Tuning the Performance of Synthetic Riboswitches using Machine Learning

ACS Synthetic Biology

Volumn 8, Issue 1, 2019, Pages 34-44

  Groher, Ann Christin ^a   Jager, Sven ^a,b   Schneider, Christopher ^a   Groher, Florian ^a   Hamacher, Kay ^b   Suess, Beatrix ^a  

^a Department of Biology

^b DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

aptamer; engineering; machine learning; riboswitch; tetracycline

Indexed keywords

APTAMER; TETRACYCLINE;

BIOLOGY; CONFORMATION; DRUG EFFECT; GENETICS; MACHINE LEARNING; PROCEDURES; RIBOSWITCH; SYNTHETIC BIOLOGY;

APTAMERS, NUCLEOTIDE; COMPUTATIONAL BIOLOGY; MACHINE LEARNING; NUCLEIC ACID CONFORMATION; RIBOSWITCH; SYNTHETIC BIOLOGY; TETRACYCLINE;

EID: 85060013523     PISSN: None     EISSN: 21615063     Source Type: Journal    
DOI: 10.1021/acssynbio.8b00207     Document Type: Article

References (39)

1. Groher, F. and Suess, B. (2014) Synthetic Riboswitches-A Tool Comes of Age. Biochim. Biophys. Acta, Gene Regul. Mech. 1839 (10), 964-973, 10.1016/j.bbagrm.2014.05.005
2. Chappell, J., Watters, K. E., Takahashi, M. K., and Lucks, J. B. (2015) A Renaissance in RNA Synthetic Biology: New Mechanisms, Applications and Tools for the Future. Curr. Opin. Chem. Biol. 28, 47-56, 10.1016/j.cbpa.2015.05.018
3. Lee, Y. J. and Moon, T. S. (2018) Design Rules of Synthetic Non-Coding RNAs in Bacteria. Methods 143, 58, 10.1016/j.ymeth.2018.01.001
4. Suess, B., Hanson, S., Berens, C., Fink, B., Schroeder, R., and Hillen, W. (2003) Conditional Gene Expression by Controlling Translation with Tetracycline-Binding Aptamers. Nucleic Acids Res. 31 (7), 1853-1858, 10.1093/nar/gkg285
5. Berens, C., Groher, F., and Suess, B. (2015) RNA Aptamers as Genetic Control Devices: The Potential of Riboswitches as Synthetic Elements for Regulating Gene Expression. Biotechnol. J. 10 (2), 246-257, 10.1002/biot.201300498
6. Schneider, C. and Suess, B. (2016) Identification of RNA Aptamers with Riboswitching Properties. Methods 97 (Supplement C), 44-50, 10.1016/j.ymeth.2015.12.001
7. Groher, F., Bofill-Bosch, C., Schneider, C., Braun, J., Jager, S., Geißler, K., Hamacher, K., and Suess, B. (2018) Riboswitching with Ciprofloxacin-Development and Characterization of a Novel RNA Regulator. Nucleic Acids Res. 46 (4), 2121, 10.1093/nar/gkx1319
8. Weigand, J. E. and Suess, B. (2007) Tetracycline Aptamer-Controlled Regulation of Pre-MRNA Splicing in Yeast. Nucleic Acids Res. 35 (12), 4179-4185, 10.1093/nar/gkm425
9. Vogel, M., Weigand, J. E., Kluge, B., Grez, M., and Suess, B. (2018) A Small, Portable RNA Device for the Control of Exon Skipping in Mammalian Cells. Nucleic Acids Res. 46 (8), 1-12, 10.1093/nar/gky062
10. Wittmann, A. and Suess, B. (2012) Engineered Riboswitches: Expanding Researchers' Toolbox with Synthetic RNA Regulators. FEBS Lett. 586 (15), 2076-2083, 10.1016/j.febslet.2012.02.038
11. Beilstein, K., Wittmann, A., Grez, M., and Suess, B. (2015) Conditional Control of Mammalian Gene Expression by Tetracycline-Dependent Hammerhead Ribozymes. ACS Synth. Biol. 4 (5), 526-534, 10.1021/sb500270h
12. Domin, G., Findeiß, S., Wachsmuth, M., Will, S., Stadler, P. F., and Mörl, M. (2016) Applicability of a Computational Design Approach for Synthetic Riboswitches. Nucleic Acids Res. 45 (7), 4108-4119, 10.1093/nar/gkw1267
13. Kötter, P., Weigand, J. E., Meyer, B., Entian, K.-D., and Suess, B. (2009) A Fast and Efficient Translational Control System for Conditional Expression of Yeast Genes. Nucleic Acids Res. 37 (18), e120-e120, 10.1093/nar/gkp578
14. Förster, U., Weigand, J. E., Trojanowski, P., Suess, B., and Wachtveitl, J. (2012) Conformational Dynamics of the Tetracycline-Binding Aptamer. Nucleic Acids Res. 40 (4), 1807-1817, 10.1093/nar/gkr835
15. Hanson, S., Bauer, G., Fink, B., and Suess, B. (2005) Molecular Analysis of a Synthetic Tetracycline-Binding Riboswitch. RNA 11, 2549-2556, 10.1261/rna.7251305
16. Xiao, H., Edwards, T. E., and Ferré-D'Amaré, A. R. (2008) Structural Basis for Specific, High-Affinity Tetracycline Binding by an In Vitro Evolved Aptamer and Artificial Riboswitch. Chem. Biol. 15 (10), 1125-1137, 10.1016/j.chembiol.2008.09.004
17. Antunes, D., Jorge, N. A. N., Caffarena, E. R., and Passetti, F. (2018) Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools. Front. Genet. 8 (January), 1-16, 10.3389/fgene.2017.00231
