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The Effect of Codon Mismatch on the Protein Translation System

  • Dinglin Zhang ,

    Contributed equally to this work with: Dinglin Zhang, Danfeng Chen

    Affiliation Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, 116023, China

  • Danfeng Chen ,

    Contributed equally to this work with: Dinglin Zhang, Danfeng Chen

    Affiliation Dalian City Fisherles Technical Extension Station, Dalian, Liaoning, 116025, China

  • Liaoran Cao,

    Affiliation Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, 116023, China

  • Guohui Li , (GHL); (HC)

    Affiliation Laboratory of Molecular Modeling and Design, State Key Laboratory of Molecular Reaction Dynamics, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning, 116023, China

  • Hong Cheng (GHL); (HC)

    Affiliation Shanghai Key Laboratory of Molecular Andrology, State Key Laboratory of Molecular Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

The Effect of Codon Mismatch on the Protein Translation System

  • Dinglin Zhang, 
  • Danfeng Chen, 
  • Liaoran Cao, 
  • Guohui Li, 
  • Hong Cheng


Incorrect protein translation, caused by codon mismatch, is an important problem of living cells. In this work, a computational model was introduced to quantify the effects of codon mismatch and the model was used to study the protein translation of Saccharomyces cerevisiae. According to simulation results, the probability of codon mismatch will increase when the supply of amino acids is unbalanced, and the longer is the codon sequence, the larger is the probability for incorrect translation to occur, making the synthesis of long peptide chain difficult. By comparing to simulation results without codon mismatch effects taken into account, the fraction of mRNAs with bound ribosome decrease faster along the mRNAs, making the 5’ ramp phenomenon more obvious. It was also found in our work that the premature mechanism resulted from codon mismatch can reduce the proportion of incorrect translation when the amino acid supply is extremely unbalanced, which is one possible source of high fidelity protein synthesis after peptidyl transfer.


Understanding of the gene translation process is important for human health[13], biotechnology [46] and evolution[3,4,7,8]. In recent years a number of technologies have been developed to characterize different features related to the gene translation and multiple roles of the coding sequence have been proposed. Recent studies suggested that the order of codons along the mRNA plays an important role in determining translation efficiency[4,911]. It was suggested that there is weak folding of mRNA molecule in the region surrounding the start codons[916], and endogenous genes tend to perform strong mRNA folding in the region after the start codon[10,17,18], which can improve the fidelity of translation initiation[10,17,19,20]. It was also suggested that the first 30–50 codons at the beginning of the open reading frame (ORF) tended to be recognized by tRNA species with lower intracellular abundance[6,21,22], resulting in slower ribosomal elongation speed in this region[6,23,24]. Fast initiation of short genes also causes a 5’ ribosomal ramp[14]. However, this process is still enigmatic with contradicting conclusions in different studies. Although there are many publications show that the codon sequence is one reason of the 5’ end ramp, other research suggested that the ramp of 5’ end is caused primarily by faster initiation in short genes, rather than by the ordering of codons within each gene[14]. In this paper, we proposed that the ribosome premature is another causeof5’ ramp.

Translation of mRNA has been studied by a variety of computational models based on the totally asymmetric simple exclusion process, justifying the role of codon ordering in determining spatial patterns of ribosomes along mRNAs[18,25]. Such models were built based on constant, inexhaustible supplies of amino acids, free ribosomes and free tRNAs in the cell. A more realistic alternation, the “Whole cell”[14] was developed to investigate the gnome scale gene translation properties. This model tracks all ribosomes and tRNAs in a cell—each of which is either freely diffusing or bound to a specific mRNA molecule at a specific codon position at any time point. Transition rates among states are parameterized in seconds so that the model describes the dynamics of translation in real time. Unlike many other models of translation, which treat each mRNA molecule in isolation and assume an inexhaustible supply of free ribosomes that initiate the message at a constant rate, “Whole cell” model keeps track of every tRNA, mRNA, and ribosome molecule in the cell simultaneously. But codon mismatch effects, leading to premature, especially under unbalanced starvation conditions[26,27], was still ignored in the “Whole cell” model. Our model are based on the “Whole cell” model, and focus on a previously ignored problem: the effect of codon mismatch and translation premature. From the simulation result of our model, the premature is one reason for the 5’ ramp and cannot be ignored. Although the chance of codon mismatch at each codon is low[28], it can be accumulated along the long mRNA sequence. In order to investigate the codon mismatch effects in translation processes[2932], we built a model to study the translation performance under two different conditions: balanced and unbalanced amino-acid supplies.


