Biology subjects

Bonvin, A.

Publications and source records attributed to Bonvin, A..

6 recordsLinked to original sources

bioRxiv2026

Full-Length Structural Modeling of Mitofusins with AlphaFold Reveals a Novel Cross-Type Dimerization and Insights into Oligomerization

Mitochondrial dynamics, involving fission and fusion, are critical for the maintenance, function, distribution, and inheritance of mitochondria, allowing their morphology to adapt to the cells physiological needs. Mitofusins, large GTPase transmembrane proteins, play a role in this process by driving the tethering and fusion of mitochondrial outer membranes. Dysfunction in mitofusins has been associated with neurodegenerative diseases such as Parkinsons, Alzheimers, Huntingtons, and Charcot-Marie-Tooth type 2A, as well as various cancers, where their dysregulated expression influences cell proliferation, invasion, and chemotherapy resistance. Despite their importance, the precise molecular mechanisms underlying mitofusin-mediated fusion remain unclear and require further structural elucidation. In fact, no complete high-resolution structures exist for mitofusins, including their yeast homolog, Fzo1. Here, we generated and analyzed full-length structural models of mitofusins using AlphaFold. Monomeric, dimeric, and tetrameric assemblies were produced, including complexes with fusion partners Ugo1 and SLC25A46. While AlphaFold predicted limited conformational diversity for isolated monomers, structural variability emerged for predicted homo- and hetero-oligomers. Notably, our models reveal a previously undescribed cross-type dimerization mode involving interactions between heptad repeat domains, not reported in current experimental structures. Comparison with recently resolved experimental data further supports the structural relevance of this interface. These full-length models allowed us to propose a new hypothetical mechanism of outer mitochondrial membrane fusion.

bioinformatics↗

bioRxiv2025

Combining AI structure prediction and integrative modelling for nanobody-antigen complexes

Nanobodies exhibit antigen binding affinities of the same order as those of antibodies, which, along with their small size and unique structural characteristics, makes them well-suited for therapeutic and diagnostic applications. The lack of coevolutionary signals in nanobody-antigen complexes together with the broad complementary determining region 3 loop (CDR3) conformational space poses a challenge for predicting the 3D structure of those complexes with computational modelling and artificial intelligence-based methods. In this context, physics-based information-driven docking can provide an alternative solution. This study evaluates the state-of-the-art of machine learning-based methods for nanobody structure prediction and benchmarks various HADDOCK workflows to model their interaction with antigens using different input nanobody ensembles and information scenarios. We propose an ensemble docking pipeline that achieves high success rates starting from nanobody structural models predicted by AlphaFold2 and ImmuneBuilder. Our results highlight the effectiveness of physics-based complex prediction of immune proteins when accurate input structures and sufficient information to guide the modelling are available.

bioinformatics↗

bioRxiv2024

The SH Protein of Mumps Virus is a Druggable Pentameric Viroporin

Viral infections are on the rise and drugs targeting viral proteins are needed. Viroporins constitute a growing group of virus-encoded transmembrane oligomeric proteins that allow passage of small molecules across the membrane. Despite sparsity in viroporin structures, recent work has revealed diversity in both the number of transmembrane helices and oligomeric states. Here we provide evidence that the small hydrophobic protein (SH) from mumps virus is a pentameric viroporin. From extensive biophysical data, a HADDOCK model of full-length SH shows its intracellular C-terminal region to form an extended structure crucial to stabilization of the pentamer. Heterologous expression of wild type SH and variants in Xenopus laevis oocytes reveals the viroporin as a chloride channel, facilitated by conserved hydroxyl-carrying residues lining the pore. The channel function of SH is inhibited by the small-molecule BIT225, highlighting the potential for antiviral targeting through SH.

biophysics↗

bioRxiv2024

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small molecules is key to understand the molecular principles of chemotherapy and for designing new and more effective drugs. During the early stage of a drug discovery program, it is customary to predict ligand-protein complexes in silico, particularly when screening large compound databases. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology to generate bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites by exploiting only information on the unbound structure and the putative binding sites. The protocol is validated on the paradigm enzyme adenylate kinase, for which we generated a significant fraction of bound-like structures. A fraction of these conformations, employed in ensemble-docking calculations, allowed to find native-like poses of substrates and inhibitors (binding to the active form of the enzyme), as well as catalytically incompetent analogs (binding the inactive form). Our protocol provides a general framework for the generation of bound-like conformations of challenging drug targets that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate proteins activity. We foresee applications in virtual screening, in the prediction of the impact of amino acid mutations on structure and dynamics, and in protein engineering.

biophysics↗

bioRxiv2022

A Cyclisation and Docking Protocol for Cyclic Peptide-Protein Modelling using HADDOCK2.4

An emerging class of therapeutic molecules are cyclic peptides with over 40 cyclic peptide drugs currently in clinical use. Their mode of action is, however, not fully understood, impeding rational drug design. Computational techniques could positively impact their design but modeling them and their interactions remains challenging due to their cyclic nature and their flexibility. This study presents a step-by-step protocol for generating cyclic peptide conformations and docking them to their protein target using HADDOCK2.4. A dataset of 30 cyclic peptide-protein complexes was used to optimize both cyclisation and docking protocols. It supports peptides cyclized via an N- and C-terminus peptide bond and/or a disulfide bond. An ensemble of cyclic peptide conformations is then used in HADDOCK to dock them onto their target protein using knowledge of the binding site on the protein side to drive the modeling. The presented protocol predicts at least one acceptable model according to CAPRI criteria (Critical Assessment of Prediction of Interaction) for each complex of the dataset when the top 10 HADDOCK-ranked single structures are considered (100% success rate top10) both in the bound and unbound docking scenario. Moreover, its performance in both bound and fully unbound docking, is similar to the state-of-the-art software in the field, Autodock CrankPep. The presented cyclisation and docking protocol should make HADDOCK a valuable tool for rational cyclic peptide-based drug design and high-throughput screening.

bioinformatics↗

bioRxiv2019

iScore: an MPI supported software for ranking protein-protein docking models based on a random walk graph kernel and support vector machines.

Computational docking is a promising tool to model three-dimensional (3D) structures of protein-protein complexes, which provides fundamental insights of protein functions in the cellular life. Singling out near-native models from the huge pool of generated docking models (referred to as the scoring problem) remains as a major challenge in computational docking. We recently published iScore, a novel graph kernel based scoring function. iScore ranks docking models based on their interface graph similarities to the training interface graph set. iScore uses a support vector machine approach with random-walk graph kernels to classify and rank protein-protein interfaces.\n\nHere, we present the software for iScore. The software provides executable scripts that fully automatize the computational workflow. In addition, the creation and analysis of the interface graph can be distributed across different processes using Message Passing interface (MPI) and can be offloaded to GPUs thanks to dedicated CUDA kernels.

bioinformatics↗