Small proteins in bacteria - Big challenges in prediction and identification.

Fuchs, S ; Engelmann, S

In: Proteomics, Jg. 23 (2023-12-01), Heft 23-24, S. e2200421

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Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Titel:	Small proteins in bacteria - Big challenges in prediction and identification.
Autor/in / Beteiligte Person:	Fuchs, S ; Engelmann, S
Link:	Full Text Finder (Volltext)
Zeitschrift:	Proteomics, Jg. 23 (2023-12-01), Heft 23-24, S. e2200421
Veröffentlichung:	Weinheim, Germany : Wiley-VCH,, 2023
Medientyp:	academicJournal
ISSN:	1615-9861 (electronic)
DOI:	10.1002/pmic.202200421
Schlagwort:	Mass Spectrometry methods Proteins metabolism Computational Biology methods
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Review Language: English [Proteomics] 2023 Dec; Vol. 23 (23-24), pp. e2200421. <i>Date of Electronic Publication: </i>2023 Aug 23. MeSH Terms: Proteins* / metabolism ; Computational Biology* / methods ; Mass Spectrometry / methods References: Ahrens, C. H., Wade, J. T., Champion, M. M., & Langer, J. D. (2022). A practical guide to small protein discovery and characterization using mass spectrometry. Journal of Bacteriology, 204, e0035321. https://doi.org/10.1128/JB.00353-21. ; Fuchs, S., Kucklick, M., Lehmann, E., Beckmann, A., Wilkens, M., Kolte, B., Mustafayeva, A., Ludwig, T., Diwo, M., Wissing, J., Jänsch, L., Ahrens, C. H., Ignatova, Z., & Engelmann, S. (2021). Towards the characterization of the hidden world of small proteins in Staphylococcus aureus, a proteogenomics approach. PLoS Genetics, 17, e1009585. https://doi.org/10.1371/journal.pgen.1009585. ; Cassidy, L., Kaulich, P. T., & Tholey, A. (2019). Depletion of high-molecular-mass proteins for the identification of small proteins and short open reading frame encoded peptides in cellular proteomes. Journal of Proteome Research, 18, 1725-1734. https://doi.org/10.1021/acs.jproteome.8b00948. ; Miravet-Verde, S., Ferrar, T., Espadas-García, G., Mazzolini, R., Gharrab, A., Sabido, E., Serrano, L., & Lluch-Senar, M. (2019). Unraveling the hidden universe of small proteins in bacterial genomes. Molecular Systems Biology, 15, e8290. https://doi.org/10.15252/msb.20188290. ; Müller, S. A., Kohajda, T., Findeiß, S., Stadler, P. F., Washietl, S., Kellis, M., Von Bergen, M., & Kalkhof, S. (2010). Optimization of parameters for coverage of low molecular weight proteins. Analytical and Bioanalytical Chemistry, 398, 2867-2881. https://doi.org/10.1007/s00216-010-4093-x. ; Petruschke, H., Anders, J., Stadler, P. F., Jehmlich, N., & Von Bergen, M. (2020). Enrichment and identification of small proteins in a simplified human gut microbiome. Journal of Proteomics, 213, 103604. https://doi.org/10.1016/j.jprot.2019.103604. ; Hadjeras, L., Bartel, J., Maier, L.-K., Maaß, S., Vogel, V., Svensson, S. L., Eggenhofer, F., Gelhausen, R., Müller, T., Alkhnbashi, O. S., Backofen, R., Becher, D., Sharma, C. M., & Marchfelder, A. (2023). Revealing the small proteome of Haloferax volcanii by combining ribosome profiling and small-protein optimized mass spectrometry. microLife, 4, 17. ; Hemm, M. R., Weaver, J., & Storz, G. (2020). Escherichia coli small proteome. EcoSal Plus, 9, https://doi.org/10.1128/ecosalplus.ESP-0031-2019. ; Fijalkowski, I., Willems, P., Jonckheere, V., Simoens, L., & van Damme, P. (2022). Hidden in plain sight: Challenges in proteomics detection of small ORF-encoded polypeptides. microLife, 3, 17. ; Storz, G., Wolf, Y. I., & Ramamurthi, K. S. (2014). Small proteins can no longer be ignored. Annual Review of Biochemistry, 83, 753-777. https://doi.org/10.1146/annurev-biochem-070611-102400. ; Hobbs, E. C., Fontaine, F., Yin, X., & Storz, G. (2011). An expanding universe of small proteins. Current Opinion in Microbiology, 14, 167-173. https://doi.org/10.1016/j.mib.2011.01.007. ; Gray, T., Storz, G., & Papenfort, K. (2022). Small proteins; Big questions. Journal of Bacteriology, 204, e0034121. https://doi.org/10.1128/JB.00341-21. ; Garai, P., & Blanc-Potard, A. (2020). Uncovering small membrane proteins in pathogenic bacteria: Regulatory functions and therapeutic potential. Molecular Microbiology, 114, 710-720. https://doi.org/10.1111/mmi.14564. ; Dimonaco, N. J., Aubrey, W., Kenobi, K., Clare, A., & Creevey, C. J. (2022). No one tool to rule them all: Prokaryotic gene prediction tool annotations are highly dependent on the organism of study. Bioinformatics, 38, 1198-1207. https://doi.org/10.1093/bioinformatics/btab827. ; Boekhorst, J., Wilson, G., & Siezen, R. J. (2011). Searching in microbial genomes for encoded small proteins. Microbial Biotechnology, 4, 