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利用串联质谱鉴定氨基酸突变的生物信息学算法

DOI: 10.1360/N052014-00099, PP. 1113-1124

Keywords: 氨基酸突变,串联质谱,鸟枪法蛋白质组学,氨基酸突变鉴定

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Abstract:

氨基酸突变能够改变蛋白的结构和功能,影响生物体的生命过程.基于串联质谱的鸟枪法蛋白质组学是目前大规模研究蛋白质组学的主要方法,但是现有的质谱数据鉴定流程为了提高鉴定结果的灵敏度往往会有意压缩数据库中的氨基酸突变信息.因此,如何挖掘数据中的氨基酸突变信息成为当前质谱数据鉴定的一个重要部分.当前应用于氨基酸突变鉴定的串联质谱鉴定方法大致可以分为3大类:基于序列数据库搜索的方法、基于序列标签搜索的算法以及基于图谱库搜索的算法.本文首先详细介绍了这3种氨基酸突变鉴定算法,并分析了各种方法的特点和不足,然后介绍了氨基酸突变鉴定的研究现状和发展方向.随着基于串联质谱的蛋白质组学的不断发展,蛋白序列中的氨基酸突变信息将被更好地解析出来,从而得以深入探讨由氨基酸突变引起的蛋白结构和功能改变,为揭示氨基酸突变的生物学意义奠定基础.

