional insights into the origin and meaning of the said invariance.

Speaker:

Dr. Tomas Silva

Title:

Tackling protonation effects and pH-dependency in RNA systems

Time:

14:00

Location:

Room 139

Abstract:

The RNA ecosystem displays a remarkable variety of biomolecules with versatile functions, supported by highly flexible structures, complex motifs, and intricate hydrogen-bond networks. Many of these interactions involve canonical and noncanonical base pairs whose stability or conformational switching can depend on protonation events. Molecular dynamics (MD) simulations, however, typically require defining protonation states a priori, and nucleobases are usually assumed to be neutral because their aqueous pKₐ values lie outside biologically relevant pH ranges. Yet several RNA systems exhibit significantly upshifted pKₐ values, enabling titration at physiological pH and reshaping hydrogen-bonding patterns and local structural motifs with direct impact on function.

Constant pH molecular dynamics (CpHMD) methods are a reliable tool to couple conformational sampling to protonation equilibria, providing robust calculations of pH-dependent properties, such as pKₐ values. Still, pH-sensitive approaches have been scarcely applied to nucleic acids despite growing evidence for pH modulation in RNA macromolecules.

In this work, we present an extension of the stochastic CpHMD method to RNA based on the standard XOL3 AMBER RNA force field and demonstrate its ability to reproduce pKₐ values across multiple RNA systems. We further introduce an implementation compatible with wt-metadynamics to enhance sampling of both conformational and protonation space, yielding more accurate pKₐ and pH-dependent observables. To showcase the potential of this framework, I will present applications ranging from small oligomers to large ribozymes, highlighting how this approach uncovers alternative protonation patterns and molecular behaviour in complex RNA environments.

Speaker:

Toon Lemmens 

Title:

Unraveling dynamical characteristics of RNA through molecular dynamics simulations

Time:

14:00

Location:

Room 132

Abstract:

RNA is a fundamental and indispensable actor in the transfer of information and functioning of life, giving rise to uniquely diverse and complex structural and interaction networks. The multifaceted nature of RNA can be demonstrated through two distinct applications. First, the RRM (RNA recognition motif) of protein TbRGG2 features a single binding pocket that recognizes poly(U)-RNA, thereby mediating protein–RNA interactions. Secondly, the kink-turn, a multifunctional recurrent structural motif, is characterized by a complex network of tertiary hydrogen bonds facilitating wide-ranging structural dynamics. Both systems were studied through atomistic molecular dynamics (MD) simulations, a powerful computational method based on calibrated set of empirical potentials, referred to as force fields (FF). Standard MD simulations were employed revealing a fast diffusion mechanism governing the interaction between the single binding pocket of RRM and poly(U)-RNA. Simultaneously, the structural complexity of Kt7 (kink-turn 7) was exploited to benchmark the system and evaluate the shortcomings of a broad selection of published RNA force fields (FFs). In addition, the kink-turn’s dynamic features such as unkinking and A-minor state transitions are being investigated through several enhanced sampling methods further unraveling RNAs distinctive characteristics. 

PhD Candidates:

14:00 Vittorio del Tatto – A distance–based framework for causal discovery from high-dimensional time series, Supervisor: Alessandro Laio, External examiners: Christoph Dellago, Massimiliano Zanin

Location:

Big Meeting Room, 7th Floor

PhD Candidates:

09:00 Davide Marcato – Lattice field theories for polymer systems, Supervisor: Angelo Rosa, External examiners: Enzo Orlandini, Friederike Schmid

11:00 Francesco Slongo – QUBO-Based Sampling of Lattice Polymers on Classical and Quantum Computers, Supervisor: Cristian Micheletti, External examiners: Andrea Pelissetto, Daniel Jost

14:15 Jonathan Sarmiento –  First-Exit Statistics in Biased, Active and Cognitive Systems, Supervisors: Domenica Bueti, Edgar Roldan, External examiners: Daniel Sanchez-Taltavull, Meik Doerpinghaus

Location:

Big Meeting Room, 7th Floor

PhD Candidates:

09:00 Ivan Gilardoni – Development and application of methods to integrate molecular simulations with experimental measurements, Supervisor: Giovanni Bussi, External examiners: Julija Zavadlav, Jürgen Köfinger

11:00 Elisa Posani – Ensemble Refinement of Cryo-Electron Microscopy Derived RNA Structures using Molecular Dynamics Simulations, Supervisors: Giovanni Bussi, Alessandra Magistrato, External examiners: Janusz Bujnicki, Giulia Palermo