308-313. https://doi.org/10.1111/j.1751-7915.2011.00261.x. ; Dinger, M. E., Pang, K. C., Mercer, T. R., & Mattick, J. S. (2008). Differentiating protein-coding and noncoding RNA: Challenges and ambiguities. PLoS Computational Biology, 4, e1000176. https://doi.org/10.1371/journal.pcbi.1000176. ; Hemm, M. R., Paul, B. J., Schneider, T. D., Storz, G., & Rudd, K. E. (2008). Small membrane proteins found by comparative genomics and ribosome binding site models. Molecular Microbiology, 70, 1487-1501. https://doi.org/10.1111/j.1365-2958.2008.06495.x. ; Tatusova, T., Dicuccio, M., Badretdin, A., Chetvernin, V., Nawrocki, E. P., Zaslavsky, L., Lomsadze, A., Pruitt, K. D., Borodovsky, M., & Ostell, J. (2016). NCBI prokaryotic genome annotation pipeline. Nucleic Acids Research, 44, 6614-6624. https://doi.org/10.1093/nar/gkw569. ; Vazquez-Laslop, N., Sharma, C. M., Mankin, A., & Buskirk, A. R. (2022). Identifying small open reading frames in prokaryotes with ribosome profiling. Journal of Bacteriology, 204, e0029421. https://doi.org/10.1128/JB.00294-21. ; Aebersold, R., & Mann, M. (2016). Mass-spectrometric exploration of proteome structure and function. Nature, 537, 347-355. https://doi.org/10.1038/nature19949. ; Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. 3rd (2013). Protein analysis by shotgun/bottom-up proteomics. Chemical Reviews, 113, 2343-2394. https://doi.org/10.1021/cr3003533. ; Maass, S., Moog, G., & Becher, D. (2019). Subcellular protein fractionation in Legionella pneumophila and preparation of the derived sub-proteomes for analysis by mass spectrometry. Methods in Molecular Biology, 1921, 445-464. https://doi.org/10.1007/978-1-4939-9048-1_28. ; Omasits, U., Quebatte, M., Stekhoven, D. J., Fortes, C., Roschitzki, B., Robinson, M. D., Dehio, C., & Ahrens, C. H. (2013). Directed shotgun proteomics guided by saturated RNA-seq identifies a complete expressed prokaryotic proteome. Genome Research, 23, 1916-1927. https://doi.org/10.1101/gr.151035.112. ; Becher, D., Hempel, K., Sievers, S., Zühlke, D., Pané-Farré, J., Otto, A., Fuchs, S., Albrecht, D., Bernhardt, J., Engelmann, S., Völker, U., Van Dijl, J. M., & Hecker, M. (2009). A proteomic view of an important human pathogen - towards the quantification of the entire staphylococcus aureus proteome. PLoS ONE, 4, e8176. https://doi.org/10.1371/journal.pone.0008176. ; Nesvizhskii, A. I. (2010). A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics. Journal of Proteomics, 73, 2092-2123. https://doi.org/10.1016/j.jprot.2010.08.009. ; Steinberg, R., & Koch, H.-G. (2021). The largely unexplored biology of small proteins in pro- and eukaryotes. The FEBS Journal, 288, 7002-7024. https://doi.org/10.1111/febs.15845. ; Basrai, M. A., Hieter, P., & Boeke, J. D. (1997). Small open reading frames: Beautiful needles in the haystack. Genome Research, 7, 768-771. https://doi.org/10.1101/gr.7.8.768. ; Orr, M. W., Mao, Y., Storz, G., & Qian, S.-B. (2020). Alternative ORFs and small ORFs: Shedding light on the dark proteome. Nucleic Acids Research, 48, 1029-1042. https://doi.org/10.1093/nar/gkz734. ; Kreitmeier, M., Ardern, Z., Abele, M., Ludwig, C., Scherer, S., & Neuhaus, K. (2022). Spotlight on alternative frame coding: Two long overlapping genes in Pseudomonas aeruginosa are translated and under purifying selection. iScience, 25, 103844. https://doi.org/10.1016/j.isci.2022.103844. ; Hücker, S. M., Ardern, Z., Goldberg, T., Schafferhans, A., Bernhofer, M., Vestergaard, G., Nelson, C. W., Schloter, M., Rost, B., Scherer, S., & Neuhaus, K. (2017). Discovery of numerous novel small genes in the intergenic regions of the Escherichia coli O157:H7 Sakai genome. PLoS ONE, 12, e0184119. https://doi.org/10.1371/journal.pone.0184119. ; Warren, A. S., Archuleta, J., Feng, W.-C., & Setubal, J. C. (2010). Missing genes in the annotation of prokaryotic genomes. BMC Bioinformatics, 11, 131. https://doi.org/10.1186/1471-2105-11-131. ; Cheng, H., Soon Chan, W., Li, Z., Wang, D., Liu, S., & Zhou, Y. (2011). Small open reading frames: Current prediction techniques and future prospect. Current Protein & Peptide Science, 12, 503-507. https://doi.org/10.2174/138920311796957667. ; Hyatt, D., Locascio, P. F., Hauser, L. J., & Uberbacher, E. C. (2012). Gene and translation initiation site prediction in metagenomic sequences. Bioinformatics, 28, 2223-2230. https://doi.org/10.1093/bioinformatics/bts429. ; Hyatt, D., Chen, G.-L., Locascio, P. F., Land, M. L., Larimer, F. W., & Hauser, L. J. (2010). Prodigal: Prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics, 11, 119. https://doi.org/10.1186/1471-2105-11-119. ; Washietl, S., Findeiß, S., Müller, S. A., Kalkhof, S., Von Bergen, M., Hofacker, I. L., Stadler, P. F., & Goldman, N. (2011). RNAcode: Robust discrimination of coding and noncoding regions in comparative sequence data. RNA, 17, 578-594. https://doi.org/10.1261/rna.2536111. ; Hanada, K., Akiyama, K., Sakurai, T., Toyoda, T., Shinozaki, K., & Shiu, S.