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

Computational study of interaction of calixarene with ebola virus structural proteins and its potential therapeutic implications

Ebola virus (EBOV) is a negative-strand RNA virus that causes hemorrhagic fever and fatal illness in humans. According to WHO, the Ebola virus caused 28,646 fatal cases and 11,323 deaths in West Africa due to hemorrhagic fever and deadly disease in humans between 2013 and 2016. Between 1976 and 2022, approximately 15,409 fatalities caused by EBOV took place worldwide. Unfortunately, no effective vaccine or drugs are available to prevent this deadly disease. In the present study, State-of-the-art tools based on in-silico methods were used to elucidate the interaction pattern of calixarene (CAL) with seven EBOV structural proteins, i.e., GP1,2, nucleoprotein (NP), polymerase cofactor (VP35), (VP40), transcription activator (VP30), VP24, and RNA-dependent RNA polymerase (L). CAL is a cage-like compound with supramolecular features. The molecular docking lead analysis using AutoDock tool has been performed to find out the binding pattern of CAL with EBOV proteins. Obtained results revealed efficient inhibitory properties of calixarene (CAL) against seven Ebola virus structural proteins i.e., GP1,2, nucleoprotein (NP), polymerase cofactor (VP35), (VP40), transcription activator (VP30), VP24, and RNA-dependent RNA polymerase (L). Molecular docking analysis shows that the interaction of CAL with VP24 was highest with the total binding energy -12.47 kcal/mol and 26.90 nM inhibitions constant. Molecular Dynamics study has also quantified the efficiency of CAL against VP24. In conclusion, the present study suggests that CAL and its derivatives could be used as inhibitors to counter EBOV infection. Furthermore, in vitro and in vivo laboratory experimentation is required to establish CAL and its derivatives as a potential inhibitor against EBOV.

Water-mediated ribonucleotide-amino acid pairs and higher-order structures at the RNA-protein interface: analysis of the crystal structure database and a topological classification

Water is essential for the formation, stability and function of RNA-protein complexes. To delineate the structural role of water molecules in shaping the interactions between RNA and proteins, we comprehensively analyzed a dataset of 329 crystal structures of these complexes to identify water-mediated hydrogen-bonded contacts at RNA-protein interface. Our survey identified a total of 4963 water bridges. We then employed a graph theory-based approach to present a robust classification scheme, encompassing triplets, quartets and quintet bridging topologies, each further delineated into sub-topologies. The frequency of water bridges within each topology decreases with the increasing degree of water node, with simple triplet water bridges outnumbering the higher-order topologies. Overall, this analysis demonstrates the variety of water-mediated interactions and highlights the importance of water as not only the medium but also the organizing principle underlying biomolecular interactions. Further, our study emphasizes the functional significance of water-mediated interactions in RNA-protein complexes, and paving the way for exploring how these interactions operate in complex biological environments. Altogether, this understanding not only enhances insights into biomolecular dynamics but also informs the rational design of RNA-protein complexes, providing a framework for potential applications in biotechnology and therapeutics. All the scripts, and data are available at https://github.com/PSCPU/waterbridges.

A primer for junior trainees: Recognition of RNA modifications by RNA-binding proteins

The complexity of RNA cannot be fully expressed with the canonical A, C, G, and U alphabet. To date, over 170 distinct chemical modifications to RNA have been discovered in living systems. RNA modifications can profoundly impact the cellular outcomes of messenger RNAs (mRNAs), transfer and ribosomal RNAs, and noncoding RNAs. Additionally, aberrant RNA modifications are associated with human disease. RNA modifications are a rising topic within the fields of biochemistry and molecular biology. The role of RNA modifications in gene regulation, disease pathogenesis, and therapeutic applications increasingly captures the attention of the scientific community. This review aims to provide undergraduates, junior trainees, and educators with an appreciation for the significance of RNA modifications in eukaryotic organisms, alongside the skills required to identify and analyze fundamental RNA-protein interactions. The pumilio RNA-binding protein and YT521-B homology (YTH) family of modified RNA-binding proteins serve as examples to highlight the fundamental biochemical interactions that underlie the specific recognition of both unmodified and modified ribonucleotides, respectively. By instilling these foundational, textbook concepts through practical examples, this review contributes an analytical toolkit that facilitates engagement with RNA modifications research at large.

Advanced sampling simulations of coupled folding and binding of phage P22 N-peptide to boxB RNA

Protein-RNA interactions are crucially important for numerous cellular processes and often involve coupled folding and binding of peptide segments upon association. The Nut-utilization site (N)-protein of bacteriophages contains an N-terminal arginine-rich motif that undergoes such a folding transition upon binding to the boxB RNA hairpin loop target structure. Molecular dynamics free energy simulations were used to calculate the absolute binding free energy of the N-peptide of bacteriophage P22 in complex with the boxB RNA hairpin motif at different salt concentrations and using two different water force field models. We obtained good agreement with experiment also at different salt concentrations for the TIP4P-D water model that has a stabilizing effect on unfolded protein structures. It allowed us to estimate the free energy contribution resulting from restricting the molecules' spatial and conformational freedom upon binding, which makes a large opposing contribution to binding. In a second set of umbrella sampling simulations to dissociate/associate the complex along a separation coordinate, we analyzed the onset of preorientation of the N-peptide and onset of structure formation relative to the RNA and its dependence on the salt concentration. Peptide orientation and conformational transitions are significantly coupled to the first contact formation between peptide and RNA. The initial contacts are mostly formed between peptide residues and the boxB hairpin loop nucleotides. A complete transition to an α-helical bound peptide conformation occurs only at a late stage of the binding process a few angstroms before the complexed state has been reached. However, the N-peptide orients also at distances beyond the contact distance such that the sizable positive charge points toward the RNA's center-of-mass. Our result may have important implications for understanding protein- and peptide-RNA complex formation frequently involving coupled folding and association processes.

