Advanced Proteomics Techniques and Applications

This study improves RNA-interactome detection using DIA SILAC over DDA SILAC, achieving higher protein detection and lower variation with single-shot analysis.

This paper introduces a membrane protein enrichment method called meSIILCC to improve detection in MS-TSA, showing increased identification and representation of membrane proteins.

The paper develops a causal model to assess and correct ratio compression caused by ion interference in multiplex proteomics data.

This paper develops a workflow called QuaNPA for analyzing newly synthesized proteins, showing its application in studying the cellular response to interferon-gamma.

This study profiles the pregnancy plasma proteome using a novel precipitation method, identifying 433 protein groups with tissue-specific associations and changes during gestation.

DeepRTAlign improves retention time alignment for large cohort LC-MS data, enhancing identification sensitivity without compromising quantitative accuracy and enabling predictive modeling for hepatocellular carcinoma recurrence.

This paper introduces an IDMS-based LC-MS/MS method for precise quantification of phytohemagglutinin in common beans, showing high linearity, sensitivity, recovery, and repeatability.

The paper presents an algorithm to identify proteomic biomarkers for health status, applying it to find 9-protein indicators for atopic dermatitis and 6-protein indicators for heart failure.

FLiPPR improves LiP-MS data analysis by formalizing data imputation, merging data, and correcting multiple hypotheses, enhancing statistical trends and metadata collection.

The paper investigates why noncanonical proteins are rarely detected by mass spectrometry in yeast, finding that their low abundance and small size, rather than instability, are the main factors.

WOMBAT-P benchmarks label-free proteomics workflows by comparing four analysis pipelines using standardized metrics and datasets.

The paper develops neural networks to predict peptide similarity between mass spectrometry runs using only MS1 data, outperforming baselines and enabling sample characterization.

The paper introduces FAXP, a method combining hydrogel expansion and mass spectrometry for high-resolution proteomics of FFPE tissues, showing improved resolution and efficiency over existing methods.

DIGEST is a web tool that designs MRM transitions for proteomics by predicting peptide fragmentation patterns and selecting optimal ion pairs to improve assay specificity and reduce experimental time.

This paper evaluates HMW fractionation as a complementary proteomics approach to identify serum biomarkers for neurodegenerative diseases, showing improved discrimination compared to direct serum analysis.

This paper develops a method to measure intracellular drug concentrations and proteomic responses in single cells, revealing drug response heterogeneity in KRASG12D inhibitor treatment.

This study analyzes rice phosphoproteomics data to identify phosphosite variations across the rice pan-genome and their functional implications.

The paper quantifies the proteome of the Drosophila melanogaster eye using GeLC-MS/MS and provides a resource for further studies.

This study identifies additional proteoform targets of ibrutinib using thermal proteome profiling, revealing off-target interactions linked to immunomodulation and cellular processes.

SpecGlobX improves peptide-spectrum match alignment by handling complex modifications, enhancing identification confidence in proteomics data.

The paper provides a comprehensive proteomic map of 13 human brain regions using high-throughput mass spectrometry, highlighting region-specific protein expression patterns and markers.

This paper introduces a workflow for ultra-low input spatial tissue proteomics, demonstrating high protein quantification from single cells and microregions in murine and human tissues.

Pepid is a flexible peptide search engine designed for bioinformatics research, enabling modifications and integration of deep learning methods while maintaining competitive performance.

DIAlignR improves quantification consistency in DIA proteomics data across multiple LC-MS/MS instruments, reducing identification errors and increasing significant protein detection.

The paper improves peptide identification by combining database-driven and de novo approaches through a deep learning-based length prediction model.

The paper introduces GPMelt, a hierarchical Gaussian process model for analyzing TPP-TR datasets, enabling unbiased analysis of unconventional melting curves and improving the detection of protein interactions.

The paper introduces a computational workflow for faster and more sensitive analysis of FPOP data, achieving 10-fold speed improvement and 50% more modified peptide spectra identification.

This study develops a high-throughput plasma proteome analysis workflow using UV-assisted digestion, 5-plex labeling, and MS-based high abundance protein removal to improve quantification of low-abundance proteins.

This paper evaluates proteomics imputation methods using downstream-centric criteria and finds that MissForest performs best, though imputation may not improve differential expression detection.

The paper maps the yeast phosphoproteome under 101 perturbations, identifying 25,000 regulated phosphosites and revealing stress response programs and regulatory networks.