Biology subjects

Bonomi, M.

Publications and source records attributed to Bonomi, M..

At least 19 recordsLinked to original sources

bioRxiv2026

NMR-measured Per-residue Electrostatic Potentials Reveal the Importance of Arginine-Lysine Balance on the Conformational Ensemble of Disordered Histone H3 Tails in Nucleosome Core Particles

Biophysical studies of chromatin have provided key insights into how the genome is regulated. Many atomic resolution structures of the fundamental building block of chromatin, the nucleosome core particle (NCP), as well as larger NCP arrays, both in isolation and in complex with effectors, or with post-translational modifications, have been elucidated. These provide in-depth pictures of key functional interactions. Considerably less information is available on the intrinsically disordered nucleosome tails that are critically important for higher order interactions in chromatin, and for transcription, DNA repair, and phase separation, and that serve as hubs for post-translational modifications. Here we focus on the tail of one of the four histones, histone-3 (H3), and measure electrostatic potentials at each of its amide proton positions in isolated NCPs via solution NMR spectroscopy. Using these potentials, in concert with molecular dynamics simulations, an ensemble of structures is calculated for WT H3 tails, as well as variants where all tail arginines are replaced by lysines (R2K) or vice versa (K2R). Our ensembles reveal that K2R tails have a higher propensity to occupy the most negatively charged region of the NCP between the DNA gyres than R2K or WT tails, while lysine enriched tails have an increased relative preference for the less negative regions near the DNA entry and exit points. The structural ensembles provide insight into differences in interactions between neighboring particles that lead to changes in diffusion and phase separation propensities between the NCP variants.

biophysics↗

bioRxiv2026

HARIBOSS++: An Integrated Platform for RNA-Targeted Drug Design

Targeting RNA with small molecules is increasingly viewed as a pivotal strategy for future therapeutics. A major limitation in studying RNA-small molecule (RNA-SM) interactions is the lack of available experimental structures. HARIBOSS established a milestone as a curated database of RNA-SM complexes derived from the Protein Data Bank. Building on its community engagement, we present HARIBOSS++, an updated and next-generation platform designed to facilitate the study of future RNA-targeted drugs. Major updates include an extension of the pocketome to encompass binding sites where small molecules interact not only with RNA but also with other biomolecules, enhanced analysis of RNA molecules by family, detailed 3D structural characterization, improved analysis of ligands chemical space, addition of experimental binding affinity as well as molecular dynamics data, when available. HARIBOSS++ opens new avenues for biologists, bioinformaticians, and machine learning researchers to investigate RNA-SM interactions on a new scale. HARIBOSS++ can be explored using a web interface freely available at https://hariboss.pasteur.cloud.

bioinformatics↗

bioRxiv2026

Single-cell multi-omics maps clonal IEL expansion and epithelial remodelling in refractory coeliac disease

Background Refractory coeliac disease type 1 (RCD1) lacks defining molecular markers, and the immune and epithelial mechanisms sustaining intestinal injury remain poorly understood. Objective To define the clonal immune and epithelial states that distinguish RCD1 from active coeliac disease (ACD) and determine their spatial organisation in the duodenal mucosa. Design We integrated single-cell RNA sequencing, CITE-seq surface proteomics and paired T-cell receptor sequencing of duodenal immune and epithelial compartments from Healthy controls (n = 6), ACD (n = 7), RCD1 (n = 9) and RCD2 (n = 2), with spatial transcriptomics in a subset of biopsies. Results RCD1 showed widespread TCR{beta} and TCR{gamma}{delta} clonal expansion across multiple IEL states, extending beyond previously defined mutation-bearing aberrant clones. Distinct IEL populations converged on a shared programme of adaptive persistence, innate-like signalling, metabolic fitness and cytoskeletal remodelling; GZMK expression marked both clonally expanded and non-clonal disease-associated states. In parallel, RCD1 epithelium showed loss of mature absorptive cell states and expansion of stress-associated, immune-interacting and regenerative programmes. Transit-amplifying cells acquired differentiation and tissue-remodelling signatures, while enteroendocrine cells expanded and developed a sensory-neurosecretory programme involving TRPA1, TRPV1, vesicle trafficking and NEUROD1 regulon activity. Spatial transcriptomics localised regenerative and enteroendocrine-associated epithelial programmes adjacent to immune-visible epithelial regions and KLRK1/GZMK-expressing IEL-rich niches in refractory tissue. Conclusion RCD1 represents a distinct mucosal state characterised by coordinated clonal IEL adaptation and epithelial remodelling, rather than simple amplification of ACD, providing a cellular framework for persistent tissue injury and disease stratification.

