Computational analysis of hydrogen bonds in protein-RNA complexes for interaction patterns

Structural analysis of protein-RNA complexes is labor-intensive, yet provides insight into the interaction patterns between a protein and RNA. As the number of protein-RNA complex structures reported has increased substantially in the last few years, a systematic method is required for automatically identifying interaction patterns. This paper presents a computational analysis of the hydrogen bonds in the most representative set of protein-RNA complexes. The analysis revealed several interesting interaction patterns. (1) While residues in the beta-sheets favored unpaired nucleotides, residues in the helices showed no preference and residues in turns favored paired nucleotides. (2) The backbone hydrogen bonds were more dominant than the base hydrogen bonds in the paired nucleotides, but the reverse was observed in the unpaired nucleotides. (3) The protein-RNA complexes contained more paired nucleotides than unpaired nucleotides, but the unpaired nucleotides were observed more frequently interacting with the proteins. And (4) Arg-U, Thr-A, Lys-A, and Asn-U were the most frequently observed pairs. The interaction patterns discovered from the analysis will provide us with useful information in predicting the structure of the RNA binding protein and the structure of the protein binding RNA.     

Discovering the interaction propensities of amino acids and nucleotides from protein-RNA complexes

With the availability of many genome sequences, the mining of biological data is attracting much attention, most of it limited to the sequences of macromolecules. Sequence data are easy to analyze as they can be treated as strings of characters, whereas the structure of a macromolecule is much more complex. We developed a set of algorithms to analyze the structures of protein-RNA complexes at the atomic level and used them to analyze protein-RNA interactions using structural data on 51 protein-RNA complexes. The analysis revealed, among other things, that: (1) polar and charged amino acids have a strong tendency to interact with nucleotides, (2) arginine and asparagine tend to hydrogen bond with uracil, and (3) histidine favors uracil in water-mediated bonding with RNA. We analyzed a large set of structural data of protein-RNA complexes involving water-mediated hydrogen bonds as well as direct hydrogen bonds. The interaction patterns discovered from the analysis provide useful information for predicting the structure of RNA that binds proteins, and of proteins that bind RNA.     

Cation-pi interactions at non-redundant protein-RNA interfaces

Cation-pi interactions have proved to be important in proteins and protein-ligand complexes. Here, cation-pi interactions are analyzed for 282 non-redundant protein-RNA interfaces. The statistical results show that this kind of interactions exists in 65% of the interfaces. The four RNA bases are ranked as Gua > Ade > Ura > Cyt according to their propensity to participate in cation-pi interactions. The corresponding ranking for the involved amino acid residues is: Arg > Lys > Asn > Gln. The same trends are obtained based on the empirical energy calculation. The Arg-Gua pairs have the greatest stability and are also most frequently observed. The number of cation-pi pairs involving unpaired bases is 2.5 times as many as those involving paired bases. Hence, cation-pi interactions show sequence and structural specificities. For the bicyclic bases, Gua and Ade, their 5-atom rings participate in cation-pi interactions somewhat more than the 6-atom rings, with percentages of 54 and 46%, respectively, which is due to the higher cation-pi participation proportion (63%) of 5-atom rings in the paired bases. These results give a general view of cation-pi interactions at protein-RNA interfaces and are helpful in understanding the specific recognition between protein and RNA.     

Molecular architecture of protein-RNA recognition sites

The molecular architecture of protein-RNA interfaces are analyzed using a non-redundant dataset of 152 protein-RNA complexes. We find that an average protein-RNA interface is smaller than an average protein-DNA interface but larger than an average protein–protein interface. Among the different classes of protein-RNA complexes, interfaces with tRNA are the largest, while the interfaces with the single-stranded RNA are the smallest. Significantly, RNA contributes more to the interface area than its partner protein. Moreover, unlike protein–protein interfaces where the side chain contributes less to the interface area compared to the main chain, the main chain and side chain contributions flipped in protein-RNA interfaces. We find that the protein surface in contact with the RNA in protein-RNA complexes is better packed than that in contact with the DNA in protein-DNA complexes, but loosely packed than that in contact with the protein in protein–protein complexes. Shape complementarity and electrostatic potential are the two major factors that determine the specificity of the protein-RNA interaction. We find that the H-bond density at the protein-RNA interfaces is similar with that of protein-DNA interfaces but higher than the protein–protein interfaces. Unlike protein-DNA interfaces where the deoxyribose has little role in intermolecular H-bonds, due to the presence of an oxygen atom at the 2′ position, the ribose in RNA plays significant role in protein-RNA H-bonds. We find that besides H-bonds, salt bridges and stacking interactions also play significant role in stabilizing protein-nucleic acids interfaces; however, their contribution at the protein–protein interfaces is insignificant.     