References

[1]  1 Collins F S, Brooks L D, Chakravarti A. A DNA polymorphism discovery resource for research on human genetic variation. Genome Res, 1998, 8: 1229-1231
[2]  2 Frazer K A, Ballinger D G, Cox D R, et al. A second generation human haplotype map of over 3.1 million SNPs. Nature, 2007, 449: 851-861
[3]  3 Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res, 2011, 39: e118
[4]  4 Nakamura Y. DNA variations in human and medical genetics: 25 years of my experience. J Hum Genet, 2009, 54: 1-8
[5]  5 Yin H, Liang Y, Yan Z, et al. Mutation spectrum in human colorectal cancers and potential functional relevance. BMC Med Genet, 2013, 14: 32
[6]  6 Martin A, Saathoff M, Kuhn F, et al. A functional ABCC11 allele is essential in the biochemical formation of human axillary odor. J Invest Dermatol, 2010, 130: 529-540
[7]  25 Mathivanan S, Ji H, Tauro B J, et al. Identifying mutated proteins secreted by colon cancer cell lines using mass spectrometry. J Proteomics, 2012, 76: 141-149
[8]  26 Stenson P D, Mort M, Ball E V, et al. The human gene mutation database: 2008 update. Genome Med, 2009, 1: 13
[9]  27 Gatlin C L, Eng J K, Cross S T, et al. Automated identification of amino acid sequence variations in proteins by HPLC/microspray tandem mass spectrometry. Anal Chem, 2000, 72: 757-763
[10]  28 Creasy D M, Cottrell J S. Error tolerant searching of uninterpreted tandem mass spectrometry data. Proteomics, 2002, 2: 1426-1434
[11]  29 Craig R, Beavis R C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics, 2004, 20: 1466-1467
[12]  30 Kersey P J, Duarte J, Williams A, et al. The International Protein Index: an integrated database for proteomics experiments. Proteomics, 2004, 4: 1985-1988
[13]  31 Bunger M K, Cargile B J, Sevinsky J R, et al. Detection and validation of non-synonymous coding SNPs from orthogonal analysis of shotgun proteomics data. J Proteome Res, 2007, 6: 2331-2340
[14]  32 Li J, Duncan D T, Zhang B. CanProVar: a human cancer proteome variation database. Hum Mutat, 2010, 31: 219-228
[15]  33 Mottaz A, David F P, Veuthey A L, et al. Easy retrieval of single amino-acid polymorphisms and phenotype information using SwissVar. Bioinformatics, 2010, 26: 851-852
[16]  34 Xi H, Park J, Ding G, et al. SysPIMP: the web-based systematical platform for identifying human disease-related mutated sequences from mass spectrometry. Nucleic Acids Res, 2009, 37: D913-D920
[17]  35 Alves G, Ogurtsov A Y, Yu Y K. RAId_DbS: mass-spectrometry based peptide identification web server with knowledge integration. BMC Genomics, 2008, 9: 505
[18]  36 Yip Y L, Famiglietti M, Gos A, et al. Annotating single amino acid polymorphisms in the UniProt/Swiss-Prot knowledgebase. Hum Mutat, 2008, 29: 361-366
[19]  37 Kawabata T, Ota M, Nishikawa K. The protein mutant database. Nucleic Acids Res, 1999, 27: 355-357
[20]  38 Chernobrovkin A L, Mitkevich V A, Popov I A, et al. Identification of single amino acid polymorphisms in MS/MS spectra of peptides. Dokl Biochem Biophys, 2011, 437: 90-93
[21]  39 Su Z D, Sun L, Yu D X, et al. Quantitative detection of single amino acid polymorphisms by targeted proteomics. J Mol Cell Biol, 2011, 3: 309-315
[22]  40 Evans V C, Barker G, Heesom K J, et al. De novo derivation of proteomes from transcriptomes for transcript and protein identification. Nat Methods, 2012, 9: 1207-1211
[23]  41 Wang X, Slebos R J, Wang D, et al. Protein identification using customized protein sequence databases derived from RNA-Seq data. J Proteome Res, 2012, 11: 1009-1017
[24]  42 Sultan M, Schulz M H, Richard H, et al. A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome. Science, 2008, 321: 956-960
[25]  43 Wang X, Zhang B. customProDB: an R package to generate customized protein databases from RNA-Seq data for proteomics search. Bioinformatics, 2013, 29: 3235-3237
[26]  44 Woo S, Cha S W, Merrihew G, et al. Proteogenomic database construction driven from large scale RNA-seq data. J Proteome Res, 2014, 13: 21-28
[27]  45 Tanner S, Shu H, Frank A, et al. InsPecT: identification of posttranslationally modified peptides from tandem mass spectra. Anal Chem, 2005, 77: 4626-4639
[28]  46 Ma B, Johnson R. De novo sequencing and homology searching. Mol Cell Proteomics, 2012, 11: O111.014902
[29]  47 Dasari S, Chambers M C, Slebos R J, et al. TagRecon: high-throughput mutation identification through sequence tagging. J Proteome Res, 2010, 9: 1716-1726
[30]  48 Dancik V, Addona T A, Clauser K R, et al. De novo peptide sequencing via tandem mass spectrometry. J Comput Biol, 1999, 6: 327-342
