Introduction Much of modern biology deals with understanding and predicting macromolecular interactions. The biotechnological possibilities inherent in being able to predict, design and manipulate macromolecular interactions are immense. The well-understood Watson-Crick pairing between nucleic acid strands facilitates the design of nucleic acids that can interact with specific DNA or RNA sequences, and this ability underlies a huge swathe of modern research and biotechnology. Given the greater functional potentialities of proteins compared to nucleic acids and the ability to target proteins to different intracellular compartments, new opportunities would emerge from the ability to design proteins to bind specific RNA or DNA sequences. Unfortunately, most protein-nucleic acid interactions are idiosyncratic, and lack the predictability necessary to engineer specific interactions. Recently, a great deal of excitement has accompanied the characterization of Transcription-Activator-Like Effectors (TALEs), a set of modular repeat proteins that bind via a predictable code to specific double-stranded DNA sequences [1], [2]. TALEs belong to the alpha-solenoid superfamily comprising proteins that consist of degenerate repeats of 30–40 amino acids, each of which forms two or three alpha-helices. This superfamily includes only one well characterized member that binds RNA: the Puf domain family. Puf domains consist of eight tandem repeats of a triple-helix motif that bind 8–9 nucleotide sites (reviewed in [3]). The residues within each motif that dictate sequence specificity have been identified, and experiments to manipulate binding specificity and protein function by exploiting this modular recognition have been successful [3], [4], [5]. This study focuses on a second class of helical repeat motif that binds RNA, the pentatricopeptide repeat (PPR). PPR proteins harbor degenerate ∼35 amino acid repeats that are related to tetratricopeptide (TPR) motifs [6]. PPR proteins localize primarily to mitochondria and chloroplasts where they influence various aspects of RNA metabolism [7]. Many PPR proteins are essential for photosynthesis or respiration, and mutations in PPR-encoding genes are associated with genetic diseases in humans (e.g. [8]). Although less widely known than Pufs and TALEs, PPR proteins are much more prevalent in nature. Protist, fungal and metazoan genomes encode roughly 5–50 PPR proteins, but the family has expanded to >400 members in plants (reviewed in [9]). The products of evolution illustrate the apparent ease with which PPR tracts can be modified to bind diverse sequences and mediate diverse functions: PPR proteins harbor between 2 and ∼30 repeats and they influence the processing, editing, splicing, stability or translation of specific organellar RNAs [7]. The remarkable evolutionary plasticity of PPR proteins is highlighted by their natural exploitation to silence rapidly evolving mitochondrial open reading frames that confer cytoplasmic male sterility in plants [10]. Results presented here demonstrate that PPR tracts bind RNA via a modular mechanism that conceptually resembles Puf-RNA recognition. However, the details of nucleotide recognition by PPR motifs differ from those for Puf repeats, revealing a diversity of independently evolved RNA recognition modes by alpha solenoid repeats. These insights provide a significant step toward the prediction of binding sites and functions for the large number of PPR proteins found in nature. Additionally, the evolutionary malleability of the PPR family implies that PPR binding specificities can be engineered to match a wide variety of desired targets. Results To develop models for sequence-specific RNA recognition by PPR tracts, we began with a focus on the maize protein PPR10, whose binding sites and mechanisms are particularly well understood [11], [12]. PPR10 consists of 19 PPR motifs and little else. PPR10 localizes to chloroplasts, and binds two different RNAs via cis-elements with considerable sequence similarity. PPR10 serves to position processed mRNA termini and stabilize adjacent RNA segments in vivo by blocking exoribonucleases intruding from either direction. PPR10 Binds RNA as a Monomer Recombinant PPR10 (rPPR10) elutes from a gel filtration column at a position corresponding to a globular homodimer [11], as does HCF152, which likewise consists almost entirely of PPR motifs [13]. Models for PPR-RNA interaction would need to incorporate homodimerization, should this be physiologically relevant. To clarify this point, we analyzed rPPR10 by sedimentation velocity analytical ultracentrifugation (SV-AUC). rPPR10 was found predominantly in two forms whose ratio changed in a concentration-dependent fashion (Figure 1A). At 3 µM, the major species sedimented at ∼5 S and had an estimated molecular weight of 84.9 kDa, close to rPPR10's monomeric molecular weight of 82.6 kDa. A two-fold increase in rPPR10 concentration shifted the distribution toward a larger species (∼6.5 S), which predominated when protein concentration was further increased to 12 µM. These results strongly suggest the ∼5 S and 6.5 S species to be monomers and dimers, respectively. Thus, rPPR10 can dimerize, but only at very high concentrations. 