Project PXD000387

Download Project Files
Project Protein Table
Project Peptide Table
Visualize in PRIDE Inspector
Follow the next three steps to open your selected project or assay in PRIDE Inspector:

  • 1.

    Download, uncompress and open PRIDE Inspector
  • 2.

    Click in the magnifier on the left top corner, paste the project or assay that you would like to open in the search box, and hit search
  • 3.

    Click in the corresponding "Download" button to download the files and visualize them



LC-MS/MS of gingiva and Periodontal ligaments


Background: Although human gingival fibroblasts (hGF) and human periodontal ligament fibroblasts (hPDLF) exhibit numerous phenotypic similarities, it has been suggested that the secretory and behavioral differences, which exist between these cell types, are a result of the membrane protein composition of these cells. Methods: Four matched pairs of hGF and hPDLF were cultured. Prior to confluence, membrane bound and associated proteins from cells of the 4th passage were extracted. The processed protein samples were identified by digestion with trypsin and sequenced using capillary-liquid chromatography tandem mass spectrometry on an Thermo Scientific LTQ-Orbitrap XL mass spectrometer. Scaffold by Proteome Software was used to quantitate and validate protein identifications derived from MS/MS sequencing results. Results: Four hundred fifty proteins were common to both hGF and hPDLF. Of the proteins identified, 214 were known membrane bound or associated proteins and 165 proteins were known nuclear associated proteins. Twenty-seven proteins, identified from the 450 proteins, common to both hGF and hPDLF, were detected in statistically significant greater quantities in either hGF or hPDLF. More specifically, 13 proteins were detected in significantly greater quantities in hGF, while 14 proteins were detected in significantly greater quantities in hPDLF. Conclusions: Distinct differences in the cellular protein catalog may reflect the dynamic role and high energy requirements of hGF in extracellular matrix remodeling and response to inflammatory challenge as well as the role of hPDGF in monitoring mechanical stress and maintaining tissue homeostasis during regeneration and remineralization. Method Details: Sequence information from the MS/MS data was processed by converting the .raw files into a merged file (.mgf) using an in-house program, RAW2MZXML_n_MGF_batch (, a Perl script). Isotope distributions for the precursor ions of the MS/MS spectra were deconvoluted to obtain the charge states and monoisotopic m/z values of the precursor ions during the data conversion. The resulting mgf files were searched using Mascot Daemon by Matrix Science version 2.3.2 (Boston, MA) and the database searched against the full SwissProt database version 2012_06 (536,489 sequences; 190,389,898 residues) or NCBI database version 20120515 (18,099,548 sequences; 6,208,559,787 residues The mass accuracy of the precursor ions were set to 20ppm, accidental pick of 13C peaks was also included into the search. The fragment mass tolerance was set to 0.5 Da. Considered variable modifications were oxidation (Met), deamidation (N and Q) and carbamidomethylation (Cys). Four missed cleavages for the enzyme were permitted. A decoy database was also searched to determine the false discovery rate (FDR) and peptides were filtered according to the FDR. The significance threshold was set at p less than 0.05 and bold red peptides is required for valid peptide identification. Proteins with a Mascot score of 50 or higher with a minimum of two unique peptides from one protein having a -b or -y ion sequence tag of five residues or better were accepted. Any modifications or low score peptide/protein identifications were manually checked for validation. Spectral Counting: Label Free Quantitation was performed using the spectral counting approach. Scaffold (version Scaffold_3.4.9, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm (Keller, A et al Anal. Chem. 2002;74(20):5383-92). Protein identifications were accepted if they could be established at greater than 95.0% probability and contained at least 1 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, AI Anal Chem. 2003 Sep 1;75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.

Sample Processing Protocol

See details in reference(s) : 24171499

Data Processing Protocol

See details in reference(s) : 24171499


Kari Green, Mass Spectrometry and Proteomics Facility

Submission Date


Publication Date



Not available

Cell Type


Experiment Type

Bottom-up proteomics

Assay count



    McKnight H, Kelsey WP, Hooper DA, Hart TC, Mariotti A; Proteomic Analysis of Human Gingival and Periodontal Ligament Fibroblasts., J Periodontol, 2013 Oct 30, PubMed: 24171499


Showing 1 - 8 of 8 results
# Accession Title Proteins Peptides Unique Peptides Spectra Identified Spectra View in Reactome
1 30500 Ging 1 1717 17290 3147 45852 12007
2 30501 PDL 1 1090 9340 1790 37125 5898
3 30502 PDL 2 1375 15762 2708 41343 9767
4 30503 GIng 2 1444 16959 2419 44867 10723
5 30504 Ging 3 1157 13481 2388 42074 8318
6 30505 PDL 3 1214 14417 2199 42384 8788
7 30506 PDL 4 1448 13418 2227 41059 8215
8 30507 Ging 4 1256 13415 2076 38734 8153