Conformational Properties of Poly(A)-Binding Protein Complexed with Poly(A) RNA

Microscopic understanding of protein-RNA interactions is important for different biological activities, such as RNA transport, translation, splicing, silencing, etc. Polyadenine (Poly(A)) binding proteins (PABPs) make up a class of regulatory proteins that play critical roles in protecting the poly(A) tails of cellular mRNAs from nuclease degradation. In this work, we performed molecular dynamics simulations to investigate the conformational modifications of human PABP protein and poly(A) RNA that occur during complexation. It is demonstrated that the intermediate linker domain of the protein transforms from a disordered coil-like structure to a helical form during the recognition process, leading to the formation of the complex. On the other hand, disordered collapsed coil-like RNA on complexation has been found to transform into a rigid extended conformation. Importantly, the binding free energy calculation showed that the thermodynamic stability of the complex is primarily guided by favorable hydrophobic interactions between the protein and the RNA.

"Boundary residues" between the folded RNA recognition motif and disordered RGG domains are critical for FUS-RNA binding

Fused in sarcoma (FUS) is an abundant RNA-binding protein, which drives phase separation of cellular condensates and plays multiple roles in RNA regulation. The RNA-binding ability of FUS protein is crucial to its cellular function. Here, our molecular simulation study on the FUS-RNA complex provides atomic resolution insights into the observations from biochemical studies and also illuminates our understanding of molecular driving forces that mediate the structure, stability, and interaction of the RNA recognition motif (RRM) and RGG domains of FUS with a stem-loop junction RNA. We observe clear cooperativity and division of labor among the ordered (RRM) and disordered domains (RGG1 and RGG2) of FUS that leads to an organized and tighter RNA binding. Irrespective of the length of RGG2, the RGG2-RNA interaction is confined to the stem-loop junction and the proximal stem regions. On the other hand, the RGG1 interactions are primarily with the longer RNA stem. We find that the C terminus of RRM, which make up the "boundary residues" that connect the folded RRM with the long disordered RGG2 stretch of the protein, plays a critical role in FUS-RNA binding. Our study provides high-resolution molecular insights into the FUS-RNA interactions and forms the basis for understanding the molecular origins of full-length FUS interaction with RNA.

c-di-GMP regulates activity of the PlzA RNA chaperone from the Lyme disease spirochete

PlzA is a c-di-GMP-binding protein crucial for adaptation of the Lyme disease spirochete Borrelia (Borreliella) burgdorferi during its enzootic life cycle. Unliganded apo-PlzA is important for vertebrate infection, while liganded holo-PlzA is important for survival in the tick; however, the biological function of PlzA has remained enigmatic. Here, we report that PlzA has RNA chaperone activity that is inhibited by c-di-GMP binding. Holo- and apo-PlzA bind RNA and accelerate RNA annealing, while only apo-PlzA can strand displace and unwind double-stranded RNA. Guided by the crystal structure of PlzA, we identified several key aromatic amino acids protruding from the N- and C-terminal domains that are required for RNA-binding and unwinding activity. Our findings illuminate c-di-GMP as a switch controlling the RNA chaperone activity of PlzA, and we propose that complex RNA-mediated modulatory mechanisms allow PlzA to regulate gene expression during both the vector and host phases of the B. burgdorferi life cycle.

Thymine DNA Glycosylase is an RNA-Binding Protein with High Selectivity for G-Rich Sequences

Thymine DNA glycosylase (TDG) is a multifaceted enzyme involved in several critical biological pathways, including transcriptional activation, DNA demethylation, and DNA repair. Recent studies have established regulatory relationships between TDG and RNA, but the molecular interactions underlying these relationships is poorly understood. Herein, we now demonstrate that TDG binds directly to RNA with nanomolar affinity. Using synthetic oligonucleotides of defined length and sequence, we show that TDG has a strong preference for binding G-rich sequences in single-stranded RNA but binds weakly to single-stranded DNA and duplex RNA. TDG also binds tightly to endogenous RNA sequences. Studies with truncated proteins indicate that TDG binds RNA primarily through its structured catalytic domain and that its disordered C-terminal domain plays a key role in regulating TDG's affinity and selectivity for RNA. Finally, we show that RNA competes with DNA for binding to TDG, resulting in inhibition of TDG-mediated excision in the presence of RNA. Together, this work provides support for and insights into a mechanism wherein TDG-mediated processes (e.g., DNA demethylation) are regulated through the direct interactions of TDG with RNA.

