Trypsin is a gold standard protease in bottom-up proteomics, but its limited proteome coverage is due to its inability to access many protein sequences. In this study, we propose a fast and robust protocol using subtilisin, protease K, and thermolysin, which are proteases with broad specificity, to overcome this limitation. We demonstrate that broad-specificity proteases allow relative quantitation of proteomes after only minutes of digestion. We used DDA and DIA to evaluate workflows based on SP3 and STRAP for each of the three enzymes, and we measured 20 concatenated fractions from a first-dimension high-pH reversed-phase separation of Jurkat cells digested with each enzyme by DDA for deep profiling.

This study aimed to elucidate the dynamics of whole-virion antigen production for vaccine development and was conducted at the very early stage of the COVID-19 pandemics. Vero E6 cells infected with the SARS-CoV-2 Italy-INMI1 isolate were collected at 1, 2, 3, 4, and 7 days post-infection under two multiplicities of infection (MOIs). Two biological replicates were systematically analyzed. The samples were subjected to in-gel tryptic digestion and analyzed by data-dependent acquisition (DDA) mass spectrometry. The aim of this study is to summarize sample preparation, DDA data acquisition, and their analysis.

The dataset (JPST003527) reports LC-MS/MS analyses performed to investigate the hydroxylation of Pro41 in human immunodeficiency virus type 2 (HIV-2) viral protein X (Vpx) by prolyl hydroxylase domain-containing protein 3 (PHD3). In vitro hydroxylation assays using synthetic Vpx peptide (residues 31-48) and recombinant PHD3 protein demonstrated selective hydroxylation of peptides containing Pro41. In addition, Chemical cross‑linking MS dataset (JPST003526) of the Vpx-PHD3 complex further provided structural information on their interaction. Collectively, these data support site‑specific proline hydroxylation in Vpx and offer insights into its association with PHD3.

This dataset describes a comprehensive proteomic analysis of human pancreatic tissue samples, including tumor and adjacent normal tissues, using state-of-the-art mass spectrometry. The goal was to explore intercellular communication in the tumor microenvironment of pancreatic ductal adenocarcinoma using standardized clinical proteomics. Proteins were extracted, digested, and analyzed by Orbitrap-based mass spectrometry in DDA and PRM modes. A total of 1,123 raw data files were generated across discovery and validation cohorts. Metadata include histology, tumor purity, and clinical background. This dataset provides a high-quality reference for pancreatic cancer research and supports biomarker discovery and tumor microenvironment analysis.