Kaeberlein, M. and Kennedy, B.K. (2007), Protein translation, 2007. Aging Cell, 6: 731-734. https://doi.org/10.1111/j.1474-9726.2007.00341.x
Erdmann-Pham, D. D., Duc, K. D., and Song, Y. S. (2020), The Key Parameters that Govern Translation Efficiency, 2020. Cell Systems, 10,2:183-192. https://www.cell.com/cell-systems/fulltext/S2405-4712(19)30464-8
Marintchev, Assen, and Gerhard Wagner. “Translation Initiation: Structures, Mechanisms and Evolution.” Quarterly Reviews of Biophysics 37.3–4 (2004): 197–284. https://doi.org/10.1017/S0033583505004026.
Gingold, H. and Pilpel, Y. (2011), Determinants of translation efficiency and accuracy, 2011. Molecular Systems Biology, 7:481. https://doi.org/10.1038/msb.2011.14
Yirong Wang, Hong Zhang, Jian Lu, Recent advances in ribosome profiling for deciphering translational regulation, Methods, Volume 176, 2020, Pages 46-54,ISSN 1046-2023, https://doi.org/10.1016/j.ymeth.2019.05.011.
Weber et al. (2023), Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium, 2023. Molecular Systems Biology, 19:e11301. https://doi.org/10.15252/msb.202211301
Li, G-W. (2015), How do bacteria tune translation efficiency, 2015. Current Opinion in Microbiology, 24:66-71. https://doi.org/10.1016/j.mib.2015.01.001.
Rodnina, M. V. (2016). The ribosome in action: Tunin of transnational efficiency and protein folding, 2016. Protein Science, 25,8: 1390-1406. https://doi.org/10.1002/pro.2950
Wang, Y., Zhang, H., Lu, J. (2019). Recent advances in ribosome profiling for deciphering translational regulation, 2019. Methods, 176:46-54. https://doi.org/10.1016/j.ymeth.2019.05.011
Ingolia NT, et al. (2009). "Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling." Science, 324(5924), 218-223.
Tuller et al. (2010). Translation efficiency is determined by both codon bias and folding energy, 2010. PNAS, 107 (8):3645-3650. https://doi.org/10.1073/pnas.0909910107
Premskaia et al. (2024). Codon-optimization in gene therapy: promises, prospects and challenges, 2024. Frontiers in Bioengineering and Biotechnology, 12. https://doi.org/10.3389/fbioe.2024.1371596
Watson, D. S. (2022). Interpretable machine learning for genomics, 2024. Human Genetics, 141:1499-1513. https://doi.org/10.1007/s00439-021-02387-9
Nieuwkoop et al. (2023). Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning, 2023. Nucleic Acids Research, 51(5):2363-2376. https://doi.org/10.1093/nar/gkad035
Li GW. How do bacteria tune translation efficiency? Curr Opin Microbiol. 2015 Apr;24:66-71. doi: 10.1016/j.mib.2015.01.001. Epub 2015 Jan 28. PMID: 25636133; PMCID: PMC4678177.
https://www.fortunebusinessinsights.com/machine-learning-market-102226
https://www.mordorintelligence.com/industry-reports/global-protein-engineering-market-industry
https://www.gminsights.com/industry-analysis/bioreactor-market
Fernandes, L.D., Moura, A.P.S.d. & Ciandrini, L. Gene length as a regulator for ribosome recruitment and protein synthesis: theoretical insights. Sci Rep 7, 17409 (2017). https://doi.org/10.1038/s41598-017-17618-1
Dongyan Zhao, John P Hamilton, Michael Hardigan, Dongmei Yin, Tao He, Brieanne Vaillancourt, Mauricio Reynoso, Germain Pauluzzi, Scott Funkhouser, Yuehua Cui, Julia Bailey-Serres, Jiming Jiang, C Robin Buell, Ning Jiang, Analysis of Ribosome-Associated mRNAs in Rice Reveals the Importance of Transcript Size and GC Content in Translation, G3 Genes|Genomes|Genetics, Volume 7, Issue 1, 1 January 2017, Pages 203–219, https://doi.org/10.1534/g3.116.036020
Paul M. Sharp, Wen-Hsiung Li, The codon adaptation index-a measure of directional synonymous codon usage bias, and its potential applications, Nucleic Acids Research, Volume 15, Issue 3, 11 February 1987, Pages 1281–1295, https://doi.org/10.1093/nar/15.3.1281
dos Reis M, Savva R, Wernisch L. Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res. 2004 Sep 24;32(17):5036-44. doi: 10.1093/nar/gkh834. PMID: 15448185; PMCID: PMC521650.
