Biology subjects

Versini, R.

Publications and source records attributed to Versini, R..

6 recordsLinked to original sources

bioRxiv2026

Deep-learning predictions of biomolecular structures : persistent limitations and new horizons extended by explicit ion addition

The advent of deep learning-driven tools such as AlphaFold has revolutionized the prediction of biomolecular structures, offering unprecedented accuracy and accessibility for proteins, RNA, and their complexes. While these tools have demonstrated remarkable success in benchmarking competitions and enabled experimentalists to generate models with ease, their widespread use has also highlighted persistent challenges. These include difficulties in assessing model confidence, limitations in predicting transmembrane domains, nucleic acids, conformational diversity, and interactions with ions or ligands, as well as the tendency to misfold intrinsically disordered regions (IDRs). In this perspective, we critically evaluate the strengths and limitations of current AI-based structure prediction tools through illustrative examples with a particular emphasis on the impact of explicit ion modelling. We notably report how the explicit addition of a few potassium cations to the prediction of IDRs or G-quadruplexes can trigger massive conformational switches compared to "dry" predictions. On this basis, we suggest modelling sequences both "dry" and in the presence of explicit potassium cations as a simple, practical way to sample alternative conformations and to expose disordered regions that current predictors tend to over-fold. We discuss the importance of reporting confidence metrics in publications to avoid overinterpretation. Furthermore, we address the unique challenges of RNA structure prediction, where data scarcity and structural complexity limit the performance of both classical and deep learning methods. Our analysis underscores the need for continued methodological advancements, integration of complementary computational tools, and expansion of high-quality experimental datasets. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/740587v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@e950e9org.highwire.dtl.DTLVardef@1bf22b3org.highwire.dtl.DTLVardef@17f5229org.highwire.dtl.DTLVardef@1eb2e9f_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2026

Integrating the MARTINI2 Coarse-Grained Force Field into HADDOCK3 for Faster Modelling of Large Biomolecular Complexes

The integration of coarse-grained (CG) approaches into docking workflows offers a powerful strategy for modelling large biomolecular assemblies with reduced computational costs. We present here the implementation of the MARTINI2 coarse-grained force field into the HADDOCK3 integrative modelling platform. This development enables the use of the CG representations and parameters within HADDOCK3 for efficient sampling and scoring of large protein-protein complexes. The implementation takes advantage of the modular and flexible architecture of HADDOCK3, allowing a seamless combination of MARTINI2 representation with the various modules. Conversion from and to all-atom models is integrated into the coarse-grained modelling workflow. The performance of the protocol is first assessed on protein-protein and protein-DNA benchmarks and then illustrated on a few representative large-scale systems, demonstrating a significant reduction in computational costs while maintaining biologically relevant accuracy.

bioinformatics↗

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

HADDOCK3: A modular and versatile platform for integrative modelling of biomolecular complexes

HADDOCK is a widely used resource for integrative modelling of a variety of biomolecular complexes that is able to incorporate experimental knowledge into physics-based calculations during complex prediction, refinement, scoring and analysis. Here we introduce HADDOCK3, the new modular version of the program, in which the original, parameterisable albeit rigid pipeline has been first broken down in a catalogue of independent modules and then enriched with powerful analysis tools and third-party integrations. Thanks to this increased flexibility, HADDOCK3 can now handle multiple integrative modelling scenarios, providing a valuable, physics-based tool to enrich and complement the predictions made by machine learning algorithms in the post-AlphaFold era. We present examples of successful applications of HADDOCK3 that were not feasible with the previous versions of HADDOCK, highlighting its expanded capabilities. The HADDOCK3 software source code is freely available from the GitHub repository (https://github.com/haddocking/haddock3) and comes with an online user guide (www.bonvinlab.org/haddock3-user-manual). All example data described in this manuscript are available at https://github.com/haddocking/haddock3-paper-data.

bioinformatics↗

bioRxiv2023

Lys716 in the transmembrane domain of yeast mitofusin Fzo1 modulates anchoring and fusion

Outer mitochondrial membrane (OMM) fusion is an important process for the cell and organism survival, as its dysfunction is linked to neurodegenerative diseases and cancer. The OMM fusion is mediated by members of the dynamin-related protein (DRP) family, named mitofusins. The exact mechanism by which the mitofusins contribute to these diseases, as well as the exact molecular fusion mechanism mediated by mitofusin, remains elusive. We have performed extensive multiscale molecular dynamics simulations using both coarse-grained and all-atom approaches to predict the dimerization of two transmembrane domain (TM) helices of the yeast mitofusin Fzo1. We identify specific residues, such as Lys716, that can modulate dimer stability. Comparison with a previous computational model reveals remarkable differences in helix crossing angles and interfacial contacts. Overall, however, the TM1-TM2 interface appears to be stable in the Martini and CHARMM force fields. Replica-exchange simulations further tune a detailed atomistic model, as confirmed by a remarkable agreement with an independent prediction of the Fzo1-Ugo1 complex by AlphaFold2. Functional implications, including a possible role of Lys716 that could affect membrane interactions during fusion, are suggested and consistent with experiments monitoring mitochondrial respiration of selected Fzo1 mutants.

bioinformatics↗

bioRxiv2023

Deep Learning-Based Prediction of A. thaliana's MCTP4 Structure and Exploration of Transmembrane Dynamics using Coarse-Grained Molecular Dynamics Simulations

Multiple C2 Domains and Transmembrane region Proteins (MCTPs) in plants have been identified as important functional and structural components of plasmodesmata cytoplasmic bridges, which are vital for cell-cell communication. MCTPs are endoplasmic reticulum (ER)-associated proteins which contain three to four C2 domains and two transmembrane regions. In this study, we created structural models of Arabidopsis MCTP4 ER-anchor transmembrane region (TMR) domain using several prediction methods based on deep learning (DL). This region, critical for driving ER association, presents a complex domain organization and remains largely unknown. Our study demonstrates that using a single deep-learning method to predict the structure of membrane proteins can be challenging. Our deep learning models presented three different conformations for the MCTP4 structure, provided by different deep learning methods, indicating the potential complexity of the proteins conformational landscape. For the first time, we used simulations to explore the behaviour of the TMR of MCTPs within the lipid bilayer. We found that the TMR of MCTP4 is not rigid, but can adopt various conformations including some not identified by deep learning tools. These findings underscore the complexity of predicting protein structures. We learned that combining different methods, such as deep learning and simulations, enhances our understanding of complex proteins.

bioinformatics↗