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How do aminoacyl-tRNA synthases distinguish between similar amino acids?

How do aminoacyl-tRNA synthases distinguish between similar amino acids?


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How do aminoacyl tRNA synthases recognize the right amino acid for their tRNA? What is the structural reason behind the selective recognition? I have difficulty in seeing how, for example, leucine and isoleucine can be selectively recognized by different binding pockets when they exhibit similar hydrophobicity, molecular volume etc.


Aminoacyl-tRNA sythetases are highly specific to their corresponding amino acid. First, the activation site, where the amino acid binds, constitutes a complex network of intermolecular interactions. For example, threonine, catalyzed by threonyl-tRNA synthetase, is very similar to valine and serine. Valine has a methyl group instead of the hydroxy group of threonine, and serine, on the other side, has no additional methyl side group. Uniquely to threonyl-tRNA synthetase, it contains a Zn2+ ion that exhibits an interaction structure specific to threonine. Nonetheless, the threonyl-tRNA synthetase could still be mischarged with serine because the zinc ion site is not specific enough, leading to 1% mischarged tRNAs. For this purpose, aminoacyl-tRNA synthetases have a second, editing site that proofreads the charged tRNA. The editing site can recognize serine and cleave it from the tRNA.

Figure: Interaction network of the activation site of the threonyl-tRNA synthetase in A. pernix. The threonine-AMP is shown in green.

Sources:

a Berg JM, Tymoczko JL, Stryer L. Biochemistry. 5th edition. New York: W H Freeman; 2002. Section 29.2, Aminoacyl-Transfer RNA Synthetases Read the Genetic Code. Available from: https://www.ncbi.nlm.nih.gov/books/NBK22356/

b Nagaoka, Yoshiyuki, et al. "EVOLUTION AND tRNA RECOGNITION OF THREONYL-tRNA SYNTHETASE FROM AN EXTREME THERMOPHILIC ARCHAEON, Aeropyrum pernix K1." Viva Origino 1.31 (2003), http://www.origin-life.gr.jp/3101/3101062/3101062.html


TRNA Aminoacylation

The second step in tRNA aminoacylation is transfer of the activated amino acid from the aa-AMP to the A76 ribose 2′-OH (for class I enzymes) or 3′-OH (for most class II enzymes). Mechanistically, the nucleophilicity of these hydroxyl groups would be considerably enhanced by deprotonation, suggesting a requirement for an active site base with a p Ka near neutral appropriately positioned within the active sites of the AARS. This same group could subsequently act to donate a proton to the AMP leaving group after bond cleavage. However, no clear candidates have emerged for conserved residues that could act as general base even when active site structures are conserved and sequence motifs have been identified for each AARS class. Alternatively, substrate-assisted catalysis, using either the phosphate group in the aa-AMP or the α-amine of the amino acid (in the aa-AMP) as the requisite base, could offer an alternative mechanistic scenario and be class-independent.

One possibility for a potential active site base was noted from the ternary complex between class I E. coli GlnRS, its cognate tRNA Gln , and the glutaminyl adenylate analog 5′-O-[N-( l -glutaminyl)-sulfamoyl]adenosine (QSI), which suggested a buried glutamate (Glu34) for this role [79] . The Glu34 carboxylate is 2.7 Å away from a crystallographic water adjacent to the tRNA 2′-OH. The proposed proton transfer relay would rely on Glu34-mediated water deprotonation, followed by deprotonation of the 2′-OH with concomitant attack on the carbonyl group of the enzyme-bound glutaminyl adenylate. The transition state of this reaction would contain a tetrahedral carbon intermediate putatively to be stabilized by the guanidino group of Arg260 [79] . This proposed mechanism for amino acid transfer contradicted the earlier suggestion, based on the GlnRS:tRNA Gln :ATP structure, that a non-bridging oxygen in Gln-AMP would deprotonate the A76 2′-OH [55] . Given the very low pKa (1.5–2) of the phosphate oxygen and that Gln was modeled into the structure, the later prediction of Glu34 as the general base was attractive [79] . In an effort to settle the mechanistic question of enzyme- or substrate-catalyzed deprotonation of the 2′-OH group, Perona and coworkers mutated both Glu34 and the nearby Glu73 and evaluated rates of reaction by steady-state and pre-steady-state approaches [80] . A Glu34Gln mutation displayed negligible change to kchem, the single turnover aminoacylation rate (although this substitution did result in an elevated Kd for the Gln substrate). The corresponding Glu73Gln substitution decreased kchem by 10 3 -fold relative to wild-type GlnRS this drop was attributed to loss of contacts with adjacent residues that position the tRNA 3′-end in the active site. Consequently, it was concluded that neither Glu residue participates in promoting A76 2′-OH deprotonation [80] .

Without an apparent general base to promote tRNA aminoacylation in either class I or II enzymes, attention was directed to the possibility of substrate-assisted catalysis. The non-bridging phosphate oxyanion offered an intriguing possibility despite its low pKa, particularly because the pKa values of the AMP reaction product are higher at 3.8 and 6.2 (The Merck Index, Rahway, NJ). Consequently, a concerted mechanism is attractive, such that the adenylate non-bridging oxygen deprotonates the 2′-OH, the resulting oxygen nucleophile attacks the adenylate carbonyl carbon, and the adjacent CO bond breaks ( Fig. 4 ). This substrate-assisted strategy is likely to be general to enzymes of both classes, and may reflect an ancestral mechanism pre-existing the emergence of the two distinct structural classes.

Fig. 4 . A general mechanism for aa-AMP-participation in tRNA aminoacylation. Derived from mechanisms previously proposed [33,81] . Ado, adenosine R, amino acid side chain cognate to the given AARS.

Evidence supporting a substrate-assisted concerted mechanism for tRNA aminoacylation like that shown in Fig. 4 is available for both classes of AARSs. Francklyn and colleagues used their E. coli HisRS:His-AMP structure with the tRNA His A76 modeled into the active site to propose a detailed, concerted mechanism for tRNA aminoacylation [33] . In this model, four key residues are in proximity to both the bound His-AMP and the terminal ribose: Arg259, Arg113, Gln127, and Glu83. The two Arg residues interact with the adenylate Sp and Rp non-bridging oxygens, respectively, while Gln contacts the α-carbonyl of the adenylate. Arg259His, Glu83Gln, and Glu83Ala variants all exhibited significantly reduced amino acid transfer rates that could have been attributed to defects in catalysis or A76 positioning. To address this uncertainty, phosphorothioate analogs of the adenylate at both Sp and Rp positions were introduced to probe the role of His-AMP in catalysis. For wild-type HisRS, the ATPαSp substitution led to a 10,000-fold loss of activity compared to only a 50-fold loss for the Rp substitution. Similar trends were observed with the Arg259His and Glu83Gln variant enzymes. Thus, the histidyl-adenylate non-bridging Sp oxygen is the likely general base for A76 3′-OH deprotonation, and histidine transfer is proposed to be a concerted, substrate-assisted reaction [33] . This mechanism is supported by density functional theory (DFT) calculations [81] .