Therapeutic Potential of Aptamer-Protein Interactions

Aptamers are single-stranded oligonucleotides (RNA or DNA) with a typical length between 25 and 100 nucleotides which fold into three-dimensional structures capable of binding to target molecules. Specific aptamers can be isolated against a large variety of targets through efficient and relatively cheap methods, and they demonstrate target-binding affinities that sometimes surpass those of antibodies. Consequently, interest in aptamers has surged over the past three decades, and their application has shown promise in advancing knowledge in target analysis, designing therapeutic interventions, and bioengineering. With emphasis on their therapeutic applications, aptamers are emerging as a new innovative class of therapeutic agents with promising biochemical and biological properties. Aptamers have the potential of providing a feasible alternative to antibody- and small-molecule-based therapeutics given their binding specificity, stability, low toxicity, and apparent non-immunogenicity. This Review examines the general properties of aptamers and aptamer-protein interactions that help to understand their binding characteristics and make them important therapeutic candidates.

Computational tools to study RNA-protein complexes

RNA is the key player in many cellular processes such as signal transduction, replication, transport, cell division, transcription, and translation. These diverse functions are accomplished through interactions of RNA with proteins. However, protein–RNA interactions are still poorly derstood in contrast to protein–protein and protein–DNA interactions. This knowledge gap can be attributed to the limited availability of protein-RNA structures along with the experimental difficulties in studying these complexes. Recent progress in computational resources has expanded the number of tools available for studying protein-RNA interactions at various molecular levels. These include tools for predicting interacting residues from primary sequences, modelling of protein-RNA complexes, predicting hotspots in these complexes and insights into derstanding in the dynamics of their interactions. Each of these tools has its strengths and limitations, which makes it significant to select an optimal approach for the question of interest. Here we present a mini review of computational tools to study different aspects of protein-RNA interactions, with focus on overall application, development of the field and the future perspectives.

Unraveling the Role of Charge Patterning in the Micellar Structure of Sequence-Defined Amphiphilic Peptoid Oligomers by Molecular Dynamics Simulations

Electrostatic interactions play a significant role in regulating biological systems and have received increasing attention due to their usefulness in designing advanced stimulus-responsive materials. Polypeptoids are highly tunable N-substituted peptidomimetic polymers that lack backbone hydrogen bonding and chirality. Therefore, polypeptoids are suitable systems to study the effect of noncovalent interactions of substituents without complications of backbone intramolecular and intermolecular hydrogen bonding. In this study, all-atom molecular dynamics (MD) simulations were performed on micelles formed by a series of sequence-defined ionic polypeptoid block copolymers consisting of a hydrophobic segment and a hydrophilic segment in an aqueous solution. By combining the results from MD simulations and experimental small-angle neutron scattering data, further insights were gained into the internal structure of the formed polypeptoid micelles, which is not always directly accessible from experiments. In addition, information was gained into the physics of the noncovalent interactions responsible for the self-assembly of weakly charged polypeptoids in an aqueous solution. While the aggregation number is governed by electrostatic repulsion of the negatively charged carboxylate (COO^–) substituents on the polypeptoid chain within the micelle, MD simulations indicate that the position of the charge on singly charged chains mediates the shape of the micelle through the charge–dipole interactions between the COO^– substituent and the surrounding water. Therefore, the polypeptoid micelles formed from the single-charged series offer the possibility for tailorable micelle shapes. In contrast, the polypeptoid micelles formed from the triple-charged series are characterized by more pronounced electrostatic repulsion that competes with more significant charge–sodium interactions, making it difficult to predict the shape of the micelles. This work has helped further develop design principles for the shape and structure of self-assembled micelles by controlling the position of charged moieties on the backbone of polypeptoid block copolymers.

RNA-Binding Macrocyclic Peptides

Being able to effectively target RNA with potent ligands will open up a large number of potential therapeutic options. The knowledge on how to achieve this is ever expanding but an important question that remains open is what chemical matter is suitable to achieve this goal. The high flexibility of an RNA as well as its more limited chemical diversity and featureless binding sites can be difficult to target selectively but can be addressed by well-designed cyclic peptides. In this review we will provide an overview of reported cyclic peptide ligands for therapeutically relevant RNA targets and discuss the methods used to discover them. We will also provide critical insights into the properties required for potent and selective interaction and suggestions on how to assess these parameters. The use of cyclic peptides to target RNA is still in its infancy but the lessons learned from past examples can be adopted for the development of novel potent and selective ligands.

Correlation in Domain Fluctuations Navigates Target Search of a Viral Peptide along RNA

Biological macromolecules often exhibit correlations in fluctuations involving distinct domains. This study decodes their functional implications in RNA-protein recognition and target-specific binding. The target search of a peptide along RNA in a viral TAR-Tat complex is closely monitored using atomistic simulations, steered molecular dynamics simulations, free energy calculations, and a machine-learning-based clustering technique. An anticorrelated domain fluctuation is identified between the tetraloop and the bulge region in the apo form of TAR RNA that sets a hierarchy in the domain-specific fluctuations at each binding event and that directs the succeeding binding footsteps. Thus, at each binding footstep, the dynamic partner selects an RNA location for binding where it senses a higher fluctuation, which is conventionally reduced upon binding. This event stimulates an alternate domain fluctuation, which then dictates sequential binding footstep/s and thus the search progresses. Our cross-correlation maps show that the fluctuations relay from one domain to another specific domain until the anticorrelation between those interdomain fluctuations sustains. Artificial attenuation of that hierarchical domain fluctuation inhibits specific RNA binding. The binding is completed with the arrival of a few long-lived water molecules that mediate slightly distant RNA-protein sites and finally stabilize the overall complex. The study underscores the functional importance of naturally designed fluctuating RNA motifs (bulge, tetraloop) and their interplay in dictating the directionality of the search in a highly dynamic environment.

