Scanning of 16S Ribosomal RNA for Peptide ... - ACS Publications

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Scanning of 16S Ribosomal RNA for Peptide Nucleic Acid Targets Anna Górska,† Agnieszka Markowska-Zagrajek,‡,† Marcin Równicki,§,† and Joanna Trylska*,† †

Centre of New Technologies, University of Warsaw, Banacha 2c, 02-097 Warsaw, Poland Department of Biology, University of Warsaw, Miecznikowa 1, 02-096 Warsaw, Poland § College of Inter-Faculty Individual Studies in Mathematics and Natural Sciences, Banacha 2c, 02-097 Warsaw, Poland ‡

S Supporting Information *

ABSTRACT: We have designed a protocol and server to aid in the search for putative binding sites in 16S rRNA that could be targeted by peptide nucleic acid oligomers. Various features of 16S rRNA were considered to score its regions as potential targets for sequence-specific binding that could result in inhibition of ribosome function. Specifically, apart from the functional importance of a particular rRNA region, we calculated its accessibility, flexibility, energetics of strand invasion by an oligomer, as well as similarity to human rRNA. To determine 16S rRNA flexibility in the ribosome context, we performed all-atom molecular dynamics simulations of the 30S subunit in explicit solvent. We proposed a few 16S RNA target sites, and one of them was tested experimentally to verify inhibition of bacterial growth by a peptide nucleic acid oligomer.

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INTRODUCTION In the history of medicine, antibiotics are one of the most successful drugs in reducing human mortality.1 However, since their introduction into human therapy, physicians have witnessed a growing number of antibiotic-resistant bacterial strains. Moreover, traditional antibiotics, as well as their semiartificial derivatives, are often not selective enough so they also destroy the natural microbiome. Therefore, there is still a need for more selective approaches. One of the most common antibiotic targets is bacterial rRNA2 because the ribosome, a protein synthesizing machine, is a vital element of the cellular metabolism. Inhibitors target the rRNA both of the small and large ribosomal subunits.3,4 Targeted sites include the catalytic center, mRNA decoding site, binding sites of tRNAs, nascent polypeptide exit tunnel, as well as other regions responsible for the interactions with external protein factors and dynamics between subunits. In the bacterial world, the small ribosomal subunit RNA (16S rRNA) is used for reconstructing phylogenetics, therefore its sequence is widely studied. The analysis of 16S rRNA multiple-sequence alignment identified conserved and variable regions within 16S rRNA sequences.5 The variable regions are used for bacterial identification, e.g., in metagenomic studies.6,7 At the same time, in the design of antimicrobials, the knowledge about 16S rRNA conservation levels enables species-specific targeting. Modifying known ribosome inhibitors has not been successful enough and most importantly has not eliminated cross-resistance. One of the ways to design new compounds could be to search for yet unexplored sites. This should be feasible because the ribosome is a large and complex ribonucleoprotein and offers many potential binding sites for interference. Unfortunately, there have been only few studies © XXXX American Chemical Society

that investigated unknown sites to inhibit the ribosome function.8−10 Since the majority of ribosome targeting inhibitors bind and interfere with rRNA, the rRNA function can also be inhibited by complementary oligonucleotides that interact through Watson−Crick and/or Hoogsteen-type base pairing. Typically such antisense oligonucleotides have been targeted against mRNAs encoding essential proteins.8,11 However, antisense oligomers, particularly DNA, complementary to exposed regions of rRNA have also been used.11,12 Since natural oligonucleotides are easily degraded by nucleases, their synthetic analogues are preferred for applications. A peptide nucleic acid (PNA) is among the most specific to natural nucleic acids.13 PNA is a neutral DNA analogue containing a backbone composed of N-(2-aminoethyl)-glycine units (Figure S1). Yet the hybridization of an oligomer to rRNA embedded in a ribosome depends on many features of the targeted rRNA region including its secondary and tertiary structure, accessibility, and dynamics. In addition, further inhibition of translation following efficient hybridization may occur only if the targeted region is either directly involved in the ribosome function or relays signals to other functional ribosome parts. The purpose of this study was to develop an algorithm to determine whether selected rRNA fragments could be good targets for sequence-specific hybridization with PNA oligomers. We have annotated and scored all overlapping potential target sites of Escherichia coli 16S rRNA in order to find the one that is Special Issue: J. Andrew McCammon Festschrift Received: February 28, 2016 Revised: April 19, 2016

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DOI: 10.1021/acs.jpcb.6b02081 J. Phys. Chem. B XXXX, XXX, XXX−XXX

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The Journal of Physical Chemistry B

Figure 1. Distribution of the mapped functional sites over the positions in 16S rRNA. Variable regions are the sites on 16S rRNA whose sequence is hyper-variable and can be used to distinguish between bacterial species, e.g., in metagenomic studies.19 They are not included in this study as functional sites. ABS stands for antibiotic binding sites and APE for A, P, and E-tRNA binding sites.20