[31]  49 Ma B, Zhang K, Hendrie C, et al. PEAKS: powerful software for peptide de novo sequencing by tandem mass spectrometry. Rapid Commun Mass Spectrom, 2003, 17: 2337-2342
[32]  50 Zhang J, Xin L, Shan B, et al. PEAKS DB: de novo sequencing assisted database search for sensitive and accurate peptide identification. Mol Cell Proteomics, 2012, 11: M111.010587
[33]  51 Han X, He L, Xin L, et al. PeaksPTM: mass spectrometry-based identification of peptides with unspecified modifications. J Proteome Res, 2011, 10: 2930-2936
[34]  52 Frank A, Pevzner P. PepNovo: de novo peptide sequencing via probabilistic network modeling. Anal Chem, 2005, 77: 964-973
[35]  53 Mann M, Wilm M. Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal Chem, 1994, 66: 4390-4399
[36]  54 Tabb D L, Saraf A, Yates J R 3rd. GutenTag: high-throughput sequence tagging via an empirically derived fragmentation model. Anal Chem, 2003, 75: 6415-6421
[37]  55 Searle B C, Dasari S, Turner M, et al. High-throughput identification of proteins and unanticipated sequence modifications using a mass-based alignment algorithm for MS/MS de novo sequencing results. Anal Chem, 2004, 76: 2220-2230
[38]  56 Han Y, Ma B, Zhang K. SPIDER: software for protein identification from sequence tags with de novo sequencing error. J Bioinform Comput Biol, 2005, 3: 697-716
[39]  57 Yuen D. SPIDER: reconstructive protein homology search with de novo sequencing tags. Master Thesis. Waterloo: University of Waterloo, 2011
[40]  58 Tabb D L, Ma Z Q, Martin D B, et al. DirecTag: accurate sequence tags from peptide MS/MS through statistical scoring. J Proteome Res, 2008, 7: 3838-3846
[41]  59 Na S, Bandeira N, Paek E. Fast multi-blind modification search through tandem mass spectrometry. Mol Cell Proteomics, 2012, 11: M111.010199
[42]  60 Ma Z Q, Dasari S, Chambers M C, et al. IDPicker 2.0: improved protein assembly with high discrimination peptide identification filtering. J Proteome Res, 2009, 8: 3872-3881
[43]  61 Na S, Jeong J, Park H, et al. Unrestrictive identification of multiple post-translational modifications from tandem mass spectrometry using an error-tolerant algorithm based on an extended sequence tag approach. Mol Cell Proteomics, 2008, 7: 2452-2463
[44]  62 Dasari S, Chambers M C, Martinez M A, et al. Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment. J Proteome Res, 2012, 11: 1686-1695
[45]  63 Ye D, Fu Y, Sun R X, et al. Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate. Bioinformatics, 2010, 26: i399-i406
[46]  64 Lam H, Deutsch E W, Eddes J S, et al. Development and validation of a spectral library searching method for peptide identification from MS/MS. Proteomics, 2007, 7: 655-667
[47]  7 Yoshiura K, Kinoshita A, Ishida T, et al. A SNP in the ABCC11 gene is the determinant of human earwax type. Nat Genet, 2006, 38: 324-330
[48]  8 Vogelstein B, Kinzler K W. Cancer genes and the pathways they control. Nat Med, 2004, 10: 789-799
[49]  9 Di Fede G, Catania M, Morbin M, et al. A recessive mutation in the APP gene with dominant-negative effect on amyloidogenesis. Science, 2009, 323: 1473-1477
[50]  10 Driscoll M C. Sickle cell disease. Pediatr Rev, 2007, 28: 259-268
[51]  11 McCarthy M I, Abecasis G R, Cardon L R, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nature Rev Genet, 2008, 9: 356-369
[52]  12 Do R, Kathiresan S, Abecasis G R. Exome sequencing and complex disease: practical aspects of rare variant association studies. Hum Mol Genet, 2012, 21: R1-R9
[53]  13 Hecht M, Bromberg Y, Rost B. News from the protein mutability landscape. J Mol Biol, 2013, 425: 3937-3948
[54]  14 Sheynkman G M, Shortreed M R, Frey B L, et al. Large-scale mass spectrometric detection of variant peptides resulting from nonsynonymous nucleotide differences. J Proteome Res, 2014, 13: 228-240
[55]  15 Apweiler R, Bairoch A, Wu C H, et al. UniProt: the universal protein knowledgebase. Nucleic Acids Res, 2004, 32: D115-D119
[56]  16 Edwards N J. Novel peptide identification from tandem mass spectra using ESTs and sequence database compression. Mol Syst Biol, 2007, 3: 102
[57]  17 Schandorff S, Olsen J V, Bunkenborg J, et al. A mass spectrometry-friendly database for cSNP identification. Nat Methods, 2007, 4: 465-466
[58]  18 Hyatt D, Pan C. Exhaustive database searching for amino acid mutations in proteomes. Bioinformatics, 2012, 28: 1895-1901
[59]  19 Abraham P, Adams R M, Tuskan G A, et al. Moving away from the reference genome: evaluating a peptide sequencing tagging approach for single amino acid polymorphism identifications in the genus Populus. J Proteome Res, 2013, 12: 3642-3651