PRI-Modeler: extracting RNA structural elements from PDB files of protein-RNA complexes

A complete understanding of protein and RNA structures and their interactions is important for determining the binding sites in protein-RNA complexes. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA, due in part to the very limited structural data so far available. We have developed a set of algorithms for extracting and visualizing secondary and tertiary structures of RNA and for analyzing protein-RNA complexes. These algorithms have been implemented in a web-based program called PRI-Modeler (protein-RNA interaction modeler). Given one or more protein data bank files of protein-RNA complexes, PRI-Modeler analyzes the conformation of the RNA, calculates the hydrogen bond (H bond) and van der Waals interactions between amino acids and nucleotides, extracts secondary and tertiary RNA structure elements, and identifies the patterns of interactions between the proteins and RNAs. This paper presents PRI-Modeler and its application to the hydrogen bond and van der Waals interactions in the most representative set of protein-RNA complexes. The analysis reveals several interesting interaction patterns at various levels. The information provided by PRI-Modeler should prove useful for determining the binding sites in protein-RNA complexes. PRI-Modeler is accessible at http://wilab.inha.ac.kr/primodeler/, and supplementary materials are available in the analysis results section at http://wilab.inha.ac.kr/primodeler/.     

The role of RNA sequence and structure in RNA--protein interactions

We investigate the sequence and structural properties of RNA-protein interaction sites in 211 RNA-protein chain pairs, the largest set of RNA-protein complexes analyzed to date. Statistical analysis confirms and extends earlier analyses made on smaller data sets. There are 24.6% of hydrogen bonds between RNA and protein that are nucleobase specific, indicating the importance of both nucleobase-specific and -nonspecific interactions. While there is no significant difference between RNA base frequencies in protein-binding and non-binding regions, distinct preferences for RNA bases, RNA structural states, protein residues, and protein secondary structure emerge when nucleobase-specific and -nonspecific interactions are considered separately. Guanine nucleobase and unpaired RNA structural states are significantly preferred in nucleobase-specific interactions; however, nonspecific interactions disfavor guanine, while still favoring unpaired RNA structural states. The opposite preferences of nucleobase-specific and -nonspecific interactions for guanine may explain discrepancies between earlier studies with regard to base preferences in RNA-protein interaction regions. Preferences for amino acid residues differ significantly between nucleobase-specific and -nonspecific interactions, with nonspecific interactions showing the expected bias towards positively charged residues. Irregular protein structures are strongly favored in interactions with the protein backbone, whereas there is little preference for specific protein secondary structure in either nucleobase-specific interaction or -nonspecific interaction. Overall, this study shows strong preferences for both RNA bases and RNA structural states in protein-RNA interactions, indicating their mutual importance in protein recognition.     

Dissecting protein-RNA recognition sites

We analyze the protein–RNA interfaces in 81 transient binary complexes taken from the Protein Data Bank. Those with tRNA or duplex RNA are larger than with single-stranded RNA, and comparable in size to protein–DNA interfaces. The protein side bears a strong positive electrostatic potential and resembles protein–DNA interfaces in its amino acid composition. On the RNA side, the phosphate contributes less, and the sugar much more, to the interaction than in protein–DNA complexes. On average, protein–RNA interfaces contain 20 hydrogen bonds, 7 that involve the phosphates, 5 the sugar 2′OH, and 6 the bases, and 32 water molecules. The average H-bond density per unit buried surface area is less with tRNA or single-stranded RNA than with duplex RNA. The atomic packing is also less compact in interfaces with tRNA. On the protein side, the main chain NH and Arg/Lys side chains account for nearly half of all H-bonds to RNA; the main chain CO and side chain acceptor groups, for a quarter. The 2′OH is a major player in protein–RNA recognition, and shape complementarity an important determinant, whereas electrostatics and direct base–protein interactions play a lesser part than in protein–DNA recognition.     

