The mechanism of error induction by the antibiotic viomycin provides insight into the fidelity mechanism of translation

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Version of Record published: June 26, 2019 (This version)
Accepted Manuscript published: June 7, 2019 (Go to version)
Accepted: June 4, 2019
Received: March 4, 2019

1. Of interest
Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

Ian R Outhwaite, Sukrit Singh ... Markus A Seeliger

Research Article Dec 4, 2023
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Abstract

Applying pre-steady state kinetics to an Escherichia-coli-based reconstituted translation system, we have studied how the antibiotic viomycin affects the accuracy of genetic code reading. We find that viomycin binds to translating ribosomes associated with a ternary complex (TC) consisting of elongation factor Tu (EF-Tu), aminoacyl tRNA and GTP, and locks the otherwise dynamically flipping monitoring bases A1492 and A1493 into their active conformation. This effectively prevents dissociation of near- and non-cognate TCs from the ribosome, thereby enhancing errors in initial selection. Moreover, viomycin shuts down proofreading-based error correction. Our results imply a mechanism in which the accuracy of initial selection is achieved by larger backward rate constants toward TC dissociation rather than by a smaller rate constant for GTP hydrolysis for near- and non-cognate TCs. Additionally, our results demonstrate that translocation inhibition, rather than error induction, is the major cause of cell growth inhibition by viomycin.

https://doi.org/10.7554/eLife.46124.001

Introduction

Viomycin is the first discovered member of the tuberactinomycin class of bacterial protein synthesis inhibiting antibiotics (Ehrlich et al., 1951; Finlay et al., 1951), commonly used to treat infections by Mycobacterium tuberculosis strains resistant to first-line drugs (WHO, 2010). It is a cyclic pentapeptide, that is naturally synthesized by a non-ribosomal peptidyl transferase (Thomas et al., 2003). Viomycin impairs the fidelity of tRNA selection (Marrero et al., 1980) and reduces the rate of mRNA translocation (Holm et al., 2016; Modolell and Vázquez, 1977) during the elongation cycle of bacterial protein synthesis. We have recently described the kinetic mechanism by which viomycin inhibits translocation (Holm et al., 2016), and here we report on the kinetic mechanism by which viomycin impairs the accuracy of AA-tRNA selection.

During genetic code translation, aminoacyl-transfer RNAs (AA-tRNAs) are delivered to the A site of the ribosome in ternary complex (TC) with elongation factor Tu (EF-Tu) and GTP. For fast and accurate protein synthesis, the ribosome must select for GTP hydrolysis those TCs which contain an AA-tRNA with a base triplet (anticodon) cognate to the base triplet on the mRNA (codon) displayed in the ribosomal A site. Those cognate AA-tRNAs will then be selected for A-site accommodation and peptidyl transfer. In quantitative terms, this means that cognate codon selection of AA-tRNAs for GTP hydrolysis and peptidyl transfer must be characterized by much higher catalytic efficiency ( $k_{c a t} / K_{M}$ ) than near-cognate codon selection. In enzyme kinetics, $k_{c a t}$ corresponds to the maximal rate of product formation at saturating substrate concentration and the Michaelis-Menten constant to the substrate concentration at which product formation rate is half maximal. The accuracy by which the ribosome discriminates against a given codon∙anticodon mismatch is defined as the ratio between the $k_{c a t} / K_{M}$ values of the cognate and the non-cognate reaction (Fersht, 1998).

Selection of AA-tRNA by the ribosome occurs in two phases: initial codon selection before GTP hydrolysis on EF-Tu and proofreading selection after GTP hydrolysis (Ruusala et al., 1982; Thompson and Stone, 1977). Initial selection begins by TC binding to the ribosomal A/T site, from which TC is either rejected by dissociation from the ribosome or accepted by the triggering of GTP hydrolysis on EF-Tu. The accuracy of initial codon selection is amplified by A-minor interactions between the codon-anticodon helix and the 16S ribosomal RNA (rRNA) monitoring bases A1492, A1493 and G530 (E. coli numbering) of the decoding center (Carter et al., 2000). It has been suggested that the monitoring bases only flip out from their binding sites in helix 44 (h44) of 16S rRNA to form hydrogen bonds with cognate but not near- or non-cognate codon-anticodon helices (Carter et al., 2000). However, in recent crystal structures of A-site accommodated cognate and near-cognate tRNAs the monitoring bases were observed in virtually identical, flipped-out, conformations in all cases (Demeshkina et al., 2012; Demeshkina et al., 2013). Further, recent cryo-EM structures (Fislage et al., 2018; Loveland et al., 2017) show that during initial selection the decoding center builds up in an identical step-wise fashion for both cognate and near-cognate tRNAs to a common final state in which all three monitoring bases are in their activated conformations. These structural data agree with kinetics data on initial TC-selection in the absence and presence of aminoglycosides (Zhang et al., 2018), suggesting that realistic modeling of initial codon selection requires at least four ribosomal states (Fislage et al., 2018; Loveland et al., 2017; Pavlov and Ehrenberg, 2018; Zhang et al., 2018).

In crystal (Pulk and Cate, 2013; Stanley et al., 2010) and cryo-EM (Brilot et al., 2013) structures of the viomycin-bound ribosome, A1492 and A1493 are seen in their active, flipped-out conformation, and viomycin is bound to a site which is occluded by the monitoring bases in their inactive, flipped-in conformation (Schuwirth et al., 2005). From these structures it seems likely that association of viomycin to the ribosome requires bases A1492 and A1493 in their flipped-out conformation and that the presence of viomycin on the ribosome will effectively block the monitoring bases from returning from their inactive, flipped-in, conformation. This interplay between viomycin and the monitoring A1492 and A1493 bases could then potentially drive activation of the third monitoring base, G530, thereby triggering GTP hydrolysis in the TC (Loveland et al., 2017). Such a conformational-selection mode of viomycin binding agrees well with our previous result that A-site-bound tRNA greatly increases the affinity of viomycin for the ribosome (Holm et al., 2016).

Here, we have examined how viomycin reduces the accuracy of tRNA selection on the mRNA translating ribosome. For this, we applied pre-steady state kinetics and mean time analysis to a cell-free protein synthesis system, reconstituted from E. coli components of high purity and in vivo like kinetic properties (Borg et al., 2015; Borg and Ehrenberg, 2015; Indrisiunaite et al., 2015; Johansson et al., 2008; Mandava et al., 2012). Our results are summarized by a kinetic model, which illustrates the mechanism of error induction by viomycin and other drugs in the tuberactinomycin class of antibiotics. We suggest that high accuracy of initial codon selection by cognate TCs is mainly achieved by much smaller backward rate constants toward dissociation of cognate than near-cognate TCs. Our data do not support the previous suggestion that cognate TCs have much larger rate constant for GTP hydrolysis than near-cognate TCs (Gromadski and Rodnina, 2004; Pape, 1999). We compare the modes of action of aminoglycosides and viomycin by highlighting their functional similarities and differences and use simple modeling techniques to estimate the frequency and distribution of viomycin-induced translational errors in the living cell. With support from the present data, we propose that translocation inhibition, rather than error induction, is the major cause of cell growth inhibition by viomycin.

Results

Viomycin acts during initial codon selection of aminoacyl-tRNAs on the ribosome

To study the impact of viomycin on translational accuracy, we designed experiments to measure its effect on the kinetic efficiency ( $k_{c a t} / K_{M}$ ) of GTP hydrolysis by EF-Tu and peptide bond formation for both cognate and near-cognate codon-anticodon interactions. A reaction mixture containing varying concentrations of viomycin and ${Phe-tRNA}_{GAA}^{Phe}$ in TC with EF-Tu·GTP was rapidly mixed in a quench-flow instrument with initiated 70S ribosomes displaying either cognate (UUC) or near-cognate (CUC) codons in the A site. For studying GTP hydrolysis, the TCs contained [³H]GTP and the 70S ribosomes had non-radioactive fMet-tRNA^fMet in the P site, while for studying peptide bond formation the TCs contained non-radioactive GTP and the 70S ribosomes had f[³H]Met-tRNA^fMet bound in the P site. The reactions were stopped at different times by addition of formic acid and the relative amounts of the reaction products were analyzed by ion-exchange chromatography ([³H]GDP) or RP-HPLC (f[³H]Met-Phe) with on-line radiation detection.

The $k_{c a t} / K_{M}$ for cognate GTP hydrolysis (blue traces in Figure 1A) did not change with increasing viomycin concentration from 0 to 1 mM and its average was estimated as 41 ± 0.6 µM⁻¹ s⁻¹ (Figure 1B). In sharp contrast, the $k_{c a t} / K_{M}$ of GTP hydrolysis for the near-cognate reaction (Red traces in Figure 1A) increased dramatically with increasing viomycin concentration (Figure 1B) from 0.053 ± 0.005 µM⁻¹ s⁻¹ in the absence of viomycin to 9.2 ± 0.7 µM⁻¹ s⁻¹ in the presence of 1 mM viomycin, corresponding to a 170-fold reduction in the accuracy of initial codon selection. Viomycin is known to bind to ribosomes with a cognate codon·anticodon interaction in the A site (Brilot et al., 2013; Feldman et al., 2010; Holm et al., 2016; Stanley et al., 2010; Zhou et al., 2012) and to strongly stabilize such complexes (Peske et al., 2004). Hence, these results imply that during initial codon selection viomycin acts on a ribosomal state from which near-cognate, but not cognate, substrates are likely to be rejected in the absence of the drug.

Figure 1

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Kinetics of tRNA selection in the presence of viomycin.

(A) Time courses of [³H] GDP formation by Phe-tRNA^Phecontaining EF-Tu TCs reacting with 0.5 µM 70S ribosomes with fMet-tRNA^fMetin the P site, displaying either a cognate UUC or near-cognate CUC codon in the A site, at the indicated viomycin concentrations. Solid lines represent fits of single exponential equations to the data. (B) of GTP hydrolysis by Phe-tRNA^Phecontaining TCs estimated from experiments such as those in (A). Solid lines represent either a fit of a constant value (UUC) or Equation 3 (CUC) to the data. (C) Time courses of f[³H] Met-Phe dipeptide formation for 1 µM Phe-tRNA^Phecontaining EF-Tu TCs reacting with 70S ribosomes with f[³H]Met-tRNA^fMetin the P site, displaying either a cognate UUC or near-cognate CUC codon in the A site, at the indicated viomycin concentrations. Solid lines represent fits of single exponential equations to the data. (D) of dipeptide formation by Phe-tRNA^Phecontaining EF-Tu TCs estimated from experiments such as those in (C). Solid lines represent either a fit of a constant value (UUC) or Equation 3 (CUC) to the data. All error bars represent SEM.

https://doi.org/10.7554/eLife.46124.002

Similar to GTP hydrolysis the $k_{c a t} / K_{M}$ of dipeptide formation with a cognate UUC codon in the ribosomal A site (blue traces in Figure 1C) did not change with the addition of viomycin and its average was estimated as 38.3 ± 0.7 µM⁻¹ s⁻¹ (Figure 1D). As with GTP hydrolysis, the $k_{c a t} / K_{M}$ of dipeptide formation with a near-cognate CUC codon in the ribosomal A site (red traces in Figure 1C) increased dramatically with increasing viomycin concentration (Figure 1D), from 0.0005 ± 0.00004 µM⁻¹ s⁻¹ in the absence of viomycin to 9.4 ± 0.8 µM⁻¹ s⁻¹ at 1 mM viomycin. This corresponds to a 21,000 fold reduction in total accuracy from 83,500 ± 8000 to 4.0 ± 0.3.

The large difference between the $k_{c a t} / K_{M}$ value for near-cognate dipeptide formation (0.0005 ± 0. 00004 µM⁻¹ s⁻¹) and near-cognate GTP hydrolysis (0.053 ± 0.005 µM⁻¹ s⁻¹) in the absence of viomycin is due to proofreading selection. The ratio of these two $k_{c a t} / K_{M}$ values estimates the accuracy of proofreading selection as 115 ± 15 (Zhang et al., 2016). In contrast, at all tested viomycin concentrations the $k_{c a t} / K_{M}$ values of near cognate GTP hydrolysis and dipeptide formation were virtually identical (Figure 1B and D). This means that viomycin-bound ribosomes are incapable of performing proofreading selection; all near- and non-cognate tRNAs that ‘survive’ initial selection go on to form peptide bonds. Furthermore, even at very low drug concentration almost all near-cognate tRNAs that pass initial selection do so due to the presence of viomycin.

