Protein-RNA interactions: exploring binding patterns with a three-dimensional superposition analysis of high resolution structures

Dissection and prediction of RNA-binding sites on proteins

RNA-binding proteins are involved in many important regulatory processes in cells and their study is essential for a complete understanding of living organisms. They show a large variability from both structural and functional points of view. However, several recent studies performed on protein-RNA crystal structures have revealed interesting common properties. RNA-binding sites usually constitute patches of positively charged or polar residues that make most of the specific and non-specific contacts with RNA. Negatively charged or aliphatic residues are less frequent at protein-RNA interfaces, although they can also be found either forming aliphatic and positive-negative pairs in protein RNA-binding sites or contacting RNA through their main chains. Aromatic residues found within these interfaces are usually involved in specific base recognition at RNA single-strand regions. This specific recognition, in combination with structural complementarity, represents the key source for specificity in protein-RNA association. From all this knowledge, a variety of computational methods for prediction of RNA-binding sites have been developed based either on protein sequence or on protein structure. Some reported methods are really successful in the identification of RNA-binding proteins or the prediction of RNA-binding sites. Given the growing interest in the field, all these studies and prediction methods will undoubtedly contribute to the identification and comprehension of protein-RNA interactions.

Crystal structure of a mirror-image L-RNA aptamer (Spiegelmer) in complex with the natural L-protein target CCL2

We report the crystal structure of a 40mer mirror-image RNA oligonucleotide completely built from nucleotides of the non-natural L-chirality in complex with the pro-inflammatory chemokine L-CLL2 (monocyte chemoattractant protein-1), a natural protein composed of regular L-amino acids. The L-oligonucleotide is an L-aptamer (a Spiegelmer) identified to bind L-CCL2 with high affinity, thereby neutralizing the chemokine's activity. CCL2 plays a key role in attracting and positioning monocytes; its overexpression in several inflammatory diseases makes CCL2 an interesting pharmacological target. The PEGylated form of the L-aptamer, NOX-E36 (emapticap pegol), already showed promising efficacy in clinical Phase II studies conducted in diabetic nephropathy patients. The structure of the L-oligonucleotide·L-protein complex was solved and refined to 2.05 Å. It unveils the L-aptamer's intramolecular contacts and permits a detailed analysis of its structure–function relationship. Furthermore, the analysis of the intermolecular drug–target interactions reveals insight into the selectivity of the L-aptamer for certain related chemokines.

Spiegelmers are ‘mirror image' L-ribose oligonucleotides being developed as therapeutics. Here the authors present a crystal structure of the therapeutic L-aptamer NOX-E36 bound to the pro-inflammatory chemokine CLL2, providing insight into NOX-E36's selectivity and mode of action.

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.

Identification of protein-interacting nucleotides in a RNA sequence using composition profile of tri-nucleotides

The RNA-protein interactions play a diverse role in the cells, thus identification of RNA-protein interface is essential for the biologist to understand their function. In the past, several methods have been developed for predicting RNA interacting residues in proteins, but limited efforts have been made for the identification of protein-interacting nucleotides in RNAs. In order to discriminate protein-interacting and non-interacting nucleotides, we used various classifiers (NaiveBayes, NaiveBayesMultinomial, BayesNet, ComplementNaiveBayes, MultilayerPerceptron, J48, SMO, RandomForest, SMO and SVM(light)) for prediction model development using various features and achieved highest 83.92% sensitivity, 84.82 specificity, 84.62% accuracy and 0.62 Matthew's correlation coefficient by SVM(light) based models. We observed that certain tri-nucleotides like ACA, ACC, AGA, CAC, CCA, GAG, UGA, and UUU preferred in protein-interaction. All the models have been developed using a non-redundant dataset and are evaluated using five-fold cross validation technique. A web-server called RNApin has been developed for the scientific community (http://crdd.osdd.net/raghava/rnapin/).

Absolute binding-free energies between standard RNA/DNA nucleobases and amino-acid sidechain analogs in different environments

Despite the great importance of nucleic acid-protein interactions in the cell, our understanding of their physico-chemical basis remains incomplete. In order to address this challenge, we have for the first time determined potentials of mean force and the associated absolute binding free energies between all standard RNA/DNA nucleobases and amino-acid sidechain analogs in high- and low-dielectric environments using molecular dynamics simulations and umbrella sampling. A comparison against a limited set of available experimental values for analogous systems attests to the quality of the computational approach and the force field used. Overall, our analysis provides a microscopic picture behind nucleobase/sidechain interaction preferences and creates a unified framework for understanding and sculpting nucleic acid-protein interactions in different contexts. Here, we use this framework to demonstrate a strong relationship between nucleobase density profiles of mRNAs and nucleobase affinity profiles of their cognate proteins and critically analyze a recent hypothesis that the two may be capable of direct, complementary interactions.

Hydration of protein-RNA recognition sites

We investigate the role of water molecules in 89 protein–RNA complexes taken from the Protein Data Bank. Those with tRNA and single-stranded RNA are less hydrated than with duplex or ribosomal proteins. Protein–RNA interfaces are hydrated less than protein–DNA interfaces, but more than protein–protein interfaces. Majority of the waters at protein–RNA interfaces makes multiple H-bonds; however, a fraction do not make any. Those making H-bonds have preferences for the polar groups of RNA than its partner protein. The spatial distribution of waters makes interfaces with ribosomal proteins and single-stranded RNA relatively ‘dry’ than interfaces with tRNA and duplex RNA. In contrast to protein–DNA interfaces, mainly due to the presence of the 2′OH, the ribose in protein–RNA interfaces is hydrated more than the phosphate or the bases. The minor groove in protein–RNA interfaces is hydrated more than the major groove, while in protein–DNA interfaces it is reverse. The strands make the highest number of water-mediated H-bonds per unit interface area followed by the helices and the non-regular structures. The preserved waters at protein–RNA interfaces make higher number of H-bonds than the other waters. Preserved waters contribute toward the affinity in protein–RNA recognition and should be carefully treated while engineering protein–RNA interfaces.