• Sites targeted by ribosome inhibitors mapped from selected crystal structures (Table S4). • Mutations causing bacterial resistance collected from the literature (Table S5). • Data from mutagenesis studies of rRNA. Yassin et al.17,18 set up a library of randomly mutated plasmids carrying the E. coli rRNA operon rnnB expressed depending on temperature. This way, they mapped dozens of mutations and determined their level of influence on the ribosome function, assuming that mutations with lethal or severe effects play an important role and can be regarded as potential new antibiotic binding sites. The distribution of the mapped functional sites over the positions in 16S rRNA is shown in Figure 1. rRNA Sequence Conservation and Composition. We characterized rRNA targets from the point of view of their sequence conservation, similarity to human rRNA, and nucleotide composition. We took into account the following sequence features: • Target purine content. The rRNA target has to be designed so that it maximizes the number of purines because PNA oligomers bind to purine targets with higher affinity.13 Also, PNA oligomers containing more pyrimidines are easier to synthesize and more soluble. Therefore, for each of the putative rRNA targets, we computed the percentage of purines. • Sequence alignment. PNA sequences were aligned multiple times against the sequence of rRNA. Such short sequences cannot be aligned using heuristics, e.g., BALST,21 therefore alignments were performed using a dynamic programming local alignment function from the BioPython package.22 The gap penalties were set to ensure that it is much harder to put a gap in the PNA sequence compared to rRNA (open gap penalty PNA: −3.5; extend gap penalty PNA: −0.1; open gap penalty rRNA: −10.5; extend gap penalty rRNA: −10.5; match: 1; mismatch: −1). We considered the sequences as aligned if the alignment score reached ≥80% of the maximal score (perfect match). So, for a 10 nucleotidelong oligomer we allowed up to two mismatches and no gaps in either sequences. • Similarity to human rRNA sequences. If a PNA or any other oligomer was to be designed with therapeutic applications in mind, it should not target human ribosomes. To ensure the selectivity of PNA oligomers to bacterial ribosomes, their sequences were aligned (as

both accessible for strand invasion by PNA and functional enough to inhibit translation. The score was based on the sequence, structure, dynamics, and other physicochemical properties of rRNA. We describe how these features were calculated and the 16S rRNA target sites for PNA oligomers were selected. Lack of data on thermodynamics of PNA/RNA duplexes of different sequences made it impossible to build a model, similar to a nearest-neighbor one developed for DNA/DNA14 or RNA/RNA duplexes,15 to estimate PNA oligomer binding energy to RNA or even its probability. Instead, we have generated a relative score for each site on 16S rRNA as an assessment of its availability as an antisense target. Based on the score, we tested one PNA sequence in its efficiency to inhibit growth of E. coli and Salmonella enterica serovar Typhimurium cells. The latter had the same sequence of the targeted region as E. coli, so we wanted to verify whether the prediction can be more general.

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THEORY AND COMPUTATIONAL DETAILS We describe the algorithm that was developed to score the RNA sites in terms of their suitability to hybridize with oligonucleotides or otherwise interact with other potential inhibitors. Mapping Functional Regions in the rRNA Sequence. We characterized the rRNA sequence from the point of view of its functional features. From the literature and crystallographic ribosome structures deposited in the Protein Data Bank (PDB),16 we annotated functional regions that we believed were relevant for our target scoring algorithm. The structures were downloaded from PDB with an in-house Python script, and the nucleotides in proximity to the antibiotics or ribosome protein factors were located. The rRNA fragments were assigned the following functional features: • Aminoacyl (A), peptidyl (P), and exit (E) tRNA binding sites (Table S1). • Inter-subunit noncovalent contacts (bridges) involving rRNA regions whose nucleotides are involved in hydrogen bonding or stacking interactions with another subunit. We listed only 16S rRNA nucleotides involved in such contacts (Table S2). • Sites interacting with protein factors modulating translation (Table S3). These sites were mapped from the T. thermophilus ribosome crystal structures; all nucleotides located closer than 10 Å from the factors were considered. B


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Figure 2. Conservation level per nucleotide position in 16S rRNA. The lower the value the more sequence-variable is the site (scale on left Y axis). The background color represents the number of sequences that were used to compute the conservation level in each of the positions (right Y axis). This is a general profile for all bacterial strains. The conservation values were taken from http://www.rna.icmb.utexas.edu.

Figure 3. SASA and ΔSASA of nucleotides in 16S rRNA. ΔSASA (blue line, scale on the right Y axis) was computed by subtracting SASA (red line, scale on the left Y axis) of the nucleotide in the complete structure from its SASA in the naked 16S rRNA. The sites where ΔSASA is equal to zero (817 nucleotides) are not covered by ribosomal proteins. Ribosomal regions with high ΔSASA denote protein interface.

V9D24). This structure had the best resolution and provided the longest 16S rRNA among the ribosome structures deposited in PDB as of June 2012. Based on the 30S structure, we computed the following descriptors. • Hydrogen bonding and stacking. Hydrogen bonds and stacking interactions per nucleotide in the crystal structure were computed using MINT.25 Stacking was computed as a sum of van der Waals and electrostatic energy terms of the bases whose distances of centers of masses were below 5.5 Å using the AMBER force field26 van der Waals parameters and partial charges. • Solvent accessible surface area (SASA) was computed with VMD27 and measure sasa tool with the probing sphere radius of 1.4 Å and mRNA and tRNAs removed. The SASA descriptor can be used in a twofold way: to eliminate the regions of rRNA buried inside the subunit28,29 or to point out the rRNA regions interacting with ribosomal proteins. These regions could also be functional because they are involved in constructing the ribonucleoprotein complex. Figure 3 presents these SASA descriptors for each 16S rRNA nucleotide. • Opening Energy. It is more difficult for a PNA oligomer to bind to the RNA region involved in a tight secondary structure because of the necessity of strand-invasion. Since rRNA secondary structure is complex, rather than counting the number of Watson−Crick pairs within a single region, we evaluated the minimal energy needed for the region to lose its secondary structure (Figure S2).