Model description

Our model takes the premature event into account (Fig 1). Consider a set of mRNA sequences and each has Ni sites (i is the index of sequence) which can either be occupied by a ribosome or be empty, ribosomes can transfer between different sites according to the following rules: given a movable ribosome randomly, if its current position j (j, 1 ≤ jNi) is between 1 and Ni− 1, then the ribosome on site j will move to site j + 1.

Fig 1. Translation schematic diagram.

When a codon mismatch occurs, the ribosome will either abort or continue to translate the left codons. When the ribosome aborts, all the work has been done will become invalid and the ribosome will return to the free pool, while some ribosomes can still reach the ending codons with incorrect peptides, which will decrease translation efficiency.

In this process, codon mismatch can occur based on a predefined probability. For mRNA i, codon j, the codon mismatch probability pj_mis can be calculated based on the number of available aminoacyl tRNAs (Eqs 15). Here pmis_based is the average mismatch probability under normal conditions, which is available from experimental results[28]; prelative_mis_aa is the average probability of mismatch for the current codon under normal amino acid supply conditions; prelative_match_aa is the average match probability for the current codon under normal amino acid supply conditions; pmis_abort is the total premature probability for a ribosome when a codon mismatch occurs. If a non-cognate amino acyl tRNA is incorporated, the peptide chain will lose specificity in the A site of the ribosome and the propagation of this error may result in premature of the peptide[28,33]. From Fig 2 [28], we can see that when a codon mismatch occurs, the premature probability for a ribosome is rather high. Because the probability of continuing an incorrect translation will be significantly reduced, the premature termination is assumed to occur immediately after the first mismatch with the defined probability in this model.

Fig 2. An initial miscoding event results in an overall drop in yield of full-length peptides.

(adapted from nature. 2009, 457(8):161, with permission from NATURE) Proposed model for the events after a miscoding event with the steps contributing to the quality control described here highlighted by green arrows. PT, peptidyl transfer.


For the current codon j, the index of matched aminoacyl tRNA type is t; x is the set of matched aminoacyl tRNA; pj_total_relative_mis is the probability of mismatch and pj_total_relative_match is the probability of match.


Here is the number of free aminoacyl tRNAs of type t. For a movable ribosome on site j, it has three possible moves. The probability of the ribosome to move to the next site correctly is pj_match; the probability for it to move to the next position, but performing incorrect translation is pj_mis × (1 –pmis_abort); and the probability of a premature termination is pj_mis × pmis_abort.

A reasonable estimation of the initiation rate under various conditions is a key problem. Here we assume that the initiation rate is the bottleneck of translation rate because the average number of ribosomes on mRNA sequences is small, indicating that the transfer between different sites should be much faster than the initiation[18,24,34]. Here we infer that the initiation rate is determined by the supply of free Methionyl-tRNAs and ribosomes (Eqs 9 and 10).

Initiation rate for one mRNA of gene i: (7) (8)

Total initiation rate: (9) (10) fi is the fraction of mRNAs of gene i that can be initialized. Ai is the number of mRNAs of type i. pi is the gene-specific initiation probability[14], Dr is the diffusion coefficient of ribosomes. Dt is the diffusion coefficient of tRNAs, τr and τt are the characteristic times of ribosomes and tRNAs[14], Rf is the number of free ribosomes, M is the number of free Methionyl-tRNAs. Because the mismatched aminoacyl tRNAs also have a certain chance to be accepted in the translation especially with an unbalanced amino acid supply, a transfer rate is introduced.

Transfer rate for one ribosome on codon: (11)

Total transfer rate: (12) (13)

If the anticodon k does not match codon j: (14)

If the anticodon k matches codon j: (15) ωj is the wobble parameter, s is tRNA competition coefficient, is the number of free tRNAs of type k. This model can also simulate the process that does not count codon mismatchby setting pmis_based = 0.

Simulation Setup Details

To investigate how protein production is affected by stress, we simulated translation under conditions of balanced and unbalanced amino-acid supply conditions. We modeled the stress of a particular amino acid by changing the abundance of its (charged) cognate tRNAs by 2x folds[14]. Here 2x is the supply coefficient. The package used for the simulations can be download from It was written in C++ and can be compiled under centos. Based on the package of “Whole cell”[14], the codon mismatch and premature features were added.