Sequence dependent variations in RNA duplex are related to non-canonical hydrogen bond interactions in dinucleotide steps

Background

Sequence determines the three-dimensional structure of RNAs, and thereby plays an important role in carrying out various biological functions. RNA duplexes containing Watson-Crick (WC) basepairs, interspersed with non-Watson-Crick basepairs, are the dominant structural unit and form the scaffold for the 3-dimensional structure of RNA. It is therefore crucial to understand the geometric variation in the dinucleotide steps that form the helices. We have carried out a detailed analysis of the dinucleotide steps formed by AU and GC Watson-Crick basepairs in RNA structures (both free and protein bound) and compared the results to that seen in DNA. Further, the effect of protein binding on these steps was examined by comparing steps in free RNA structures with protein bound RNA structures.

Results

Characteristic sequence dependent geometries are observed for the RR, RY and YR type of dinucleotide steps in RNA. Their geometric parameters show correlated variations that are different from those observed in B-DNA helices. Subtle, but statistically significant differences are seen in roll, slide and average propeller-twist values, between the dinucleotide steps of free RNA and protein bound RNA structures. Many non-canonical cross-strand and intra-strand hydrogen bonds were identified that can stabilise the RNA dinucleotide steps, among which YR steps show presence of many new unreported interactions.

Conclusions

Our work provides for the first time a detailed analysis of the conformational preferences exhibited by Watson-Crick basepair containing steps in RNA double helices. Overall, the WC dinucleotide steps show considerable conformational variability. Furthermore, we have identified hydrogen bond interactions in several of the dinucleotide steps that could play a role in determining the preferred geometry, in addition to the intra-basepair hydrogen bonds and stacking interactions. Protein binding affects the conformation of the steps that are in direct contact, as well as allosterically affect the steps that are not in direct physical contact.

SPECIFIC BINDING OF A SHORT miRNA SEQUENCE BY ZINC KNUCKLES OF Lin28: A MOLECULAR DYNAMICS SIMULATION STUDY

Protein Lin28 recognizes pre-let-7 microRNAs (miRNAs) through direct interactions between its zinc-knuckle type zinc-finger (ZnF) domains and the terminal loop of pre-let-7, resulting in the inhibition of the synthesis of mature let-7 miRNAs. Despite the physiological importance, the involved conformational changes and energetic factors contributing to the binding affinity and specificity remain unclear. We conducted molecular dynamics (MD) simulations in conjunction with molecular mechanics/generalized born surface area (MM/GBSA) and energy decomposition calculations to investigate the RNA binding-induced conformational changes of the ZnFs and the residual level energetic factors that influence the binding affinity and specificity. We showed that the binding of the RNA results in the inter-domain conformational changes of the two ZnF domains, including changes of the spatial relationships of several nucleobase-binding amino acids. We also observed mutation-induced weakening of the affinity of the Lin28–pre-let-7 binding, which reveals the importance of the stacking interactions between the side-chains of Tyr140, His148, His162 and the bases of nucleic acid G2 and G5 in the specific recognition of pre-let-7 by the ZnFs of Lin28.

Prediction of RNA binding proteins comes of age from low resolution to high resolution

Networks of protein-RNA interactions is likely to be larger than protein-protein and protein-DNA interaction networks because RNA transcripts are encoded tens of times more than proteins (e.g. only 3% of human genome coded for proteins), have diverse function and localization, and are controlled by proteins from birth (transcription) to death (degradation). This massive network is evidenced by several recent experimental discoveries of large numbers of previously unknown RNA-binding proteins (RBPs). Meanwhile, more than 400 non-redundant protein-RNA complex structures (at 25% sequence identity or less) have been deposited into the protein databank. These sequences and structural resources for RBPs provide ample data for the development of computational techniques dedicated to RBP prediction, as experimentally determining RNA-binding functions is time-consuming and expensive. This review compares traditional machine-learning based approaches with emerging template-based methods at several levels of prediction resolution ranging from two-state binding/non-binding prediction, to binding residue prediction and protein-RNA complex structure prediction. The analysis indicates that the two approaches are complementary and their combinations may lead to further improvements.

Computational prediction of associations between long non-coding RNAs and proteins

Background

Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro.

Results

By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA–protein pair. This score can be used to measure the interactions between an RNA–protein pair. This method effectively discriminates interacting and non-interacting RNA–protein pairs and predicts RNA–protein interactions within a given complex. Applying this method on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins.

Conclusions

Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process.

Nucleotide-specific recognition of iron-responsive elements by iron regulatory protein 1

IRP1 [iron regulatory protein (IRP) 1] is a bifunctional protein with mutually exclusive end-states. In one mode of operation, IRP1 binds iron-responsive element (IRE) stem-loops in messenger RNAs encoding proteins of iron metabolism to control their rate of translation. In its other mode, IRP1 serves as cytoplasmic aconitase to correlate iron availability with the energy and oxidative stress status of the cell. IRP1/IRE binding occurs through two separate interfaces, which together contribute about two-dozen hydrogen bonds. Five amino acids make base-specific contacts and are expected to contribute significantly to binding affinity and specificity of this protein:RNA interaction. In this mutagenesis study, each of the five base-specific amino acids was changed to alter binding at each site. Analysis of IRE binding affinity and translational repression activity of the resulting IRP1 mutants showed that four of the five contact points contribute uniquely to the overall binding affinity of the IRP1:IRE interaction, while one site was found to be unimportant. The stronger-than-expected effect on binding affinity of mutations at Lys379 and Ser681, residues that make contact with the conserved nucleotides G16 and C8, respectively, identified them as particularly critical for providing specificity and stability to IRP1:IRE complex formation. We also show that even though the base-specific RNA-binding residues are not part of the aconitase active site, their substitutions can affect the aconitase activity of holo-IRP1, positively or negatively.