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.

Conservation in the Iron Responsive Element Family

Iron responsive elements (IREs) are mRNA stem-loop targets for translational control by the two iron regulatory proteins IRP1 and IRP2. They are found in the untranslated regions (UTRs) of genes that code for proteins involved in iron metabolism. There are ten “classic” IRE types that define the conserved secondary and tertiary structure elements necessary for proper IRP binding, and there are 83 published “IRE-like” sequences, most of which depart from the established IRE model. Here are structurally-guided discussions regarding the essential features of an IRE and what is important for IRE family membership.

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.

Functional Roles of RNA-Binding Proteins in Plant Signaling

RNA-binding proteins (RBPs) are typical proteins that bind RNA through single or multiple RNA-binding domains (RBDs). These proteins have a functional role in determining the fate or function of the bound RNAs. A few hundred RBPs were known through in silico prediction based on computational assignment informed by structural similarity and the presence of classical RBDs. However, RBPs lacking such conventional RBDs were omitted. Owing to the recent mRNA interactome capture technology based on UV-crosslinking and fixing proteins to their mRNA targets followed by affinity capture purification and identification of RBPs by tandem mass spectrometry, several hundreds of RBPs have recently been discovered. These proteome-wide studies have colossally increased the number of proteins implicated in RNA binding and unearthed hundreds of novel RBPs lacking classical RBDs, such as proteins involved in intermediary metabolism. These discoveries provide wide insights into the post-transcriptional gene regulation players and their role in plant signaling, such as environmental stress conditions. In this review, novel discoveries of RBPs are explored, particularly on the evolving knowledge of their role in stress responses. The molecular functions of these RBPs, particularly focusing on those that do not have classical RBDs, are also elucidated at the systems level.

Structure specific recognition of telomeric repeats containing RNA by the RGG-box of hnRNPA1

The telomere repeats containing RNA (TERRA) is transcribed from the C-rich strand of telomere DNA and comprises of UUAGGG nucleotides repeats in humans. The TERRA RNA repeats can exist in single stranded, RNA-DNA hybrid and G-quadruplex forms in the cell. Interaction of TERRA RNA with hnRNPA1 has been proposed to play critical roles in maintenance of telomere DNA. hnRNPA1 contains an N-terminal UP1 domain followed by an RGG-box containing C-terminal region. RGG-motifs are emerging as key protein motifs that recognize the higher order nucleic acid structures as well as are known to promote liquid-liquid phase separation of proteins. In this study, we have shown that the RGG-box of hnRNPA1 specifically recognizes the TERRA RNA G-quadruplexes that have loops in their topology, whereas it does not interact with the single-stranded RNA. Our results show that the N-terminal UP1 domain in the presence of the RGG-box destabilizes the loop containing TERRA RNA G-quadruplex efficiently compared to the RNA G-quadruplex that lacks loops, suggesting that unfolding of G-quadruplex structures by UP1 is structure dependent. Furthermore, we have compared the telomere DNA and TERRA RNA G-quadruplex binding by the RGG-box of hnRNPA1 and discussed its implications in telomere DNA maintenance.

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.

Posttranscriptional Regulation of tnaA by Protein-RNA Interaction Mediated by Ribosomal Protein L4 in Escherichia coli

Some ribosomal proteins have extraribosomal functions in addition to ribosome translation function. The extraribosomal functions of several r-proteins control operon expression by binding to own-operon transcripts. Previously, we discovered a posttranscriptional, RNase E-dependent regulatory role for r-protein L4 in the stabilization of stress-responsive transcripts. Here, we found an additional extraribosomal function for L4 in regulating the tna operon by L4-intergenic spacer mRNA interactions. L4 binds to the transcribed spacer RNA between tnaC and tnaA and alters the structural conformation of the spacer RNA, thereby reducing the translation of TnaA. Our study establishes a previously unknown L4-mediated mechanism for regulating gene expression, suggesting that bacterial cells have multiple strategies for controlling levels of tryptophanase in response to varied cell growth conditions.

