Themes in RNA-protein recognition

Interactome dynamics during heat stress signal transmission and reception

Among evolved molecular mechanisms, cellular stress response to altered environmental conditions to promote survival is among the most fundamental. The presence of stress-induced unfolded or misfolded proteins and molecular registration of these events constitute early steps in cellular stress response. However, what stress-induced changes in protein conformations and protein-protein interactions within cells initiate stress response and how these features are recognized by cellular systems are questions that have remained difficult to answer, requiring new approaches. Quantitative in vivo chemical cross-linking coupled with mass spectrometry (qXL-MS) is an emerging technology that provides new insight on protein conformations, protein-protein interactions and how the interactome changes during perturbation within cells, organelles, and even tissues. In this work, qXL-MS and quantitative proteome analyses were applied to identify significant time-dependent interactome changes that occur prior to large-scale proteome abundance remodeling within cells subjected to heat stress. Interactome changes were identified within minutes of applied heat stress, including stress-induced changes in chaperone systems as expected due to altered functional demand. However, global analysis of all interactome changes revealed the largest significant enrichment in the gene ontology molecular function term of RNA binding. This group included more than 100 proteins among multiple components of protein synthesis machinery, including mRNA binding, spliceosomes, and ribosomes. These interactome data provide new conformational insight on the complex relationship that exists between transcription, translation and cellular stress response mechanisms. Moreover, stress-dependent interactome changes suggest that in addition to conformational stabilization of RNA-binding proteins, adaptation of RNA as interacting ligands offers an additional fitness benefit resultant from generally lower RNA thermal stability. As such, RNA ligands also serve as fundamental temperature sensors that signal stress through decreased conformational regulation of their protein partners as was observed in these interactome dynamics.

PNAbind: Structure-based prediction of protein-nucleic acid binding using graph neural networks

The recognition and binding of nucleic acids (NAs) by proteins depends upon complementary chemical, electrostatic and geometric properties of the protein-NA binding interface. Structural models of protein-NA complexes provide insights into these properties but are scarce relative to models of unbound proteins. We present a deep learning approach for predicting protein-NA binding given the apo structure of a protein (PNAbind). Our method utilizes graph neural networks to encode spatial distributions of physicochemical and geometric properties of the protein molecular surface that are predictive of NA binding. Using global physicochemical encodings, our models predict the overall binding function of a protein and can discriminate between specificity for DNA or RNA binding. We show that such predictions made on protein structures modeled with AlphaFold2 can be used to gain mechanistic understanding of chemical and structural features that determine NA recognition. Using local encodings, our models predict the location of NA binding sites at the level of individual binding residues. Binding site predictions were validated against benchmark datasets, achieving AUROC scores in the range of 0.92-0.95. We applied our models to the HIV-1 restriction factor APOBEC3G and show that our predictions are consistent with experimental RNA binding data.

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.

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.

DNA and RNA Binding Proteins: From Motifs to Roles in Cancer

DNA and RNA binding proteins (DRBPs) are a broad class of molecules that regulate numerous cellular processes across all living organisms, creating intricate dynamic multilevel networks to control nucleotide metabolism and gene expression. These interactions are highly regulated, and dysregulation contributes to the development of a variety of diseases, including cancer. An increasing number of proteins with DNA and/or RNA binding activities have been identified in recent years, and it is important to understand how their activities are related to the molecular mechanisms of cancer. In addition, many of these proteins have overlapping functions, and it is therefore essential to analyze not only the loss of function of individual factors, but also to group abnormalities into specific types of activities in regard to particular cancer types. In this review, we summarize the classes of DNA-binding, RNA-binding, and DRBPs, drawing particular attention to the similarities and differences between these protein classes. We also perform a cross-search analysis of relevant protein databases, together with our own pipeline, to identify DRBPs involved in cancer. We discuss the most common DRBPs and how they are related to specific cancers, reviewing their biochemical, molecular biological, and cellular properties to highlight their functions and potential as targets for treatment.

