Combination of Multiple Spectral Libraries Improves the Current Search Methods Used to Identify Missing Proteins in the Chromosome-Centric Human Proteome Project.
Approximately 2.9 billion long base-pair human reference genome sequences are known to encode some 20 000 representative proteins. However, 3000 proteins, that is, ~15% of all proteins, have no or very weak proteomic evidence and are still missing. Missing proteins may be present in rare samples in very low abundance or be only temporarily expressed, causing problems in their detection and protein profiling. In particular, some technical limitations cause missing proteins to remain unassigned. For example, current mass spectrometry techniques have high limits and error rates for the detection of complex biological samples. An insufficient proteome coverage in a reference sequence database and spectral library also raises major issues. Thus, the development of a better strategy that results in greater sensitivity and accuracy in the search for missing proteins is necessary. To this end, we used a new strategy, which combines a reference spectral library search and a simulated spectral library search, to identify missing proteins. We built the human iRefSPL, which contains the original human reference spectral library and additional peptide sequence-spectrum match entries from other species. We also constructed the human simSPL, which contains the simulated spectra of 173 907 human tryptic peptides determined by MassAnalyzer (version 2.3.1). To prove the enhanced analytical performance of the combination of the human iRefSPL and simSPL methods for the identification of missing proteins, we attempted to reanalyze the placental tissue data set (PXD000754). The data from each experiment were analyzed using PeptideProphet, and the results were combined using iProphet. For the quality control, we applied the class-specific false-discovery rate filtering method. All of the results were filtered at a false-discovery rate of <1% at the peptide and protein levels. The quality-controlled results were then cross-checked with the neXtProt DB (2014-09-19 release). The two spectral libraries, iRefSPL and simSPL, were designed to ensure no overlap of the proteome coverage. They were shown to be complementary to spectral library searching and significantly increased the number of matches. From this trial, 12 new missing proteins were identified that passed the following criterion: at least 2 peptides of 7 or more amino acids in length or one of 9 or more amino acids in length with one or more unique sequences. Thus, the iRefSPL and simSPL combination can be used to help identify peptides that have not been detected by conventional sequence database searches with improved sensitivity and a low error rate.
Yonsei Proteome Research Center, Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University , 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea.
Jin-Young
JY
Cho
Yonsei Proteome Research Center, Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University , 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea.
Hyoung-Joo
HJ
Lee
Yonsei Proteome Research Center, Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University , 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea.
Seul-Ki
SK
Jeong
Yonsei Proteome Research Center, Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University , 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea.
Kwang-Youl
KY
Kim
Korea Basic Science Institute , Ochang, Korea.
Kyung-Hoon
KH
Kwon
Korea Basic Science Institute , Ochang, Korea.
Jong Shin
JS
Yoo
Center for Computational Medicine and Bioinformatics, University of Michigan , 100 Washtenaw Avenue, Ann Arbor 48109, Michigan United States.
Gilbert S
GS
Omenn
Department of Biomedical Science, Faculty of Medicine and Health Science, Macquarie University , New South Wales 2109, Australia.
Mark S
MS
Baker
Northeastern University , Boston, Massachusetts 02115, United States.
William S
WS
Hancock
Yonsei Proteome Research Center, Department of Integrated OMICS for Biomedical Science and Department of Biochemistry, College of Life Science and Biotechnology, Yonsei University , 50 Yonsei-Ro, Seodaemoon-gu, Seoul 120-749, Korea.
Young-Ki
YK
Paik
D000595:Amino Acid Sequence; D000818:Animals; D002877:Chromosomes, Human; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D015894:Genome, Human; D006801:Humans; D013058:Mass Spectrometry; D008969:Molecular Sequence Data; D010455:Peptides / Q000032:analysis; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D020543:Proteome; D040901:Proteomics / Q000379:methods*
D010455:Peptides; D011506:Proteins; D020543:Proteome
Chromosome-Centric Human Proteome Project; missing protein; proteomics; spectral library search