Unravelling peptidomes by in silico mining

• Authors: Johannes Koehbach , Kathryn A.V. Jackson
• Languages of publication: EN

Abstracts

EN

Peptides of great number and diversity occur in
all domains of life and exhibit a range of pharmaceutically
relevant bioactivities. The complexity of biological
samples including human cells or tissues, plant extracts
or animal venom cocktails, often impedes the discovery
of novel bioactive peptides using mass spectrometrybased
peptidomics analysis. An increasing number of
publicly available genome and transcriptome datasets,
together with refined bioinformatics analysis, allows for
rapid identification of novel peptides which may have
been previously unrecognized. Moreover, a combination
of information extracted from in silico mining approaches
together with data derived from mass spectrometrybased
studies provides new impetus for future peptidome
analyses, including the discovery of novel bioactive
peptides that can serve as starting points for drug
development.

Keywords

EN

genome; transcriptome; peptidomics; massspectrometry; short ORF; peptides

• Publisher: De Gruyter Open
• Journal: Peptidomics
• Year: 2015
• Volume: 2
• Issue: 1

Dates

received

1 - 12 - 2014

accepted

11 - 2 - 2015

online

31 - 3 - 2015

Contributors

author

Johannes Koehbach

• School of Biomedical Sciences, The University
  of Queensland, 4072 St. Lucia QLD, Australia

author

Kathryn A.V. Jackson

• School of Biomedical Sciences, The University
  of Queensland, 4072 St. Lucia QLD, Australia