RNA pentaloop structures as effective targets of regulators belonging to the RsmA/CsrA protein family

In the Gac/Rsm signal transduction pathway of Pseudomonas fluorescens CHA0, the dimeric RNA-binding proteins RsmA and RsmE, which belong to the vast bacterial RsmA/CsrA family, effectively repress translation of target mRNAs containing a typical recognition sequence near the translation start site. Three small RNAs (RsmX, RsmY, RsmZ) with clustered recognition sequences can sequester RsmA and RsmE and thereby relieve translational repression. According to a previously established structural model, the RsmE protein makes optimal contacts with an RNA sequence 5'- (A)/(U)CANGGANG(U)/(A)-3', in which the central ribonucleotides form a hexaloop. Here, we questioned the relevance of the hexaloop structure in target RNAs. We found that two predicted pentaloop structures, AGGGA (in pltA mRNA encoding a pyoluteorin biosynthetic enzyme) and AAGGA (in mutated pltA mRNA), allowed effective interaction with the RsmE protein in vivo. By contrast, ACGGA and AUGGA were poor targets. Isothermal titration calorimetry measurements confirmed the strong binding of RsmE to the AGGGA pentaloop structure in an RNA oligomer. Modeling studies highlighted the crucial role of the second ribonucleotide in the loop structure. In conclusion, a refined structural model of RsmE-RNA interaction accommodates certain pentaloop RNAs among the preferred hexaloop RNAs.

Nucleotide binding architecture for secreted cytotoxic endoribonucleases

Vertebrate secreted RNases are small cationic protein endowed with an endoribonuclease activity that belong to the RNase A superfamily and display diverse cytotoxic activities. In an effort to unravel their mechanism of action, we have analysed their nucleotide binding recognition patterns. General shared features with other nucleotide binding proteins were deduced from overall statistics on the available structure complexes at the Protein Data Bank and compared with the particularities of selected representative endoribonuclease families. Results were compared with other endoribonuclease representative families and with the overall protein-nucleotide interaction features. Preferred amino acids and atom types involved in pair bonding interactions were identified, defining the spatial motives for phosphate, base and ribose building blocks. Together with the conserved catalytic triad at the active site, variability was observed for secondary binding subsites that may contribute to the proper substrate alignment and could explain the distinct substrate preference patterns. Highly conserved binding patterns were identified for the pyrimidine and purine subsites at the main and secondary base subsites. Particular substitution could be ascribed to specific adenine or guanine specificities. Distribution of evolutionary conserved residues were compared to search for the structure determinants that underlie their diverse catalytic efficiency and those that may account for putative physiological substrate targets or other non-catalytic biological activities that contribute to the antipathogen role of the RNases involved in the host defence system. A side by side comparison with another endoribonuclease superfamily of secreted cytotoxic proteins, the microbial RNases, was carried on to analyse the common features and peculiarities that rule their substrate recognition. The data provides the structural basis for the development of applied therapies targeting cellular nucleotide polymers.

Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection

RNA-protein interaction plays an important role in various cellular processes, such as protein synthesis, gene regulation, post-transcriptional gene regulation, alternative splicing, and infections by RNA viruses. In this study, using Gene Ontology Annotated (GOA) and Structural Classification of Proteins (SCOP) databases an automatic procedure was designed to capture structurally solved RNA-binding protein domains in different subclasses. Subsequently, we applied tuned multi-class SVM (TMCSVM), Random Forest (RF), and multi-class ℓ1/ℓq-regularized logistic regression (MCRLR) for analysis and classifying RNA-binding protein domains based on a comprehensive set of sequence and structural features. In this study, we compared prediction accuracy of three different state-of-the-art predictor methods. From our results, TMCSVM outperforms the other methods and suggests the potential of TMCSVM as a useful tool for facilitating the multi-class prediction of RNA-binding protein domains. On the other hand, MCRLR by elucidating importance of features for their contribution in predictive accuracy of RNA-binding protein domains subclasses, helps us to provide some biological insights into the roles of sequences and structures in protein-RNA interactions.

Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure

RNA-protein interactions play important roles in various biological processes. The precise detection of RNA-protein interaction sites is very important for understanding essential biological processes and annotating the function of the proteins. In this study, based on various features from amino acid sequence and structure, including evolutionary information, solvent accessible surface area and torsion angles (φ, ψ) in the backbone structure of the polypeptide chain, a computational method for predicting RNA-binding sites in proteins is proposed. When the method is applied to predict RNA-binding sites in three datasets: RBP86 containing 86 protein chains, RBP107 containing 107 proteins chains and RBP109 containing 109 proteins chains, better sensitivities and specificities are obtained compared to previously published methods in five-fold cross-validation tests. In order to make further examination for the efficiency of our method, the RBP107 dataset is used as training set, RBP86 and RBP109 datasets are used as the independent test sets. In addition, as examples of our prediction, RNA-binding sites in a few proteins are presented. The annotated results are consistent with the PDB annotation. These results show that our method is useful for annotating RNA binding sites of novel proteins.