-H. (2010). sORF finder: A program package to identify small open reading frames with high coding potential. Bioinformatics, 26, 399-400. https://doi.org/10.1093/bioinformatics/btp688. ; de Souza, E. V., Dalberto, P. F., Machado, V. P., Canedo, A., Saghatelian, A., Machado, P., Basso, L. A., & Bizarro, C. V. (2022). μProteInS-a proteogenomics pipeline for finding novel bacterial microproteins encoded by small ORFs. Bioinformatics, 38, 2612-2614. https://doi.org/10.1093/bioinformatics/btac115. ; Clauwaert, J., Menschaert, G., & Waegeman, W. (2019). DeepRibo: A neural network for precise gene annotation of prokaryotes by combining ribosome profiling signal and binding site patterns. Nucleic Acids Research, 47, e36. https://doi.org/10.1093/nar/gkz061. ; Ndah, E., Jonckheere, V., Giess, A., Valen, E., Menschaert, G., & Van Damme, P. (2017). REPARATION: Ribosome profiling assisted (re-)annotation of bacterial genomes. Nucleic Acids Research, 45, e168. https://doi.org/10.1093/nar/gkx758. ; Bartholomäus, A., Kolte, B., Mustafayeva, A., Goebel, I., Fuchs, S., Benndorf, D., Engelmann, S., & Ignatova, Z. (2021). smORFer: A modular algorithm to detect small ORFs in prokaryotes. Nucleic Acids Research, 49, e89. https://doi.org/10.1093/nar/gkab477. ; Badger, J. H., & Olsen, G. J. (1999). CRITICA: Coding region identification tool invoking comparative analysis. Molecular Biology and Evolution, 16, 512-524. https://doi.org/10.1093/oxfordjournals.molbev.a026133. ; Cerqueira, F. R., & Vasconcelos, A. T. R. (2020). OCCAM: Prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques. Database (Oxford), 2020, baaa067. https://doi.org/10.1093/database/baaa067. ; Oheigeartaigh, S. S., Armisen, D., Byrne, K. P., & Wolfe, K. H. (2014). SearchDOGS bacteria, software that provides automated identification of potentially missed genes in annotated bacterial genomes. Journal of Bacteriology, 196, 2030-2042. https://doi.org/10.1128/JB.01368-13. ; Keller, O., Kollmar, M., Stanke, M., & Waack, S. (2011). A novel hybrid gene prediction method employing protein multiple sequence alignments. Bioinformatics, 27, 757-763. https://doi.org/10.1093/bioinformatics/btr010. ; Sommer, M. J., & Salzberg, S. L. (2021). Balrog: A universal protein model for prokaryotic gene prediction. PLoS Computational Biology, 17, e1008727. https://doi.org/10.1371/journal.pcbi.1008727. ; Besemer, J., Lomsadze, A., & Borodovsky, M. (2001). GeneMarkS: A self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Research, 29, 2607-2618. https://doi.org/10.1093/nar/29.12.2607. ; Delcher, A. L., Bratke, K. A., Powers, E. C., & Salzberg, S. L. (2007). Identifying bacterial genes and endosymbiont DNA with Glimmer. Bioinformatics, 23, 673-679. https://doi.org/10.1093/bioinformatics/btm009. ; Camacho, C., Coulouris, G., Avagyan, V., Ma, N., Papadopoulos, J., Bealer, K., & Madden, T. L. (2009). BLAST+: Architecture and applications. BMC Bioinformatics, 10, 421. https://doi.org/10.1186/1471-2105-10-421. ; Pearson, W. R. (2000). Flexible sequence similarity searching with the FASTA3 program package. Methods in Molecular Biology, 132, 185-219. https://doi.org/10.1385/1-59259-192-2:185. ; Gelhausen, R., Müller, T., Svensson, S. L., Alkhnbashi, O. S., Sharma, C. M., Eggenhofer, F., & Backofen, R. (2022). RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria. Briefings in Bioinformatics, 23, bbab549. https://doi.org/10.1093/bib/bbab549. ; Korandla, D. R., Wozniak, J. M., Campeau, A., Gonzalez, D. J., & Wright, E. S. (2020). AssessORF: Combining evolutionary conservation and proteomics to assess prokaryotic gene predictions. Bioinformatics, 36, 1022-1029. https://doi.org/10.1093/bioinformatics/btz714. ; Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990). Basic local alignment search tool. Journal of Molecular Biology, 215, 403-410. https://doi.org/10.1016/S0022-2836(05)80360-2. ; Pearson, W. R., & Lipman, D. J. (1988). Improved tools for biological sequence comparison. Proceedings of the National Academy of Sciences of the United States of America, 85, 2444-2448. https://doi.org/10.1073/pnas.85.8.2444. ; Darling, A. E., Carey, L., & Feng, W.