Biology subjects

Giulini, M.

Publications and source records attributed to Giulini, M..

9 recordsLinked to original sources

bioRxiv2026

Patient-derived monoclonal anti-HPA-1a can induce platelet activation through FcγRIIa

Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a pregnancy-associated disorder caused by maternal alloantibodies targeting paternally inherited human platelet antigens (HPAs). These antibodies traverse the placenta and bind to fetal platelets, causing thrombocytopenia and potentially severe complications including intracranial hemorrhage (ICH). Beyond platelets, antibodies may also target other fetal cells such as endothelial cells and placental trophoblasts thereby exacerbating disease severity. Anti-HPA-1a antibodies, the primary cause of FNAIT, display functional heterogeneity due to differences in epitope specificity, glycosylation, which complicate disease severity prediction. Here, we report the structural and functional characterization of two novel anti-HPA-1a antibodies, D-204 and M-204, derived from a mother with a severe FNAIT case. In comparison to D-204 and other existing anti-HPA-1a antibodies such as B2G1 and 26.4, M-204 bound IIb{beta}3 with different kinetics, reaching only half-maximal binding, as determined by flow cytometry. Notably, only M-204 induced platelet aggregation. To investigate the underlying mechanism behind this aggregation, various antibody formats were generated (Fab, Fab2, bispecific, and IgG-Fc dead). Aggregation required both Fab-arms to engage HPA-1a and intact binding to Fc{gamma}RIIa. Structural models and crystal structures suggest the dual engagement of both Fab-arms is required for Fc{gamma}RIIA-engagement which is a unique feature of M-204 among known HPA-1 monoclonals. Our findings highlight the functional diversity of anti-HPA-1a antibodies and provide new insights into their pathogenic mechanisms.

immunology↗

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↗

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↗

bioRxiv2025

Improved structural modelling of antibodies and their complexes with clustered diffusion ensembles

MotivationGaining structural insights into antibody-antigen complexes is crucial for understanding antigen recognition mechanisms and advancing therapeutic antibody design. However, accurate prediction of the structure of highly variable complementarity-determining region 3 on the antibody heavy chain (CDR-H3 loop) remains a significant challenge due to its increased length and conformational variability. While AlphaFold2-multimer (AF2) has made substantial progress in protein structure prediction, its application on antibodies and antibody-antigen complexes is limited by the weak evolutionary signals in the CDR region and the lack of structural diversity in its output. ResultsTo address these limitations, we propose a workflow that combines AlphaFlow to generate ensembles of potential loop conformations with integrative modelling of antibody-antigen complexes with HADDOCK. Improving the structural diversity of the H3 loop increases the success rate of subsequent docking tasks. Our analysis shows that while AF2 generally predicts accurate antibody structures, it struggles with the H3 loop. In cases where AF2 mispredicts the loop, we leverage AlphaFlow to generate ensembles of loop conformations via diffusion-based sampling, followed by clustering to produce a structurally diverse set of models. We demonstrate that these ensembles significantly improve antibody-antigen docking performance compared to the standard AF2 ensembles. Availability and implementationThe input datasets and codes involved in this research are available at https://github.com/haddocking/alphaflow-antibodies. All the resulting modelling data are available from Zenodo (https://zenodo.org/records/14906314).

bioinformatics↗

bioRxiv2024

Integrative modeling in the age of machine learning: a summary of HADDOCK strategies in CAPRI rounds 47-55

The HADDOCK team participated in CAPRI rounds 47-55 as both server, manual predictor, and scorers. Throughout these CAPRI rounds, we used a plethora of computational strategies to predict the structure of protein complexes. Of the 10 targets comprising 24 interfaces, we achieved acceptable or better models for 3 targets in the human category and 1 in the server category. Our performance in the scoring challenge was slightly better, with our simple scoring protocol being the only one capable of identifying an acceptable model for Target 234. This result highlights the robustness of the simple, fully physics-based HADDOCK scoring function, especially when applied to highly flexible antibody-antigen complexes. Inspired by the significant advances in machine learning for structural biology and the dramatic improvement in our success rates after the public release of Alphafold2, we identify the integration of classical approaches like HADDOCK with AI-driven structure prediction methods as a key strategy for improving the accuracy of model generation and scoring.

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

Modelling Protein-Glycan Interactions with HADDOCK

The term glycan refers to a broad category of molecules composed of monosaccharides units linked to each other in a variety of ways, whose structural diversity is related to different functions in living organisms. Among others, glycans are recognized by proteins with the aim of carrying information and for signalling purposes. Determining the three-dimensional structures of protein-protein-glycan complexes is essential both for the understanding of the mechanisms glycans are involved in, and for applications such as drug design. In this context, molecular docking approaches are of undoubted importance as complementary approaches to experiments. In this study, we show how HADDOCK can be efficiently used for the prediction of protein-glycan complexes. Using a benchmark of 89 complexes, starting from their bound or unbound forms, and assuming some knowledge of the binding site on the protein, our protocol reaches a 70% and 40% top 5 success rate on bound and unbound datasets, respectively. We show that the main limiting factor is related to the complexity of the glycan to be modelled and the associated conformational flexibility.

bioinformatics↗

bioRxiv2023

Towards the accurate modelling of antibody-antigen complexes from sequence using machine learning and information-driven docking

Antibody-antigen complex modelling is an important step in computational workflows for therapeutic antibody design. While experimentally determined structures of both antibody and the cognate antigen are often not available, recent advances in machine learning-driven protein modelling have enabled accurate prediction of both antibody and antigen structures. Here, we analyse the ability of protein-protein docking tools to use machine learning generated input structures for information-driven docking. We find that HADDOCK can generate accurate models of antibodyantigen complexes using an ensemble of antibody structures generated by machine learning tools and AlphaFold2 predicted antigen structures. Targeted docking using knowledge of the complementary determining regions on the antibody and some information about the targeted epitope allows the generation of high quality models of the complex with reduced sampling, resulting in a computationally cheap protocol that outperforms the ZDOCK baseline. The data set used to benchmark the docking protocols in this study is available at github.com/haddocking/ai-antibodies. The docking models will be deposited at data.sbgrid.org/labs/32/ upon acceptance.

bioinformatics↗

bioRxiv2023

ARCTIC-3D: Automatic Retrieval and ClusTering of Interfaces in Complexes from 3D structural information

The formation of a stable complex between proteins lies at the core of a wide variety of biological processes and has been the focus of countless experiments. The huge amount of information contained in the protein structural interactome in the Protein Data Bank can now be used to characterise and classify the existing biological interfaces. We here introduce ARCTIC-3D, a fast and user-friendly data mining and clustering software to retrieve data and rationalise the interface information associated with the protein input data. We demonstrate its use by various examples ranging from showing the increased interaction complexity of eukaryotic proteins, 20% of which on average have more than 3 different interfaces compared to only 10% for prokaryotes, to associating different functions to different interfaces. In the context of modelling biomolecular assemblies, we introduce the concept of "recognition entropy", related to the number of possible interfaces of the components of a protein-protein complex, which we demonstrate to correlate with the modelling difficulty. The identified interface clusters can also be used to generate various combinations of interface-specific restraints for integrative modelling. The ARCTIC-3D software is freely available at https://github.com/haddocking/arctic3d and can be accessed as a web-service at https://wenmr.science.uu.nl/arctic-3d

bioinformatics↗