Advanced Proteomics Techniques and Applications

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.

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.

The paper introduces MS-EmpiReS, a method to detect differential alternative splicing in proteomics data, identifying cancer-relevant splicing candidates and showing correlation with RNA sequencing.

This paper improves fluorosequencing by optimizing fluorophores, reducing dye interactions, and creating an end-to-end workflow, demonstrating its potential for high-sensitivity clinical proteomics.

The paper introduces a deep proteomics platform called Proteonano with high sensitivity and wide dynamic range, identifying novel biomarkers for diabetic kidney disease progression.

This study compares FAIMS and quadrupole gas-phase fractionation in peptide identification, finding that FAIMS improves results mainly by reducing co-isolation of precursor ions and chimeric spectra.

The paper introduces DPKS, an explainable machine learning framework for identifying proteome states by calculating protein importance in predictive models.

SCP Viz is a graphical tool for analyzing single-cell proteomics data, enabling visualization and transformation of protein abundance across cells and subpopulations.

This paper demonstrates that nanoparticle-based proteomics workflows enable accurate and scalable deep plasma proteomics with high precision and statistical power for biomarker discovery.

The paper analyzes time-series human plasma proteome data to study changes in protein expression related to Covid-19 infection and vaccination.

This paper shows that acetic acid improves peptide signal and proteome coverage in sub-nanogram and single cell proteomics compared to formic acid.

einprot provides an R package for reproducible analysis of quantitative proteomics data, generating reports with interactive visualizations and customizable workflows.

The paper improves inter-link detection in cross-linking mass spectrometry by using a fused database and context-sensitive subgrouping, increasing coverage by 30-75% while maintaining low error rates.

The paper introduces π-HelixNovo, a Transformer-based model for de novo peptide sequencing that outperforms existing methods and improves taxonomic resolution in gut metaproteomes.

The paper introduces an automated proteomics sample preparation platform that improves throughput for drug target identification.

The paper introduces SDC-ACE, a method to enrich S-palmitoylated peptides, and applies it to study S-palmitoylation in mouse brain and tissues, revealing its role in diabetes and obesity.

QuantUMS, a machine-learning module in DIA-NN, weights MS1 and fragment signals by quality-derived variance estimates, improving DIA precision, removing ratio compression in high-accuracy mode, and scoring each quantity's reliability.

This paper develops a method for in situ cell-type specific proteome analysis using antibody-mediated biotinylation and validates it on mouse brain tissue.

This study evaluates the Astral mass analyzer's performance in single-cell proteomics using DIA, showing improved proteome depth and quantitative precision for ultra-low input samples.

This paper develops a high-speed LC/MS/MS system achieving 1,000 samples per day with high sensitivity and depth, demonstrating its application in spatial proteomics of mouse brain.

The paper introduces Narrow-window DIA, a fast proteomics method enabling high-throughput and comprehensive proteome analysis with high precision and accuracy.

This study benchmarks 12 DIA analysis workflows for shotgun proteomics samples, finding DIA-NN and Spectronaut in library-free mode offer highest sensitivity with good reproducibility and accuracy.

The paper introduces Chronologer, a deep learning tool for accurate peptide retention time prediction, outperforming existing methods by leveraging harmonized datasets of post-translational modifications.

BiATNovo improves de novo peptide sequencing from DIA mass spectrometry data by using attention-based bidirectional models and post-processing, achieving better accuracy for long peptides and amino acid-level predictions.

μPhos is a phosphoproteomics platform that enables the identification of 5,000 phosphorylation sites from 5 µg of sample, supporting extensive perturbation experiments.

The paper introduces PTM-TBA, a software pipeline that identifies and localizes post-translational modifications in proteoforms using top-down and bottom-up mass spectrometry combined with UniProt annotations.

PPIAT is a web tool that integrates protein interaction data and mass spectrometry information to identify physical protein-protein interactions.

This study improves variant peptide identification in proteomics by integrating retention time and fragmentation predictors into a proteogenomic pipeline, enhancing discrimination between correct and incorrect matches.

DIP-MS resolves protein complex isoforms using deep learning and mass spectrometry, revealing new subunits in human prefoldin complexes.