Seminars:

14:00 Jürgen Köfinger – Max Planck Institute of Biophysics – Encoding prior knowledge in ensemble refinement

14:45 Julija Zavadlav – Technical University of Munich – Multiscale modeling with ML potentials

15:45 Janusz Bujnicki – International Institute of Molecular and Cell Biology in Warsaw – Structural bioinformatics of RNA folding, interactions, and modifications

16:30 Giulia Palermo – University of California Riverside – Advancing Gene Editing through Computational Methods and Deep Learning

Location:

Big Meeting Room, 7th Floor

Speaker:

Prof. Sergei Nechaev

Title:

Fractional Brownian motion meets polymer topology. Crumpled globule as a bridge between mineral and living worlds

Time:

14:30

Location:

Room 132

Abstract:

We investigate statistical and topological properties of fractional Brownian motion (fBm) with the fractal dimension D_f > 2 in the tree-dimensional space. Our study is motivated by an attempt to mimic the statistics of collapsed unknotted polymer rings, which are known to form hierarchical crumpled globules (CG) with D_f=3 at large scales. We demonstrate that with the increase of D_f, typical conformations become less knotted. Distribution of the knot complexity for various fractal dimensions of chains suggests a relationship between fractal dimension and polymer topology. This finding could have an important impact: replacing topology by a fractal dimension would tremendously simplify the problem of generating compact self-avoiding polymer conformations since  topological constraints are washed out of the consideration. Considering relaxational properties of the elastic network constituting crumpled globule (CG), we conjecture that CG could be a bridge between “mineral” and “living” worlds playing a role of an elementary “molecular machine”.

Speaker:

Dr. uca Giorgetti

Title:

Towards a quantitative understanding of chromosome structure and transcriptional regulation

Time:

14:30

Location:

Zoom Seminar – Stream in Room 132

Abstract:

Control of gene expression relies on tens of thousands of distal enhancer sequences. Genetic variation within these regions is a major driver of evolution, but is also causal to developmental disorders and complex human disease. Despite their central role in gene regulation in health and disease, the principles by which enhancers select and control their target genes remain largely unknown: What are the molecular mechanisms that transmit regulatory information from enhancers to promoters? How are they related to their physical interactions? Are these mechanisms universal or rather depend on sequence-, locus- and tissue-specific contexts?

My group addresses these fundamental questions using experimental and theoretical approaches at the interface of molecular biology and biophysics. Our research is declined in three interconnected axes of research: 1) we use quantitative assays relying on engineered genomic regions to measure and perturb enhancer-promoter communication in the absence of confounding effects, and uncover fundamental regulatory principles; 2) we measure the dynamics of chromosome structure and transcription at single-molecule resolution in single cells; and 3) we develop mathematical and physical models to predict and interpret our experiments and formulate new hypotheses.

Speaker:

Prof. Felix Ritort

Title:

Heatomics for life

Time:

14:30

Location:

TBA

Abstract:

Heat is essential for life. In living organisms, the generation of mechanical and chemical work to accomplish specific tasks inevitably leads to energy dissipation in the degraded form of heat—the most fundamental signature of life. Despite major progress in processing large biological data sets across all omics layers, we still lack a clear understanding of how to measure heat generation at the nanoscale, known as the entropy production rate, σ. In this talk, I present a recently introduced variance sum rule for measuring σ with attowatt sensitivity (1 aW = 10^-18 Watts) from the flickering motion of a microscopic probe [1,2,3]. We apply it to produce the first heat map of human red blood cells and to derive σ from hidden degrees of freedom in molecular processes. The variance sum rule provides a new resource for energy inference, enabling σ measurements in living and active matter.

[1] Di Terlizzi, I., Gironella, M., Herráez-Aguilar, D., Betz, T., Monroy, F., Baiesi, M., & Ritort, F. (2024). Variance sum rule for entropy production. Science, 383(6686), 971-976.

[2] Di Terlizzi, I., Baiesi, M., & Ritort, F. (2024). Variance sum rule: proofs and solvable models. New Journal of Physics, 26, 063013.

[3] Roldán, É. (2024). Thermodynamic probes of life. Science, 383(6686), 952-953.