-c. (2003). The design, implementation and evaluation of mpiBLAST. In ClusterWorld Conference & Expo and the 4th International Conference on Linux Clusters: The HPC Revolution, June 23 to 26 in 2003 in San Jose, California. ; Pearson, W. R. (2013). An introduction to sequence similarity (“homology”) searching. In Current protocols in bioinformatics, John Wiley & Sons, Inc. (Chapter 3, 3.1.1-3.1.8). https://doi.org/10.1002/0471250953.bi0301s42. ; Sberro, H., Fremin, B. J., Zlitni, S., Edfors, F., Greenfield, N., Snyder, M. P., Pavlopoulos, G. A., Kyrpides, N. C., & Bhatt, A. S. (2019). Large-scale analyses of human microbiomes reveal thousands of small, novel genes. Cell, 178, 1245-1259.e14. https://doi.org/10.1016/j.cell.2019.07.016. ; Baumgartner, D., Kopf, M., Klähn, S., Steglich, C., & Hess, W. R. (2016). Small proteins in cyanobacteria provide a paradigm for the functional analysis of the bacterial micro-proteome. BMC Microbiology, 16, 285. https://doi.org/10.1186/s12866-016-0896-z. ; Fremin, B. J., Bhatt, A. S., Kyrpides, N. C., & Global Phage Small Open Reading Frame Consortium. (2022). Thousands of small, novel genes predicted in global phage genomes. Cell Reports, 39, 110984. https://doi.org/10.1016/j.celrep.2022.110984. ; Megrian, D., Taib, N., Jaffe, A. L., Banfield, J. F., & Gribaldo, S. (2022). Ancient origin and constrained evolution of the division and cell wall gene cluster in Bacteria. Nature Microbiology, 7, 2114-2127. https://doi.org/10.1038/s41564-022-01257-y. ; Yang, Z. (2007). PAML 4: Phylogenetic analysis by maximum likelihood. Molecular Biology and Evolution, 24, 1586-1591. https://doi.org/10.1093/molbev/msm088. ; Lin, M. F., Jungreis, I., & Kellis, M. (2011). PhyloCSF: A comparative genomics method to distinguish protein coding and non-coding regions. Bioinformatics, 27, i275-i282. https://doi.org/10.1093/bioinformatics/btr209. ; Wood, D. E., Lin, H., Levy-Moonshine, A., Swaminathan, R., Chang, Y.-C., Anton, B. P., Osmani, L., Steffen, M., Kasif, S., & Salzberg, S. L. (2012). Thousands of missed genes found in bacterial genomes and their analysis with COMBREX. Biology Direct, 7, 37. https://doi.org/10.1186/1745-6150-7-37. ; Choi, H.-P., Juarez, S., Ciordia, S., Fernandez, M., Bargiela, R., Albar, J. P., Mazumdar, V., Anton, B. P., Kasif, S., Ferrer, M., & Steffen, M. (2013). Biochemical characterization of hypothetical proteins from Helicobacter pylori. PLoS ONE, 8, e66605. https://doi.org/10.1371/journal.pone.0066605. ; Akita, H., Kimura, Z.-I., Yusoff, M. Z. M., Nakashima, N., & Hoshino, T. (2016). Draft genome sequence of Burkholderia sp. strain CCA53, isolated from leaf soil. Genome Announcements, 4, e00630. https://doi.org/10.1128/genomeA.00630-16. ; Akita, H., Matsushika, A., & Kimura, Z.-I. (2019). Enterobacter oligotrophica sp. nov., a novel oligotroph isolated from leaf soil. MicrobiologyOpen, 8, e00843. https://doi.org/10.1002/mbo3.843. ; Venturini, E., Svensson, S. L., Maaß, S., Gelhausen, R., Eggenhofer, F., Li, L., Cain, A. K., Parkhill, J., Becher, D., Backofen, R., Barquist, L., Sharma, C. M., Westermann, A. J., & Vogel, J. (2020). A global data-driven census of Salmonella small proteins and their potential functions in bacterial virulence. microLife, 1. https://doi.org/10.1093/femsml/uqaa002. ; Froschauer, K., Svensson, S. L., Gelhausen, R., Fiore, E., Kible, P., Klaude, A., Kucklick, M., Fuchs, S., Eggenhofer, F., Engelmann, S., Backofen, R., & Sharma, C. M. (2022). Complementary Ribo-seq approaches map the translatome and provide a small protein census in the foodborne pathogen Campylobacter jejuni. bioRxiv. https://doi.org/10.1101/2022.11.09.515450. ; Hadjeras, L., Heiniger, B., Maaß, S., Scheuer, R., Gelhausen, R., Azarderakhsh, S., Barth-Weber, S., Backofen, R., Becher, D., Ahrens, C. H., Sharma, C. M., & Evguenieva-Hackenberg, E. (2023). Unraveling the small proteome of the plant symbiont Sinorhizobium meliloti by ribosome profiling and proteogenomics. microLife, 4, 22. ; Baek, J., Lee, J., Yoon, K., & Lee, H. (2017). Identification of unannotated small genes in Salmonella. G3: Genes, Genomes, Genetics, 7, 983-989. https://doi.org/10.1534/g3.116.036939. ; Stringer, A., Smith, C., Mangano, K., & Wade, J. T. (2021). Identification of novel translated small open reading frames in Escherichia coli using complementary ribosome profiling approaches. Journal of Bacteriology, 204, JB0035221. https://doi.org/10.1128/JB.00352-21. ; Weaver, J., Mohammad, F., Buskirk, A. R., & Storz, G. (2019). Identifying small proteins by ribosome profiling with stalled initiation complexes. mBio, 10, e02819 https://doi.org/10.1128/mBio.02819-18. ; Willems, P., Fijalkowski, I., & Van Damme, P. (2020). Lost and found: Re-searching and re-scoring proteomics data aids genome annotation and improves proteome coverage. mSystems, 5, e00833-e00820. https://doi.org/10.1128/mSystems.00833-20. ; Smith, C., Canestrari, J. G., Wang, A. J., Champion, M. M., Derbyshire, K. M., Gray, T. A., & Wade, J. T. (2022). Pervasive translation in Mycobacterium tuberculosis. eLife, 11, e73980. https://doi.org/10.7554/eLife.73980. ; Crappé, J., Van Criekinge, W., Trooskens, G., Hayakawa, E., Luyten, W., Baggerman, G., & Menschaert, G. (2013). Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs. BMC Genomics, 14, 648. https://doi.org/10.1186/1471-2164-14-648. ; Sharma, C. M., Hoffmann, S., Darfeuille, F., Reignier, J., Findeiß, S., Sittka, A., Chabas, S., Reiche, K., Hackermüller, J., Reinhardt, R., Stadler, P. F., & Vogel, J. (2010). The primary transcriptome of the major human pathogen Helicobacter pylori. Nature, 464, 250-255. https://doi.org/10.1038/nature08756. ; Nicolas, P., Mäder, U., Dervyn, E., Rochat, T., Leduc, A., Pigeonneau, N., Bidnenko, E., Marchadier, E., Hoebeke, M., Aymerich, S., Becher, D., Bisicchia, P., Botella, E., Delumeau, O., Doherty, G., Denham, E. L., Fogg, M. J., Fromion, V., Goelzer, A., … Noirot, P. (2012). Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis. Science, 335, 1103-1106. https://doi.org/10.1126/science.1206848. ; Jäger, D., Sharma, C. M., Thomsen, J., Ehlers, C., Vogel, J., & Schmitz, R. A. (2009). Deep sequencing analysis of the Methanosarcina mazei Gö1 transcriptome in response to nitrogen availability. Proceedings of the National Academy of Sciences of the United States of America, 106, 21878-21882. https://doi.org/10.1073/pnas.0909051106. ; Lasa, I., Toledo-Arana, A., Dobin, A., Villanueva, M., De Los Mozos, I. R., Vergara-Irigaray, M., Segura, V., Fagegaltier, D., Penadés, J. R., Valle, J., Solano, C., & Gingeras, T. R. (2011). Genome-wide antisense transcription drives mRNA processing in bacteria. Proceedings of the National Academy of Sciences of the United States of America, 108, 20172-20177. https://doi.org/10.1073/pnas.1113521108. ; Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10, 57-63. https://doi.org/10.1038/nrg2484. ; Laass, S., Monzon, V. A., Kliemt, J., Hammelmann, M., Pfeiffer, F., Förstner, K. U., & Soppa, J. (2019). Characterization of the transcriptome of Haloferax volcanii, grown under four different conditions, with mixed RNA-Seq. PLoS ONE, 14, e0215986. https://doi.org/10.1371/journal.pone.0215986. ; Gimpel, M., & Brantl, S. (2017). Dual-function small regulatory RNAs in bacteria. Molecular Microbiology, 103, 387-397. https://doi.org/10.1111/mmi.13558. ; Prasse, D., Thomsen, J., De Santis, R., Muntel, J., Becher, D., & Schmitz, R. A. (2015). First description of small proteins encoded by spRNAs in Methanosarcina mazei strain Gö1. Biochimie, 117, 138-148. https://doi.org/10.1016/j.biochi.2015.04.007. ; Giess, A., Jonckheere, V., Ndah, E., Chyżyńska, K., van Damme, P., & Valen, E. (2017). Ribosome signatures aid bacterial translation initiation site identification. BMC Biology, 15, 76. https://doi.org/10.1186/s12915-017-0416-0. ; Shell, S. S., Wang, J., Lapierre, P., Mir, M., Chase, M. R., Pyle, M. M., Gawande, R., Ahmad, R., Sarracino, D. A., Ioerger, T. R., Fortune, S. M., Derbyshire, K. M., Wade, J. T., & Gray, T. A. (2015). Leaderless transcripts and small proteins are common features of the mycobacterial translational landscape. PLoS Genetics, 11, e1005641. https://doi.org/10.1371/journal.pgen.1005641. ; Meydan, S., Marks, J., Klepacki, D., Sharma, V., Baranov, P. V., Firth, A. E., Margus, T., Kefi, A., Vázquez-Laslop, N., & Mankin, A. S. (2019). Retapamulin-assisted ribosome profiling reveals the alternative bacterial proteome. Molecular Cell, 74, 481-493.e6. https://doi.org/10.1016/j.molcel.2019.02.017. ; Nakahigashi, K., Takai, Y., Kimura, M., Abe, N., Nakayashiki, T., Shiwa, Y., Yoshikawa, H., Wanner, B. L., Ishihama, Y., & Mori, H. (2016). Comprehensive identification of translation start sites by tetracycline-inhibited ribosome profiling. DNA Research, 23, 193-201. https://doi.org/10.1093/dnares/dsw008. ; Fremin, B. J., Nicolaou, C., & Bhatt, A. S. (2021). Simultaneous ribosome profiling of hundreds of microbes from the human microbiome. Nature Protocols, 16, 4676-4691. https://doi.org/10.1038/s41596-021-00592-4. ; Hwang, J.