PRince: a web server for structural and physicochemical analysis of protein-RNA interface

We have developed a web server, PRince, which analyzes the structural features and physicochemical properties of the protein–RNA interface. Users need to submit a PDB file containing the atomic coordinates of both the protein and the RNA molecules in complex form (in ‘.pdb’ format). They should also mention the chain identifiers of interacting protein and RNA molecules. The size of the protein–RNA interface is estimated by measuring the solvent accessible surface area buried in contact. For a given protein–RNA complex, PRince calculates structural, physicochemical and hydration properties of the interacting surfaces. All these parameters generated by the server are presented in a tabular format. The interacting surfaces can also be visualized with software plug-in like Jmol. In addition, the output files containing the list of the atomic coordinates of the interacting protein, RNA and interface water molecules can be downloaded. The parameters generated by PRince are novel, and users can correlate them with the experimentally determined biophysical and biochemical parameters for better understanding the specificity of the protein–RNA recognition process. This server will be continuously upgraded to include more parameters. PRince is publicly accessible and free for use. Available at http://www.facweb.iitkgp.ernet.in/~rbahadur/prince/home.html.

Selectivity of stop codon recognition in translation termination is modulated by multiple conformations of GTS loop in eRF1

Translation termination in eukaryotes is catalyzed by two release factors eRF1 and eRF3 in a cooperative manner. The precise mechanism of stop codon discrimination by eRF1 remains obscure, hindering drug development targeting aberrations at translation termination. By solving the solution structures of the wild-type N-domain of human eRF1 exhibited omnipotent specificity, i.e. recognition of all three stop codons, and its unipotent mutant with UGA-only specificity, we found the conserved GTS loop adopting alternate conformations. We propose that structural variability in the GTS loop may underline the switching between omnipotency and unipotency of eRF1, implying the direct access of the GTS loop to the stop codon. To explore such feasibility, we positioned N-domain in a pre-termination ribosomal complex using the binding interface between N-domain and model RNA oligonucleotides mimicking Helix 44 of 18S rRNA. NMR analysis revealed that those duplex RNA containing 2-nt internal loops interact specifically with helix α1 of N-domain, and displace C-domain from a non-covalent complex of N-domain and C-domain, suggesting domain rearrangement in eRF1 that accompanies N-domain accommodation into the ribosomal A site.

Sequence coevolution between RNA and protein characterized by mutual information between residue triplets

Coevolving residues in a multiple sequence alignment provide evolutionary clues of biophysical interactions in 3D structure. Despite a rich literature describing amino acid coevolution within or between proteins and nucleic acid coevolution within RNA, to date there has been no direct evidence of coevolution between protein and RNA. The ribosome, a structurally conserved macromolecular machine composed of over 50 interacting protein and RNA chains, provides a natural example of RNA/protein interactions that likely coevolved. We provide the first direct evidence of RNA/protein coevolution by characterizing the mutual information in residue triplets from a multiple sequence alignment of ribosomal protein L22 and neighboring 23S RNA. We define residue triplets as three positions in the multiple sequence alignment, where one position is from the 23S RNA and two positions are from the L22 protein. We show that residue triplets with high mutual information are more likely than residue doublets to be proximal in 3D space. Some high mutual information residue triplets cluster in a connected series across the L22 protein structure, similar to patterns seen in protein coevolution. We also describe RNA nucleotides for which switching from one nucleotide to another (or between purines and pyrimidines) results in a change in amino acid distribution for proximal amino acid positions. Multiple crystal structures for evolutionarily distinct ribosome species can provide structural evidence for these differences. For one residue triplet, a pyrimidine in one species is a purine in another, and RNA/protein hydrogen bonds are present in one species but not the other. The results provide the first direct evidence of RNA/protein coevolution by using higher order mutual information, suggesting that biophysical constraints on interacting RNA and protein chains are indeed a driving force in their evolution.

Dissecting the protein-RNA interface: the role of protein surface shapes and RNA secondary structures in protein-RNA recognition

Protein-RNA interactions are essential for many biological processes. However, the structural mechanisms underlying these interactions are not fully understood. Here, we analyzed the protein surface shape (dented, intermediate or protruded) and the RNA base pairing properties (paired or unpaired nucleotides) at the interfaces of 91 protein-RNA complexes derived from the Protein Data Bank. Dented protein surfaces prefer unpaired nucleotides to paired ones at the interface, and hydrogen bonds frequently occur between the protein backbone and RNA bases. In contrast, protruded protein surfaces do not show such a preference, rather, electrostatic interactions initiate the formation of hydrogen bonds between positively charged amino acids and RNA phosphate groups. Interestingly, in many protein-RNA complexes that interact via an RNA loop, an aspartic acid is favored at the interface. Moreover, in most of these complexes, nucleotide bases in the RNA loop are flipped out and form hydrogen bonds with the protein, which suggests that aspartic acid is important for RNA loop recognition through a base-flipping process. This study provides fundamental insights into the role of the shape of the protein surface and RNA secondary structures in mediating protein-RNA interactions.

Prediction of dinucleotide-specific RNA-binding sites in proteins

Background

Regulation of gene expression, protein synthesis, replication and assembly of many viruses involve RNA–protein interactions. Although some successful computational tools have been reported to recognize RNA binding sites in proteins, the problem of specificity remains poorly investigated. After the nucleotide base composition, the dinucleotide is the smallest unit of RNA sequence information and many RNA-binding proteins simply bind to regions enriched in one dinucleotide. Interaction preferences of protein subsequences and dinucleotides can be inferred from protein-RNA complex structures, enabling a training-based prediction approach.