Speaker:

Prof. Roberto Cerbino

Title:

Using cellular phase transitions to understand cancer

Time:

14:30

Location:

TBA

Abstract:

In this talk, I will explore how collective cell dynamics arise from phase transitions that modulate tissue mechanics, with key consequences for both normal physiology and disease. A central focus will be on the transition between jammed and unjammed states in epithelial tissues—shifts that determine whether tissues behave as rigid solids or viscoelastic fluids. I will present our recent discovery that the small GTPase RAB5a acts as a molecular switch for unjamming. Its overexpression drives coordinated behaviors—such as flocking in 2D monolayers and coherent rotations in 3D spheroids—by lowering cell surface tension and aligning cellular motility. Intriguingly, RAB5a is upregulated in highly invasive mammary tumors, suggesting a link between unjamming and the mechanical capacity of spheroids to remodel and degrade the extracellular matrix. I will also discuss how the unjammed state generates giant density fluctuations that stress the nucleus, leading to envelope rupture and DNA leakage into the cytosol. This triggers the cGAS-STING pathway, a key immune sensor whose role in cancer remains to be fully characterized. Overall, this work stresses the importance of tissue physical states in tumor progression and suggests that targeting the mechanical landscape of tumors may offer novel therapeutic opportunities.

Speaker:

Matteo Boccalini

Title:

Exploring RNA Destabilization Mechanisms in Biomolecular Condensates through Atomistic Simulations

Time:

14:00

Location:

TBA

Abstract:

Biomolecular condensates are currently recognized to play a key role in organizing cellular space and in orchestrating biochemical processes. Despite an increasing interest in characterizing their internal organization at the molecular scale, not much is known about how the densely crowded environment within these condensates affects the structural properties of recruited macromolecules. Here we adopted explicit-solvent all-atom simulations based on a combination of enhanced sampling approaches to investigate how the conformational ensemble of an RNA hairpin is reshaped in a highly-concentrated peptide solution that mimics the interior of a biomolecular condensates. Our simulations indicate that RNA structure is greatly perturbed by this distinctive physico-chemical environment, which weakens RNA secondary structure and promotes extended non-native conformations. The resulting high-resolution picture reveals that RNA unfolding is driven by the effective solvation of nucleobases through hydrogen bonding and stacking interactions with surrounding peptides. This solvent effect can be modulated by the aminoacid composition of the model condensate as proven by the differential RNA behaviour observed in the case of arginine-rich and lysine-rich peptides.

Speaker:

Dr. Gareth A. Tribello

Title:

Reconnaissance Metadynamics Rides Again

Time:

14:00

Location:

Room 132

Abstract:

In my talk I will discuss the Adaptive Topography of Landscape for Accelerated Sampling (ATLAS) method. This is a new biasing method that I recently developed with Federico Giberti and Michele Ceriotti that is capable of working with many CVs. The root idea of ATLAS is to apply a divide-and-conquer strategy where the high-dimensional CVs space is divided into basins, each of which is described by an automatically-determined, low-dimensional set of variables. A well-tempered metadynamics-like bias is constructed as a function of these local variables. Indicator functions associated with the basins switch on and off the local biases, so that the sampling is performed on a collection of low-dimensional CV spaces, that are smoothly combined to generate an effectively high-dimensional bias. The unbiased Boltzmann distribution is recovered through reweighing, making the evaluation of conformational and thermodynamic properties straightforward. The decomposition of the free-energy landscape in local basins can be updated iteratively as the simulation discovers new (meta)stable states.

In the paper, we say that new tools like ATLAS are useful because when you use more standard methods such as umbrella sampling or metadynamics you are forced to operate with a small number of collective variables.  Towards the end of my talk I will discuss why I am no longer convinced this is true. My hope in doing so is to encourage conversation with the audience about this work and its potential usages. I firmly believe that we should be doing more to stimulate conversation when we give in-person talks as this justifies the expense associated with bringing everyone to one place.