above) against sequences of both cytoplasmic and mitochondrial human rRNA. • Conservation level. One of the negative side-effects of the traditional antibiotics is a rapid drop of the diversity of the gut microbiota. This phenomenon is limited if narrow spectra antibiotics are used. Since the binding strength of PNA oligomers is particularly sensitive to mismatches,13 in principle, PNA could be used as a precise antimicrobial against pathogenic strains once targeting 16S rRNA low conservation sites (Figure 2). To score conservation, we took into account the conservation level estimated for each nucleotide position in the 16S rRNA sequence. Van de Peer et al.5 aligned thousands of bacterial 16S rRNA sequences. For each position in the alignment, they computed the conservation level using the following function C = ∑Pi log2(4*Pi) + Pδ log2(Pδ), where Pi represents the nucleotide frequency at a particular position in the alignment i ∈ (A,C,G,U), and Pδ the frequency of deletion. However, to design an oligomer against a particular bacterial group, this analysis would have to be deepened. The profile for the targeted group would need to be recomputed according to the current state of the database and compared against the profile of 16S rRNA sequences from the human-gut microbiota studies.23 rRNA Properties in the Crystal Structure of the 30S Subunit. We used the crystal structure of the E. coli 30S subunit resolved with the 3.0 Å resolution (PDB entry 4 C


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Figure 4. Opening energies of 16S rRNA 10-mers. Nucleotide position denotes the starting point in 16S rRNA of the 10-nucleotide window used for the calculations. Blue line denotes the average opening energy computed for the secondary structures of E. coli 16S rRNAs stored in FraBase32 (PDB entries: 4V9D, 4V85, 4V7T, 4V7S, 4V56, 4V53, 4V50, 4V4Q, 2AVY, 4V4H). Light blue denotes the standard deviation corresponding to variability among structures. Variability is both due to imperfections in the crystal structures and secondary structure mapping, but also reflects the natural variation and flexibility of the ribosome structure. Sites with ease of opening and high standard deviation were scored highly.

Figure 5. Number of nucleotides participating in hydrogen bonds calculated from the starting crystal structure (blue, Single) and average from the production trajectory (red, Dynamic). The latter was computed by counting the number of nucleotides creating at least one hydrogen bond in each frame of the last 30 ns of production trajectory and averaging. The “WC-edge pairs” denotes the hydrogen bonds formed via the Watson−Crick edge and “non-WC edge pairs” via other nucleotide edges.

M NaCl. MD simulations were performed with NAMD36 in CHARMM36 force field37 using the particle mesh Ewald method and the SHAKE algorithm with a time step of 2 fs. The cutoff parameter for the van der Waals and electrostatic interactions was set to 12 Å, the switching distance to 15 Å, and the pair list distance to 18 Å. First, the solvent was energy minimized, then it was gradually heated, with the solute fixed. The temperature was raised from 30 K, increasing 1 K every 500 steps up to 300 K. Next, the system was equilibrated. Initial constraints applied to the solute atoms were gradually decreased in 0.1 ns runs using the following decreasing force constants: (1) 25 →1 kcal/mol, (2) 1 →0.0076 kcal/mol, (3) 0.0075 →0.0042 kcal/mol, (4) 0.0042 →0.00167 kcal/mol. Finally, the restraints were removed, and the system was equilibrated for 20 ns at 300 K. A detailed description of the protocol is presented in Table S6. For the analysis, we used the last 30 ns of the production simulation with an average RMSD of the heavy atoms from the starting structure of 4.5 ± 0.2 Å and the radius of gyration of 67.7 ± 0.1 Å (Figure S3). We also computed the total number of the hydrogen bonds between nucleotides and van der Waals energy for the stacked bases (Figure S4). Root mean square fluctuations (RMSF) were used to score the internal mobility: the higher the RMSF of a particular nucleotide stretch, the easier it should be for an oligomer to bind. RMSF carries similar information as the B-factor in crystallographic studies

The opening energy was computed by subtracting the free energy of the closed conformation of the target RNA region from the free energy of the open conformation with no secondary structure. The free energy was estimated using the Nearest Neighbor Model implemented in RNAeval of the ViennaRNA package that was parametrized based on experimental melting temperatures.30,31 The higher the energy value, the easier it is to open the targeted region. We mapped the entire 16S rRNA, based on several crystal structures, by computing the differences in the free energy of the target in the native structure and sequentially opened fragments (Figure 4). The 10-nucleotide stretches were used for this energetic scan of 16S rRNA. 16S rRNA Flexibility from Molecular Dynamics. The ribosome is a dynamic macromolecule, and its internal flexibility has been proven in many simulations (for reviews, see refs 33−35). To include and quantify the dynamical aspects of the rRNA structure in the scoring of targets, we performed all-atom molecular dynamics (MD) simulation of the small ribosomal subunit. Using MD, we determined local flexibility of rRNA in the context of the entire 30S subunit. The 30S subunit structure with tRNAs removed was used as a starting conformation (PDB entry 4V9D24). The solute was protonated and was solvated with explicit waters extending 15 Å from any atom of the solute. Ions were added to achieve 0.15 D