Condition 1: a balanced amino acid supply condition in which the amount of each aminoacyl tRNAs is multiplied by the same supply coefficient 2x. When x = 0, the system is in normal amino acid supplycondition[14].

Condition 2: an unbalanced amino acid supply condition. Here we take Arg imbalance as an example to investigate the performance of the translation system. So only the amount of Arginyl-tRNA is multiplied by a supply coefficient 2x.

Condition 3: random starvation condition that all amino-acid supplies are randomly modified, different amino acids have different coefficients less than one. In fact this condition is also an unbalanced condition.

Normal amino acid supply condition: the ratio of amino acid supply is defined with the relative copy numbers of charged aminoacyl tRNAs (Table 1) [14].

In our simulation, pmis_abort was set to be 0.5, pmis_based was set to be 0.001[28]. 3,795 genes and 60,000 mRNAs were adopted from S. cerevisiae[14]. To investigate the effects of mistranslation, we studied the translation under three conditions: Condition 1, Condition 2and Condition 3 as previously mentioned. Equilibrations in all simulation systems were achieved in the first 2x109 steps, followed by another 100 seconds of simulation for data collection.

Results and Discussions

Incorrect translation proportion is high under unbalanced conditions

There will be 2 types of productions: one is the correct translation production which is the correct translation of the whole codon sequence. The second is incorrect translation production in which the peptide is synthesized with codon mismatch or is shorter than the correct peptide. The fraction of the three fractions were got from the simulation result and are defined as the following: (16) (17) (18)

Compared to the simulation under Condition 1, the fraction of incorrect translation under unbalanced conditions is relatively high (Fig 3). The fraction of incorrect translation is minor and does not perform large fluctuations under Condition 1, while under Condition 2, the fraction of incorrect translation increases with the stress of unbalanced amino acid supply. Under Condition 3, all the 10 fractions of incorrect translation are higher than the normal conditions. This is an evidence of the premature termination mechanism, which can contribute to the reduction of incorrect production when the supply of amino acid is extremely unbalanced.

Fig 3. The relationship between incorrect translation proportion and amino acid supply.

In Fig 3a, 3b and 3c, the vertical axis shows the total length fraction of the three kinds of released peptide chains which are the premature chains, mature chains with error, and the incorrect chains. In Fig 3a and 3b, the horizontal axis shows the amino acids supply coefficient. So in Fig 3a, all amino acid numbers were multiplied by the same coefficient, and in Fig 3b, only the number of Arg was multiplied by the coefficient while the number of other amino acids were kept unchanged. In Fig 3c, 10 simulations were run under Condition 3 independently and the horizontal axis shows the simulation index. The result shows that stress of unbalanced amino acid supply can lead to incorrect translation.

Although the chance of codon mismatch is low under balanced conditions, accumulation of codon mismatch can lead to a high proportion of incorrect translation of long mRNAs (Fig 4a, 4b and 4c) and the chance can be increased under the Condition 2 (Fig 4b) and Condition 3 (Fig 4c). In Fig 4a, the fraction of incorrect peptide length does not change very much when there are more charged tRNA available, because under normal condition, the proportion of charged tRNAs can almost meet the demand of protein synthesis, and so is the balanced conditions of more charged tRNA available. So the fraction of mismatch on a codon almost remains unchanged (Fig 4d and 4f). But in Fig 4b, the fraction of incorrect peptide length increases fast with more unbalanced supplies. The reason is that when the number of Arginyl-tRNAs decreases or increases based on normal condition, the proportion of charged tRNAs cannot meet the demand of protein synthesis any longer. When the number of Arginyl-tRNAs decreases, the fraction of mismatch on Arg related codons will significantly increase, while the fraction of mismatch on other codons will decrease. When the number of Arginyl-tRNAs increases, the fraction of mismatch on Arg related codons will decrease, while the fraction of mismatch on other codons will increase (Fig 4e and 4g). Thus, study of codon mismatch effects is essential to build the model of the translation process.

Fig 4. Incorrect translation proportion increases with codon sequence length.