Measuring thermodynamic preferences to form non-native conformations in nucleic acids using ultraviolet melting

Thermodynamic preferences to form non-native conformations are crucial for understanding how nucleic acids fold and function. However, they are difficult to measure experimentally because this requires accurately determining the population of minor low-abundance (<10%) conformations in a sea of other conformations. Here, we show that melting experiments enable facile measurements of thermodynamic preferences to adopt nonnative conformations in DNA and RNA. The key to this "delta-melt" approach is to use chemical modifications to render specific minor non-native conformations the major state. The validity and robustness of delta-melt is established for four different non-native conformations under various physiological conditions and sequence contexts through independent measurements of thermodynamic preferences using NMR. Delta-melt is faster relative to NMR, simple, and cost-effective and enables thermodynamic preferences to be measured for exceptionally low-populated conformations. Using delta-melt, we obtained rare insights into conformational cooperativity, obtaining evidence for significant cooperativity (1.0 to 2.5 kcal/mol) when simultaneously forming two adjacent Hoogsteen base pairs. We also measured the thermodynamic preferences to form G-C⁺ and A-T Hoogsteen and A-T base open states for nearly all 16 trinucleotide sequence contexts and found distinct sequence-specific variations on the order of 2 to 3 kcal/mol. This rich landscape of sequence-specific non-native minor conformations in the DNA double helix may help shape the sequence specificity of DNA biochemistry. Thus, melting experiments can now be used to access thermodynamic information regarding regions of the free energy landscape of biomolecules beyond the native folded and unfolded conformations.

DFpin: Deep learning-based protein-binding site prediction with feature-based non-redundancy from RNA level

The interaction between proteins and RNA is closely related to various human diseases. Computer-aided drug design can be facilitated by detecting the RNA sites that bind proteins. However, due to the aggregation of binding sites in RNA sequences, high sample similarity occurs when extracting RNA fragments by using a sliding window. Considering these problems, we present a method, DFpin, to predict protein-interacting nucleotides in RNA. To retain more key nucleotide sites, we used the redundancy method based on feature similarity, that is, feature redundancy is removed based on the RNA mono-nucleotide composition to maintain the diversity of RNA samples and avoid the residue of redundant data. In addition, to extract key abstract features and avoid over-fitting, we used the cascade structure of a deep forest model to predict protein-interacting nucleotides. Overall, DFpin demonstrated excellent classification with 85.4% accuracy and 93.3% area under the curve. Compared with other methods, the accuracy of DFpin was better, suggesting that feature-based redundancy removal and deep forest can help predict nucleotides of protein interactions. The source code and all dataset are available at: https://github.com/zhaoxj-tech/DFpin.git.

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.

Role of salt-bridging interactions in recognition of viral RNA by arginine-rich peptides

Interactions between RNA molecules and proteins are critical to many cellular processes and are implicated in various diseases. The RNA-peptide complexes are good model systems to probe the recognition mechanism of RNA by proteins. In this work, we report studies on the binding-unbinding process of a helical peptide from a viral RNA element using nonequilibrium molecular dynamics simulations. We explored the existence of various dissociation pathways with distinct free-energy profiles that reveal metastable states and distinct barriers to peptide dissociation. We also report the free-energy differences for each of the four pathways to be 96.47 ± 12.63, 96.1 ± 10.95, 91.83 ± 9.81, and 92 ± 11.32 kcal/mol. Based on the free-energy analysis, we further propose the preferred pathway and the mechanism of peptide dissociation. The preferred pathway is characterized by the formation of sequential hydrogen-bonding and salt-bridging interactions between several key arginine amino acids and the viral RNA nucleotides. Specifically, we identified one arginine amino acid (R8) of the peptide to play a significant role in the recognition mechanism of the peptide by the viral RNA molecule.

Mass Spectrometry of Nucleic Acid Noncovalent Complexes

Nucleic acids have been among the first targets for antitumor drugs and antibiotics. With the unveiling of new biological roles in regulation of gene expression, specific DNA and RNA structures have become very attractive targets, especially when the corresponding proteins are undruggable. Biophysical assays to assess target structure as well as ligand binding stoichiometry, affinity, specificity, and binding modes are part of the drug development process. Mass spectrometry offers unique advantages as a biophysical method owing to its ability to distinguish each stoichiometry present in a mixture. In addition, advanced mass spectrometry approaches (reactive probing, fragmentation techniques, ion mobility spectrometry, ion spectroscopy) provide more detailed information on the complexes. Here, we review the fundamentals of mass spectrometry and all its particularities when studying noncovalent nucleic acid structures, and then review what has been learned thanks to mass spectrometry on nucleic acid structures, self-assemblies (e.g., duplexes or G-quadruplexes), and their complexes with ligands.

