Advanced Proteomics Techniques and Applications

Anubis authenticates ancient proteins using deamidation patterns and random forest classification, demonstrating reliability with ancient and degraded beta-lactoglobulin.

This study uses standard-flow targeted proteomics to quantify 12 enzymes in Arabidopsis thaliana's glucosinolate biosynthetic pathway, revealing correlations between transcript and protein levels under normal conditions and uncoupling under methyl-jasmonate treatment.

DDA-BERT improves PSM rescoring in proteomics using a transformer-based model trained on 95 million human spectra, outperforming existing methods across species and sample types.

SysQuan uses SILAC mouse tissues as internal standards to enable affordable absolute quantitation of the human proteome, improving comparability across studies and laboratories.

EasyAb is a high-throughput workflow for antibody-based PTM peptide enrichment coupled to mass spectrometry, enhancing sensitivity for large-scale PTM analysis in diverse cell systems.

This study introduces a robust multiplex DIA-MS workflow with machine learning and statistical filtering to profile protein turnover in cisplatin-resistant cancer cells, revealing correlations between transcript regulation and protein degradation.

SearchNovo improves peptide sequencing by combining database search and de novo methods, outperforming existing models on multiple datasets.

PyProteinInference enables joint protein inference analysis of MS/MS data to compute protein-level FDR and support biological interpretation.

The paper presents Turnover-PPT, a resource mapping protein and phosphoprotein turnover across mouse tissues and brain regions, revealing tissue-specific protein lifetimes and phosphorylation's impact on stability.

This study uses phosphoproteomics to show that inflammatory stimulation of fibroblasts primarily drives resource and stress management rather than specific inflammatory effector functions.

The paper evaluates high-throughput mass spectrometry methods using VAMS devices to identify differentially expressed proteins in lung cancer patients and healthy controls, finding 36 discriminatory biomarkers with 88.9% AUC.

This study compares Thermolysin and Trypsin/LysC proteolysis for enhancing proteomic analysis of ovarian cancer extracellular vesicles, finding Thermolysin improves peptide complexity and identifies unique proteins like Ly6E and NODAL.

This study characterizes gender- and age-related differences in the proteome of murine primary peritoneal mesothelial cells using bottom-up proteomics.

The paper quantifies proteomes and RNA levels in human testis cells to infer post-transcriptional regulation using a Bayesian model called BayesPG.

DigestR is an open-source tool for visualizing LC-MS proteomics data from natural protein catabolism, demonstrating its ability to identify proteolytic cut sites in human and parasite systems.

This study quantifies kinase protein abundance in human tissues using proteomic mass spectrometry, revealing heterogeneity in breast cancer subtypes and enabling clinical applications.

The paper identifies protein-protein interaction modules in mammalian cells using XL-MS data and bioinformatics, revealing hierarchical organization into compartments.

MetaLab enables comprehensive DDA and DIA metaproteomics analysis by supporting both modes without requiring DDA results, improving peptide identification in human gut microbiome studies.

The paper updates the Unipept software to support analysis of non-tryptic and semi-tryptic peptides, improving performance while maintaining accuracy.

The paper introduces UFP-MS, a fast proteomics method, and applies it to study drug effects on mouse tissues, revealing tissue-specific metabolic changes.

This study uses quantitative proteomics to analyze human and mouse gastruloids at four stages, revealing protein dynamics, discordance with transcriptomes, and gene networks linked to developmental defects.

This study presents a temporal atlas of protein and PTM abundance in cerebral organoids, showing significant changes during development related to neuronal differentiation and energy metabolism.

The paper evaluates normalization strategies for creating a pan-cancer protein expression dataset from CPTAC, finding global quantile normalization most effective for cross-cohort comparisons.

ProteoRift predicts peptide properties from mass spectrometry data using a deep attention-based multitask network, reducing search space and improving speed while maintaining accuracy.

The paper introduces a platform for large-scale plasma proteomics studies that improves the detection of low-abundant proteins and enables biomarker discovery in neurodegenerative diseases.

This paper introduces a deep learning-based approach for peptide identification in mass spectrometry proteomics that outperforms existing tools by using spectra and peptide sequences directly.

The paper develops a method to efficiently remove over-labelled peptides in proteomics samples, improving identification rates and quantitation precision.

This paper develops a rapid de novo sequencing workflow for COVID-19 neutralizing antibodies, achieving high accuracy in mixed antibody mixtures.

The paper introduces a modified decision tree model for proteomic data analysis, improving generalization across Alzheimer's disease datasets and identifying ferroptosis-related proteins as important in dementia resistance.

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