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Figure 6. Number of target regions with the same score for different lengths of the target 16S rRNA stretches (between 10 and 15 nucleotides). The longer the region, the higher (better) its score, which is especially visible for the 36−40 score range. For the purpose of this comparison, the number related to functionality was normalized by the length of the region and scaled in the same way as the rest of the descriptors. RMSF2 * 8 * π 2

Typhimurium, there was a collection of possible alignments with 5/13 and 6/13 matches, in the case of 16S rRNA and 23S rRNA, respectively. None overlapped the targeted region.

with a relation B = .38 The comparison of 3 crystallographic and trajectory-based fluctuations is shown in Figure S5. Quantification of the hydrogen bonding in the starting and trajectory average structures is presented in Figure 5. The criteria for hydrogen bond detection were: minimal angle 140 degrees and the maximal length of 4.0 Å as the acceptor−donor distance. Both secondary and tertiary structures of rRNA are dynamic. These data provide information on how a nucleotide is “constrained” by the surrounding structure and are another indication of its accessibility for PNA or any other oligomer type.

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RESULTS AND DISCUSSION Design of the Score and Selection of rRNA Targets. We have defined and calculated a number of descriptors characterizing the regions in rRNA in terms of their structural and energetic accessibility to hybridization with PNA and functional hindrance by PNA. From these data we composed a joint score to select the final target sites in 16S rRNA. Here, we describe our PNA selection and design of the score for the E. coli rRNA since one E. coli 16S rRNA region was selected as a target for experimental verification of the inhibition of bacterial growth. However, the same 16S rRNA region was found in S. enterica serovar Typhimurium, so the latter bacteria was also used to verify extendability of the score. Overall, the targets selected in this study could be also aligned with 16S rRNA sequences of other bacteria and tested. The descriptors that we took into account for the E. coli 16S rRNA targets were the functionality of the region, opening energy, SASA, RMSF, MD-averaged stacking energy and total number of hydrogen bonds, and purine percentage. As the SASA descriptor, we used SASA of the rRNA target in the ribonucleoprotein complex, characterizing the accessibility of the target, and not ΔSASA (Figure 3). Oligomers targeting protein−rRNA interfaces could influence the formation of the 30S and 50S subunits, but here we did not take this mechanism into account. Also, note that some descriptors are correlated, e.g., regions with a low number of hydrogen bonds would have low opening energy and high RMSF. On the other hand, RMSF and B-factors report similar properties, so we did not include both but only the trajectory-derived RMSF. For the experiment on inhibition of bacterial growth, we did not take into account the alignment with human rRNA sequences and conservation level because our goal was to verify the designed rRNA scoring function only on the bacterial target. However, these descriptors can be taken into account further to study the toxicity of selected PNAs to eukaryotic cells.

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EXPERIMENT A PNA oligomer targeted to nucleotides 830−839 of 16S RNA with a sequence Nter(KFF)3K-eg1-GCACAACCTC-KCter was purchased from Panagene, Korea (eg1 stands for the 8-amino3,6-dioxaoctanoic acid linker.) The E. coli K12 strain and Salmonella enterica serovar Typhimurium were used to determine the minimum concentration of peptide−PNA that prevented visibly detectable bacterial growth after overnight exposure to this compound (minimal inhibitory concentration, MIC). MIC were determined with the broth microdilution method. Cells were grown in cation-adjusted Mueller−Hinton Broth (MHB) to exponential phase and diluted to 5 × 105 CFU/mL. The culture was added to the wells of a sterile 96well plate and incubated at 37 °C for 20 h in different concentrations of peptide−PNA.39 Each sample was shaken shortly before the measurement. The turbidity of the samples was determined at 600 nm using a plate reader. The statistical analysis (ordinary One Way Anova and Tukey’s multiple comparisons test) was performed using GraphPad Prism 6. A noncomplementary control sequence Nter(KFF)3K-eg1TTATATATATGGC-KCter was selected in a way not to align with any rRNA region (with the alignment procedure as abovedescribed.) We found six best equally scored alignments of the control sequence to the E. coli 16S rRNA and all had only 5 out of 13 matches. None of the possible best alignments overlapped with the targeted region. For 23S rRNA the three best alignments had only 6/13 matches. Similarly for S. E


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Figure 7. (a) Descriptors of 16S rRNA regions. Each colored circular stripe shows the score for one descriptor averaged over 10-mer 16S rRNA stretches. Coloring scale is shown on the right. To present the range and variability in the data, the values in (a) are shown before scaling to the integer 0 to 10 so the maximal value is not always favorable for oligomer binding. (b) The total scaled and summed score averaged over the 10nucleotide fragments as a function of the 16S rRNA sequence. The positions at which the score reaches over 34 are highlighted in yellow (the choice of the threshold is arbitrary.).