Characteristic Features of Protein Interaction with Single- and Double-Stranded RNA

The review discusses differences between the specific protein interactions with single- and double-stranded RNA molecules using the data on the structure of RNA-protein complexes. Proteins interacting with the single-stranded RNAs form contacts with RNA bases, which ensures recognition of specific nucleotide sequences. Formation of such contacts with the double-stranded RNAs is hindered, so that the proteins recognize unique conformations of the RNA spatial structure and interact mainly with the RNA sugar-phosphate backbone.

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.

RNA-dependent assembly of chimeric antigen nanoparticles as an efficient H5N1 pre-pandemic vaccine platform

Highly pathogenic avian influenza viruses (HPAIVs) pose a significant threat to human health, with high mortality rates, and require effective vaccines. We showed that, harnessed with novel RNA-mediated chaperone function, hemagglutinin (HA) of H5N1 HPAIV could be displayed as an immunologically relevant conformation on self-assembled chimeric nanoparticles (cNP). A tri-partite monomeric antigen was designed including: i) an RNA-interaction domain (RID) as a docking tag for RNA to enable chaperna function (chaperna: chaperone + RNA), ii) globular head domain (gd) of HA as a target antigen, and iii) ferritin as a scaffold for 24 mer-assembly. The immunization of mice with the nanoparticles (~46 nm) induced a 25-30 fold higher neutralizing capacity of the antibody and provided cross-protection from homologous and heterologous lethal challenges. This study suggests that cNP assembly is conducive to eliciting antibodies against the conserved region in HA, providing potent and broad protective efficacy.

RNA-assisted self-assembly of monomeric antigens into virus-like particles as a recombinant vaccine platform

Representing highly ordered repetitive structures of antigen macromolecular assemblies, virus-like particles (VLPs) serve as a high-priority vaccine platform against emerging viral infections, as alternatives to traditional cell culture-based vaccines. RNAs can function as chaperones (Chaperna) and are extremely effective in promoting protein folding. Beyond their canonical function as translational adaptors, tRNAs may moonlight as chaperones for the kinetic control of macromolecular antigen assembly. Capitalizing on genomic RNA co-assembly in infectious virions, we present the first report of a biomimetic assembly of viral capsids that was assisted by non-viral host RNAs into genome-free, non-infectious empty particles. Here, we demonstrate the assembly of bacterially-produced soluble norovirus VP1 forming VLPs (n = 180) in vitro. A tRNA-interacting domain (tRID) was genetically fused with the VP1 capsid protein, as a tRNA docking tag, in the bacterial host to transduce chaperna function for de novo viral antigen folding. tRID/tRNA removal prompted the in vitro assembly of monomeric antigens into highly ordered repetitive structures that elicited robust protective immune responses after immunization. The chaperna-based assembly of monomeric antigens will impact the development and deployment of VLP vaccines for emerging and re-emerging viral infections.

Chaperna: linking the ancient RNA and protein worlds

As a mental framework for the transition of self-replicating biological forms, the RNA world concept stipulates a dual function of RNAs as genetic substance and catalyst. The chaperoning function is found intrinsic to ribozymes involved in protein synthesis and tRNA maturation, enriching the primordial RNA world with proteins of biological relevance. The ribozyme-resident protein folding activity, even before the advent of protein-based molecular chaperone, must have expedited the transition of the RNA world into the present protein theatre.

