A detailed list of command line options and their usage is provided as a Supplemental Document.
#Similarity ratio calculator code#
The source code for this script is freely available at. LC-MS/MS data files are converted to the mzXML format and then parsed by an in-house developed python script. For data sets in which no corresponding MS2 spectra were obtained, the identity of the peptide was determined by the peptide’s accurate mass, retention time, charge state distribution and pattern of modification. All target peptides contained MS2 identifications in at least one data set. Proteins were identified and SILAC protein ratios were determined by the MassMatrix database search engine. A blank was run between each sample injection to check carryover and a bovine histone standard was run every 10 runs as a quality control. After washing at 90% B for 1 min, the column was equilibrated at 2% B for 24 min. Using a 120 min gradient beginning with 2% mobile phase B, the phase B linearly increased to 5% in 10 min, from 5% to 15% in 20 min, from 15% to 30% in 45 min, from 30% to 50% in 15 min, and from 50% to 90% in 5 min. Histones were separated on a 0.2 mm × 150 mm C 18 column (3 μm, 200Å, Michrom Bioresources Inc., Auburn, CA) at a flow rate of 2 μL/min with mobile phase A containing H 2O with 0.1% Formic acid (FA) and mobile phase B containing ACN with 0.1% FA. An implementation of this approach is presented wherein changes in histones PTMs were quantified in a model yeast system.Įluting peptides were separated by reversed-phase HPLC (Dionex Ultimate 3000 capillary/nano HPLC system, Dionex, Sunnyvale, CA) and mass analyzed with a Thermo Fisher LTQ Orbitrap XL (ThermoFisher, San Jose, CA). The primary utility of this algorithm is directed at the accurate and precise SILAC quantification of peptide posttranslational modifications. Comparisons are made with MassMatrix and MaxQuant to emphasize the complimentary use of the algorithm alongside database search and feature detection algorithms. The paper describes both the important aspects of normalization factor estimation and peptide ratio calculation. In this paper a simple yet robust computational strategy is put forth to assist in determining peptide SILAC ratios for features (either identified or unidentified) in LC-MS/MS data sets. However, a more robust strategy is required for peptide ratios. The use of median ratios is appropriate for estimation of the SILAC ratio for a protein. Similarly, a protein’s SILAC ratio is taken from the median ratio of all assigned peptides. If the peptide is identified with the builtin Andromeda seach engine, MaxQuant will then assign a peptide sequence and use the median ratio to approximate its SILAC value. For SILAC experiments, MaxQuant will attempt to calculate a SILAC ratio for every detected feature. MaxQuant has the potential to be used for peptide level SILAC due to its feature detection capabilities.
Another widely used tools for SILAC ratio quantitation is MaxQuant. Peptide level changes are especially important for characterizaiton of peptide modifications. SPeCtRA (SILAC Peptide Count Ratio Analysis) take a very different approach in which the MS 2 spectra are used for protein quantitation 10. Other applications, such as Xpress 7, ASAPRatio 8 and MSQuant 9 determine the SILAC ratios using peak area integration of extracted ion chromatograms (XIC).
The SILAC ratios for each protein are aggregated from the multiple peptide IDs to generate the overal protein ratio. MassMatrix 2, SILACtor 3, Census 4, MaxQuant 5 and Uniquant 6) calculate protein relative abundances from the heavy and light abundances of the MS1 precusor ions of identified peptides.
#Similarity ratio calculator software#
Software applications exists that facilitate protein SILAC data interpretation. heavy labeled signals, changes in protein relative amount can be measured. By comparing the relative abundance of the light vs. For each proteolytic fragmentation, the mass spectra is represented by a light and heavy set of signals with a distinct mass difference that equals the incorporated label. The digest is then analyzed by liquid chromatography tandem mass spectrometry (LC-MS/MS) to identify proteins and assess changes in protein abundance across sample treatments.
The light and heavy labeled cells are mixed followed by the protein extraction and proteolytic digestion. In a classic SILAC experiment, cells are metabolically labeled by culturing them in media that contains either light or heavy stable isotope-labeled amino acids. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) is a multiplex quantitative proteomic technique that uses metabolic labeling of proteins with isotopically heavy amino acids to determine changes in protein relative abundance 1.
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