[60]  20 Falkner J A, Falkner J W, Yocum A K, et al. A spectral clustering approach to MS/MS identification of post-translational modifications. J Proteome Res, 2008, 7: 4614-4622
[61]  21 Sherry S T, Ward M H, Kholodov M, et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res, 2001, 29: 308-311
[62]  22 Forbes S A, Tang G, Bindal N, et al. COSMIC (the Catalogue of Somatic Mutations in Cancer): a resource to investigate acquired mutations in human cancer. Nucleic Acids Res, 2010, 38: D652-D657
[63]  23 Hamosh A, Scott A F, Amberger J S, et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res, 2005, 33: D514-D517
[64]  24 Li J, Su Z, Ma Z Q, et al. A bioinformatics workflow for variant peptide detection in shotgun proteomics. Mol Cell Proteomics, 2011, 10: M110.006536
[65]  65 Hoopmann M R, Moritz R L. Current algorithmic solutions for peptide-based proteomics data generation and identification. Curr Opin Biotechnol, 2013, 24: 31-38
[66]  66 Ji C, Arnold R J, Sokoloski K J, et al. Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra. Proteomics, 2013, 13: 756-765
[67]  67 Lam H, Deutsch E W, Eddes J S, et al. Building consensus spectral libraries for peptide identification in proteomics. Nat Methods, 2008, 5: 873-875
[68]  68 Craig R, Cortens J C, Fenyo D, et al. Using annotated peptide mass spectrum libraries for protein identification. J Proteome Res, 2006, 5: 1843-1849
[69]  69 Fu Y, Xiu L Y, Jia W, et al. DeltAMT: a statistical algorithm for fast detection of protein modifications from LC-MS/MS data. Mol Cell Proteomics, 2011, 10: M110.000455
[70]  70 Yates J R 3rd, Morgan S F, Gatlin C L, et al. Method to compare collision-induced dissociation spectra of peptides: potential for library searching and subtractive analysis. Anal Chem, 1998, 70: 3557-3565
[71]  71 Lam H. Building and searching tandem mass spectral libraries for peptide identification. Mol Cell Proteomics, 2011, 10: R111.008565
[72]  72 Hu Y, Li Y, Lam H. A semi-empirical approach for predicting unobserved peptide MS/MS spectra from spectral libraries. Proteomics, 2011, 11: 4702-4711
[73]  73 Hu Y, Lam H. Expanding tandem mass spectral libraries of phosphorylated peptides: advances and applications. J Proteome Res, 2013, 12: 5971-5977
[74]  74 Joachims T. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Dordrecht: Kluwer Academic Publishers, 2002
[75]  76 Davies H, Bignell G R, Cox C, et al. Mutations of the BRAF gene in human cancer. Nature, 2002, 417: 949-954
[76]  77 Tanner S, Shen Z, Ng J, et al. Improving gene annotation using peptide mass spectrometry. Genome Res, 2007, 17: 231-239
[77]  78 Tanner S, Payne S H, Dasari S, et al. Accurate annotation of peptide modifications through unrestrictive database search. J Proteome Res, 2008, 7: 170-181
[78]  79 Resing K A, Meyer-Arendt K, Mendoza A M, et al. Improving reproducibility and sensitivity in identifying human proteins by shotgun proteomics. Anal Chem, 2004, 76: 3556-3568
[79]  80 Yen C Y, Russell S, Mendoza A M, et al. Improving sensitivity in shotgun proteomics using a peptide-centric database with reduced complexity: protease cleavage and SCX elution rules from data mining of MS/MS spectra. Anal Chem, 2006, 78: 1071-1084
[80]  81 Keller A, Nesvizhskii A I, Kolker E, et al. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal Chem, 2002, 74: 5383-5392
[81]  82 Snyder M, Du J, Gerstein M. Personal genome sequencing: current approaches and challenges. Genes Dev, 2010, 24: 423-431
[82]  83 Ng P C, Levy S, Huang J, et al. Genetic variation in an individual human exome. PLoS Genet, 2008, 4: e1000160
[83]  84 Gonzalez-Perez A, Perez-Llamas C, Deu-Pons J, et al. IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods, 2013, 10: 1081-1082
[84]  85 Ng P C, Henikoff S. Predicting deleterious amino acid substitutions. Genome Res, 2001, 11: 863-874
[85]  86 Adzhubei I A, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods, 2010, 7: 248-249
[86]  87 Liu X, Jian X, Boerwinkle E. dbNSFP v2. 0: a database of human non-synonymous SNVs and their functional predictions and annotations. Hum Mutat, 2013, 34: E2393-E2402
[87]  88 Snyder M, Weissman S, Gerstein M. Personal phenotypes to go with personal genomes. Mol Syst Biol, 2009, 5: 273
[88]  75 Desiere F, Deutsch E W, King N L, et al. The PeptideAtlas project. Nucleic Acids Res, 2006, 34: D655-D658

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