The score was computed for all possible rRNA stretches of the lengths of 10 to 15 nucleotides and for the 16S rRNA positions from 2 to 1536, in a maximally overlapping manner. Because for PNA oligomers the best inhibition of bacterial growth was obtained for the oligomer lengths between 9 and 12, we selected 10-nucleotide regions as 10-mer PNA targets.40 Also, shorter PNA sequences are less hydrophobic (thus more soluble), cheaper to synthesize, and easier to deliver to cells. The scores are shown in Figure 6. Longer regions achieve better scores, but in the part of highly scored regions of the score above >40, the difference is relatively small. The collected data are available via a web server that can be accessed at http://bionano.cent.uw.edu.pl/Software or http:// riboscanner.cent.uw.edu.pl. After submitting the sequence of the oligomer or RNA region to be targeted, the server will align the sequence to E. coli and human rRNA sequences (cytoplasmic and mitochondrial.) The best matching rRNA region with calculated descriptors is displayed. The server extends the results also to 23S rRNA of the large subunit but without the dynamic characteristics since we have not yet performed MD simulation for the large ribosomal subunit. Figure 7a presents the calculated descriptors and scores for each 16S rRNA region. The most outer circle (also showing nucleotide numbering) quantifies the functionalities for each

The score from the chosen descriptors was calculated in the following manner. Functionalities were directly counted: the more nucleotides within the target region are annotated as functional, the better. For each region, the opening energy was calculated based on the secondary structure acquired in the three-dimensional structures of the ribosome. The standard deviation of the opening energy was not included, since it would be redundant with the descriptors from MD simulations. For SASA, stacking energy, RMSF and hydrogen bonding average values were assigned. The number of purines of the targeted site was computed directly. Note that each of the above characteristics has a different unit and range of values. Therefore, we scaled each of them to acquire a value between 0 and 10. For some characteristics the score was reversed, e.g., the lower the number of hydrogen bonds the easier it is for an oligomer to strand-invade the region, so low numbers of hydrogen bonds in the target were given a high score. As a result each descriptor was an integer between 0 and 10, where 10 denotes that the site is optimal for targeting with a PNA oligomer from the point of view of this descriptor. The global score was a sum of the scaled values of each descriptor. There are no data that would allow us to introduce weights or a more complicated form of the score. F


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The Journal of Physical Chemistry B Table 1. List of Highly Scored 16S rRNA Regionsa nucleotide no in 16S RNA

length

67 → 88 444 → 468 688 → 705 765 → 796 829 → 851 1010 → 1037 1126 → 1144 1146 → 1187 1391 → 1454 1481 → 1506

22 25 18 32 23 28 19 42 64 26

average score 39.0 36.5 38.6 37.9 40.6 38.1 35.8 35.4 38.6 38.8

± ± ± ± ± ± ± ± ± ±

2.8 2.0 2.3 3.5 4.4 2.6 1.5 0.9 3.7 2.7

hyper-variable regions

functionality B4 intersubunit bridge − B7a intersubunit bridge, E-site, tetracycline and kasugamycin binding sites E-site, kasugamycin binding site, mutations gentamycin, IF3 binding site RF1 binding site B2a intersubunit bridge, P-site, kasugamycin binding site, mutations mutation P-site, B2a, B5, B5a, B6 intersubunit bridges, mutations A- and P-site, EF-G, IF1, RF1, RF2 binding site, B2a intersubunit bridge, kasugamycin binding site, mutations

V1 V3 − − V5 V6 − − − −

The table shows average scores (with standard deviation) for possible 10-mer 16S rRNA stretches of the longer regions listed in the first column. The sites for which at least four neighboring regions reached the score of 34 (as highlighted in Figure 7b) are listed. Dashes mean that the regions were not classified as functional or are located in the conserved regions. The regions are marked on the structure of the 30S subunit in Figure 8. a

rRNA stretch (antibiotic binding sites, intersubunit bridges, A/ P/E tRNA sites and mutations). Toward the inner circles are the opening energy, average stacking energy, RMSF, the maximal number of average hydrogen-bonds, the percentage of purines, and the total score (in shades of red). The two most inner rings represent the conservation level and similarities to human rRNA sequences. These were not taken into account while computing the global score in this work because they would not influence the outcome of the E. coli growth inhibition experiment. The descriptors not included here can still be used for further selection from the highly scored sites. The global score (as a sum of rescaled descriptors) averaged over the 10-nucleotide stretches is shown in Figure 7b with the sites that globally scored over 34 highlighted. The increase in the score between 1400 and 1500 is due to the functionality of this region including many intersubunit bridges and A- and PtRNA binding sites. These potential target sites are also listed in Table 1 and mapped in Figure 8 on the three-dimensional structure of the 30S subunit. From the sites listed in Table 1, the 829 → 851 region was further selected because it is characterized with the best global score. Moreover, the target is located within the V5 hyper-variable region of 16S rRNA and does not have similarities with human rRNA. We did not select the regions in proximity or covering the A- or P-tRNA binding sites since these have already been studied,41−43 but a high score for this region is a good verification of our protocol. The target site 829 → 851 shown in Figure 9 covers almost all of helix 26 that spans the nucleotide range 829 to 857 and is a component of the central domain of 16S rRNA. Sequencewise, this region belongs to the V5 variable region (nucleotides 822 to 879). Helix 26 comprises several non-Watson−Crick type base pairs, and this may be the reason why it was characterized with a relatively high opening energy and high RMSF. We have not found any literature on the role of helix 26 in the ribosomal machinery. Automatic mapping from crystal structures of ribosomes showed that nucleotides in the helix 26 region are involved in the interactions with initiation factor 3 (PDB entry 1HR0) and gentamycin (PDB entry 4V53). Within the 829 → 851 region there are 14 possible 10-mer 16S rRNA stretches to choose as a target. The deviation of their score is just 4.4 so all of them scored highly. Narrowing the selection can be made using some additional characteristics that were not included in the computation of the global score. Table S7 lists all possible 10-nucleotide long targets along with their

