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Computational Proteomics

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Systems Biology Application in Synthetic Biology

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Abstract

Mass Spectrometry (MS) based high throughput proteomics generates huge amount of data, which necessitates the use of computational tools and statistical software for interpreting their biological significance. Herein, we have explored the application of computational proteomics in the bottom-up approach for MS-based protein identification and quantitation. Commonly used scoring systems for interaction proteomics and various tools used in metaproteomic analyses have also been documented. Finally, community standards for proteomics data handling and publicly available proteomics data repositories have been discussed.

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References

  1. Colinge J, Bennett KL (2007) Introduction to computational proteomics. PLoS Comput Biol 3(7):e114

    Article  PubMed  PubMed Central  Google Scholar 

  2. Nilsson T, Mann M, Aebersold R et al (2010) Mass spectrometry in high-throughput proteomics: ready for the big time. Nat Methods 7(9):681–685. doi:10.1038/nmeth0910-681

    Article  CAS  PubMed  Google Scholar 

  3. Cottrell JS (2011) Protein identification using MS/MS data. J Proteomics 74(10):1842–1851. doi:10.1016/j.jprot.2011.05.014

    Article  CAS  PubMed  Google Scholar 

  4. Perkins DN, Pappin DJ, Creasy DM et al (1999) Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20(18):3551–3567

    Article  CAS  PubMed  Google Scholar 

  5. Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 5(11):976–989. doi:10.1016/1044-0305(94)80016-2

    Article  CAS  PubMed  Google Scholar 

  6. Fenyö D, Beavis RC (2003) A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. Anal Chem 75(4):768–774

    Article  PubMed  Google Scholar 

  7. Searle BC (2010) Scaffold: a bioinformatic tool for validating MS/MS-based proteomic studies. Proteomics 10(6):1265–1269. doi:10.1002/pmic.200900437

    Article  CAS  PubMed  Google Scholar 

  8. Neubert H, Bonnert TP, Rumpel K et al (2008) Label-free detection of differential protein expression by LC/MALDI mass spectrometry. J Proteome Res 7(6):2270–2279. doi:10.1021/pr700705u

    Article  CAS  PubMed  Google Scholar 

  9. Nesvizhskii AI, Aebersold R (2005) Interpretation of shotgun proteomic data: the protein inference problem. Mol Cell Proteomics 4(10):1419–1440

    Article  CAS  PubMed  Google Scholar 

  10. Geer LY, Markey SP, Kowalak JA (2004) Open mass spectrometry search algorithm. J Proteome Res 3(5):958–964

    Article  CAS  PubMed  Google Scholar 

  11. Rabilloud T, Lelong C (2011) Two-dimensional gel electrophoresis in proteomics: a tutorial. J Proteomics 74(10):1829–1841. doi:10.1016/j.jprot.2011.05.040

    Article  CAS  PubMed  Google Scholar 

  12. Paoletti AC, Parmely TJ, Tomomori-Sato C (2006) Quantitative proteomic analysis of distinct mammalian mediator complexes using normalized spectral abundance factors. Proc Natl Acad Sci 103(50):18928–18933

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lu P, Vogel C, Wang R (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation. Nat Biotechnol 25(1):117–124

    Article  CAS  PubMed  Google Scholar 

  14. Ntai I, Kim K, Fellers RT et al (2014) Applying label-free quantitation to top down proteomics. Anal Chem 86(10):4961–4968. doi:10.1021/ac500395k

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Müller T, Schrötter A, Loosse C et al (2011) Sense and nonsense of pathway analysis software in proteomics. J Proteome Res 10(12):5398–5408. doi:10.1021/pr200654k

    Article  PubMed  Google Scholar 

  16. Nikitin A, Egorov S, Daraselia N et al (2003) Pathway studio – the analysis and navigation of molecular networks. Bioinformatics 19(16):2155–2157

    Article  CAS  PubMed  Google Scholar 

  17. Kim MS, Pinto SM, Getnet D et al (2014) A draft map of the human proteome. Nature 509(7502):575–581. doi:10.1038/nature13302

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Saha S, Kaur P, Ewing RM (2010) The bait compatibility index: computational bait selection for interaction proteomics experiments. J Proteome Res 9(10):4972–4981. doi:10.1021/pr100267t

    Article  CAS  PubMed  Google Scholar 

  19. Gavin AC, Aloy P, Grandi P et al (2006) Proteome survey reveals modularity of the yeast cell machinery. Nature 440(7084):631–636

    Article  CAS  PubMed  Google Scholar 

  20. Sardiu ME, Cai Y, Jin J (2008) Probabilistic assembly of human protein interaction networks from label-free quantitative proteomics. Proc Natl Acad Sci 105(5):1454–1459. doi:10.1073/pnas.0706983105

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Dazard JE, Saha S, Ewing RM (2012) ROCS: a reproducibility index and confidence score for interaction proteomics studies. BMC Bioinforma 13:128. doi:10.1186/1471-2105-13-128

    Article  Google Scholar 

  22. Sowa ME, Bennett EJ, Gygi SP et al (2009) Defining the human deubiquitinating enzyme interaction landscape. Cell 138(2):389–403. doi:10.1016/j.cell.2009.04.042

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Mellacheruvu D, Wright Z, Couzens AL et al (2013) The CRAPome: a contaminant repository for affinity purification-mass spectrometry data. Nat Methods 10(8):730–736. doi:10.1038/nmeth.2557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Choi H, Larsen B, Lin ZY et al (2011) SAINT: probabilistic scoring of affinity purification-mass spectrometry data. Nat Methods 8(1):70–73. doi:10.1038/nmeth.1541

