Advanced Proteomics Techniques and Applications

This study evaluates diaPASEF for high-sensitivity identification and quantification of HLA-I peptides, demonstrating improved detection of neoantigens in cancer models.

This study examines the time- and dose-dependent effects of CIGB-300 on the proteome of squamous lung cancer cells, identifying potential biomarkers and mechanisms of action.

The paper develops a machine learning method to identify reliable gold standards for protein complex composition prediction by integrating evolutionary conservation and size exclusion chromatography data.

The paper introduces two new PTM crosstalk scores to analyze histone proteoform dynamics in mouse organs during aging and demonstrates their application using proteoform data.

This study improves an in vivo crosslinking workflow to identify protein-protein interactions, finding 56 novel nuclear PPIs and showing increased crosslinking of low-abundance proteins.

The paper updates MaxQuant for isobaric labeling data, showing improved accuracy and reduced batch effects using weighted median normalization.

PepCentric enables fast proteogenomics searches across large mass spectrometry datasets by using 2-D fragment indexing to validate novel peptides and proteomic variations.

The paper introduces TMTCal, a TMT-based multipoint internal calibration method for proteomics, demonstrating its application in quantifying proteome changes in ovarian cancer cells under cisplatin treatment.

PepGo is a deep learning and tree search-based model for de novo peptide sequencing that outperforms existing methods by integrating Transformer networks with Monte Carlo Tree Search.

This study quantifies protein expression in rat brains, identifies pQTLs, and links them to behavioral traits and human disorders.

The paper assesses MS-based identifications of unannotated human microproteins, finding large variation in reported proteins and PSM quality across studies.

ProteoAutoNet improves CF-MS analysis by integrating a multi-database search strategy with robotics, enhancing throughput and identifying novel protein interactions in thyroid cancer.

MS2Rescore improves peptide identification and taxonomic specificity in metaproteomics by using machine learning-based rescoring, achieving lower false discovery rates with minimal sensitivity loss.

The paper identifies and characterizes non-genetic and genetic protein variants in human proteomes, showing they contribute to functional diversity.

The paper presents a scalable MBR algorithm for proteomics cohort analysis, enabling faster processing of large datasets with results comparable to existing methods.

The paper introduces a graph-based method to annotate internal fragment ions in tandem mass spectra, improving proteoform sequencing and modification localization.

The paper compares DIA and TMT proteomics methods on an Orbitrap Astral mass spectrometer, finding comparable proteome depth and quantitative consistency.

This paper introduces a method using 2-mercaptoethanol and DMSO to specifically modify cysteine residues, reducing offsite modifications in proteome analysis.

The paper applies molecularly-guided single-cell spatial proteomics to study proteomic differences in healthy and diseased mammalian brains, revealing insights into Parkinson's disease.

The paper quantifies how growth-dependent dilution and protein degradation affect protein abundance across human B-cells, tissues, and proteoforms, showing dilution explains 40% of abundance changes.

This paper develops targeted mass spectrometry assays to detect and quantify endogenous ORF1p and ORF2p proteins, demonstrating sensitive and robust quantitation of ORF2p for the first time.

The paper identifies sulfotyrosine (sY) sites in the human phosphoproteome by distinguishing them from phosphorylation using mass error shifts and filters out false positives, finding 31 convincing sY sites.

The paper introduces a deep learning model to improve citrullination site identification in proteomes, achieving high precision and sensitivity in human and plant tissues.

This study uses Deep Visual Proteomics to analyze SRCC proteomics, identifying DNA replication stress and immune-related features, suggesting pembrolizumab may be more effective than chemotherapy.

This study quantitatively analyzes plasma proteome changes in vaccinated individuals using a nanoparticle-based proteomic workflow, identifying proteins associated with vaccine response and COVID-19 infection.

This paper describes an automated AC-MS platform with DIA-MS for efficient kinase profiling and inhibitor selectivity assessment.

prolfquapp simplifies differential expression analysis in quantitative proteomics by providing a command-line tool with dynamic HTML reports and integration with existing analysis tools.

This study examines how different cell storage conditions affect single-cell proteomic profiles using DIA-diaPASEF in 293T cells, finding significant changes in morphology and proteome composition.

PeptideForest improves peptide identification by integrating multiple search engines and increases PSMs with q-value <1% without compromising quantification quality.