Figure 8. Structure of the 30S ribosomal subunit (PDB entry 4GD2): (a) solvent-exposed and (b) 50S-subunit interaction side. Proteins are shown in gray and RNA in blue. Selected regions listed in Table 1 are in green.

score, purine percentage and conservation level. For experimental verification, we selected the 830 → 839 target. Inhibition of Bacterial Growth by a PNA Oligomer Targeted to 16S rRNA 830−839 Region. A PNA oligomer complementary to the 830−839 region of 16S RNA was G


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Typhimurium treated with peptide−PNA are shown in Figures S6 and S7, respectively. For the (KFF)3K peptide alone the MIC values were higher than 25 μM for E. coli and 20 μM for S. Typhimurium. We also checked the influence of a control noncomplementary peptide−PNA sequence and MIC for this sequence in E. coli was higher than 25 μM, i.e., the highest concentrations used in this study for the inhibitor peptide− PNA.

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CONCLUSION We have used a range of physicochemical and bioinformaticsbased criteria to score the ribosome rRNA sites and predict the capability of oligomer hybridization to these sites, as well as its effectiveness in inhibition of protein synthesis. As a test oligomer we used PNA. PNA hybridization depends on such features as rRNA target accessibility, secondary and tertiary structures of the target, and its flexibility. Dynamic properties were derived from all-atom molecular dynamics simulation of the small ribosomal subunit. This approach required calculating and merging various types of data into one global score for each 16S rRNA nucleotide stretch. In addition, we analyzed the functionality of the rRNA regions in order to ensure not only binding of the oligonucleotide but also inhibition of the ribosome function. We confirmed the inhibition of bacterial growth by adding a PNA oligomer with a sequence targeted to one of the best scored sites in 16S rRNA in E. coli and S. enterica serovar Typhimurium, whose rRNA had the same sequence in this particular site. However, future studies are needed to test other sites that scored well to be able to generate data on the biological effect of the oligomers. We have developed a comprehensive protocol to predict the best possible sites in 16S rRNA for targeting by PNA. We integrated various 16S rRNA features that could impact the hybridization of PNA oligomers to the rRNA targets. The protocol can be adjusted for different classes of synthetic oligonucleotides, not only PNA. Although PNA was found to be nontoxic to human cells, it has problems with solubility if it contains too many purines; its performance depends on the used broth, and transport to bacterial cells requires a cellpenetrating peptide.40 Also, an analogous protocol could be used to design oligonucleotides for other nucleic acid targets.

Figure 9. Region of nucleotides 829 → 851 for targeting with PNA (in orange) marked on the structure of the 30S subunit and the selected 10-mer target 830 → 839 (in red). 16S rRNA is in blue, and ribosomal proteins are in cyan.

selected for the experiment. There was only one perfect alignment of this region to E. coli 16S rRNA and the best match to 23S rRNA comprised a gap. Also, the reverse complement of the target and control sequence was aligned against E. coli rRNA to ensure it does not produce any significant alignments. Even though the antiparallel PNA/RNA duplex formation is preferred (5′ RNA to C-ter PNA), PNA binding in the parallel mode cannot be excluded and had to be verified in terms of sequence complementarity.44 The best alignments were characterized with eight mismatches for 16S rRNA and three mismatches for 23S rRNA. Considering high sensitivity of PNA to mismatches, the selected region seemed to be a good target. Bacterial cells do not uptake nucleic acid oligomers, so PNA was attached to a cell penetrating peptide (KFF)3K, which is used to deliver PNA to bacterial cells by many researchers working with PNA.11,45 However, the efficiency of delivery of PNA by this peptide has not been quantified and may depend on bacterial cell types. In the experiment, various concentrations of peptide−PNA were added to either E. coli or S. enterica serovar Typhimurium cultures. Figure 10A shows

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jpcb.6b02081. Tables S1−S7 and Figures S1−S7 (PDF)

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Figure 10. Concentration-dependent E. coli (A) and S. enterica serovar Typhimurium (B) growth inhibition after 20 h of incubation by the peptide−PNA. G.C. denotes growth control, i.e., bacterial culture without the peptide−PNA. Error bars represent the mean ± SEM for three independent experiments. The differences between G.C. and tested samples are denoted as not significant (P ≥ 0.05), marginally significant (P < 0.05)*, and highly significant (P < 0.001)***.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]; phone: +48 225543600. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS J.T. is grateful to Prof. Andy McCammon for research mentorship during her postdoctoral work and many years after, as well as continuous encouragement and support. A.G. and J.T. thank Marcin Grynberg for helpful discussions. A.M.Z. thanks Nadja Patenge for helpful suggestions on experimental procedures pertaining to PNA. Authors acknowledge support

that after 20 h, E. coli growth was completely inhibited at concentrations of peptide−PNA of 15, 20, and 25 μM giving MIC equal to 15 μM. A visible decrease of E. coli growth was also observed with 10 μM of peptide−PNA. The growth of S. Typhimurium was completely inhibited at peptide−PNA concentrations of 5, 10, and 20 μM giving MIC of 5 μM (Figure 10B). The kinetics of bacterial growth of E. coli and S. H