Results

We analyzed basic statistics of amino acid-dinucleotide contacts in protein-RNA complexes and found their pairing preferences could be identified. Using a standard approach to represent protein subsequences by their evolutionary profile, we trained neural networks to predict multiclass target vectors corresponding to 16 possible contacting dinucleotide subsequences. In the cross-validation experiments, the accuracies of the optimum network, measured as areas under the curve (AUC) of the receiver operating characteristic (ROC) graphs, were in the range of 65-80%.

Conclusions

Dinucleotide-specific contact predictions have also been extended to the prediction of interacting protein and RNA fragment pairs, which shows the applicability of this method to predict targets of RNA-binding proteins. A web server predicting the 16-dimensional contact probability matrix directly from a user-defined protein sequence was implemented and made available at: http://tardis.nibio.go.jp/netasa/srcpred.

Crystal structure of human mitochondrial PheRS complexed with tRNA(Phe) in the active "open" state

Monomeric human mitochondrial phenylalanyl-tRNA synthetase (PheRS), or hmPheRS, is the smallest known enzyme exhibiting aminoacylation activity. HmPheRS consists of only two structural domains and differs markedly from heterodimeric eukaryotic cytosolic and bacterial analogs both in the domain organization and in the mode of tRNA binding. Here, we describe the first crystal structure of mitochondrial aminoacyl-tRNA synthetase (aaRS) complexed with tRNA at a resolution of 3.0 Å. Unlike bacterial PheRSs, the hmPheRS recognizes C74, the G1-C72 base pair, and the "discriminator" base A73, proposed to contribute to tRNA(Phe) identity in the yeast mitochondrial enzyme. An interaction of the tRNA acceptor stem with the signature motif 2 residues of hmPheRS is of critical importance for the stabilization of the CCA-extended conformation and its correct placement in the synthetic site of the enzyme. The crystal structure of hmPheRS-tRNA(Phe) provides direct evidence that the formation of the complex with tRNA requires a significant rearrangement of the anticodon-binding domain from the "closed" to the productive "open" state. Global repositioning of the domain is tRNA modulated and governed by long-range electrostatic interactions.

Computational analysis of RNA-protein interaction interfaces via the Voronoi diagram

Cellular functions are mediated by various biological processes including biomolecular interactions, such as protein-protein, DNA-protein and RNA-protein interactions in which RNA-Protein interactions are indispensable for many biological processes like cell development and viral replication. Unlike the protein-protein and protein-DNA interactions, accurate mechanisms and structures of the RNA-Protein complexes are not fully understood. A large amount of theoretical evidence have shown during the past several years that computational geometry is the first pace in understanding the binding profiles and plays a key role in the study of intricate biological structures, interactions and complexes. In this paper, RNA-Protein interaction interface surface is computed via the weighted Voronoi diagram of atoms. Using two filter operations provides a natural definition for interface atoms as classic methods. Unbounded parts of Voronoi facets that are far from the complex are trimmed using modified convex hull of atom centers. This algorithm is implemented to a database with different RNA-Protein complexes extracted from Protein Data Bank (PDB). Afterward, the features of interfaces have been computed and compared with classic method. The results show high correlation coefficients between interface size in the Voronoi model and the classical model based on solvent accessibility, as well as high accuracy and precision in comparison to classical model.

A coarse-grained force field for Protein-RNA docking

The awareness of important biological role played by functional, non coding (nc) RNA has grown tremendously in recent years. To perform their tasks, ncRNA molecules typically unite with protein partners, forming ribonucleoprotein complexes. Structural insight into their architectures can be greatly supplemented by computational docking techniques, as they provide means for the integration and refinement of experimental data that is often limited to fragments of larger assemblies or represents multiple levels of spatial resolution. Here, we present a coarse-grained force field for protein-RNA docking, implemented within the framework of the ATTRACT program. Complex structure prediction is based on energy minimization in rotational and translational degrees of freedom of binding partners, with possible extension to include structural flexibility. The coarse-grained representation allows for fast and efficient systematic docking search without any prior knowledge about complex geometry.

Classification of pseudo pairs between nucleotide bases and amino acids by analysis of nucleotide-protein complexes

Nucleotide bases are recognized by amino acid residues in a variety of DNA/RNA binding and nucleotide binding proteins. In this study, a total of 446 crystal structures of nucleotide–protein complexes are analyzed manually and pseudo pairs together with single and bifurcated hydrogen bonds observed between bases and amino acids are classified and annotated. Only 5 of the 20 usual amino acid residues, Asn, Gln, Asp, Glu and Arg, are able to orient in a coplanar fashion in order to form pseudo pairs with nucleotide bases through two hydrogen bonds. The peptide backbone can also form pseudo pairs with nucleotide bases and presents a strong bias for binding to the adenine base. The Watson–Crick side of the nucleotide bases is the major interaction edge participating in such pseudo pairs. Pseudo pairs between the Watson–Crick edge of guanine and Asp are frequently observed. The Hoogsteen edge of the purine bases is a good discriminatory element in recognition of nucleotide bases by protein side chains through the pseudo pairing: the Hoogsteen edge of adenine is recognized by various amino acids while the Hoogsteen edge of guanine is only recognized by Arg. The sugar edge is rarely recognized by either the side-chain or peptide backbone of amino acid residues.

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.

