Biology subjects

Bonvin, A. M. J. J.

Publications and source records attributed to Bonvin, A. M. J. J..

At least 19 recordsLinked to original sources

bioRxiv2026

PowerFit: An updated framework for fast rigid-body fitting into cryo-EM density maps.

Cryo-electron microscopy (cryo-EM) has become a central method for determining structures of molecular assemblies, requiring reliable tools to interpret the density maps. Despite recent advances in AI-driven model building, at intermediate-to-low resolution (4-15 [a]) accurate rigid-body fitting of existing atomic structures into cryo-EM maps remains an essential step in the modelling process. Here we present an updated version of our software package PowerFit. It performs an exhaustive translational and rotational search against the cryo-EM density map, implementing sensitive correlation functions. The updated release preserves the fitting accuracy and sensitivity of the original method while substantially improving the computational performance through modernised CPU and GPU implementations. In addition, to facilitate the interpretation of results, it provides an HTML report with interactive 3D visualisation using MolViewSpec. The same HTML report is used to generate the result page when using PowerFit via its web service. The report enables immediate inspection and validation of the results. The software's accessibility has been enhanced by updating it from Python 2 to Python 3, enabling straightforward installation via the Python Package Index (PyPI) and simple integration into computational pipelines. PowerFit is freely available at https://github.com/haddocking/powerfit, as a web service at https://wenmr.science.uu.nl/powerfit or in JupyterLite directly in a web browser at https://www.bonvinlab.org/powerfit/jupyterlite.html.

bioinformatics↗

bioRxiv2026

MIMOSA: Guiding De Novo Antibody Design with Natural Interaction Fingerprints

De novo antibody design promises to transform therapeutic antibody and nanobody discovery. Yet, success remains inconsistent across targets, with generative models often yielding no experimentally validated binders. Here we introduce MIMOSA (MIMic-Oriented Structural Antibody generation), a model-agnostic, inference-time framework that constrains diffusion-based antibody design pipelines to reproduce the interaction geometry and chemistry of a target's known cognate binder interface. Rather than searching for a productive interface from scratch, MIMOSA constrains generation around residue identities and geometries already known to support binding, while allowing the generative model to complete the surrounding antibody interface. We test MIMOSA on different interface topologies: contiguous motifs that fit within a single CDR loop and spatially dispersed hotspots distributed across the paratope. Applied without retraining to two architecturally distinct generators (RFAntibody and BoltzGen), this framework delivers experimentally validated binders-achieving sub-micromolar to single-digit nanomolar affinities-at 20-26% hit rates on three targets on which unconstrained methods produce zero or near-zero binders: KEAP1, uPA, and IL-8. By turning any target with a structurally characterised cognate binder into an accessible de novo design problem, this framework broadens the practical reach of generative antibody design to targets where unconstrained approaches currently fail.

bioinformatics↗

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↗

bioRxiv2025

AbTune: Layer-wise selective fine-tuning of protein language models for antibodies

AbstractO_ST_ABSMotivationC_ST_ABSAntibodies play central roles in immune defense and are widely used as therapeutic agents. However, the high structural and sequence diversity of antigen-binding loops, combined with limited experimental data and weak co-evolutionary signals, makes it difficult to develop generalizable predictive models. ResultsWe investigate test-time fine-tuning strategies to improve protein language model (pLM) performance in low-data settings, with a focus on antibody-related tasks. Systematic evaluations show that carefully constrained fine-tuning improves performance while preserving generalization. In particular, depth-selective fine-tuning consistently outperforms full-depth fine-tuning, with optimal performance achieved when tuning 50-75% of model layers for medium- to small-sized pLMs. We introduce AbTune, a test-time fine-tuning framework leveraging this depth-controlled adaptation strategy. Across antibody structure prediction, mutation effect prediction, and binding affinity prediction, AbTune outperforms standard pLM baselines and task-specific predictors on most benchmarks. We further analyze representation shifts, sequence-dependent adaptation behavior, and overfitting indicators, showing that fine-tuning depth, duration, and perplexity jointly determine performance. Availabilityhttps://github.com/haddocking/AbTune

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

AlphaFastPPi: a fast AlphaFold-Multimer-based pipeline for high-throughput screening

MotivationDiscovering new protein-protein interactions (PPIs) across entire proteomes offers vast potential for understanding novel protein functions and elucidate system properties within or between an organism. While recent advances in computational structural biology, particularly AlphaFold-Multimer, have facilitated this task, scaling for large-scale screenings remains a challenge, requiring significant computational resources. ResultsWe evaluated the impact of reducing the number of models generated by AlphaFold-Multimer from five to one on the methods ability to distinguish true PPIs from false ones. Our evaluation was conducted on a dataset containing both intra- and inter-species PPIs, which included proteins from bacterial and eukaryotic sources. We demonstrate that reducing the sampling does not compromise the accuracy of the method, offering a 5 time faster, efficient, and environmentally friendly solution for PPI predictions. AvailabilityThe code used in this article is available at https://github.com/MIDIfactory/AlphaFastPPi. Note that the same can be achieved using the latest version of AlphaPulldown available at https://github.com/KosinskiLab/AlphaPulldown.