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(20) Cannone, J. J.; Subramanian, S.; Schnare, M. N.; Collett, J. R.; D’Souza, L. M.; Du, Y.; Feng, B.; Lin, N.; Madabusi, L. V.; Müller, K. M.; et al. The comparative RNA web (CRW) site: an online database of comparative sequence and structure information for ribosomal, intron, and other RNAs. BMC Bioinf. 2002, 3, 2. (21) Altschul, S.; Gish, W.; Miller, W.; Myers, E. W.; Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 1990, 215, 403−410. (22) Cock, P. J. a.; Antao, T.; Chang, J. T.; Chapman, B. a.; Cox, C. J.; Dalke, A.; Friedberg, I.; Hamelryck, T.; Kauff, F.; Wilczynski, B.; et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics 2009, 25, 1422− 1423. (23) Turnbaugh, P. J.; Ley, R. E.; Hamady, M.; Fraser-Liggett, C.; Knight, R.; Gordon, J. I. The human microbiome project: exploring the microbial part of ourselves in a changing world. Nature 2007, 449, 804−810. (24) Dunkle, J. A.; Wang, L.; Feldman, M. B.; Pulk, A.; Chen, V. B.; Kapral, G. J.; Noeske, J.; Richardson, J. S.; Blanchard, S. C.; Cate, J. H. D. Structures of the bacterial ribosome in classical and hybrid states of tRNA binding. Science 2011, 332, 981−984. (25) Górska, A.; Jasiński, M.; Trylska, J. MINT: software to identify motifs and short-range interactions in trajectories of nucleic acids. Nucleic Acids Res. 2015, 43, e114. (26) Wang, J.; Cieplak, P.; Kollman, P. A. How well does a restrained electrostatic potential (RESP) model perform in calculating conformational energies of organic and biological molecules? J. Comput. Chem. 2000, 21, 1049−1074. (27) Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graphics 1996, 14, 33−38. (28) Lee, B.; Richards, F. M. The interpretation of protein structures: estimation of static accessibility. J. Mol. Biol. 1971, 55, 379−400. (29) Connolly, M. L. Solvent-accessible surfaces of proteins and nucleic acids. Science 1983, 221, 709−713. (30) Lorenz, R.; Bernhart, S. H.; Höner Zu Siederdissen, C.; Tafer, H.; Flamm, C.; Stadler, P. F.; Hofacker, I. L. ViennaRNA Package 2.0. Algorithms Mol. Biol. 2011, 6, 26. (31) Mathews, D. H.; Disney, M. D.; Childs, J. L.; Schroeder, S. J.; Zuker, M.; Turner, D. H. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl. Acad. Sci. U. S. A. 2004, 101, 7287−7292. (32) Popenda, M.; Błazewicz, M.; Szachniuk, M.; Adamiak, R. W. RNA FRABASE version 1.0: an engine with a database to search for the three-dimensional fragments within RNA structures. Nucleic Acids Res. 2008, 36, D386−D391. (33) Sanbonmatsu, K. Y. Computational studies of molecular machines: The ribosome. Curr. Opin. Struct. Biol. 2012, 22, 168−174. (34) Trylska, J. Simulating activity of the bacterial ribosome. Q. Rev. Biophys. 2009, 42, 301−316. (35) Trylska, J. Coarse-grained models to study dynamics of nanoscale biomolecules and their applications to the ribosome. J. Phys.: Condens. Matter 2010, 22, 453101. (36) Phillips, J. C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R. D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem. 2005, 26, 1781− 1802. (37) Denning, E. J.; Priyakumar, U. D.; Nilsson, L.; MacKerell, A. D. Impact of 2′-hydroxyl sampling on the conformational properties of RNA: Update of the CHARMM all-atom additive force field for RNA. J. Comput. Chem. 2011, 32, 1929−1943. (38) Kuzmanic, A.; Zagrovic, B. Determination of Ensemble-Average Pairwise Root Mean-Square Deviation from Experimental B-Factors. Biophys. J. 2010, 98, 861−871. (39) Wiegand, I.; Hilpert, K.; Hancock, R. E. W. Agar and broth dilution methods to determine the minimal inhibitory concentration (MIC) of antimicrobial substances. Nat. Protoc. 2008, 3, 163−175. (40) Good, L.; Awasthi, S. K.; Dryselius, R.; Larsson, O.; Nielsen, P. E. Bactericidal antisense effects of peptide-PNA conjugates. Nat. Biotechnol. 2001, 19, 360−364.

from the National Science Centre (DEC-2012/05/B/NZ1/ 00035 and DEC-2014/12/W/ST5/00589). Calculations were performed using the McCammon group resources at UCSD (National Biomedical Computation Resource, NIH P41 GM103426) and resources of the Interdisciplinary Centre for Mathematical and Computational Modelling, U. Warsaw (ICM/KDM G31-4).