Structure-based analysis of protein-RNA interactions using the program ENTANGLE

Until recently, drawing general conclusions about RNA recognition by proteins has been hindered by the paucity of high-resolution structures. We have analyzed 45 PDB entries of protein-RNA complexes to explore the underlying chemical principles governing both specific and non-sequence specific binding. To facilitate the analysis, we have constructed a database of interactions using ENTANGLE, a JAVA-based program that uses available structural models in their PDB format and searches for appropriate hydrogen bonding, stacking, electrostatic, hydrophobic and van der Waals interactions. The resulting database of interactions reveals correlations that suggest the basis for the discrimination of RNA from DNA and for base-specific recognition. The data illustrate both major and minor interaction strategies employed by families of proteins such as tRNA synthetases, ribosomal proteins, or RNA recognition motifs with their RNA targets. Perhaps most surprisingly, specific RNA recognition appears to be mediated largely by interactions of amide and carbonyl groups in the protein backbone with the edge of the RNA base. In cases where a base accepts a proton, the dominant amino acid donor is arginine, whereas in cases where the base donates a proton, the predominant acceptor is the backbone carbonyl group, not a side-chain group. This is in marked contrast to DNA-protein interactions, which are governed predominantly by amino acid side-chain interactions with functional groups that are presented in the accessible major groove. RNA recognition often proceeds through loops, bulges, kinks and other irregular structures that permit use of all the RNA functional groups and this is seen throughout the protein-RNA interaction database.     

Recognition of nucleic acid bases and base-pairs by hydrogen bonding to amino acid side-chains

Sequence-specific protein-nucleic acid recognition is determined, in part, by hydrogen bonding interactions between amino acid side-chains and nucleotide bases. To examine the repertoire of possible interactions, we have calculated geometrically plausible arrangements in which amino acids hydrogen bond to unpaired bases, such as those found in RNA bulges and loops, or to the 53 possible RNA base-pairs. We find 32 possible interactions that involve two or more hydrogen bonds to the six unpaired bases (including protonated A and C), 17 of which have been observed. We find 186 "spanning" interactions to base-pairs in which the amino acid hydrogen bonds to both bases, in principle allowing particular base-pairs to be selectively targeted, and nine of these have been observed. Four calculated interactions span the Watson-Crick pairs and 15 span the G:U wobble pair, including two interesting arrangements with three hydrogen bonds to the Arg guanidinum group that have not yet been observed. The inherent donor-acceptor arrangements of the bases support many possible interactions to Asn (or Gln) and Ser (or Thr or Tyr), few interactions to Asp (or Glu) even though several already have been observed, and interactions to U (or T) only if the base is in an unpaired context, as also observed in several cases. This study highlights how complementary arrangements of donors and acceptors can contribute to base-specific recognition of RNA, predicts interactions not yet observed, and provides tools to analyze proposed contacts or design novel interactions.     

A new residue-nucleotide propensity potential with structural information considered for discriminating protein-RNA docking decoys

Understanding the key factors that influence the preferences of residue-nucleotide interactions in specific protein-RNA interactions has remained a research focus. We propose an effective approach to derive residue-nucleotide propensity potentials through considering both the types of residues and nucleotides, and secondary structure information of proteins and RNAs from the currently largest nonredundant and nonribosomal protein-RNA interaction database. To test the validity of the potentials, we used them to select near-native structures from protein-RNA docking poses. The results show that considering secondary structure information, especially for RNAs, greatly improves the predictive power of pair potentials. The success rate is raised from 50.7 to 65.5% for the top 2000 structures, and the number of cases in which a near-native structure is ranked in top 50 is increased from 7 to 13 out of 17 cases. Furthermore, the exclusion of ribosomes from the database contributes 8.3% to the success rate. In addition, some very interesting findings follow: (i) the protein secondary structure element π-helix is strongly associated with RNA-binding sites; (ii) the nucleotide uracil occurs frequently in the most preferred pairs in which the unpaired and non-Watson-Crick paired uracils are predominant, which is probably significant in evolution. The new residue-nucleotide potentials can be helpful for the progress of protein-RNA docking methods, and for understanding the mechanisms of protein-RNA interactions.     