Viomycin stabilizes a GTPase-deficient TC in contact with both cognate and near-cognate codons on the ribosome

Viomycin is known to strongly stabilize peptidyl-tRNA in the ribosomal A site (Holm et al., 2016; Peske et al., 2004). To address whether viomycin also stabilizes TCs in the A site during initial codon selection, we estimated the rate of dissociation of both cognate and near-cognate tRNAs in TC (TC ^H84A) with a GTPase-deficient mutant of EF-Tu (EF-Tu^H84A). In this EF-Tu mutant, an essential histidine in the G-domain has been replaced by alanine (Daviter et al., 2003), but formation of TC ^H84A is unhindered and the mutant TC carries out all partial reactions during initial codon selection, excluding GTP hydrolysis (Daviter et al., 2003; Gromadski and Rodnina, 2004). Initiated 70S ribosomes with f[³H]Met-tRNA^fMet in the P site and a cognate (UUC) or near-cognate (CUC) codon in the A site were equilibrated with TC^H84A containing $P h e - {t R N A}_{G A A}^{P h e}$ and GTP in the presence of varying concentrations of viomycin. TC^H84As were chased from the A site by addition of GTPase proficient TC, containing WT EF-Tu (EF-Tu^WT) and either $P h e - {t R N A}_{G A A}^{P h e}$ or $L e u - {t R N A}_{G A G}^{L e u 2}$ , whichever was cognate for the codon in the A site. The dissociation rate for TC^H84A s from the A site, defined as the inverse of the average dissociation time, was then estimated from the rate of f[³H]Met-Phe or f[³H]Met-Leu formation (supplementary methods).

The rate of TC^H84A dissociation from ribosomes displaying the cognate UUC codon (Figure 2A) was 1.21 ± 0.088 s⁻¹ in the absence of viomycin and decreased from 0.616 ± 0.044 s⁻¹ in the presence of 1 µM viomycin to 0.198 ± 0.0275 s⁻¹ at 10 µM viomycin. The corresponding viomycin-induced increase in dissociation mean time ( $τ_{d i s s}$ ) is shown in Figure 2B. In comparison, dissociation of ${Phe-tRNA}_{GAA}^{Phe}$ - containing TC ^H84A from ribosomes displaying the near-cognate CUC codon (Figure 2C) was too fast to be estimated using manual mixing techniques in the absence of viomycin, consistent with previous reports (Gromadski and Rodnina, 2004; Johansson et al., 2008; Pape, 1999). In the presence of 50 µM viomycin, the apparent near-cognate dissociation rate was 0.264 ± 0.055 s⁻¹ and decreased modestly to 0.209 ± 0.066 s⁻¹ at 200 µM viomycin.

Figure 2

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Stabilization of ternary complex on the ribosome by viomycin on both cognate and near-cognate codons.

(A) Time courses of chase experiments were f[³H]Met-Phe dipeptide is formed after chasing of 5 µM containing EF-Tu^H84ATCs from 70S ribosomes with f[³H] Met-tRNA^fMetin the P site, displaying a cognate UUC codon in the A site, by 0.5 µM containing EF-Tu^WTTCs at the indicated viomycin concentrations. Solid lines represent fits of single exponential equations to the data. (B) Mean times of f[³H] Met-Phe dipeptide formation reflecting the mean times of containing EF-Tu^H84ATC dissociation estimated from experiments such as those in (A). Error bars represent SEM. The solid line represents a linear fit to the data. (C) Time courses of chase experiments where f[³H]Met-Leu dipeptide is formed after chasing of 5 µM Phe-tRNA^Phe_GAAcontaining EF-Tu^H84ATCs from 70S ribosomes with f[³H] Met-tRNA^fMetin the P site, displaying a near-cognate CUC codon in the A site, by 0.5 µM containing EF-Tu^WTTCs at the indicated viomycin concentrations. Solid lines represent fits of single exponential equations to the data.

https://doi.org/10.7554/eLife.46124.003

Even in the absence of viomycin, dissociation of cognate TC is much slower (Figure 2A) than the forward rate constant of GTP hydrolysis (Figure 1A). This indicates that the frequency of cognate tRNA rejection from the state probed by these experiments is very small. However, the fact that cognate TCs are frequently rejected by the ribosome (Geggier et al., 2010; Johansson et al., 2012; Zhang et al., 2015; Zhang et al., 2016) suggests the existence of an early initial binding state from which TC rapidly dissociates, as suggested previously (Geggier et al., 2010; Gromadski and Rodnina, 2004; Pape et al., 1998). Together, the decrease in the cognate dissociation rate with increasing viomycin concentration (Figure 2B) and the lack of an effect of viomycin on the cognate kinetic efficiency (k_cat/K_m) observed above (Figure 1B and D) implies that viomycin does not stabilize this initial binding state. The drug might, however, further stabilize a late binding state from which cognate TC continues to GTP hydrolysis with high probability whether or not the ribosome is viomycin bound. A correspondingly large viomycin-dependent stabilization of near-cognate TC in the very same late binding state would readily explain the viomycin-induced increase in kinetic efficiency of the near-cognate reaction (Figure 2) and the decrease in dissociation rate of TC in the cognate reaction (Figure 2A) (Carter et al., 2000).

A kinetic model for inhibition of translational fidelity by viomycin

As shown above, viomycin binding stabilizes both cognate and near-cognate TC on the ribosome but increases the kinetic efficiency only for near-cognate reactions, even though cognate TCs are frequently rejected by the ribosome. These observations can be accounted for by the existence of an initial binding state where any TC lacks codon∙anticodon interaction (Figure 3) in accordance with previous work on initial selection (Geggier et al., 2010; Gromadski and Rodnina, 2004; Loveland et al., 2017; Pape et al., 1998; Pavlov and Ehrenberg, 2018; Zhang et al., 2018). In this case, cognate and near-cognate TCs have equal probability of dissociating from the ribosome rather than proceeding to formation of codon·anticodon contact in the decoding site and subsequent activation of the monitoring bases. In these latter states, near-cognate tRNA has high probability of moving backward to the preceding state while cognate tRNA has high probability of moving forward to the upcoming state. Viomycin binds to the state with activated monitoring bases, and when this happens any TC present in the A site is prevented from moving backwards, eventually leading to GTP hydrolysis by EF-Tu and subsequent peptide bond formation with 100% probability. The viomycin dependence of $k_{c a t} / K_{M}$ for GTP hydrolysis in such a mechanism is given by (supplementary methods):

{⟮ k_{c a t} / K_{M} ⟯}^{c, n c} = \frac{k_{1}}{1 + \frac{q_{2}}{k_{2}} ⟮ 1 + \frac{q_{3}^{c, n c} q_{4}^{c, n c}}{k_{3} (k_{4} + k_{V} [V])} ⟯}

which for cognate substrates simplifies to (supplementary methods):

{(\frac{k_{c a t}}{K_{M}})}^{c} = \frac{k_{1}}{1 + \frac{q_{2}}{k_{2}}}

Here $k_{1}$ and $q_{2}$ are the rate constants for ternary complex association to and dissociation from the initial binding state C₂. Parameters $k_{2}$ and $q_{3}^{c, n c}$ are the rate constants for entry into and return from the first codon recognition state, C₃. Parameters $k_{3}$ and $q_{4}^{c, n c}$ are the rate constants for entry into and return from the second codon recognition state, C₄ where k₄ is the rate constant for GTP hydrolysis by EF-Tu (Figure 3). The suffixes c and nc denote parameters that vary between cognate and near-cognate reactions, respectively. With a cognate codon·anticodon interaction $q_{3}^{c} q_{4}^{c} / (k_{3} k_{4})$ is expected to be much smaller than one, due to small backward and large forward rate constants (Pavlov and Ehrenberg, 2018; Zhang et al., 2018), which is what gives rise to the simplified expression for the $k_{c a t} / K_{M}$ (Equation 2). With a near-cognate codon·anticodon interaction $q_{3}^{n c} q_{4}^{n c} / (k_{3} k_{4})$ is expected to be much larger than one, due to comparatively large backward rate constants (Pavlov and Ehrenberg, 2018; Zhang et al., 2018). This leads to much larger $k_{c a t} / K_{M}$ for cognate than for near-cognate reactions and thereby high accuracy. It also explains why the cognate $k_{c a t} / K_{M}$ is insensitive to viomycin while the near-cognate $k_{c a t} / K_{M}$ increases sharply with increasing drug concentration leading to sharply decreasing accuracy.

Figure 3

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Kinetic model for viomycin action during tRNA selection.

TC binds to a ribosome with an empty A site with rate constant k₁to form a viomycin-insensitive initial binding complex where the codon·anticodon interaction is not yet established. From this state either the TC dissociates with rate constant q₂or the ribosome proceeds along the selection pathway, with rate constant k₂to codon anticodon contact. From this state the ribosome can either return to the initial binding state with rate constantq₃or proceed to a viomycin-sensitive state, with rate constant k₃, where the codon·anticodon interaction is monitored by the activated monitoring bases A1492 and A1493. In this state three events can take place; the ribosome can return to the previous state with rate constant q₄, the ribosome can proceed with hydrolysis of EF-Tu bound GTP with rate constant k₄or viomycin can bind with rate constant *k_vio*. After GTP hydrolysis viomycin-free ribosomes can either form a peptide bond with rate constant k₅or reject the tRNA in the A site with rate constant *k_F*. Viomycin-bound ribosomes are unable to reject the tRNA present in the A site and therefore will always proceed with GTP hydrolysis and peptide bond formation regardless of the nature of the codon·anticodon interaction.

https://doi.org/10.7554/eLife.46124.004

It follows from Equation 1 that in the presence of viomycin the normalized accuracy, A, for initial codon selection, defined as the ratio between cognate and near-cognate $k_{c a t} / K_{M}$ - values for GTP hydrolysis, is approximated by:

A = \frac{1 + \frac{q_{2}}{k_{2}} (1 + \frac{q_{3}^{n c} q_{4}^{n c}}{k_{3} (k_{4} + k_{V} [V])})}{1 + \frac{q_{2}}{k_{2}}}

It is clear from Equation 3 that the value of A depends on the ratio between the selective back reaction product $q_{3}^{n c} q_{4}^{n c}$ and the non-selective forward rate constant k₃ multiplied by a non-selective total forward rate constant, $k_{4} + k_{V} [V]$ , leading directly or via viomycin binding to GTP hydrolysis. As explained further in the Discussion and supplementary materials, we have assumed rate constants $k_{4}$ and $k_{V}$ to be the same in cognate and near-cognate cases. This is supported by crystal (Demeshkina et al., 2012; Demeshkina et al., 2013) and cryo-EM (Fislage et al., 2018; Loveland et al., 2017) structures showing virtually identical geometries for cognate and near-cognate codon·anticodon interactions as well as kinetics data (Zhang et al., 2018). It follows directly from Equation 3 that as long as $k_{V} [V] > > k_{4}$ we can write (Geggier et al., 2010; Gromadski and Rodnina, 2004; Johansson et al., 2008; Pape et al., 1998; Wohlgemuth et al., 2010) (supplementary methods):

{(k_{c a t} / K_{M})}^{n c} = {(k_{c a t} / K_{M})}^{c} \cdot \frac{[V]}{[V] + K_{I}^{n c}},

where $K_{I}^{n c}$ is the viomycin concentration at which the accuracy, A, has decreased to just two:

K_{I}^{n c} = \frac{q_{2}}{q_{2} + k_{2}} \cdot \frac{q_{3}^{n c} q_{4}^{n c}}{k_{3} k_{V}}

By fitting of Equation 4 to the experimental data points in Figure 1B and Figure 1D the $K_{I}$ value for ${tRNA}_{GAA}^{Phe}$ reading the near-cognate codon CUC was estimated as (3.1 ± 0.2) mM from GTP hydrolysis (Figure 1B) and as (3.3 ± 0.1) mM from dipeptide formation (Figure 1D). While these concentrations may appear high, it should be noted that at a viomycin concentration equal to $K_{I}$ the near cognate k_cat/K_m parameter is half that of the cognate one meaning that accuracy has been reduced to only 2. The ribosome would be unable to produce functional proteins even at far lower drug concentrations. Another type of $K_{I}$ value can be estimated for the cognate reaction from the chase experiment data in Figure 2B by linear fitting of the following expression (supplementary methods):

τ_{d i s s} = τ_{d i s s}^{0} + \frac{1}{q_{V}} (1 + \frac{[V]}{K_{I}^{c}}),

Here, the first term, $τ_{d i s s}^{0}$ , is a contribution to the mean time of TC dissociation that remains unaltered as the fraction of viomycin-bound ribosomes increases from zero to 100%. Parameter $q_{V}$ is the rate constant for viomycin dissociation from the ribosome and $K_{I}^{c}$ is the viomycin concentration at which the rate of viomycin rebinding to a ribosome with bound TC is equal to the rate of TC dissociation when unhindered by rebinding of viomycin. Note that all ribosomes are assumed to be viomycin bound in Equation 6. It follows that $K_{I}^{c}$ is given by (supplementary material)

K_{I}^{c} = \frac{q_{2}}{q_{2} + k_{2}} \cdot \frac{q_{3}^{c} q_{4}^{c}}{k_{3} k_{V}}

This gives a $K_{I}^{c}$ value for ${tRNA}_{GAA}^{Phe}$ reading the cognate codon UUC of (9.4 ± 3.3) µM.