In Fig 4a, 4b and 4c, some of mRNAs were not translated for enough times, which could have a significant effect on the final result. So for each simulation result, the mRNAs were sorted with the correctly translated times, and the top 100 were selected. The vertical axis shows the total length fraction of the incorrect chains. The horizontal axis shows the codon sequence length. In Fig 4a and 4b, the incorrect translation fraction is plotted against the codon sequence length with 5 different supply coefficients; In Fig 4c, there are 5 curves for each simulation, different charged aminoacyl tRNA coefficient was randomly set less than 1; Fig 4d and 4e show the fraction of mismatch events on a codon, which is equal to the number of mismatch events on the codon divided by the number of all events on the same codon. Fig 4f and 4g show the fraction of mismatch events of a codon, which is equal to the number of mismatch events on the codon divided by the number of mismatch events on all codons.

Mismatch is one reason of 5’-to-3’ ramp

Codon mismatch effects can be used to explain some well-known phenomenon in the translation process. In order to remove the effects of faster initiation of short mRNAs, 10410 mRNAs with codon sequence length larger than 500 codons were selected. To study the effect of mismatch on the 5’ ramp, the simulation with and without mismatch were run and the fraction of mRNAs with bound ribosome at each site was averaged over all the mRNAs. For each case (Fig 5a, 5b, 5c and 5d), a faster decline of fraction of mRNAs with bound ribosome is found through the elongation process when the mismatch events are considered (Fig 5b and 5d), which suggests that the mismatch premature events play an important role to form the 5’ ramp. The tests under Condition 3 with and without codon mismatch effects counted were also done (Fig 5e). There are 200 different random supply configurations and for each supply configuration, the simulation with and without codon mismatch effects counted were done. Finally the fraction of mRNAs with bound ribosome of each site was averaged over all the 200 simulations results. The result also shows that the curve declines faster when codon mismatch effects are counted.

Fig 5. The effect of mismatch on 5’-to-3’ ramp.

The vertical axis shows the fraction of mRNAs with bound ribosome. The horizontal axis shows the codon positions. Fraction of mRNAs with bound ribosome in each site was averaged over all the mRNAs that were larger than 500 codons. Fig 5a and 5b: simulation results with codon mismatch effects counted; Fig 5c and 5d: simulation results with no codon mismatch effects counted; Fig 5a and 5c: simulation results are under Condition 1; Fig 5b and 5d: simulation results are under Condition 2; Fig 5e: simulation results are under Condition 3 and the final fraction of mRNAs with bound ribosome of each site was averaged over the 200 simulations results.

Since the incorrect but complete translation would not affect the fraction of mRNAs with bound ribosome, it can be inferred that the fraction decrease along mRNA is, at least partly, caused by the premature termination mechanism. If a premature termination occurs, ribosome cannot translate the left codons, which will do contribution to a higher ribosome density in 5’ zones and lower ribosome density in 3’ zones.

In Fig 5, another observation is the lower fraction of mRNAs with bound ribosome when amino acids is more available, because when there are more charged aminoacyl tRNAs available, the ribosome will move faster from one codon to the next. But the initiation rate is the bottleneck for the whole translation process, which cannot be speeded as fast as the codon translation rate. So the increase of ribosome flux cannot be increased as fast as the increase of the charged aminoacyl tRNA,which will lead to a lower fraction of mRNAs with bound ribosome. Moreover, this effect is alleviated in Condition 2 under which only the number of Arginyl-tRNA fluctuates. When the number of Arginyl-tRNA increases, only the Arginyl-tRNA related codons will be affected significantly. That is to say only the ribosome at Arginyl-tRNA related codons site will translate faster than before, which cannot affect the fraction of mRNAs with bound ribosome very much. But when the Arginyl-tRNA drops in large number, all the codons will be affected because the ribosome on the Arginyl-tRNA related codons site moves slower, which will lead more stalled ribosomes, then the fraction of mRNAs with bound ribosome will increase significantly.

Codon mismatch affects the rate of correct translation

Incorrect translation productions waste cell energies, decrease the rate of correct translation and even do harm to the living cell. The rate of the amino acid synthesis is about 5 aa/s under normal amino acid supply condition, which agrees with the results from empirical measurements[35,36]. In this work, the rates of correct translation under a variety of unbalanced amino acid supplies conditions were studied (Fig 6). It is interesting to see that the increase of Arg affects the translation rate in different manners. With a supply coefficient smaller than 20, the translation rate increases with more Arg supplied due to the increase in reagent concentration. However, the trend is then reversed, which suggests that the degree of amino acid imbalance plays a more important role, causing a higher chance of codon mismatch and higher probability of incorrect translation. Thus, the codon mismatch is key to the determination of translation consequence.

