Arthur Declercq

Finely tuned DDA-PASEF-based immunopeptidomics workflow enhances HLA class I coverage

This study optimizes a DDA-PASEF-based immunopeptidomics workflow to enhance HLA class I peptide coverage in low-input samples.

Evaluating the use of non-linear models in data-driven rescoring of peptide-spectrum matches

The paper evaluates non-linear models for peptide-spectrum match rescoring in proteomics, finding that complex models increase PSMs but introduce biased FDR estimates.

Predicting and Elucidating Peptide Retention Mechanisms with Graph Attention Networks

The paper introduces a deep learning model to predict peptide retention times in liquid chromatography and identifies key factors influencing retention through saliency mapping.

iDeepLC: chemical structure information yields improved retention time prediction of peptides with unseen modifications

iDeepLC improves retention time prediction for peptides with unseen modifications by incorporating chemical structural information, outperforming previous models.

Collisional cross-section prediction for multiconformational peptide ions with IM2Deep

The paper improves IM2Deep to predict collisional cross-sections for peptides with multiple conformations, using a comprehensive dataset and tailored architecture.

DeepLC introduces transfer learning for accurate LC retention time prediction and adaptation to substantially different modifications and setups

DeepLC uses transfer learning to improve LC retention time prediction across different experimental setups and peptide modifications.

Maximizing immunopeptidomics-based bacterial epitope discovery by multiple search engines and rescoring

The paper improves bacterial epitope discovery by integrating multiple search engines and rescoring, increasing peptide identification by 27% and detecting 18 new bacterial peptides.

Machine learning-based rescoring with MS2Rescore boosts peptide identification and taxonomic specificity in metaproteomics

MS2Rescore improves peptide identification and taxonomic specificity in metaproteomics by using machine learning-based rescoring, achieving lower false discovery rates with minimal sensitivity loss.

TIMS2Rescore: A DDA-PASEF optimized data-driven rescoring pipeline based on MS2Rescore

TIMS2Rescore is a data-driven rescoring pipeline optimized for DDA-PASEF data that improves protein identification in proteomics analyses.

MS2Rescore: Data-driven rescoring dramatically boosts immunopeptide identification rates

MS2Rescore improves immunopeptide identification rates by integrating retrained MS2PIP, DeepLC, and Percolator, achieving higher spectrum identification rates and unique peptide counts.