Advanced Proteomics Techniques and Applications

PIPI-C identifies PTM crosstalks in mass spectrometry data using a combinatorial optimization framework, revealing upregulated PTM patterns in cancer datasets.

The paper develops the Maize PeptideAtlas resource to analyze maize proteomes using MS/MS data, identifying peptides and proteins across diverse genetic backgrounds and conditions.

This study standardizes protein corona characterization across proteomics centers, improving data consistency and reproducibility through unified protocols and workflows.

The paper identifies issues in crosslinking mass spectrometry analysis where heuristic methods can distort error estimation, leading to false protein-protein interaction discoveries and proposes ways to correct this.

The paper introduces mCP, a streamlined workflow for detecting protein complexes with reduced fractionation, and demonstrates its application in analyzing cardiac tissue.

Casanovo translates tandem mass spectra into peptide sequences using a transformer model, outperforming existing methods and improving analysis in immunopeptidomics and metaproteomics.

This paper discusses machine learning strategies to overcome data challenges in mass spectrometry-based proteomics, emphasizing the need for standardized datasets and larger data sizes to improve model performance.

The paper evaluates diaPASEF for HCP analysis in biopharmaceutical processes, showing improved quantification and throughput compared to standard methods.

This paper introduces CASA, a targeted mass spectrometry method for protein quantification, and demonstrates its application in determining stoichiometries of kinetochore components in budding yeast.

The paper introduces a one-step N-terminomics method using tip-based SCX chromatography, enabling high-throughput and sensitive profiling of N-terminal peptides.

This study compares two EGFR-targeted drugs using ABPP to reveal site-specific binding differences and off-target effects at low concentrations.

ThunderBolt provides an interactive web platform for analyzing large proteomics datasets through guided exploration, preprocessing, and statistical analysis.

This study introduces PulsPhos, a method to quantify site-specific phosphorylation lifetimes in living cells and applies it to pharmacological perturbations.

This study develops a proteogenomic pipeline to detect amino acid misincorporation in Candida albicans and shows increased misincorporation at CUG sites without affecting other codons.

The study shows that carrier proteome limits vary with mass analyzer type, suggesting that using analyzers with higher linear dynamic range can improve isobaric peptide signal amplification.

The paper introduces LRP-IS-PRM, a method using long-read RNAseq to inform targeted proteomics with IS-PRM, identifying five new protein isoforms in human stem cells.

This paper introduces a hybrid method combining direct infusion shotgun proteome analysis with nanoparticle protein corona enrichment to achieve rapid and efficient plasma proteomic profiling.

The paper introduces Acyl-Trap, a modified suspension trap assay for detecting and quantifying S-acylated proteins with low input, and identifies new S-acylation sites in mouse brain.

This study presents an automated single-cell proteomics workflow that identifies over 4,000 proteins per cell and detects key regulatory proteins in LPS-perturbed THP-1 cells.

The paper introduces a single-sample workflow, MTBE-SP3, for simultaneous proteomic and metabolomic analysis, showing it reduces technical variation and enables consistent results across biological matrices.

This study evaluates how different experimental workflows affect fecal metaproteomic results, showing that DIA-PASEF improves protein identification and small protein recovery compared to DDA.

The paper introduces a hidden Markov model method for protein identification from noisy signature data, showing good performance on a human protein database under various conditions.

The paper re-purposes a machine learning model for de novo peptide sequencing as a score function for database search, improving detection power over existing methods.

The paper improves the accuracy of antibody sequence reconstruction from bottom-up proteomics data by addressing mass coincidence errors in de novo sequencing.

This paper introduces a deep learning method for imputing missing values in label-free quantitative proteomics data and shows its effectiveness in identifying differentially abundant proteins in alcohol-related liver disease.

This paper develops a UPLC-MRM assay to absolutely quantify structural proteins of VSV-GP, including processed GP1/GP2 and full-length GPC, to assess viral processing and infectivity.

The paper identifies proteoform truncations in human and plant proteomes and describes their functional implications and evolutionary conservation.

MAGMa is a tool for differential expression analysis in mass-spectrometry proteomic data, showing improved sensitivity and specificity in detecting true signals.

This study uses single-cell proteomics to identify proteins distinguishing two adjacent cell types in Arabidopsis root cells.

LooMS improves peptide identification in DIA datasets by using an unbiased sample generation strategy and integrating results with DiaNN to reduce false discovery rates.