• Bioinformatics and Biosystems
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• 제1권2호
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• pp.109-114
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• 2006

프로테오믹스에서 얻는 탄뎀 질량 스펙트럼은 효소로 가수분해된 펩타이드의 전구이온(precursor ion) 분자량과 펩타이드에 에너지를 가하여 생성된 이온조각(fragment ion)들의 분자량값들로 구성된다. 탄뎀 질량스펙트럼의 전구이온 분자량은 단백질 서열 데이터베이스에서의 검객 과정에서 가장 먼저 고려하는 값이다. 단백질 검색 프로그램은 단백질 서열 중에 스펙트럼의 전구이온으로부터 계산된 분자량과 일치하는 펩타이드 서열들을 찾아내고, 이들 중의 하나를 이온조각들의 분자량 정보를 이용해서 선택한다. 이 때에 전구이온의 분자량은 사용자가 지정한 오차범위 내에서 일치하는 감을 검색하는데, 이때의 오차범위는 질량분석기의 정확도에 따라 결정된다. 본 논문에서는 인간 혈액의 혈장시료로부터 FT LTQ 질량분석기를 통해 얻어진 탄뎀 질량 스펙트럼에서 전구이온 분자량의 분포를 역순서열을 이용하여 분석하였다. 전구이온 분자량의 분포를 재해석하여 실험값의 정확도를 보정하고 단백질 동정의 성능을 향상시키는 방법을 모색하였다.

• Journal of Microbiology and Biotechnology
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• 제21권4호
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• pp.421-429
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• 2011

For precise identification of a Lactobacillus K1 isolate, LC-MS/MS analysis of the putative surface layer protein was performed. The results obtained from LTQ-FT-ICR mass spectrometry confirmed that the analyzed protein spot is the surface layer protein originating from Lb. helveticus species. Moreover, the identified protein has the highest similarity with the surface layer protein from Lb. helveticus R0052. To evaluate the proteomic study, multilocus sequence analysis of selected housekeeping gene sequences was performed. Combination of 16S rRNA sequencing with partial sequences for the genes encoding the RNA polymerase alpha subunit (rpoA), phenylalanyl-tRNA synthase alpha subunit (pheS), translational elongation factor Tu (tuf), and Hsp60 chaperonins (groEL) also allowed to classify the analyzed isolate as Lb. helveticus. Further classification at the strain level was achieved by sequencing of the slp gene. This gene showed 99.8% identity with the corresponding slp gene of Lb. helveticus R0052, which is in good agreement with data obtained by nano-HPLC coupled to an LTQ-FT-ICR mass spectrometer. Finally, LC-MS/MS analysis of surface layer proteins extracted from three other Lactobacillus strains proved that the proposed method is the appropriate molecular tool for the identification of S-layer-possessing lactobacilli at the species and even strain levels.

• Interdisciplinary Bio Central
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• 제1권3호
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• pp.9.1-9.6
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• 2009

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.