Ribosomal computing: implementation of the computational method.
Computational model
Gene sequence
Mathematical model
Mutation
Protein
Protein synthesis
Ribosomal computing
Ribosome
Simulator
Stalling
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
03 Oct 2024
03 Oct 2024
Historique:
received:
30
03
2024
accepted:
23
09
2024
medline:
3
10
2024
pubmed:
3
10
2024
entrez:
2
10
2024
Statut:
epublish
Résumé
Several computational and mathematical models of protein synthesis have been explored to accomplish the quantitative analysis of protein synthesis components and polysome structure. The effect of gene sequence (coding and non-coding region) in protein synthesis, mutation in gene sequence, and functional model of ribosome needs to be explored to investigate the relationship among protein synthesis components further. Ribosomal computing is implemented by imitating the functional property of protein synthesis. In the proposed work, a general framework of ribosomal computing is demonstrated by developing a computational model to present the relationship between biological details of protein synthesis and computing principles. Here, mathematical abstractions are chosen carefully without probing into intricate chemical details of the micro-operations of protein synthesis for ease of understanding. This model demonstrates the cause and effect of ribosome stalling during protein synthesis and the relationship between functional protein and gene sequence. Moreover, it also reveals the computing nature of ribosome molecules and other protein synthesis components. The effect of gene mutation on protein synthesis is also explored in this model. The computational model for ribosomal computing is implemented in this work. The proposed model demonstrates the relationship among gene sequences and protein synthesis components. This model also helps to implement a simulation environment (a simulator) for generating protein chains from gene sequences and can spot the problem during protein synthesis. Thus, this simulator can identify a disease that can happen due to a protein synthesis problem and suggest precautions for it.
Sections du résumé
BACKGROUND
BACKGROUND
Several computational and mathematical models of protein synthesis have been explored to accomplish the quantitative analysis of protein synthesis components and polysome structure. The effect of gene sequence (coding and non-coding region) in protein synthesis, mutation in gene sequence, and functional model of ribosome needs to be explored to investigate the relationship among protein synthesis components further. Ribosomal computing is implemented by imitating the functional property of protein synthesis.
RESULT
RESULTS
In the proposed work, a general framework of ribosomal computing is demonstrated by developing a computational model to present the relationship between biological details of protein synthesis and computing principles. Here, mathematical abstractions are chosen carefully without probing into intricate chemical details of the micro-operations of protein synthesis for ease of understanding. This model demonstrates the cause and effect of ribosome stalling during protein synthesis and the relationship between functional protein and gene sequence. Moreover, it also reveals the computing nature of ribosome molecules and other protein synthesis components. The effect of gene mutation on protein synthesis is also explored in this model.
CONCLUSION
CONCLUSIONS
The computational model for ribosomal computing is implemented in this work. The proposed model demonstrates the relationship among gene sequences and protein synthesis components. This model also helps to implement a simulation environment (a simulator) for generating protein chains from gene sequences and can spot the problem during protein synthesis. Thus, this simulator can identify a disease that can happen due to a protein synthesis problem and suggest precautions for it.
Identifiants
pubmed: 39358680
doi: 10.1186/s12859-024-05945-w
pii: 10.1186/s12859-024-05945-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
321Informations de copyright
© 2024. The Author(s).
Références
Plaza PI, Blanco G, Lambertucci SA. Implications of bacterial, viral and mycotic microorganisms in vultures for wildlife conservation, ecosystem services and public health. Ibis. 2020;162(4):1109–24.
doi: 10.1111/ibi.12865
Karim N, Afroj S, Lloyd K, Oaten LC, Andreeva DV, Carr C, Farmery AD, Kim ID, Novoselov KS. Sustainable personal protective clothing for healthcare applications: a review. ACS Nano. 2020;14(10):12313–40.
doi: 10.1021/acsnano.0c05537
pubmed: 32866368
pmcid: 7518242
Weischenfeldt J, Symmons O, Spitz F, Korbel JO. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat Rev Genet. 2013;14(2):125–38.
doi: 10.1038/nrg3373
pubmed: 23329113
Li M, Wang IX, Li Y, Bruzel A, Richards AL, Toung JM, Cheung VG. Widespread RNA and DNA sequence differences in the human transcriptome. Science. 2011;333(6038):53–8.
doi: 10.1126/science.1207018
pubmed: 21596952
pmcid: 3204392
Lodish, Harvey F. Translational control of protein synthesis. Annu Rev Biochem. 1976;45(1):39–72 (Annual Reviews 4139 El Camino Way, PO Box 10139, Palo Alto, CA 94303-0139, USA).