    Article  CAS  PubMed  Google Scholar 

  25. Teo G, Liu G, Zhang J et al (2014) SAINTexpress: improvements and additional features in Significance Analysis of INTeractome software. J Proteomics 100:37–43. doi:10.1016/j.jprot.2013.10.023

    Article  CAS  PubMed  Google Scholar 

  26. Mathivanan S, Periaswamy B, Gandhi TK et al (2006) An evaluation of human protein-protein interaction data in the public domain. BMC Bioinforma 7(5):S19

    Article  Google Scholar 

  27. Goel R, Muthusamy B, Pandey A et al (2011) Human protein reference database and human proteinpedia as discovery resources for molecular biotechnology. Mol Biotechnol 48(1):87–95. doi:10.1007/s12033-010-9336-8

    Article  CAS  PubMed  Google Scholar 

  28. Ruepp A, Waegele B, Lechner M et al (2010) CORUM: the comprehensive resource of mammalian protein complexes – 2009. Nucleic Acids Res 38(Database issue):D497–D501. doi:10.1093/nar/gkp914

    Article  CAS  PubMed  Google Scholar 

  29. Orchard S, Ammari M, Aranda B et al (2014) The MIntAct project – IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res 42(Database issue):D358–D363. doi:10.1093/nar/gkt1115

    Article  CAS  PubMed  Google Scholar 

  30. Salwinski L, Miller CS, Smith AJ et al (2004) The database of interacting proteins: 2004 update. Nucleic Acids Res 32(Database issue):D449–D451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Oughtred R, Chatr-Aryamontri A, Breitkreutz BJ (2016) BioGRID: a resource for studying biological interactions in yeast. Cold Spring Harb Protoc 2016(1):pdb.top080754. doi:10.1101/pdb.top080754

    Google Scholar 

  32. Szklarczyk D, Franceschini A, Wyder S et al (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43(Database issue):D447–D452. doi:10.1093/nar/gku1003

    Article  PubMed  Google Scholar 

  33. Huttlin EL, Ting L, Bruckner RJ et al (2015) The BioPlex network: a systematic exploration of the human interactome. Cell 162:425–440

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Yang X, Boehm JS, Yang X et al (2011) A public genome-scale lentiviral expression library of human ORFs. Nat Methods 8(8):659–661. doi:10.1038/nmeth.1638

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hettich RL, Pan C, Chourey K et al (2013) Metaproteomics: harnessing the power of high performance mass spectrometry to identify the suite of proteins that control metabolic activities in microbial communities. Anal Chem 85(9):4203–4214. doi:10.1021/ac303053e

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Abraham PE, Giannone RJ, Xiong W et al (2014) Metaproteomics: extracting and mining proteome information to characterize metabolic activities in microbial communities. Curr Protoc Bioinformatics 46:13.26:13.26.1–13.26.14

    Google Scholar 

  37. Mesuere B, Debyser G, Aerts M et al (2015) The Unipept metaproteomics analysis pipeline. Proteomics 15(8):1437–1442. doi:10.1002/pmic.201400361

    Article  CAS  PubMed  Google Scholar 

  38. Muth T, Behne A, Heyer R et al (2015) The MetaProteomeAnalyzer: a powerful open-source software suite for metaproteomics data analysis and interpretation. J Proteome Res 14(3):1557–1565. doi:10.1021/pr501246w

    Article  CAS  PubMed  Google Scholar 

  39. Penzlin A, Lindner MS, Doellinger J et al (2014) Pipasic: similarity and expression correction for strain-level identification and quantification in metaproteomics. Bioinformatics 30(12):i149–i156. doi:10.1093/bioinformatics/btu267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Taylor CF, Paton NW, Lilley KS et al (2007) The minimum information about a proteomics experiment (MIAPE). Biotechnology 25(8):887–893

    CAS  Google Scholar 

  41. Hermjakob H, Montecchi-Palazzi L, Bader G et al (2004) The HUPO PSI’s molecular interaction format – a community standard for the representation of protein interaction data. Nat Biotechnol 22(2):177–183

    Article  CAS  PubMed  Google Scholar 

  42. Demir E, Cary MP, Paley S et al (2010) The BioPAX community standard for pathway data sharing. Nat Biotechnol 28(9):935–942. doi:10.1038/nbt.1666

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Riffle M, Eng JK (2009) Proteomics data repositories. Proteomics 9(20):4653–4663. doi:10.1002/pmic.200900216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vizcaíno JA, Côté RG, Csordas A et al (2013) The PRoteomics IDEntifications (PRIDE) database and associated tools: status in 2013. Nucleic Acids Res 41(Database issue):D1063–D1069. doi:10.1093/nar/gks1262

    Article  PubMed  Google Scholar 

  45. Smith BE, Hill JA, Gjukich MA (2011) Tranche distributed repository and ProteomeCommons.org. Methods Mol Biol 696:123–145

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Centre of Excellence in Bioinformatics, Bose Institute, Kolkata, India

    Debasree Sarkar & Sudipto Saha

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  1. Debasree Sarkar
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  2. Sudipto Saha
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Correspondence to Sudipto Saha .

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Editors and Affiliations

  1. Computational and Systems Biology Lab, National Centre for Cell Science, Pune, India

    Shailza Singh

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Sarkar, D., Saha, S. (2016). Computational Proteomics. In: Singh, S. (eds) Systems Biology Application in Synthetic Biology. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2809-7_2

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