immunology↗

bioRxiv2026

Integrative Ensemble Modeling reveals RNA conformations targetable by small molecules

RNA molecules explore heterogeneous conformational ensembles that are essential for their biological function and molecular recognition, yet this intrinsic flexibility poses a major challenge for structure-based drug discovery. In particular, the absence of well-defined binding pockets in static structures limits the identification of ligandable sites. Here, we present an integrative ensemble-based approach that combines enhanced-sampling molecular dynamics simulations with Nuclear Magnetic Resonance data to characterize the conformational landscape of the HIV-1 TAR RNA at atomic resolution. Starting from extensive sampling, we refined the resulting conformational distribution through maximum-entropy reweighting to achieve quantitative agreement with experimental data. Analysis of the reweighted ensemble reveals a diverse set of conformational substates, including compact arrangements that exhibit pocket features compatible with ligand recognition and overlap with known ligand-bound structures. At the same time, highly ligandable conformations, which are only marginally populated, might nonetheless be critical for RNA recognition. Our results demonstrate that integrative ensemble modeling can reveal pharmacologically relevant RNA conformations that are not apparent from experimental static structures, providing a framework for ensemble-based strategies in RNA-targeted drug discovery.

biophysics↗

bioRxiv2026

Structural insights into the inactive state of the adhesion GPCR ADGRV1

Adhesion G protein-coupled receptors (aGPCRs) are involved in numerous physiological processes, including cell-cell and cell-matrix interactions, and are associated with several human diseases. ADGRV1 is a member of the aGPCR family and plays a significant role in the sensorineural systems. Mutations of ADGRV1 are linked to the Usher syndrome, a genetic disorder causing deafness and blindness in human. However, the molecular mechanisms that control the activity of ADGRV1 remain unclear. In this study, we present the high-resolution cryo-electron microscopy structure of the inactive ADGRV1 receptor in complex with the nanobody RE02, providing detailed insights into its transmembrane domain, intracellular loop conformations and the inactive orthosteric site. Functional cellular assays revealed that ADGRV1 exhibits a weak constitutive activation independent of the tethered agonist peptide, primarily coupling with Gi proteins. These findings suggest that ADGRV1 may employ an alternative activation mechanism, distinct that from other aGPCRs reported so far. Our structural analysis highlights that ADGRV1 does not follow the conventional tethered agonist activation mechanism. This is due to the divergent sequence of its tethered agonist peptide, as well as the lack of residues critical for conformational transitions toward the active state in other aGPCRs. Moreover, the large intracellular loop 3 (ICL3) adopts a closed conformation, tightly packed against the transmembrane region of ADGRV1. This suggests that the ICL3 loop can compete with G-protein binding, ultimately acting as an additional barrier for ADGRV1 activation. Significance StatementAdhesion G protein-coupled receptors (aGPCRs) regulate essential processes such as cell communication and sensory function, yet the mechanisms controlling many family members remain poorly understood. We report the first high-resolution cryo-electron microscopy structure of the human receptor ADGRV1, a protein linked to Usher syndrome, a major genetic cause of deafness and blindness. Structural and functional analyses reveal that ADGRV1 displays weak constitutive signaling and likely operates through an activation mechanism distinct from the canonical tethered agonist model that defines most aGPCRs. Unique structural features, including a closed intracellular loop (ICL3) that may limit G-protein engagement, suggest an alternative regulatory strategy. These findings expand the conceptual framework of aGPCR activation and provide a foundation for understanding ADGRV1 function in sensory physiology and disease.

biochemistry↗

bioRxiv2026

Learning Dynamic Protein Representations at Scale with Distograms

Protein function and other biological properties often depend on structural dynamics, yet most machine-learning predictors rely on static representations. Physics-based molecular simulations can describe conformational variability but remain computationally prohibitive at scale. Generative models provide a more efficient alternative, though their ability to produce accurate conformational ensembles is still limited. In this work, we bypass expensive simulations by leveraging residue-residue distance probability distributions (distograms) from structure predictors such as AlphaFold2. Our approach provides a scalable way to encode dynamic information into protein representations, aiming to improve function prediction without explicit conformational sampling.

