Protein-RNA interactions: structural analysis and functional classes

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes

Protein-RNA interactions play vital roles in plethora of biological processes such as regulation of gene expression, protein synthesis, mRNA processing and biogenesis. Identification of RNA-binding residues (RBRs) in proteins is essential to understand RNA-mediated protein functioning, to perform site-directed mutagenesis and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence-structure gap. In this study, we have extensively studied protein-RNA interface in known RNA-binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5-fold cross-validations, the sequence pattern derived dipeptide composition based BRF model (DCP-BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein-RNA interactions. This may serve to accelerate investigations on functional roles of many novel RBPs.

Guanidinium–amino acid hydrogen-bonding interactions in protein crystal structures: implications for guanidinium-induced protein denaturation

Structural analysis of guanidinium–amino acid interaction pairs in protein crystal structures is coupled with an effective scheme for classifying the optimized pairs, to gain understanding of the guanidinium:protein hydrogen bonding modes.

qNABpredict: Quick, accurate, and taxonomy-aware sequence-based prediction of content of nucleic acid binding amino acids

Protein sequence-based predictors of nucleic acid (NA)-binding include methods that predict NA-binding proteins and NA-binding residues. The residue-level tools produce more details but suffer high computational cost since they must predict every amino acid in the input sequence and rely on multiple sequence alignments. We propose an alternative approach that predicts content (fraction) of the NA-binding residues, offering more information than the protein-level prediction and much shorter runtime than the residue-level tools. Our first-of-its-kind content predictor, qNABpredict, relies on a small, rationally designed and fast-to-compute feature set that represents relevant characteristics extracted from the input sequence and a well-parametrized support vector regression model. We provide two versions of qNABpredict, a taxonomy-agnostic model that can be used for proteins of unknown taxonomic origin and more accurate taxonomy-aware models that are tailored to specific taxonomic kingdoms: archaea, bacteria, eukaryota, and viruses. Empirical tests on a low-similarity test dataset show that qNABpredict is 100 times faster and generates statistically more accurate content predictions when compared to the content extracted from results produced by the residue-level predictors. We also show that qNABpredict's content predictions can be used to improve results generated by the residue-level predictors. We release qNABpredict as a convenient webserver and source code at http://biomine.cs.vcu.edu/servers/qNABpredict/. This new tool should be particularly useful to predict details of protein-NA interactions for large protein families and proteomes.

Molecular Dynamics Simulations of Protein RNA Complexes by Using an Advanced Electrostatic Model

Protein-RNA interactions are integral to the biological functions of RNA. It is well recognized that molecular dynamics (MD) simulations of protein-RNA complexes are more challenging than those of each component. The difficulty arises from the strong electrostatic interactions and the delicate balance between various types of physical forces at the interface. Previously, MD simulations of protein-RNA complexes have predominantly employed fixed-charge force fields. Although force field modifications have been developed to address problems identified in the simulations, some protein-RNA structures are still hard to reproduce by simulations. Here, we present MD simulations of two representative protein-RNA complexes using the AMOEBA polarizable force field. The van der Waals parameters were refined to reproduce accurate quantum-mechanical data of base-base and base-amino acid interactions. It was found that the refined parameters produced a more stable hydrogen-bond network in the interface. One of the complexes remained stable during the short simulations, whereas it could quickly break down in previous simulations using fixed-charge force fields. There was reversible breaking and formation of hydrogen bonds that are observed in the crystal structure, which may indicate the difference in solution and crystal structures. While further improvement and validation of the force fields are still needed, this work demonstrates that polarizable force fields are promising for the study of protein-RNA complexes.

Transient RNA Interactions Leave a Covalent Imprint on a Viral Capsid Protein

The hepatitis B virus (HBV) is the leading cause of persistent liver infections. Its DNA-based genome is synthesized through reverse transcription of an RNA template inside the assembled capsid shell. In addition to the structured assembly domain, the capsid protein harbors a C-terminal extension that mediates both the enclosure of RNA during capsid assembly and the nuclear entry of the capsid during infection. The arginine-rich motifs within this extension, though common to many viruses, have largely escaped atomic-scale investigation. Here, we leverage solution and solid-state nuclear magnetic resonance spectroscopy at ambient and cryogenic temperatures, under dynamic nuclear polarization signal enhancement, to investigate the organization of the genome within the capsid. Transient interactions with phosphate groups of the RNA backbone confine the arginine-rich motifs to the interior capsid space. While no secondary structure is induced in the C-terminal extension, interactions with RNA counteract the formation of a disulfide bond, which covalently tethers this peptide arm onto the inner capsid surface. Electrostatic and covalent contributions thus compete in the spatial regulation of capsid architecture. This disulfide switch represents a coupling mechanism between the structured assembly domain of the capsid and the enclosed nucleic acids. In particular, it enables the redox-dependent regulation of the exposure of the C-terminal extension on the capsid surface, which is required for nuclear uptake of the capsid. Phylogenetic analysis of capsid proteins from hepadnaviruses points toward a function of this switch in the persistence of HBV infections.

Exploring the Nature of Hydrogen Bonding between RNA and Proteins: A Comprehensive Analysis of RNA : Protein Complexes

A nonredundant dataset of ∼300 high (up to 2.5 Å) resolution X-ray structures of RNA:protein complexes were analyzed for hydrogen bonds between amino-acid residues and canonical ribonucleotides (rNs). The identified 17100 contacts were classified based on the identity (rA, rC, rG or rU) and interacting fragment (base, sugar, or ribose) of the rN, the nature (polar or nonpolar) and interacting moiety (main chain or side chain) of the amino-acid residue, as well as the rN and amino-acid atoms participating in the hydrogen bonding. 80 possible hydrogen-bonding combinations (4 (rNs) X 20 (amino acids)) involve a wide variety of RNA and protein types and are present in multiple occurrences in almost all PDB files. Comparison with the analogously-selected DNA:protein complexes reveals that the absence of 2'-OH group in DNA mainly accounts for the differences in DNA:protein and RNA:protein hydrogen bonding. Search for intrinsically-stable base:amino acid pairs containing single or multiple hydrogen bonds reveals 37 unique pairs, which may act as well-defined RNA:protein interaction motifs. Overall, our work collectively analyzes the largest set of nucleic acid-protein hydrogen bonds to date, and therefore highlights several trends that may help frame structural rules governing the physiochemical characteristics of RNA:protein recognition.

Statistical potentials for RNA-protein interactions optimized by CMA-ES

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.

