Abstract
The sequence of amino acid monomers in the primary structure of a protein is decided by the corresponding sequence of codons (triplets of nucleic acid monomers) on the template messenger RNA (mRNA). The polymerization of a protein, by incorporation of the successive amino acid monomers, is carried out by a molecular machine called ribosome. We develop a stochastic kinetic model that captures the possibilities of mis-reading of mRNA codon and prior mis-charging of a tRNA. By a combination of analytical and numerical methods, we obtain the distribution of the times taken for incorporation of the successive amino acids in the growing protein in this mathematical model. The corresponding exact analytical expression for the average rate of elongation of a nascent protein is a ‘biologically motivated’ generalization of the Michaelis–Menten formula for the average rate of enzymatic reactions. This generalized Michaelis–Menten-like formula (and the exact analytical expressions for a few other quantities) that we report here display the interplay of four different branched pathways corresponding to selection of four different types of tRNA.
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References
Basu A, Chowdhury D (2007) Traffic of interacting ribosomes: effects of single-machine mechanochemistry on protein synthesis. Phys Rev E 75:021902
Blomberg C (2007) Physics of life: the physicist’s road to biology. Elsevier, Amsterdam
Chen J, Tsai A, OLeary SE, Petrov A, Puglisi JD (2012) Unraveling the dynamics of ribosome translocation. Curr Opin Struct Biol 22:804
Chowdhury D (2013a) Stochastic mechano-chemical kinetics of molecular motors: a multidisciplinary enterprise from a physicist’s perspective. Phys Rep 529:1
Chowdhury D (2013b) Modeling stochastic kinetics of molecular machines at multiple levels: from molecules to modules. Biophys J 104:2331
Chowdhury D (2014) Michaelis–Menten at 100 and allosterism at 50: driving molecular motors in a hailstorm with noisy ATPase engines and allosteric transmission. FEBS J 281:601
Cleland WW (1975) Partition analysis and the concept of net rate constant as tools in enzyme kinetics. Biochemistry 14:3220
Cochella L, Green R (2005) Fidelity in protein synthesis. Curr Biol 136:R536–R540
de Pouplana LR, Santos MAS, Zhu JH, Farabaugh PJ, Javid B (2014) Protein mistranslation: friend or foe? Trends Biochem Sci 39:355–362
Fan Y, Wu J, Ung MH, De Lay N, Cheng C, Ling J (2015) Protein mistranslation protects bacteria against oxidative stress. Nucl Acids Res 43:1740
Fluitt A, Pienaar E, Viljoen H (2007) Ribosome kinetics and aa-tRNA competition determine rate and fidelity of peptide synthesis. Comput Biol Chem 31:335
Frank J (ed) (2011) Molecular machines in biology. Cambridge University Press, Cambridge
Frank J, Gonzalez RL (2010) Structure and dynamics of a processive Brownian motor: the translating ribosome. Annu Rev Biochem 79:381–412
Garai A, Chowdhury D, Chowdhury D, Ramakrishnan TV (2009) Stochastic kinetics of ribosomes: single motor properties and collective behavior. Phys Rev E 80:011908
Grima R, Leier A (2017) Exact product formation rates for stochastic enzyme kinetics. J Phys Chem B 121:13–23
Grima R, Walter NG, Schnell S (2014) Single-molecule enzymology à la Michaelis–Menten. FEBS J 281:518
Johansson M, Lovmar M, Ehrenberg M (2008) Rate and accuracy of bacterial protein synthesis. Curr Opin Microbiol 11:141–147
Johnson KA (2013) A century of enzyme kinetic analysis, 1913 to 2013. FEBS Lett 587:2753
Johnson KA, Goody RS (2011) The original Michaelis constant: translation of the 1913 Michaelis-Menten paper. Biochemistry 50:8264–8269. doi:10.1021/bi201284u
Kim S (2014) Aminoacyl-tRNA synthetases in biology and medicine. Springer, Berlin
Kinz-Thompson C, Sharma AK, Frank J, Gonzalez RL Jr, Chowdhury D (2015) Quantitative connection between ensemble thermodynamics and single-molecule kinetics: a case study using cryogenic electron microscopy and single-molecule Fluorescence Resonance Energy Transfer investigations of the ribosome. J Phys Chem B 119:10888–10901