From these expressions, it can be seen that $K_{I}$ increases when the compounded back rate constant for rejection of tRNA from the codon recognition states, $q_{3}^{c / n c} q_{4}^{c / n c}$ , increases. This is because larger values of $q_{3}^{c / n c} q_{4}^{c / n c}$ leave a smaller time window for viomycin to bind before the tRNA is rejected. $K_{I}$ decreases when the ratio $q_{2} / k_{2}$ decreases. Small values of this ratio mean that each time a TC returns to the non-selective initial binding state it has a larger probability to return to the codon-selective states for GTPase activation, affording viomycin multiple chances to bind. Note that $K_{I}$ is completely insensitive to the rate constant for GTP hydrolysis k₄.

The viomycin sensitivity of a mismatched codon·anticodon pair correlates strongly with the accuracy of initial codon selection

The model presented above predicts that viomycin sensitivity ( $K_{I}^{n c}$ ) for any codon·anticodon pair depends on the accuracy of initial codon selection as defined by the product of the near-cognate back reaction rate constants $q_{3}^{n c}$ and $q_{4}^{n c}$ (Equations 4 and 5) in the absence as well as presence of viomycin. For different codon·anticodon pairs initial codon selectivity varies over more than two orders of magnitude (Johansson et al., 2012; Zhang et al., 2015) implying that viomycin sensitivity could also vary significantly from pair to pair. To test this prediction, we measured how the $k_{c a t} / K_{M}$ of dipeptide formation varied with viomycin concentration for ${t R N A}_{G A A}^{P h e}$ reading three additional near-cognate codons (Figure 4A); AUC, UAC or UUA (the underlined base differs from the cognate codon UUC). We quantified the viomycin sensitivity of each codon by estimating its $K_{I}$ value by fitting of Equation 4 to plots of $k_{c a t} / K_{M}$ versus viomycin concentration (Figure 4B). We also estimated the accuracies of initial selection by measuring $k_{c a t} / K_{M}$ for GTP hydrolysis with the three codons in the absence of viomycin. Both sets of experiments were carried out exactly as described above. This gave $K_{I}$ values of 7 ± 0.3 mM for AUC, 29.0 ± 2.7 mM for UAC and 3.25 ± 0.20 mM for UUA and accuracies of initial selection of 2400 ± 120 for AUC, 6400 ± 670 for UAC and 580 ± 47 for UUA.

Figure 4

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Correlation between initial selection accuracy and viomycin sensitivity for four codon∙anticodon pairs.

(A) Time courses of f[³H]Met-Phe dipeptide formation for 1 µM Phe-tRNA^Phe_GAAcontaining TCs reacting with 70S ribosomes, displaying either a cognate UUC codon or one of the near-cognate codons CUC, AUC, UAC or UUAin the A site (the underlined base differs from the cognate codon), in the presence of 250 µM viomycin. Solid lines represent fits of single exponential equations to the data. (B) of dipeptide formation by Phe-tRNA^Phe_GAAcontaining TCs reacting with 70S ribosomes, displaying the near-cognate codons CUC, AUC, UAC or UUAin the A site, at varying concentrations of viomycin. Solid lines represent fits of Equation 3 to the data. (C) The accuracy of initial selection for Phe-tRNA^Phecontaining TCs reading the indicated codons plotted against the concentration of viomycin required to increase the efficiency of each near-cognate reaction to half that of the cognate reaction ( ). The dotted line is a linear regression of the data illustrating the correlation predicted by Equation 2. All error bars represent SEM.

https://doi.org/10.7554/eLife.46124.005

Plotting the accuracies of initial selection versus the $K_{I}$ values shows a clear correlation between the two (Figure 4C). This indicates that differences in viomycin sensitivity and accuracy of initial selection between different codon·anticodon pairs depend on differences in the same elemental rate constants. This is in line with the hypothesis that the rate constant $k_{4}$ is neutral to the cognate or near-cognate nature of the codon∙anticodon interaction and that accuracy only varies with the product $q_{3}^{n c} q_{4}^{n c}$ (Equation 3) (Geggier et al., 2010; Thompson, 1988; Gromadski and Rodnina, 2004; Pape, 1999).

A model to quantify viomycin-induced translational errors

We can now construct a model to estimate the frequency of extra missense errors induced by viomycin during translation. The probability that a given tRNA is trapped by viomycin on a ribosome displaying a given codon in the A site will depend on the intracellular concentration of that tRNA species as well as on how long it remains bound in the ribosomal A site during initial selection. This leads to the following expression for the probability that a missense error is caused by viomycin (supplementary methods):

P (E) = \frac{1}{1 + \frac{[T_{3}^{c}]}{\sum_{k} [T_{3}]_{k}^{n c} \frac{[V i o]}{[V i o] + K_{I k}}}}

Here, ${[T_{3}]}_{k}^{n c}$ is the concentration of near- or non-cognate TC species $k$ , ${[T_{3}]}^{c}$ is the cognate TC concentration and $K_{I k}$ is the $K_{I}$ value for tRNA of type $k$ reading the codon in the A site. As an example, from our in vitro experiments; 80 ± 8 nM viomycin is required to double the rate at which tRNA^Phe reads the codon CUC; this would roughly double the error rate assuming that there were equal concentrations of UUC and CUC displaying ribosomes in the reaction mixture.

Discussion

Based on the results presented above, we have constructed a kinetic model for how viomycin reduces the fidelity of mRNA decoding (Figure 3). We also show that the loss of translational fidelity due to viomycin is to a good approximation governed by a single kinetic parameter ( $K_{I}^{n c}$ ) for each codon·anticodon pair, and we have precisely determined its value for four such pairs. In this model, when a ternary complex first binds to the ribosome the codon·anticodon interaction is not yet established, the ribosome is not yet sensitive to viomycin and the monitoring bases are inactive. Subsequent establishment of codon·anticodon interaction and activation of the monitoring bases then leads to a highly selective ribosomal state to which viomycin can bind. The viomycin sensitivity of a ribosome with a given codon·anticodon pair in the A site is defined by the $K_{I}^{n c}$ -value, which depends on how much time the ribosome spends in this viomycin-sensitive ‘codon recognition’ state before TC dissociates (Equation 5). Viomycin binding to this state effectively traps the tRNA present in the A site, abolishing the ability of the ribosome to discriminate between cognate and non-cognate tRNAs in both initial selection and proofreading selection, committing the viomycin-bound ribosome to GTP hydrolysis and peptide bond formation with 100% probability.

It has been suggested that most of the high accuracy of translation is achieved through larger forward rate constants of GTP hydrolysis and tRNA accommodation for cognate than for near- and non-cognate substrates (Gromadski and Rodnina, 2004; Pape, 1999). Such a mechanism would imply that a large part of the variation in the accuracy of initial selection between different mismatched codon·anticodon pairs comes from variation of the rate of GTP hydrolysis rather than from variation of the tRNA rejection rate. More recently, high -resolution ribosome structures from crystallography (Demeshkina et al., 2012; Demeshkina et al., 2013) and cryo-EM (Fislage et al., 2018; Loveland et al., 2017) have revealed that cognate and near-cognate codon-anticodon complexes from tRNAs (Demeshkina et al., 2012; Demeshkina et al., 2013) or TCs (Loveland et al., 2017) have very similar structures. This suggests the existence of a highly selective state in which cognate and near-cognate TCs have the same orientation in the A/T state and, by inference, the same rate constant for GTP hydrolysis. This suggestion of a codon·anticodon insensitive rate constant k₄ is fully compatible with earlier kinetics results showing that the maximal rate of GTP hydrolysis (k_cat) is lower in near-cognate than cognate cases and becomes equal upon addition of aminoglycosides (Pape, 1999; Pavlov and Ehrenberg, 2018; Zhang et al., 2018). We suggest that in those earlier studies the increase in the Michaelis-Menten parameter k_cat, due to decreasing back reaction rate constants on drug addition, was instead mistakenly interpreted (Gromadski and Rodnina, 2004; Pape, 1999) as an increase of the catalytic rate constant, k₄, for GTP hydrolysis (Pavlov and Ehrenberg, 2018; Zhang et al., 2018).

Furthermore, our observation of a strong correlation between the accuracy of initial codon selection in the absence of viomycin and the viomycin sensitivity ( $K_{I}^{n c}$ ) (Figure 4C) is fully in line with the present hypothesis of a codon-anticodon insensitive rate constant for GTP hydrolysis. This type of correlation requires that virtually all the variation in accuracy between different codon·anticodon pairs comes from variation of tRNA rejection rates rather than from variation of GTP hydrolysis rates. If all non-cognate tRNAs remained on the ribosome for approximately the same amount of time and accuracy was primarily determined by their propensity to trigger GTP hydrolysis, that is by variation in $k_{4}$ (Gromadski and Rodnina, 2004), we would observe approximately the same $K_{I}^{n c}$ for all codon·anticodon pairs (Equation 5), which is not the case (Figure 4C).

In all available structures of viomycin-bound ribosomes the drug is positioned between rRNA helices h44 and H69 in the space vacated by the bases A1492 and A1493 when they flip out to interact with the codon·anticodon minihelix (Brilot et al., 2013; Pulk and Cate, 2013; Stanley et al., 2010; Zhou et al., 2012). Of these structures two contain an A-site tRNA (Brilot et al., 2013; Stanley et al., 2010) and in both cases it is a cognate tRNA; leaving open the question of how viomycin can bind rapidly to a ribosome with a non-cognate tRNA where A1492 and A1493 are thought to occupy the drug binding site (Carter et al., 2000). Rapid binding of viomycin to non-cognate ribosome·tRNA complexes is explained by recent observations that A1492 and A1493 flip out after initial binding of a tRNA to the A site regardless of Watson-Crick base pairing between the codon and the anticodon (Loveland et al., 2017). The less a given codon·anticodon helix can be stabilized by interactions with A1492 and A1493 the less energetically favorable the flipped-out conformation becomes. Thus, the more easily a tRNA can be rejected by the ribosome the less time A1492 and A1493 spend in their flipped-out conformation. This link between the accuracy and the length of the time window during which the viomycin binding site is open explains our observed correlation between accuracy and viomycin sensitivity. Thus, it is likely that the bases A1492 and A1493 rapidly fluctuate between their active flipped-out and inactive flipped-in conformations when any tRNA is present in the A site. Such a model has been suggested previously from studies of A-site dynamics in the absence of tRNA (Fourmy et al., 1998; Sanbonmatsu, 2006; Vaiana and Sanbonmatsu, 2009) and recently based on structural studies of tRNA selection by both mammalian and bacterial ribosomes (Fislage et al., 2018; Loveland et al., 2017; Shao et al., 2016). In particular, Loveland et al. (2017) shows that flipping-out of A1492 and A1493 happens early in decoding for both cognate and near-cognate tRNAs, binding of viomycin would then force A1492 and A1493 to remain in their flipped-out positions, leading to activation of G530, followed by closure of the 30S subunit and GTP hydrolysis. The complete absence of proofreading selection by viomycin-bound ribosomes further implies that the A1492 and A1493 play a role also in this process and that proofreading may be mediated by the same conformational changes of the decoding center as initial selection.

Equation 6 provides a model to evaluate the probability for a given viomycin-induced missense error at any codon. To fully evaluate this expression for the situation in a living cell, it is necessary to know the concentration of all tRNA species as well as the $K_{I}^{n c}$ values for all codon·anticodon pairs. However, some conclusions can be drawn even without this information. The viomycin-induced error frequency is large when the concentration of cognate tRNA is small and when there is a high concentration of near- or non-cognate tRNAs that are not efficiently discriminated against during initial selection. These are the same conditions that cause naturally occurring translational error hot-spots, implying that in the cell viomycin primarily acts to enhance such pre-existing hot-spots. Further, since proofreading selection is completely disabled on viomycin-bound ribosomes, it is unable to carry out its suggested function in neutralizing error hot-spots in initial selection (Zhang et al., 2016). This means that viomycin will alter not just the overall frequency of translational errors but also their distribution.

The $K_{I}^{n c}$ values estimated here are remarkably large considering how little viomycin is required to significantly reduce the rate of translocation (Holm et al., 2016) but direct comparison of the error-induction and translocation inhibition effects of viomycin is difficult as it is largely unknown how changes in the translational error rate affect bacterial growth rate. From the available data (Hughes, 1991), it seems that small changes in translational fidelity cause significantly smaller changes in growth rate than what is caused by comparable changes in translation speed. Given the parameter estimates in this and our previous study on translocation inhibition by viomycin (Holm et al., 2016), the error-inducing effect of the drug is likely responsible for only a small fraction of its antimicrobial activity under typical laboratory conditions, but may be more important in the clinical setting. The clinical target of the tuberactinomycins, the slow growing M. tuberculosis, normally maintains a smaller number of ribosomes per cell compared to faster growing bacteria (Cox, 2003). It could therefore potentially significantly reduce the effectiveness of translation speed inhibition, but not inhibition of translational accuracy, by overproduction of ribosomes (Dennis, 1976; Feldman et al., 2010; Maitra and Dill, 2016).