18. Al-Jarrah, O. Y., Muhaidat, S., Karagiannidis, G. K., Taha, K. et al. (2015) Efficient Machine Learning for Big Data: A Review. Big Data Res. 2 (3), 87-93, 10.1016/j.bdr.2015.04.001
19. Nguyen, N. G., Tran, V. A., Ngo, D. L., Phan, D., Lumbanraja, F. R., Faisal, M. R., Abapihi, B., Kubo, M., and Satou, K. (2016) DNA Sequence Classification by Convolutional Neural Network. J. Biomed. Sci. Eng. 09 (05), 280-286, 10.4236/jbise.2016.95021
20. Bedbrook, C. N., Yang, K. K., Rice, A. J., Gradinaru, V., and Arnold, F. H. (2017) Machine Learning to Design Integral Membrane Channelrhodopsins for Efficient Eukaryotic Expression and Plasma Membrane Localization. PLoS Comput. Biol. 13 (10), 1-21, 10.1371/journal.pcbi.1005786
21. Giguère, S., Laviolette, F., Marchand, M., Tremblay, D., Moineau, S., Liang, X., Biron, é., and Corbeil, J. (2015) Machine Learning Assisted Design of Highly Active Peptides for Drug Discovery. PLoS Comput. Biol. 11 (4), 1-21, 10.1371/journal.pcbi.1004074
22. Osorio, D., Rondón-Villarreal, P., and Torres, R. (2015) Peptides: A Package for Data Mining of Antimicrobial Peptides. R J. 7 (1), 4-14
23. Ryvkin, P., Leung, Y. Y., Ungar, L. H., Gregory, B. D., and Wang, L.-S. (2014) Using Machine Learning and High-Throughput RNA Sequencing to Classify the Precursors of Small Non-Coding RNAs. Methods 67 (1), 28-35, 10.1016/j.ymeth.2013.10.002
24. Fiannaca, A., La Rosa, M., La Paglia, L., Rizzo, R., and Urso, A. (2017) NRC: Non-Coding RNA Classifier Based on Structural Features. BioData Min. 10 (1), 27, 10.1186/s13040-017-0148-2
25. Jiang, X., Yang, J., Zhang, H., Zou, H., Wang, C., and Xian, M. (2012) In Vitro Assembly of Multiple DNA Fragments Using Successive Hybridization. PLoS One 7 (1), 2-7, 10.1371/journal.pone.0030267
26. Singh, S. and Singh, R. (2016) Application of Supervised Machine Learning Algorithms for the Classification of Regulatory RNA Riboswitches. Briefings Funct. Genomics 16 (2), 99-105, 10.1093/bfgp/elw005
27. Biau, G. (2012) Analysis of a Random Forests Model. J. Mach. Learn. Res. 13, 1063-1095
28. Lorenz, R., Bernhart, S. H., zu Siederdissen, C., Tafer, H., Flamm, C., Stadler, P. F., and Hofacker, I. L. (2011) ViennaRNA Package 2.0. Algorithms Mol. Biol. 6 (26), 26, 10.1186/1748-7188-6-26
29. Andronescu, M., Condon, A., Hoos, H. H., Mathews, D. H., and Murphy, K. P. (2007) Efficient Parameter Estimation for RNA Secondary Structure Prediction. Bioinformatics 23 (13), 19-28, 10.1093/bioinformatics/btm223
30. Dombrowsky, M. J., Jager, S., Schiller, B., Mayer, B., Stammler, S., and Hamacher, K. (2018) StreaMD: Advanced Analysis of Molecular Dynamics Using R. J. Comput. Chem. 39, 1666, 10.1002/jcc.25197
31. Schiller, B., Jager, S., Hamacher, K., and Strufe, T. StreaM-A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs. In Algorithms for Computational Biology. AlCoB 2015. Lecture Notes in Computer Science; Dediu, A. H., Hernandez-Quiroz, F., Martin-Vide, C., and Rosenblueth, D., Eds.; Springer, 2015; Vol. 9199.
32. Buß, O., Jager, S., Dold, S.-M., Zimmermann, S., Hamacher, K., Schmitz, K., and Rudat, J. (2016) Statistical Evaluation of HTS Assays for Enzymatic Hydrolysis of β-Keto Esters. PLoS One 11 (1), e0146104 10.1371/journal.pone.0146104
33. Ishwaran, H., and Kogalur, U. B. Random Forests for Survival, Regression, and Classification (RF-SRC). GitHub, 2017.
34. Ishwaran, H. and Kogalur, U. B. (2007) Random Survival Forests for R. R News 7 (2), 25-31
35. Cock, P. J. A., Antao, T., Chang, J. T., Chapman, B. A., Cox, C. J., Dalke, A., Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B. et al. (2009) Biopython: Freely Available Python Tools for Computational Molecular Biology and Bioinformatics. Bioinformatics 25 (11), 1422-1423, 10.1093/bioinformatics/btp163
36. Chollet, F. Keras. GitHub, 2015.
37. Ehrlinger, J. GgRandomForests: Visually Exploring a Random Forest for Regression. 2015, arXiv:1501.07196. arXiv e-Print archive. https://arxiv.org/abs/1501.07196.
38. Kozak, M. (1999) Initiation of Translation in Prokaryotes and Eukaryotes. Gene 234 (2), 187-208, 10.1016/S0378-1119(99)00210-3
39. Schneider, C., Bronstein, L., Diemer, J., Koeppl, H., and Suess, B. (2017) ROC'n'Ribo: Characterizing a riboswitching expression system by modeling single-cell data. ACS Synth. Biol. 6 (7), 1211-1224, 10.1021/acssynbio.6b00322