Kramer EB, Farabaugh PJ (2007) The frequency of translational misreading errors in E. coli is largely determined by tRNA competition. RNA 13:87
Kramer EB, Vallabhaneni H, Mayer LM, Farabaugh PJ (2010) A comprehensive analysis of translational missense errors in the yeast Saccharomyces cerevisiae. RNA 16:1797
Ling J, Reynolds N, Ibba M (2009) Aminoacyl-tRNA synthesis and translational quality control. Annu Rev Micorobiol 63:61–78
Michaelis L, Menten MML (2013) The kinetics of invertin action: translated by T.R.C. Boyde. FEBS Lett 587:2712–2720. doi:10.1016/j.febslet.2013.07.015
Michel D, Ruelle P (2013) Seven competing ways to recover the Michaelis–Menten equation reveal the alternative approaches to steady state modeling. J Math Chem 51:2271
Netzer N et al (2010) Innate immune and chemically triggered oxidative stress modifies translational fidelity. Nature 462:522
Pan T (2013) Adaptive translation as a mechanism of stress response and adaptation. Annu Rev Genet 47:121–137
Parker J (1989) Errors and alternatives in reading the universal genetic code. Microbiol Rev 53:273–298
Reynolds NM, Lazazzera BA, Ibba M (2010) Cellular mechanisms that control mistranslation. Nat Rev Microbiol 8:849–856
Rittie L, Perbal B (2008) Enzymes used in molecular biology: a useful guide. J Cell Commun Signal 2:25
Rodnina MV (2012) Quality control of mRNA decoding on the bacterial ribosome. Adv Protein Chem Struct Biol 86:95–128
Rodnina MV, Wintermeyer W, Green R (eds) (2011) Ribosomes: structure, function, and dynamics. Springer, Berlin
Ruan B, Palioura S, Sabina J, Marvin-Guy L, Kochhar S, LaRossa RA, Söll D (2008) Quality control despite mistranslation caused by an ambiguous genetic code. Proc Natl Acad Sci USA 105:16502–16507
Rudorf S, Lipowsky R (2015) Protein synthesis in E. coli: dependence of codon-specific elongation on tRNA concentration and codon usage. PLoS One 10:e0134994
Schnell S, Maini PK (2003) A century of enzyme kinetics. Should we believe in the Km and vmax estimates? Comments Theor Biol 8:169
Shah P, Gilchrist MA (2010) Effect of correlated tRNA abundances on translation errors and evolution of codon usage bias. PLoS Genet 6(9):e1001128
Sharma AK, Chowdhury D (2010) Quality control by a mobile molecular workshop: quality versus quantity. Phys Rev E 82:031912
Sharma AK, Chowdhury D (2011a) Distribution of dwell times of a ribosome: effects of infidelity, kinetic proofreading and ribosome crowding. Phys Biol 8:026005
Sharma AK, Chowdhury D (2011b) Stochastic theory of protein synthesis and polysome: ribosome profile on a single mRNA transcript. J Theor Biol 289:36–46
Spirin AS (2002) Ribosomes. Springer, Berlin
Uemura S, Aitken CE, Korlach J, Flusberg BA, Turner SW, Puglisi JD (2010) Real-time tRNA transit on single translating ribosomes at codon resolution. Nature 464:1012
Xie P (2013a) Dynamics of tRNA occupancy and dissociation during translation by the ribosome. J Theor Biol 316:49–60
Xie XS (2013b) Enzyme kinetics, past and present. Science 342:1457
Yadavalli SS, Ibba M (2012) Quality control in aminoacyl-tRNA synthesis. Adv Protein Chem Struct Biol 86:1–43
Zaher HS, Green R (2009) Fidelity at the molecular level: lessons from protein synthesis. Cell 136:746–762
Zouridis H, Hatzimanikatis V (2008) Effects of codon distributions and tRNA competition on protein translation. Biophys J 95:1018
Acknowledgements
DC thanks Joachim Frank, Ruben Gonzalez Jr. and Michael Ibba for useful correspondence. We also thank Joachim Frank, Adil Moghal and Alex Mogilner for valuable comments on a draft of this manuscript. We thank the anonymous referees for useful suggestions and for drawing our attention to a very recent paper. This work is supported by “Prof. S. Sampath Chair” professorship (DC) and a J.C. Bose National Fellowship (DC). DC also acknowledges hospitality of the Biological Physics Group of the Max-Planck Institute for the Physics of Complex Systems at Dresden, under the Visitors Program, during the initial stages of this work.
