Introduction: In the mass spectrometry-based proteomics, biological samples are analyzed to identify proteins by mass spectrometer and database search. Database search is the process to select the best matches to the experimental mass spectra among the amino acid sequence database and we identify th...
Introduction: In the mass spectrometry-based proteomics, biological samples are analyzed to identify proteins by mass spectrometer and database search. Database search is the process to select the best matches to the experimental mass spectra among the amino acid sequence database and we identify the protein as the matched sequence. The match score is defined to find the matches from the database and declare the highest scored hit as the most probable protein. According to the score definition, search result varies. In this study, the difference among search results of different search engines or different databases was investigated, in order to suggest a better way to identify more proteins with higher reliability. Materials and Methods: The protein extract of human mesenchymal stem cell was separated by several bands by one-dimensional electrophorysis. One-dimensional gel was excised one by one, digested by trypsin and analyzed by a mass spectrometer, FT LTQ. The tandem mass (MS/MS) spectra of peptide ions were applied to the database search of X!Tandem, Mascot and Sequest search engines with IPI human database and SwissProt database. The search result was filtered by several threshold probability values of the Trans-Proteomic Pipeline (TPP) of the Institute for Systems Biology. The analysis of the output which was generated from TPP was performed. Results and Discussion: For each MS/MS spectrum, the peptide sequences which were identified from different conditions such as search engines, threshold probability, and sequence database were compared. The main difference of peptide identification at high threshold probability was caused by not the difference of sequence database but the difference of the score. As the threshold probability decreases, the missed peptides appeared. Conversely, in the extremely high threshold level, we missed many true assignments. Conclusion and Prospects: The different identification result of the search engines was mainly caused by the different scoring algorithms. Usually in proteomics high-scored peptides are selected and low-scored peptides are discarded. Many of them are true negatives. By integrating the search results from different parameter and different search engines, the protein identification process can be improved.
Introduction: In the mass spectrometry-based proteomics, biological samples are analyzed to identify proteins by mass spectrometer and database search. Database search is the process to select the best matches to the experimental mass spectra among the amino acid sequence database and we identify the protein as the matched sequence. The match score is defined to find the matches from the database and declare the highest scored hit as the most probable protein. According to the score definition, search result varies. In this study, the difference among search results of different search engines or different databases was investigated, in order to suggest a better way to identify more proteins with higher reliability. Materials and Methods: The protein extract of human mesenchymal stem cell was separated by several bands by one-dimensional electrophorysis. One-dimensional gel was excised one by one, digested by trypsin and analyzed by a mass spectrometer, FT LTQ. The tandem mass (MS/MS) spectra of peptide ions were applied to the database search of X!Tandem, Mascot and Sequest search engines with IPI human database and SwissProt database. The search result was filtered by several threshold probability values of the Trans-Proteomic Pipeline (TPP) of the Institute for Systems Biology. The analysis of the output which was generated from TPP was performed. Results and Discussion: For each MS/MS spectrum, the peptide sequences which were identified from different conditions such as search engines, threshold probability, and sequence database were compared. The main difference of peptide identification at high threshold probability was caused by not the difference of sequence database but the difference of the score. As the threshold probability decreases, the missed peptides appeared. Conversely, in the extremely high threshold level, we missed many true assignments. Conclusion and Prospects: The different identification result of the search engines was mainly caused by the different scoring algorithms. Usually in proteomics high-scored peptides are selected and low-scored peptides are discarded. Many of them are true negatives. By integrating the search results from different parameter and different search engines, the protein identification process can be improved.
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제안 방법
The gel band was digested into peptides by trypsin and analyzed by tandem mass (MS/MS) spectrometry. All MS/MS experiments for peptide identification were performed a Nano-LC/MS system consisting of a Surveyor HPLC system and a 7-tesla LTQ-FT mass spectrometer (Finnigan, San Jose) equipped with a nano-ESI source. Ten microliter of each sample with digested peptides was separated on a homemade microcapillary column of length 100mm packed with C18 in 75 µm silica tubing.
In this study, we compared the peptides and proteins which were identified from different search engines and filtered by different threshold probabilities. At first, we aimed to check whether two search engines identify different sequences for one MS/MS spectrum.
In this study, we tried to compare the peptide sequences identified for one MS/MS spectrum by different search engines or by different threshold probability. At first, it was checked whether one MS/MS spectrum could be identified by different peptide sequences with low error rate in different search engines.
대상 데이터
In this analysis, three major search engines of Mascot, Sequest and X!Tandem were used. As the sequence database, IPI human database v3.49 (EBI, UK) and Swiss-Prot database v51.6 (EBI, UK) were chosen. They are less redundant appropriately for the database search of proteomics experimental data than NCBI nr database.
성능/효과
Figure 5. From the assumption that the PeptideProphet probability of Sequest is underestimated and compared the Sequest result of p=0.90, p=0.80 with those of the other search engines of p=0.95, throughput of Sequest at high threshold probability, probably PeptideProphet underestimated Sequest score and assigned the PeptideProphet probability lower. At Figure 5, we tried to compare Sequest p=0.
참고문헌 (15)
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