ProteinQuant Suite: a bundle of automated software tools for label‐free quantitative proteomics

Abstract

In simplifying the evaluation and quantification of high-throughput label-free quantitative proteomic data, we introduce ProteinQuant Suite. It comprises three standalone complementary computer utilities, namely ProtParser, ProteinQuant, and Turbo RAW2MGF. ProtParser is a filtering utility designed to evaluate database search results. Filtering is performed according to different criteria that are defined by the end-user. ProteinQuant then utilizes this parsed list of peptides and proteins in conjunction with mzXML or mzData files generated from the raw files for quantification. This quantification is based on the automatic detection and integration of chromatographic peaks representative of the liquid chromatography/mass spectrometry (LC/MS) elution profiles of identified peptides. Turbo RAW2MGF was developed to extend the applicability of ProteinQuant Suite to data collected from different types of mass spectrometers. It directly processes raw data files generated by Xcalibur, a ThermoElectron data acquisition software, and generates a MASCOT generic file (MGF). This file format is needed since the protein identification results generated by the database search employing this file format include information required for the precise identification and quantification of chromatographic peaks. The performance of ProteinQuant Suite was initially validated using LC/MS/MS generated for a mixture of standard proteins as well as standard proteins spiked in a complex biological matrix such as blood serum. Automated quantification of the collected data resulted in calibration curves with R(2) values higher than 0.95 with linearity spanning over more than 2 orders of magnitude with peak quantification reproducibility better than 15% (RSD). ProteinQuant Suite was also applied to confirm the binding preference of standard glycoproteins to Con A lectin using a sample consisting of both standard glycoproteins and proteins.

A quantitative investigation of fucosylated serum glycoproteins with application to esophageal adenocarcinoma

“…The .MGF files were generated using TURBO RAW2MGF, a utility developed in-house as part of a software bundle [20]. The MASCOT search engine was used to search the UniProt protein database, and search results were filtered with ProteinParser according to the following parameters: 1 missed cleavage allowed from tryptic digestion, ± 0.03 m/z tolerance for precursors, +2 and +3 charges, oxidation of methionine (variable modification), ion score ≥ 30, expect ≤ 0.1, accept only bold red queries, and a minimum peptide mass of 600.00 Da.…”

“…Additionally, .MGF files were searched against a randomized version of the UniProt database that was previously generated using the decoy database generator utility available from Matrix Science (Boston, MA) and filtered using the same parameters as described above for the regular searches. The identified peptides from each LC-MS run were combined into a master peptide file [20], which was used for quantification of proteins as described previously. Briefly, instrument raw files were converted from .RAW format to .mzXML using the ReAdW utility that is freely available at http://sourceforge.net/projects/sashimi/files.…”

“…Briefly, instrument raw files were converted from .RAW format to .mzXML using the ReAdW utility that is freely available at http://sourceforge.net/projects/sashimi/files. LC peak areas for identified peptides were then defined by their edges and integrated using ProteinQuant [20]. The following parameters were defined in ProteinQuant: ±0.03 m/z, an apex reassignment window of ± 3.5 minutes, peak width ≤ 1 minute with an intensity threshold of 3-times the baseline intensity, and peptide areas were normalized to the total area for all identified peptides.…”

“…normalization to all measured features has been utilized in our experiments. Through first compiling a master file of all identified peptides, including their m/z and retention time information, it is possible to measure the relative abundance of each peptide contained therein as described previously [20]. Such an approach requires reproducible chromatography, and it becomes more effective with the use of MS at high resolution.…”

“…Understandably, this method will not provide a totally accurate comparison for two separate LC-MS/MS experiments, in which different peptides are identified, because each peptide has a unique ionization efficiency determined by the gas-phase basicity, and, perhaps of greater significance, an LC-MS/MS experiment that results in a higher number of peptide identifications will result in a protein measurement that is based on a higher number of extracted ion profiles. However, the use of a master peptide file will address both of these concerns by ensuring that each LC-MS/MS data set is mined for all peptides that are endogenous to the sample [20]. In this way, each protein measurement is computed from the summation of the same identified peptides, thereby facilitating cross-experiment comparison of measurements.…”