Experimental evidence also suggests that class I AARSs rely on aa-AMP assisted catalysis using a non-bridging oxygen to promote deprotonation, in this case of the 2′-OH on the 3′-end of the tRNA substrate. The deleterious impact of sulfur substitution for either non-bridging oxygen in the α-phosphate of ATP was recognized for MetRS activity as early as 1982: one stereoisomer (termed ATPαSA) cause a

180-fold drop in Vmax, while the other (ATPαSB) completely eliminated enzyme activity [82] . With the mechanism shown in Fig. 4 in mind, it seems likely that the sulfur in ATPαSB replaced the non-bridging oxygen that would be critical for deprotonation of the attacking 2′-hydroxyl group. In 2000, First and colleagues used results from detailed pre-steady-state kinetics to propose a 6-membered ring transition state with the Tyr-AMP non-bridging oxygen on the phosphate positioned to deprotonate the 2′-OH on tRNA Tyr [83] . And, in 2017, Aboelnga and Gauld computationally examined the tRNA aminoacylation mechanisms of the E. coli GlnRS and T. maritima ND-GluRS. Molecular dynamics simulations, combined with QM and QM/MM calculations, led to a mechanistic proposal similar to that shown in Fig. 4 . In these cases, an ordered water molecule was positioned between the non-bridging phosphate oxygen in the aa-AMP and the 2′-OH in the tRNA. Thus, the transition state would proceed through an 8-membered ring and the required relay of proton transfers would proceed via aa-AMP-mediated deprotonation of this water molecule with immediate reprotonation using the proton from the 2′-OH. Immediate nucleophilic attack at the α-phosphate would complete the cycle [84] . Whether or not water participates in proton transfer for all class I AARSs remains to be determined.

A possible exception to the general case of aa-AMP-assisted deprotonation of the A76 hydroxyl is the class II ThrRS. Comparison of E. coli ThrRS:AMP and ThrRS:tRNA Thr :AMP structures indicates that a conserved His residue (His309) reorients upon tRNA binding to contact the A76 2′-OH [34] . Mutation of His309 decreased aminoacylation, and this loss was attributed to the transfer step, with a decrease in ktrans of > 200-fold. The authors argue that the A76 2′-OH is also active in the transfer step, as substitution with 2′-deoxy or 2′-fluoro decreased, but did not abolish, ktrans with losses of 10 2 –10 3 relative to the native tRNA. Participation by the adenylate non-bridging oxygen in deprotonation of the A76 3′-OH was discounted, as phosphorothioate substitutions did not significantly affect amino acid transfer [34] . Double mutant cycle analysis between the His309Ala and 2′-deoxy or 2′-fluoro substitutions indicates thermodynamic coupling, leading to a catalytic model for Thr-tRNA Thr synthesis that invokes proton relay from His309 to A76 2′-OH to the nucleophilic A76 3′-OH ( Fig. 5 A ). Subsequent DFT calculations alternatively suggested a possible role for the substrate threonine amino group as the general base. ThrRS relies on an active site Zn 2 + cofactor for amino acid selection and activation by coordinating to the α-amine and β-hydroxy groups in Thr (and non-cognate Ser) (described in more detail later in this chapter). The proximity of this Zn 2 + ion and the calculated lability of the bond between the Zn 2 + and the α-amine promote the basicity of the amine ( Fig. 5 B) [85] . This novel mechanism has not been experimentally tested.

Fig. 5 . tRNA aminoacylation mechanisms proposed for ThrRS. (A) His309 acts as a catalytic base to initiate a relay of proton transfers that activates the 3′-OH as a nucleophile [32] . (B) The α-amine in the Thr-AMP substrate acts as the base that deprotonates the 3′-OH. Thr-tRNA Thr (right) is the product that would arise from either pathway [83] .


Zinc ion mediated amino acid discrimination by threonyl-tRNA synthetase

Accurate translation of the genetic code depends on the ability of aminoacyl-tRNA synthetases to distinguish between similar amino acids. In order to investigate the basis of amino acid recognition and to understand the role played by the zinc ion present in the active site of threonyl-tRNA synthetase, we have determined the crystal structures of complexes of an active truncated form of the enzyme with a threonyl adenylate analog or threonine. The zinc ion is directly involved in threonine recognition, forming a pentacoordinate intermediate with both the amino group and the side chain hydroxyl. Amino acid activation experiments reveal that the enzyme shows no activation of isosteric valine, and activates serine at a rate 1,000-fold less than that of cognate threonine. This study demonstrates that the zinc ion is neither strictly catalytic nor structural and suggests how the zinc ion ensures that only amino acids that possess a hydroxyl group attached to the β-position are activated.


Results

Dataset

Based on all available structures in the PDB, 424 (189 Class I, 235 Class II) three-dimensional structures of aaRSs co-crystallized with their corresponding amino acid ligands were analyzed. The selected data covers aaRSs of 56 different species in total, 180 from eukaryotes, 213 from bacteria, and 31 from archaea (SI Appendix Fig. S1). In total, 70 human structures are part of the dataset. Each protein chain that contains a protein-ligand complex of a catalytic aaRS domain was considered. Data was available for each of the 20 aaRSs, plus the non-standard aaRSs pyrrolysyl-tRNA synthetase (PylRS) and phosphoseryl-tRNA synthetase (SepRS). Unfortunately, Class I LysRS could not be considered for analysis. The single structure of this enzyme from Pyrococcus horikoshii (PDB-ID: 1irx), which is part of the dataset, does not contain any co-crystallized amino acid ligand. The numbers of protein-ligand complexes available for each aaRS are given in SI Appendix Fig. S2. For twelve aaRSs, protein-ligand complexes were available in both pre-activation and post-activation reaction states, i.e. co-crystallized with either amino acid or aminoacyl ligand (SI Appendix Fig. S3). Out of all analyzed structures, 240 are in pre-activation and 184 in post-activation state. Out of the post-activation complexes, 72 are adenosine monophosphate (AMP) esters and 112 are non-hydrolysable analogs, mainly sulfamoyl derivatives.

Interaction features

The frequencies of observed non-covalent binding site interactions in respect of the aaRS class and the type of interaction are shown in Table 1. In general, hydrophobic interactions are the most prevalent interactions for Class I aaRSs with a frequency of 44.60% with respect to the total number of interactions, while hydrogen bonds are most frequently observed in Class II aaRSs with 59.23% frequency. Five (hydrogen bonds, hydrophobic interactions, salt bridges, (pi ) -stacking, and metal complexes) interaction types were observed in aaRSs. No (pi ) -cation interactions were observed to be involved in amino acid binding. Water bridges were excluded from the interaction analysis. Some aaRS structures deposited in the PDB are resolved including water, but other structures do not contain water molecules. In these cases, no water bridges can be detected using PLIP, despite them existing in vivo, which would lead to an experimental bias. Nonetheless, water molecules are known to mediate important interactions for ligand recognition 48 and their role should not be underestimated.

Amino acid recognition

The annotation of non-covalent protein-ligand interactions allowed to characterize interaction preferences of each aaRS at the level of individual atoms of their amino acid ligands. This analysis highlights the preferred modes of binding for each of the 22 amino acid ligands. Figure 2 shows the occurring interactions for each aaRS based on the analysis with PLIP. Each interaction is annotated with its occupancy, i.e. the relative frequency of occurrence in respect of the total number of structures for this aaRS. Binding site features are neglected at this point and all interactions are shown with respect to the amino acid ligand.