References

• [1] Newman D.J., Cragg G.M., Natural products as sources of newdrugs over the 30 years from 1981 to 2010, J. Nat. Prod., 2012,75, 311-335.
• [2] Lipinski C.A., Drug-like properties and the causes of poorsolubility and poor permeability, J. Pharmacol. Toxicol.Methods, 2000, 44, 235-249.[Crossref]
• [3] Craik D.J., Fairlie D.P., Liras S., Price D., The future of peptidebaseddrugs, Chem. Biol. Drug Des., 2013, 81, 136-147.[Crossref]
• [4] Gruber C.W., Muttenthaler M., Freissmuth M., Ligand-basedpeptide design and combinatorial peptide libraries to targetG protein-coupled receptors, Curr. Pharm. Des., 2010, 16,3071-3088.[Crossref]
• [5] Goodson J.L., Nonapeptides and the evolutionary patterning ofsociality, Prog. Brain Res., 2008, 170, 3-15.[Crossref]
• [6] Brogden K.A., Ackermann M., McCray P.B., Jr., Tack B.F., Antimicrobialpeptides in animals and their role in host defences, Int.J. Antimicrob. Agents, 2003, 22, 465-478.[Crossref]
• [7] Zasloff M., Antimicrobial peptides of multicellular organisms,Nature, 2002, 415, 389-395.
• [8] Schrader M., Selle H., The process chain for peptidomicbiomarker discovery, Dis. Markers, 2006, 22, 27-37.[Crossref]
• [9] Martelli C., Iavarone F., Vincenzoni F., Cabras T., ManconiB., Desiderio C., Messana I., Castagnola M., Top-downpeptidomics of bodily fluids, Peptidomics, 2013, 1, 47-64.
• [10] Finoulst I., Pinkse M., Van Dongen W., Verhaert P., Samplepreparation techniques for the untargeted LC-MS-baseddiscovery of peptides in complex biological matrices, J.Biomed. Biotechnol., 2011, 2011, 245291.
• [11] Gruber C.W., Muttenthaler M., Discovery of defense- andneuropeptides in social ants by genomemining, PLoS ONE,2012, DOI: 10.1371/journal.pone.0032559.[Crossref]
• [12] Koehbach J., Attah A.F., Berger A., Hellinger R., Kutchan T.M.,Carpenter E.J., Rolf M., Sonibare M.A., Moody J.O., Wong G.K.,et al., Cyclotide discovery in Gentianales revisited-identificationand characterization of cyclic cystine-knot peptidesand their phylogenetic distribution in Rubiaceae plants,Biopolymers, 2013, 100, 438-452.[Crossref]
• [13] Frith M.C., Forrest A.R., Nourbakhsh E., Pang K.C., Kai C., KawaiJ., Carninci P., Hayashizaki Y., Bailey T.L., Grimmond S.M., TheAbundance of Short Proteins in the Mammalian Proteome, PLoSGenet., 2006, DOI: 10.1371/journal.pgen.0020052.[Crossref]
• [14] Jin A.H., Dutertre S., Kaas Q., Lavergne V., Kubala P., Lewis R.J.,Alewood P.F., Transcriptomic messiness in the venom ductof Conus miles contributes to conotoxin diversity, Mol. Cell.Proteomics, 2013, 12, 3824-3833.[Crossref]
• [15] Schrader M., Schulz-Knappe P., Fricker L.D., Historicalperspective of peptidomics, EuPA Open Proteom, 2014, 3,171-182.
• [16] Cole A.M., Hong T., Boo L.M., Nguyen T., Zhao C., Bristol G.,Zack J.A., Waring A.J., Yang O.O., Lehrer R.I., Retrocyclin: aprimate peptide that protects cells from infection by T- andM-tropic strains of HIV-1, Proc. Natl. Acad. Sci. U. S. A., 2002,99, 1813-1818.12[Crossref]
• [17] Bachmann B.O., Van Lanen S.G., Baltz R.H., Microbial genomemining for accelerated natural products discovery: is arenaissance in the making?, J. Ind. Microbiol. Biotechnol.,2014, 41, 175- 184.[Crossref]
• [18] Goecks J., Nekrutenko A., Taylor J., Team T.G., Galaxy:a comprehensive approach for supporting accessible,reproducible, and transparent computational research in thelife sciences, Genome Biol., 2010, http://genomebiology.com/2010/11/8/R86.
• [19] Okonechnikov K., Golosova O., Fursov M., UGENE-team, UniproUGENE: a unified bioinformatics toolkit, Bioinformatics, 2012,28, 1166-1167.[Crossref]
• [20] Lavergne V., Dutertre S., Jin A.H., Lewis R.J., Taft R.J., AlewoodP.F., Systematic interrogation of the Conus marmoreusvenom duct transcriptome with ConoSorter reveals 158 novelconotoxins and 13 new gene superfamilies, BMC Genomics,2013, http://www.biomedcentral.com/1471- 2164/14/708.
• [21] Misof B., Liu S., Meusemann K., Peters R.S., Donath A.,Mayer C., Frandsen P.B., Ware J., Flouri T., Beutel R.G., et al.,Phylogenomics resolves the timing and pattern of insectevolution, Science, 2014, 346, 763-767.
