Alexey I. Nesvizhskii

This study identifies conventionally and unconventionally secreted proteins from HUVECs under inflammatory stress, revealing novel UCPS cargo and their functional roles.

The paper shows that sepsis reprograms monocytes into a pro-inflammatory state and shifts their lineage toward neutrophil-like cells, increasing lung injury risk in survivors.

This study shows that loss of FICD rescues motor impairments and reverses oligodendrocyte maturation deficits in a SCA3 mouse model by restoring ER homeostasis and myelination.

The paper evaluates photon- and electron-based fragmentation methods for glycopeptide analysis using Omnitrap-LCMS, finding UVPD and EID provide detailed structural information comparable to traditional methods.

The paper presents a comprehensive spatial proteome map of human tissues and cancers, enabling drug response analysis and target discovery.

The paper introduces MIRA-MS, a real-time AI-guided acquisition method that improves quantification of non-canonical HLA peptides in clear cell renal cell carcinoma.

This study shows that the SUN1 H118Y variant impairs CUL3 neddylation, leading to insulin resistance, and that inhibiting neddylation can restore insulin signaling.

The paper introduces TMT-Integrator, a tool for analyzing isobaric proteomic data, showing improved performance in protein and phosphorylation site quantification compared to existing tools.

The paper introduces a modular pipeline for neoantigen discovery, verification, and prioritization in cancer research using immunopeptidogenomic data.

This paper introduces MBG, a method to expand glycopeptide identification in glycoproteomics by leveraging MS1 signals from displaced glycopeptides, improving detection of low-abundant and modified glycans.

This paper develops a high-throughput single-cell proteomic method for PBMCs, achieving 660 cells per day with 750 protein identifications per cell and identifying low-frequency cell types.

The paper introduces a new detailed mass offset search method in MSFragger to improve the interpretation of complex post-translational modifications.

This study shows TRAF6 regulates glucose homeostasis via mitophagy in islets, with both Parkin-dependent and -independent pathways involved.

This study identifies LONP1's role in mitochondrial protein folding and shows its impact on beta cell survival and hyperglycemia in type 2 diabetes.

The paper develops a deep learning model that integrates multiple fragmentation techniques into standard LC-MS workflows, improving proteome coverage and identification rates.

The paper identifies SorlA as a novel PAI-1 interactor required for lung fibrosis, showing SorlA deficiency protects against fibrosis in mice and SorlA is upregulated in human IPF lung tissue.

MSFragger-DDA+ improves peptide identification sensitivity by searching the full isolation window and handling co-fragmented peptides, enhancing proteomics data analysis.

This paper introduces a novel framework for harmonizing pan-cancer proteomics data, demonstrating its effectiveness in improving dataset integration and revealing prognostic markers.

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

This paper introduces RiboTIE, a transformer model for mapping RNA translation, showing high precision and sensitivity in normal and cancerous tissues.

The paper quantifies protein abundance across human tissues and identifies genetic factors influencing protein levels, revealing both shared and tissue-specific genetic architectures.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data by generating pseudo-MS/MS spectra for direct peptide identification and quantification.

The paper improves experimental design and normalization for multiplex proteomics by introducing reference samples and technical replicates, and presents ProMix to enhance analysis performance.

This study benchmarks label-free and TMT-MS2 methods for spatial proteome mapping, showing label-free provides deeper coverage and higher dynamic range, while TMT-MS2 offers faster throughput.

FragPipe-Analyst provides an R shiny web server and R package for downstream analysis and visualization of quantitative proteomics data from FragPipe.

Koina provides an open-source, accessible ML/DL prediction service for proteomics, improving integration with existing tools like FragPipe.

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.

MSFragger-Labile improves the analysis of labile post-translational modifications by incorporating modification-specific fragment ions into mass spectrometry searches.