In silico characterization and prediction of global protein-mRNA interactions in yeast

Post-transcriptional gene regulation is mediated through complex networks of protein-RNA interactions. The targets of only a few RNA binding proteins (RBPs) are known, even in the well-characterized budding yeast. In silico prediction of protein-RNA interactions is therefore useful to guide experiments and to provide insight into regulatory networks. Computational approaches have identified RBP targets based on sequence binding preferences. We investigate here to what extent RBP-RNA interactions can be predicted based on RBP and mRNA features other than sequence motifs. We analyze global relationships between gene and protein properties in general and between selected RBPs and known mRNA targets in particular. Highly translated RBPs tend to bind to shorter transcripts, and transcripts bound by the same RBP show high expression correlation across different biological conditions. Surprisingly, a given RBP preferentially binds to mRNAs that encode interaction partners for this RBP, suggesting coordinated post-transcriptional auto-regulation of protein complexes. We apply a machine-learning approach to predict specific RBP targets in yeast. Although this approach performs well for RBPs with known targets, predictions for uncharacterized RBPs remain challenging due to limiting experimental data. We also predict targets of fission yeast RBPs, indicating that the suggested framework could be applied to other species once more experimental data are available.

Stacking interactions in PUF-RNA complexes

Stacking interactions between amino acids and bases are common in RNA-protein interactions. Many proteins that regulate mRNAs interact with single-stranded RNA elements in the 3' UTR (3'-untranslated region) of their targets. PUF proteins are exemplary. Here we focus on complexes formed between a Caenorhabditis elegans PUF protein, FBF, and its cognate RNAs. Stacking interactions are particularly prominent and involve every RNA base in the recognition element. To assess the contribution of stacking interactions to formation of the RNA-protein complex, we combine in vivo selection experiments with site-directed mutagenesis, biochemistry, and structural analysis. Our results reveal that the identities of stacking amino acids in FBF affect both the affinity and specificity of the RNA-protein interaction. Substitutions in amino acid side chains can restrict or broaden RNA specificity. We conclude that the identities of stacking residues are important in achieving the natural specificities of PUF proteins. Similarly, in PUF proteins engineered to bind new RNA sequences, the identity of stacking residues may contribute to "target" versus "off-target" interactions, and thus be an important consideration in the design of proteins with new specificities.

Predicting RNA-binding residues from evolutionary information and sequence conservation

Abstract

PRIDB: a Protein-RNA interface database

The Protein–RNA Interface Database (PRIDB) is a comprehensive database of protein–RNA interfaces extracted from complexes in the Protein Data Bank (PDB). It is designed to facilitate detailed analyses of individual protein–RNA complexes and their interfaces, in addition to automated generation of user-defined data sets of protein–RNA interfaces for statistical analyses and machine learning applications. For any chosen PDB complex or list of complexes, PRIDB rapidly displays interfacial amino acids and ribonucleotides within the primary sequences of the interacting protein and RNA chains. PRIDB also identifies ProSite motifs in protein chains and FR3D motifs in RNA chains and provides links to these external databases, as well as to structure files in the PDB. An integrated JMol applet is provided for visualization of interacting atoms and residues in the context of the 3D complex structures. The current version of PRIDB contains structural information regarding 926 protein–RNA complexes available in the PDB (as of 10 October 2010). Atomic- and residue-level contact information for the entire data set can be downloaded in a simple machine-readable format. Also, several non-redundant benchmark data sets of protein–RNA complexes are provided. The PRIDB database is freely available online at http://bindr.gdcb.iastate.edu/PRIDB.

Structural basis for the dual RNA-recognition modes of human Tra2-β RRM

Human Transformer2-β (hTra2-β) is an important member of the serine/arginine-rich protein family, and contains one RNA recognition motif (RRM). It controls the alternative splicing of several pre-mRNAs, including those of the calcitonin/calcitonin gene-related peptide (CGRP), the survival motor neuron 1 (SMN1) protein and the tau protein. Accordingly, the RRM of hTra2-β specifically binds to two types of RNA sequences [the CAA and (GAA)(2) sequences]. We determined the solution structure of the hTra2-β RRM (spanning residues Asn110-Thr201), which not only has a canonical RRM fold, but also an unusual alignment of the aromatic amino acids on the β-sheet surface. We then solved the complex structure of the hTra2-β RRM with the (GAA)(2) sequence, and found that the AGAA tetra-nucleotide was specifically recognized through hydrogen-bond formation with several amino acids on the N- and C-terminal extensions, as well as stacking interactions mediated by the unusually aligned aromatic rings on the β-sheet surface. Further NMR experiments revealed that the hTra2-β RRM recognizes the CAA sequence when it is integrated in the stem-loop structure. This study indicates that the hTra2-β RRM recognizes two types of RNA sequences in different RNA binding modes.

Evolution in action: N and C termini of subunits in related T = 4 viruses exchange roles as molecular switches

The T = 4 tetravirus and T = 3 nodavirus capsid proteins undergo closely similar autoproteolysis to produce the N-terminal beta and C-terminal, lipophilic gamma polypeptides. The gamma peptides and the N termini of beta also act as molecular switches that determine their quasi equivalent capsid structures. The crystal structure of Providence virus (PrV), only the second of a tetravirus (the first was NomegaV), reveals conserved folds and cleavage sites, but the protein termini have completely different structures and the opposite functions of those in NomegaV. N termini of beta form the molecular switch in PrV, whereas gamma peptides play this role in NomegaV. PrV gamma peptides instead interact with packaged RNA at the particle two-folds by using a repeating sequence pattern found in only four other RNA- or membrane-binding proteins. The disposition of peptide termini in PrV is closely related to those in nodaviruses, suggesting that PrV may be closer to the primordial T = 4 particle than NomegaV.