-Y., & Buskirk, A. R. (2017). A ribosome profiling study of mRNA cleavage by the endonuclease RelE. Nucleic Acids Research, 45, 327-336. https://doi.org/10.1093/nar/gkw944. ; Meydan, S., Vázquez-Laslop, N., & Mankin, A. S. (2018). Genes within genes in bacterial genomes. Microbiology Spectrum, 6, https://doi.org/10.1128/microbiolspec.rwr-0020-2018. ; Saito, K., Green, R., & Buskirk, A. R. (2020). Translational initiation in E. coli occurs at the correct sites genome-wide in the absence of mRNA-rRNA base-pairing. eLife, 9, e55002. https://doi.org/10.7554/eLife.55002. ; Gelsinger, D. R., Dallon, E., Reddy, R., Mohammad, F., Buskirk, A. R., & Diruggiero, J. (2020). Ribosome profiling in archaea reveals leaderless translation, novel translational initiation sites, and ribosome pausing at single codon resolution. Nucleic Acids Research, 48, 5201-5216. https://doi.org/10.1093/nar/gkaa304. ; Jaffe, J. D., Berg, H. C., & Church, G. M. (2004). Proteogenomic mapping as a complementary method to perform genome annotation. Proteomics, 4, 59-77. https://doi.org/10.1002/pmic.200300511. ; Nesvizhskii, A. I. (2014). Proteogenomics: Concepts, applications and computational strategies. Nature Methods, 11, 1114-1125. https://doi.org/10.1038/nmeth.3144. ; Omasits, U., Varadarajan, A. R., Schmid, M., Goetze, S., Melidis, D., Bourqui, M., Nikolayeva, O., Québatte, M., Patrignani, A., Dehio, C., Frey, J. E., Robinson, M. D., Wollscheid, B., & Ahrens, C. H. (2017). An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics. Genome Research, 27, 2083-2095. https://doi.org/10.1101/gr.218255.116. ; Hecht, A., Glasgow, J., Jaschke, P. R., Bawazer, L. A., Munson, M. S., Cochran, J. R., Endy, D., & Salit, M. (2017). Measurements of translation initiation from all 64 codons in E. coli. Nucleic Acids Research, 45, 3615-3626. https://doi.org/10.1093/nar/gkx070. ; Singhal, P., Jayaram, B., Dixit, S. B., & Beveridge, D. L. (2008). Prokaryotic gene finding based on physicochemical characteristics of codons calculated from molecular dynamics simulations. Biophysical Journal, 94, 4173-4183. https://doi.org/10.1529/biophysj.107.116392. ; Bartel, J., Varadarajan, A. R., Sura, T., Ahrens, C. H., Maaß, S., & Becher, D. (2020). Optimized proteomics workflow for the detection of small proteins. Journal of Proteome Research, 19, 4004-4018. https://doi.org/10.1021/acs.jproteome.0c00286. ; Varadarajan, A. R., Goetze, S., Pavlou, M. P., Grosboillot, V., Shen, Y., Loessner, M. J., Ahrens, C. H., & Wollscheid, B. (2020). A proteogenomic resource enabling integrated analysis of listeria genotype-proteotype-phenotype relationships. Journal of Proteome Research, 19, 1647-1662. https://doi.org/10.1021/acs.jproteome.9b00842. ; Cassidy, L., Prasse, D., Linke, D., Schmitz, R. A., & Tholey, A. (2016). Combination of bottom-up 2D-LC-MS and semi-top-down GelFree-LC-MS enhances coverage of proteome and low molecular weight short open reading frame encoded peptides of the archaeon methanosarcina mazei. Journal of Proteome Research, 15, 3773-3783. https://doi.org/10.1021/acs.jproteome.6b00569. ; Kaulich, P. T., Cassidy, L., Weidenbach, K., Schmitz, R. A., & Tholey, A. (2020). Complementarity of different SDS-PAGE gel staining methods for the identification of short open reading frame-encoded peptides. Proteomics, 20, 2000084. https://doi.org/10.1002/pmic.202000084. ; Kaulich, P. T., Cassidy, L., Bartel, J., Schmitz, R. A., & Tholey, A. (2021). Multi-protease approach for the improved identification and molecular characterization of small proteins and short open reading frame-encoded peptides. Journal of Proteome Research, 20, 2895-2903. https://doi.org/10.1021/acs.jproteome.1c00115. ; Impens, F., Rolhion, N., Radoshevich, L., Bécavin, C., Duval, M., Mellin, J., García Del Portillo, F., Pucciarelli, M. G, Williams, A. H., & Cossart, P. (2017). N-terminomics identifies Prli42 as a membrane miniprotein conserved in Firmicutes and critical for stressosome activation in Listeria monocytogenes. Nature Microbiology, 2, 17005. https://doi.org/10.1038/nmicrobiol.2017.5. ; Smith, L. M., & Kelleher, N. L. (2013). Proteoform: A single term describing protein complexity. Nature Methods, 10, 186-187. https://doi.org/10.1038/nmeth.2369. ; Berry, I. J., Steele, J. R., Padula, M. P., & Djordjevic, S. P. (2016). The application of terminomics for the identification of protein start sites and proteoforms in bacteria. Proteomics, 16, 257-272. https://doi.org/10.1002/pmic.201500319. ; Mcdonald, L., & Beynon, R. J. (2006). Positional proteomics: Preparation of amino-terminal peptides as a strategy for proteome simplification and characterization. Nature Protocols, 1, 1790-1798. https://doi.org/10.1038/nprot.2006.317. ; Tran, J. C., & Doucette, A. A. (2008). Gel-eluted liquid fraction entrapment electrophoresis: An electrophoretic method for broad molecular weight range proteome separation. Analytical Chemistry, 80, 1568-1573. https://doi.org/10.1021/ac702197w. ; Hughes, C. S., Foehr, S., Garfield, D. A., Furlong, E. E., Steinmetz, L. M., & Krijgsveld, J. (2014). Ultrasensitive proteome analysis using paramagnetic bead technology. Molecular Systems Biology, 10, 757. https://doi.org/10.15252/msb.20145625. ; Gao, Z., Chang, C., Yang, J., Zhu, Y., & Fu, Y. (2019). AP3: An advanced proteotypic peptide predictor for targeted proteomics by incorporating peptide digestibility. Analytical Chemistry, 91, 8705-8711. https://doi.org/10.1021/acs.analchem.9b02520. ; Qeli, E., Omasits, U., Goetze, S., Stekhoven, D. J., Frey, J. E., Basler, K., Wollscheid, B., Brunner, E., & Ahrens, C. H. (2014). Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data. Journal of Proteomics, 108, 269-283. https://doi.org/10.1016/j.jprot.2014.05.011. ; Meier-Credo, J., Preiss, L., Wüllenweber, I., Resemann, A., Nordmann, C., Zabret, J., Suckau, D., Michel, H., Nowaczyk, M. M., Meier, T., & Langer, J. D. (2022). Top-down identification and sequence analysis of small membrane proteins using MALDI-MS/MS. Journal of the American Society for Mass Spectrometry, 33, 1293-1302. https://doi.org/10.1021/jasms.2c00102. ; Zhang, F., Ge, W., Ruan, G., Cai, X., & Guo, T. (2020). Data-independent acquisition mass spectrometry-based proteomics and software tools: A glimpse in 2020. Proteomics, 20, 1900276. https://doi.org/10.1002/pmic.201900276. ; Cassidy, L., Helbig, A. O., Kaulich, P. T., Weidenbach, K., Schmitz, R. A., & Tholey, A. (2021). Multidimensional separation schemes enhance the identification and molecular characterization of low molecular weight proteomes and short open reading frame-encoded peptides in top-down proteomics. Journal of Proteomics, 230, 103988. https://doi.org/10.1016/j.jprot.2020.103988. ; Cristobal, A., Marino, F., Post, H., van den Toorn, H. W. P., Mohammed, S., & Heck, A. J. R. (2017). Toward an optimized workflow for middle-down proteomics. Analytical Chemistry, 89, 3318-3325. https://doi.org/10.1021/acs.analchem.6b03756. ; Olsen, J. V., & Mann, M. (2004). Improved peptide identification in proteomics by two consecutive stages of mass spectrometric fragmentation. Proceedings of the National Academy of Sciences of the United States of America, 101, 13417-13422. https://doi.org/10.1073/pnas.0405549101. ; Wysocki, V. H., Tsaprailis, G., Smith, L. L., & Breci, L. A. (2000). Mobile and localized protons: A framework for understanding peptide dissociation. Journal of Mass Spectrometry, 35, 1399-1406. https://doi.org/10.1002/1096-9888(200012)35:12<1399::AID-JMS86>3.0.CO;2-R. ; Zubarev, R. A. (2004). Electron-capture dissociation tandem mass spectrometry. Current Opinion in Biotechnology, 15, 12-16. https://doi.org/10.1016/j.copbio.2003.12.002. ; Syka, J. E. P., Coon, J. J., Schroeder, M. J., Shabanowitz, J., & Hunt, D. F. (2004). Peptide and protein sequence analysis by electron transfer dissociation mass spectrometry. Proceedings of the National Academy of Sciences of the United States of America, 101, 9528-9533. https://doi.org/10.1073/pnas.0402700101. ; Burger, T. (2018). Gentle introduction to the statistical foundations of false discovery rate in quantitative proteomics. Journal of Proteome Research, 17, 12-22. https://doi.org/10.1021/acs.jproteome.7b00170. ; Zhang, K., Fu, Y., Zeng, W.-F., He, K., Chi, H., Liu, C., Li, Y.-C., Gao, Y., Xu, P., & He, S.-M. (2015). A note on the false discovery rate of novel peptides in proteogenomics. Bioinformatics, 31, 3249-3253. https://doi.org/10.1093/bioinformatics/btv340. ; 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. (2013). Peptidomic discovery of short open reading frame-encoded peptides in human cells. Nature Chemical Biology, 9, 59-64. https://doi.org/10.1038/nchembio.1120. ; Käll, L., Canterbury, J. D., Weston, J., Noble, W. S., & Maccoss, M. J. (2007). Semi-supervised learning for peptide identification from shotgun proteomics datasets. Nature Methods, 4, 923-925. https://doi.org/10.1038/nmeth1113. ; Granholm, V., Kim, S., Navarro, J. C. F., Sjölund, E., Smith, R. D., & Käll, L. (2014). Fast and accurate database searches with MS-GF+Percolator. Journal of Proteome Research, 13, 890-897. https://doi.org/10.1021/pr400937n. ; Gessulat, S., Schmidt, T., Zolg, D. P., Samaras, P., Schnatbaum, K., Zerweck, J., Knaute, T., Rechenberger, J., Delanghe, B., Huhmer, A., Reimer, U., Ehrlich, H.-C., Aiche, S., Kuster, B., & Wilhelm, M. (2019). Prosit: Proteome-wide prediction of peptide tandem mass spectra by deep learning. Nature Methods, 16, 509-518. https://doi.org/10.1038/s41592-019-0426-7. ; Tiwary, S., Levy, R., Gutenbrunner, P., Salinas Soto, F., Palaniappan, K. K., Deming, L., Berndl, M., Brant, A., Cimermancic, P., & Cox, J. (2019). High-quality MS/MS spectrum prediction for data-dependent and data-independent acquisition data analysis. Nature Methods, 16, 519-525. https://doi.org/10.1038/s41592-019-0427-6. ; Silva, A. S. C., Bouwmeester, R., Martens, L., & Degroeve, S. (2019). Accurate peptide fragmentation predictions allow data driven approaches to replace and improve upon proteomics search engine scoring functions. Bioinformatics, 35, 5243-5248. https://doi.org/10.1093/bioinformatics/btz383. ; Ferguson, J. T., Wenger, C. D., Metcalf, W. W., & Kelleher, N. L. (2009). Top-down proteomics reveals novel protein forms expressed in Methanosarcina acetivorans. Journal of the American Society for Mass Spectrometry, 20, 1743-1750. https://doi.org/10.1016/j.jasms.2009.05.014. ; Kohlstaedt, M., Buschmann, S., Xie, H., Resemann, A., Warkentin, E., Langer, J. D., & Michel, H. (2016). Identification and characterization of the novel subunit CcoM in the cbb3(3)cytochrome c oxidase from Pseudomonas stutzeri ZoBell. mBio, 7, e01921-01915. https://doi.org/10.1128/mBio.01921-15. ; Ying, Y.