The recognition of individual amino acids by aaRSs mapped to their ligands. The ligands are grouped by physicochemical properties 49 and aaRS class. Different types of non-covalent protein-ligand interactions were determined with PLIP 46 and assigned to individual atoms of the ligand using subgraph isomorphism detection 50 . Backbone atoms of the ligand are depicted as circles without filled interior. The relative occupancy of each interaction in respect of the total number of investigated structures (number in parentheses for each aaRS) is given by pie charts. Interactions with an occupancy below 0.1 are neglected. Interactions for which a unique mapping to an individual atom is not possible due to ambiguous isomorphism, e.g. for the side chain of valine, were assigned to multiple atoms. (pi ) -stacking interactions are shown in dark green and refer to all atoms of the aromatic ring structures in TyrRSs, TrpRSs and PheRSs. Some aaRSs prevent the mischarging of their tRNAs via error correction mechanisms (“editing”) 51 . The aaRSs conducting error correction are typeset in bold.

Class I

In general, Class I aaRSs interact mainly via hydrogen bonds and hydrophobic interactions with the ligand. The backbone atoms of all Class I ligands feature hydrogen bonding with the primary amine group. The occupancy of this interaction is high throughout all Class I aaRSs, indicating a pivotal role of this interaction for ligand fixation. Additionally, the oxygen atom of the ligand’s carboxyl group is involved in hydrogen bonding except for glutaminyl-tRNA synthetase (GlnRS), isoleucyl-tRNA synthetase (IleRS), and valyl-tRNA synthetase (ValRS). The same atom forms additional salt bridges in leucyl-tRNA synthetase (LeuRS), arginyl-tRNA synthetase (ArgRS), methionyl-tRNA synthetase (MetRS), and glutamyl-tRNA synthetase (GluRS). The side chains of the aliphatic amino acids leucine, isoleucine, and valine are exclusively bound via hydrophobic interactions. ArgRS and GluRS form salt bridges between binding site residues and the charged carboxyl and guanidine groups of the ligand, respectively. Glutamine is bound by GlnRS via conserved hydrogen bonds to the amide group and hydrophobic interactions with beta and delta carbon atoms. The two aromatic amino acids tyrosine and tryptophan are recognized by (pi ) -stacking interactions and extensive hydrophobic contact networks. Tryptophan is bound preferably from one side of its indole group at positions one, six, and seven. The sulfur atom of the cysteinyl-tRNA synthetase (CysRS) ligand forms a metal complex with a zinc ion in both structures. MetRSs bind their ligand with a highly conserved hydrophobic interaction with the beta carbon atom.

Class II

Class II aaRSs consistently interact with the backbone atoms of the ligand via hydrogen bonds and salt bridges. The primary amine group forms hydrogen bonds with high occupancy and is involved in metal complex formation in threonyl-tRNA synthetases (ThrRSs) and seryl-tRNA synthetases (SerRSs). The carboxyl oxygen atoms of the ligands are bound by a combination of hydrogen bonding and electrostatic salt bridge interactions. The overall backbone interaction pattern is highly conserved within Class II aaRSs. Closer investigation revealed that a previously described structural motif of two arginine residues 43 , responsible for ATP fixation, seems to be involved in stabilizing the amino acid carboxyl group with its N-terminal arginine residue. The charged amino acid ligands in histidyl-tRNA synthetase (HisRS) and LysRS form highly conserved hydrogen bonds with the binding site residues. Other specificity-conferring interactions include (pi ) -stacking interactions and hydrophobic contacts observed for phenylalanine-tRNA synthetase (PheRS), metal complex formation for ThrRS and SerRS with zinc, and salt bridges as well as hydrogen bonds for aspartyl-tRNA synthetase (AspRS). The amino acids alanine and proline are bound by alanyl-tRNA synthetases (AlaRSs) and prolyl-tRNA synthetases (ProRSs) via hydrophobic interactions. No specificity-conferring interactions can be described for the smallest amino acid glycine due to absence of a side chain. Hence, glycyl-tRNA synthetase (GlyRS) can only form interactions with the backbone atoms of the ligand. Furthermore, asparaginyl-tRNA synthetases (AsnRSs) mediate highly conserved hydrogen bonds with the amide group of their asparagine ligand. The non-standard amino acid pyrrolysine is bound by PylRS via several hydrogen bonds and hydrophobic interactions with the pyrroline group. SepRSs employ mainly salt bridge interactions to fixate the phosphate group of the phosphoserine ligand.

Conserved Interaction Patterns

Class I aaRSs show a strong conservation of hydrogen bonds with the primary amine group of the amino acid ligand with 83.16% of all structures forming this interaction. Interactions with the carboxyl group are less conserved with a frequency of 32.65% for hydrogen bonds and 28.57% for salt bridges, respectively. In this context, the salt bridges with the carboxyl group are a form of extra strong hydrogen bonding 52 . Interaction patterns with the backbone atoms of the amino acid ligand are strikingly consistent within Class II aaRSs. This class forms hydrogen bonds with the primary amine group in 92.15% of all structures. Additionally, hydrogen bonds with the oxygen atom of the carboxyl group occur in 65.70% of all structures and salt bridges with the same atom are formed in 39.26% of all Class II protein-ligand complexes.

Similar recognition requires editing mechanisms

Various aaRSs are known to conduct pre- or post-transfer editing (see the work of Perona and Gruic-Sovulj 51 for a detailed discussion of editing mechanisms) in order to ensure proper mapping of amino acids to their cognate tRNAs. The similarity of interaction preferences depicted in Fig. 2 suggests that groups of very similar amino acids require editing mechanisms for their correct handling. Especially the three aliphatic amino acids isoleucine, leucine, and valine are bound via unspecific and weak hydrophobic interactions, substantiating the necessity of editing mechanisms observed for their aaRSs 53 and that substrate hydrophobicity cannot entirely account for specificity 54 . Distinction between those three similar amino acids is proposed to happen via the “double sieve” 52 mechanism. Exemplarily for IleRS, amino acids larger than isoleucine are excluded with the “first sieve” at the aminoacylation site, whereas smaller amino acids (like valine and leucine) are sorted out by the editing domain, functioning as a finer “sieve”. Specificity can therefore be accomplished by steric selection based on side chain length and shape at the editing site 53 . A similar trend can be observed, e.g., for AlaRS 55 in order to distinguish alanine from serine or glycine.

