A Residue Number System (Rns) Anti-Codon Table for Protein Synthesis
DOI:
https://doi.org/10.26437/ajar.v10i2.811Keywords:
Algorithmically. anti-codons. genetic. protein synthesis. wobblingAbstract
Purpose: This study aims to optimise the representation and processing of genetic information through RNS encoding.
Design/Methodology/Approach: The RNS anti-codon table is constructed as a table of RNS genetic code using the concept of number trees. The complementarity of bases suggests the swap of bases leading to the generation of 64 anti-codons for all possible codons of the genetic code. These are algorithmically reduced to less than 40 known anti-codons due to wobbling.
Findings: The finding indicates that the decimal values change as the moduli sets vary, but the residue digits remain the same. Codons and anti-codons are static with some bases, in this case, moduli set and vary with the third base.
Research Limitations: The current RNS implementation may experience overflow issues when dealing with extensive protein sequences and difficulty in verifying results across different organism types
Practical Implications: This research answers the compelling cases of a quarternary number system in molecular biology applications. In the era of Artificial Intelligence (AI) and machine learning and the desire for gene editing, gene therapy, and personalised medicine, digital implementations are enhanced with number systems.
Social Implication: The findings demonstrate the far-reaching impact of implementing RNS anti-codon tables in protein synthesis, highlighting the need for careful consideration and planning in its deployment and integration into society.
Originality/ Value: This research represents a significant departure from conventional approaches to genetic information processing, introducing multiple layers of innovation that advance theoretical understanding and practical applications in the field.
References
Afriyie, Y. (2021). A Novel Exploitation of Errors in Redundant Residue Number System Architecture. American Journal of Applied Sciences. https://doi.org/10.3844/ajassp.2021.96.106
Akanni G., Eseyin, J. B. & Gbolagade, K. A. (2022) A Residue Number System and Secret Key Crypto System Review in Cyber Security.International Journal of Innovative Science and Research Technology.
Base, D. N. A., Codons, R. N. A., & Base, D. N. A. (n.d.). 20 Amino Acids In Human Protein. 20.
Chervyakov, N., Lyakhov, P., Babenko, M., Lavrinenko, I., Deryabin, M., Lavrinenko, A., ... & Kaplun, D. (2020). A division algorithm in a redundant residue number system using fractions. Applied Sciences, 10(2), 695.
Crick, F. H. C. (1966). Codon-Anticodon Pairing: The Wobble Hypothesis. 548–555.
Das, J. K., Sengupta, A., Choudhury, P. P., & Roy, S. (2021). Mapping sequence to feature vector using numerical representation of codons targeted to amino acids for alignment-free sequence analysis. Gene, 766, 145096.
Dubey, S., Verma, D. K., & Kumar, M. (2024). Severe acute respiratory syndrome Coronavirus-2 GenoAnalyzer and mutagenic anomaly detector using FCMFI and NSCE. International Journal of Biological Macromolecules, 258, 129051.
Elias, P. (2014). Relative efficiency of anticodons in reading the valine codons during protein synthesis in vitro in Vitro * in Reading the Valine. THE JOURNAL OF BIOLOGICAL CHEMISTRY, 9258(May). https://doi.org/10.1016/S0021-9258(18)50379-3
Ganesh, R. B., & Maerkl, S. J. (2022). Biochemistry of aminoacyl tRNA Synthetase and tRNAs and their engineering for cell-free and synthetic cell applications. Frontiers in bioengineering and biotechnology, 10, 918659.
Gao, L., Behrens, A., Rodschinka, G., Forcelloni, S., Wani, S., Strasser, K., & Nedialkova, D. D. (2024). Selective gene expression maintains human tRNA anticodon pools during differentiation. In Nature Cell Biology (Vol. 26, Issue 1). Springer US. https://doi.org/10.1038/s41556-023-01317-3
Harrison, S. A., Palmeira, R. N., Halpern, A., & Lane, N. (2022). A biophysical basis for the emergence of the genetic code in protocells. Biochimica et Biophysica Acta (BBA)-Bioenergetics, 1863(8), 148597.
Khanam, R., Hussain, M., Hill, R., & Allen, P. (2024). A comprehensive review of convolutional neural networks for defect detection in industrial applications. IEEE Access.
Marshall, P. (2021). Biology transcends the limits of computation. Progress in Biophysics and Molecular Biology, 165, 88-101.
Mejía-Almonte, C., Busby, S. J., Wade, J. T., van Helden, J., Arkin, A. P., Stormo, G. D., ... & Collado-Vides, J. (2020). Redefining fundamental concepts of transcription initiation in bacteria. Nature Reviews Genetics, 21(11), 699-714.
Mohamed, B. A. (2022). The development of ALICE-tRNA-sequencing and its use in exploring the role of tRNAs in translational control. 31.
Mohanta, T. K., Mohanta, Y. K., Al-harrasi, A., Sharma, N., & Sciences, M. (2022). Anticodon Table of the Chloroplast Genome and Identification of Putative Quadruplet Anticodons in Chloroplast tRNAs.
Morange, M. (2009). The Central Dogma of molecular biology. Resonance, 14(3), 236–247. https://doi.org/10.1007/s12045-009-0024-6
Olsen, E. B. (2018, May). Rns hardware matrix multiplier for high precision neural network acceleration:" rns tpu". In 2018 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
Panawala, L. (2017). Difference Between Codon and Anticodon. https://www.researchgate.net/publication/314255928
Salman, A., Biziaev, N., Shuvalova, E., & Alkalaeva, E. (2024). mRNA context and translation factors determine decoding in alternative nuclear genetic codes. BioEssays, 2400058.
Schiessel, H. (2021). Biophysics for beginners: a journey through the cell nucleus. Jenny Stanford Publishing.
Sun, L., Zhao, L., & Peng, R. Y. (2021). Research progress in the effects of terahertz waves on biomacromolecules. Military medical research, 8, 1-8.
Taylor, F. J. R., & Coates, D. (1989). The code within the codons. Biosystems, 22(3), 177-187.
Weiss, J. L., Decker, J. C., Bolano, A., & Krahn, N. (2024). Tuning tRNAs for improved translation. Frontiers in Genetics, 15, 1436860.
Wu, S., Li, X., & Wang, G. (2022). tRNA-like structures and their functions. FEBS Journal, 289(17), 5089–5099. https://doi.org/10.1111/febs.16070
Yu, L., Cao, Y., Yang, J. Y., & Yang, P. (2022). Benchmarking clustering algorithms on
estimating the number of cell types from single-cell RNA-sequencing data. Genome biology, 23(1), 49.
Zahoor, A., Hauq, S., Bashir, U., Hamadani, A., & Shabir, S. (2024). A meshwork of artificial
intelligence and biology: The future of science. In A Biologist s Guide to Artificial Intelligence (pp. 315-333). Academic Press.
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