Funding was provided by Science and Engineering Research Board.
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Appendices
Appendix A: Master Equations and Steady-State Probabilities
The master equations governing the time evolution of the probabilities can be written as:
with the normalization condition
The steady-state solutions \(P_{\mu }\) of equations (26) are given by
where
Appendix B: Intuitive Derivation of the Expression for Average Rate of Translation
Following Cleland’s approach Cleland (1975) for replacing complex network of biochemical pathways by a simpler equivalent network and deriving the effective rates of the transitions of that network, we derive the expression for the average velocity of the ribosome in our model. To illustrate the method, we consider a simpler reaction
The effective rate constant, \(k_{1}'\), for \(X \xrightarrow {k_{1}'} Y,\) is given by
The same treatment can be applied to our model.
Let us first assume that only correctly charged cognate tRNAs are present (i.e., assuming that mis-charged cognate, near-cognate and non-cognate tRNAs are absent in the surrounding). For the five consecutive steps of the cycle, we denote the transit times by \(T_{1}\), \(T_{2}\), \(T_{3}\), \(T_{4}\) and \(T_{5}\), respectively. It is straightforward to see that
Therefore, for the above cycle, i.e., when only correctly charged cognate tRNA molecules are present in the surrounding, the average velocity of the ribosome would be
Similarly, for the other three cycles we can specify the transit times in a similar manner. For mis-charged cognate tRNA, the corresponding symbols are \(T'_{1},T'_{2},T'_{3},T'_{4},T'_{5}\), respectively, while for near-cognate tRNA, ths symbols are \(T''_{1},T''_{2},T''_{3},T''_{4},T''_{5}\), respectively. For non-cognate tRNA, we have \(T'''_{1},T'''_{2},T'''_{3},T'''_{4},T'''_{5}\), respectively.
Next, let us consider the general case when all the four different substrates are present in the surrounding; the kinetics of the system is described by full model shown in Fig. 3. The transit times are analogous to resistances in electrical circuits, which means that for a series of reaction, the transit times are additive and for parallel reaction pathways, the reciprocals of the transit times are additive. Hence, the average velocity for the complete model is
where
Appendix C: Master Equations and Derivation of Dwell Time Distribution
The master equations governing the time evolution of the probabilities are identical to those given in Appendix A, except for the following:
and the normalization condition becomes
Here, we take the initial conditions to be \(P_{1}(0)=1\), and \(P_{2}(0)=P_{3}(0)=P_{4}(0)=P_{5}(0)=P'_{2}(0) =P'_{3}(0)=P''_{2}(0)=P''_{3}(0)=P'''_{2}(0) =P'''_{3}(0)=P^{*}_{4}(0)=P^{*}_{5}(0)= \widetilde{P_{1}}(0)=0\).
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Dutta, A., Chowdhury, D. A Generalized Michaelis–Menten Equation in Protein Synthesis: Effects of Mis-Charged Cognate tRNA and Mis-Reading of Codon. Bull Math Biol 79, 1005–1027 (2017). https://doi.org/10.1007/s11538-017-0266-5
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DOI: https://doi.org/10.1007/s11538-017-0266-5