Protein music of enhanced musicality by music style guided exploration of diverse amino acid properties

Inspired by the traceable analogies between protein sequences and music notes, protein music has been composed from amino acid sequences for popularizing science and sourcing melodies. Despite the continuous development of protein-to-music algorithms, the musicality of protein music lags far behind human music. Musicality may be enhanced by fine-tuned protein-to-music mapping to the features of a specific music style. We analyzed the features of a music style (Fantasy-Impromptu style), and used the quantized musical features to guide broad exploration of diverse amino acid properties (104 properties, sequence patterns and variations) for developing a novel protein-to-music algorithm of enhanced musicality. This algorithm was applied to 18 proteins of various biological functions. The derived music pieces consistently exhibited enhanced musicality with respect to existing protein music. Music style guided exploration of diverse amino acid properties enable protein music composition of enhanced musicality, which may be further developed and applied to a wider variety of music styles.

Structural basis for tRNA methylthiolation by the radical SAM enzyme MiaB

Numerous post-transcriptional modifications of transfer RNAs have vital roles in translation. The 2-methylthio-N⁶-isopentenyladenosine (ms²i⁶A) modification occurs at position 37 (A37) in transfer RNAs that contain adenine in position 36 of the anticodon, and serves to promote efficient A:U codon-anticodon base-pairing and to prevent unintended base pairing by near cognates, thus enhancing translational fidelity^1-4. The ms²i⁶A modification is installed onto isopentenyladenosine (i⁶A) by MiaB, a radical S-adenosylmethionine (SAM) methylthiotransferase. As a radical SAM protein, MiaB contains one [Fe₄S₄]_RS cluster used in the reductive cleavage of SAM to form a 5'-deoxyadenosyl 5'-radical, which is responsible for removing the C² hydrogen of the substrate⁵. MiaB also contains an auxiliary [Fe₄S₄]_aux cluster, which has been implicated^6-9 in sulfur transfer to C² of i⁶A37. How this transfer takes place is largely unknown. Here we present several structures of MiaB from Bacteroides uniformis. These structures are consistent with a two-step mechanism, in which one molecule of SAM is first used to methylate a bridging µ-sulfido ion of the auxiliary cluster. In the second step, a second SAM molecule is cleaved to a 5'-deoxyadenosyl 5'-radical, which abstracts the C² hydrogen of the substrate but only after C² has undergone rehybridization from sp² to sp³. This work advances our understanding of how enzymes functionalize inert C-H bonds with sulfur.

Structures and target RNA preferences of the RNA-binding protein family of IGF2BPs: An overview

Insulin-like growth factor 2 mRNA-binding proteins (IMPs, IGF2BPs) act in mRNA transport and translational control but are oncofetal tumor marker proteins. The IMP protein family represents a number of bona fide multi-domain RNA-binding proteins with up to six RNA-binding domains, resulting in a high complexity of possible modes of interactions with target mRNAs. Their exact mechanism in stability control of oncogenic mRNAs is only partially understood. Our and other laboratories' recent work has significantly pushed the understanding of IMP protein specificities both toward RNA engagement and between each other from NMR and crystal structures serving the basis for systematic biochemical and functional investigations. We here summarize the known structural and biochemical information about IMP RNA-binding domains and their RNA preferences. The article also touches on the respective roles of RNA secondary and protein tertiary structures for specific RNA-protein complexes, including the limited knowledge about IMPs' protein-protein interactions, which are often RNA mediated.

Anatomy of noncovalent interactions between the nucleobases or ribose and π-containing amino acids in RNA-protein complexes

A set of >300 nonredundant high-resolution RNA–protein complexes were rigorously searched for π-contacts between an amino acid side chain (W, H, F, Y, R, E and D) and an RNA nucleobase (denoted π–π interaction) or ribose moiety (denoted sugar–π). The resulting dataset of >1500 RNA–protein π-contacts were visually inspected and classified based on the interaction type, and amino acids and RNA components involved. More than 80% of structures searched contained at least one RNA–protein π-interaction, with π–π contacts making up 59% of the identified interactions. RNA–protein π–π and sugar–π contacts exhibit a range in the RNA and protein components involved, relative monomer orientations and quantum mechanically predicted binding energies. Interestingly, π–π and sugar–π interactions occur more frequently with RNA (4.8 contacts/structure) than DNA (2.6). Moreover, the maximum stability is greater for RNA–protein contacts than DNA–protein interactions. In addition to highlighting distinct differences between RNA and DNA–protein binding, this work has generated the largest dataset of RNA–protein π-interactions to date, thereby underscoring that RNA–protein π-contacts are ubiquitous in nature, and key to the stability and function of RNA–protein complexes.

Alterations in Glucose Metabolism Due to Decreased Expression of Heterogeneous Nuclear Ribonucleoprotein M in Pancreatic Ductal Adenocarcinoma

Simple Summary

Pancreatic cancer has one of the worst prognoses when compared to those of other cancer subtypes. One of the reasons is the resistance of this tumor to the hypovascular environment (an environment with low blood flow and low supply of oxygen and nutrients (especially glucose)). However, the detailed mechanism remains elusive. Recently, it has been reported that heterogeneous ribonuclear protein M (HNRNPM) is a splicing factor associated with malignant tumors. Thus, in this study, we investigated the expression and effects of HNRNPM in pancreatic ductal adenocarcinoma (PDA). We revealed that HNRNPM was highly expressed in pancreatic tissues but expression decreased in PDA tissues. Furthermore, we found that knockdown of HNRNPM protein expression under low-glucose conditions altered glucose metabolism and prolonged cell survival by suppressing glucose consumption. These results suggest that reduced expression of HNRNPM in PDAs may be involved in adaptation to a hypovascular environment, and that therapeutic agents for this target may lead to improved prognosis for pancreatic cancer.

Abstract

The prognosis of pancreatic cancer is considerably worse than that of other cancers, as early detection of pancreatic cancer is difficult and due to its hypovascular environment, which involves low blood flow and a low supply of oxygen and nutrients. Moreover, pancreatic cancer demonstrates a mechanism that allows it to survive in a hypovascular environment. However, the detailed mechanism remains elusive. Recently, it has been reported that heterogeneous ribonuclear protein M (HNRNPM) is a splicing factor associated with malignant tumors. Thus, in this study, we investigated the expression and effects of HNRNPM in pancreatic ductal adenocarcinoma (PDA). We observed that HNRNPM expression, which is highly expressed in pancreatic tissues, was reduced in PDA tissues. Additionally, knockdown of HNRNPM under low-glucose conditions that mimic a hypovascular environment was shown to alter glucose metabolism and prolong cell survival by suppressing glucose consumption. These results suggest that the decreased expression of HNRNPM in PDA may be involved in its adaptation to a hypovascular environment.