Escherichia coli ribosomal protein (r-protein) L4 has extraribosomal biological functions. Previously, we described L4 as inhibiting RNase E activity through protein-protein interactions. Here, we report that from stabilized transcripts regulated by L4-RNase E, mRNA levels of tnaA (encoding tryptophanase from the tnaCAB operon) increased upon ectopic L4 expression, whereas TnaA protein levels decreased. However, at nonpermissive temperatures (to inactivate RNase E), tnaA mRNA and protein levels both increased in an rne temperature-sensitive [rne(Ts)] mutant strain. Thus, L4 protein fine-tunes TnaA protein levels independently of its inhibition of RNase E. We demonstrate that ectopically expressed L4 binds with transcribed spacer RNA between tnaC and tnaA and downregulates TnaA translation. We found that deletion of the 5′ or 3′ half of the spacer compared to the wild type resulted in a similar reduction in TnaA translation in the presence of L4. In vitro binding of L4 to the tnaC-tnaA transcribed spacer RNA results in changes to its secondary structure. We reveal that during early stationary-phase bacterial growth, steady-state levels of tnaA mRNA increased but TnaA protein levels decreased. We further confirm that endogenous L4 binds to tnaC-tnaA transcribed spacer RNA in cells at early stationary phase. Our results reveal the novel function of L4 in fine-tuning TnaA protein levels during cell growth and demonstrate that r-protein L4 acts as a translation regulator outside the ribosome and its own operon.

IMPORTANCE Some ribosomal proteins have extraribosomal functions in addition to ribosome translation function. The extraribosomal functions of several r-proteins control operon expression by binding to own-operon transcripts. Previously, we discovered a posttranscriptional, RNase E-dependent regulatory role for r-protein L4 in the stabilization of stress-responsive transcripts. Here, we found an additional extraribosomal function for L4 in regulating the tna operon by L4-intergenic spacer mRNA interactions. L4 binds to the transcribed spacer RNA between tnaC and tnaA and alters the structural conformation of the spacer RNA, thereby reducing the translation of TnaA. Our study establishes a previously unknown L4-mediated mechanism for regulating gene expression, suggesting that bacterial cells have multiple strategies for controlling levels of tryptophanase in response to varied cell growth conditions.

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.

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.

Recent Advances in Machine Learning Based Prediction of RNA-protein Interactions

The interactions between RNAs and proteins play critical roles in many biological processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical, and clinical studies. Many experimental methods can be used to determine RNA-protein interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific and condition-specific, as well as these interactions are weak and frequently compete with each other, those experimental techniques can not be made full use of to discover the complete spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been devoted to developing high quality computational techniques to study the interactions between RNAs and proteins. Many important progresses have been achieved with the application of novel techniques and strategies, such as machine learning techniques. Especially, with the development and application of CLIP techniques, more and more experimental data on RNA-protein interaction under specific biological conditions are available. These CLIP data altogether provide a rich source for developing advanced machine learning predictors. In this review, recent progresses on computational predictors for RNA-protein interaction were summarized in the following aspects: dataset, prediction strategies, and input features. Possible future developments were also discussed at the end of the review.

Evolutionary Trends in RNA Base Selectivity Within the RNase A Superfamily

There is a growing interest in the pharmaceutical industry to design novel tailored drugs for RNA targeting. The vertebrate-specific RNase A superfamily is nowadays one of the best characterized family of enzymes and comprises proteins involved in host defense with specific cytotoxic and immune-modulatory properties. We observe within the family a structural variability at the substrate-binding site associated to a diversification of biological properties. In this work, we have analyzed the enzyme specificity at the secondary base binding site. Towards this end, we have performed a kinetic characterization of the canonical RNase types together with a molecular dynamic simulation of selected representative family members. The RNases' catalytic activity and binding interactions have been compared using UpA, UpG and UpI dinucleotides. Our results highlight an evolutionary trend from lower to higher order vertebrates towards an enhanced discrimination power of selectivity for adenine respect to guanine at the secondary base binding site (B2). Interestingly, the shift from guanine to adenine preference is achieved in all the studied family members by equivalent residues through distinct interaction modes. We can identify specific polar and charged side chains that selectively interact with donor or acceptor purine groups. Overall, we observe selective bidentate polar and electrostatic interactions: Asn to N1/N6 and N6/N7 adenine groups in mammals versus Glu/Asp and Arg to N1/N2, N1/O6 and O6/N7 guanine groups in non-mammals. In addition, kinetic and molecular dynamics comparative results on UpG versus UpI emphasize the main contribution of Glu/Asp interactions to N1/N2 group for guanine selectivity in lower order vertebrates. A close inspection at the B2 binding pocket also highlights the principal contribution of the protein ß6 and L4 loop regions. Significant differences in the orientation and extension of the L4 loop could explain how the same residues can participate in alternative binding modes. The analysis suggests that within the RNase A superfamily an evolution pressure has taken place at the B2 secondary binding site to provide novel substrate-recognition patterns. We are confident that a better knowledge of the enzymes' nucleotide recognition pattern would contribute to identify their physiological substrate and eventually design applied therapies to modulate their biological functions.

Ribosomal RNA-Aminoglycoside Hygromycin B Interaction Energy Calculation within a Density Functional Theory Framework

We intend to investigate the drug-binding energy of each nucleotide inside the aminoglycoside hygromycin B (hygB) binding site of 30S ribosomal RNA (rRNA) subunit by using the molecular fractionation with conjugate caps (MFCC) strategy based on the density functional theory (DFT), considering the functional LDA/PWC, OBS, and the dielectric constant parametrization. Aminoglycosides are bactericidal antibiotics that have high affinity to the prokaryotic rRNA, inhibiting the synthesis of proteins by acting on the main stages of the translation mechanism, whereas binding to rRNA 16S, a component of the 30S ribosomal subunit in prokaryotes. The identification of the nucleotides presenting the most negative binding energies allows us to stabilize hygB in a suitable binding pocket of the 30S ribosomal subunit. In addition, it should be highlighted that mutations in these residues may probably lead to resistance to ribosome-targeting antibiotics. Quantum calculations of aminoglycoside hygromycin B-ribosome complex might contribute to further quantum studies with antibiotics like macrolides and other aminoglycosides.