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REFERENCES

(1) Martinez, J. L. The role of natural environments in the evolution of resistance traits in pathogenic bacteria. Proc. R. Soc. London, Ser. B 2009, 276, 2521−2530. (2) Green, R.; Noller, H. F. Ribosomes and Translation. Annu. Rev. Biochem. 1997, 66, 679−716. (3) Wilson, D. N. The AâĂ ŞZ of bacterial translation inhibitors. Crit. Rev. Biochem. Mol. Biol. 2009, 44, 393−433. (4) McCoy, L. S.; Xie, Y.; Tor, Y. Antibiotics that target protein synthesis. Wiley Interdiscip. Rev. RNA 2011, 2, 209−232. (5) Van de Peer, Y.; Chapelle, S.; De Wachter, R. A quantitative map of nucleotide substitution rates in bacterial rRNA. Nucleic Acids Res. 1996, 24, 3381−3391. (6) Handelsman, J. Metagenomics: Application of Genomics to Uncultured Microorganisms. Microbiol. Mol. Biol. Rev. 2004, 68, 669− 685. (7) Wu, D.; Jospin, G.; Eisen, J. A. Systematic Identification of Gene Families for Use as ”Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLoS One 2013, 8, e77033. (8) Rasmussen, L.; Sperling-Petersen, H.; Mortensen, K. Hitting bacteria at the heart of the central dogma: sequence-specific inhibition. Microb. Cell Fact. 2007, 6, 24. (9) Opalińska, J. B.; Gewirtz, A. M. Nucleic-acid therapeutics: basic principles and recent applications. Nat. Rev. Drug Discovery 2002, 1, 503−514. (10) Folini, M.; Zaffaroni, N. Targeting telomerase by antisensebased approaches: perspectives for new anti-cancer therapies. Curr. Pharm. Des. 2005, 11, 1105−1117. (11) Hatamoto, M.; Ohashi, A.; Imachi, H. Peptide nucleic acids (PNAs) antisense effect to bacterial growth and their application potentiality in biotechnology. Appl. Microbiol. Biotechnol. 2010, 86, 397−402. (12) Trylska, J.; Thoduka, S. G.; Da̧browska, Z. Using sequencespecific oligonucleotides to inhibit bacterial rRNA. ACS Chem. Biol. 2013, 8, 1101−1109. (13) Nielsen, P. E. Peptide nucleic acid. A molecule with two identities. Acc. Chem. Res. 1999, 32, 624−630. (14) SantaLucia, J., Jr.; Hicks, D. Thermodynamics of DNA Structural Motifs. Annu. Rev. Biophys. Biomol. Struct. 2004, 33, 415− 440. (15) Xia, T.; SantaLucia, J.; Burkard, M. E.; Kierzek, R.; Schroeder, S. J.; Jiao, X.; Cox, C.; Turner, D. H. Thermodynamic parameters for an expanded nearest-neighbor model for formation of RNA duplexes with Watson-Crick base pairs. Biochemistry 1998, 37, 14719−14735. (16) Berman, H. M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T. N.; Weissig, H.; Shindyalov, I. N.; Bourne, P. E. The Protein Data Bank. Nucleic Acids Res. 2000, 28, 235−242. (17) Yassin, A.; Fredrick, K.; Mankin, A. S. Deleterious mutations in small subunit ribosomal RNA identify functional sites and potential targets for antibiotics. Proc. Natl. Acad. Sci. U. S. A. 2005, 102, 16620− 16625. (18) Yassin, A.; Mankin, A. S. Potential new antibiotic sites in the ribosome revealed by deleterious mutations in RNA of the large ribosomal subunit. J. Biol. Chem. 2007, 282, 24329−24342. (19) Chakravorty, S.; Helb, D.; Burday, M.; Connell, N.; Alland, D. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 2007, 69, 330− 339. I


Article

The Journal of Physical Chemistry B (41) Patenge, N.; Pappesch, R.; Krawack, F.; Walda, C.; Mraheil, M. A.; Jacob, A.; Hain, T.; Kreikemeyer, B. Inhibition of Growth and Gene Expression by PNA-peptide Conjugates in Streptococcus pyogenes. Mol. Ther.–Nucleic Acids 2013, 2, e132. (42) Abelian, A.; Walsh, A. P.; Lentzen, G.; Aboul-Ela, F.; Gait, M. J. Targeting the A site RNA of the Escherichia coli ribosomal 30 S subunit by 2′-O-methyl oligoribonucleotides: a quantitative equilibrium dialysis binding assay and differential effects of aminoglycoside antibiotics. Biochem. J. 2004, 383, 201−208. (43) Hatamoto, M.; Nakai, K.; Ohashi, A.; Imachi, H. Sequencespecific bacterial growth inhibition by peptide nucleic acid targeted to the mRNA binding site of 16S rRNA. Appl. Microbiol. Biotechnol. 2009, 84, 1161−1168. (44) Nielsen, P. E., Ed. Peptide Nucleic Acids Protocols and Applications, 2nd ed.; Horizon Bioscience: Norfolk, U.K., 2004. (45) Soofi, M. A.; Seleem, M. N. Targeting Essential Genes in Salmonella enterica Serovar Typhimurium with Antisense Peptide Nucleic Acid. Antimicrob. Agents Chemother. 2012, 56, 6407−6409.

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