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Volume 9, Issue 1

TransAAP: an automated annotation pipeline for membrane transporter prediction in bacterial genomes Open Access

Abstract

Membrane transporters are a large group of proteins that span cell membranes and contribute to critical cell processes, including delivery of essential nutrients, ejection of waste products, and assisting the cell in sensing environmental conditions. Obtaining an accurate and specific annotation of the transporter proteins encoded by a micro-organism can provide details of its likely nutritional preferences and environmental niche(s), and identify novel transporters that could be utilized in small molecule production in industrial biotechnology. The Transporter Automated Annotation Pipeline (TransAAP) (http://www.membranetransport.org/transportDB2/TransAAP_login.html) is a fully automated web service for the prediction and annotation of membrane transport proteins in an organism from its genome sequence, by using comparisons with both curated databases such as the TCDB (Transporter Classification Database) and TDB, as well as selected Pfams and TIGRFAMs of transporter families and other methodologies. TransAAP was used to annotate transporter genes in the prokaryotic genomes in the National Center for Biotechnology Information (NCBI) RefSeq; these are presented in the transporter database TransportDB (http://www.membranetransport.org) website, which has a suite of data visualization and analysis tools. Creation and maintenance of a bioinformatic database specific for transporters in all genomic datasets is essential for microbiology research groups and the general research/biotechnology community to obtain a detailed picture of membrane transporter systems in various environments, as well as comprehensive information on specific membrane transport proteins.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): annotation , bioinformatics , genomics , membrane transporters and web server
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License. This article was made open access via a Publish and Read agreement between the Microbiology Society and the corresponding author’s institution.
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2023-01-18
2024-04-24
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References

  1. Bohnsack MT, Schleiff E. The evolution of protein targeting and translocation systems. Biochim Biophys Acta 2010; 1803:1115–1130 [View Article]
     [Google Scholar]
  2. Stillwell W. Membrane transport. In An Introduction to Biological Membranes Amsterdam: Elsevier; 2016 pp 423–451
     [Google Scholar]
  3. Saier MH. Vectorial metabolism and the evolution of transport systems. J Bacteriol 2000; 182:5029–5035 [View Article] [PubMed]
     [Google Scholar]
  4. Saier MH. Tracing pathways of transport protein evolution. Mol Microbiol 2003; 48:1145–1156 [View Article] [PubMed]
     [Google Scholar]
  5. Ren Q, Chen K, Paulsen IT. TransportDB: a comprehensive database resource for cytoplasmic membrane transport systems and outer membrane channels. Nucleic Acids Res 2007; 35:D274–D279 [View Article]
     [Google Scholar]
  6. Elbourne LDH, Hassan KA, Ren Q, Cameron AD, Henderson PJF et al. Microbial solute transporters. In Schmidt T. eds Encyclopedia of Microbiology Amsterdam: Elsevier; 2019 pp 157–173
     [Google Scholar]
  7. Saier MH, Tran CV, Barabote RD. TCDB: the Transporter Classification Database for membrane transport protein analyses and information. Nucleic Acids Res 2006; 34:D181–D186 [View Article]
     [Google Scholar]
  8. Paulsen IT, Sliwinski MK, Saier MH. Microbial genome analyses: global comparisons of transport capabilities based on phylogenies, bioenergetics and substrate specificities. J Mol Biol 1998; 277:573–592 [View Article] [PubMed]
