Dataset - Proteomics S. elongatus PCC 7942 Limited Proteolysis Structural Proteomics (JM-BH-DP3)

Created on 2024-10-16T17:27:40+00:00 by LN Anderson; Last updated 2025-07-14T17:54:40+00:00 and is pending public release.

S. elongatus PCC 7942 Limited Proteolysis Structural Proteomics (JM-BH-DP3)

The purpose of this experiment was to investigate structural alterations in proteins involved in central carbon metabolism and photosynthetic electron transfer pathways in Synechococcus elongatus PCC 7942. Sample data was obtained from S. elongatus cell lysates using three complementary mass spectrometry (MS) techniques limited proteolysis (LiP-MS), thermal proteome profiling (TPP-MS), and global redox (Redox-MS) for Label-free quantification (LFQ) and global proteomics analysis. LiP-MS samples were treated with pronase and a non-specific protease at an enzyme:substrate ratio of 1:200. TPP-MS samples were treated across 10 temperatures ranging from 37 – 82 ºC in 5 ºC increments and labeled for tandem mass tag 10-plex (TMT10). Redox-MS raw measurement datasets were reanalyzed from a previously reported dataset (MC-DP), see linked dataset below for more information.

Accessible Digital Data Downloads*

Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files, computed outputs, and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using either MSGF+ (v2024.03.26), FragPipe (v.4.0), MSFragger (v.22.1), PlexedPiper, and/or custom Microbial Isolate LiP Analysis Workflow (located at Zenodo). Processed proteomic data downloads include a sample naming key, normalized quantification results files, and protein annotated abundance files. 

*Corresponding primary publication is under review.

Linked Primary Data

MassIVE accessions pending.

Funding Acknowledgments

The research data described here was funded in whole or in part by the Predictive Phenomics Initiative (PPI) at Pacific Northwest National Laboratory (PNNL). This work was conducted under the Laboratory Directed Research and Development Program at PNNL. A portion of this research was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the U.S. Department of Energy (DOE) Office of Science located at PNNL. PNNL is a multiprogram national laboratory operated by Battelle for the DOE under Contract No. DE-AC05-76RL01830.

Citation Policy

In efforts to enable discovery, reproducibility, and reuse of PPI-funded project dataset citations in accordance with best practices (as outlined by the FORCE11 Data Citation Principles), we ask that all reuse of project data and metadata download materials acknowledge all primary and secondary dataset citations and corresponding journal articles where applicable.

Data Licensing

Creative Commons Attribution 4.0 International (CC BY 4.0)