10.1371/journal.pgen.1002910.g001 Figure 1 Sedimentation Velocity Analytical Ultracentrifugation of rPPR10 and rPPR10/RNA Complexes. (A) SV-AUC analysis of rPPR10 at 3, 6, and 12 µM. (B) SV-AUC analysis of rPPR10 (3 µM) in the presence of its 17-nt minimal RNA ligand (1.5 µM or 3 µM). The assignment of the two species at ∼5S in the top panel as either PPR10 monomer or PPR10/RNA is ambiguous, as variation in apparent S value can result when multiple species of similar abundance are in equilibrium. The root-mean-squared-deviations ranged between .007 and .013. The trace species at low S values may result from contaminating MBP and TEV protease, whereas those of larger size may represent higher order PPR10 oligomers. To determine which form of PPR10 binds RNA, rPPR10 was analyzed by SV-AUC in the presence of its 17-nt minimal RNA ligand. This RNA is small in comparison with rPPR10 (5 kDa versus 84 kDa) and does not contribute significant signal with the interference optical system used for these experiments. With rPPR10 at 3 µM and RNA at half that concentration, PPR10 monomers partitioned into two species of similar abundance with an S value near 5 S (Figure 1B). The concentration, sedimentation rate, and RNA-dependence of the second ∼5S species strongly suggest it to be a PPR10 monomer bound to RNA. The pair of species near 5S collapsed into a single ∼5 S species when the RNA concentration was increased to be equimolar with PPR10 (3 µM). As this concentration is much higher than the Kd for the PPR10-RNA interaction ( C, C>U, or U = C. With this knowledge, the engineering of PPR tracts to bind a wide variety of RNA sequences is within reach. However, prediction of the natural binding sites of PPR proteins, and prediction of off-target binding by engineered PPR proteins remains challenging for two reasons. First, the natural diversity of amino acid identities at positions 6 and 1′ implies a degenerate code, and less than two-thirds of naturally occurring combinations can currently be interpreted. Second, an understanding of the energetic parameters required to establish a physiologically meaningful PPR/RNA interaction and the energetic costs of mismatches at various positions along a PPR/RNA duplex will be required to accurately predict potential binding sites. The prediction of microRNA targets is similar in concept and provides a glimpse into the challenge to come: despite the simplicity of RNA base pairing rules, the parameters that dictate microRNA targets are still being worked out [21]. Prediction of binding sites is further complicated by the fact that gaps in a PPR/RNA duplex can be tolerated in some contexts, as exemplified by PPR10's natural targets (Figure 2A). Indeed, the optimal alignments of the P-class PPR proteins HCF152 and CRP1 also contain a gap, with the predicted protein/RNA duplex containing non-contiguous segments of either RNA (PPR10 and CRP1) or protein (HCF152). These gaps break the protein-RNA duplex into two segments in a manner that resembles Puf-RNA duplexes, which require contiguous protein-RNA matches at each end but can accommodate various flipped base conformations in the central region [22]. Our findings imply considerable flexibility in the length of the “looped out” RNA between contiguous PPR-RNA segments. These RNA loops may be analogous to internal loops in RNA duplexes, which adopt diverse architectures due to the great flexibility of the RNA backbone and to the wealth of opportunities for non-canonical base-base interactions (reviewed in [23], [24]). Our alignments of P-class PPR proteins to their cognate RNAs include contiguous duplexes consisting of no more than nine motifs and eight nucleotides. This is reminiscent of the binding of 8–9 nucleotides by the eight repeats in Puf proteins (reviewed in [25]). The number of contiguous interactions between helical repeats and RNA bases may be constrained by the minimum distance between parallel alpha helices. The minimum theoretical helix-helix distance is c. 9.5 Å [26], which is approached by the helix-helix distance in Puf motifs [27]. In contrast, adjacent nucleotides in Puf:RNA complexes are 7 Å apart, close to the maximally extended conformation, and resulting in a distance mismatch that is only partially accommodated by curvature of the RNA-binding surface. A similar constraint may limit the maximum number of contiguous RNA bases bound by tandem PPR motifs. There is no evidence for gaps in the alignments between PLS-class editing factors and their RNA targets. However, the representation of amino acids at position 6 differs between P and S versus L-type PPR motifs. Thus, we suspect that L motifs do not bind nucleotide bases, allowing a ‘mini-gap’ every third nucleotide that may relax the structural constraints. The well-defined code for RNA recognition by Puf domains provides a means to engineer proteins to bind specified RNA sequences. Results presented here imply that PPR tracts could be exploited for similar purposes. In fact, PPR tracts may well offer functionalities beyond those achievable with engineered Puf domains due to their more flexible architecture. Unlike Puf