A quantum chemical view of the interaction of RNA nucleobases and base pairs with the side chains of polar amino acids

Hydrogen bonding between amino acids and nucleobases is important for RNA-protein recognition. As a first step toward understanding the physicochemical features of these contacts, the present work employs density functional theory calculations to critically analyze the intrinsic structures and strength of all theoretically possible model hydrogen-bonded complexes involving RNA nucleobase edges and polar amino acid side chains. Our geometry optimizations uncover a number of unique complexes that involve variable hydrogen-bonding characteristics, including conventional donor-acceptor interactions, bifurcated interactions and single hydrogen-bonded contacts. Further, significant strength of these complexes in the gas phase (-27 kJ mol^-1 to -226 kJ mol^-1) and solvent phase (-19 kJ mol^-1 to -78 kJ mol^-1) points toward the ability of associated contacts to provide stability to RNA-protein complexes. More importantly, for the first time, our study uncovers the features of complexes involving protonated nucleobases, as well as those involving the weakly polar cysteine side chain, and thereby highlights their potential importance in biological processes that involve RNA-protein interactions. Additional analysis on select base pair-amino acid complexes uncovers the ability of amino acid side chain to simultaneously interact with both nucleobases of the base pair, and highlights the greater strength of such interactions compared to base-amino acid interactions. Overall, our analysis provides a basic physicochemical framework for understanding the molecular basis of nucleic acid-protein interactions. Further, our quantum chemical data can be used to design better algorithms for automated search of these contacts at the RNA-protein interface.Communicated by Ramaswamy H. Sarma.

SCRIBER: accurate and partner type-specific prediction of protein-binding residues from proteins sequences

Motivation

Accurate predictions of protein-binding residues (PBRs) enhances understanding of molecular-level rules governing protein–protein interactions, helps protein–protein docking and facilitates annotation of protein functions. Recent studies show that current sequence-based predictors of PBRs severely cross-predict residues that interact with other types of protein partners (e.g. RNA and DNA) as PBRs. Moreover, these methods are relatively slow, prohibiting genome-scale use.

Results

We propose a novel, accurate and fast sequence-based predictor of PBRs that minimizes the cross-predictions. Our SCRIBER (SeleCtive pRoteIn-Binding rEsidue pRedictor) method takes advantage of three innovations: comprehensive dataset that covers multiple types of binding residues, novel types of inputs that are relevant to the prediction of PBRs, and an architecture that is tailored to reduce the cross-predictions. The dataset includes complete protein chains and offers improved coverage of binding annotations that are transferred from multiple protein–protein complexes. We utilize innovative two-layer architecture where the first layer generates a prediction of protein-binding, RNA-binding, DNA-binding and small ligand-binding residues. The second layer re-predicts PBRs by reducing overlap between PBRs and the other types of binding residues produced in the first layer. Empirical tests on an independent test dataset reveal that SCRIBER significantly outperforms current predictors and that all three innovations contribute to its high predictive performance. SCRIBER reduces cross-predictions by between 41% and 69% and our conservative estimates show that it is at least 3 times faster. We provide putative PBRs produced by SCRIBER for the entire human proteome and use these results to hypothesize that about 14% of currently known human protein domains bind proteins.

Availability and implementation

SCRIBER webserver is available at http://biomine.cs.vcu.edu/servers/SCRIBER/.

Supplementary information

Supplementary data are available at Bioinformatics online.

How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms

RNA-binding proteins (RBPs) comprise a large class of over 2,000 proteins that interact with transcripts in all manner of RNA-driven processes. The structures and mechanisms that RBPs use to bind and regulate RNA are incredibly diverse. In this review, we take a look at the components of protein-RNA interaction, from the molecular level to multi-component interaction. We first summarize what is known about protein-RNA molecular interactions based on analyses of solved structures. We additionally describe software currently available for predicting protein-RNA interaction and other resources useful for the study of RBPs. We then review the structure and function of seventeen known RNA-binding domains and analyze the hydrogen bonds adopted by protein-RNA structures on a domain-by-domain basis. We conclude with a summary of the higher-level mechanisms that regulate protein-RNA interactions.

Structural Patterns and Stabilities of Hydrogen-Bonded Pairs Involving Ribonucleotide Bases and Arginine, Glutamic Acid, or Glutamine Residues of Proteins from Quantum Mechanical Calculations

Ribonucleotide:protein interactions play crucial roles in a number of biological processes. Unlike the RNA:protein interface where van der Waals contacts are prevalent, the recognition of a single ribonucleotide such as ATP by a protein occurs predominantly through hydrogen-bonding interactions. As a first step toward understanding the role of hydrogen bonding in ribonucleotide:protein recognition, the present work employs density functional theory to provide a detailed quantum-mechanical analysis of the structural and energetic characteristics of 18 unique hydrogen-bonded pairs involving the nucleobase/nucleoside moiety of four canonical ribonucleotides and the side chains of three polar amino-acid residues (arginine, glutamine, and glutamic acid) of proteins. In addition, we model five new pairs that are till now not observed in crystallographically identified ribonucleotide:protein complexes but may be identified in complexes crystallized in the future. We critically examine the characteristics of each pair in its ribonucleotide:protein crystal structure occurrence and (gas phase and water phase) optimized intrinsic structure. We further evaluated the interaction energy of each pair and characterized the associated hydrogen bonds using a number of quantum mechanics-based relationships including natural bond orbital analysis, quantum theory atoms in molecules analysis, Iogansen relationships, Nikolaienko-Bulavin-Hovorun relationships, and noncovalent interaction-reduced density gradient analysis. Our analyses reveal rich variability in hydrogen bonds in the crystallographic as well as intrinsic structure of each pair, which includes conventional O/N-H···N/O and C-H···O hydrogen bonds as well as donor/acceptor-bifurcated hydrogen bonds. Further, we identify five combinations of nucleobase and amino acid moieties; each of which exhibits at least two alternate (i.e., multimodal) structures that interact through the same nucleobase edge. In fact, one such pair exhibits four multimodal structures; one of which possesses unconventional "amino-acceptor" hydrogen bonding with comparable (-9.4 kcal mol^-1) strength to the corresponding conventional (i.e., amino:donor) structure (-9.2 kcal mol^-1). This points to the importance of amino-acceptor hydrogen bonds in RNA:protein interactions and suggests that such interactions must be considered in the future while studying the dynamics in the context of molecular recognition. Overall, our study provides preliminary insights into the intrinsic features of ribonucleotide:amino acid interactions, which may help frame a clearer picture of the molecular basis of RNA:protein recognition and further appreciate the role of such contacts in biology.

