Advanced Proteomics Techniques and Applications

dnoise reduces Bruker timsTOF data size with minimal impact on analysis results.

ChemIntelligence enables antibody-free, ultra-low-input profiling of lysine lactylation and diverse acyl-proteomes using chemical derivatization and computational methods.

Bromo, a pairwise transformer, ranks a protein's peptide precursors by expected mass spectrometry response, beating XGBoost, PREGO and PeptideRanger on held-out yeast data, and can be fine-tuned per instrument.

This study evaluates the trade-off between spatial resolution and proteome depth in spatial proteomics using SLACS, showing that few-cell sampling maintains proteomic information while increasing spatial specificity.

The paper identifies recurrent non-canonical proteoforms in AML using integrative proteogenomics, revealing 5,849 unannotated proteoforms, including 1,987 with no homology to annotated proteins.

XMAn Update expands a database of mutated peptides by integrating cancer-related variants from COSMIC and CGC datasets to improve variant detection in mass spectrometry analyses.

The paper quantifies proteins and transcripts in the same cell, finding that the proteome defines stable cell states while the transcriptome reflects transitions.

The paper profiles the nuclear proteome of Trypanosoma cruzi using mass spectrometry and identifies protein interactions and modifications, providing a resource for further studies.

This study maps organ-specific endothelial surface proteomes in vivo to identify tissue-selective vascular targets for therapy.

This paper introduces ProtInt, a deep learning framework for integrating proteomic data from cancer cell lines and tumors, showing improved performance over existing methods.

The paper describes a chemical proteomics workflow that reduces protein input and improves sensitivity for profiling post-translational modifications.

This paper introduces a normalization framework to decouple protocol and instrument effects in multicenter proteomics studies, demonstrating its application to the PME13 dataset.

MARLOWE is a computational tool for taxonomic characterization of unknown forensic samples using de novo peptide identification, achieving high specificity in species-level identification.

This paper demonstrates that using ionbot with OpenProt improves detection of non-canonical proteins by reducing FDR and increasing reproducibility compared to TPP.

STEP-PTM enables integrated quantitative analysis of proteome, metabolome, and multiple PTM classes from single biological samples using a modular TMT-multiplexed workflow.

This study benchmarks rescoring methods across proteomics search engines and datasets, finding that prediction-based approaches improve identification consensus and reduce variability.

MetaPilot improves metaproteomics analysis by using genome-aware search-space refinement, enhancing peptide identification in DDA and DIA datasets.

This paper uses spatial proteomics to reveal proteome heterogeneity and ECM remodeling in human atherosclerotic plaques across different regions.

This study compares serum preprocessing workflows in proteomics to assess their impact on biological readouts in systemic juvenile idiopathic arthritis.

borgonovo improves protein sequence accuracy from tandem mass spectra by consensus assembly of redundant de novo peptide reads, achieving 0.87 mean fixed-window identity on therapeutic antibodies.

The paper introduces a visualization framework for top-down proteomics data, called proteoform barcode, which displays proteoform abundance and functional information, enabling disease diagnosis.

This paper introduces a protein language model-based decoy generation method for proteomics and benchmarks it against classical decoy strategies, finding it less distinguishable by neural networks but not superior in search performance.

The paper develops an automated FFPE deparaffinization workflow to improve protein yield and reproducibility in melanoma studies.

This study benchmarks four spectral prediction platforms for neuropeptidomics, finding no single model captures neuropeptide fragmentation well and highlighting the importance of neutral loss fragments and hybrid libraries.

This study uses multimodal mass spectrometry to accurately map co-occurring histone modifications on intact proteins, enabling residue-level assignment of modifications and positional isomers.

This paper introduces Peptergents, a detergent-free method for native membrane proteomics that preserves protein structure and function, enabling better analysis of membrane proteins and their interactions.

GenomeProt integrates genomics, transcriptomics, and proteomics to characterise proteoforms, discovering novel proteins and variant-containing peptides in human brain and melanoma samples.

This study maps protein-protein interactions in Arabidopsis using cross-linking mass spectrometry, identifying thousands of interactions across subcellular compartments and structural complexes.

MSstatsResponse improves detection of drug-protein interactions in chemoproteomics by using a semi-parametric statistical model that avoids fixed curve shapes, enhancing accuracy and robustness in low-replicate and low-dose settings.

The paper introduces RyboCypher, a platform that maps the dark transcriptome to unannotated peptides, identifying cryptic proteins in cancer cell lines, tumors, and healthy tissues.