Advanced Proteomics Techniques and Applications

This paper introduces a detergent-free method using amphipathic peptides to extract and purify membrane proteins, enabling improved structural and mass spectrometry analysis.

The paper introduces a single-cell proteomics workflow using an inkjet printer and nanowell chip to improve protein recovery and throughput.

The paper integrates COFRADIC and sel-TRAP data to refine Nat substrate specificities and uncover cryptic Nt-acetylome substrates from alternative translation initiation.

NIFty is a classification pipeline for single-cell proteomics that handles missing data, avoids circular analysis, and mitigates batch effects without correction.

This paper uses single-cell proteomics to identify novel cell phenotypes in Marfan mouse aorta, revealing sex differences and angiotensin signaling roles in aneurysm formation.

The paper optimizes label-free single-cell proteomics workflows to achieve robust quantification of up to 5,000 proteins per Hela cell at 55 samples-per-day throughput.

The paper introduces Peptonizer2000, a tool that improves taxonomic identification in metaproteomics by combining peptide scores and taxon links with statistical models, showing higher specificity and confidence.

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.

The paper introduces TMT-Integrator, a tool for analyzing isobaric proteomic data, showing improved performance in protein and phosphorylation site quantification compared to existing tools.

The paper presents an experimental strategy to map subcellular protein localization in plants using mass spectrometry, annotating thousands of proteins across multiple species and demonstrating conservation and dynamic changes.

TreeMS2 constructs molecular phylogenies from raw MS/MS data, revealing relationships based on molecular phenotype and identifying deviations from genetic-based trees.

This study investigates whether protein quantification and physical normalization are necessary in proteomics by comparing normalized and unnormalized sample inputs, showing that computational normalization can reduce variability in some cases.

This paper introduces a high-resolution multi-pass Astral analyzer mode for TMT proteomics, enabling 35-plex quantification with improved depth and precision.

This paper introduces a single-cell proteomics pipeline for analyzing human microglia, identifying 1039 proteins per cell and revealing mitochondrial protein variation across subtypes.

Pho-Tip is a one-pot dephosphorylation method that improves the detection of low-abundant phosphorylated peptides in DIA phosphoproteomics data.

The paper assesses peptide proteotypicity in-house for targeted proteomics by synthesizing and verifying peptides from three plasma proteins.

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

This paper introduces Rapid HAMMOC, a phosphoproteomics workflow that improves sensitivity and reduces sample loss, identifying more phosphosites from low-input samples.

This study uses single-cell spatial proteomics to map human liver zonation patterns and shows their disruption in fibrosis.

This study develops a high-sensitivity spatial proteomics method, SP-Max, to analyze protein expression changes in pancreatic tissues from normal to cancerous states.

pmultiqc generates metadata-aware QC reports for proteomics data using standardized metrics and integrates with existing workflows and cloud services.

The paper introduces a cost-effective single-cell proteomics method to analyze skin tumors in CYLD cutaneous syndrome, identifying a novel tumor-associated macrophage signature.

The paper evaluates library-free DIA workflows for LiP-MS and introduces DIA-LiPA, a pipeline that improves structural interpretation of limited proteolysis data.

This study quantifies the yeast peroxisomal proteome under different metabolic conditions, revealing significant changes in protein abundance related to metabolic functions.

xTracer identifies peptides in PAMAF data by integrating chromatogram and mobiligram correlations, improving identification accuracy over single-domain methods.

This study uses PAMAF mode HRIM-MS to quantify succinylation sites in mouse kidney, showing Sirt5 regulates succinylation of mitochondrial proteins involved in metabolism.

This study evaluates chemical decrosslinking to improve proteome coverage in FFPE tissues, finding that 3,4-diaminobenzoic acid enhances precursor identifications in pancreatic samples.

The paper generates a benchmark dataset to evaluate proteomics instruments and workflows using high-throughput LC gradients across multiple platforms and organisms.

AbNovoBench benchmarks de novo sequencing algorithms for monoclonal antibodies using a large dataset and provides an online platform for antibody sequencing workflows.

The paper introduces AFDIP, a chemical proteomics method that identifies drug target binding sites with high structural resolution using digestion rate analysis and MD simulations.