Binding site geometry and cavity volume

We investigated binding site geometry and cavity volume in order to quantify their potential contribution to amino acid recognition. Known editing mechanisms in aaRSs are focused on the prevention or correction of tRNA mischarging within one aaRS class (intra-class), e.g. the amino acids isoleucine, leucine, and valine belong to Class I. However, GluRSs and AspRSs have a highly similar interaction pattern of hydrogen bonds and salt bridges with the carboxyl group and weak hydrophobic interactions. Both aaRSs do not use editing and are handled by different aaRS classes. In this case, the geometry and size of the binding site can act as an additional layer of selectivity a mechanism also exploited by ValRS 53,56 . To quantify the contribution of binding site geometry, seven structures of GluRS and six structures of AspRS were superimposed with respect to their common adenine substructure using the Fit3D 57 software. As this superimposition can solely be computed for protein-ligand complexes which resemble the post-reaction state, only a subset of the structures was used. The results show that the ligands of GluRSs and AspRSs are oriented towards different sides of a plane defined by their common adenine substructure (Fig. 3A). There is a significant difference (Mann-Whitney U p<0.01) in ligand orientation, described by the torsion angle between phosphate and the amino acid substructure of the ligand (Fig. 3B). Class I GluRSs feature a torsion angle of 54.64 ± 7.12 (^) , whereas the torsion angle of Class II AspRSs is −65.02 ± 7.40 (^) . Furthermore, the volume of the specificity-conferring moiety of the binding site (see Fig. 1) was estimated with the POVME 58 algorithm. It differs significantly (Mann-Whitney U p<0.01) between GluRS (147.00 ± 22.31 Å (^3) ) and AspRS (73.34 ± 17.12 Å (^3) ). This trend can be observed for all Class I and Class II structures, respectively. An analysis of all representative structures for Class I and Class II aaRSs shows that Class I binding sites are significantly (Mann-Whitney U p<0.01) larger on average (Fig. 3C). While Class I binding cavities have a mean volume of 143.40 ± 39.62 Å (^3) , Class II binding sites are on average 90.36 ± 32.09 Å (^3) in volume.

Binding geometry and binding cavity volume analysis. (A) Binding geometry of GluRSs and AspRSs. Aminoacyl ligands of Class I GluRSs and Class II AspRSs in post-activation state aligned with Fit3D 57 with respect to their adenine substructure. The midpoints of non-covalent interactions 46 with binding site residues are depicted as small spheres. Blue is hydrogen bond, yellow is salt bridge, and gray is hydrophobic interaction. (B) Distribution of torsion angles between the phosphate and amino acid substructure of the ligand. The orientation of the ligand in the binding site differs significantly (Mann–Whitney U (p<0.01) ) between GluRSs and AspRSs. (C) The volume of the specificity-conferring moiety of the binding site, estimated with the POVME algorithm 58 , differs significantly between Class I and Class II aaRSs (Mann-Whitney U (p<0.01) ).

Interaction patterns of individual aaRSs

In addition to the investigation of interaction preferences from the ligand point-of-view, the binding sites of each aaRS were analyzed regarding the residues that form interactions with the amino acid ligand. Because each aaRS is backed by multiple proteins from diverse organisms with considerably divergent sequences, we devised a computational abstraction to allow the reader to infer amino acids of individual proteins via a structure-driven multiple sequence alignments (MSAs) (see "Methods" section). Original sequence numbers for each position can be inferred with the mapping tables published along with this manuscript (see Data Availability). Each row in the table corresponds to the artificial sequence position, whereas each column gives the original position for each structure in our dataset as defined by the PDB. Figure 4A shows a sequence logo 59 representation of binding site interactions for AlaRS. Each colored position in the sequence logo represents interactions occurring at this position. Highly conserved interactions can be observed at renumbered position 135. The corresponding hydrogen bond and salt bridge interactions are formed with the backbone atoms of the ligand. On the protein side, this interaction is mediated by a conserved arginine residue that corresponds to the N-terminal residue of the previously described Arginine Tweezers motif 43 . Another prominent interaction is formed by valine at renumbered position 293. This residue interacts with the beta carbon atom of the alanine ligand via hydrophobic interactions. In some structures, this hydrophobic interaction is complemented by an alanine residue at renumbered position 325. Aspartic acid at renumbered position 323 is highly conserved in AlaRSs and seems to be involved in amino acid fixation via hydrogen bonding of the primary amine group. Overall, the specificity-conferring interactions with the small side chain of alanine are hydrophobic contacts. An example for amino acid recognition in AlaRSs is given in Fig. 4B. The structure of bacterial Escherichia coli AlaRS forms the whole array of observed interactions. Sequence logos of the remaining aaRSs are given in SI Appendix Figs. S4–S24. Based on the interactions between binding site residues and the ligand, a qualitative summary of specificity-conferring mechanisms and key residues was composed (Table 2). Moreover, the ligand size and count of observed interactions was checked for dependence. There is a weak but significant positive correlation between the average number of interacting binding site residues for each aaRS and the number of all non-hydrogen atoms of the amino acid ligand (Pearson r=0.32, p<0.01). This indicates that the number of formed interactions generally increases with ligand size. However, smaller amino acids do not necessarily have a less complex recognition pattern. ThrRSs, for example, bind their amino acid ligand with on average more than a dozen binding site residues, while ValRSs employ on average five binding site residues. The hydroxyl group of threonine allows for an extended range of non-covalent interactions to be formed with binding site residues compared to valine, where only hydrophobic contacts can be established. Distributions of interacting binding site residues for each aaRS are given in SI Appendix Fig. S25.

Interaction patterns of AlaRS. (A) Sequence logo 59 of representative sequences for AlaRSs. Non-covalent interactions with the amino acid ligand occurring at certain positions are indicated by colored circles. Filled circles are interactions with the side chain atoms, while hollow circles are interactions with any of the backbone atoms of the amino acid ligand. Blue is hydrogen bond, yellow is salt bridge, gray is hydrophobic interaction. (B) Depiction of interactions in the binding site (blue stick model) of an AlaRS from Escherichia coli (PDB:3hxz chain A) with its ligand (orange stick model). Here, hydrogen bonds (solid blue lines) and hydrophobic interactions (dashed gray lines) are established. The sequence positions of the interacting residues are given in accordance to the MSA (black) as well as the original structure (red). Figure created with PyMol 60 . Double bonds are indicated by parallel line segments, aromatic bonds by circular dashed lines.

Quantitative comparison of ligand recognition

To allow for a quantitative analysis and comparison of ligand recognition between several aaRSs, interaction and binding site features were represented as binary vectors, so-called interaction fingerprints (see "Methods" section). Based on these fingerprints, the Jaccard distance was computed for each pair of structures to represent the dissimilarity in ligand recognition. Subsequently, the Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) algorithm 61 was used for dimensionality reduction and embedding of the high-dimensional fingerprints into two dimensions for visualization. This embedding is considered to be the recognition space of aaRSs. The two-dimensional visualization of this recognition space (Fig. 5) can be seen as a map describing the similarity in ligand recognition across all aaRSs. Thereby, each data point corresponds to a single amino acid binding site that was characterized by interaction and binding site features. In general, a similar recognition mechanism between two aaRSs can be assumed if they are located close to each other in this map. The more distant two aaRSs are from each other, the less similar their amino acid recognition. However, it has to be noted that the applied dimension reduction does not perfectly conserve distances. Figure 5A shows the embedding results for all aaRSs in the dataset colored according to the aaRS classes. A Principal Component Analysis (PCA) of the same data is given in SI Appendix Fig. S26. For each aaRS the average position of all data points in the embedding space was calculated and is shown as one-letter code label. Figure 5B shows the same data colored according to the physicochemical properties of the amino acid ligand, i.e. positive (lysine, arginine, and histidine), aromatic (phenylalanine, tyrosine, and tryptophan), negative (aspartic acid and glutamic acid), polar (asparagine, cysteine, glutamine, proline, serine, and threonine), and unpolar (glycine, alanine, isoleucine, leucine, methionine, and valine).