• [22] Christie A.E., Prediction of the peptidomes of Tigriopuscalifornicus and Lepeophtheirus salmonis (Copepoda,Crustacea), Gen. Comp. Endocrinol., 2014, 201, 87-106.
• [23] Christie A.E., Expansion of the Litopenaeus vannamei andPenaeus monodon peptidomes using transcriptome shotgunassembly sequence data, Gen. Comp. Endocrinol., 2014, 206,235-254.
• [24] Altschul S.F., Gish W., Miller W., Myers E.W., Lipman D.J., Basiclocal alignment search tool, J. Mol. Biol., 1990, 215, 403-410.
• [25] Artimo P., Jonnalagedda M., Arnold K., Baratin D., Csardi G., deCastro E., Duvaud S., Flegel V., Fortier A., Gasteiger E., et al.,ExPASy: SIB bioinformatics resource portal, Nucleic Acids Res.,2012, 40, W597-W603.[Crossref]
• [26] Sievers F., Wilm A., Dineen D., Gibson T.J., Karplus K., Li W.,Lopez R., McWilliam H., Remmert M., Söding J., et al., Fast,scalable generation of high-quality protein multiple sequencealignments using Clustal Omega, Mol. Syst. Biol., 2011, DOI:10.1038/msb.2011.75.[Crossref]
• [27] Petersen T.N., Brunak S., von Heijne G., Nielsen H., SignalP 4.0:discriminating signal peptides from transmembrane regions,Nat. Meth., 2011, 8, 785-786.
• [28] Birney E., Clamp M., Durbin R., GeneWise and Genomewise,Genome Res., 2004, 14, 988-995.[Crossref]
• [29] Christie A.E., Neuropeptide discovery in Ixodoidea: An in silicoinvestigation using publicly accessible expressed sequencetags, Gen. Comp. Endocrinol., 2008, 157, 174-185.
• [30] Stewart M.J., Favrel P., Rotgans B., Wang T., Zhao M., Sohail M.,O‘Connor W.A., Elizur A., Henry J., Cummins S.F., Neuropeptidesencoded by the genomes of the Akoya pearl oyster Pinctatafucata and Pacific oyster Crassostrea gigas: a bioinformatic andpeptidomic survey, BMC Genomics, 2014, http://www.biomedcentral.com/1471-2164/15/840.
• [31] Wang S., Luo X., Zhang S., Yin C., Dou Y., Cai X., Identificationof putative insulin-like peptides and components of insulinsignaling pathways in parasitic platyhelminths by the use ofgenome-wide screening, FEBS J., 2014, 281, 877-893.13
• [32] Liu C., Li H., In Silico Prediction of Post-translational Modifications,In: Yu B & Hinchcliffe M. (Eds.), Methods in MolecularBiology, 1st ed., Humana Press, New York, 2011.
• [33] Castellana N.E., Payne S.H., Shen Z., Stanke M., Bafna V.,Briggs S.P., Discovery and revision of Arabidopsis genes byproteogenomics, Proc. Natl. Acad. Sci. U.S.A., 2008, 105,21034-21038.[Crossref]
• [34] Andrews S.J., Rothnagel J.A., Emerging evidence for functionalpeptides encoded by short open reading frames, Nat. Rev.Genet., 2014, 15, 193-204.[Crossref]
• [35] Pauli A., Valen E., Schier A.F., Identifying (non-)coding RNAsand small peptides: Challenges and opportunities, BioEssays,2014, DOI: 10.1002/bies.201400103.[Crossref]
• [36] Bazzini A.A., Johnstone T.G., Christiano R., Mackowiak S.D.,Obermayer B., Fleming E.S., Vejnar C.E., Lee M.T., Rajewsky N.,Walther T.C., et al., Identification of small ORFs in vertebratesusing ribosome footprinting and evolutionary conservation,EMBO J., 2014, 33, 981-993.[Crossref]
• [37] Slavoff S.A., Mitchell A.J., Schwaid A.G., Cabili M.N., Ma J.,Levin J.Z., Karger A.D., Budnik B.A., Rinn J.L., Saghatelian A.,Peptidomic discovery of short open reading frame–encodedpeptides in human cells, Nat. Chem. Biol., 2013, 9, 59-64.
• [38] Ma J., Ward C.C., Jungreis I., Slavoff S.A., Schwaid A.G., NeveuJ., Budnik B.A., Kellis M., Saghatelian A., Discovery of humansORF-encoded polypeptides (SEPs) in cell lines and tissue, J.Proteome Res., 2014, 13, 1757-1765.[Crossref]
• [39] Lu Y., Zhuang Y., Liu J., Mining antimicrobial peptides fromsmall open reading frames in Ciona intestinalis, J. Pept. Sci.,2014, 20, 25-29.[Crossref]
• [40] Crappe J., Van Criekinge W., Trooskens G., Hayakawa E.,Luyten W., Baggerman G., Menschaert G., Combining in silicoprediction and ribosome profiling in a genome-wide search fornovel putatively coding sORFs, BMC Genomics, 2013, http://www.biomedcentral.com/1471-2164/14/648.
• [41] Yang X., Tschaplinski T.J., Hurst G.B., Jawdy S., Abraham P.E.,Lankford P.K., Adams R.M., Shah M.B., Hettich R.L., LindquistE., et al., Discovery and annotation of small proteins usinggenomics, proteomics, and computational approaches,Genome Res., 2011, 21, 634-641.[Crossref]
