Biology subjects

Ludaic, M.

Publications and source records attributed to Ludaic, M..

3 recordsLinked to original sources

bioRxiv2026

ABAG-Rank: Improving Model Selection of AlphaFold Antibody-Antigen Complexes by Learning to Rank

MotivationAlphaFold has transformed structural biology with an unprecedented accuracy in modeling protein structures and their interactions with biomolecules, with AlphaFold3 (AF3) achieving state-of-the-art performance. However, AF3 and other methods often struggle to accurately predict the structure of protein complexes that lack strong co-evolutionary information, such as antibody-antigen (Ab-Ag) complexes. One of the fundamental issues is that AF3 often generates accurate predictions, but fails to reliably distinguish them from the much larger set of incorrect ones. ResultsTo address this, we propose ABAG-Rank, a deep neural network that provides an efficient and robust solution for model selection of Ab-Ag interactions from a pool of structural ensembles predicted with AlphaFold. Built on the permutation-invariant DeepSets architecture, ABAG-Rank can process variable-sized ensembles of structural decoys and is directly applicable to prediction settings in which the number of candidates may vary. We train a model on a redundancy-reduced set of all known antibody-antigen complexes and find that simple geometric descriptors, along with confidence scores from AlphaFold, provide rich information about interface quality without requiring intensive physics-based calculations. Our experiments demonstrate that ABAG-Rank significantly outperforms AF3 internal scoring and the ranking performance of existing deep learning baselines. ImplementationSource code can be found at: https://github.com/tadteo/ABAG-Rank

bioinformatics↗

bioRxiv2025

Evaluating Deep Learning Based Structure Prediction Methods on Antibody-Antigen Complexes

MotivationAlphaFold2 significantly improved the prediction of protein complex structures. However, its accuracy is lower for interactions without coevolutionary signals, such as host-pathogen and antibody-antigen interactions. Two strategies have been developed to address this limitation: massive sampling and replacing the evoformer with the pairformer, which does not rely on coevolution, as introduced in AlphaFold3, thereby enabling more structural reasoning by the network. ResultsIn this study, we benchmark structure prediction methods on unseen antibody-antigen complexes. We found that increased sampling improves the chances of generating a correct protein model, roughly in a log-linear manner. However, the internal quality estimates by AlphaFold often cannot identify the best predicted structures for each target, resulting in a significant loss of performance for the top-ranked protein model compared with the best model. For all methods, a significant challenge remains the identification of the best model. We also show that AlphaFold3 outperforms AlphaFold2, Boltz-1, and Chai-1. Furthermore, AlphaFold3 performance declines significantly for complexes that lack structural similarity to the training set, indicating that it has to some extent learned to detect remote structural similarities. Availability and implementationAll code is available from github.com/samuelfromm/abag-benchmark-set/ and all data from DOI:10.5281/zenodo.17978681. The latter repository also contains the code. Supplementary informationSupplementary information is available online.

bioinformatics↗

bioRxiv2025

Limits of deep-learning-based RNA prediction methods

In recent years, tremendous advances have been made in predicting protein structures and protein-protein interactions. However, progress in predicting RNA structure, either alone or in complex with other macromolecules, has been less prominent, though some recent developments have been reported. It remains unclear whether the improved prediction accuracy is sustained for novel RNA structures. Here, we use an independent benchmark to evaluate the performance of the latest methods. First, we show that state-of-the-art methods can sometimes predict the structure of single-chain RNA strands, with accurate models observed for RNAs with well-defined or regular secondary structures. Next, our evaluation was extended to RNA complexes, where prediction accuracy was notably higher for those involving extensive canonical base pairing. Additionally, a structural similarity analysis revealed that prediction success strongly correlates with resemblance to known structures, indicating that current methods recognise recurring motifs rather than generalising to novel folds. Finally, we also noted that the accuracy estimates for RNA models are far from accurate. Therefore, it is not possible to reliably identify the correctly predicted models with todays methods. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/651414v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@fbdd3eorg.highwire.dtl.DTLVardef@17a31c0org.highwire.dtl.DTLVardef@158391corg.highwire.dtl.DTLVardef@10d78a0_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