Recognition space analysis of all aaRSs. (A) Embedding 61 space of interaction fingerprints for all aaRS structures in the dataset. Scaling is in arbitrary units. The data points are colored according to the aaRS class. One letter code labels are given for each aaRS based on the averaged coordinates in the embedding space. An asterisk indicates the non-standard amino acids phosphoserine (J*) and pyrrolysine (O*). (B) Embedding space of interaction fingerprints for all aaRS structures in the dataset except phosphoserine and pyrrolysine. Scaling is in arbitrary units. One-letter codes of amino acid ligands are used to identify each aaRS. Every data point represents an individual protein-ligand complex. The color of the data points encodes the physicochemical properties 49 of the ligand.

Class I

In terms of amino acid binding both aaRS classes seem to employ different overall mechanism they separate almost perfectly in the embedding space. Especially aromatic amino acid recognition in Class I tryptophanyl-tRNA synthetases (TrpRSs) and tyrosyl-tRNA synthetases (TyrRSs) is distinct from Class II aaRSs and forms two outgroups in the embedding space. Remarkably, two different recognition mechanisms exist for TrpRSs, indicated by two clusters approximately at positions (−2.0,6.0) and (1.0,8.5) of the embedding space, respectively. The cluster at position (−2.0,6.0) is formed by structures from bacteria and archaea, while the cluster at position (1.0,8.5) is formed by eukaryotes and archaea and is in proximity to TyrRSs. Closer investigation of two representatives from these clusters shows two distinct forms of amino acid recognition for TrpRSs. Human aaRSs employ a tyrosine residue in order to bind the amine group of the indole ring, while prokaryotes employ different residues (SI Appendix Fig. S27). The Class I aaRSs that are closest to Class II are GluRSs and CysRSs. A cluster of high density is formed by Class I IleRS, MetRS, and ValRS, which handle aliphatic amino acids. This indicates closely related recognition mechanisms and difficult discrimination between these amino acids.

Class II

For Class II aaRSs the recognition space is less structured. Nonetheless, clusters are formed that coincide with individual Class II aaRSs, e.g. a distinct recognition mechanism in AlaRSs. The aaRSs handling the small and polar amino acids threonine, serine, and proline are closely neighbored in the embedding space. Recognition of GlyRSs seems to be diverse GlyRSs are not grouped in the embedding space. However, the recognition of glycine, which has no side chain, is limited by definition and thus the fingerprinting approach might fail to capture subtle recognition features. AspRSs and AsnRSs are located next to each other in the embedding space. Their recognition mechanisms seem to be very similar as the only difference between these two amino acids is the carboxylate and amide group, respectively.

Mechanisms that drive specificity

In order to quantify the influence of different aspects of binding site evolution on amino acid recognition by aaRSs, different interaction fingerprint designs were compared against each other. Each design includes varying levels of information and combinations thereof: the sequence composition of the enzyme’s binding site (Seq), non-covalent interactions formed between side chains of the enzyme’s binding site and the amino acid ligand (Int), whether pre- or post-transfer correction (i.e. “editing”) is conducted (Ed), and the overall volume of the enzyme’s binding cavity (Vol). To assess the segregation power of each fingerprint variant, the mean silhouette coefficient 62 , a quantification for the error in clustering methods, over all data points was calculated. This score allows to assess to which extent the recognition of one aaRS differs from other aaRSs and how similar it is within its own group. Perfect discrimination between all amino acids would give a value close to one, while a totally random assignment corresponds to a value of zero. Negative values indicate that the recognition of a different aaRS is rated to be more similar than the recognition of the same aaRS. Figure 6 shows the results of this comparison. When using fingerprints describing the sequence composition of the enzyme’s binding site (Seq (_ ext ) ), the mean silhouette coefficient over all samples is −0.0510, which indicates many overlapping data points and unspecific recognition. By including non-covalent interactions (Seq, Int) the value increases to 0.1361. If pre- or post-transfer correction mechanisms are considered (Seq, Int, Ed), the silhouette coefficient improves further to 0.2731. Adding information about the binding cavity volume (Seq, Int, Ed, Vol) slightly increases the quality of the embedding to 0.2757. The silhouette coefficients for error correction and volume-based fingerprints were calculated as baseline comparison. If only pre- or post-transfer correction mechanisms (Ed) are considered the mean silhouette coefficient amounts to −0.3027. For binding cavity volume (Vol) the mean silhouette coefficient is −0.4682.

Relation to physicochemical properties of the ligands

In order to investigate whether the fingerprinting approach is a simple encoding of the physicochemical properties of the amino acids, the results were related to experimentally determined phase transfer free energies for the side chains of amino acids from water ( (Delta G_) ) and vapor ( (Delta G_) ) to cyclohexane 3,63 . These energies are descriptors for the size and polarity of amino acid side chains and underlie both, the rules of protein folding and the genetic code 64 . The Spearman’s rank correlation between pairwise distances for each aaRS in the recognition space and physicochemical property space is weak with ( ho ) =0.2564 and p (<0.01) (see SI Appendix Fig. S28). This indicates that the fingerprinting approach used in this study is a true high-dimensional representation of the complex binding mechanisms of amino acid recognition in aaRSs. This assumption is supported by a PCA (SI Appendix Fig. S26) of the fingerprint data, where the first two principal components account for only 9.24% and 8.44% of the covered variance, respectively.

Comparison of different fingerprint designs that include the sequence composition of the enzyme’s binding site (Seq), non-covalent interactions formed between side chains of the enzyme’s binding site and the amino acid ligand (Int), pre- or post-transfer correction (i.e. “editing”) mechanisms (Ed), and volume of the enzyme’s binding cavity (Vol). Simple sequence-based fingerprints (Seq (_ ext ) ) are a 20-dimensional representation of binding site composition. The line plot shows the silhouette coefficient 62 for each embedding. Points represent mean values, error bars are calculated based on all silhouette coefficients for each data point.


Discussion

In this study, we characterize the translocation of threonine, α-aminobutyrate, and cysteine during editing by ValRS. For all three amino acids, the translocation rates are similar to their respective overall editing rates (2.7–3.5 s −1 ). The rate of translocation is considerably slower than the maximum rate of hydrolysis of exogenously added misacylated tRNA Val (20–40 s −1 ) (18, 19). Thus, once the misactivated amino acid is translocated to the site for editing, the chemical step for hydrolysis is relatively instantaneous. Consequently, under normal circumstances where a noncognate amino acid is mixed with tRNA Val , translocation is rate-limiting for editing.