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↗

bioRxiv2023

A new antibiotic from an uncultured bacterium binds to an immutable target

Antimicrobial resistance is a leading mortality factor worldwide. Here we report the discovery of clovibactin, a new antibiotic, isolated from uncultured soil bacteria. Clovibactin efficiently kills drug-resistant bacterial pathogens without detectable resistance. Using biochemical assays, solid-state NMR, and atomic force microscopy, we dissect its mode of action. Clovibactin blocks cell wall synthesis by targeting pyrophosphate of multiple essential peptidoglycan precursors (C55PP, Lipid II, LipidWTA). Clovibactin uses an unusual hydrophobic interface to tightly wrap around pyrophosphate, but bypasses the variable structural elements of precursors, accounting for the lack of resistance. Selective and efficient target binding is achieved by the irreversible sequestration of precursors into supramolecular fibrils that only form on bacterial membranes that contain lipid-anchored pyrophosphate groups. Uncultured bacteria offer a rich reservoir of antibiotics with new mechanisms of action that could replenish the antimicrobial discovery pipeline.

microbiology↗

bioRxiv2023

A novel antifolate suppresses growth of FPGS-deficient cells and overcomes methotrexate resistance

Cancer cells make extensive use of the folate cycle to sustain increased anabolic metabolism. Multiple chemotherapeutic drugs interfere with the folate cycle, including methotrexate and 5-fluorouracil that are commonly applied for the treatment of leukemia and colorectal cancer (CRC), respectively. Despite high success rates, therapy-induced resistance causes relapse at later disease stages. Depletion of folylpolyglutamate synthase (FPGS), which normally promotes intracellular accumulation and activity of both natural folates and methotrexate, is linked to methotrexate and 5-fluorouracil resistance and its association with relapse illustrates the need for improved intervention strategies. In this study, we characterize a novel antifolate (C1) that, like methotrexate, potently inhibits dihydrofolate reductase (DHFR) and downstream one-carbon metabolism. Contrary to methotrexate, however, C1 displays optimal efficacy in FPGS-deficient contexts, due to decreased competition with intracellular folate concentrations for interaction with DHFR. Indeed, we show that FPGS-deficient patient-derived CRC organoids display enhanced sensitivity to C1-induced growth inhibition, while FPGS-high CRC organoids are more sensitive to methotrexate. Our results thus argue that polyglutamylation-independent antifolates can be applied to exert selective pressure on FPGS-deficient cells during chemotherapy, employing a vulnerability created by polyglutamylation deficiency.

biochemistry↗

bioRxiv2022

Improving the Quality of Co-evolution Intermolecular Contact Prediction with DisVis

The steep rise in available protein sequences and structures has paved the way for bioinformatics approaches to predict residue-residue interactions in protein complexes. Multiple sequence alignments are commonly used in intermolecular contact predictions to identify co-evolving residues. These contacts, however, often include false positives (FPs), which may impair their use to predict three dimensional structures of biomolecular complexes and affect the accuracy of the generated models. Previously, we have developed DisVis to identify false positive data in mass spectrometry cross-linking data. DisVis allows to assess the accessible interaction space between two proteins consistent with a set of distance restraints. Here, we investigate if a similar approach could be applied to co-evolution predicted contacts in order to improve their precision prior to using them for modelling complexes. In this work we analyze co-evolution contact predictions with DisVis in order to identify putative FPs for a set of 26 protein-protein complexes. Next, the DisVis-reranked and the original co-evolution contacts are used to model the complexes with our integrative docking software HADDOCK using different filtering scenarios. Our results show that HADDOCK is robust with respect to the precision of the predicted contacts due to the 50% random contact removal during docking and using DisVis filtering for low precision contact data. DisVis can thus have a beneficial effect on low quality data, but overall HADDOCK can accommodate FP restraints without negatively impacting the quality of the resulting models. Other more precision-sensitive docking protocols might, however, benefit from the increased precision of the predicted contacts after DisVis filtering.

bioinformatics↗

bioRxiv2022

Thriving in the heat - Lysine acetylation stabilizes the quaternary structure of a Mega-Dalton hyperthermoactive PEP-synthase

Over time structural adaptations enabled proteins and enzymes to have sufficient stability and flexibility to perform the basic functions of life under various environmental conditions. The catalytic cores of key metabolic enzymes of hyperthermophilic archaea work at a temperature range of 80-120 {degrees}C, similar to the conditions wher the earliest life forms may have thrived. Here we characterize a key enzyme of the central carbon metabolism of Pyrococcus furious, through an integrative approach combining structural mass spectrometry, cryo-electron microscopy, mass photometry and molecular modelling with molecular dynamics simulations. From our investigation, we unveil the structural organization of phosphoenolpyruvate synthase (PPSA). Its 24-meric assembly - weighing over 2 MDa - harbors flexible distal domains, whose proper functioning and coordination depends on widespread chemical acetylation of lysine residues. This non-enzymatic post-translational modification, along with other types of lysine modifications, also occurs on most other major protein complexes of P. furiosus. These modifications likely originated in the chemically favorable primordial conditions and gradually became highly specialized and enzyme-driven in more distantly related mesophiles and Eukaryotes.