Structural basis of chimpanzee APOBEC3H dimerization stabilized by double-stranded RNA

APOBEC3H (A3H) is a mammal-specific cytidine deaminase that potently restricts the replication of retroviruses. Primate A3Hs are known to exert key selective pressures against the cross-species transmission of primate immunodeficiency viruses from chimpanzees to humans. Despite recent advances, the molecular structures underlying the functional mechanisms of primate A3Hs have not been fully understood. Here, we reveal the 2.20-Å crystal structure of the chimpanzee A3H (cpzA3H) dimer bound to a short double-stranded RNA (dsRNA), which appears to be similar to two recently reported structures of pig-tailed macaque A3H and human A3H. In the structure, the dsRNA-binding interface forms a specialized architecture with unique features. The analysis of the dsRNA nucleotides in the cpzA3H complex revealed the GC-rich palindrome-like sequence preference for dsRNA interaction, which is largely determined by arginine residues in loop 1. In cells, alterations of the cpzA3H residues critical for the dsRNA interaction severely reduce intracellular protein stability due to proteasomal degradation. This suggests that cpzA3H stability is regulated by the dsRNA-mediated dimerization as well as by unknown cellular machinery through proteasomal degradation in cells. Taken together, these findings highlight unique structural features of primate A3Hs that are important to further understand their cellular functions and regulation.

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.

Enhancement of RNA/Ligand Association Kinetics via an Electrostatic Anchor

The diverse biological processes mediated by RNA rest upon its recognition of various ligands, including small molecules and nucleic acids. Nevertheless, a recent literature survey suggests that RNA molecular recognition of these ligands is slow, with association rate constants orders of magnitude below the diffusional limit. Thus, we were prompted to consider strategies for increasing RNA association kinetics. Proteins can accelerate ligand association via electrostatic forces, and here, using the Tetrahymena group I ribozyme, we provide evidence that electrostatic forces can accelerate RNA/ligand association. This RNA enzyme (E) catalyzes cleavage of an oligonucleotide substrate (S) by an exogenous guanosine (G) cofactor. The G 2′- and 3′-OH groups interact with an active site metal ion, termed M_C, within E·S·G, and we perturbed each of these contacts via −NH₃⁺ substitution. New and prior data indicate that G(2′NH₃⁺) and G(3′NH₃⁺) bind as strongly as G, suggesting that the −NH₃⁺ substituents of these analogues avoid repulsive interactions with M_C and make alternative interactions. Unexpectedly, removal of the adjacent −OH via −H substitution to give G(2′H,3′NH₃⁺) and G(2′NH₃⁺_,3′H) enhanced binding, in stark contrast to the deleterious effect of these substitutions on G binding. Pulse–chase experiments indicate that the −NH₃⁺ moiety of G(2′H,3′NH₃⁺) increases the rate of G association. These results suggest that the positively charged −NH₃⁺ group can act as a molecular “anchor” to increase the residence time of the encounter complex and thereby enhance productive binding. Electrostatic anchors may provide a broadly applicable strategy for the development of fast binding RNA ligands and RNA-targeted therapeutics.

Individually double minimum-distance definition of protein-RNA binding residues and application to structure-based prediction

Identifying protein-RNA binding residues is essential for understanding the mechanism of protein-RNA interactions. So far, rigid distance thresholds are commonly used to define protein-RNA binding residues. However, after investigating 182 non-redundant protein-RNA complexes, we find that it would be unsuitable for a certain amount of complexes since the distances between proteins and RNAs vary widely. In this work, a novel definition method was proposed based on a flexible distance cutoff. This method can fully consider the individual differences among complexes by setting a variable tolerance limit of protein-RNA interactions, i.e. the double minimum-distance by which different distance thresholds are achieved for different complexes. In order to validate our method, a comprehensive comparison between our flexible method and traditional rigid methods was implemented in terms of interface structure, amino acid composition, interface area and interaction force, etc. The results indicate that this method is more reasonable because it incorporates the specificity of different complexes by extracting the important residues lost by rigid distance methods and discarding some redundant residues. Finally, to further test our double minimum-distance definition strategy, we developed a classifier to predict those binding sites derived from our new method by using structural features and a random forest machine learning algorithm. The model achieved a satisfactory prediction performance and the accuracy on independent data sets reaches to 85.0%. To the best of our knowledge, it is the first prediction model to define positive and negative samples using a flexible cutoff. So the comparison analysis and modeling results have demonstrated that our method would be a very promising strategy for more precisely defining protein-RNA binding sites.