References

F. Giberti, G. A. Tribello and M. Ceriotti, Global Free-Energy Landscapes as a Smoothly Joined Collection of Local Maps. J. Chem. Theory and Comput. 17, 3292-3308 (2021)

Speaker:

Prof. Marco Baiesi (UniPd, Italy)

Title:

Kinetic bounds for nonequilibrium systems

Time:

12:00

Location:

Room 132

Abstract:

Dissipation, or entropy production, characterizes irreversibility and states deviating slightly from equilibrium conditions. However, far from equilibrium, dynamical fluctuations also depend on kinetic aspects such as the average jumping rate or the diffusivity of the system. These features, which in the years acquired names such as “traffic,” “dynamical activity,” or “frenesy,” measure the degree of agitation and are essential for developing a statistical mechanical description of generic nonequilibrium regimes. Examples show how frenetic aspects help understand the nonequilibrium linear response. Writing such a response as a difference between a dissipative and a frenetic term allows for identifying homeostatic regimes in biological conditions, where both terms cancel each other, or even regimes with a negative differential response. Frenesy also constrains the precision of currents (in a “kinetic uncertainty relation”) and even provides a lower bound to the entropy production in entirely irreversible conditions. This lower bound empirically may be more precise in estimating dissipation than directly applying the theoretical expression for the entropy production in Markov jump systems.

Speaker:

Prof. Sebastian Springer (Constructor University, Bremen)

Title:

Redesigning MHC class I proteins for tumor immunotherapy

Time:

11:30

Location:

Room 132

Abstract:

Killer T cells of the immune system can destroy virus-infected and tumor cells. These aberrant cells can be recognized because at their surface, they have MHC (major histocompatibility complex) class I proteins that display peptide fragments from the inside of the cell. My group is interested in all aspects of MHC class I proteins, especially their biochemistry, i.e., folding and ligand binding. Recombinant MHC class I proteins have a biotechnological role: they bind to tumor- or virus-specific T cells and can be used to detect, isolate, and activate them. We have used molecular dynamics simulations (in collaboration with Martin Zacharias in Munich) to introduce mutations in MHC class I proteins that make them more suitable for T cell detection: especially, we have generated peptide-empty forms that can shorten the production time from weeks to seconds. We are now looking to introduce further modifications, expand the approach to other proteins, and also screen for small molecules that support the stable conformation of the proteins. For this, we are open for collaboration.

Speaker:

Dr. Elisa Frezza (Université Paris Cité)

Title:

Modelling RNA and protein-RNA complexes using different computational approaches

Time:

14:30

Location:

Room 132

Abstract:

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The recent pandemic of SARS-CoV-2 has highlighted the importance of studying RNA molecules and their interplay with proteins, which is key to any physiological phenomenon.1 In fact, many viruses (e.g. HIV, Hepatitis C, Coronavirus) manipulate cellular machineries to ensure their replication, for instance to translate their mRNA.2 RNA functions crucially rely on both the specific tridimensional (3D) folding of the molecule, which in turn depends on the sequence and on how nucleobases pair through hydrogen bonds,3,4 and its conformation. This relationship is even more crucial for protein-RNA complexes. RNA molecules can undergo conformational changes based on different stimuli or following the binding to a ligand or another biomolecule that often goes beyond local rearrangements. Hence, RNA flexibility is crucial for its function and underlies not only RNA folding but also the majority of RNA interactions with other molecular species. However, its flexibility is very complex allowing the adoption of distinct conformations, and it is one of the major reasons why obtaining high-resolution 3D structures via X-ray crystallography, NMR, or cryo-electron microscopy is still a challenging task as shown by the relatively small number of bound or unbound structures deposited in the Nucleic Acid Data Bank (NDB).5 Moreover, the limited number of known complex structures is constrained by the inherent flexibility of proteins and/or RNA and the size of the complexes. To overcome that, low resolution data have been used to model these structures, like chemical probing data. In this context, our research activities develop on three main axes: i) the study of chemical reactivity data such as SHAPE (Selective 2′ Hydroxyl Acylation analyzed by Primer Extension)6 using multiscale approaches with the aim to develop an integrative approach;7,8 ii) the investigation of protein-RNA complexes and the dynamics of their interfaces; iii) the prediction of RNA conformational changes using internal normal mode analysis (iNMA). For the first axis, we conducted all-atom and QM/MM calculations on a model system, and for the first time documented in the literature we were capable to get molecular insights on the reaction mechanism and to determine the most stable reactive conformations. For the second axis, we studied different protein-RNA complexes using standard and enhanced all-atom MD simulations.9,10 In particular, a systematic study was conducted to set-up a computational strategy to investigate the dynamics of the interfaces in combination with the structural changes at the level of the protein and the RNA. A hierarchical clustering based on the contacts at the interface was proposed and allowed to determine alternative interfaces along our simulations that seem to have a biological relevance.10 Finally, despite all the approximations, for the third axis our study shows that despite all the approximations iNMA is a suitable method to take into account RNA flexibility and describe its conformational changes, opening thus the route to its applicability in any integrative approach where these properties are crucial.11