• [42] Kastenmayer J.P., Ni L., Chu A., Kitchen L.E., Au W.-C., YangH., Carter C.D., Wheeler D., Davis R.W., Boeke J.D., et al.,Functional genomics of genes with small open reading frames(sORFs) in S. cerevisiae, Genome Res., 2006, 16, 365-373.[Crossref]
• [43] Galindo M.I., Pueyo J.I., Fouix S., Bishop S.A., Couso J.P.,Peptides Encoded by Short ORFs Control Development andDefine a New Eukaryotic Gene Family, PLoS Biol., 2007, DOI:10.1371/journal.pbio.0050106.[Crossref]
• [44] Oyama M., Kozuka-Hata H., Suzuki Y., Semba K., YamamotoT., Sugano S., Diversity of Translation Start Sites May DefineIncreased Complexity of the Human Short ORFeome, Mol. Cell.Proteomics, 2007, 6, 1000-1006.[Crossref]
• [45] Hanada K., Akiyama K., Sakurai T., Toyoda T., Shinozaki K., ShiuS.-H., sORF finder: a program package to identify small openreading frames with high coding potential, Bioinformatics,2010, 26, 399-400.[Crossref]
• [46] Hanada K., Zhang X., Borevitz J.O., Li W.-H., Shiu S.-H., A largenumber of novel coding small open reading frames in theintergenic regions of the Arabidopsis thaliana genome aretranscribed and/or under purifying selection, Genome Res.,2007, 17, 632-640.14[Crossref]
• [47] Blankenberg D., Kuster G.V., Coraor N., Ananda G., LazarusR., Mangan M., Nekrutenko A., Taylor J., Galaxy: A Web-BasedGenome Analysis Tool for Experimentalists, Curr. Protoc. Mol.Biol, 2010, DOI: 10.1002/0471142727.mb1910s89.[Crossref]
• [48] Giardine B., Riemer C., Hardison R.C., Burhans R., Elnitski L.,Shah P., Zhang Y., Blankenberg D., Albert I., Taylor J., et al., Galaxy: A platform for interactive large-scale genome analysis,Genome Res., 2005, 15, 1451-1455.[Crossref]
• [49] Pruess M., Apweiler R., Bioinformatics Resources for In SilicoProteome Analysis, J. Biomed. Biotechnol., 2003, 4, 231-236.[Crossref]
• [50] Le T.T., Lehnert S., Colgrave M.L., Neuropeptidomics applied tostudies of mammalian reproduction, Peptidomics, 2013, 1, 1-13.
• [51] Romanova E.V., Dowd S.E., Sweedler J.V., Quantitation ofendogenous peptides using mass spectrometry basedmethods, Curr. Opin. Chem. Biol., 2013, 17, 801-808.[Crossref]
• [52] Hashempour H., Koehbach J., Daly N.L., Ghassempour A.,Gruber C.W., Characterizing circular peptides in mixtures:sequence fragment assembly of cyclotides from a violet plantby MALDITOF/TOF mass spectrometry, Amino Acids, 2013, 44,581-595.[Crossref]
• [53] Ueberheide B.M., Fenyö D., Alewood P.F., Chait B.T., Rapidsensitive analysis of cysteine rich peptide venom components,Proc. Natl. Acad. Sci. U. S. A., 2009, 106, 6910-6915.[Crossref]
• [54] Góngora-Castillo E., Buell C.R., Bioinformatics challenges in denovo transcriptome assembly using short read sequences inthe absence of a reference genome sequence, Nat. Prod. Rep.,2013, 30, 490- 500.[Crossref]
• [55] Cahais V., Gayral P., Tsagkogeorga G., Melo-Ferreira J.,Ballenghien M., Weinert L., Chiari Y., Belkhir K., RanwezV., Galtier N., Reference-free transcriptome assembly innon-model animals from next-generation sequencing data,Mol. Ecol. Resour., 2012, 12, 834-845.[Crossref]
• [56] Jakubowski J.A., Keays D.A., Kelley W.P., Sandall D.W., BinghamJ.P., Livett B.G., Gayler K.R., Sweedler J.V., Determiningsequences and post-translational modifications of novelconotoxins in Conus victoriae using cDNA sequencing and massspectrometry, J. Mass Spectrom., 2004, 39, 548- 557.[Crossref]
• [57] Ma M., Gard A.L., Xiang F., Wang J., Davoodian N., LenzP.H., Malecha S.R., Christie A.E., Li L., Combining in silicotranscriptome mining and biological mass spectrometry forneuropeptide discovery in the Pacific white shrimp Litopenaeusvannamei, Peptides, 2010, 31, 27-43.[Crossref]
• [58] Safavi-Hemami H., Hu H., Gorasia D.G., Bandyopadhyay P.K.,Veith P.D., Young N.D., Reynolds E.C., Yandell M., OliveraB.M., Purcell A.W., Combined proteomic and transcriptomicinterrogation of the venom gland of Conus geographusuncovers novel components and functional compartmentalization,Mol. Cell. Proteomics, 2014, 13, 938-953.[Crossref]