Computational evolution of an RNA-binding protein towards enhanced oxidized-RNA binding

The oxidation of RNA has been implicated in the development of many diseases. Among the four ribonucleotides, guanosine is the most susceptible to oxidation, resulting in the formation of 8-oxo-7,8-dihydroguanosine (8-oxoG). Despite the limited knowledge about how cells regulate the detrimental effects of oxidized RNA, cellular factors involved in its regulation have begun to be identified. One of these factors is polynucleotide phosphorylase (PNPase), a multifunctional enzyme implicated in RNA turnover. In the present study, we have examined the interaction of PNPase with 8-oxoG in atomic detail to provide insights into the mechanism of 8-oxoG discrimination. We hypothesized that PNPase subunits cooperate to form a binding site using the dynamic SFF loop within the central channel of the PNPase homotrimer. We evolved this site using a novel approach that initially screened mutants from a library of beneficial mutations and assessed their interactions using multi-nanosecond Molecular Dynamics simulations. We found that evolving this single site resulted in a fold change increase in 8-oxoG affinity between 1.2 and 1.5 and/or selectivity between 1.5 and 1.9. In addition to the improvement in 8-oxoG binding, complementation of K12 Δpnp with plasmids expressing mutant PNPases caused increased cell tolerance to H₂O₂. This observation provides a clear link between molecular discrimination of RNA oxidation and cell survival. Moreover, this study provides a framework for the manipulation of modified-RNA protein readers, which has potential application in synthetic biology and epitranscriptomics.

Scoring Functions for Protein-RNA Complex Structure Prediction: Advances, Applications, and Future Directions

Protein-RNA interaction is among the most essential of biological events in living cells, being involved in protein synthesizing, RNA processing and transport, DNA transcription, and regulation of gene expression, and many other critical bio-molecular activities. A thorough understanding of this interaction is of paramount importance in fundamental study of a variety of vital cellular processes and therapeutic application for remedy of a broad range of diseases. Experimental high-resolution 3D structure determination is the primary source of knowledge for protein-RNA complexes. However, due to technical limitations, the existing techniques for experimental structure determination couldn't match the demand from fast growing interest in academia and industry. This problem necessitates the alternative high-throughput computational method for protein-RNA complex structure prediction. Similar to the in silico methods used for protein-protein and protein-DNA interactions, a reliable prediction of protein-RNA complex structure requires a scoring function with commensurate discriminatory power. Derived from determined structures and purposed to predict the to-be-determined structures, the scoring function is not only a predictive tool but also a gauge of our knowledge of protein-RNA interaction. In this review, we present an overview of the status of existing scoring functions and the scientific principle behind their constructions as well as their strengths and limitations. Finally, we will discuss about future directions of the scoring function development for protein-RNA structure prediction.

A structure-based model for the prediction of protein-RNA binding affinity

Protein-RNA recognition is highly affinity-driven and regulates a wide array of cellular functions. In this study, we have curated a binding affinity data set of 40 protein-RNA complexes, for which at least one unbound partner is available in the docking benchmark. The data set covers a wide affinity range of eight orders of magnitude as well as four different structural classes. On average, we find the complexes with single-stranded RNA have the highest affinity, whereas the complexes with the duplex RNA have the lowest. Nevertheless, free energy gain upon binding is the highest for the complexes with ribosomal proteins and the lowest for the complexes with tRNA with an average of -5.7 cal/mol/Å2 in the entire data set. We train regression models to predict the binding affinity from the structural and physicochemical parameters of protein-RNA interfaces. The best fit model with the lowest maximum error is provided with three interface parameters: relative hydrophobicity, conformational change upon binding and relative hydration pattern. This model has been used for predicting the binding affinity on a test data set, generated using mutated structures of yeast aspartyl-tRNA synthetase, for which experimentally determined ΔG values of 40 mutations are available. The predicted ΔGempirical values highly correlate with the experimental observations. The data set provided in this study should be useful for further development of the binding affinity prediction methods. Moreover, the model developed in this study enhances our understanding on the structural basis of protein-RNA binding affinity and provides a platform to engineer protein-RNA interfaces with desired affinity.

Discovery of Small-Molecule Modulators of Protein-RNA Interactions by Fluorescence Intensity-Based Binding Assay

Protein-RNA interactions mediate various cellular processes, the dysregulation of which has been associated with a list of diseases. Thus, novel experimental tools for monitoring protein-RNA interactions are highly desirable to identify new chemical modulators of these therapeutic targets. In this study, we constructed simple fluorescence intensity-based protein-RNA binding assays by testing multiple environment-sensitive organic fluorophores. We selected the oncogenic interaction between Lin28 and the let-7 microRNA and the important immunomodulatory Roquin-Tnf CDE interaction as representative targets. We adapted this assay to high-throughput screening for the identification of pyrazolyl thiazolidinedione-type molecules as potent small-molecule inhibitors of protein-microRNA interactions. We clearly showed the structure-activity relationships of this new class of Lin28-let-7 interaction inhibitors, and confirmed that cellular mature let-7 microRNAs and their target genes could be modulated upon treatment with the pyrazolyl thiazolidinedione-type inhibitor. We expect that our simple and adaptable screening approach can be applied for the development of various assay systems aimed at the identification of bioactive small molecules targeting protein-RNA interactions.

Dissecting macromolecular recognition sites in ribosome: implication to its self-assembly

Interactions between macromolecules play a crucial role in ribosome assembly that follows a highly coordinated process involving RNA folding and binding of ribosomal proteins (r-proteins). Although extensive studies have been carried out to understand macromolecular interactions in ribosomes, most of them are confined to either large or small ribosomal-subunit of few species. A comparative analysis of macromolecular interactions across different domains is still missing. We have analyzed the structural and physicochemical properties of protein-protein (PP), protein-RNA (PR) and RNA-RNA (RR) interfaces in small and large subunits of ribosomes, as well as in between the two subunits. Additionally, we have also developed Random Forest (RF) classifier to catalog the r-proteins. We find significant differences as well as similarities in macromolecular recognition sites between ribosomal assemblies of prokaryotes and eukaryotes. PR interfaces are substantially larger and have more ionic interactions than PP and RR interfaces in both prokaryotes and eukaryotes. PP, PR and RR interfaces in eukaryotes are well packed compared to those in prokaryotes. However, the packing density between the large and the small subunit interfaces in the entire assembly is strikingly low in both prokaryotes and eukaryotes, indicating the periodic association and dissociation of the two subunits during the translation. The structural and physicochemical properties of PR interfaces are used to predict the r-proteins in the assembly pathway into early, intermediate and late binders using RF classifier with an accuracy of 80%. The results provide new insights into the classification of r-proteins in the assembly pathway.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ε_in). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GB^GBn1 model with ε_in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GB^GBn1 model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein-RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems.