biochemistry↗

bioRxiv2021

DeepRank-GNN: A Graph Neural Network Framework to Learn Patterns in Protein-Protein Interfaces

Gaining structural insights into the protein-protein interactome is essential to understand biological phenomena and extract knowledge for rational drug design or protein engineering. We have previously developed DeepRank, a deep-learning framework to facilitate pattern learning from protein-protein interfaces using Convolutional Neural Network (CNN) approaches. However, CNN is not rotation invariant and data augmentation is required to desensitize the network to the input data orientation which dramatically impairs the computation performance. Representing protein-protein complexes as atomic- or residue-scale rotation invariant graphs instead enables using graph neural networks (GNN) approaches, bypassing those limitations. We have developed DeepRank-GNN, a framework that converts protein-protein interfaces from PDB 3D coordinates files into graphs that are further provided to a pre-defined or user-defined GNN architecture to learn problem-specific interaction patterns. DeepRank-GNN is designed to be highly modularizable, easily customized, and is wrapped into a user-friendly python3 package. Here, we showcase DeepRank-GNNs performance for scoring docking models using a dedicated graph interaction neural network (GINet). We show that this graph-based model performs better than DeepRank, DOVE and HADDOCK scores and competes with iScore on the CAPRI score set. We show a significant gain in speed and storage requirement using DeepRank-GNN as compared to DeepRank. DeepRank-GNN is freely available from https://github.com/DeepRank/DeepRank-GNN. Contacta.m.j.j.bonvin@uu.nl

bioinformatics↗

bioRxiv2021

MetaScore: A novel machine-learning based approach to improve traditional scoring functions for scoring protein-protein docking conformations

Protein-protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and timeconsuming experimental approaches for determining 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking - the so-called scoring problem - still has considerable room for improvement. We present here MetaScore, a new machine-learning based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using a rich set of features extracted from the respective protein-protein interfaces. These include physico-chemical properties, energy terms, interaction propensity-based features, geometric properties, interface topology features, evolutionary conservation and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging of the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of nine traditional SFs included in this work in terms of success rate and hit rate evaluated over the top 10 predicted conformations; (ii) An ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by judiciously leveraging machine-learning.

bioinformatics↗

bioRxiv2021

Interface Refinement of Low-to-Medium Resolution Cryo-EM Complexes using HADDOCK2.4

A wide range of cellular processes require the formation of multimeric protein complexes. The rise of cryo-electron microscopy (cryo-EM) has enabled the structural characterization of these protein assemblies. The produced density maps can, however, still suffer from limited resolution, impeding the process of resolving structures at atomic resolution. In order to solve this issue, monomers can be fitted into low-to-medium resolution maps. Unfortunately, the produced models frequently contain atomic clashes at the protein-protein interfaces (PPIs) as intermolecular interactions are typically not considered during monomer fitting. Here, we present a refinement approach based on HADDOCK2.4 to remove intermolecular clashes and optimize PPIs. A dataset of 14 cryo-EM complexes was used to test eight protocols. The best performing protocol, consisting of a semi-flexible simulated annealing refinement with restraints on the centroids of the monomers, was able to decrease intermolecular atomic clashes by 98% without significantly deteriorating the quality of the cryo-EM density fit.

bioinformatics↗

bioRxiv2021

Shape-restrained modelling of protein-small molecule complexes with HADDOCK

Small molecule docking remains one of the most valuable computational techniques for the structure prediction of protein-small molecule complexes. It allows us to study the interactions between compounds and the protein receptors they target at atomic detail, in a timely and efficient manner. Here we present a new protocol in HADDOCK, our integrative modelling platform, which incorporates homology information for both receptor and compounds. It makes use of HADDOCKs unique ability to integrate information in the simulation to drive it toward conformations which agree with the provided data. The focal point is the use of shape restraints derived from homologous compounds bound to the target receptors. We have developed two protocols: In the first, the shape is composed of fake atom beads based on the position of the heavy atoms of the homologous template compound, whereas in the second the shape is additionally annotated with pharmacophore data, for some or all beads. For both protocols, ambiguous distance restraints are subsequently defined between those beads and the heavy atoms of the ligand to be docked. We have benchmarked the performance of these protocols with a fully unbound version of the widely used DUD-E dataset. In this unbound docking scenario, our template/shape-based docking protocol reaches an overall success rate of 81% on 99 complexes, which is close to the best results reported for bound docking on the DUD-E dataset. Table of contents graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/447890v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@15faf1dorg.highwire.dtl.DTLVardef@e1d311org.highwire.dtl.DTLVardef@1e8225eorg.highwire.dtl.DTLVardef@1285ed0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