     [Google Scholar]
  9. Saier MH, Paulsen IT. Paralogous genes encoding transport proteins in microbial genomes. Res Microbiol 1999; 150:689–699 [View Article] [PubMed]
     [Google Scholar]
  10. Saier MH. A functional-phylogenetic classification system for transmembrane solute transporters. Microbiol Mol Biol Rev 2000; 64:354–411 [View Article] [PubMed]
     [Google Scholar]
  11. Saier MH, Reddy VS, Moreno-Hagelsieb G, Hendargo KJ, Zhang Y et al. The transporter classification database (TCDB): 2021 update. Nucleic Acids Res 2021; 49:D461–D467 [View Article]
     [Google Scholar]
  12. Böhm A, Diez J, Diederichs K, Welte W, Boos W. Structural model of MalK, the ABC subunit of the maltose transporter of Escherichia coli: implications for mal gene regulation, inducer exclusion, and subunit assembly. J Biol Chem 2002; 277:3708–3717 [View Article]
     [Google Scholar]
  13. Boos W, Shuman H. Maltose/maltodextrin system of Escherichia coli: transport, metabolism, and regulation. Microbiol Mol Biol Rev 1998; 62:204–229 [View Article] [PubMed]
     [Google Scholar]
  14. Newman MJ, Foster DL, Wilson TH, Kaback HR. Purification and reconstitution of functional lactose carrier from Escherichia coli. J Biol Chem 1981; 256:11804–11808 [PubMed]
     [Google Scholar]
  15. Viitanen P, Garcia ML, Foster DL, Kaczorowski GJ, Kaback HR. Mechanism of lactose translocation in proteoliposomes reconstituted with lac carrier protein purified from Escherichia coli. 2. Deuterium solvent isotope effects. Biochemistry 1983; 22:2531–2536 [View Article]
     [Google Scholar]
  16. Abramson J, Smirnova I, Kasho V, Verner G, Kaback HR et al. Structure and mechanism of the lactose permease of Escherichia coli. Science 2003; 301:610–615 [View Article]
     [Google Scholar]
  17. Kaback HR, Guan L. It takes two to tango: the dance of the permease. J Gen Physiol 2019; 151:878–886 [View Article]
     [Google Scholar]
  18. Elferink MG, Driessen AJ, Robillard GT. Functional reconstitution of the purified phosphoenolpyruvate-dependent mannitol-specific transport system of Escherichia coli in phospholipid vesicles: coupling between transport and phosphorylation. J Bacteriol 1990; 172:7119–7125 [View Article]
     [Google Scholar]
  19. Postma PW, Lengeler JW, Jacobson GR. Phosphoenolpyruvate:carbohydrate phosphotransferase systems of bacteria. Microbiol Rev 1993; 57:543–594 [View Article] [PubMed]
     [Google Scholar]
  20. Sweet G, Gandor C, Voegele R, Wittekindt N, Beuerle J et al. Glycerol facilitator of Escherichia coli: cloning of glpF and identification of the glpF product. J Bacteriol 1990; 172:424–430 [View Article] [PubMed]
     [Google Scholar]
  21. Büchel DE, Gronenborn B, Müller-Hill B. Sequence of the lactose permease gene. Nature 1980; 283:541–545 [View Article]
     [Google Scholar]
  22. Kaback HR, Sahin-Tóth M, Weinglass AB. The kamikaze approach to membrane transport. Nat Rev Mol Cell Biol 2001; 2:610–620 [View Article] [PubMed]
     [Google Scholar]
  23. Paulsen IT, Sliwinski MK, Nelissen B, Goffeau A, Saier MH. Unified inventory of established and putative transporters encoded within the complete genome of Saccharomyces cerevisiae. FEBS Lett 1998; 430:116–125 [View Article]
     [Google Scholar]
  24. Paulsen IT, Nguyen L, Sliwinski MK, Rabus R, Saier MH. Microbial genome analyses: comparative transport capabilities in eighteen prokaryotes. J Mol Biol 2000; 301:75–100 [View Article]
     [Google Scholar]
  25. Elbourne LDH, Tetu SG, Hassan KA, Paulsen IT. TransportDB 2.0: a database for exploring membrane transporters in sequenced genomes from all domains of life. Nucleic Acids Res 2017; 45:D320–D324 [View Article] [PubMed]