In Silico Prediction and Validation of Novel RNA Binding Proteins and Residues in the Human Proteome

Deciphering a complete landscape of protein-RNA interactions in the human proteome remains an elusive challenge. We computationally elucidate RNA binding proteins (RBPs) using an approach that complements previous efforts. We employ two modern complementary sequence-based methods that provide accurate predictions from the structured and the intrinsically disordered sequences, even in the absence of sequence similarity to the known RBPs. We generate and analyze putative RNA binding residues on the whole proteome scale. Using a conservative setting that ensures low, 5% false positive rate, we identify 1511 putative RBPs that include 281 known RBPs and 166 RBPs that were previously predicted. We empirically demonstrate that these overlaps are statistically significant. We also validate the putative RBPs based on two major hallmarks of their RNA binding residues: high levels of evolutionary conservation and enrichment in charged amino acids. Moreover, we show that the novel RBPs are significantly under-annotated functionally which coincides with the fact that they were not yet found to interact with RNAs. We provide two examples of our novel putative RBPs for which there is recent evidence of their interactions with RNAs. The dataset of novel putative RBPs and RNA binding residues for the future hypothesis generation is provided in the Supporting Information.

The Expansion Segments of 28S Ribosomal RNA Extensively Match Human Messenger RNAs

Eukaryote ribosomal RNAs (rRNAs) have expanded in the course of phylogeny by addition of nucleotides in specific insertion areas, the expansion segments. These number about 40 in the larger (25–28S) rRNA (up to 2,400 nucleotides), and about 12 in the smaller (18S) rRNA (<700 nucleotides). Expansion of the larger rRNA shows a clear phylogenetic increase, with a dramatic rise in mammals and especially in hominids. Substantial portions of expansion segments in this RNA are not bound to ribosomal proteins, and may engage extraneous interactants, including messenger RNAs (mRNAs). Studies on the ribosome-mRNA interaction have focused on proteins of the smaller ribosomal subunit, with some examination of 18S rRNA. However, the expansion segments of human 28S rRNA show much higher density and numbers of mRNA matches than those of 18S rRNA, and also a higher density and match numbers than its own core parts. We have studied that with frequent and potentially stable matches containing 7–15 nucleotides. The expansion segments of 28S rRNA average more than 50 matches per mRNA even assuming only 5% of their sequence as available for such interaction. Large expansion segments 7, 15, and 27 of 28S rRNA also have copious long (≥10-nucleotide) matches to most human mRNAs, with frequencies much higher than in other 28S rRNA parts. Expansion segments 7 and 27 and especially segment 15 of 28S rRNA show large size increase in mammals compared to other metazoans, which could reflect a gain of function related to interaction with non-ribosomal partners. The 28S rRNA expansion segment 15 shows very high increments in size, guanosine, and cytidine nucleotide content and mRNA matching in mammals, and especially in hominids. With these segments (but not with other 28S rRNA or any 18S rRNA expansion segments) the density and number of matches are much higher in 5′-terminal than in 3′-terminal untranslated mRNA regions, which may relate to mRNA mobilization via 5′ termini. Matches in the expansion segments 7, 15, and 27 of human 28S rRNA appear as candidates for general interaction with mRNAs, especially those associated with intracellular matrices such as the endoplasmic reticulum.

RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.

Structural Changes of RNA in Complex with Proteins in the SRP

The structural flexibility of RNA allows it to exist in several shapes and sizes. Thus, RNA is functionally diverse and is known to be involved in processes such as catalysis, ligand binding, and most importantly, protein recognition. RNA can adopt different structures, which can often dictate its functionality. When RNA binds onto protein to form a ribonucleoprotein complex (RNP), multiple interactions and conformational changes occur with the RNA and protein. However, there is the question of whether there is a specific pattern for these changes to occur upon recognition. In particular when RNP complexity increases with the addition of multiple proteins/RNA, it becomes difficult to structurally characterize the overall changes using the current structural determination techniques. Hence, there is a need to use a combination of biochemical, structural and computational modeling to achieve a better understanding of the processes that RNPs are involved. Nevertheless, there are well-characterized systems that are evolutionarily conserved [such as the signal recognition particle (SRP)] that give us important information on the structural changes of RNA and protein upon complex formation.

Analysis of Evolutionarily Independent Protein-RNA Complexes Yields a Criterion to Evaluate the Relevance of Prebiotic Scenarios

A central difficulty facing study of the origin of life on Earth is evaluating the relevance of different proposed prebiotic scenarios. Perhaps the most established feature of the origin of life was the progression through an RNA World, a prebiotic stage dominated by functional RNA. We use the appearance of proteins in the RNA World to understand the prebiotic milieu and develop a criterion to evaluate proposed synthetic scenarios. Current consensus suggests that the earliest amino acids of the genetic code were anionic or small hydrophobic or polar amino acids. However, the ability to interact with the RNA World would have been a crucial feature of early proteins. To determine which amino acids would be important for the RNA World, we analyze non-biological protein-aptamer complexes in which the RNA or DNA is the result of in vitro evolution. This approach avoids confounding effects of biological context and evolutionary history. We use bioinformatic analysis and molecular dynamics simulations to characterize these complexes. We find that positively charged and aromatic amino acids are over-represented whereas small hydrophobic amino acids are under-represented. Binding enthalpy is found to be primarily electrostatic, with positively charged amino acids contributing cooperatively to binding enthalpy. Arginine dominates all modes of interaction at the interface. These results suggest that proposed prebiotic syntheses must be compatible with cationic amino acids, particularly arginine or a biophysically similar amino acid, in order to be relevant to the invention of protein by the RNA World.