Protein-RNA contacts at crystal packing surfaces

The crystal packing surfaces comprising protein-RNA interactions were analyzed for 50 RNA-protein crystal structures in the Protein Data Bank database. Protein-RNA crystal contacts, which represent nonspecific protein-RNA interfaces, were investigated for their amino acid propensities, hydrogen bond patterns, and backbone and side chain interactions. When compared to biologically relevant interactions, the protein-RNA crystal contacts exhibit similarities as well as differences with respect to the principles of protein-RNA interactions. Similar to what was observed at cognate protein-RNA interfaces, positively charged amino acids have high propensities at noncognate protein-RNA interfaces and preferentially form hydrogen bonds with RNA phosphate groups. In contrast, nonpolar residues are less frequently associated with noncognate interactions. These results highlight the important roles of both electrostatic and hydrogen bonding interactions, facilitated by positively charged amino acids, in mediating both specific and nonspecific protein-RNA interactions.     

A protein-RNA docking benchmark (I): nonredundant cases

We have developed a nonredundant protein-RNA docking benchmark dataset, which is derived from the available bound and unbound structures in the Protein Data Bank involving polypeptide and nucleic acid chains. It consists of nine unbound-unbound cases where both the protein and the RNA are available in the free form. The other 36 cases are of unbound-bound type where only the protein is available in the free form. The conformational change upon complex formation is calculated by a distance matrix alignment method, and based on that, complexes are classified into rigid, semi-flexible, and full flexible. Although in the rigid body category, no significant conformational change accompanies complex formation, the fully flexible test cases show large domain movements, RNA base flips, etc. The benchmark covers four major groups of RNA, namely, t-RNA, ribosomal RNA, duplex RNA, and single-stranded RNA. We find that RNA is generally more flexible than the protein in the complexes, and the interface region is as flexible as the molecule as a whole. The structural diversity of the complexes in the benchmark set should provide a common ground for the development and comparison of the protein-RNA docking methods. The benchmark can be freely downloaded from the internet.     

Detailed analysis of RNA-protein interactions within the bacterial ribosomal protein L5/5S rRNA complex

The crystal structure of ribosomal protein L5 from Thermus thermophilus complexed with a 34-nt fragment comprising helix III and loop C of Escherichia coli 5S rRNA has been determined at 2.5 A resolution. The protein specifically interacts with the bulged nucleotides at the top of loop C of 5S rRNA. The rRNA and protein contact surfaces are strongly stabilized by intramolecular interactions. Charged and polar atoms forming the network of conserved intermolecular hydrogen bonds are located in two narrow planar parallel layers belonging to the protein and rRNA, respectively. The regions, including these atoms conserved in Bacteria and Archaea, can be considered an RNA-protein recognition module. Comparison of the T. thermophilus L5 structure in the RNA-bound form with the isolated Bacillus stearothermophilus L5 structure shows that the RNA-recognition module on the protein surface does not undergo significant changes upon RNA binding. In the crystal of the complex, the protein interacts with another RNA molecule in the asymmetric unit through the beta-sheet concave surface. This protein/RNA interface simulates the interaction of L5 with 23S rRNA observed in the Haloarcula marismortui 50S ribosomal subunit.     

Computational methods for prediction of protein-RNA interactions

Understanding the molecular mechanism of protein-RNA recognition and complex formation is a major challenge in structural biology. Unfortunately, the experimental determination of protein-RNA complexes by X-ray crystallography and nuclear magnetic resonance spectroscopy (NMR) is tedious and difficult. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental observations, computational predictions can be sufficiently accurate to prompt functional hypotheses and guide experiments, e.g. to identify individual amino acid or nucleotide residues. In this article we review 10 methods for predicting protein-RNA interactions, seven of which predict RNA-binding sites from protein sequences and three from structures. We also developed a meta-predictor that uses the output of top three sequence-based primary predictors to calculate a consensus prediction, which outperforms all the primary predictors. In order to fully cover the software for predicting protein-RNA interactions, we also describe five methods for protein-RNA docking. The article highlights the strengths and shortcomings of existing methods for the prediction of protein-RNA interactions and provides suggestions for their further development.     

RNA binding properties of the coat protein from bacteriophage GA.