doi: 10.1146/annurev.bi.45.070176.000351
pubmed: 786155
Barnes DJ, Chu DF. Introduction to modelling for biosciences. London: Springer; 2010.
doi: 10.1007/978-1-84996-326-8
Mehra A, Hatzimanikatis V. An algorithmic framework for genome-wide modeling and analysis of translation networks. Biophys J. 2006;90(4):1136–46 (Elsevier).
doi: 10.1529/biophysj.105.062521
pubmed: 16299083
Garai A, Chowdhury D, Chowdhury D, Ramakrishnan TV. Stochastic kinetics of ribosomes: single motor properties and collective behavior. Phys Rev E: Stat, Nonlinear, Soft Matter Phys. 2009;80(1): 011908.
doi: 10.1103/PhysRevE.80.011908
Zhao Y-B, Krishnan J. Probabilistic Boolean network modelling and analysis framework for mRNA translation. IEEE/ACM Trans Comput Biol Bioinf. 2015;13(4):754–66 (IEEE).
doi: 10.1109/TCBB.2015.2478477
Khatter H, Myasnikov AG, Mastio L, Billas IML, Birck C, Stella S, Klaholz BP. Purification, characterization and crystallization of the human 80S ribosome. Nucl Acids Res. 2014;42(6):e49–e49 (Oxford University Press).
doi: 10.1093/nar/gkt1404
pubmed: 24452798
pmcid: 3973290
Heinrich R, Rapoport TA. Mathematical modelling of translation of mRNA in eucaryotes; steady states, time-dependent processes and application to reticulocytest. J Theory Biol. 1980;86(2):279–313 (Elsevier).
doi: 10.1016/0022-5193(80)90008-9
von der Haar T. Mathematical and computational modelling of ribosomal movement and protein synthesis: an overview. Comput Struct Biotechnol J. 2012;1:1–7.
Tour JM. Molecular electronics. Synth Test Compon, Acc Chem Res. 2000;33(11):791–804.
doi: 10.1021/ar0000612
Boneh D, Dunworth C, Lipton RJ, Sgall J. On the computational power of DNA. Discret Appl Math. 1996;71(1–3):79–94.
doi: 10.1016/S0166-218X(96)00058-3
Ehrenfeucht A, Harju T, Petre I, Rozenberg G. Characterizing the micronuclear gene patterns in ciliates. Theory Comput Syst. 2002;35(5):501–19.
doi: 10.1007/s00224-002-1043-9
Balan MS, Krithivasan K, Sivasubramanyam Y. Peptide computing-universality and complexity. Berlin: Springer; 2001. p. 290–9.
Nolte DD. Mind at light speed: anew kind of intelligence. New York: Simon, and Schuster; 2001.
Pratima C, Mayukh S, Prasun G. Computing in ribosomes: performing boolean logic using mrna-ribosome system, VLSI (ISVLSI), 2016 IEEE computer society annual symposium on, 2016; pp. 260–265, USA.
Pratima C, Mayukh S, Prasun G. Computing in ribosomes: implementing sequential circuits using mRNA-ribosome system, nanoelectronic and information systems (iNIS), In: 2016 IEEE International Symposium on, 2016. pp. 230–235, India.
Pratima C, Prasun G. Realizing all logic operations using mRNA-ribosome system as a post Si alternative. Nanoelectronic, and information systems (iNIS), In: 2017 IEEE international symposium on, 2017. pp. 40–45, India.
Spirin AS. Ribosome as a molecular machine. FEBS Lett. 2002;514:2–10.
doi: 10.1016/S0014-5793(02)02309-8
pubmed: 11904172
Merrick WC, Pavitt GD. Protein synthesis initiation in eukaryotic cells. Cold Spring Harb Perspect Biol. 2018;10(12):a033092 (Cold Spring Harbor Lab).
doi: 10.1101/cshperspect.a033092
pubmed: 29735639
pmcid: 6280705
Lee K, Holland-Staley CA, Cunningham PR. Genetic approaches to studying protein synthesis: effects of mutations at Ψ516 and A535 in Escherichia coli 16S rRNA. J Nutr. 2001;131(11):2994S-3004S (Elsevier).
doi: 10.1093/jn/131.11.2994S
pubmed: 11694635
Hardesty B, Kramer G. Structure, function, and genetics of ribosomes. Berlin: Springer Science Business Media; 2012.