• [59] Kersten R.D., Yang Y.L., Xu Y., Cimermancic P., Nam S.J., FenicalW., Fischbach M.A., Moore B.S., Dorrestein P.C., A massspectrometry-guided genome mining approach for naturalproduct peptidogenomics, Nat. Chem. Biol., 2011, 7, 794-802.[Crossref]
• [60] Mohimani H., Kersten R.D., Liu W.T., Wang M., Purvine S.O.,Wu S., Brewer H.M., Pasa-Tolic L., Bandeira N., Moore B.S., etal., Automated genome mining of ribosomal peptide naturalproducts, ACS Chem. Biol., 2014, 9, 1545-1551.15[Crossref]
• [61] Mohimani H., Liu W.T., Kersten R.D., Moore B.S., DorresteinP.C., Pevzner P.A., NRPquest: Coupling Mass Spectrometry andGenome Mining for Nonribosomal Peptide Discovery, J. Nat.Prod., 2014, 77, 1902-1909.[Crossref]
• [62] Medema M.H., Paalvast Y., Nguyen D.D., Melnik A., DorresteinP.C., Takano E., Breitling R., Pep2Path: Automated MassSpectrometry-Guided Genome Mining of Peptidic NaturalProducts, PLoS Comput. Biol., 2014, DOI: 10.1371/journal.pcbi.1003822.
• [63] Clark R.J., Fischer H., Nevin S.T., Adams D.J., Craik D.J., Thesynthesis, structural characterization, and receptor specificityof the alpha-conotoxin Vc1.1, J. Biol. Chem., 2006, 281,23254-23263.
• [64] Koehbach J., O‘Brien M., Muttenthaler M., Miazzo M., Akcan M.,Elliott A.G., Daly N.L., Harvey P.J., Arrowsmith S., GunasekeraS., et al., Oxytocic plant cyclotides as templates for peptide Gprotein-coupled receptor ligand design, Proc. Natl. Acad. Sci.U. S. A., 2013, 110, 21183-21188.[Crossref]
• [65] Ladoukakis E., Pereira V., Magny E., Eyre-Walker A., Couso J.P.,Hundreds of putatively functional small open reading framesin Drosophila, Genome Biol., 2011, http://genomebiology.com/2011/12/11/R118.
• [66] Clamp M., Fry B., Kamal M., Xie X., Cuff J., Lin M.F., Kellis M.,Lindblad-Toh K., Lander E.S., Distinguishing protein-codingand noncoding genes in the human genome, Proc. Natl. Acad.Sci. U.S.A., 2007, 104, 19428-19433.[Crossref]
• [67] Koehbach J., Stockner T., Bergmayr C., Muttenthaler M., GruberC.W., Insights into the molecular evolution of oxytocin receptorligand binding, Biochem. Soc. Trans., 2013, 41, 197-204.[Crossref]
• [68] Gruber C.W., Physiology of invertebrate oxytocin andvasopressin neuropeptides, Exp. Physiol., 2014, 99, 55-61.[Crossref]
• [69] Ingolia N.T., Ghaemmaghami S., Newman J.R.S., Weissman J.S.,Genome-Wide Analysis in Vivo of Translation with NucleotideResolution Using Ribosome Profiling, Science, 2009, 324,218-223.
• [70] Guttman M., Russell P., Ingolia N.T., Weissman J.S., Lander E.S.,Ribosome Profiling Provides Evidence that Large NoncodingRNAs Do Not Encode Proteins, Cell, 2013, 154, 240-251.
• [71] Tamura K., Stecher G., Peterson D., Filipski A., Kumar S.,MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0,Mol. Biol. Evol., 2013, 30, 2725-2729.
• [72] Edgar R.C., MUSCLE: multiple sequence alignment with highaccuracy and high throughput, Nucleic Acids Res., 2004, 32,1792-1797.[Crossref]
• [73] Sigrist C.J.A., de Castro E., Cerutti L., Cuche B.A., Hulo N.,Bridge A., Bougueleret L., Xenarios I., New and continuingdevelopments at PROSITE, Nucleic Acids Res., 2013, 41,D344-D347.[Crossref]
• [74] Rawlings N.D., Waller M., Barrett A.J., Bateman A., MEROPS:the database of proteolytic enzymes, their substrates andinhibitors, Nucleic Acids Res., 2014, 42, D503-D509.[Crossref]
• [75] Stanke M., Keller O., Gunduz I., Hayes A., Waack S.,Morgenstern B., AUGUSTUS: ab initio prediction of alternativetranscripts, Nucleic Acids Res., 2006, 34, W435-W439.[Crossref]
• [76] Lin M.F., Jungreis I., Kellis M., PhyloCSF: a comparativegenomics method to distinguish protein coding andnon-coding regions, Bioinformatics, 2011, 27, i275-i282.16[Crossref]
• [77] Ferrè F., Clote P., DiANNA: a web server for disulfideconnectivity prediction, Nucleic Acids Res., 2005, 33,W230-W232.[Crossref]
• [78] Xue Y., Liu Z., Cao J., Ma Q., Gao X., Wang Q., Jin C., Zhou Y.,Wen L., Ren J., GPS 2.1: enhanced prediction of kinase-specificphosphorylation sites with an algorithm of motif lengthselection, Protein Eng. Des. Sel., 2011, 24, 255-260.[Crossref]
• [79] Consortium T.U., Activities at the Universal Protein Resource(UniProt), Nucleic Acids Res., 2014, 42, D191-D198.17 [Crossref]

Identifiers

DOI

10.1515/ped-2015-0002