(1) Glisovic, T.; Bachorik, J. L.; Yong, J.; Dreyfuss, G. RNA-Binding Proteins and Post-Transcriptional Gene Regulation. FEBS Letters 2008, 582 (14), 1977–1986.
(2) Deforges, J.; Locker, N.; Sargueil, B. mRNAs That Specifically Interact with Eukaryotic Ribosomal Subunits. Biochimie 2015, 114, 48–57.
(3) Lilley, D. M. J. How RNA Acts as a Nuclease: Some Mechanistic Comparisons in the Nucleolytic Ribozymes. Biochemical Society Transactions 2017, 45 (3), 683–691.
(4) Incarnato, D.; Oliviero, S. The RNA Epistructurome: Uncovering RNA Function by Studying Structure and Post-Transcriptional Modifications. Trends in Biotechnology 2017, 35 (4), 318–333. https://doi.org/10.1016/j.tibtech.2016.11.002.
(5) Berman, H. M.; Olson, W. K.; Beveridge, D. L.; Westbrook, J.; Gelbin, A.; Demeny, T.; Hsieh, S. H.; Srinivasan, A. R.; Schneider, B. The Nucleic Acid Database. A Comprehensive Relational Database of Three-Dimensional Structures of Nucleic Acids. Biophysical Journal 1992, 63 (3), 751–759. https://doi.org/10.1016/S0006-3495(92)81649-1.
(6) Deigan, K. E.; Li, T. W.; Mathews, D. H.; Weeks, K. M. Accurate SHAPE-Directed RNA Structure Determination. PNAS 2009, 106 (1), 97–102.
(7) Frezza, E.; Courban, A.; Allouche, D.; Sargueil, B.; Pasquali, S. The Interplay between Molecular Flexibility and RNA Chemical Probing Reactivities Analyzed at the Nucleotide Level via an Extensive Molecular Dynamics Study. Methods 2019, 162–163, 108–127.
(8) De Bisschop, G.; Allouche, D.; Frezza, E.; Masquida, B.; Ponty, Y.; Will, S.; Sargueil, B. Progress toward SHAPE Constrained Computational Prediction of Tertiary Interactions in RNA Structure. Non-Coding RNA 2021, 7 (4), 71.
(9) Berta, D.; Badaoui, M.; Martino, S. A.; Buigues, P. J.; Pisliakov, A. V.; Elghobashi-Meinhardt, N.; Wells, G.; Harris, S. A.; Frezza, E.; Rosta, E. Modelling the Active SARS-CoV-2 Helicase Complex as a Basis for Structure-Based Inhibitor Design. Chem Sci 2021, 12 (40), 13492–13505.
(10) Sabei, A.; Hognon, C.; Martin, J.; Frezza, E. Dynamics of Protein–RNA Interfaces Using All-Atom Molecular Dynamics Simulations. J. Phys. Chem. B 2024, 128 (20), 4865–4886.
(11) Sabei, A.; Caldas Baia, T. G.; Saffar, R.; Martin, J.; Frezza, E. Internal Normal Mode Analysis Applied to RNA Flexibility and Conformational Changes. J. Chem. Inf. Model. 2023, 63 (8), 2554–2572.

Speaker:

Dr. Lorenzo Casalino (University of California)

Title:

Multiscale Computational Microscopy of Viruses

Time:

14:30

Location:

Room 132

Abstract:

By leveraging a set of computational modeling approaches, simulation techniques, analysis algorithms, and visualization tools, the ‘computational microscope’ serves as a cornerstone for gaining atomic-level insights into the microscopic world of the microbiome, including viruses. This enables the exploration of structural and dynamical features or properties that may remain cryptic to experimental approaches alone. In my talk, I will showcase several examples where we used multiscale all-atom molecular dynamics simulations to investigate the conformational plasticity of different viral glycoproteins, including those found in SARS-CoV-2 and influenza viruses. These studies elucidated the pivotal contribution of glycans in shaping the dynamics and antigenicity of the viral glycoproteins, thereby expanding the functional annotation of the glycan shield. Moreover, whole-virion simulations allowed us to illuminate the dynamical interplay of the influenza virus glycoproteins in situ, revealing sites of vulnerability that could be exploited for the development of future vaccines.