A structural dissection of protein–RNA interactions based on different RNA base areas of interfaces

Protein–RNA interactions are very common cellular processes, but the mechanisms of interactions are not fully understood, mainly due to the complicated RNA structures. By the elaborate investigation on RNA structures of protein–RNA complexes, it was firstly found in this paper that RNAs in these complexes could be clearly classified into three classes (high, medium and low) based on the different levels of P_base (the percentage of base area buried in the RNA interface). In view of the three RNA classes, more detailed analyses on protein–RNA interactions were comprehensively performed from various aspects, including interface area, structure, composition and interaction force, so as to achieve a deeper understanding of the recognition specificity for the three classes of protein–RNA interactions. According to our classification strategy, the three complex classes have significant differences in terms of almost all properties. Complexes in the high class have short and extended RNA structures and behave like protein–ssDNA interactions. Their hydrogen bonds and hydrophobic interactions are strong. For complexes in low class, their RNA structures are mainly double-stranded, like protein–dsDNA interactions, and electrostatic interactions frequently occur. The complexes in medium class have the longest RNA chains and largest average interface area. Meanwhile, they do not show any preference for the interaction force. On average, in terms of composition, secondary structures and intermolecular physicochemical properties, significant feature preferences can be observed in high and low complexes, but no highly specific features are found for medium complexes. We found that our proposed P_base is an important parameter which can be used as a new determinant to distinguish protein–RNA complexes. For high and low complexes, we can more easily understand the specificity of the recognition process from the interface features than for medium complexes. In the future, medium complexes should be our research focus to further structurally analyze from more feature aspects. Overall, this study may contribute to further understanding of the mechanism of protein–RNA interactions on a more detailed level.

Qualitative and quantitative measurements of the influence of structure and composition of RNA interfaces on protein–RNA interactions.

Comprehensive review and empirical analysis of hallmarks of DNA-, RNA- and protein-binding residues in protein chains

Proteins interact with a variety of molecules including proteins and nucleic acids. We review a comprehensive collection of over 50 studies that analyze and/or predict these interactions. While majority of these studies address either solely protein–DNA or protein–RNA binding, only a few have a wider scope that covers both protein–protein and protein–nucleic acid binding. Our analysis reveals that binding residues are typically characterized with three hallmarks: relative solvent accessibility (RSA), evolutionary conservation and propensity of amino acids (AAs) for binding. Motivated by drawbacks of the prior studies, we perform a large-scale analysis to quantify and contrast the three hallmarks for residues that bind DNA-, RNA-, protein- and (for the first time) multi-ligand-binding residues that interact with DNA and proteins, and with RNA and proteins. Results generated on a well-annotated data set of over 23 000 proteins show that conservation of binding residues is higher for nucleic acid- than protein-binding residues. Multi-ligand-binding residues are more conserved and have higher RSA than single-ligand-binding residues. We empirically show that each hallmark discriminates between binding and nonbinding residues, even predicted RSA, and that combining them improves discriminatory power for each of the five types of interactions. Linear scoring functions that combine these hallmarks offer good predictive performance of residue-level propensity for binding and provide intuitive interpretation of predictions. Better understanding of these residue-level interactions will facilitate development of methods that accurately predict binding in the exponentially growing databases of protein sequences.

Whole Ribosome NMR: Dipolar Couplings and Contributions to Whole Cells

Solid-state NMR is a powerful tool for quantifying chemical composition and structure in complex assemblies and even whole cells. We employed N{P} REDOR NMR to obtain atomic-level distance propensities in intact ¹⁵N-labeled E. coli ribosomes. The experimental REDOR dephasing of shift-resolved lysyl amine nitrogens by phosphorus was comparable to that expected from a calculation of N-P distances involving the lysines included in the crystal structure coordinates. Among the nitrogen contributions to the REDOR spectra, the strongest dephasing emerged from the dipolar couplings to phosphorus involving nitrogen peaks ascribed primarily to rRNA, and the weakest dephasing arose from protein amide nitrogens. This approach is applicable to any macromolecular system and provides quantitative comparisons of distance proximities between shift-resolved nuclei of one type and heteronuclear dephasing spins. Enhanced molecular specificity could be achieved through the use of spectroscopic filters or specific labeling. Furthermore, ribosome ¹³C and ¹⁵N CPMAS spectra were compared with those of whole cells from which the ribosomes were isolated. Whole-cell signatures of ribosomes were identified and should be of value in comparing overall cellular ribosome content in whole-cell samples.

A pair-conformation-dependent scoring function for evaluating 3D RNA-protein complex structures

Computational prediction of RNA-protein complex 3D structures includes two basic steps: one is sampling possible structures and another is scoring the sampled structures to pick out the correct one. At present, constructing accurate scoring functions is still not well solved and the performances of the scoring functions usually depend on used benchmarks. Here we propose a pair-conformation-dependent scoring function, 3dRPC-Score, for 3D RNA-protein complex structure prediction by considering the nucleotide-residue pairs having the same energy if their conformations are similar, instead of the distance-only dependence of the most existing scoring functions. Benchmarking shows that 3dRPC-Score has a consistent performance in three test sets.

The Genetic Code and RNA-Amino Acid Affinities

A significant part of the genetic code likely originated via a chemical interaction, which should be experimentally verifiable. One possible verification relates bound amino acids (or perhaps their activated congeners) and ribonucleotide sequences within cognate RNA binding sites. To introduce this interaction, I first summarize how amino acids function as targets for RNA binding. Then the experimental method for selecting relevant RNA binding sites is characterized. The selection method’s characteristics are related to the investigation of the RNA binding site model treated at the outset. Finally, real binding sites from selection and also from extant natural RNAs (for example, the Sulfobacillus guanidinium riboswitch) are connected to the genetic code, and by extension, to the evolutionary progression that produced the code. During this process, peptides may have been produced directly on an instructive amino acid binding RNA (a DRT; Direct RNA Template). Combination of observed stereochemical selectivity with adaptation and co-evolutionary refinement is logically required, and also potentially sufficient, to create the striking order conserved throughout the present coding table.

Selecting high-quality negative samples for effectively predicting protein-RNA interactions

Background

The identification of Protein-RNA Interactions (PRIs) is important to understanding cell activities. Recently, several machine learning-based methods have been developed for identifying PRIs. However, the performance of these methods is unsatisfactory. One major reason is that they usually use unreliable negative samples in the training process.

Methods

For boosting the performance of PRI prediction, we propose a novel method to generate reliable negative samples. Concretely, we firstly collect the known PRIs as positive samples for generating positive sets. For each positive set, we construct two corresponding negative sets, one is by our method and the other by random method. Each positive set is combined with a negative set to form a dataset for model training and performance evaluation. Consequently, we get 18 datasets of different species and different ratios of negative samples to positive samples.

