Konstantinos Kalogeropoulos

This paper introduces InstaNovo-FM, a self-supervised foundation model for proteomics that learns from tandem mass spectra to enable transferable applications across datasets and instruments.

InstaNovo-P improves phosphorylated peptide detection and site localization in phosphoproteomics by using a transformer-based model fine-tuned on phosphoproteomics data.

This study identifies and analyzes tissue-specific protease dynamics in the pig respiratory tract during influenza A virus infection, revealing distinct protease activity patterns and their roles in antiviral responses and pathogenesis.

This paper introduces a workflow for direct protein sequencing using AI-driven methods, validated across diverse proteins to improve accuracy and reduce ambiguity in therapeutic and scientific applications.

This study shows that increased meprin α expression in mice leads to a psoriasis-like skin phenotype through degradation of dermokine, promoting hyperproliferation and inflammation.

This study benchmarks mass spectrometry-based antibody sequencing methods, revealing biases related to sequence, concentration, protease, and bioinformatics tools.

InstaNovo is a transformer model for de novo peptide sequencing that outperforms existing methods, enabling accurate identification of peptides and discovery of novel sequences and organisms.

CLIPPER 2.0 is a computational tool for peptide-level annotation and analysis of positional proteomics data, demonstrating its use on GluC and MMP9 cleavage in HeLa lysates.

This study compares three protein structure prediction tools on snake venom toxins with no reference templates, finding AlphaFold2 performs best, while ColabFold is computationally less intensive.