Antibiotics of the aminoglycoside class bind to the ribosome in a site that partially overlaps with that of viomycin (Carter et al., 2000; Stanley et al., 2010), suggesting that aminoglycosides and viomycin have overlapping modes of action. The detailed effects of three types of aminoglycosides on the accuracy of tRNA selection were recently investigated in a study (Zhang et al., 2018) which, together with the present study, clarifies differences and similarities of the modes of action of these two groups of antibiotics. Unlike viomycin, aminoglycosides bind to the ribosome with high affinity independently of the presence of an A-site-bound tRNA or ternary complex (Feldman et al., 2010; Pape et al., 2000; Zhang et al., 2018). Like viomycin, aminoglycosides alter the equilibrium between the active and inactive conformations of the monitoring bases A1492 and A1493, although the aminoglycoside-induced equilibrium shift is much smaller (Fislage et al., 2018; Zhang et al., 2018) than that of viomycin. Thus, the modes of action of these two classes of drugs are structurally similar in that they both force the ribosome into a state where the A-site tRNA is stabilized by activation of the monitoring bases. At the same time, their modes of action are kinetically distinct, since the two drugs bind to the ribosome during different stages of the ternary complex selection process and viomycin must be present at a much higher concentration for effective error induction than an aminoglycoside.

In summary, we have provided a quantitative kinetic model for the error-inducing effect of viomycin which together with our previous study on translocation inhibition (Holm et al., 2016) covers both known functions of the tuberactinomycin antibiotics. The model for initial selection of tRNA by the ribosome and the function of the monitoring bases suggested by our results is strongly supported by recent structural studies (Loveland et al., 2017; Shao et al., 2016) and calls into question prevailing ideas of how the high accuracy of translation is achieved. Our models and methods can be used to characterize the antimicrobial mechanisms of other tuberactinomycins and potential new tuberactinomycin derivatives and to understand the mechanisms of tuberactinomycin resistance mutations, which is highly relevant in terms of treatment of tuberculosis and related diseases.

Materials and methods

Buffers and reagents

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All experiments were performed at 37°C in HEPES-polymix buffer (95 mM KCl, 5 mM NH₄Cl, 0.5 mM CaCl₂, 8 mM putrescine, 1 mM spermidine, 5 mM Mg(OAc)₂, 1 mM dithioerythritol and 5 mM HEPES pH 7.5). All reaction mixes contained 10 mM phosphoenolpyruvate (PEP), 1 µg/ml pyruvate kinase (PK) and 0.1 µg/ml myokinase (MK). His-tagged initiation factors IF1, IF2 and IF3, elongation factor Ts, and phenylalanine and leucine aminoacyl tRNA-synthetases were purified using nickel-affinity chromatography (HisTrap GE Healthcare). Wild-type elongation factor Tu was prepared as in Ehrenberg et al. (1990). All protein concentrations were determined using the Bradford assay. Ribosomes (E. coli MRE600) and f[³H]Met-tRNA^fMet were prepared according to Antoun et al. (2004); ribosome concentration was determined spectrophotometrically. XR7 mRNAs with coding sequences AUG-UUC, AUG-CUC, AUG-UAC and AUG-UUA were prepared as in Borg and Ehrenberg (2015), see theGV Appendix I for full mRNA sequences. tRNA^Phe was prepared as in Holm et al. (2016). [³H]Met and [³H]GTP were from Perkin-Elmer, viomycin was from USP, all other chemicals were from either Merck or Sigma-Aldrich.

Construction and purification of EF-Tu^H84A

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The wild type tufA gene from E. coli Mg1655 was cloned in the pET21b vector with a C-terminal hexahistidine tag. Using this construct, the Histidine at position 84 was changed to Alanine by following the standard protocol from the QIAGEN site directed mutagenesis kit. Successful mutation was confirmed by DNA sequencing. His-tagged EF-Tu^H84A was overexpressed in E. coli BL21(DE3) and purified using nickel-affinity chromatography (HisTrap GE Healthcare). The identity and purity of the H84A mutant protein was confirmed by mass spectrometry.

GTP-hydrolysis experiments

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Two mixtures were prepared. The ribosome mixture contained 70S ribosomes (1.0–2.0 µM), IF1, IF2 and IF3 (2 µM each), fMet-tRNA^fMet(1.5–3.0 µM), mRNA (3 µM), GTP (1 mM) and ATP (1 mM). The TC mixture contained EF-Tu (0.3–0.6 µM), phenylalanine (200 µM), PheRS (0.5 µM), tRNA^Phe (2 µM), viomycin (0–2000 µM), [³H]GTP (0.3–0.6 µM) and ATP (2 mM). After 15 min incubation at 37°C, equal volumes of the two mixes were rapidly mixed and the reaction quenched at different time points with formic acid (17% final concentration) using a quench-flow instrument (RQF-3 KinTek corp.). After quenching, the samples were centrifuged at 20,800 g. The supernatant, containing the [³H]GTP and [³H]GDP was analyzed by anion exchange chromatography with on-line scintillation counting (β-RAM model 4 IN/US systems). A Mono-Q GL column (GE Healthcare) was used and the mobile phase was a multistep gradient of 0–2 M NaCl in 20 mM Tris (pH 7.5).

Dipeptide formation experiments

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Two mixtures were prepared. The ribosome mixture contained 70S ribosomes (0.5 µM), IF1, IF2 and IF3 (1 µM each), f[³H]Met-tRNA^fMet (1 µM), mRNA (2 µM), GTP (1 mM) and ATP (1 mM). The TC mixture contained EF-Tu (1–10 µM), EF-Ts (1 µM), phenylalanine (200 µM), PheRS (0.5 µM), tRNA^Phe (12 µM), viomycin (0–2000 µM), GTP (1 mM) and ATP (1 mM). After 15 min incubation at 37°C, equal volumes of the two mixes were rapidly mixed and the reaction quenched at different time points with formic acid (17% final concentration) using a quench-flow instrument (RQF-3 KinTek corp.). After quenching, the samples were centrifuged at 20,800 g and the supernatant discarded. The pellet was dissolved in 165 µl 0.5 M KOH to cleave the peptides from the tRNA. After 10 min 13 µl of 100% formic acid was added, the samples were centrifuged at 20,800 g and the radioactive peptides in the supernatant were analyzed by RP-HPLC using a H₂O/MeOH/trifluoroacetic acid (58/42/0.1 by volume) mobile phase and a C-18 column (Merck) with on-line scintillation counting (β-RAM model 4 IN/US systems) to quantify the relative amounts of f[³H]Met and f[³H]Met-Phe.

EF-Tu^H84A chase experiments

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Three mixtures were prepared. The ribosome mixture contained 70S ribosomes (0.75 µM), IF1, IF2 and IF3 (1 µM each), f[³H]Met-tRNA^fMet (1 µM), mRNA (2 µM), GTP (1 mM) and ATP (1 mM). The first TC mixture contained EF-Tu^H84A (15 µM), phenylalanine (200 µM), PheRS (0.5 µM), tRNA^Phe (15 µM), viomycin (0–400 µM), GTP (1 mM) and ATP (1 mM). The second TC mixture contained EF-Tu (1.5 or 12 µM), EF-Ts (1 µM), phenylalanine (200 µM) or leucine (200 µM), PheRS (0.5 µM) or LeuRS (0.5 µM), tRNA^Phe (2 µM) or bulk tRNA of which 2 µM was tRNA^Leu2 and an additional 10 µM were other leucine tRNA isoacceptors, viomycin (0–600 µM), GTP (1 mM) and ATP (1 mM). All three mixes were incubated at 37°C for 15 min. During the experiment, one volume of the ribosome mixture was mixed with one volume of the first TC mixture, the resulting mixture was incubated for 5–10 s and then one volume of the second TC mixture was added. The reaction was quenched at different time points after the addition of the second TC mixture using formic acid (17% final). The samples were treated as the quench flow peptide samples above.

Data analysis and curve fitting

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Reaction rates were estimated by fitting of single exponential functions to the experimental time courses except for the GTP hydrolysis reactions without viomycin on near-cognate codons which were analyzed as in Johansson et al. (2012). $k_{c a t} / K_{M}$ values were estimated by fitting of the Michaelis-Menten equation to plots of reaction rates versus concentration. The linear regression in Figure 4C was based on the method in York et al. (2004). All curve-fittings were implemented in Matlab R2015b. For derivations of the equations used in the main text see Appendix I.

Appendix 1

mRNA sequences

Complete nucleotide sequences for the mRNA molecules used. The start codon and A-site codon are in bold.

UUC (cognate for tRNA^Phe)

5’-GAAUUCGGGCCCUUGUUAACAAUUAAGGAGGUAUUAAAUGUUCUCUAAUUGCAGAAAAAAAAAAAAAAAAAAAAA-3’

AUC (near-cognate, first position mismatch)

5’-GAAUUCGGGCCCUUGUUAACAAUUAAGGAGGUAUUAAAUGAUCUCUAAUUGCAGAAAAAAAAAAAAAAAAAAAAA-3’

CUC (near-cognate, first position mismatch)

5’-GAAUUCGGGCCCUUGUUAACAAUUAAGGAGGUAUUAAAUGCUCUCUAAUUGCAGAAAAAAAAAAAAAAAAAAAAA-3’

UAC (near-cognate, second position mismatch)

5’-GAAUUCGGGCCCUUGUUAACAAUUAAGGAGGUAUUAAAUGUACUCUAAUUGCAGAAAAAAAAAAAAAAAAAAAAA-3’

UUA (near-cognate, third position mismatch)

5’-GAAUUCGGGCCCUUGUUAACAAUUAAGGAGGUAUUAAAUGUUAUCUAAUUGCAGAAAAAAAAAAAAAAAAAAAAA-3’

Data analysis and curve fitting

Introduction to the four-step scheme for initial codon selection

A four-step scheme for viomycin action on initial codon selection by transfer RNA in ternary complex with EF-Tu and GTP may be formulated as (Loveland et al., 2017; Pavlov and Ehrenberg, 2018; Zhang et al., 2018):

\begin{array}{ll} R_{1} + T_{3} + V i o ⇌_{q_{2}}^{k_{1}} C_{2} + V i o ⇌_{q_{3}^{x}}^{k_{2}} C_{3} + V i o ⇌_{q_{4}^{x}}^{k_{3}} C_{4} + \overset{k_{4}}{\to} \\ (q_{V}) ↑↓ k_{V} [V] \\ C_{4} + \overset{k_{4 V}}{\to} \end{array}

R₁ is the ribosome with initiator tRNA in the P site and an empty A site programmed with a codon cognate (x=c) or near-cognate (x=nc) to the Phe-tRNA^Phe containing ternary complex, T3. In state C2, codon∙anticodon contact has yet to be established. In C3, there is codon-anticodon contact (Zhang et al., 2016) with partially inactive monitoring bases (Loveland et al., 2017) and an open 30S subunit. In C4, all monitoring bases (A1492, A1493 and G530) are active and the 30S subunit is closed (Fislage et al., 2018; Loveland et al., 2017; Zhang et al., 2018). Complex C3 moves to state C4 with rate constant k3. State C4 is subjected to GTP hydrolysis with rate constant, moves backward to state C3 with rate constant or moves to state C4V by binding to viomycin with compounded rate constant. State C4V is either subjected to GTP hydrolysis with rate constant, butmay return to state C4 through very slow viomycin dissociation. Therefore, viomycin dissociation from C4V is significant only in chase experiments with a GTPase-deficient mutant of EF-Tu, as described below and in Gromadski and Rodnina (2004) and Zhang et al. (2018). In scheme 1, binding of viomycin occurs only to complex C4, in line with the proposal that the monitoring bases are inactive (Zhang et al., 2016) or partially active in state C3 (Fislage et al., 2018; Loveland et al., 2017) (see also Discussion). To account for all present data, we also make the assumption that structures C₃ and C₄ are comparatively rapidly equilibrating (Pavlov and Ehrenberg, 2018) (see also Discussion), so that:

\begin{array}{ll} R_{1} + T_{3} + V i o ⇌_{q_{2}}^{k_{1}} C_{2} + V i o ⇌_{q_{3 4}^{x}}^{k_{2}} C_{34} + V i o \overset{k_{34}^{x}}{\to} \\ (q_{V}) ↑↓ k_{V}^{x} [V] \\ C_{4} + \overset{k_{4 V}}{\to} \end{array}

Compounded parameters are related to the primary parameters in Equation 1 through

q_{34}^{x} = q_{3}^{x} \cdot \frac{K_{34}^{x}}{K_{34}^{x} + 1}, k_{34}^{x} = k_{4} \cdot \frac{1}{K_{34}^{x} + 1}, k_{V}^{x} = k_{V} \cdot \frac{1}{K_{34}^{x} + 1}, K_{34}^{x} = \frac{q_{4}^{x}}{k_{3}}

To derive Michaelis-Menten parameters for scheme 2, we use the mean-time approach (Bilgin et al., 1992; Borg and Ehrenberg, 2015; Borg et al., 2016), as described below.