Woo Seo S, Yang JS, Kim I, Yang J et al., Predictive design of mRNA translation initiation region to control prokaryotic translation efficiency. Metabolic Engineering. January 2013, Volume 15, Pages 67–74, https://doi.org/10.1016/j.ymben.2012.10.006Get
Cambray, G., Guimaraes, J. & Arkin, A. Evaluation of 244,000 synthetic sequences reveals design principles to optimize translation in Escherichia coli. Nat Biotechnol 36, 1005–1015 (2018). https://doi.org/10.1038/nbt.4238
Verma, M., Choi, J., Cottrell, K.A. et al. A short translational ramp determines the efficiency of protein synthesis. Nat Commun 10, 5774 (2019). https://doi.org/10.1038/s41467-019-13810-1
de Smit, M. H., & van Duin, J. (1990). Secondary structure of the ribosome binding site determines translational efficiency: a quantitative analysis. Proceedings of the National Academy of Sciences of the United States of America, 87(19), 7668–7672. https://doi.org/10.1073/pnas.87.19.7668
Tuller, T., Carmi, A., Vestsigian, K., Navon, S., Dorfan, Y., Zaborske, J., Pan, T., Dahan, O., Furman, I., & Pilpel, Y. (2010). An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell, 141(2), 344–354. https://doi.org/10.1016/j.cell.2010.03.031
Gingold, H. and Pilpel, Y. (2011), Determinants of translation efficiency and accuracy, 2011. Molecular Systems Biology, 7:481. https://doi.org/10.1038/msb.2011.14
Shao, B., Yan, J., Zhang, J. et al. Riboformer: a deep learning framework for predicting context-dependent translation dynamics. Nat Commun 15, 2011 (2024). https://doi.org/10.1038/s41467-024-46241-8
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer. https://doi.org/10.1007/978-0-387-84858-7
Arella, D., Dilucca, M. & Giansanti, A. Codon usage bias and environmental adaptation in microbial organisms. Mol Genet Genomics 296, 751–762 (2021). https://doi.org/10.1007/s00438-021-01771-4
Hinnebusch, A. G., Ivanov, I. P., & Sonenberg, N. (2016). Translational control by 5'-untranslated regions of eukaryotic mRNAs. Science (New York, N.Y.), 352(6292), 1413–1416. https://doi.org/10.1126/science.aad9868
Morgan, G. J., Burkhardt, D. H., Kelly, J. W., & Powers, E. T. (2018). Translation efficiency is maintained at elevated temperature in Escherichia coli. The Journal of biological chemistry, 293(3), 777–793. https://doi.org/10.1074/jbc.RA117.000284
Haft, R. J., Keating, D. H., Schwaegler, T., Schwalbach, M. S., Vinokur, J., Tremaine, M., Peters, J. M., Kotlajich, M. V., Pohlmann, E. L., Ong, I. M., Grass, J. A., Kiley, P. J., & Landick, R. (2014). Correcting direct effects of ethanol on translation and transcription machinery confers ethanol tolerance in bacteria. Proceedings of the National Academy of Sciences of the United States of America, 111(25), E2576–E2585. https://doi.org/10.1073/pnas.1401853111
Zhang, Y., Burkhardt, D. H., Rouskin, S., Li, G. W., Weissman, J. S., & Gross, C. A. (2018). A Stress Response that Monitors and Regulates mRNA Structure Is Central to Cold Shock Adaptation. Molecular cell, 70(2), 274–286.e7. https://doi.org/10.1016/j.molcel.2018.02.035
Wanting Liu, Lunping Xiang, Tingkai Zheng, Jingjie Jin, Gong Zhang, TranslatomeDB: a comprehensive database and cloud-based analysis platform for translatome sequencing data, Nucleic Acids Research, Volume 46, Issue D1, 4 January 2018, Pages D206–D212, https://doi.org/10.1093/nar/gkx1034
Nakahigashi, K., Takai, Y., Shiwa, Y. et al. Effect of codon adaptation on codon-level and gene-level translation efficiency in vivo. BMC Genomics 15, 1115 (2014). https://doi.org/10.1186/1471-2164-15-1115
Boël, G., Letso, R., Neely, H. et al. Codon influence on protein expression in E. coli correlates with mRNA levels. Nature 529, 358–363 (2016). https://doi.org/10.1038/nature16509
Grzegorz Kudla et al., Coding-Sequence Determinants of Gene Expression in Escherichia coli.Science324,255-258(2009).DOI:10.1126/science.1170160
Rudolph, K. L., Schmitt, B. M., Villar, D., White, R. J., Marioni, J. C., Kutter, C., & Odom, D. T. (2016). Codon-Driven Translational Efficiency Is Stable across Diverse Mammalian Cell States. PLoS genetics, 12(5), e1006024. https://doi.org/10.1371/journal.pgen.1006024
Fernandes, L. D., Moura, A. P. S., & Ciandrini, L. (2017). Gene length as a regulator for ribosome recruitment and protein synthesis: theoretical insights. Scientific reports, 7(1), 17409. https://doi.org/10.1038/s41598-017-17618-1