The 2.2-Å structure of the HP0958 protein from Helicobacter pylori reveals a kinked anti-parallel coiled-coil hairpin domain and a highly conserved ZN-ribbon domain

We have determined the 2.2-Å structure of the HP0958 protein from the human gastric pathogen Helicobacter pylori. HP0958 is essential for flagellum formation and motility. It functions as a chaperone for RpoN (σ(54)) and also controls the stability and translation of mRNA for the major flagellin subunit FlaA. The protein is composed of a highly elongated and kinked coiled-coil hairpin domain (residues 1-170), followed by a C(4) Zn-ribbon domain (residues 174-238). The Zn-ribbon domain is rich in aromatic and positively charged amino acid residues. Electrophoretic mobility shift assays identified residues in a positively charged region of the Zn-ribbon domain of HP0958 whose mutation alters the mobility of an HP0958-flaA mRNA complex. Mutation of surface residues in the coiled-coil domain did not result in an observable change in the mobility of the HP0958-flaA transcript complex. The data thus suggest the arrangement of HP0958 into distinct structural and functional domains.

Mechanism of microRNA-target interaction: molecular dynamics simulations and thermodynamics analysis

MicroRNAs (miRNAs) are endogenously produced ∼21-nt riboregulators that associate with Argonaute (Ago) proteins to direct mRNA cleavage or repress the translation of complementary RNAs. Capturing the molecular mechanisms of miRNA interacting with its target will not only reinforce the understanding of underlying RNA interference but also fuel the design of more effective small-interfering RNA strands. To address this, in the present work the RNA-bound (Ago-miRNA, Ago-miRNA-target) and RNA-free Ago forms were analyzed by performing both molecular dynamics simulations and thermodynamic analysis. Based on the principal component analysis results of the simulation trajectories as well as the correlation analysis in fluctuations of residues, we discover that: 1) three important (PAZ, Mid and PIWI) domains exist in Argonaute which define the global dynamics of the protein; 2) the interdomain correlated movements are so crucial for the interaction of Ago-RNAs that they not only facilitate the relaxation of the interactions between residues surrounding the RNA binding channel but also induce certain conformational changes; and 3) it is just these conformational changes that expand the cavity of the active site and open putative pathways for both the substrate uptake and product release. In addition, by thermodynamic analysis we also discover that for both the guide RNA 5′-end recognition and the facilitated site-specific cleavage of the target, the presence of two metal ions (of Mg²⁺) plays a predominant role, and this conclusion is consistent with the observed enzyme catalytic cleavage activity in the ternary complex (Ago-miRNA-mRNA). Our results find that it is the set of arginine amino acids concentrated in the nucleotide-binding channel in Ago, instead of the conventionally-deemed seed base-paring, that makes greater contributions in stabilizing the binding of the nucleic acids to Ago.

One of the biggest surprises at the beginning of the ‘post-genome era’ was the discovery of numerous genes encoding microRNAs. The number of microRNA genes is estimated to be nearly 1% of that of protein-coding genes, which were found in genomes of such diverse organisms as Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, and Homo sapiens. Their products, tiny RNAs (miRNAs and siRNAs), are thought to bind to Argonaute (Ago) proteins and form effector complexes to direct mRNA cleavage or repress translation of complementary RNAs, during development, organogenesis, and very likely during many other processes. The cellular interactions between the miRNAs and their target RNAs associating with Ago are only beginning to be revealed, and details of this interaction mechanism at molecular level are still poorly understood. In this article we propose the possible mechanisms of miRNA-target interaction with special emphasis on their structural dynamic and thermodynamic aspects. The results of our model suggest the chemical and physical factors and effects that may be responsible for the miRNA-Ago assembly and miRNA-target recognition.

Aptamers as therapeutics

Aptamers are single-stranded oligonucleotides that fold into defined architectures and bind to targets such as proteins.

In binding proteins they often inhibit protein–protein interactions and thereby may elicit therapeutic effects such as antagonism.

Aptamers are discovered using SELEX (systematic evolution of ligands by exponential enrichment), a directed in vitro evolution technique in which large libraries of degenerate oligonucleotides are iteratively and alternately partitioned for target binding. They are then amplified enzymatically until functional sequences are identified by the sequencing of cloned individuals.

For most therapeutic purposes, aptamers are truncated to reduce synthesis costs, modified at the sugars and capped at their termini to increase nuclease resistance, and conjugated to polyethylene glycol or another entity to reduce renal filtration rates.

The first aptamer approved for a therapeutic application was pegaptanib sodium (Macugen; Pfizer/Eyetech), which was approved in 2004 by the US Food and Drug Administration for macular degeneration.

Eight other aptamers are currently undergoing clinical evaluation for various haematology, oncology, ocular and inflammatory indications.

Aptamers are ultimately chemically synthesized in a readily scalable process in which specific conjugation points are introduced with defined stereochemistry.

Unlike some protein therapeutics, aptamers do not elicit antibodies, and because aptamers generally contain sugars modified at their 2′-positions, Toll-like receptor-mediated innate immune responses are also abrogated.

As aptamers are oligonucleotides they can be readily assembled into supramolecular multi-component structures using hybridization.

Owing to the fact that binding to appropriate cell-surface targets can lead to internalization, aptamers can also be used to deliver therapeutic cargoes such as small interfering RNA.

Supramolecular assemblies of aptamers and delivery agents have already been demonstrated in vivo and may pave the way for further therapeutic strategies with this modality in the future.

Aptamers are oligonucleotide sequences that are capable of recognizing target proteins with an affinity and specificity rivalling that of antibodies. In this article, Keefe and colleagues discuss the development, properties and therapeutic potential of aptamers, highlighting those currently in the clinic.

Nucleic acid aptamers can be selected from pools of random-sequence oligonucleotides to bind a wide range of biomedically relevant proteins with affinities and specificities that are comparable to antibodies. Aptamers exhibit significant advantages relative to protein therapeutics in terms of size, synthetic accessibility and modification by medicinal chemistry. Despite these properties, aptamers have been slow to reach the marketplace, with only one aptamer-based drug receiving approval so far. A series of aptamers currently in development may change how nucleic acid therapeutics are perceived. It is likely that in the future, aptamers will increasingly find use in concert with other therapeutic molecules and modalities.