Derivation of k_cat/K_m-parameters in cognate and near-cognate cases

To derive the kcat/Km parameters for scheme 2, we use ordinary differential equations for the time evolution of probabilitiesand for ternary complex, T3, to be in ribosome bound states C2 and C34, respectively. The reaction starts in complex C2 (p2(0)=1) and continues until it ends by T3 dissociation from C2, GTP hydrolysis in viomycin binding to followed by GTP hydrolysis. We note that in this approximation viomycin binding to in the following step leads to GTP hydrolysis with 100% probability:

\begin{array}{ll} \frac{d p_{2}}{d t} & = - (k_{2} + q_{2}) p_{2} + q_{34}^{x} p_{34} \\ \frac{d p_{34}}{d t} & = k_{2} p_{2} - (k_{34}^{x} + k_{V}^{x} [V] + q_{34}^{x}) p_{34} \\ p_{2} (0) & = 1, p_{34} (0) = 0. \end{array}

Mean times for T3 being in state “i” are defined as (Bilgin et al., 1992; Borg and Ehrenberg, 2015; Borg et al., 2016):

τ_{i} = \int_{0}^{\infty} p i (t) d t; i = 2 o r 3 / 4

Integrating equation system Equation 4 from zero to infinite time, one obtains algebraic equations for the mean times:

\begin{array}{ll} 1 & = (k_{2} + q_{2}) τ_{2}^{x} - q_{34}^{x} τ_{34}^{x} \\ 0 & = k_{2} τ_{2}^{x} - (k_{34}^{x} + q_{34}^{x} + k_{V}^{x} [V]) τ_{34}^{x} \end{array}

The solution is

\begin{array}{ll} τ_{2}^{x} & = \frac{k_{34}^{x} + k_{V}^{x} [V] + q_{34}^{x}}{(q_{2} + k_{2}) (k_{34}^{x} + k_{V}^{x} [V]) + q_{2} q_{34}^{x}}, \\ τ_{34}^{x} & = \frac{k_{2}}{(q_{2} + k_{2}) (k_{34}^{x} + k_{V}^{x} [V]) + q_{2} q_{34}^{x}} \end{array}

In general, k_cat/K_m is defined by the association rate constant, k₁, multiplied by the probability, $p_{G T P}^{x}$ , that ternary complex is subjected to GTP hydrolysis rather than dissociation from the ribosome (Johansson et al., 2008):

{(\frac{k_{c a t}}{K_{m}})}^{x} = k_{1} p_{G T P}^{x} = k_{1} τ_{34}^{x} (k_{34}^{x} + [V] k_{V}^{x}) = \frac{k_{1}}{1 + a_{2} (1 + a_{34}^{x})}

where

\begin{array}{ll} a_{2} & = q_{2} / k_{2}, \\ a_{34}^{x} & = \frac{q_{34}^{x} K_{34}^{x}}{k_{34}^{x} + k_{V}^{x} [V]} = \frac{q_{3}^{x} q_{4}^{x}}{k_{3} (k_{4} + k_{V} [V])} \end{array}

In analogy with initial codon selection in the presence of aminoglycosides we define a ‘current’ accuracy, an ‘effective’ initial codon selection and an ‘intrinsic’ initial codon selection (Pavlov and Ehrenberg, 2018; Zhang et al., 2018). The ‘current’ accuracy, A([V]), for initial codon selection as function of the viomycin concentration, we define as:

A ([V]) = \frac{1 + a_{2} (1 + a_{34}^{n c})}{1 + a_{2} (1 + a_{34}^{c})} \approx \frac{a_{2}}{1 + a_{2}} a_{34}^{n c} = \frac{a_{2}}{1 + a_{2}} \frac{q_{3}^{x} q_{4}^{x}}{k_{3} (k_{4} + k_{V} [V])}

The ‘effective’ initial codon selection as function of the viomycin concentration, d_ev, we define as:

\begin{array}{ll} d_{e v} = l i m A ([V])_{(a_{2} \to \infty)} = \frac{1 + a_{34}^{n c}}{1 + a_{34}^{c}} \approx a_{34}^{n c} = \frac{1}{k_{3}} \frac{q_{3}^{n c} q_{4}^{n c}}{k_{3} k_{4} + k_{V} [V]} \end{array}

The ‘intrinsic’ initial codon selection as function of the viomycin concentration, $D ([V])$ , we define as

D = \lim A {([V])}_{(a_{34}^{c} \to \infty)} = \frac{a_{34}^{n c}}{a_{34}^{c}} = \frac{q_{3}^{n c} q_{4}^{n c}}{q_{3}^{c} q_{4}^{c}}

Since $a_{34}^{n c}$ is inversely proportional to the viomycin concentration at high values it follows from Equation 11 that viomycin decreases the effective initial codon selection and from Equation 12 that the intrinsic initial codon selection remains unaltered at changing drug concentration. A proposition here is that viomycin and aminoglycosides act according to partially similar principles since also aminoglycoside addition reduces d_e and leaves D unaltered (Zhang et al., 2018). The fundamental assumption leading to Equations 11 and 12 is that rate constant k₄ in Equation 9 is the same in cognate and near-cognate cases and not much larger in the former cases as previously claimed (Gromadski and Rodnina, 2004; Pape, 1999). This point is further discussed in the next section below where the maximal rate of GTP hydrolysis (k_cat) has been defined by its proper average value (Pavlov and Ehrenberg, 2018). We note that when k_V[V]>>k₄ the approximation

{(\frac{k_{c a t}}{K_{m}})}^{n c} = {(\frac{k_{c a t}}{K_{m}})}^{c} \frac{[V]}{[V] + K_{I}},

where

K_{I} = \frac{a_{2}}{(1 + a_{2})} \frac{q_{3}^{n c} q_{4}^{n c}}{k_{V} k_{3}}

is valid. Under this condition of high viomycin concentration, the ratio ( $K_{I}^{n c 1} / K_{I}^{n c 2}$ ) between the K_I-values for two near-cognate reactions approximates the ratio ( $A^{n c 1} / A^{n c 2}$ ) between the A_I-values for the corresponding current accuracies when estimated in the absence of viomycin (Equation 10):

\frac{K_{I}^{n c 1}}{K_{I}^{n c 2}} = \frac{A^{n c 1}}{A^{n c 2}} = \frac{q_{3}^{n c 1} q_{4}^{n c 1}}{q_{3}^{n c 2} q_{4}^{n c 2}}

as seen experimentally. The point here is that if the forward rate constant for GTP hydrolysis, k₄, were different in the two cases, then the similarity between the K_I- and A-ratios in Equation 15 would be invalid.

Derivation of k_cat-parameters in cognate and near-cognate cases

In this section, we use mean time analysis to derive expressions for the k_cat-values of scheme (2). The relevant differential equations for the scheme in Equation 2 are in this case given by

\begin{array}{ll} \frac{d p_{2}}{d t} & = - k_{2} p_{2} + q_{34}^{x} p_{34} \\ \frac{d p_{34}}{d t} & = k_{2} p_{2} - (k_{34}^{x} + k_{V}^{x} [V] + q_{34}^{x}) p_{34} \\ \frac{d p_{4}}{d t} & = k_{V}^{x} [V] p_{34} - k_{4 V} p_{4} \\ p_{2} (0) & = 1, p_{34} (0) = 0, p_{4} (0) = 0. \end{array}

Integration from zero to infinite time leads to the following algebraic equations:

\begin{array}{ll} 1 & = k_{2} τ_{2} - q_{34}^{x} τ_{34} \\ 0 & = k_{2} τ_{2} - (q_{34}^{x} + k_{34}^{x} + k_{V}^{x} [V]) τ_{34} \\ 0 & = k_{V}^{x} [V] τ_{34} - k_{4 V} τ_{4} \end{array}

The solution is

\begin{array}{ll} τ_{2} & = \frac{1}{k_{2}} (1 + \frac{q_{3}^{x} q_{4}^{x} / k_{3}}{k_{4} + k_{V} [V]}), \\ τ_{34} & = \frac{1 + q_{4}^{x} / k_{3}}{k_{4} + k_{V} [V]}, \\ τ_{4 V} & = \frac{1}{k_{4 V}} \frac{k_{V} [V]}{k_{4} + k_{V} [V]} . \end{array}

The minimal time for GTP hydrolysis in ternary complex at saturating ribosome concentration, $τ$ , equal to the inverse of k_cat, corresponds to the sum of the times in Equation 18:

τ = 1 / k_{c a t} = τ_{2} + τ_{34} + τ_{4 V}

In cognate cases, the minimal reaction time is approximated by:

τ = \frac{1}{k_{c a t}} = τ_{2} + τ_{34} + τ_{4} = {k_{4 V} = k_{4}} = \frac{1}{k_{2}} + \frac{1}{k_{4}}

As expected it is seen that the cognate k_cat-parameter does not respond to viomycin addition.

In near cognate cases, the minimal reaction time is given by:

τ = \frac{1}{k_{c a t}} = {k_{4} = k_{4 V}} = \frac{1}{k_{2}} + \frac{1}{k_{4}} + \frac{1}{(k_{4} + k_{V} [V])} (\frac{q_{3}^{n c} q_{4}^{n c}}{k_{2} k_{3}} + \frac{q_{4}^{n c}}{k_{3}})

Since $\frac{q_{3}^{n c} q_{4}^{n c}}{k_{2} k_{3}} >> 1$ it follows that k_cat is much smaller in near-cognate than cognate cases and increases in proportion to increasing $k_{V} [V]$ when $k_{V} [V] >> 1$ >> in spite of uniform and constant $k_{4}$ -values. From this, we contend that the allegedly small near-cognate k₄-values estimated in the absence of drugs and their increase with aminoglycoside (Gromadski and Rodnina, 2004; Pape, 1999; Pape, 1999) or, as here, viomycin addition, reflect drug-induced variations in k_cat-values rather than k₄-values (Pavlov and Ehrenberg, 2018; Zhang et al., 2018).

Effect of viomycin on the rate of dissociation of GTPase deficient ternary complex

Here, we adapt scheme 2 above to the chase experiments described in the main text, where a pre-bound, GTPase deficient ternary complex, T_3m, containing the EF-Tu mutant (m) H84A is chased by a native ternary complex, T₃:

\begin{array}{ll} R_{1} + T_{3 m} + V i o \leftarrow_{q_{2}}^{} C_{2 m} + V i o ⇌_{q_{3 4}^{x}}^{k_{2}} C_{34 m} + V i o \\ (q_{V}) ↑↓ k_{V}^{x} [V] \\ C_{4 V m} \end{array}

$T_{3 m}$ is pre-bound to the ribosome in either one of the states $C_{2 m}$ , $C_{34 m}$ or $C_{4 V m}$ with probabilities $P_{2}^{x} (0)$ , $P_{34}^{x} (0)$ or $P_{4 V}^{x} (0)$ , respectively, as determined by the stability of each T₃-bound complex:

\begin{array}{ll} P_{2}^{x} (0) & = 1 / (1 + \frac{k_{2}}{q_{3}^{x}} \cdot \frac{1 + K_{34}^{x}}{K_{34}^{x}} + \frac{k_{2}}{q_{3}^{x}} \cdot \frac{k_{V} [V]}{q_{V} K_{34}^{x}}) = 1 / N \\ P_{34}^{x} (0) & = \frac{k_{2}}{q_{3}^{x}} \cdot \frac{1 + K_{34}^{x}}{K_{34}^{x}} / (1 + \frac{k_{2}}{q_{3}^{x}} \cdot \frac{1 + K_{34}^{x}}{K_{34}^{x}} + \frac{k_{2}}{q_{3}^{x}} \cdot \frac{k_{V} [V]}{q_{V} K_{34}^{x}}) = \frac{k_{2}}{q_{3}^{x}} \cdot \frac{1 + K_{34}^{x}}{K_{34}^{x}} / N \\ P_{4 V}^{x} (0) & = \frac{k_{2}}{q_{3}^{x}} \cdot \frac{k_{V} [V]}{q_{V} K_{34}^{x}} / (1 + \frac{k_{2}}{q_{3}^{x}} \cdot \frac{1 + K_{34}^{x}}{K_{34}^{x}} + \frac{k_{2}}{q_{3}^{x}} \cdot \frac{k_{V} [V]}{q_{V} K_{34}^{x}}) = \frac{k_{2}}{q_{3}^{x}} \cdot \frac{[V]}{K_{V} K_{34}^{x}} / N \end{array}