bioinformatics↗

bioRxiv2025

Integrative Biophysical Illumination of the 3D GPCRome Dynamics

Achieving high-resolution insights into protein dynamics remains a major challenge, particularly for membrane proteins such as G protein-coupled receptors (GPCRs) - a highly dynamic class of cell surface receptors and key drug targets. Here, we present an integrative strategy that bridges computational and experimental biophysical methods for probing protein dynamics. To illustrate its potential, we developed GPCRmd-NMR, a platform that synergistically combines molecular dynamics (MD) simulations with high-resolution nuclear magnetic resonance (NMR) spectroscopy across the 3D GPCRome. The resulting dataset maps over 20 billion NMR signals to more than 7 million conformational snapshots of GPCRs, enabling structure-resolved analysis at an unprecedented scale. Application across multiple GPCRs provides access to previously underexplored functional GPCR dynamics, receptor states, and sites, including microswitches and hidden ligand entrance gateways. Our integrated framework offers a powerful solution to long-standing challenges faced by both experimental and computational researchers. To support broad accessibility, GPCRmd-NMR is available as an open-access, web-based framework equipped with user-friendly analysis tools (https://www.gpcrmd.org/gpcrmd-nmr-tool) and is readily applicable to other dynamic protein families beyond GPCRs and other types of biophysical data.

bioinformatics↗

bioRxiv2025

Post-translational acylation drives folding and activity of the CyaA bacterial toxin.

Post-translational modifications critically shape protein conformation and function, yet how they regulate bacterial toxins remains elusive. The adenylate cyclase (CyaA) toxin is a major virulence factor of Bordetella pertussis, the causative agent of whooping cough. CyaA is produced as an inactive precursor, proCyaA, which is activated by acylation of two lysine residues within the bacterium. Once acylated and secreted, CyaA invades innate immune cells and disrupts their phagocytic functions. High-resolution structural characterization of CyaA has remained elusive due to its size, multi-domain organization, flexibility, and aggregation propensity. Here, we overcome these challenges and generate the first structural ensembles of both non-acylated and acylated CyaA in solution by combining experimental data with integrative modeling. Coarse-grained molecular dynamics simulations reveal that acylation is critical to stabilize the native fold and to favorably orient CyaA on the target membrane. Overall, our findings reveal how post-translational acylation triggers native folding and provide mechanistic insights into the early steps of host cell intoxication.

biophysics↗

bioRxiv2025

Product-stabilized filamentation by human glutamine synthetase allosterically tunes metabolic activity

To maintain metabolic homeostasis, enzymes must adapt to fluctuating nutrient levels through mechanisms beyond gene expression. Here, we demonstrate that human glutamine synthetase (GS) can reversibly polymerize into filaments aided by a composite binding site formed at the filament interface by the product, glutamine. Time-resolved cryo-electron microscopy (cryo-EM) confirms that glutamine binding stabilizes these filaments, which in turn exhibit reduced catalytic specificity for ammonia at physiological concentrations. This inhibition appears induced by a conformational change that remodulates the active site loop ensemble gating substrate entry. Metadynamics ensemble refinement revealed >10 [A] conformational range for the active site loop and that the loop is stabilized by transient contacts. This disorder is significant, as we show that the transient contacts which stabilize this loop in a closed conformation are essential for catalysis both in vitro and in cells. We propose that GS filament formation constitutes a negative-feedback mechanism, directly linking product concentration to the structural and functional remodeling of the enzyme.