     [Google Scholar]
  26. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J et al. BLAST+: architecture and applications. BMC Bioinformatics 2009; 10:421 [View Article]
     [Google Scholar]
  27. Ren Q, Kang KH, Paulsen IT. TransportDB: a relational database of cellular membrane transport systems. Nucleic Acids Res 2004; 32:D284–D288 [View Article]
     [Google Scholar]
  28. NCBI Resource Coordinators Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2018; 46:D8–D13 [View Article]
     [Google Scholar]
  29. Eddy SR, Pearson WR. Accelerated profile HMM searches. PLoS Comput Biol 2011; 7:e1002195 [View Article]
     [Google Scholar]
  30. Mistry J, Chuguransky S, Williams L, Qureshi M, Salazar GA et al. Pfam: the protein families database in 2021. Nucleic Acids Res 2021; 49:D412–D419 [View Article]
     [Google Scholar]
  31. Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res 2013; 41:D387–D395 [View Article]
     [Google Scholar]
  32. Li W, O’Neill KR, Haft DH, DiCuccio M, Chetvernin V et al. RefSeq: expanding the Prokaryotic Genome Annotation Pipeline reach with protein family model curation. Nucleic Acids Res 2021; 49:D1020–D1028 [View Article] [PubMed]
     [Google Scholar]
  33. Galperin MY, Wolf YI, Makarova KS, Vera Alvarez R, Landsman D et al. COG database update: focus on microbial diversity, model organisms, and widespread pathogens. Nucleic Acids Res 2021; 49:D274–D281 [View Article] [PubMed]
     [Google Scholar]
  34. Krogh A, Larsson B, von Heijne G, Sonnhammer EL. Predicting transmembrane protein topology with a hidden Markov model: application to complete genomes. J Mol Biol 2001; 305:567–580 [View Article] [PubMed]
     [Google Scholar]
  35. Hallgren J, Tsirigos KD, Pedersen MD, Almagro Armenteros JJ, Marcatili P et al. DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks. bioRxiv 2022487609 [View Article]
     [Google Scholar]
  36. Wilkens S. Structure and mechanism of ABC transporters. F1000Prime Rep 2015; 7:14 [View Article]
     [Google Scholar]
  37. Higgins CF, Hiles ID, Salmond GP, Gill DR, Downie JA et al. A family of related ATP-binding subunits coupled to many distinct biological processes in bacteria. Nature 1986; 323:448–450 [View Article] [PubMed]
     [Google Scholar]
  38. Rees DC, Johnson E, Lewinson O. ABC transporters: the power to change. Nat Rev Mol Cell Biol 2009; 10:218–227 [View Article] [PubMed]
     [Google Scholar]
  39. Thomas C, Tampé R. Structural and mechanistic principles of ABC transporters. Annu Rev Biochem 2020; 89:605–636 [View Article]
     [Google Scholar]
  40. Keseler IM, Gama-Castro S, Mackie A, Billington R, Bonavides-Martínez C et al. The EcoCyc database in 2021. Front Microbiol 2021; 12:711077 [View Article]
     [Google Scholar]
  41. Hassan KA, Elbourne LDH, Li L, Gamage HKAH, Liu Q et al. An ace up their sleeve: a transcriptomic approach exposes the AceI efflux protein of Acinetobacter baumannii and reveals the drug efflux potential hidden in many microbial pathogens. Front Microbiol 2015; 6:333 [View Article] [PubMed]
     [Google Scholar]
  42. Saier MH. Computer-aided analyses of transport protein sequences: gleaning evidence concerning function, structure, biogenesis, and evolution. Microbiol Rev 1994; 58:71–93 [View Article] [PubMed]
     [Google Scholar]
  43. Saier MH. Phylogenetic approaches to the identification and characterization of protein families and superfamilies. Microb Comp Genomics 1996; 1:129–150 [View Article] [PubMed]
     [Google Scholar]
  44. Saier MH. Molecular phylogeny as a basis for the classification of transport proteins from bacteria, archaea and eukarya. Adv Microb Physiol 1998; 40:81–136 [View Article] [PubMed]
     [Google Scholar]