Fig 6. The effect of mismatch on the rate of correct translation.

The vertical axis shows the average correct translation rate of one ribosome in a second. In Fig 6a and 6b, the horizontal axis shows the amino acids supply coefficient. The number of amino acid is equal to the normal condition number multiplied by the coefficient. So in Fig 6a, all amino acid numbers are multiplied by the same coefficient, and in Fig 6b, only the number of arg is multiplied by the coefficient, other amino acids keep unchanged. In Fig 6c, all the 10 simulations are under the Condition 3 that all amino acid supplies are randomly modified with the coefficient less than 1, and different amino acid has different coefficient.


All the above simulations are based on the assumption that amino acids are supplied continuously. But the actual environment is more complex than that. For example, there may be an extreme condition that the supply of some amino acids is ceased. As far as the model concerned, without the contribution of premature, there will be deadlock states on corresponding codon positions and the process of translation event will be paused without releasing the ribosome and the nascent peptide. From our results, it can be inferred that the premature termination can help avoid entering into the deadlock states. If cells release some incomplete nascent peptides through the premature mechanism and recover some scarce amino acids through the recycling mechanism[3739], some of the stalled ribosomes caused by the deficient amino acids will continue to translate the left codons. Thus, although the codon mismatch does reduce the rate of valid work when amino acids supply is balanced, it can also be helpful when supply is unbalanced. Mismatch may be a way for living creatures to adapt to stressful environments. Due to the limitation of computational power and simulation algorithms, a number of simplifications were made in our model. The initialization, translation and termination should be each separated into several steps[4042], which are not counted in this work. Additionally, in real biological environments the numbers of free tRNAs, ribosomes and mRNAs are changing continuously[25,4345], while in our model these numbers are fixed. Some regulatory mechanisms[39,46,47] involved in the translation process are also ignored in the process of the simulation and other kinds of mismatch error that exist in the translation process[29,48] are also ignored. We mainly considered two supply conditions. One is balanced and the other is unbalanced. Considering the unbalanced supply of different amino acids have the similar effect, we just take the Arg as an example. In this model we have investigated the starvation condition that one amino acid is very deficient, but in fact, such a condition is terrible, almost no cell can keep live or cells have already adjusted the metabolic pathways to suit the terrible conditions. Based on the assumption that cells can survive and the amino acid metabolism pathway is not changed, we just only focus on the translation system behaviour and properties. It is more easier to get unusual behaviour under terrible conditions. Hopefully more parameters can be achieved in further experimental studies on various transition systems to improve this model.

Supporting Information


This work is supported by grants from the National Science Foundation of China [No. 31370714], the Ministry of Science and Technology of China 973 project [No. 2012CB721002]

Author Contributions

Conceived and designed the experiments: GHL HC. Performed the experiments: DLZ DFC. Analyzed the data: DLZ. Contributed reagents/materials/analysis tools: DLZ. Wrote the paper: LRC.