The close similarities in the translocation rate constants measured for threonine and cysteine establish that the editing domain is not designed to select a specific misactivated amino acid. This possibility is supported by our observation that α-aminobutyrate also is efficiently translocated during editing, even though it is an unnatural amino acid in E. coli (hence, the existence of evolutionary pressure on the E. coli editing domain to select α-aminobutyrate as a substrate is not obvious). Instead, to carry out its role as the “fine sieve” in the double-sieve-editing model, the editing domain probably is structured to accept all misactivated amino acids and to strictly prevent the entrance of the activated cognate amino acid.

The translocation of misactivated amino acid during ValRS editing is thought to occur primarily by the posttransfer pathway, that is, by the deacylation of mischarged tRNA Val (20). A possible molecular mechanism for posttransfer editing in IleRS has been inferred from the structure of IleRS complexed to tRNA Ile (9). Specifically, the last three nucleotides of the tRNA (74–76) are proposed to adopt two conformations in the IleRS⋅tRNA Ile complex. The hairpinned conformation projects the A 76 nucleotide into the active site of the enzyme, where it can be aminoacylated, whereas the stacked conformation places the A 76 nucleotide in the editing domain, where an incorrect amino acid (attached to A 76 ) can be hydrolyzed. The switch from the hairpinned to the stacked conformation could therefore provide the mechanism for translocation during editing.

A similar mechanism might be responsible for the translocation of misactivated amino acids during editing by ValRS. In such a mechanism, the rate of translocation will depend primarily on the rate of the tRNA switching from the hairpinned to the stacked conformation, and not on the side chain of the amino acid attached to the tRNA. This prediction is consistent with our observation that threonine, α-aminobutyrate, and cysteine are translocated with similar efficiencies. It also implies that there is no physical contact between the amino acid side chain and the surface of the enzyme that lies between the synthetic active site and the editing site. If the side chain of an aminoacyl group made contact with the enzyme during translocation, then the difference between the more polar threonine and cysteine, on the one hand, and the hydrophobic α-aminobutyrate, on the other, would probably be reflected in different rates of translocation. Thus, we infer that the amino acid side chain points out, away from the surface of the protein during translocation.


How do aminoacyl-tRNA synthases distinguish between similar amino acids? - Biology

a School of Biology, Biomedical Sciences Research Complex, University of St Andrews, St Andrews, Fife KY16 9ST, UK
E-mail: [email protected]

b Arab Academy for Science, Technology, and Maritime Transport (AASTMT), Cairo Campus, Egypt

c Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Waddington 1 Building, King's Buildings, Edinburgh, UK

Abstract

Cyclodipeptide synthases (CDPSs) produce a variety of cyclic dipeptide products by utilising two aminoacylated tRNA substrates. We sought to investigate the minimal requirements for substrate usage in this class of enzymes as the relationship between CDPSs and their substrates remains elusive. Here, we investigated the Bacillus thermoamylovorans enzyme, BtCDPS, which synthesises cyclo(L-Leu–L-Leu). We systematically tested where specificity arises and, in the process, uncovered small molecules (activated amino esters) that will suffice as substrates, although catalytically poor. We solved the structure of BtCDPS to 1.7 Å and combining crystallography, enzymatic assays and substrate docking experiments propose a model for how the minimal substrates interact with the enzyme. This work is the first report of a CDPS enzyme utilizing a molecule other than aa-tRNA as a substrate providing insights into substrate requirements and setting the stage for the design of improved simpler substrates.


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Quality control of the translation machinery

Faithful translation of the mRNA codons into protein is essential for cellular physiology. The fidelity of the translation machinery firstly depends on the specific coupling of amino acids to their cognate tRNA species, which is catalyzed by aminoacyl-tRNA synthetases (aaRSs) (Fig 4a and 4b). aaRS is capable of discriminating its cognate substrates from structurally analogous tRNAs and amino acids [39]. Subsequently, eukaryotic elongation factor 1A (eEF-1A) or prokaryotic EF-Tu delivers the aminoacyl-tRNA to the ribosome A site for elongation of nascent peptide chain after proper codon–anticodon recognition [40]. Thus, aaRSs are cardinal in protecting protein synthesis against misacylation [39], but their specificity is not absolute. For instance, in E. coli, four types of misacylated-tRNA—including Cys-tRNA Pro , Ser-tRNA Thr , Glu-tRNA Gln , and Asp-tRNA Asn —do not evoke a correctional reaction [41]. In both mice and bacteria, serine is prone to be misacylated by alanyl-tRNA synthetases (AlaRSs) [42]. In mycobacteria, an increase in the substitution of glutamic acid→glutamine and aspartic acid→asparagine by translational misincorporation has been linked to phenotypic resistance to rifampicin treatment [43]. Thus, beneficial mistranslation in both prokaryotes and eukaryotes may exist and improve their survival or facilitate drug resistance [43–45]. Apart from misdecoding, misacylation of amino acids to tRNA molecules is another important source of mistranslated proteins, despite the presence of mechanisms preventing such events.

(a) aa-tRNAs are synthesized by sampling from the amino acid pool and tRNA pool and require catalysis by aaRSs. This process may accidently introduce misacylated aa-tRNAs, because the types of tRNAs and amino acids are difficult to be distinguished by involved aminoacyl synthetase because of analogous structures. (b) During elongation, tRNA wobbling will increase translation efficiency. Misincorporation can also be introduced because of tRNA misdecoding (amino acid misincorporation caused by excessive wobble decoding), especially when certain codon-paired tRNA species are missing. Finally, the fidelity of translation machinery will be impaired and produce mutated proteome, including RNA and DNA polymerases, aaRSs, and accessories. (c) Mistranslation of RdRP in RNA viruses will augment generation of a mutated virome (quasispecies) and facilitate viral evolution and adaption. (d) Similarly, mistranslation of cellular DNA replication-related enzymes and relative proteins amplifies mutagenesis in the genome and contributes to cancer development. aaRS, aminoacyl-tRNA synthetase aa-tRNA, aminoacyl-tRNA RdRP, RNA-dependent RNA polymerase tRNA, transfer RNA.

How could tRNA wobbling guarantee faithful decoding by the codon–anticodon duplex? During elongation, eEF-1A or EF-Tu delivers amino acid–coupled tRNA to the ribosome A site [40]. Subsequently, the ribosome rechecks the codon–anticodon duplex that involves the highly conserved G530, A1492, and A1493 of 16S RNA via stabilization of the first two Watson-Crick pairs of the duplex [31, 46]. A correct confirmation of the codon–anticodon duplex will induce a conformational domain closure in the ribosome and result in the formation of the appropriate peptide bond and elongate the nascent protein [47]. Analysis of X-ray structures suggests that the positions 1 and 2 of the A codon are obligatory Watson-Crick base pairs. In prokaryotes, when U•G and G•U wobbles at the first or second codon–anticodon position, the decoding center forces this pair to adopt the geometry close to that of a canonical C•G pair [40]. Using nuclear magnetic resonance (NMR) relaxation dispersion, it has recently been revealed that dG•dT misincorporation during replication is likely mediated via tautomerization and ionization [37]. As discussed, these Watson-Crick-like mismatches may further contribute to tRNA wobbling and consequently misdecoding [5]. Although the hydrogen bond is the major force to form codon–anticodon pairs [1], the van der Waals forces, steric complementarity, and shape acceptance may concurrently contribute to the codon–anticodon recognition essentially for quality control [3, 40].