  45. Majd H, King MS, Palmer SM, Smith AC, Elbourne LD et al. Screening of candidate substrates and coupling ions of transporters by thermostability shift assays. Elife 2018; 7:e38821 [View Article]
     [Google Scholar]
  46. Calgin MK, Sahin F, Turegun B, Gerceker D, Atasever M et al. Expression analysis of efflux pump genes among drug-susceptible and multidrug-resistant Mycobacterium tuberculosis clinical isolates and reference strains. Diagn Microbiol Infect Dis 2013; 76:291–297 [View Article] [PubMed]
     [Google Scholar]
  47. Li J, Lin L, Li H, Tian C, Ma Y. Transcriptional comparison of the filamentous fungus Neurospora crassa growing on three major monosaccharides D-glucose, D-xylose and L-arabinose. Biotechnol Biofuels 2014; 7:31 [View Article]
     [Google Scholar]
  48. Goswami D, Kaur J, Surade S, Grell E, Michel H. Heterologous production and functional and thermodynamic characterization of cation diffusion facilitator (CDF) transporters of mesophilic and hyperthermophilic origin. Biol Chem 2012; 393:617–629 [View Article] [PubMed]
     [Google Scholar]
  49. Moodley C, Reid SJ, Abratt VR. Molecular characterisation of ABC-type multidrug efflux systems in Bifidobacterium longum. Anaerobe 2015; 32:63–69 [View Article]
     [Google Scholar]
  50. Shaheen A, Ismat F, Iqbal M, Haque A, De Zorzi R et al. Characterization of putative multidrug resistance transporters of the major facilitator-superfamily expressed in Salmonella Typhi. J Infect Chemother 2015; 21:357–362 [View Article]
     [Google Scholar]
  51. Hassan KA, Fagerlund A, Elbourne LDH, Vörös A, Kroeger JK et al. The putative drug efflux systems of the Bacillus cereus group. PLoS One 2017; 12:e0176188 [View Article]
     [Google Scholar]
  52. Zeigler Allen L, Allen EE, Badger JH, McCrow JP, Paulsen IT et al. Influence of nutrients and currents on the genomic composition of microbes across an upwelling mosaic. ISME J 2012; 6:1403–1414 [View Article] [PubMed]
     [Google Scholar]
  53. Kell DB, Swainston N, Pir P, Oliver SG. Membrane transporter engineering in industrial biotechnology and whole cell biocatalysis. Trends Biotechnol 2015; 33:237–246 [View Article] [PubMed]
     [Google Scholar]
  54. Wang G, Møller-Hansen I, Babaei M, D’Ambrosio V, Christensen HB et al. Transportome-wide engineering of Saccharomyces cerevisiae. Metab Eng 2021; 64:52–63 [View Article]
     [Google Scholar]
  55. Claus S, Jezierska S, Elbourne LDH, Van Bogaert I. Exploring the transportome of the biosurfactant producing yeast Starmerella bombicola. BMC Genomics 2022; 23:22 [View Article]
     [Google Scholar]
  56. Bateman A, Martin M-J, Orchard S, Magrane M, Agivetova R. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res 2021; 49:D480–D489 [View Article] [PubMed]
     [Google Scholar]
  57. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article] [PubMed]
     [Google Scholar]
  58. Lagoa D, Faria JP, Liu F, Cunha E, Henry CS et al. TranSyT, an innovative framework for identifying transport systems. bioRxiv 2022441738 [View Article]
     [Google Scholar]
  59. Mishra NK, Chang J, Zhao PX. Prediction of membrane transport proteins and their substrate specificities using primary sequence information. PLoS One 2014; 9:e100278 [View Article]
     [Google Scholar]
  60. Reddy VS, Saier MH. BioV Suite – a collection of programs for the study of transport protein evolution. FEBS J 2012; 279:2036–2046 [View Article]
     [Google Scholar]
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TransAAP: an automated annotation pipeline for membrane transporter prediction in bacterial genomes
M Gen 9, 000927 (2023); https://doi.org/10.1099/mgen.0.000927
/content/journal/mgen/10.1099/mgen.0.000927
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