  1. 1. Li GW, Oh E, Weissman JS. The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature. 2012; 484: 538–541. pmid:22456704
  2. 2. Weygand-Durasevic I, Ibba M. New Roles for Codon Usage. Science. 2010; 329: 1473–1474. pmid:20847254
  3. 3. Cannarozzi G, Schraudolph NN, Faty M, von Rohr P, Friberg MT, Roth AC, et al. A role for codon order in translation dynamics. Cell. 2010; 141: 355–367. pmid:20403329
  4. 4. Barash Y, Calarco JA, Gao W, Pan Q, Wang X, Shai O, et al. Deciphering the splicing code. Nature. 2010; 465: 53–59. pmid:20445623
  5. 5. Gingold H, Pilpel Y. Determinants of translation efficiency and accuracy. Mol Syst Biol. 2011; 7: 481. pmid:21487400
  6. 6. Tuller T, Carmi A, Vestsigian K, Navon S, Dorfan Y, Zaborske J, et al. An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell. 2010; 141: 344–354. pmid:20403328
  7. 7. Novoa EM, Ribas de Pouplana L. Speeding with control: codon usage, tRNAs, and ribosomes. Trends Genet. 2012; 28: 574–581. pmid:22921354
  8. 8. Hershberg R, Petrov DA. Selection on codon bias. Annu Rev Genet. 2008; 42: 287–299. pmid:18983258
  9. 9. Tuller T, Waldman YY, Kupiec M, Ruppin E. Translation efficiency is determined by both codon bias and folding energy. Proc Natl Acad Sci U S A. 2010; 107: 3645–3650. pmid:20133581
  10. 10. Kozak M. Regulation of translation via mRNA structure in prokaryotes and eukaryotes. Gene. 2005; 361: 13–37. pmid:16213112
  11. 11. Robbins-Pianka A, Rice MD, Weir MP. The mRNA landscape at yeast translation initiation sites. Bioinformatics. 2010; 26: 2651–2655. pmid:20819958
  12. 12. Gu W, Zhou T, Wilke CO. A universal trend of reduced mRNA stability near the translation-initiation site in prokaryotes and eukaryotes. PLoS Comput Biol. 2010; 6: e1000664. pmid:20140241
  13. 13. Eyrewalker A, Bulmer M. Reduced Synonymous Substitution Rate at the Start of Enterobacterial Genes. Nucleic Acids Research. 1993; 21: 4599–4603. pmid:8233796
  14. 14. Shah P, Ding Y, Niemczyk M, Kudla G, Plotkin JB. Rate-Limiting Steps in Yeast Protein Translation. Cell. 2013; 153: 1589–1601. pmid:23791185
  15. 15. Kudla G, Murray AW, Tollervey D, Plotkin JB. Coding-sequence determinants of gene expression in Escherichia coli. Science. 2009; 324: 255–258. pmid:19359587
  16. 16. Goodman DB, Church GM, Kosuri S. Causes and Effects of N-Terminal Codon Bias in Bacterial Genes. Science. 2013; 342: 475–479. pmid:24072823
  17. 17. Kozak M. Downstream secondary structure facilitates recognition of initiator codons by eukaryotic ribosomes. Proc Natl Acad Sci U S A. 1990; 87: 8301–8305. pmid:2236042
  18. 18. Tuller T, Veksler-Lublinsky I, Gazit N, Kupiec M, Ruppin E, Ziv-Ukelson M. Composite effects of gene determinants on the translation speed and density of ribosomes. Genome Biology. 2011; 12:
  19. 19. Zur H, Tuller T. New Universal Rules of Eukaryotic Translation Initiation Fidelity. Plos Computational Biology. 2013; 9:
  20. 20. Kochetov AV, Palyanov A, Titov II, Grigorovich D, Sarai A, Kolchanov NA. AUG_hairpin: prediction of a downstream secondary structure influencing the recognition of a translation start site. BMC Bioinformatics. 2007; 8: 318. pmid:17760957
  21. 21. Fredrick K, Ibba M. How the sequence of a gene can tune its translation. Cell. 2010; 141: 227–229. pmid:20403320
  22. 22. Pechmann S, Frydman J. Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding. Nat Struct Mol Biol. 2013; 20: 237–243. pmid:23262490
  23. 23. Dana A, Tuller T. Properties and determinants of codon decoding time distributions. BMC Genomics. 2014; 15 Suppl 6: S13. pmid:25572668
  24. 24. Ingolia NT, Ghaemmaghami S, Newman JRS, Weissman JS. Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling. Science. 2009; 324: 218–223. pmid:19213877
  25. 25. Reuveni S, Meilijson I, Kupiec M, Ruppin E, Tuller T. Genome-Scale Analysis of Translation Elongation with a Ribosome Flow Model. Plos Computational Biology. 2011; 7:
  26. 26. Harley CB, Pollard JW, Stanners CP, Goldstein S. Model for Messenger-Rna Translation during Amino-Acid Starvation Applied to the Calculation of Protein Synthetic Error Rates. Journal of Biological Chemistry. 1981; 256: 786–794.