Not an inside job: non-coded amino acids compromise the genetic code

The sophistication of the editing mechanisms that prevent gene translation errors indicates that amino acid misincorporation is generally a problem to be avoided. Mistranslation is considered invariably deleterious and often caused by confusion between similar proteogenic amino acids. These views are being challenged. The evidence linking misincorporation of dietary non-proteogenic amino acids to human disease continues to grow, and a report in this issue of The EMBO Journal demonstrates the importance of preventing non-proteogenic amino acid misincorporation for cellular homeostasis (Cvetesic etਊl, 2014).

See also: N Cvetesic et al (August 2014)

The genetic code holds the key to translate 64 codons into 20-odd amino acids. The enzymes that aminoacylate tRNAs, aminoacyl-tRNA synthetases (ARS), are the keepers of the code as they create the molecular link between amino acids and triplet information in the tRNA. ARS form two families of enzymes with a peculiar symmetric organization that clusters them in groups that recognize chemically similar amino acids. These two families possibly emerged from an ancestral complex of two proteins around a single tRNA molecule that evolved to increase the number of cognate substrates as the genetic code grew to its extant size. This expansion in cognate substrates logically involved the gradual incorporation of relatively similar side chains to those that were previously used (Ribas de Pouplana & Schimmel, 2001).

The extent to which some proteogenic amino acids are similar to each other𠅊s well as the structural organization of the ARS themselves𠅎xplain the difficulty in discriminating between certain residues during tRNA aminoacylation. To make matters worse, several nonprotein amino acids, which are ubiquitous in many cellular metabolic pathways, can also be mistakenly incorporated into proteins through ARS recognition errors that also require editing reactions to be corrected (Jakubowski, 2012).

Linus Pauling was the first to note that the chemical proximity between some side chains makes it impossible for ARS to discriminate between them with a tolerable error rate (Pauling, 1958). Hence the necessity of editing activities to remove incorrectly charged amino acids was postulated. A “second sieve” model for aminoacylation editing was proposed by Alan Fersht, and later proven to exist in several ARS (reviewed in Yadavalli & Ibba, 2012).

Valine, isoleucine, and leucine are good examples of amino acids requiring proofreading due to their chemical similarity. The discovery of a common editing domain shared by the ARS cognate to these three residues reinforced the notion that misincorporations would mostly involve related proteogenic amino acids, and that such errors always need to be corrected. However, mistranslation need not be limited to proteogenic amino acids and, in some cases, it may offer adaptive advantages to cells.

In this issue of The EMBO Journal Gruic-Sovulj and colleagues elegantly demonstrate that the editing domain of leucyl-tRNA synthetase (LeuRS) is not designed to fend off the misincorporation of isoleucine, as was previously thought. Earlier reports that suggested otherwise were marred by an unsuspected contamination of leucine in commercial preparations of isoleucine. Once the contaminating cognate amino acid is removed from the reaction the authors clearly show, by kinetic, structural, thermodynamic and in vivo approaches, that isoleucine is in fact a very poor substrate for LeuRS, which gets discriminated early in the reaction cycle and is not incorporated (substantially) to tRNA (Cvetesic etਊl, 2014). This clearly obviates a need for isoleucine editing (Fig ​ (Fig1 1 ).

Contrary to previous belief isoleucine is very effectively discriminated by the synthetic active site of LeuRS, and is not activated nor transferred to tRNALeu. Norvaline is easily charged to tRNA, and requires a posterior docking into the editing domain of the enzyme to prevent its incorporation into proteins.

Norvaline, a non proteogenic amino acid that in microaerobic conditions accumulates in the cytosol of E.਌oli (Soini etਊl, 2008) is, on the other hand, an excellent analog of leucine and is readily mischarged to tRNA Leu by LeuRS. However, accumulation of norvaline-tRNA Leu is prevented by the editing domain of LeuRS (Cvetesic etਊl, 2012).

A beautiful physiologic explanation to this biochemistry is offered in the paper when the authors show that E.਌oli grown under aerobic conditions do not require editing by LeuRS, whereas this activity becomes essential when intracellular concentrations of norvaline increase as a result of growth in microaerobic conditions.

Norvaline thus joins the ranks of non-proteogenic amino acids that can be misincorporated into proteins and cause toxicity. Indeed, recent reports have established links between several types of human neurodegeneration and the ingestion of non-proteogenic amino acids. For example, beta-methylamino-L-alanine is an amino acid analog taken up in the diet, and mischarged by seryl-tRNA synthetases respectively due to its similarity to serine (Dunlop etਊl, 2013). It is still unclear why the nervous system is more affected by this insult than other tissues.

Opposite to the previous examples, a body of literature is also starting to accumulate that reports on cellular strategies that utilize mistranslation to improve biologic fitness. For example, the adaptive nature of random variations in the proteome caused by amino acid misincorporation has been demonstrated in Candida albicans. The proteome of this pathogenic fungus undergoes generalized serine to leucine substitutions as an adaptive strategy that increases the virulence of this species (Moura etਊl, 2010).

The existence of an adaptive mistranslation has been confirmed in bacteria and human cells, and we now know that the mis-methiolation of proteins is a strategy used across the phylogenetic tree to minimize the damage caused by oxidative stress (Pan, 2013). Thus, amino acid misincorporation needs not be a deleterious mistake, but can sometimes be seen as a beneficial relaxation in translation fidelity that increases the fitness of the organism.


The return of pretransfer editing in protein synthesis

The accuracy with which the genetic information contained in protein-coding genes is faithfully translated into the corresponding sequence of amino acids has long fascinated biologists. Before the mechanisms of transcription and protein synthesis had been uncovered in the exquisite molecular detail we know today, some of the inherent problems of faithful gene expression were obvious. Crick's seminal adaptor hypothesis (1) predicted the existence of many then-unknown components of translation, including the aminoacyl-tRNA synthetases. The aminoacyl-tRNA synthetases in effect define the genetic code by catalyzing a 2-step reaction that pairs amino acids with their cognate tRNAs to provide substrates for ribosomal protein synthesis. In the first step, an amino acid is condensed with ATP to form an aminoacyl-adenylate. In the second reaction, the aminoacyl group is transferred to the 3′ end of the tRNA. The aminoacyl-tRNA synthetases also provide a critical safeguard to maintain fidelity during translation of the genetic code by discriminating against and, when necessary, editing noncognate amino acids. Crick was quick to point out that specificity would be of paramount importance to the synthetases, because their function in protein synthesis would require them to precisely distinguish similar amino acids such as isoleucine and valine. Linus Pauling (2), who reasoned that small differences in binding energy between aliphatic amino acids would not provide the level of discrimination necessary for faithful protein synthesis, had also noted this particular problem in molecular recognition. This discrepancy, between the specificity achievable during recognition and the accuracy required for translation, was resolved with the discovery of editing.