Secondly, sequence-based features are extracted to represent each of PRIs and protein-RNA pairs in the datasets. A filter-based method is employed to cut down the dimensionality of feature vectors for reducing computational cost. Finally, the performance of support vector machine (SVM), random forest (RF) and naive Bayes (NB) is evaluated on the generated 18 datasets.

Results

Extensive experiments show that comparing to using randomly-generated negative samples, all classifiers achieve substantial performance improvement by using negative samples selected by our method. The improvements on accuracy and geometric mean for the SVM classifier, the RF classifier and the NB classifier are as high as 204.5 and 68.7%, 174.5 and 53.9%, 80.9 and 54.3%, respectively.

Conclusion

Our method is useful to the identification of PRIs.

Structure alignment-based classification of RNA-binding pockets reveals regional RNA recognition motifs on protein surfaces

Background

Many critical biological processes are strongly related to protein-RNA interactions. Revealing the protein structure motifs for RNA-binding will provide valuable information for deciphering protein-RNA recognition mechanisms and benefit complementary structural design in bioengineering. RNA-binding events often take place at pockets on protein surfaces. The structural classification of local binding pockets determines the major patterns of RNA recognition.

Results

In this work, we provide a novel framework for systematically identifying the structure motifs of protein-RNA binding sites in the form of pockets on regional protein surfaces via a structure alignment-based method. We first construct a similarity network of RNA-binding pockets based on a non-sequential-order structure alignment method for local structure alignment. By using network community decomposition, the RNA-binding pockets on protein surfaces are clustered into groups with structural similarity. With a multiple structure alignment strategy, the consensus RNA-binding pockets in each group are identified. The crucial recognition patterns, as well as the protein-RNA binding motifs, are then identified and analyzed.

Conclusions

Large-scale RNA-binding pockets on protein surfaces are grouped by measuring their structural similarities. This similarity network-based framework provides a convenient method for modeling the structural relationships of functional pockets. The local structural patterns identified serve as structure motifs for the recognition with RNA on protein surfaces.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1410-1) contains supplementary material, which is available to authorized users.

Evoking picomolar binding in RNA by a single phosphorodithioate linkage

RNA aptamers are synthetic oligonucleotide-based affinity molecules that utilize unique three-dimensional structures for their affinity and specificity to a target such as a protein. They hold the promise of numerous advantages over biologically produced antibodies; however, the binding affinity and specificity of RNA aptamers are often insufficient for successful implementation in diagnostic assays or as therapeutic agents. Strong binding affinity is important to improve the downstream applications. We report here the use of the phosphorodithioate (PS2) substitution on a single nucleotide of RNA aptamers to dramatically improve target binding affinity by ∼1000-fold (from nanomolar to picomolar). An X-ray co-crystal structure of the α-thrombin:PS2-aptamer complex reveals a localized induced-fit rearrangement of the PS2-containing nucleotide which leads to enhanced target interaction. High-level quantum mechanical calculations for model systems that mimic the PS2 moiety and phenylalanine demonstrate that an edge-on interaction between sulfur and the aromatic ring is quite favorable, and also confirm that the sulfur analogs are much more polarizable than the corresponding phosphates. This favorable interaction involving the sulfur atom is likely even more significant in the full aptamer-protein complexes than in the model systems.

Improved Accuracy in RNA-Protein Rigid Body Docking by Incorporating Force Field for Molecular Dynamics Simulation into the Scoring Function

RNA-protein interactions play fundamental roles in many biological processes. To understand these interactions, it is necessary to know the three-dimensional structures of RNA-protein complexes. However, determining the tertiary structure of these complexes is often difficult, suggesting that an accurate rigid body docking for RNA-protein complexes is needed. In general, the rigid body docking process is divided into two steps: generating candidate structures from the individual RNA and protein structures and then narrowing down the candidates. In this study, we focus on the former problem to improve the prediction accuracy in RNA-protein docking. Our method is based on the integration of physicochemical information about RNA into ZDOCK, which is known as one of the most successful computer programs for protein-protein docking. Because recent studies showed the current force field for molecular dynamics simulation of protein and nucleic acids is quite accurate, we modeled the physicochemical information about RNA by force fields such as AMBER and CHARMM. A comprehensive benchmark of RNA-protein docking, using three recently developed data sets, reveals the remarkable prediction accuracy of the proposed method compared with existing programs for docking: the highest success rate is 34.7% for the predicted structure of the RNA-protein complex with the best score and 79.2% for 3,600 predicted ones. Three full atomistic force fields for RNA (AMBER94, AMBER99, and CHARMM22) produced almost the same accurate result, which showed current force fields for nucleic acids are quite accurate. In addition, we found that the electrostatic interaction and the representation of shape complementary between protein and RNA plays the important roles for accurate prediction of the native structures of RNA-protein complexes.

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors

BACKGROUND

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers.

RESULTS

In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631.

CONCLUSIONS

The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

Use of chemical modification and mass spectrometry to identify substrate-contacting sites in proteinaceous RNase P, a tRNA processing enzyme

Among all enzymes in nature, RNase P is unique in that it can use either an RNA- or a protein-based active site for its function: catalyzing cleavage of the 5′-leader from precursor tRNAs (pre-tRNAs). The well-studied catalytic RNase P RNA uses a specificity module to recognize the pre-tRNA and a catalytic module to perform cleavage. Similarly, the recently discovered proteinaceous RNase P (PRORP) possesses two domains – pentatricopeptide repeat (PPR) and metallonuclease (NYN) – that are present in some other RNA processing factors. Here, we combined chemical modification of lysines and multiple-reaction monitoring mass spectrometry to identify putative substrate-contacting residues in Arabidopsis thaliana PRORP1 (AtPRORP1), and subsequently validated these candidate sites by site-directed mutagenesis. Using biochemical studies to characterize the wild-type (WT) and mutant derivatives, we found that AtPRORP1 exploits specific lysines strategically positioned at the tips of it's V-shaped arms, in the first PPR motif and in the NYN domain proximal to the catalytic center, to bind and cleave pre-tRNA. Our results confirm that the protein- and RNA-based forms of RNase P have distinct modules for substrate recognition and cleavage, an unanticipated parallel in their mode of action.

