Advanced Proteomics Techniques and Applications

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

EPSD 2.0 updates the eukaryotic phosphorylation site database with 2.7 million experimentally identified p-sites across 223 species, integrating new data and annotations from multiple sources.

The paper introduces SPEC, a proteomics sample preparation method that improves proteome depth and reproducibility from limited samples.

The paper introduces msqrob2TMT, a mixed model-based workflow for differential protein abundance analysis in labelled proteomics, showing improved performance over existing tools.

DIAVariant identifies low abundance peptide variants from DIA-MS data without relying on reference databases, improving variant discovery while maintaining specificity.

The paper introduces iDIA, a method that enhances sensitivity for quantifying regulatory proteins in single cells, revealing lineage priming and cell-cycle coordination in hematopoietic stem and progenitor cells.

This paper introduces a method to isolate N-termini for proteome analysis, demonstrating its application in identifying cleaved proteins during apoptosis.

TopLib is a software package for building and searching top-down mass spectral libraries, offering faster and more reproducible proteoform identification than conventional methods.

nanoPhos improves phosphoproteomics sensitivity and enables cell-type resolved spatial analysis of mouse brain tissue.

The paper develops a deep learning model that integrates multiple fragmentation techniques into standard LC-MS workflows, improving proteome coverage and identification rates.

This paper introduces a method to improve quantitation in DIA proteomics by imputing peptide retention time boundaries, leading to more accurate quantifications and better estimation of radiation exposure.

The paper develops an automated SP3-TMT sample preparation workflow for proteomics, showing it matches manual methods in performance while improving throughput, cost, and error reduction.

Machine learning integration of over 25,000 mass spectrometry experiments yields 15,326 human protein complexes covering 13,769 proteins, plus 511 AlphaFold-derived mutually exclusive subunit pairs.

The paper evaluates three proteomics sample preparation methods, finding that automated SP3 (autoSP3) provides high consistency and scalability for proteomics workflows.

This paper introduces HTA-proteases for de novo antibody sequencing, achieving higher peptide read coverage and accuracy compared to trypsin or chymotrypsin.

The paper introduces SPIED-DIA, a phosphoproteomics method, to identify synergistic signaling responses in colon cancer cells to MEK inhibition and growth factor stimulation.

This study analyzes PTMs in the urinary proteome of SHRSP rats across different ages to identify stage-specific and shared PTM alterations linked to hypertension progression.

The paper introduces PTMdecoder, a tool for deconvoluting multiplexed mass spectra to identify and quantify isobaric modification peptidoforms with high sensitivity and accuracy.

This paper develops an automated sample preparation platform for bottom-up proteomics, demonstrating improved reproducibility, throughput, and suitability for drug development.

This paper evaluates kinase activity inference methods using phosphoproteomics data and benchmarking approaches, finding that curated kinase-substrate libraries significantly impact results.

This study uses Orbitrap Astral mass spectrometry with TMT to analyze proteomic changes in mouse brain and kidney tissues across age and sex, revealing tissue-specific and non-linear age-related protein dynamics.

JMod improves sequence identification and quantification in multiplexed DIA proteomics by jointly modeling overlapping mass spectra, enabling higher throughput with maintained accuracy.

This paper optimizes BONCAT for low-disruption labeling of Arabidopsis proteins, showing improved growth and effective enrichment under stress conditions.

timePlex increases LC-MS throughput by multiplexing samples in the time domain, achieving combinatorial scaling and enabling high-throughput proteomics.

PSMtags enhance peptide detection and sequence identification in proteomics, enabling 9-plexDIA and significantly increasing sample throughput.

SPROUTS_DB catalogs serum-derived contaminants in extracellular vesicle proteomics to improve data reliability by distinguishing contaminants from true proteins.

The paper identifies over 12,000 blood proteins using proteomic strategies and develops a database to catalog their detectability and abundance across different conditions.

This study introduces MAP-MS, a method to improve proteomic dynamic range using Orbitrap mass spectrometers without hardware or software changes, achieving nearly 2x dynamic range with minimal impact.

This paper evaluates six plasma proteomic technologies, finding that Seer Proteograph XT provides the highest proteomic depth and quantification, while Olink Explore HT shows comparable LOD but worse LOQ.

The paper introduces a rapid and cost-effective method for phosphotyrosine peptide enrichment, enabling sensitive detection of phosphotyrosine sites in biological samples.