-L., Hu, Z.-L., Zhang, S., Qing, Y., Fragasso, A., Maglia, G., Meller, A., Bayley, H., Dekker, C., & Long, Y.-T. (2022). Nanopore-based technologies beyond DNA sequencing. Nature Nanotechnology, 17, 1136-1146. https://doi.org/10.1038/s41565-022-01193-2. ; Restrepo-Pérez, L., Joo, C., & Dekker, C. (2018). Paving the way to single-molecule protein sequencing. Nature Nanotechnology, 13, 786-796. https://doi.org/10.1038/s41565-018-0236-6. ; Nivala, J., Marks, D. B., & Akeson, M. (2013). Unfoldase-mediated protein translocation through an α-hemolysin nanopore. Nature Biotechnology, 31, 247-250. https://doi.org/10.1038/nbt.2503. ; Brinkerhoff, H., Kang, A. S. W., Liu, J., Aksimentiev, A., & Dekker, C. (2021). Multiple rereads of single proteins at single-amino acid resolution using nanopores. Science, 374, 1509-1513. https://doi.org/10.1126/science.abl4381. ; Hu, Z.-L., Huo, M.-Z., Ying, Y.-L., & Long, Y.-T. (2021). Biological nanopore approach for single-molecule protein sequencing. Angewandte Chemie (International ed in English), 60, 14738-14749. https://doi.org/10.1002/anie.202013462. ; Callahan, N., Tullman, J., Kelman, Z., & Marino, J. (2020). Strategies for development of a next-generation protein sequencing platform. Trends in Biochemical Sciences, 45, 76-89. https://doi.org/10.1016/j.tibs.2019.09.005. ; Chinappi, M., & Cecconi, F. (2018). Protein sequencing via nanopore based devices: A nanofluidics perspective. Journal of Physics: Condensed Matter, 30, 204002. https://doi.org/10.1088/1361-648X/aababe. ; Jackson, D. W., Suzuki, K., Oakford, L., Simecka, J. W., Hart, M. E., & Romeo, T. (2002). Biofilm formation and dispersal under the influence of the global regulator CsrA of Escherichia coli. Journal of Bacteriology, 184, 290-301. https://doi.org/10.1128/JB.184.1.290-301.2002. ; Shimizu, K. (2013). Regulation systems of bacteria such as Escherichia coli in response to nutrient limitation and environmental stresses. Metabolites, 4, 1-35. https://doi.org/10.3390/metabo4010001. ; Janzon, L., Löfdahl, S., & Arvidson, S. (1989). Identification and nucleotide sequence of the delta-lysin gene, hld, adjacent to the accessory gene regulator (agr) of Staphylococcus aureus. Molecular & General Genetics, 219, 480-485. https://doi.org/10.1007/BF00259623. ; Ziebandt, A.-K., Kusch, H., Degner, M., Jaglitz, S., Sibbald, M. J. J. B., Arends, J. P., Chlebowicz, M. A., Albrecht, D., Pantuček, R., Doškar, J., Ziebuhr, W., Bröker, B. M., Hecker, M., Van Dijl, J. M., & Engelmann, S. (2010). Proteomics uncovers extreme heterogeneity in the Staphylococcus aureus exoproteome due to genomic plasticity and variant gene regulation. Proteomics, 10, 1634-1644. https://doi.org/10.1002/pmic.200900313. ; Wen, B., Zeng, W.-F., Liao, Y., Shi, Z., Savage, S. R., Jiang, W., & Zhang, B. (2020). Deep learning in proteomics. Proteomics, 20, 1900335. https://doi.org/10.1002/pmic.201900335. ; Tran, N. H., Zhang, X., Xin, L., Shan, B., & Li, M. (2017). De novo peptide sequencing by deep learning. Proceedings of the National Academy of Sciences of the United States of America, 114, 8247-8252. https://doi.org/10.1073/pnas.1705691114. ; Karunratanakul, K., Tang, H.-Y., Speicher, D. W., Chuangsuwanich, E., & Sriswasdi, S. (2019). Uncovering thousands of new peptides with sequence-mask-search hybrid de novo peptide sequencing framework. Molecular & Cellular Proteomics, 18, 2478-2491. https://doi.org/10.1074/mcp.TIR119.001656. ; Mardis, E. R. (2017). DNA sequencing technologies: 2006-2016. Nature Protocols, 12, 213-218. https://doi.org/10.1038/nprot.2016.182. Grant Information: SPP2002 DFG priority program; EN 712/4-1 DFG priority program; INST 188/365-1 FUGG DFG Institutional Fund (DFG); GRK PROCOMPAS (DFG) Contributed Indexing: Keywords: bioinformatics; bottom-up proteomics; databases; mass spectrometry; protein identification; proteogenomics; top-down proteomics Substance Nomenclature: 0 (Proteins) Entry Date(s): Date Created: 20230823 Date Completed: 20231220 Latest Revision: 20231220 Update Code: 20231220

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