The primary and compounded parameters in Equation 22 are defined by Scheme one and Scheme 2, respectively, and are related to each other as shown in Equation 3. Here we have also defined the viomycin-binding constant as $K_{V} = q_{V} / [V]$ . The differential equations corresponding to the scheme in Equation 16 are:

\begin{array}{ll} \frac{d p_{2 m}}{d t} & = - (q_{2} + k_{2}) p_{2 m} + q_{34}^{x} p_{34 m} \\ \frac{d p_{34 m}}{d t} / & = - (k_{V}^{x} [V] + q_{34}^{x}) p_{34 m} + k_{2} p_{2 m} + q_{V} p_{4 V m} \\ \frac{d p_{4 V m}}{d t} / & = - q_{V} p_{4 V m} + k_{V}^{x} [V] p_{34 m} \end{array}

Integrating these equations with the initial conditions in Equation 22 from zero to infinite time lead to the following set of algebraic equations for the average times $τ_{4 V m}$ , $τ_{34 m}$ and $τ_{2 m}$ the system spends in states $C_{4 V m}$ , $C_{34 m}$ and $C_{2 m}$ , respectively:

\begin{array}{ll} P_{2 m} & = (q_{2} + k_{2}) τ_{2 m} - q_{34}^{x} τ_{34 m} \\ P_{34 m} & = (k_{V}^{x} [V] + q_{34}^{x}) τ_{34 m} - k_{2} τ_{2 m} - q_{V} τ_{4 V m} \\ P_{4 V m} & = q_{V} τ_{4 V m} - k_{V}^{x} [V] τ_{34 m} \end{array}

The general solution to the algebraic Equation 25 is:

τ_{2} = 1 / q_{2},

\begin{array}{ll} τ_{34} & = \frac{1}{q_{34}^{x}} (P_{34} (0) + P_{4 V} (0) + \frac{k_{2}}{q_{2}}), \\ τ_{4 V} & = \frac{1}{q_{V}} (P_{4 V} (0) + k_{V} [V] τ_{34}) \end{array}

Writing the initial conditions in the elementary parameters of the scheme in Equation 1 gives:

\begin{array}{ll} P_{4 V} (0) & = \frac{[V]}{K_{V}} \frac{k_{2} k_{3}}{q_{3}^{x} q_{4}^{x}} / N, \\ P_{34} (0) & = \frac{k_{2}}{q_{3}^{x}} \frac{k_{3} + q_{4}^{x}}{q_{4}^{x}} / N, \\ P_{2} (0) & = 1 / N \end{array}

When chasing a cognate ternary complex we expect ribosomal states $C_{34}$ and $C_{4 V}$ to dominate which leads to the following approximation:

τ_{d i s s}^{c} = \frac{k_{3} (k_{2} + q_{2})}{q_{2} q_{3}^{c} q_{4}^{c}} + \frac{1}{q_{V}} \frac{[V]}{[V] + K_{V}} + \frac{[V]}{K_{V}} \frac{k_{3} (k_{2} + q_{2})}{q_{2} q_{3}^{c} q_{4}^{c}}

The first term in Equation 28 is the chase time in the absence of viomycin. The second term is the dissociation time for viomycin multiplied by the probability that viomycin is ribosome bound at the beginning of the chase. The third term reflects the prolonged chase time that is due to rebinding of viomycin that dissociates during the chase before dissociation of ternary complex. In near-cognate cases, we expect the back reactions in the schemes in Equations 1 and 2 to be large, which leads to the following approximations for the average times in the different states:

τ_{d i s s} = τ_{2} + τ_{34} + τ_{4 V}

where

\begin{array}{ll} τ_{20} & = \frac{1}{q_{2}}, \\ τ_{34} & = \frac{1}{q_{3}^{n c}} (\frac{[V]}{K_{I} + [V]} + \frac{k_{2}}{q_{2}}) \\ τ_{4 V} & = \frac{1}{q_{V}} (\frac{[V]}{K_{I} + [V]} + k_{V} [V] [\frac{1}{q_{3}^{n c}} (\frac{[V]}{K_{I} + [V]} + \frac{k_{2}}{q_{2}})]) \end{array}

and

K_{I} = \frac{K_{V} q_{3}^{n c} q_{4}^{n c}}{k_{2} k_{3}}

The near-cognate chase experiment harbours several challenges. One is that dissociation of ternary complex from a viomycin-lacking ribosome is too fast to be measured with standard quench flow or stopped flow techniques. Another challenge is that the K_I-value in equation 30 is much larger than the K_V-value due to large near-cognate backward rate constants.

We note that the ratio between the K_I-value in Equation 10 above and $τ_{0}$ in Equation 26 gives

K_{I} \cdot τ_{0} \approx \frac{q_{2}}{(k_{2} + q_{2})} \frac{q_{3}^{n c} q_{4}^{n c}}{k_{V} k_{3}} \cdot \frac{1}{q_{2}} \frac{(k_{2} + q_{2}) k_{3}}{q_{3}^{c} q_{4}^{c}} = \frac{1}{k_{V}} \frac{q_{3}^{n c} q_{4}^{n c}}{q_{3}^{c} q_{4}^{c}}

It is clear that chase experiments in principle allows for estimations of k_V, q_V and $τ_{0}$ which allows for determination of the intrinsic selectivity $D = \frac{q_{3}^{n c} q_{4}^{n c}}{q_{3}^{c} q_{4}^{c}}$ in Equation 12 from the K_I-value in Equation 10 and $τ_{d i s s}$ in Equation 26.

Chase times and dissociation times in chase experiments

In experiments where a GTPase-deficient ternary complex, $T_{3 m}$ , is chased by a wild-type ternary complex, $T_{3,}$ , at a finite ratio between the free concentrations of $T_{3,}$ and $T_{3 m}$ the average chase time $τ_{c h a s e}$ is related to $τ_{d i s s}$ by a factor $p_{c h a s e}$ . This factor is the probability that dissociation of $T_{3 m}$ leads to a successful chase which in our experiments is signified by the peptidyl transfer reaction. Accordingly, $p_{c h a s e}$ is given by

p_{c h a s e} = \frac{[T_{3}] (k_{c a t} / K_{m})}{[T_{3}] (k_{c a t} / K_{m}) + [T_{3 m}] {(k_{c a t} / K_{m})}_{m}}

Probability $p_{c h}$ connects the experimentally measured chase time, $τ_{c h \exp}^{c}$ , through the relation

τ_{d i s s} = τ_{c h a s e} p_{c h a s e}

https://doi.org/10.7554/eLife.46124.007

Data availability

All data generated or analysed during this study are included in the manuscript.

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Decision letter

Alan G Hinnebusch

Reviewing Editor; Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States
James L Manley

Senior Editor; Columbia University, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

[Editors’ note: a previous version of this study was rejected after peer review, but the authors submitted for reconsideration. The first decision letter after peer review is shown below.]

Thank you for submitting your work entitled "Insights into the fidelity mechanism of mRNA decoding from characterization of viomycin induced miscoding in translation" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor, Alan Hinnebusch, and a Senior Editor. The reviewers have opted to remain anonymous.

Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife.

Summary:

This paper examines the mechanism of miscoding in translation by the drug viomycin, concluding that drug binding increases initial selection of the near-cognate tRNA complexes rather GTP hydrolysis by the near-cognate ternary complex. Based on the results obtained and various theoretical considerations, the paper also challenges two aspects of the current induced-fit model for tRNA selection accuracy, concluding that (i) accuracy in the tRNA selection phase of decoding is enforced primarily by increased rates of near-cognate tRNA dissociation resulting from a higher rate of the back reaction from the codon recognition state to the initial binding state, rather than from a reduced rate of GTP hydrolysis for non-cognate versus cognate tRNAs; and (ii) interactions of bases A1492/A1493 with the tRNA anticodon stem loop is not restricted to cognate tRNA, with the unstated implication that these interactions play no important role in monitoring codon:anticodon helices in the A-site. Using pre-steady-state kinetic analysis with a quench-flow apparatus, they measure the catalytic efficiency (k_cat/K_m) for cognate versus near-cognate codons in the A-site in the presence of increasing concentrations of viomycin, for both GTP hydrolysis by the EF-Tu-GTP-aatRNAPhe ternary complex in the step of initial selection prior to proof-reading and for peptide-bond formation following proof-reading. The results show no effect of viomycin on k_cat / K_m for cognate UUC, but a marked increase in k_cat / K_m for near-cognate CUC; which leads to a large reduction in accuracy, defined as the ratio of (k_cat / K_m)-cognate to (k_cat / K_m)-near-cognate. Rather than finding that the near-cognate codon exhibits a much smaller k_cat / K_mfor peptide bond formation versus GTP hydrolysis owing to proof-reading, they are identical in the presence of drug, which means that proof-reading is disabled by the drug. They conclude, reasonably, that drug binding increases initial selection of the near-cognate complexes and that all near-cognates that pass initial selection go on to form peptide bonds.

The authors construct a kinetic model for drug action based on a current model in which the initial binding of cognate and near-cognate occurs at the same rate and only after transition to a codon-recognition state is the near-cognate tRNA rejected, returning to the initial binding state if GTP hydrolysis does not occur first. They propose that viomycin binds to the codon-recognition state and traps the tRNA in the A-site, preventing dissociation to the initial binding state and allowing GTP hydrolysis to occur. This has little effect on cognate tRNA because the rate of conversion to the initial binding state is slow and/or GTP hydrolysis is fast; but it dramatically increases the efficiency for near-cognate by preventing conversion to the initial binding state. In principle the drug could also increase the rate of GTP hydrolysis by near-cognate, but they approximate the equations that give a complete theoretical description of the kinetics with an expression in which the drug concentration required to increase the efficiency of each near-cognate reaction to one-half that of the cognate reaction, Ki, is governed by only the forward and back rates of the conversion from initial binding to codon-recognition states, and is independent of the rate of GTP hydrolysis. They go on to repeat the previous experiment for three different near-cognate codons and find a linear relationship between accuracy, (k_cat / K_m)-cognate/(k_cat / K_m)-near-cognate and K_I for the four different near-cognates. They claim that this relationship indicates that the accuracy of initial selection for different near-cognates is determined primarily by different rates of tRNA rejection (the back reaction to initial binding state) rather than different rates of GTP hydrolysis in the manner proposed by the current model.

In the Discussion, they present the argument that, because viomycin binding to the A site requires that rRNA bases A1492/A1493 are in their "flipped out" conformation capable of interacting with the anticodon stem-loop of A site tRNA, that drug binding in the presence of a near-cognate codon requires that flipping out of the bases is not restricted to cognate codons, at odds with the recent model that these bases mediate an induced fit in the A site that triggers GTP hydrolysis. Thus, they propose that A1492/A1493 flipping out occurs dynamically when any tRNA is present in the A-site.

Summary of reviewer's comments:

There is agreement among the reviewers that your analysis of the mechanism of viomycin action has been well-designed and executed, and that it leads convincingly to the conclusion that drug binding increases initial selection of the near-cognate complexes, with attendant GTP hydrolysis, and that all near-cognates that pass initial selection go on to form peptide bonds owing to the fact that viomycin disables proofreading. In addition, it is considered significant that the inhibition of selection accuracy by the drug is in the millimolar range, whereas viomycin is known to inhibit tRNA translocation at low μM concentrations, implying that the main effect of viomycin on cellular translation is by inhibiting translocation and not by inducing decoding errors.

However, two of the reviewers object strongly to the second, more theoretical aspects of the paper where you argue from the linear relationship in Figure 3C that the accuracy of initial selection for different near-cognates is determined primarily by different rates of tRNA rejection rather than different rates of GTP hydrolysis-at odds with a leading model based on the work of Rodnina and colleagues. Both reviewers felt that this was too speculative, and that to argue convincingly against the prevailing model, it is necessary to directly measure ternary complex and/or aa-tRNA association/dissociation rates, in particular the dissociation rates of cognate and near-cognate tRNA, as you cannot currently determine whether viomycin slows down the dissociation of the ternary complex from the ribosome (as you conclude) or that viomycin accelerates GTP hydrolysis for near-cognates. It is difficult to follow the logic of your arguments, and it is questionable whether the approximation of the full set of equations required for a complete theoretical description of the kinetics you adopted in order to analyze the relationship between accuracy and K_I is valid. The reviewing editor also had difficulty following these arguments and was concerned that the logic used to claim that accuracy is determined primarily by different rates of tRNA rejection rather than GTP hydrolysis may be circular, as you have used an approximation of the parameter K_I in deriving equations 3-4 that doesn't include a term for GTP hydrolysis.

