Wassim Gabriel

The paper develops a chemical proteomics workflow to profile protein citrullination dynamics, achieving high sensitivity and throughput in detecting citrullinated peptides across biological systems.

Modanovo is a de novo peptide sequencing model that handles post-translational modifications, outperforming existing methods and complementing database searches in phosphoproteomics.

The paper maps isozyme-specific substrate recognition of PAD1-4 by identifying citrullination sites across the proteome, revealing distinct sequence preferences and key residues determining substrate specificity.

This paper introduces Prosit-PTM, a deep learning model for PTM discovery that improves zero-shot prediction using a synthetic dataset and chemically-informed encoding.

This study compares Scribe and database search engines in metaproteomic analysis of a ground-truth microbiome dataset, finding Scribe detects more proteins and quantifies microbial composition more accurately.

The paper introduces a deep learning model to improve citrullination site identification in proteomes, achieving high precision and sensitivity in human and plant tissues.

Koina provides an open-source, accessible ML/DL prediction service for proteomics, improving integration with existing tools like FragPipe.

The paper trains a deep learning model to predict fragment ion intensities, improving immunopeptide identification rates using timsTOF instruments.

MegaGO assesses functional similarity between meta-omics datasets using semantic GO term similarity, offering fast and scalable analysis.