  27. 27. Fluitt A, Pienaar E, Vijoen H. Ribosome kinetics and aa-tRNA competition determine rate and fidelity of peptide synthesis. Computational Biology and Chemistry. 2007; 31: 335–346. pmid:17897886
  28. 28. Zaher HS, Green R. Quality control by the ribosome following peptide bond formation. Nature. 2009; 457: 161–U151. pmid:19092806
  29. 29. Hausmann CD, Praetorius-Ibba M, Ibba M. An aminoacyl-tRNA synthetase:elongation factor complex for substrate channeling in archaeal translation. Nucleic Acids Research. 2007; 35: 6094–6102. pmid:17766929
  30. 30. Cathopoulis TJT, Chuawong P, Hendrickson TL. Conserved discrimination against misacylated tRNAs by two mesophilic elongation factor Tu orthologs. Biochemistry. 2008; 47: 7610–7616. pmid:18627126
  31. 31. Becker HD, Kern D. Thermus thermophilus: A link in evolution of the tRNA-dependent amino acid amidation pathways. Proceedings of the National Academy of Sciences of the United States of America. 1998; 95: 12832–12837. pmid:9789000
  32. 32. Schrader JM, Chapman SJ, Uhlenbeck OC. Understanding the Sequence Specificity of tRNA Binding to Elongation Factor Tu using tRNA Mutagenesis. Journal of Molecular Biology. 2009; 386: 1255–1264. pmid:19452597
  33. 33. Shen PS, Park J, Qin YD, Li XM, Parsawar K, Larson MH, et al. Rqc2p and 60S ribosomal subunits mediate mRNA-independent elongation of nascent chains. Science. 2015; 347: 75–78. pmid:25554787
  34. 34. Piques M, Schulze WX, Hohne M, Usadel B, Gibon Y, Rohwer J, et al. Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis. Molecular Systems Biology. 2009; 5:
  35. 35. Waldron C, Lacroute F. Effect of growth rate on the amounts of ribosomal and transfer ribonucleic acids in yeast. J Bacteriol. 1975; 122: 855–865. pmid:1097403
  36. 36. Karpinets TV, Greenwood DJ, Sams CE, Ammons JT. RNA: protein ratio of the unicellular organism as a characteristic of phosphorous and nitrogen stoichiometry and of the cellular requirement of ribosomes for protein synthesis. Bmc Biology. 2006; 4:
  37. 37. Williams CW, Elmendorf HG. Identification and analysis of the RNA degrading complexes and machinery of Giardia lamblia using an in silico approach. Bmc Genomics. 2011; 12:
  38. 38. Soudet J, Gelugne JP, Belhabich-Baumas K, Caizergues-Ferrer M, Mougin A. Immature small ribosomal subunits can engage in translation initiation in Saccharomyces cerevisiae. Embo Journal. 2010; 29: 80–92. pmid:19893492
  39. 39. Fujii K, Kitabatake M, Sakata T, Miyata A, Ohno M. A role for ubiquitin in the clearance of nonfunctional rRNAs. Genes & Development. 2009; 23: 963–974.
  40. 40. Dever TE, Green R. The Elongation, Termination, and Recycling Phases of Translation in Eukaryotes. Cold Spring Harbor Perspectives in Biology. 2012; 4:
  41. 41. Weinger JS, Parnell KM, Dorner S, Green R, Strobel SA. Substrate-assisted catalysis of peptide bond formation by the ribosome. Nature Structural & Molecular Biology. 2004; 11: 1101–1106.
  42. 42. Hernandez G. Was the initiation of translation in early eukaryotes IRES-driven? Trends in Biochemical Sciences. 2008; 33: 58–64. pmid:18242094
  43. 43. Qian WF, Yang JR, Pearson NM, Maclean C, Zhang JZ. Balanced Codon Usage Optimizes Eukaryotic Translational Efficiency. Plos Genetics. 2012; 8:
  44. 44. Garcia-Martinez J, Aranda A, Perez-Ortin JE. Genomic run-on evaluates transcription rates for all yeast genes and identifies gene regulatory mechanisms. Molecular Cell. 2004; 15: 303–313. pmid:15260981
  45. 45. Reid DW, Nicchitta CV. Primary Role for Endoplasmic Reticulum-bound Ribosomes in Cellular Translation Identified by Ribosome Profiling. Journal of Biological Chemistry. 2012; 287: 5518–5527. pmid:22199352
  46. 46. Hofmann K. Ubiquitin-binding domains and their role in the DNA damage response. DNA Repair. 2009; 8: 544–556. pmid:19213613
  47. 47. Reyes-Turcu FE, Ventii KH, Wilkinson KD. Regulation and Cellular Roles of Ubiquitin-Specific Deubiquitinating Enzymes. Annual Review of Biochemistry. 2009; 78: 363–397. pmid:19489724
  48. 48. Effraim PR, Wang JN, Englander MT, Avins J, Leyh TS, Gonzalez RL, et al. Natural amino acids do not require their native tRNAs for efficient selection by the ribosome. Nature Chemical Biology. 2009; 5: 947–953. pmid:19915542