Céline Marquet

The paper evaluates missense variant effect predictors using CAGI6 data, finding strong performance across settings and highlighting biases in some predictors.

TMVisDB provides a database and visualization tool for transmembrane proteins, enabling large-scale analysis of predicted structures and topologies without requiring costly AlphaFold3-style predictions.

VespaG is a fast missense variant effect predictor using protein Language Models, achieving high accuracy on the ProteinGym benchmark.

PINDER provides a benchmark dataset for evaluating protein-protein interaction docking models, revealing overestimation of performance on leaky test sets and showing improvements with interface-clustered training.

The paper introduces a machine learning model, IDBindT5, to predict binding residues in intrinsically disordered protein regions, achieving balanced accuracy comparable to state-of-the-art methods.

The paper predicts mutational outcomes for 1.5M missense variants across 72 protein families using alignment-based methods, showing high-quality and compute-efficient results for entire proteomes.

LambdaPP provides fast, accessible protein feature predictions including structure and function, using AI models and embeddings from protein Language Models.

TMvisDB provides a resource for browsing and visualizing predicted transmembrane proteins with topological annotations, using AlphaFold DB and TMbed predictions.