Biology subjects

Roel-Touris, J.

Publications and source records attributed to Roel-Touris, J..

7 recordsLinked to original sources

bioRxiv2026

SNAC-DB: An ML-Ready Database for Antibody and NANOBODY(R) VHH-Antigen Complexes with Expanded Structural Diversity and Real-World Benchmarking

Predicting antibody and NANOBODY(R) VHH-antigen complexes remains a critical challenge for state-of-the-art structure prediction models, limiting their impact in therapeutic discovery pipelines. We introduce SNAC-DB, an ML-ready database and curation pipeline enriched with structural biology expertise, designed to accelerate model accuracy and generalization by providing 31-37% expanded structural diversity over existing resources like SAbDab through comprehensive re-curation that extracts maximum value from available experimental structures. SNAC-DB expands coverage by capturing often-overlooked complexes and accurately identifying complete multi-chain epitopes through improved biological-assembly-based logic. Built for ML practitioners, SNAC-DB provides standardized formats with multi-threshold structure-based clustering to enable principled sample weighting during training. Using a rigorous benchmark of public PDB entries deposited post-May 2024 plus confidential therapeutic structures, we evaluate seven leading models (Protenix-v1, OpenFold-3p2, RosettaFold-3, Boltz-2, Boltz-1x, Chai-1, and AlphaFold2.3-multimer) with evaluation methodology tailored to antibody/NAN-OBODY(R) VHH-antigen complexes to ensure correct handling of multi-chain epitopes, revealing systematic performance gaps: success rates rarely exceed 25%, confidence-based ranking fails to identify best predictions even when accurate structures exist in ensembles, and all models consistently struggle with therapeutically relevant NANOBODY(R) VHHs. Systematic evaluation of sampling strategies demonstrates that while generating 1000 samples per target substantially increases the likelihood of producing accurate structures (oracle selection improves from 11.9% to 50.5%), confidence-based ranking remains nearly flat (between 10.9% and 14.9%), revealing that improved ranking mechanisms represent a more tractable path to performance gains. Finally, fine-tuning GeoDock on SNAC-DB yields higher success rates than training on SAbDab (11.0% vs. 7.1% for antibodies; 7.0% vs. 4.0% for NANOBODY(R) VHHs), suggesting that SNAC-DBs expanded structural diversity translates to improved model generalization. Significance StatementComputational antibody/NANOBODY(R) VHH design shows promise but remains unreliable for therapeutic development. SNAC-DB provides 31-37% expanded structural diversity through comprehensive data curation, immediately accelerating model development. Benchmarking seven leading AI models reveals accuracy rarely exceeds 25% on therapeutic targets, with confidence-based ranking failing to identify correct structures even when they exist in model outputs. Training on SNAC-DB increases prediction accuracy, validating that high-quality, diverse training data is critical for advancing computational methods toward clinical impact.

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↗

bioRxiv2023

The structural landscape of the immunoglobulin fold by large-scale de novo design

De novo designing immunoglobulin-like frameworks that allow for functional loop diversification shows great potential for crafting antibody-like scaffolds with fully customizable structures and functions. In this work, we combined de novo parametric design with deep-learning methods for protein structure prediction and design to explore the structural landscape of 7-stranded immunoglobulin domains. After screening folding of nearly 4 million designs, we have assembled a structurally diverse library of [~]50,000 immunoglobulin domains with high-confidence AlphaFold2 predictions and structures diverging from naturally occurring ones. The designed dataset enabled us to identify structural requirements for the correct folding of immunoglobulin domains, shed light on {beta}-sheet-{beta}-sheet rotational preferences and how these are linked to functional properties. Our approach eliminates the need for preset loop conformations and opens the route to large-scale de novo design of immunoglobulin-like frameworks.

bioinformatics↗

bioRxiv2022

Design of extended metal-binding β-sandwiches from de novo immunoglobulin domains