Structural insights into methylated DNA recognition by the C-terminal zinc fingers of the DNA reader protein ZBTB38

Methyl-CpG-binding proteins (MBPs) are selective readers of DNA methylation that play an essential role in mediating cellular transcription processes in both normal and diseased cells. This physiological function of MBPs has generated significant interest in understanding the mechanisms by which these proteins read and interpret DNA methylation signals. Zinc finger and BTB domain-containing 38 (ZBTB38) represents one member of the zinc finger (ZF) family of MBPs. We recently demonstrated that the C-terminal ZFs of ZBTB38 exhibit methyl-selective DNA binding within the ((A/G)TmCG(G/A)(mC/T)(G/A)) context both in vitro and within cells. Here we report the crystal structure of the first four C-terminal ZBTB38 ZFs (ZFs 6-9) in complex with the previously identified methylated consensus sequence at 1.75 Å resolution. From the structure, methyl-selective binding is preferentially localized at the 5' mCpG site of the bound DNA, which is facilitated through a series of base-specific interactions from residues within the α-helices of ZF7 and ZF8. ZF6 and ZF9 primarily stabilize ZF7 and ZF8 to facilitate the core base-specific interactions. Further structural and biochemical analyses, including solution NMR spectroscopy and electrophoretic mobility gel shift assays, revealed that the C-terminal ZFs of ZBTB38 utilize an alternative mode of mCpG recognition from the ZF MBPs structurally evaluated to date. Combined, these findings provide insight into the mechanism by which this ZF domain of ZBTB38 selectively recognizes methylated CpG sites and expands our understanding of how ZF-containing proteins can interpret this essential epigenetic mark.

RNA-protein interactions in an unstructured context

Despite their importance, our understanding of noncovalent RNA-protein interactions is incomplete. This especially concerns the binding between RNA and unstructured protein regions, a widespread class of such interactions. Here, we review the recent experimental and computational work on RNA-protein interactions in an unstructured context with a particular focus on how such interactions may be shaped by the intrinsic interaction affinities between individual nucleobases and protein side chains. Specifically, we articulate the claim that the universal genetic code reflects the binding specificity between nucleobases and protein side chains and that, in turn, the code may be seen as the Rosetta stone for understanding RNA-protein interactions in general.

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.

Structure of an RNA Aptamer that Can Inhibit HIV-1 by Blocking Rev-Cognate RNA (RRE) Binding and Rev-Rev Association

HIV-1 Rev protein mediates nuclear export of unspliced and partially spliced viral RNAs for production of viral genomes and structural proteins. Rev assembles on a 351-nt Rev response element (RRE) within viral transcripts and recruits host export machinery. Small (<40-nt) RNA aptamers that compete with the RRE for Rev binding inhibit HIV-1 viral replication. We determined the X-ray crystal structure of a potential anti-HIV-1 aptamer that binds Rev with high affinity (K_d = 5.9 nM). The aptamer is structurally similar to the RRE high-affinity site but forms additional contacts with Rev unique to its sequence. Exposed bases of the aptamer interleave with the guanidinium groups of two arginines of Rev, forming stacking interactions and hydrogen bonds. The aptamer also obstructs an oligomerization interface of Rev, blocking Rev self-assembly. We propose that this aptamer can inhibit HIV-1 replication by interfering with Rev-RRE, Rev-Rev, and possibly Rev-host protein interactions.

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.

An archaeal aminoacyl-tRNA synthetase complex for improved substrate quality control

Aminoacyl-tRNA synthetases (aaRSs) decode genetic information by coupling tRNAs with cognate amino acids. In the archaeon Methanothermobacter thermautotrophicus arginyl- and seryl-tRNA synthetase (ArgRS and SerRS, respectively) form a complex which enhances serylation and facilitates tRNA^Ser recycling through its association with the ribosome. Yet, the way by which complex formation participates in Arg-tRNA^Arg synthesis is still unresolved. Here we utilized pull down and surface plasmon resonance experiments with truncated ArgRS variants to demonstrate that ArgRS uses its N-terminal domain to establish analogous interactions with both SerRS and cognate tRNA^Arg, providing a rationale for the lack of detectable SerRS•[ArgRS•tRNA^Arg] complex. In contrast, stable ternary ArgRS•[SerRS•tRNA^Ser] complex was easily detected supporting the model wherein ArgRS operates in serylation by modulating SerRS affinity toward tRNA^Ser. We also found that the interaction with SerRS suppresses arginylation of unmodified tRNA^Arg by ArgRS, which, by itself, does not discriminate against tRNA^Arg substrates lacking posttranscriptional modifications. Hence, there is a fundamentally different participation of the protein partners in Arg-tRNA and Ser-tRNA synthesis. Propensity of the ArgRS•SerRS complex to exclude unmodified tRNAs from translation leads to an attractive hypothesis that SerRS•ArgRS complex might act in vivo as a safeguarding switch that improves translation accuracy.

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.