There was also objections to your second argument, that because viomycin binding requires A1492/A1493 to be in the flipped-out conformation, its binding to near-cognate/ribosome complexes necessitates that A1492/A1493 be flipped out for near-cognate as well as cognate tRNA-at odds with proposals that flipping out of A1492/A1493 is restricted to cognate tRNAs as a key step of the induced fit mechanism that accelerates GTP hydrolysis for cognate tRNAs. Your conclusion has been criticized because there is no crystal structure of ribosome/drug/near-cognate tRNA ternary complexes, and so the mode of viomycin binding in the presence of near-cognate tRNA is unknown; and also because there is no way to exclude that viomycin simply binds first and flips out the bases prior to tRNA binding. It was also noted in the discussion session that the available structural data do not assess the codon-recognition state, which is necessarily transient and distinct from the state probed by Ramakrishan and colleagues by stalling the ribosome with non-hydrolyzable GTP analogues or GTPase deficient mutants. Thus, we do not yet know the nature of the codon-anticodon interaction in the codon recognition state and viomycin's interactions with the ribosome and/or the codon-anticodon pair. Again, it was stated in the reviews that you should experimentally measure the conformational dynamics of A1492/A1493 during accommodation and viomycin binding to make mechanistic claims on this topic.

Finally, there was the criticism that extended into the discussion session among the reviewers, that you have failed to cite or discuss the previous study of Pape et al. (Rodnina) on the mechanistically similar antibiotic paromomycin, which involved separate measurements of the effect of paromomycin on GTP hydrolysis and aa-tRNA dissociation (measured using non-hydrolyzable GTP), leading to the conclusion that both steps are affected in the case of near-cognate aa-tRNA. It was objected that your study argues with that work without citing it, but with less experimental data (i.e. no tRNA dissociation analyses) to make the argument and using complex mathematical analyses based on questionable assumptions. While it was noted that there are reasons to question the conclusions of Pape et al., challenging them should be justified by the appropriate experimental data, such as measuring tRNA disassociation rates using a GTPase-deficient mutant of EF-Tu.

The reviewing editor originally recommended that the paper be sent out for review primarily because if your speculative arguments were judged to be compelling by expert reviewers, then they would have the impact of swaying the field towards accepting a revision of the tRNA selection mechanisms proposed by others in the field. However, with these speculations being strongly challenged by two of the three reviewers, it is now judged that the solid aspects of the paper concerning the mechanism of viomycin action and implications for its biological impact are not of sufficient general interest on their own for eLife.

Reviewer #2:

The manuscript by Holm et al. presents a clear kinetic description of the established miscoding impact of viomycin on tRNA selection (REF 5 and Wurmbach and Nierhaus Eur. J. Biochem 1983) using modern pre-steady state methods. A big picture message of the manuscript is that viomycin operates by preventing the ribosome from efficiently rejecting near-cognate tRNA at early stages of the selection process, which has larger implications regarding the fidelity mechanism. The authors' key conclusion from their finding is that viomycin impacts tRNA selection at a step prior to GTP hydrolysis and after conserved decoding site residues have contacted the mRNA-tRNA pair (i.e. during early steps of initial selection). This conclusion seems to be well supported by the data presented and fits with some, but not other, group's interpretations of the selection mechanism. Thus, the presented findings may have the broader impact of swaying the field towards accepting revision of the tRNA selection mechanisms proposed by Rodnina and colleagues that currently serves as the most widely accepted mechanistic framework. The experiments appear to have been rigorously performed, properly analyzed and kinetically modeled. The manuscript is well written and the findings appear to be appropriately interpreted in the context of existing literature. For these reasons, publication is recommended.

Reviewer #3:

Holm and Sanyal performed kinetic analysis of ribosome miscoding induced by the antibiotic viomycin. The authors measured the rate of GTP hydrolysis by EF-Tu and the rate of dipeptide formation to examine the effect of viomycin on initial tRNA selection and complete accommodation of tRNA to the A site, respectively. The authors found that accommodation of cognate tRNA is not affected by viomycin. By contrast, viomycin dramatically enhances rates of GTP hydrolysis and dipetide formation in the case of near-cognate tRNA. Kinetic data also suggest that viomycin completely abrogates proofreading step of tRNA selection. The experiments are well-executed. Observations made by the authors are of substantial interest to the protein synthesis community.

However, the authors went further and made a number of additional conclusions that are not related to experimental results in an obvious way. ("Viomycin binds to the ribosome during initial selection of tRNA, after binding of ternary complex but before GTP hydrolysis by EF-Tu". "In contrast to current ideas about 'induced-fit', accuracy in initial selection is achieved primarily by increase dissociation rates for near-cognate tRNAs rather than by decreased rates of GTP hydrolysis. Furthermore, the 'monitoring' bases A1492 and A1493 rapidly fluctuate between active and inactive conformations both when cognate and near-cognate tRNAs are present in the A site"). These conclusions are inferred from kinetic analysis of the authors' data. The authors acknowledge that they did not directly measure most of the rate constants required to obtain the complete kinetic description of tRNA accommodation. In particular, the authors did not directly measure the rate of tRNA disassociation of the codon recognition complex (the Rodnina's group used GTPase deficient mutant of EF-Tu to determine this rate constant experimentally). Hence, the authors' experiments cannot directly delineate whether viomycin slows down the dissociation of the EF-Tu ternary complex from the ribosome (as the authors conclude) or viomycin accelerates GTP hydrolysis. Furthermore, the authors did not directly examine the conformational dynamics of bases A1492 and A1493 of 16S rRNA during accommodation and viomycin binding. I could not completely follow the line of theoretical arguments that the authors used in support of their conclusions. I also could not fully understand the authors' justification for the "approximation" of complete kinetic description of the tRNA accommodation process to a simpler kinetic equation (equation 3) that the authors used. Hence, I might not be able to adequately judge the validity of main conclusions of the manuscript. Nevertheless, it seems to me that the authors' conclusions are made based on a number of assumptions that are not directly drawn from experimental results. Besides, the manuscript does not seem very accessible to a wide audience of readers and is rather addressed to a narrow group of scientists specialized in the kinetics of the tRNA accommodation process. I therefore doubt that this manuscript is suitable for publication in eLife.

Reviewer #4:

Holm and Sanyal employ pre-steady-state kinetics to study how the translation inhibitor viomycin affects the accuracy of aminoacyl-tRNA selection. The miscoding effect of viomycin has been reported before, e.g. by Marrero et al., 1980, also by Wurmbach and Nierhaus, 1982 (the authors do not cite the latter paper). The current study provides a more detailed assessment of miscoding of near-UUC codons by Phe-tRNAPhe. The authors find that the rates of GTP hydrolysis and dipeptide formation on near-cognate tRNA increase with the increasing concentrations of viomycin. The authors interpret that the concentration dependence reflects an equilibrium shift in the binding of near-cognate aminoacyl-tRNA to the A site. The authors find that the inhibition of selection accuracy (Ki) is in the millimolar range. This contrasts the effect of viomycin on tRNA translocation, which viomycin inhibits at low μM concentrations (Holms et al., 2016). As such, the study suggests that the main effect of viomycin on cellular translation is via the translocation step and not via decoding errors.

In addition to the conclusion about the mechanism of action of viomycin, the authors attempt to extend their findings to understand the mechanism of aa-tRNA decoding. Specifically, they ask whether the accuracy of selection is achieved via (1) "induced fit" of the ternary complex resulting in slow GTP hydrolysis on near-cognate aa-tRNA or (2) dissociation of near-cognate aa-tRNA. The authors also discuss the involvement of the decoding-center nucleotides A1492 and A1493 in tRNA selection. This part is speculative because the authors' findings do not directly report on tRNA association/dissociation or dynamics of the decoding center or even the binding mode (site and conformation) of viomycin in the presence of near-cognate tRNA. The rationalization of decoding accuracy in this work is not robust because it relies heavily on previous work, including crystal structures of near-cognate tRNAs or structures of viomycin-bound ribosomes. A mechanistic scheme is presented, in which viomycin binds following the ternary complex – but how was the order established, in which viomycin and the ternary complex bind? Surprisingly, the authors do not discuss a kinetic study of aa-tRNA miscoding by paromomycin (Pape, Wintermeyer and Rodnina, 2000), which addresses individual steps of ternary complex acceptance/rejection. That earlier work is particularly relevant because paromomycin' and viomycin's binding sites within h44 overlap and these antibiotics induce nearly identical rearrangements of A1492-A1493, so their mechanisms of tRNA miscoding are likely similar.

In summary, the detailed biochemical dissection of viomycin action on decoding in this work is interesting as it suggests that miscoding is not the primary mode of viomycin's antimicrobial action. However the implications for a general mechanism of translation accuracy are indirect and would require substantial additional work (e.g. direct measurements of ternary complex and/or aa-tRNA association/dissociation rates).

[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration. After a subsequent appeal, the manuscript was accepted for publication.]

Thank you for submitting your work entitled "The mechanism of error induction by the antibiotic viomycin provides insight into the fidelity mechanism of translation" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The reviewers have opted to remain anonymous.

Although both reviewers felt that the work was well done, noting improvements over the original version of the paper, they also agreed that the findings add only incrementally to knowledge regarding the mechanisms of viomycin action and decoding in relation to previously published biochemical and structural studies on these topics. As such, neither felt that the paper satisfies the journal's standards for publishing work of the greatest importance to the field, and indicated that it would be of interest to only a very small group of specialists.

Reviewer #1:

Holm et al. performed kinetic analysis of ribosome miscoding induced by antibiotic viomycin. This manuscript is much improved in comparison to the earlier version of the paper. The manuscript is well written. Experiments and data analysis were meticulously done. The authors' conclusions are compelling. Important experiments with GTPase deficient mutant of EF-Tu that were missing from the previous version of the paper are now included. However, my enthusiasm is dampened because the manuscript does not offer many new insights regarding the mechanism of viomycin action or the mechanism of decoding. Most of authors' observations are consistent with previously published biochemical and structural studies of decoding/viomycin including authors' own works (e.g. Holm et al., 2016, Zhang et al., 2018). Furthermore, the authors conclude (and state this in the Abstract of manuscript) that viomycin-induced miscoding is not the cause of cell growth inhibition by viomycin. This conclusion further diminishes biological and medicinal significance of the study.

Reviewer #2:

The authors biochemically dissect how viomycin induces miscoding. They demonstrate that viomycin increases the rates of (1) EF-Tu-catalyzed GTP hydrolysis and (2) peptide bond formation for the near-cognate ternary complexes. Viomycin sensitivity (Ki) of different near-sense codons correlates with the accuracy for cognate tRNA over these codons (Figure 4), consistent with interference of viomycin with the decoding step(s). A competition assay is used to demonstrate that viomycin stabilizes near-cognate tRNA on the ribosome with catalytically-inactive EF-Tu, suggesting that the mechanism for GTPase activation on near-cognate tRNAs is due to stabilization of the ASL in the decoding center. These findings are consistent with previous biochemical and structural work showing that viomycin potently stabilizes tRNA in the A site, thus inducing miscoding and inhibiting translocation.

Although this work describes a well-designed and detailed biochemical study of viomycin's effect on near-cognate tRNA, these findings only incrementally add to what's been known. Increased binding of near-cognate tRNA to the A site in the presence of viomycin was demonstrated earlier (e.g. in Wurmbach and Nierhaus, Eur J. Biochem, 1983, but this work is not cited in the submitted manuscript). Next, the effect of viomycin on A1492 and A1493 constitutes bulk of the mechanistic insights discussed in the paper. But these insights are based almost entirely on previous structural studies because present biochemical assays do not directly report on the conformational changes in the decoding center. This discussion could as well be part of a review article, in the absence of the presented data. Furthermore, in vivo relevance of increased miscoding is not shown in this work. It is possible that these in vitro findings are not relevant because translocation inhibition is the prevalent mode of action of viomycin, so that mistranslated proteins do not contribute to cellular toxicity.

A minor note: The authors somewhat unexpectedly (for a broader readership) bring up aminoglycosides in the Introduction of the decoding mechanism. How relevant is this given that viomycin is not an aminoglycoside? If the modes of aminoglycosides and viomycin are deemed similar, what is the rationale for this study in the light of the known mechanism of miscoding by aminoglycosides?

https://doi.org/10.7554/eLife.46124.009

Author response

[Editors’ note: the author responses to the first round of peer review follow.]

Summary of the review: Our manuscript concerns the mechanism of action of a ribosome targeting antibiotic drug, viomycin. In this work we show specifically how viomycin acts on selection of tRNA by the ribosome to induce missense errors in reading of the genetic code. We propose a complete mechanistic description of this mode of action of the drug and additionally, through comparison with our previous work on inhibition of translocation by viomycin, suggest that this is not the primary mode of action of the drug. This part of our manuscript was well received by the reviewers.