The coat protein of bacteriophage GA, a group II RNA phage, binds to a small RNA hairpin corresponding to its replicase operator. Binding is specific, with a Ka of 71 microM -1. This interaction differs kinetically from the analogous coat protein-RNA hairpin interactions of other RNA phage and also deviates somewhat in its pH and salt dependence. Despite 46 of 129 amino acid differences between the GA and group I phage R17 coat proteins, the binding sites are fairly similar. The essential features of the GA coat protein binding site are a based-paired stem with an unpaired purine and a four nucleotide loop having an A at position -4 and a purine at -7. Unlike the group I phage proteins, the GA coat protein does not distinguish between two alternate positions for the unpaired purine and does not show high specificity for a pyrimidine at position -5 of the loop.     

Themes in RNA-protein recognition.

Atomic resolution structures are now available for more than 20 complexes of proteins with specific RNAs. This review examines two main themes that appear in this set of structures. A "groove binder" class of proteins places a protein structure (alpha-helix, 310-helix, beta-ribbon, or irregular loop) in the groove of an RNA helix, recognizing both the specific sequence of bases and the shape or dimensions of the groove, which are sometimes distorted from the normal A-form. A second class of proteins uses beta-sheet surfaces to create pockets that examine single-stranded RNA bases. Some of these proteins recognize completely unstructured RNA, and in others RNA secondary structure indirectly promotes binding by constraining bases in an appropriate orientation. Thermodynamic studies have shown that binding specificity is generally a function of several factors, including base-specific hydrogen bonds, non-polar contacts, and mutual accommodation of the protein and RNA-binding surfaces. The recognition strategies and structural frameworks used by RNA binding proteins are not exotically different from those employed by DNA-binding proteins, suggesting that the two kinds of nucleic acid-binding proteins have not evolved independently.     

Structural insights into SRP RNA: an induced fit mechanism for SRP assembly

Proper assembly of large protein-RNA complexes requires sequential binding of the proteins to the RNA. The signal recognition particle (SRP) is a multiprotein-RNA complex responsible for the cotranslational targeting of proteins to biological membranes. Here we describe the crystal structure at 2.6-A resolution of the S-domain of SRP RNA from the archeon Methanococcus jannaschii. Comparison of this structure with the SRP19-bound form reveals the nature of the SRP19-induced conformational changes, which promote subsequent SRP54 attachment. These structural changes are initiated at the SRP19 binding site and transmitted through helix 6 to looped-out adenosines, which form tertiary RNA interaction with helix 8. Displacement of these adenosines enforces a conformational change of the asymmetric loop structure in helix 8. In free RNA, the three unpaired bases A195, C196, and C197 are directed toward the helical axis, whereas upon SRP19 binding the loop backbone inverts and the bases are splayed out in a conformation that resembles the SRP54-bound form. Nucleotides adjacent to the bulged nucleotides seem to be particularly important in the regulation of this loop transition. Binding of SRP19 to 7S RNA reveals an elegant mechanism of how protein-induced changes are directed through an RNA molecule and may relate to those regulating the assembly of other RNPs.     

The influence of the local sequence environment on RNA loop structures

RNA folding is assumed to be a hierarchical process. The secondary structure of an RNA molecule, signified by base-pairing and stacking interactions between the paired bases, is formed first. Subsequently, the RNA molecule adopts an energetically favorable three-dimensional conformation in the structural space determined mainly by the rotational degrees of freedom associated with the backbone of regions of unpaired nucleotides (loops). To what extent the backbone conformation of RNA loops also results from interactions within the local sequence context or rather follows global optimization constraints alone has not been addressed yet. Because the majority of base stacking interactions are exerted locally, a critical influence of local sequence on local structure appears plausible. Thus, local loop structure ought to be predictable, at least in part, from the local sequence context alone. To test this hypothesis, we used Random Forests on a nonredundant data set of unpaired nucleotides extracted from 97 X-ray structures from the Protein Data Bank (PDB) to predict discrete backbone angle conformations given by the discretized η/θ-pseudo-torsional space. Predictions on balanced sets with four to six conformational classes using local sequence information yielded average accuracies of up to 55%, thus significantly better than expected by chance (17%-25%). Bases close to the central nucleotide appear to be most tightly linked to its conformation. Our results suggest that RNA loop structure does not only depend on long-range base-pairing interactions; instead, it appears that local sequence context exerts a significant influence on the formation of the local loop structure.