Site-Directed Chemical Probing to map transient RNA/protein interactions

RNA-protein interactions are at the bases of many biological processes, forming either tight and stable functional ribonucleoprotein (RNP) complexes (i.e. the ribosome) or transitory ones, such as the complexes involving RNA chaperone proteins. To localize the sites where a protein interacts on an RNA molecule, a common simple and inexpensive biochemical method is the footprinting technique. The protein leaves its footprint on the RNA acting as a shield to protect the regions of interaction from chemical modification or cleavages obtained with chemical or enzymatic nucleases. This method has proven its efficiency to study in vitro the organization of stable RNA-protein complexes. Nevertheless, when the protein binds the RNA very dynamically, with high off-rates, protections are very often difficult to observe. For the analysis of these transient complexes, we describe an alternative strategy adapted from the Site Directed Chemical Probing (SDCP) approach and we compare it with classical footprinting. SDCP relies on the modification of the RNA binding protein to tether an RNA probe (usually Fe-EDTA) to specific protein positions. Local cleavages on the regions of interaction can be used to localize the protein and position its domains on the RNA molecule. This method has been used in the past to monitor stable complexes; we provide here a detailed protocol and a practical example of its application to the study of Escherichia coli RNA chaperone protein S1 and its transitory complexes with mRNAs.

Crystal structure of a DNA aptamer bound to PvLDH elucidates novel single-stranded DNA structural elements for folding and recognition

Structural elements are key elements for understanding single-stranded nucleic acid folding. Although various RNA structural elements have been documented, structural elements of single-stranded DNA (ssDNA) have rarely been reported. Herein, we determined a crystal structure of PvLDH in complex with a DNA aptamer called pL1. This aptamer folds into a hairpin-bulge contact by adopting three novel structural elements, viz, DNA T-loop-like motif, base-phosphate zipper, and DNA G·G metal ion zipper. Moreover, the pL1:PvLDH complex shows unique properties compared with other protein:nucleic acid complexes. Generally, extensive intermolecular hydrogen bonds occur between unpaired nucleotides and proteins for specific recognitions. Although most protein-interacting nucleotides of pL1 are unpaired nucleotides, pL1 recognizes PvLDH by predominant shape complementarity with many bridging water molecules owing to the combination of three novel structural elements making protein-binding unpaired nucleotides stable. Moreover, the additional set of Plasmodium LDH residues which were shown to form extensive hydrogen bonds with unpaired nucleotides of 2008s does not participate in the recognition of pL1. Superimposition of the pL1:PvLDH complex with hLDH reveals steric clashes between pL1 and hLDH in contrast with no steric clashes between 2008s and hLDH. Therefore, specific protein recognition mode of pL1 is totally different from that of 2008s.

Amino Acid Specific Effects on RNA Tertiary Interactions: Single-Molecule Kinetic and Thermodynamic Studies

In light of the current models for an early RNA-based universe, the potential influence of simple amino acids on tertiary folding of ribozymal RNA into biochemically competent structures is speculated to be of significant evolutionary importance. In the present work, the folding-unfolding kinetics of a ubiquitous tertiary interaction motif, the GAAA tetraloop-tetraloop receptor (TL-TLR), is investigated by single-molecule fluorescence resonance energy transfer spectroscopy in the presence of natural amino acids both with (e.g., lysine, arginine) and without (e.g., glycine) protonated side chain residues. By way of control, we also investigate the effects of a special amino acid (e.g., proline) and amino acid mimetic (e.g., betaine) that contain secondary or quaternary amine groups rather than a primary amine group. This combination permits systematic study of amino acid induced (or amino acid like) RNA folding dynamics as a function of side chain complexity, pK_a, charge state, and amine group content. Most importantly, each of the naturally occurring amino acids is found to destabilize the TL-TLR tertiary folding equilibrium, the kinetic origin of which is dominated by a decrease in the folding rate constant (k_dock), also affected by a strongly amino acid selective increase in the unfolding rate constant (k_undock). To further elucidate the underlying thermodynamics, single-molecule equilibrium constants (K_eq) for TL-TLR folding have been probed as a function of temperature, which reveal an amino acid dependent decrease in both overall exothermicity (ΔΔH° > 0) and entropic cost (-TΔΔS° < 0) for the overall folding process. Temperature-dependent studies on the folding/unfolding kinetic rate constants reveal analogous amino acid specific changes in both enthalpy (ΔΔH^⧧) and entropy (ΔΔS^⧧) for accessing the transition state barrier. The maximum destabilization of the TL-TLR tertiary interaction is observed for arginine, which is consistent with early studies of arginine and guanidine ion-inhibited self-splicing kinetics for the full Tetrahymena ribozyme [ Yarus , M. ; Christian , E. L. Nature 1989 , 342 , 349 - 350 ; Yarus , M. Science 1988 , 240 , 1751 - 1758 ].

Template-Based Modeling of Protein-RNA Interactions

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

Structures of protein-RNA complexes are important for characterization of biological processes. The number of experimentally determined protein-RNA complexes is limited. Thus modeling of these complexes is important. Reliable structural predictions of proteins and their complexes are provided by comparative modeling, which takes advantage of similar complexes with experimentally determined structures. Thus, in the case of protein-RNA complexes, it is important to determine if similar proteins and RNAs bind in a similar way. We show that, similarly to the earlier published results on protein-protein complexes, such correlation of the protein-RNA binding mode and the monomers similarity indeed exists, and is stronger when the similarity is determined by structure rather than sequence alignment. The data shows clear transition from random to similar binding mode with the increase of the structural similarity of the monomers. On the basis of the results we designed and implemented a predictive tool, which should be useful for the biological community interested in modeling of protein-RNA interactions.

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.