Prediction of protein-RNA binding sites by a random forest method with combined features

MOTIVATION

Protein-RNA interactions play a key role in a number of biological processes, such as protein synthesis, mRNA processing, mRNA assembly, ribosome function and eukaryotic spliceosomes. As a result, a reliable identification of RNA binding site of a protein is important for functional annotation and site-directed mutagenesis. Accumulated data of experimental protein-RNA interactions reveal that a RNA binding residue with different neighbor amino acids often exhibits different preferences for its RNA partners, which in turn can be assessed by the interacting interdependence of the amino acid fragment and RNA nucleotide.

RESULTS

In this work, we propose a novel classification method to identify the RNA binding sites in proteins by combining a new interacting feature (interaction propensity) with other sequence- and structure-based features. Specifically, the interaction propensity represents a binding specificity of a protein residue to the interacting RNA nucleotide by considering its two-side neighborhood in a protein residue triplet. The sequence as well as the structure-based features of the residues are combined together to discriminate the interaction propensity of amino acids with RNA. We predict RNA interacting residues in proteins by implementing a well-built random forest classifier. The experiments show that our method is able to detect the annotated protein-RNA interaction sites in a high accuracy. Our method achieves an accuracy of 84.5%, F-measure of 0.85 and AUC of 0.92 prediction of the RNA binding residues for a dataset containing 205 non-homologous RNA binding proteins, and also outperforms several existing RNA binding residue predictors, such as RNABindR, BindN, RNAProB and PPRint, and some alternative machine learning methods, such as support vector machine, naive Bayes and neural network in the comparison study. Furthermore, we provide some biological insights into the roles of sequences and structures in protein-RNA interactions by both evaluating the importance of features for their contributions in predictive accuracy and analyzing the binding patterns of interacting residues.

AVAILABILITY

All the source data and code are available at http://www.aporc.org/doc/wiki/PRNA or http://www.sysbio.ac.cn/datatools.asp

CONTACT

lnchen@sibs.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Simple, recurring RNA binding sites for L-arginine

Seven new arginine binding motifs have been selected from a heterogeneous RNA pool containing 17, 25, and 50mer randomized tracts, yielding 131 independently derived binding sites that are multiply isolated. The shortest 17mer random region is sufficient to build varied arginine binding sites using five different conserved motifs (motifs 1a, 1b, 1c, 2, and 4). Dissociation constants are in the fractional millimolar to millimolar range. Binding sites are amino acid side-chain specific and discriminate moderately between L- and D-stereoisomers of arginine, suggesting a molecular focus on side-chain guanidinium. An arginine coding triplet (codon/anticodon) is highly conserved within the largest family of Arg sites (72% of all sequences), as has also been found in minimal, most prevalent RNA binding sites for Ile, His, and Trp.

Geometric similarity between protein-RNA interfaces

A new method is described to measure the geometric similarity between protein-RNA interfaces quantitatively. The method is based on a procedure that dissects the interface geometry in terms of the spatial relationships between individual amino acid nucleotide pairs. Using this technique, we performed an all-on-all comparison of 586 protein-RNA interfaces deposited in the current Protein Data Bank, as the result, an interface-interface similarity score matrix was obtained. Based upon this matrix, hierarchical clustering was carried out which yielded a complete clustering tree for the 586 protein-RNA interfaces. By investigating the organizing behavior of the clustering tree and the SCOP classification of protein partners in complexes, a geometrically nonredundant, diverse data set (representative data set) consisting of 45 distinct protein-RNA interfaces was extracted for the purpose of studying protein-RNA interactions, RNA regulations, and drug design. We classified protein-RNA interfaces into three types. In type I, the families and interface structural classes of the protein partners, as well as the interface geometries are all similar. In type II, the interface geometries and the interface structural classes are similar, whereas the protein families are different. In type III, only the interface geometries are similar but the protein families and the interface structural classes are distinct. Furthermore, we also show two new RNA recognition themes derived from the representative data set.

Analysis of interactions between ribosomal proteins and RNA structural motifs

Background

One important goal of structural bioinformatics is to recognize and predict the interactions between protein binding sites and RNA. Recently, a comprehensive analysis of ribosomal proteins and their interactions with rRNA has been done. Interesting results emerged from the comparison of r-proteins within the small subunit in T. thermophilus and E. coli, supporting the idea of a core made by both RNA and proteins, conserved by evolution. Recent work showed also that ribosomal RNA is modularly composed. Motifs are generally single-stranded sequences of consecutive nucleotides (ssRNA) with characteristic folding. The role of these motifs in protein-RNA interactions has been so far only sparsely investigated.

Results

This work explores the role of RNA structural motifs in the interaction of proteins with ribosomal RNA (rRNA). We analyze composition, local geometries and conformation of interface regions involving motifs such as tetraloops, kink turns and single extruded nucleotides. We construct an interaction map of protein binding sites that allows us to identify the common types of shared 3-D physicochemical binding patterns for tetraloops. Furthermore, we investigate the protein binding pockets that accommodate single extruded nucleotides either involved in kink-turns or in arbitrary RNA strands. This analysis reveals a new structural motif, called tripod.

It corresponds to small pockets consisting of three aminoacids arranged at the vertices of an almost equilateral triangle. We developed a search procedure for the recognition of tripods, based on an empirical tripod fingerprint.

Conclusion

A comparative analysis with the overall RNA surface and interfaces shows that contact surfaces involving RNA motifs have distinctive features that may be useful for the recognition and prediction of interactions.