bioinformatics↗

bioRxiv2025

Advancing in silico drug design with Bayesian refinement of AlphaFold models

Virtual screening has become an indispensable tool in modern structure-based drug discovery, enabling the identification of candidate molecules by computationally evaluating their potential to bind target proteins. The accuracy of such screenings critically depends on the quality of the target structures employed. Recent advances in protein structure prediction, particularly AlphaFold2, have revolutionized this field with unprecedented accuracy. However, AlphaFold2 models often exhibit limitations in local structural details, especially within binding pockets, which limit their utility for small molecule docking. In contrast, molecular dynamics simulations with accurate atomistic force fields can refine protein structures, but lack the ability to leverage the structural information provided by deep learning approaches. Here, we introduce bAIes, an integrative method that bridges this gap by combining physics-based force fields with data-driven predictions through Bayesian inference. Crucially, bAIes demonstrates a superior ability to discriminate between binders and non-binders in virtual screening campaigns, outperforming both AlphaFold2 and molecular dynamics-refined models. By enhancing the usability of AlphaFold2 models without requiring extensive experimental or computational resources, bAIes offers a convenient solution to a longstanding challenge in structure-based drug design, potentially accelerating the early phases of drug discovery.

bioinformatics↗

bioRxiv2025

Atomic resolution ensembles of intrinsically disordered and multi-domain proteins with Alphafold

Intrinsically disordered proteins are ubiquitous in biological systems and play essential roles in a wide range of biological processes and diseases. Despite recent advances in high-resolution structural biology techniques and breakthroughs in deep learning-based protein structure prediction, accurately determining structural ensembles of IDPs at atomic resolution remains a major challenge. Here we introduce bAIes, a Bayesian framework that integrates AlphaFold2 predictions with physico-chemical molecular mechanics force fields to generate accurate atomic-resolution ensembles of IDPs. We show that bAIes produces structural ensembles that match a wide range of high- and low-resolution experimental data across diverse systems, achieving accuracy comparable to atomistic molecular dynamics simulations but at a fraction of their computational cost. Furthermore, bAIes outperforms state-of-the-art IDP models based on coarse-grained potentials as well as deep-learning approaches. Our findings pave the way for integrating structural information from modern deep-learning approaches with molecular simulations, advancing ensemble-based understanding of disordered proteins.

bioinformatics↗

bioRxiv2024

Towards accurate, force field independent conformational ensembles of intrinsically disordered proteins

Determining accurate atomic resolution conformational ensembles of intrinsically disordered proteins (IDPs) is extremely challenging. Molecular dynamics (MD) simulations provide atomistic conformational ensembles of IDPs, but their accuracy is highly dependent on the quality of physical models, or force fields, used. Here, we demonstrate how to determine accurate atomic resolution conformational ensembles of IDPs by integrating all-atom MD simulations with experimental data from nuclear magnetic resonance (NMR) spectroscopy and small-angle x-ray scattering (SAXS) with a simple, robust and fully automated maximum entropy reweighting procedure. We demonstrate that when this approach is applied with sufficient experimental data, IDP ensembles derived from different MD force fields converge to highly similar conformational distributions. The maximum entropy reweighting procedure presented here facilitates the integration of MD simulations with extensive experimental datasets and enables the calculation of accurate, force-field independent atomic resolution conformational ensembles of IDPs.

biophysics↗

bioRxiv2024

All atom molecular dynamics simulations enable ensemble refinement of flexible and mismodelled cryo-EM derived RNA structures

The advent of single-particle cryogenic electron microscopy (cryo-EM) has enabled near-atomic resolution imaging of large macromolecules, enhancing functional insights. However, current cryo-EM refinement tools condense all single-particle images into a single structure, which can misrepresent highly flexible molecules like RNAs. Here, we combine molecular dynamics simulations with cryo-EM density maps to better account for the structural dynamics of a complex and biologically relevant RNA macromolecule. Namely, using metainference, a Bayesian method, we reconstruct an ensemble of structures of the group II intron ribozyme, which better match experimental data, and we reveal inaccuracies of single-structure approaches in modeling flexible regions. An analysis of all RNA-containing structures deposited in the PDB reveal that this issue affects most cryo-EM structures in the 2.5--4 [A] range. Thus, RNA structures determined by cryo-EM require careful handling, and our method may be broadly applicable to other RNA systems.

molecular biology↗

bioRxiv2023

Accurate model and ensemble refinement using cryo-electron microscopy maps and Bayesian inference