Topology of RNA-protein nucleobase-amino acid π-π interactions and comparison to analogous DNA-protein π-π contacts

The present work analyzed 120 high-resolution X-ray crystal structures and identified 335 RNA-protein π-interactions (154 nonredundant) between a nucleobase and aromatic (W, H, F, or Y) or acyclic (R, E, or D) π-containing amino acid. Each contact was critically analyzed (including using a visual inspection protocol) to determine the most prevalent composition, structure, and strength of π-interactions at RNA-protein interfaces. These contacts most commonly involve F and U, with U:F interactions comprising one-fifth of the total number of contacts found. Furthermore, the RNA and protein π-systems adopt many different relative orientations, although there is a preference for more parallel (stacked) arrangements. Due to the variation in structure, the strength of the intermolecular forces between the RNA and protein components (as determined from accurate quantum chemical calculations) exhibits a significant range, with most of the contacts providing significant stability to the associated RNA-protein complex (up to -65 kJ mol(-1)). Comparison to the analogous DNA-protein π-interactions emphasizes differences in RNA- and DNA-protein π-interactions at the molecular level, including the greater abundance of RNA contacts and the involvement of different nucleobase/amino acid residues. Overall, our results provide a clearer picture of the molecular basis of nucleic acid-protein binding and underscore the important role of these contacts in biology, including the significant contribution of π-π interactions to the stability of nucleic acid-protein complexes. Nevertheless, more work is still needed in this area in order to further appreciate the properties and roles of RNA nucleobase-amino acid π-interactions in nature.

The role of RNA conformation in RNA-protein recognition

Interactions between protein and RNA play a key role in many biological processes in the gene expression pathway. Those interactions are mediated through a variety of RNA-binding protein domains, among them the highly abundant RNA recognition motif (RRM). Here we studied protein-RNA complexes from different RNA binding domain families solved by NMR and x-ray crystallography. Characterizing the structural properties of the RNA at the binding interfaces revealed an unexpected number of nucleotides with unusual RNA conformations, specifically found in RNA-RRM complexes. Moreover, we observed that the RNA nucleotides that are directly involved in interactions with the RRM domains, via hydrogen bonds and hydrophobic contacts, are significantly enriched with unique RNA conformations. Further examination of the sequences binding the RRM domain showed a preference for G nucleotides in syn conformation to precede or to follow U nucleotides in the anti-conformation, and U nucleotides in C2' endo conformation to precede U and G nucleotides possessing the more common C3' endo conformation. These findings imply a possible mode of RNA recognition by the RRM domains which enables the recognition of a wide variety of different RNA sequences and shapes. Overall, this study suggests an additional way by which the RRM domain recognizes its RNA target, involving a conformational readout.

Position-dependent interactions of Y-box protein 2 (YBX2) with mRNA enable mRNA storage in round spermatids by repressing mRNA translation and blocking translation-dependent mRNA decay

Many mRNAs encoding proteins needed for the construction of the specialized organelles of spermatozoa are stored as translationally repressed, free messenger ribonucleoproteins in round spermatids, to be actively translated in elongating and elongated spermatids. The factors that repress translation in round spermatids, however, have been elusive. Two lines of evidence implicate the highly abundant and well-known translational repressor, Y-box protein 2 (YBX2), as a critical factor: First, protamine 1 (Prm1) and sperm-mitochondria cysteine-rich protein (Smcp) mRNAs are prematurely recruited onto polysomes in Ybx2-knockout mouse round spermatids. Second, mutations in 3' untranslated region (3'UTR) cis-elements that abrogate YBX2 binding activate translation of Prm1 and Smcp mRNAs in round spermatids of transgenic mice. The abundance of YBX2 and its affinity for variable sequences, however, raise questions of how YBX2 targets specific mRNAs for repression. Mutations to the Prm1 and Smcp mRNAs in transgenic mice reveal that strong repression in round spermatids requires YBX2 binding sites located near the 3' ends of their 3'UTRs as locating the same sites in upstream positions produce negligible repression. This location-dependence implies that the assembly of repressive complexes is nucleated by adjacent cis-elements that enable cooperative interactions of YBX2 with co-factors. The available data suggest that, in vertebrates, YBX2 has the important role of coordinating the storage of translationally repressed mRNAs in round spermatids by inhibiting translational activity and the degradation of transcripts via translation-dependent deadenylation. These insights should facilitiate future experiments designed to unravel how YBX2 targets mRNAs for repression in round spermatids and how mutations in the YBX2 gene cause infertility in humans. Mol. Reprod. Dev. 83: 190-207, 2016. © 2016 Wiley Periodicals, Inc.

Variation in the ribosome interacting loop of the Sec61α from Giardia lamblia

The interaction between the ribosome and the endoplasmic reticulum-located Sec61 protein translocon is mediated through an arginine residue of Sec61α, which is conserved in all prokaryotic and eukaryotic orthologues characterized to date. Using in silico approaches we report that instead of arginine, this ribosome-interaction function is most likely discharged by a lysine residue in the protist Giardia lamblia. This functional substitution of the R with a K in GlSec61α may have taken place to accommodate a G-rich rRNA.

Aromatic residues in RNase T stack with nucleobases to guide the sequence-specific recognition and cleavage of nucleic acids

RNase T is a classical member of the DEDDh family of exonucleases with a unique sequence preference in that its 3'-to-5' exonuclease activity is blocked by a 3'-terminal dinucleotide CC in digesting both single-stranded RNA and DNA. Our previous crystal structure analysis of RNase T-DNA complexes show that four phenylalanine residues, F29, F77, F124, and F146, stack with the two 3'-terminal nucleobases. To elucidate if the π-π stacking interactions between aromatic residues and nucleobases play a critical role in sequence-specific protein-nucleic acid recognition, here we mutated two to four of the phenylalanine residues in RNase T to tryptophan (W mutants) and tyrosine (Y mutants). The Escherichia coli strains expressing either the W mutants or the Y mutants had slow growth phenotypes, suggesting that all of these mutants could not fully substitute the function of the wild-type RNase T in vivo. DNA digestion assays revealed W mutants shared similar sequence specificity with wild-type RNase T. However, the Y mutants exhibited altered sequence-dependent activity, digesting ssDNA with both 3'-end CC and GG sequences. Moreover, the W and Y mutants had reduced DNA-binding activity and lower thermal stability as compared to wild-type RNase T. Taken together, our results suggest that the four phenylalanine residues in RNase T not only play critical roles in sequence-specific recognition, but also in overall protein stability. Our results provide the first evidence showing that the π-π stacking interactions between nucleobases and protein aromatic residues may guide the sequence-specific activity for DNA and RNA enzymes.