domains, whose 8-repeat organization is conserved throughout the eucaryotes, natural PPR proteins have between 2 and ∼30 repeats and rapidly evolve to bind new RNA sequences and fulfill new functions (reviewed in [9]). The unusually long surface for RNA interaction that is presented by long PPR tracts has the potential to sequester an extended RNA segment, which can impact RNA function in novel ways [12]. PPR proteins play essential roles in all eucaryotes by enabling the expression of specific mitochondrial and chloroplast genes. Even for well-studied PPR proteins such as human LRPPRC (e.g. [8]), the exact binding sites still await discovery. The results and approaches described here offer the potential to eliminate this bottleneck by permitting candidate sites to be postulated from simple sequence analysis, providing information that will have broad application in the medical and agricultural sciences. Materials and Methods Expression of rPPR10 rPPR10 and its variants were expressed in E. coli and purified as in [11]. In brief, mature PPR10 (lacking the plastid targeting peptide) was expressed as a fusion to maltose binding protein (MBP), purified by amylose affinity chromatography, separated from MBP by cleavage with TEV protease, and further purified by gel filtration chromatography in 250 mM NaCl, 50 mM Tris-HCl pH 7.5, 5 mM ß-mercaptoethanol. The elution peak was diluted in the same buffer for AUC, or dialyzed against 400 mM NaCl, 50 mM Tris-HCl pH 7.5, 5 mM ß-mercaptoethanol, 50% glycerol prior to use in RNA binding assays. PPR10 variants were obtained by PCR-mutagenesis using the following primers (lower case indicates mutations): TD Variant: 5′ GGTCTGTTGCCAgACGCATTCACG; 5′ CGTGAATGCGTcTGGCAACAGACC; 5′ GCTGTGACGTACAcCGAGCTCGCCGGAACG ; 5′ CGTTCCGGCGAGCTCGgTGTACGTCACAGC ; 5′ CACCTGGAGCAACGCGgTGTACGTGACGACGCAC. TN Variant: 5′ CGTGAATGCGTtTGGCAACAGACCC; 5′ GGGTCTGTTGCCAaACGCATTCACG ; 5′ GAACGGCTGCCAGCCAaAcGCTGTGACGTAC ; 5′ CGgTGTACGTCACAGCgTtTGGCTGGCAGCCG. NN Variant: 5′ GGAGCAGAACGGCTGCCAGCCAaacGCTGTGACG; 5′ CGTCACAGCgttTGGCTGGCAGCCGTTCTGCTCC. ND Variant: 5′ GGTCTGTTGCCAgACGCATTCACG; 5′ CGTGAATGCGTcTGGCAACAGACC. NS Variant: 5′ GCTGCCAGCCAagcGCTGTGACG; 5′ CGTCACAGCgctTGGCTGGCAGC;5′ GTCTGTTGCCAagcGCATTCACGTACAACACC; 5′ GGTGTTGTACGTGAATGCgctTGGCAACAGAC Analytical Ultracentrifugation SV-AUC was performed in a Beckman Optima XL-I ultracentrifuge with a Beckman An60Ti rotor. 400 µl of sample and 410 µl of reference buffer were analyzed in a 1.2 cm double-sector standard AUC cell. Experiments were run at 20°C at 50,000 rpm and monitored with an interference optical system. Data were collected at 3 min intervals for 8 hrs, and analyzed with SedFit [28], using a partial specific volume for rPPR10 of 0.73543 calculated from its amino acid composition. The residuals in all experiments were randomly distributed, and 95% of the residuals had a value C, 5′-3′; reverse indicates N->C, 3′-5′. Offset: distance from start of RNA sequence to first PPR motif. Gap position: nucleotide at which gap introduced between protein motifs. Gap length: length of gap in nucleotides. 17-mer: position (from 1 to 35) within the PPR motifs used to constitute the 17-mer sequence of amino acids used for the alignment. P-value: probability that amino acids and nucleotides are arranged independently of each other, as calculated by Fisher's Exact Test. None of the 29400 alignments exceed the threshold for significance at the 5% level if a threshold corrected for the total number of tests is used (5% threshold using the Šidák correction = 1.74E-06). (PDF) Click here for additional data file. Table S2 Correlations between amino acids at specific positions within PPR motifs and aligned nucleotides. Contingency tables (amino acids versus nucleotides) were constructed from the alignments in Figure 2 and Figure S1. Each 20×4 table was tested for independent assortment of amino acids and nucleotides using a chi-squared test (after first removing any empty rows from the table). P-values from the tests are shown in the table, with those values that are significant for both P and S motifs highlighted (a 1% significance threshold was used, corrected for multiple tests using the Šidák correction). Rows: amino acid positions within the motifs. Columns: 0 indicates the motif aligned with the nucleotide, −1 the preceding motif, +1 the following motif. (PDF) Click here for additional data file. Table S3 Correlations between amino acids at positions 6, 1′ and aligned nucleotides. The tables show frequencies of co-occurrence of amino acids and nucleotides from the alignments in Figure 2 and Figure S1. A. P motifs, positions 6, 1′ versus each nucleotide. B. S motifs, positions 6, 1′ versus each nucleotide. C. P motifs, position 6 versus purines (R), pyrimidines (Y). D. S motifs, position 6 versus purines (R), pyrimidines (Y). P-values were calculated using G-tests. P-values in A and B are for the most positively correlated nucleotide. Significance was evaluated at 5% allowing for multiple testing (using the Šidák correction). Green shading indicates significantly correlated, magenta shading indicates significantly anti-correlated. (PDF) Click here for additional data file.