We then go further and show that the effects of viomycin on tRNA selection constrain the possible biochemical mechanisms through which the high accuracy of translation is achieved. We argue that our results contradict the prevailing ‘induced fit’ model. We also argue based on our results and on the published structures of the viomycin-bound ribosome that the so-called ‘monitoring bases’ A1492 and A1493 of the 16S rRNA are much more dynamic than has been proposed previously based on structural studies. These points raised in our manuscript were strongly criticized in the review.

What has been done: We have carried out a new set of experiments using a GTPase deficient mutant of EF-Tu to directly measure stabilization of ternary complexes on the ribosome by viomycin. These experiments address several concerns brought up in the review. We now show directly that viomycin strongly stabilizes both cognate and near/non cognate ternary complexes on the ribosome before GTP hydrolysis. We show that this stabilization is enough to completely explain the accuracy reducing effects of the drug. This provides direct experimental proof for the approximations made in our model.

A key argument in our manuscript concerns a correlation between the accuracy of a particular mismatched codon·anticodon pair during initial selection and the sensitivity of that pair to viomycin. We find that the more accurate a given mismatch is the more viomycin is required to yield a given misreading frequency for that mismatch. That is, the easier it is for the ribosome to reject a tRNA from the A site the harder it is for viomycin to bind to the ribosome while that tRNA is present. This propensity of viomycin to bind, while a tRNA is present in the ribosomal A site but before GTP hydrolysis by EF-Tu has occurred, is quantified in our manuscript by a single kinetic parameter, a K_I value.

When estimating these K_I values from our experimental data we rely on an approximation that was criticized in the review. We have updated the main text and that of the supplementary material to further clarify the nature of this approximation. To state it shortly we ignore the effect of the forward rate of GTP hydrolysis for near-cognate tRNA on the size of the K_I value because it is so much smaller than the effective backwards rate of tRNA rejection. This assumption is true by definition for near/non cognate tRNA, it is also independent of the exact structure of the kinetic model used, if it were not true then the tRNA in question would be more likely to be accepted by the ribosome than to be rejected, and would by definition be a cognate tRNA.

Therefore, what we find is that the effective backwards rate of tRNA rejection, as measured by the K_I value, and the accuracy of initial selection are strongly correlated. This is the evidence we rely on to strengthen our claim that the accuracy must be determined by tRNA rejection rates, rather than tRNA acceptance rates.

In our manuscript we also propose a hypothesis for the role of the monitoring bases A1492 and A1493 during tRNA selection. Since directly measuring the dynamics of these bases during tRNA selection on the ribosome is currently impossible by any experimental technique that we are aware of, we base our suggestion on our biochemical results and preexisting structures of viomycin-bound ribosomes.

We suggest that the conformation of the decoding center with A1492, A1493 and G530 interacting with the codon anticodon helix, is a necessary prerequisite for GTPase activation. To proceed to GTP hydrolysis any tRNA, be it cognate or non-cognate has to pass through this state. The propensity for a given tRNA to trigger GTP hydrolysis is then directly proportional to the lifetime of this state. The monitoring bases enhance the accuracy of tRNA selection by greatly decreasing the energy level of this state for cognate but not near/non-cognate tRNA. This same hypothesis has now been suggested by the Ramakrishnan lab (Shao et al., 2016) and demonstrated by Korestelev lab (Loveland et al., 2018). Our lab together with Joachim Frank’s lab also reached the same structural conclusion (Fislage et al., 2018). We do not claim to have directly observed the proposed behavior by the monitoring bases, but we do suggest that such a mechanism is compatible with our experimental data as well as all existing structural data on both viomycin-bound ribosomes and ribosomes with cognate or near-cognate tRNAs in the A site.

References:

Shao, S., Murray, J., Brown, A., Taunton, J., Ramakrishnan, V., and Hegde, R.S. (2016). Decoding Mammalian Ribosome-mRNA States by Translational GTPase Complexes. Cell 167, 1229-1240 e1215. 10.1016/j.cell.2016.10.046

Loveland, A.B., Demo, G., Grigorieff, N., and Korostelev, A.A. (2017). Ensemble cryo-EM elucidates the mechanism of translation fidelity. Nature 546, 113-117.

Fislage, M., Zhang, J., Brown, Z.P., Mandava, C.S., Sanyal, S., Ehrenberg, M., and Frank, J. (2018). Cryo-EM shows stages of initial codon selection on the ribosome by aa-tRNA in ternary complex with GTP and the GTPase-deficient EF-TuH84A. Nucleic Acids Res 46, 5861-5874.

[Editors' note: the author responses to the re-review follow.]

Although both reviewers felt that the work was well done, noting improvements over the original version of the paper, they also agreed that the findings add only incrementally to knowledge regarding the mechanisms of viomycin action and decoding in relation to previously published biochemical and structural studies on these topics. As such, neither felt that the paper satisfies the journal's standards for publishing work of the greatest importance to the field, and indicated that it would be of interest to only a very small group of specialists.

Reviewer #1:

Holm et al. performed kinetic analysis of ribosome miscoding induced by antibiotic viomycin. […]. This conclusion further diminishes biological and medicinal significance of the study.

Reviewer #2:

The authors biochemically dissect how viomycin induces miscoding. They demonstrate that viomycin increases the rates of (1) EF-Tu-catalyzed GTP hydrolysis and (2) peptide bond formation for the near-cognate ternary complexes. […] If the modes of aminoglycosides and viomycin are deemed similar, what is the rationale for this study in the light of the known mechanism of miscoding by aminoglycosides?

As you know, the current manuscript roots from an earlier manuscript submitted to eLife in 2016, with title “Insights into the fidelity mechanism of mRNA decoding from characterization of viomycin induced miscoding in translation”. In that manuscript, we presented with careful and precise biochemical work, how the antibiotic viomycin affects the accuracy of decoding. More importantly, we presented two vital insights about the fidelity mechanism of the decoding process on the ribosome, which were new to the field and not in line with views presented in the literature. These were, in brief:

i) The widely accepted ‘induced fit’ model for initial selection is incorrect; instead the accuracy in initial codon selection by ternary complex is decided by differential rejection rates of the cognate and near-cognate tRNAs before GTP hydrolysis by EF-Tu rather than by varied GTP hydrolysis rates for cognate and near-cognate tRNAs as proposed earlier.

ii) The monitoring bases in the decoding center, A1492 and A1493 flip out dynamically and interact with the codon anticodon helix irrespective of whether the tRNA is cognate or non/near cognate. We hypothesized that the propensity for a given tRNA to trigger GTP hydrolysis by EF-Tu and being accepted is directly proportional to the lifetime of this flipped-out state.

Two of the three referees and the reviewing editor reacted strongly against both of these findings (in 2016). The reviewing editor writes – ‘you find….that flipping out of the bases is not restricted to cognate codons, at odds with the recent model that these bases mediate an induced fit in the A site that triggers GTP hydrolysis’, and also, ‘its (viomycin’s) binding to near-cognate/ribosome complexes necessitates that A1492/A1493 be flipped out for near-cognate as well as cognate tRNA-at odds with proposals that flipping out of A1492/A1493 is restricted to cognate tRNAs as a key step of the induced fit mechanism that accelerates GTP hydrolysis for cognate tRNAs.’ The main complaint from one reviewer was that without a proper structure our hypothesis about the decoding mechanism could not stand. The reviewing editor criticized our conclusion because there was no crystal structure of ribosome/drug/near-cognate tRNA ternary complexes. We feel that two of the reviewers and the reviewing editor may have been preoccupied with the idea of the ‘induced fit’ model and were not open to results opposing it in the absence of high-resolution structures. There was however, no criticism concerning viomycin’s mode of action.

While we worked to gather more biochemical results to support our model of decoding, a paper presenting high-resolution structures of these very decoding states was published by the lab of Andrei Korostolev (Loveland et al., 2018). The results in this paper are fully in line with our proposed mechanism (already in 2016) that irrespective of the cognate or near/non cognate nature of the codon the monitoring bases are dynamically flipping in and out of h69. Now in 2019, when we submitted a more complete work presenting this mechanistic model for the fidelity of initial selection, supported by this recent structural data, our manuscript was rejected on the grounds that ‘the findings add only incrementally to knowledge regarding the mechanisms of viomycin action and decoding in relation to previously published biochemical and structural studies on these topics’.

We strongly object to the statement quoted in the paragraph above. We would like to point out that our state-of-the-art quantitative biochemical data are complementary to the recent structural data, not mutually exclusive. While the proposed mechanism of decoding can be speculated qualitatively from the now published structures, the quantitative aspects of our work go far beyond what can be deduced from just these structures. We were surprised that the reviewers and the reviewing editor minimized the importance of biochemical validation and extensions of the hypotheses proposed from structure, and imply that such biochemical experiments are useless.

We also question the referees’ reasoning based on comparison with our previous study on translocation inhibition by viomycin (Holm et al., 2016), that our manuscript is unimportant due to the demonstration that viomycin induced decoding error is not the main cause of the drug’s antimicrobial effect. We disagree with this argument. First, we feel both pathways need to be studied mechanistically to determine which of the two inhibition mechanisms is the strongest. Second, it has been demonstrated for other drugs (e.g. apramycin, an aminoglycoside), which cause both misreading and inhibition of translocation that the induction of decoding errors is correlated with the strength of the side effects observed in patients. Hence the demonstration of the fact that error-induction is not relevant to the antimicrobial activity of viomycin, a drug with severe side effects, is in our mind highly important for drug-development purposes.

Reviewer #2 is surprised that we mention aminoglycosides in the paper since the paper is about viomycin (which is not an aminoglycoside). What we have discussed is that in spite of the fact that these two classes of drugs share overlapping binding sites on the ribosome their modes of action are strikingly different, something that was not suggested from previous studies. In fact, the current opinion in the field is misleading. It is believed that as the drugs share a common binding site they must operate in the same way. Hence, we explicitly compare viomycin with aminoglycosides in our manuscript. Here the referee’s reasoning itself demonstrates why biochemical studies such as ours are an essential complement to structural studies as none of these questions could be addressed by the previous structural studies mentioned by both referees.

Reviewer #2 also suggests that our finding was already known from an article by Wurmbach and Nierhaus, Eur J. Biochem, 1983. We are aware of this paper, which presented a semi quantitative study on an incomplete translation system. We therefore feel it provides little mechanistic insight about viomycin’s actions on bacterial protein synthesis and chose not to discuss it.

This extended review and the editorial process has led us losing our priority in solving pertinent issues regarding fidelity mechanism of decoding on the ribosome. As both the past and the present rejections appear to be based on bias from the reviewers, we feel obliged to appeal the editorial decision and ask for a new review of the current manuscript.

https://doi.org/10.7554/eLife.46124.010

Article and author information

Author details

Mikael Holm

Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

Contribution
Conceptualization, Data curation, Formal analysis, Validation, Methodology, Writing—original draft, Writing—review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-4361-9554
Chandra Sekhar Mandava

Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

Contribution
Resources, Investigation, Writing—review and editing

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-3028-3270
Måns Ehrenberg

Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

Contribution
Formal analysis, Writing—review and editing

Competing interests
No competing interests declared
Suparna Sanyal

Department of Cell and Molecular Biology, Uppsala University, Uppsala, Sweden

Contribution
Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Project administration, Writing—review and editing

For correspondence
suparna.sanyal@icm.uu.se

Competing interests
No competing interests declared

"This ORCID iD identifies the author of this article:" 0000-0002-7124-792X

Funding

Swedish Research Council (2018-05498 (NT))

Suparna Sanyal

Carl Tryggers Stiftelse för Vetenskaplig Forskning (CTS 18: 338)

Suparna Sanyal

Wenner-Gren Foundation (UPD2017-0238)

Suparna Sanyal

Knut och Alice Wallenbergs Stiftelse (KAW 2011.0081 to RiboCORE)

Suparna Sanyal

Knut och Alice Wallenbergs Stiftelse (KAW 2017.0055)

Suparna Sanyal

Swedish Research Council (2016-06264 (Research Environment))

Suparna Sanyal

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This work is funded by the research grants from the Swedish Research Council (2018–05498 (NT), 2016–06264 (Research Environment)), Carl-Tryggers Foundation (CTS 18: 338), Wenner-Gren Foundation (UPD2017-0238) and the Knut and Alice Wallenberg Foundation (KAW 2011.0081 to RiboCORE and KAW 2017.0055) to Suparna Sanyal. The authors also thank Raymond Fowler for expert technical assistance for the purification of translation factors.

Senior Editor

James L Manley, Columbia University, United States

Reviewing Editor

Alan G Hinnebusch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, United States

Version history

Received: March 4, 2019
Accepted: June 4, 2019
Accepted Manuscript published: June 7, 2019 (version 1)
Version of Record published: June 26, 2019 (version 2)

Copyright

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.