Biology subjects

Dumortier, B.

Publications and source records attributed to Dumortier, B..

3 recordsLinked to original sources

bioRxiv2026

Repurposing PeTriBERT for Protein Protein Interaction Prediction with Sequence Structure Fusion

Recent advances in artificial intelligence have enabled models to capture protein sequence and structural features. We present PeTriPPI2, a hybrid framework for protein--protein interaction (PPI) prediction that combines sequence embeddings from ESM-2 with structural representations from PeTriBERT, an encoder originally designed for inverse folding. We evaluate this sequence--structure approach on the Pinder dataset. On a test set of 2,342 protein pairs, the selected checkpoint achieves 0.898 accuracy, 0.917 precision, 0.876 recall, and 0.896 F1 score for the interacting class (AUROC 0.963). Compared with SpatialPPIv2 on the same test data, PeTriPPI2 has higher precision (0.917 versus 0.886) but lower recall (0.876 versus 0.942) and F1 score (0.896 versus 0.913). Component ablations indicate that both the sequence and structural pathways contribute to this configuration.

bioinformatics↗

bioRxiv2025

Boosting Protein-Protein Interaction Detection with AlphaFold Multimer and Transformers

In 2021, DeepMind released AlphaFold 2, an AI-driven algorithm that revolutionized protein folding predictions by achieving error rates comparable to traditional experimental methods. Its remarkable performance has inspired researchers to extend its applications beyond its original purpose, leveraging AlphaFold 2 and its output features to tackle various unresolved biological challenges. In this paper, we discuss previous works that explored the repurposing of AlphaFold 2 for the detection of peptide- protein interactions (PPI). We propose a novel methodology where the outputs of a lightweight version of AlphaFold 2 Multimer are enhanced by a fine-tuned transformer encoder and benchmarked against classic machine learning algorithms. We study the performance of our approach on a curated version of Propedia, a peptide-protein dataset, and show that the boosting models significantly improve the detection of PPIs. Additionally, we provide HAPI (Hacky API), a freely available Python library designed to facilitate the seamless integration and use of AlphaFold 2 in computational workflows, further empowering the scientific community.

bioinformatics↗

bioRxiv2022

PeTriBERT : Augmenting BERT withtridimensional encoding for inverse protein foldingand design

Protein is biology workhorse. Since the recent break-through of novel folding methods, the amount of available structural data is increasing, closing the gap between data-driven sequence-based and structure-based methods. In this work, we focus on the inverse folding problem that consists in predicting an amino-acid primary sequence from protein 3D structure. For this purpose, we introduce a simple Transformer model from Natural Language Processing augmented 3D-structural data. We call the resulting model PeTriBERT: Proteins embedded in tridimensional representation in a BERT model. We train this small 40-million parameters model on more than 350 000 proteins sequences retrieved from the newly available AlphaFoldDB database. Using PetriBert, we are able to in silico generate totally new proteins with a GFP-like structure. These 9 of 10 of these GFP structural homologues have no ressemblance when blasted on the whole entry proteome database. This shows that PetriBert indeed capture protein folding rules and become a valuable tool for de novo protein design.

bioinformatics↗