Yusupov MM, Yusupova GZ, Baucom A, Lieberman K, Earnest TN, Cate JHD, Noller HF. Crystal structure of the ribosome at 5.5 Å resolution. Science. 2001;292(5518):883–96.
doi: 10.1126/science.1060089
pubmed: 11283358
Noller HF. Evolution of protein synthesis from an RNA world. Cold Spring Harb Perspect Biol. 2012;4(4):a003681 (Cold Spring Harbor Lab).
doi: 10.1101/cshperspect.a003681
pubmed: 20610545
pmcid: 3312679
Kozak M. Point mutations define a sequence flanking the AUG initiator codon that modulates translation by eukaryotic ribosomes. Cell. 1986;44:283–92.
doi: 10.1016/0092-8674(86)90762-2
pubmed: 3943125
Bayfield MA, Thompson J, Dahlberg AE. The A2453-C2499 wobble base pair in Escherichia coli 23S ribosomal RNA is responsible for pH sensitivity of the peptidyltransferase active site conformation. Nucl Acids Res. 2004;32:5512–8.
doi: 10.1093/nar/gkh888
pubmed: 15479786
pmcid: 524298
Hodgkin J. Genetic suppression, WormBook: the online review of C. elegans biology. 2005;1–13.
Giegé R, Puglisi JD, Florentz C. tRNA structure and aminoacylation efficiency. Prog Nucl Acid Res Mol Biol. 1993;45:129–206.
doi: 10.1016/S0079-6603(08)60869-7
Ibba M, Söll D. Aminoacyl-tRNA synthesis. Annu Rev Biochem. 2000;69(1):617–50.
doi: 10.1146/annurev.biochem.69.1.617
pubmed: 10966471
Sprinzl M, Horn C, Brown M, Ioudovitch A, Steinberg S. Compilation of tRNA sequences and sequences of tRNA genes. Nucl Acids Res. 1998;26:148–53.
doi: 10.1093/nar/26.1.148
pubmed: 9399820
pmcid: 147216
Selmer M, Dunham CM, Murphy FV, Weixlbaumer A, Petry S, Kelley AC, Weir JR, Ramakrishnan V. Structure of the 70S ribosome complexed with mRNA and tRNA. Science. 2006;313:1935–42.
doi: 10.1126/science.1131127
pubmed: 16959973
Pelham, Hugh RB, JACKSON, Richard J. An efficient mRNA-dependent translation system from reticulocyte lysates. Eur J Biochem. 1976;67:247–56.
doi: 10.1111/j.1432-1033.1976.tb10656.x
pubmed: 823012
Yusupova GZ, Yusupov MM, Cate JHD, Noller HF. The Path of Messenger RNA through the Ribosome. Cell. 2001;106:233–41.
doi: 10.1016/S0092-8674(01)00435-4
pubmed: 11511350
Nagata S, Hamasaki T, Uetake K, Masuda H, Takagaki K, Oka N, Wada T, Ohgi T, Yano J. Synthesis and biological activity of artificial mRNA prepared with novel phosphorylating reagents. Nucl Acids Res. 2010;38(21):7845–57 (Oxford University Press).
doi: 10.1093/nar/gkq638
pubmed: 20660478
pmcid: 2995060
Orelle C, Carlson ED, Szal T, Florin T, Jewett MC, Mankin AS. Protein synthesis by ribosomes with tethered subunits. Nature. 2015;524:119.
doi: 10.1038/nature14862
pubmed: 26222032
Chen H, Bjerknes M, Kumar R, Jay E. Determination of the optimal aligned spacing between the Shine-Dalgarno sequence and the translation initiation codon of Escherichia coli m RNAs. Nucl Acids Res. 1994;22(23):4953–7.
doi: 10.1093/nar/22.23.4953
pubmed: 7528374
pmcid: 523762
Alberts B. Molecular Biology of the Cell. Milton Park: Taylor and Francis Group; 2018.
Chatterjee P, Ghosal P. Computing in ribosome: logic gates implementation using the mRNA-ribosome system. CSI Trans ICT. 2017;6:39–50.
doi: 10.1007/s40012-017-0183-7
Ramu H, Mankin A, Vazquez-Laslop N. Programmed drug-dependent ribosome stalling. Mol Microbiol. 2009;71(4):811–24 (Wiley Online Library).
doi: 10.1111/j.1365-2958.2008.06576.x
pubmed: 19170872
Chatterjee P, Ghosal P. Realization of arithmetic operations using a combined computational unit in ribosomal computing. J Inst Eng (India): Ser B. 2023;104(2):461–73.
Wohlgemuth I, Pohl C, Mittelstaet J, Konevega AL, Rodnina MV. Evolutionary optimization of speed and accuracy of decoding on the ribosome. Philos Trans R Soc B: Biol Sci. 2011;366(1580):2979–89.
doi: 10.1098/rstb.2011.0138