It was observed that although isoleucyl-tRNA synthetase did indeed recognize and activate valine, the intermediate valyl-adenylate was subsequently hydrolyzed in a tRNA-dependent reaction (3). The net result is that although isoleucyl-tRNA synthetase can use both isoleucine and valine, only the cognate product isoleucyl-tRNA Ile accumulates. Numerous studies of isoleucyl-tRNA and other synthetases have provided a general picture of the structure and mechanism of editing (Fig. 1). It had long been known that editing could occur either before (pretransfer) or after (posttransfer) amino acids are covalently attached to tRNA, an essential component of the protein synthesis machinery. In both cases the net result is the same, synthesis and release of noncognate aminoacyl-tRNA is prevented and translational accuracy is maintained. In recent years the biological relevance of the pretransfer route had come under question, and the prevailing dogma was that, with a few notable exceptions, editing was essentially a posttransfer process. In this issue of PNAS, Martinis and coworkers (4) now show that posttransfer editing can mask pretransfer editing activity, a finding with far-reaching implications for both quality control and the evolution of protein synthesis.

Posttransfer editing can mask pretransfer editing activity.

Pretransfer and posttransfer editing of noncognate amino acids by aminoacyl-tRNA synthetases. The amino acid (AA) is activated in an ATP-dependent reaction at the active site (AS) to form an enzyme-bound aminoacyl-adenylate (AA-AMP). In pretransfer editing, AA-AMP is hydrolyzed directly, thereby preventing the synthesis of a noncognate aminoacyl-tRNA. In posttransfer editing, AA-AMP is a substrate for esterification of the 3′ end of the tRNA, which then translocates to the editing site (ES) where the AA is removed. PPi, pyrophosphate.

The posttransfer reaction either occurs in cis, before the noncognate aminoacyl-tRNA is released, or in trans catalyzed by synthetases or specific hydrolases (5). A large number of biochemical and structural studies have clearly shown that this reaction occurs in a second active site that is distinct from the ancient catalytic core. Less frequently, the noncognate aminoacyl-adenylate will be hydrolyzed before tRNA esterifcation in a so-called pretransfer reaction (6). However, the relationship between these 2 pretransfer and posttransfer editing pathways has remained unclear in most systems. In particular, the mechanism and physiological relevance of pretransfer editing has continued to be contentious. Many of the points of dispute arise from the inherent instability of aminoacyl-adenylates, which makes their study difficult in vitro and almost impossible in vivo. In addition, much of the controversy surrounding the pretransfer pathway had come from difficulties in envisioning the molecular mechanism by which an unstable aminoacyl-adenylate could translocate some 30 Å from the active site before hydrolysis. Indeed, several examples of pretransfer editing by synthetases that lack a separate editing domain have been reported (7 ⇓ –9).

Leucyl-tRNA synthetase (LeuRS) presents an ideal model system for studying the relationship between, and relative significance of, pretransfer and posttransfer editing. In addition to the well-documented modularity of the conserved CP1 editing domain (10 ⇓ –12), different LeuRSs show widely divergent editing mechanisms. For example, Escherichia coli LeuRS is known to use a posttransfer editing mechanism to deacylate tRNAs charged with a variety of noncognate amino acids, including isoleucine, valine, norvaline, and methionine. Most notably for the present study, although it has been proposed that the yeast enzyme predominantly relies on pretransfer editing, the E. coli enzyme shows no such activity and solely uses posttransfer editing (13). In their study, Martinis and coworkers (4) set out to exploit the known properties of the E. coli enzyme to probe the relationship between pretransfer and posttransfer editing in more detail.

Previous studies had suggested that particular residues are needed for the effective transfer of editing substrates from the synthetic active site to the hydrolytic editing site in the CP1 module of LeuRS (14). To study this postulated translocation pathway Martinis and coworkers (4) used a combination of LeuRS mutants defective in posttransfer editing and CP1 deletion mutants of both E. coli (ecΔCP1) and yeast mitochondrial (ymΔCP1) LeuRSs. The ΔCP1 deletion mutants were found to retain significant cognate aminoacylation activity (Leu-tRNA Leu synthesis), although they did show some loss compared with the wild type. This modest loss in leucylation by the ΔCP1 mutants was not unexpected and was consistent with suggestions that the CP1 domain stabilizes enzyme–tRNA interactions. When tested for hydrolysis in trans of noncognate Ile-tRNA Leu , both of the ΔCP1 deletion mutants display negligible levels of deacylation, confirming that, as expected, these mutants retain little or no posttransfer editing activity. Martinis and coworkers next performed a final control and tested the ability of the mutants to synthesize mischarged tRNAs, a typical property of enzymes in which posttransfer editing has been disrupted. Whereas the editing-site mutant T252Y showed robust Ile-tRNA Ile synthesis as predicted, the ecΔCP1 and ymΔCP1 mutants were incapable of mischarging tRNA Leu with Ile. Pyrophosphate exchange assays performed to test the misactivation of Ile and other noncognate substrates showed that the ecΔCP1 mutant misactivated Ile to a level comparable with that of wild type. The ability of ecΔCP1 to misactivate Ile, together with its inability to form Ile-tRNA, suggested the existence of a dormant pretransfer editing activity, which is only apparent when the posttransfer editing CP1 domain is removed. Finally, by using inactive tRNAs modified to lack the site for amino acid attachment, pretransfer editing activity was shown to be strongly stimulated by tRNA despite the absence of the CP1 domain.

In unveiling pretransfer editing activity in E. coli LeuRS, an enzyme believed to solely depend on posttransfer editing for quality control, Martinis and coworkers (4) raise a number of questions about the origin and function of editing. The most immediate result of their study is to force a reassessment of tRNA-dependent pretransfer editing. Their work clearly shows that translocation to a distinct editing domain is not a prerequisite for tRNA-dependent pretransfer editing as had previously been proposed, which, in turn, raises the question as to how tRNA facilitates editing without itself being aminoacylated. Presumably this would involve increasing the rate of hydrolysis within the active site itself and/or accelerating adenylate dissociation as a prelude to spontaneous hydrolysis, both of which were recently shown to contribute to tRNA-independent editing by prolyl-tRNA synthetase (6). However this particular pretransfer editing mechanism is eventually resolved, the observation that the extant E. coli LeuRS:tRNA Leu pair contains the functional remnants of a rudimentary ribonucleoprotein has some interesting evolutionary implications. The tRNA dependence of this hidden pretransfer editing activity is consistent with the coevolution of tRNAs and quality-control mechanisms, helping to explain how structurally similar amino acids were added to the genetic code without compromising the fidelity of translation.

The biggest remaining mystery of this study is why the E. coli LeuRS has retained a “hidden” capacity for pretransfer editing that would appear to be redundant given that it already has robust posttransfer activity. Editing is not ubiquitous to synthetases as illustrated by the fact that some enzymes, including human mitochondrial LeuRS, have lost their proofreading capacity and rely instead on accurate substrate recognition (15 ⇓ –17). In this context, the retention of 2 distinct editing pathways is all the more surprising and would seem to suggest that the pretransfer reaction of E. coli LeuRS may actually be required for an activity other than translational quality control. No matter what this activity might turn out to be, to paraphrase Mark Twain, reports of the death of pretransfer editing are greatly exaggerated.


Watch the video: Aminoacyl tRNA synthetase Meaning (November 2022).