The antigen binding sites of antibodies, and derivatives such as scFvs, consist of an immunoglobulin structural framework that anchors hypervariable loops, and that is arranged as a dimer of {beta}-sandwiches (from the heavy and light chains) packing face-to-face. Yet, the naturally occurring dimer orientation of antibodies is not well suited for engineering rigid single-chain formats of increased stability and solubility, which are two key limitations of engineered antibodies. In this work, we computationally designed an extended 12-stranded {beta}-sandwich as a rigid single-chain dimer of de novo immunoglobulin domains interacting through an alternative edge-to-edge arrangement. The extended {beta}-sandwich was found to be hyperstable and structurally accurate as confirmed through X-ray crystallography. We functionalized this design by inserting a long EF-hand calcium-binding motif into the {beta}-hairpin bridge between both domains, showing high metal-binding affinity while remaining soluble and stable in solution. Finally, we propose a few designs with two EF-hand motifs in either or the same side of the scaffold with high-confidence structure predictions; altogether suggesting the robustness and versatility of our scaffold to harbor long functional loops. Our extended {beta}-sandwiches are structurally divergent to natural antibodies and open new avenues for incorporating multiple loop binding sites either for increased or bispecific activities.

biochemistry↗

bioRxiv2021

De Novo Design of Immunoglobulin-like Domains

Antibodies and antibody derivatives such as nanobodies contain immunoglobulin-like (Ig) {beta}-sandwich scaffolds which anchor the hypervariable antigen-binding loops and constitute the largest growing class of drugs. Current engineering strategies for this class of compounds rely on naturally existing Ig frameworks, which can be hard to modify and have limitations in manufacturability, designability and range of action. Here we develop design rules for the central feature of the Ig fold architecture - the non-local cross-{beta} structure connecting the two {beta}-sheets - and use these to de novo design highly stable seven-stranded Ig domains, confirm their structures through X-ray crystallography, and show they can correctly scaffold functional loops. Our approach opens the door to the design of a new class of antibody-like scaffolds with tailored structures and superior biophysical properties.

biochemistry↗

bioRxiv2019

An overview of data-driven HADDOCK strategies in CAPRI rounds 38-45.

Our information-driven docking approach HADDOCK has demonstrated a sustained performance since the start of its participation to CAPRI. This is due, in part, to its ability to integrate data into the modelling process, and to the robustness of its scoring function. We participated in CAPRI both as server and as manual predictors.\n\nIn CAPRI rounds 38-45, we have used various strategies depending on the information at hand. These ranged from imposing restraints to a few residues identified from literature as being important for the interaction, to binding pockets identified from homologous complexes or template-based refinement / CA-CA restraint-guided docking from identified templates. When relevant, symmetry restraints were used to limit the conformational sampling. We also tested for a large decamer target a new implementation of the MARTINI coarse-grained force field in HADDOCK. Overall in the current rounds, we obtained acceptable or better predictions for 13 and 11 server and manual submissions, respectively, out of the 22 interfaces. Our server performance (acceptable models) was better (59%) than the manual (50%) one, in which we typically experiment with various combinations of protocols and data sources. Again, our simple scoring function based on a linear combination of intermolecular van der Waals and electrostatic energies and an empirical desolvation term demonstrated a good performance in the scoring experiment with a 63% success rate across all 22 interfaces.\n\nAn analysis of model quality indicates that, while we are consistently performing well in generating acceptable models, there is room for improvement for generating/identifying higher quality models.

bioinformatics↗

bioRxiv2019

Less is more: Coarse-grained integrative modeling of large biomolecular assemblies with HADDOCK

Predicting the 3D structure of protein interactions remains a challenge in the field of computational structural biology. This is in part due to difficulties in sampling the complex energy landscape of multiple interacting flexible polypeptide chains. Coarse-graining approaches, which reduce the number of degrees of freedom of the system, help address this limitation by smoothing the energy landscape, allowing an easier identification of the global energy minimum. They also accelerate the calculations, allowing to model larger assemblies. Here, we present the implementation of the MARTINI coarse-grained force field for proteins into HADDOCK, our integrative modelling platform. Docking and refinement are performed at the coarse-grained level and the resulting models are then converted back to atomistic resolution through a distance restraints-guided morphing procedure. Our protocol, tested on the largest complexes of the protein docking benchmark 5, shows an overall ~7-fold speed increase compared to standard all-atom calculations, while maintaining a similar accuracy and yielding substantially more near-native solutions. To showcase the potential of our method, we performed simultaneous 7 body docking to model the 1:6 KaiC-KaiB complex, integrating mutagenesis and hydrogen/deuterium exchange data from mass spectrometry with symmetry restraints, and validated the resulting models against a recently published cryo-EM structure.

bioinformatics↗