Converting cryo-electron microscopy (cryo-EM) data into high-quality structural models is a challenging problem of outstanding importance. Current refinement methods often generate unbalanced models in which physico-chemical quality is sacrificed for excellent fit to the data. Furthermore, these techniques struggle to represent the conformational heterogeneity averaged out in low-resolution regions of density maps. Here we introduce EMMIVox, a Bayesian inference approach to determine single-structure models as well as structural ensembles from cryo-EM maps. EMMIVox automatically balances experimental information with accurate physico-chemical models of the system and the surrounding environment, including waters, lipids, and ions. Explicit treatment of data correlation and noise as well as inference of accurate B-factors enable determination of structural models and ensembles with both excellent fit to the data and high stereochemical quality, thus outperforming state-of-the-art refinement techniques. EMMIVox represents a flexible approach to determine high-quality structural models that will contribute to advancing our understanding of the molecular mechanisms underlying biological functions.

bioinformatics↗

bioRxiv2023

Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN

The rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Most in silico tools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field.

bioinformatics↗

bioRxiv2022

Clustering conformational ensembles of intrinsically disordered proteins with t-distributed stochastic neighbor embedding

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, A{beta}42, and a C-terminal fragment of -synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered sub-states within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information and provide interpretable visualizations of the conformational heterogeneity within each ensemble and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs. SignificanceGrouping heterogeneous conformations of IDPs into "structurally similar" clusters facilitates a clearer understanding of the properties of IDP conformational ensembles and provides insights into "structural ensemble: function" relationships. In this work, we provide a unique approach for clustering IDP ensembles efficiently using a non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), to create clusters with structurally similar IDP conformations. We show how this can be used for meaningful biophysical analyses such as understanding the binding mechanisms of IDPs such as -synuclein and Amyloid {beta}42 with small drug molecules. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=136 SRC="FIGDIR/small/516231v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@8ef906org.highwire.dtl.DTLVardef@7f14e5org.highwire.dtl.DTLVardef@134f9eeorg.highwire.dtl.DTLVardef@104a2ef_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Determination of the structure and dynamics of the fuzzy coat of an amyloid fibril of IAPP using cryo-electron microscopy

In recent years, major advances in cryo-electron microscopy (cryo-EM) have enabled the routine determination of complex biomolecular structures at atomic resolution. An open challenge for this approach, however, concerns large systems that exhibit continuous dynamics. To address this problem, we developed the metadynamic electron-microscopy metainference (MEMMI) method, which incorporates metadynamics, an enhanced conformational sampling approach, into the metainference method of integrative structural biology. MEMMI enables the simultaneous determination of the structure and dynamics of large heterogeneous systems by combining cryo-EM density maps with prior information through molecular dynamics, while at the same time modelling the different sources of error. To illustrate the method, we apply it to elucidate the dynamics of an amyloid fibril of the islet amyloid polypeptide (IAPP). The resulting conformational ensemble provides an accurate description of the structural variability of the disordered region of the amyloid fibril, known as fuzzy coat. The conformational ensemble also reveals that in nearly half of the structural core of this amyloid fibril the side-chains exhibit liquid-like dynamics despite the presence of the highly ordered network backbone of hydrogen bonds characteristic of the cross-{beta} structure of amyloid fibrils.

biophysics↗

bioRxiv2022

HARIBOSS: a curated database of RNA-small molecules structures to aid rational drug design

RNA molecules are implicated in numerous fundamental biological processes and many human pathologies, such as cancer, neurodegenerative disorders, muscular diseases, and bacterial infections. Modulating the mode of action of disease-implicated RNA molecules can lead to the discovery of new therapeutical agents and even address pathologies linked to 8undruggable9 protein targets. This modulation can be achieved by direct targeting of RNA with small molecules. As of today, only a few RNA-targeting small molecules are used clinically. One of the main obstacles that has hampered the development of a rational drug design protocol to target RNA with small molecules is the lack of a comprehensive understanding of the molecular mechanisms at the basis of RNA-small molecule recognition. Here, we present HARIBOSS, a curated collection of RNA-small molecule structures determined by X-ray crystallography, Nuclear Magnetic Resonance spectroscopy and cryo-electron microscopy. HARIBOSS facilitates the exploration of drug-like compounds known to bind RNA, the analysis of ligands and pockets properties, and ultimately the development of in silico strategies to identify RNA-targeting small molecules. HARIBOSS can be explored via a web interface available at http://hariboss.pasteur.cloud.

bioinformatics↗