Advanced Proteomics Techniques and Applications

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.

A machine learning model trained on proteomics data from 183 human datasets identifies tissue and cell-type specific proteins with high accuracy.

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

prolfqua provides an R package for proteomics differential expression analysis with quality control, normalization, and statistical modeling, showing sensitive and specific results.

The paper introduces real-time library search (RTLS) to improve cross-link identification in XL-MS by leveraging intensity ratios of diagnostic peaks, enhancing identification depth in complex samples.

DeepDetect predicts peptide detectability using digestibility features, achieving higher accuracy than existing methods across multiple proteases.

The paper introduces an ion mobility-integrated gas phase fractionation (IM-GPF) method for rapid diaPASEF library generation, outperforming direct deconvolution and approaching offline fractionation performance.

The paper re-mines serum proteomics data to identify post-translational modifications associated with Zika and dengue infections, highlighting specific modifications linked to each virus.

The paper shows that subculture memory effects increase variation in omics data from perturbation studies, improving deconvolution and revealing causal networks.

The paper analyzes lysosomal proteomes across six cell lines to identify cell-type specific differences and novel lysosomal proteins.

The paper introduces Slim-TPCA, an optimized method for proteome-wide characterization of protein complex dynamics, showing improved throughput and accuracy in identifying complex dissociation and association under glucose deprivation.

This study compares proteome coverage and extraction efficiency of different solubilization agents in proteo-metabolome analysis using SPM-LLE and direct cell lysis.

This paper creates a mass spectrometry-based atlas of ECM proteins across 25 mouse organs, revealing shared and tissue-specific ECM protein profiles.

The paper demonstrates that super-resolution mass spectrometry with least-squares fitting enables highly-multiplexed, accurate, and precise shotgun proteomics at the MS2 level.

IQMMA improves protein quantitation in DDA proteomics by integrating multiple feature detection algorithms to extract more accurate MS1 intensities.

This paper introduces plexDIA, a framework for increasing proteomics throughput and depth through multiplexed DIA, enabling more accurate protein quantification and single-cell analysis.

This study identifies and quantifies matrisome proteins in healthy mouse kidneys using two extraction methods, finding 113 proteins including 22 new to the Matrisome Database.

The paper introduces midiaPASEF, a DIA scan mode that enhances information content in proteomics, enabling precise precursor m/z determination and efficient automated processing.

HLAProphet improves HLA protein quantification by using personalized allele information from DNA sequencing, increasing peptide identifications and aligning with RNA expression and loss of heterozygosity.

The paper introduces an MS3-trigger algorithm that improves the sensitivity and specificity of MS3-based crosslinker identification in crosslinking mass spectrometry.

Scribe improves library searching for DDA experiments by predicting fragmentation and retention times for all peptides, outperforming traditional database searching in sensitivity and precision.

MS1Connect measures similarity between mass spectrometry runs using MS1 scans, outperforming baselines in species prediction and correlating with MS2 scan similarities.

This paper integrates semi-supervised machine learning into the ANN-SoLo tool to improve spectral library searching for open modification peptides, increasing identification rates.

The paper updates the spectrum_utils Python package to integrate mass spectrometry standards, enhance data processing, and unify visualization across proteomics and metabolomics applications.

The paper introduces an upgraded CRIMP tool for detecting crosslinked peptides in XL-MS data, showing improved sensitivity and resolution of protein interactions.

This study develops multienzyme deep learning models for de novo peptide sequencing from mass spectrometry data, showing improved generalizability and sequence coverage across diverse protease digests and species.

The paper introduces TargetDecoy, a Bioconductor package to assess and control the quality of the target-decoy approach for peptide identification in proteomics.

The paper improves the peptide indexing component of the Tide search engine, reducing resource usage and enabling larger databases, while exploring the impact of digestion parameters and PTMs on statistical power.

The paper introduces SEC-MX, a method to simultaneously study protein assembly-states and phosphorylation events, and demonstrates its application in HEK293 and HCT116 cells.

Positional scanning peptide arrays profile substrate sequence specificities across 303 human serine/threonine kinases, enabling computational kinase-substrate annotation and signaling pathway inference across the human phosphoproteome.