Binding specificities of human RNA-binding proteins toward structured and linear RNA sequences

RNA-binding proteins (RBPs) regulate RNA metabolism at multiple levels by affecting splicing of nascent transcripts, RNA folding, base modification, transport, localization, translation, and stability. Despite their central role in RNA function, the RNA-binding specificities of most RBPs remain unknown or incompletely defined. To address this, we have assembled a genome-scale collection of RBPs and their RNA-binding domains (RBDs) and assessed their specificities using high-throughput RNA-SELEX (HTR-SELEX). Approximately 70% of RBPs for which we obtained a motif bound to short linear sequences, whereas ∼30% preferred structured motifs folding into stem-loops. We also found that many RBPs can bind to multiple distinctly different motifs. Analysis of the matches of the motifs in human genomic sequences suggested novel roles for many RBPs. We found that three cytoplasmic proteins-ZC3H12A, ZC3H12B, and ZC3H12C-bound to motifs resembling the splice donor sequence, suggesting that these proteins are involved in degradation of cytoplasmic viral and/or unspliced transcripts. Structural analysis revealed that the RNA motif was not bound by the conventional C3H1 RNA-binding domain of ZC3H12B. Instead, the RNA motif was bound by the ZC3H12B's PilT N terminus (PIN) RNase domain, revealing a potential mechanism by which unconventional RBDs containing active sites or molecule-binding pockets could interact with short, structured RNA molecules. Our collection containing 145 high-resolution binding specificity models for 86 RBPs is the largest systematic resource for the analysis of human RBPs and will greatly facilitate future analysis of the various biological roles of this important class of proteins.

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.

A Non-amyloid Prion Particle that Activates a Heritable Gene Expression Program

Spatiotemporal gene regulation is often driven by RNA-binding proteins that harbor long intrinsically disordered regions in addition to folded RNA-binding domains. We report that the disordered region of the evolutionarily ancient developmental regulator Vts1/Smaug drives self-assembly into gel-like condensates. These proteinaceous particles are not composed of amyloid, yet they are infectious, allowing them to act as a protein-based epigenetic element: a prion [SMAUG⁺]. In contrast to many amyloid prions, condensation of Vts1 enhances its function in mRNA decay, and its self-assembly properties are conserved over large evolutionary distances. Yeast cells harboring [SMAUG⁺] downregulate a coherent network of mRNAs and exhibit improved growth under nutrient limitation. Vts1 condensates formed from purified protein can transform naive cells to acquire [SMAUG⁺]. Our data establish that non-amyloid self-assembly of RNA-binding proteins can drive a form of epigenetics beyond the chromosome, instilling adaptive gene expression programs that are heritable over long biological timescales.

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.

γ-AApeptides as a New Strategy for Therapeutic Development

A new class of peptidomimetics termed as "γ-AApeptides" was recently developed by our group. Similar to other peptidomimetics, γ-AApeptides are resistant to proteolytic degradation, and possess limitless potential to introduce chemically diverse functional groups. γ-AApeptides have shown great promise in biomedical applications. In this article, we will review a few examples of γ-AApeptides with biological potential. Certain γ-AApeptides can permeate cell membranes and therefore they can be used as potential drug carrier. γ-AApeptides can also bind to HIV RNA with high specificity and affinity, suggesting their potential application as anti-HIV agents. Moreover, they can mimic host-defense peptides and display potent and broad-spectrum activity towards a range of drug-resistant bacterial pathogens. They are also potential anti-cancer agents. For instance, they have shown great promise in targeted imaging of tumor in mouse model, and they are also capable of disrupting p53/DNA interactions, and thus antagonize STAT3 signaling pathway. Recently, from combinatorial screening, γ-AApeptides are identified to inhibit Aβ peptide aggregation, and thus they can be developed into potential anti- Alzheimer's disease agent.

In Silico Structural Modeling and Analysis of Elongation Factor-1 Alpha and Elongation Factor-like Protein

Translation elongation factor-1alpha (EF-1α) or its paralog elongation factor-like proteins (EFL) interact with an aminoacyl-transfer RNA (aa-tRNA) to play its essential role in elongation of peptide-chain during protein synthesis. Species usually have either an EF-1α or EFL protein; however, some species have both EF-1α and EFL (dual-EF-containing species). In the dual-EF-containing species, EF-1α appeared to be highly divergent in the sequence. Homology modeling and surface analysis of EF-1α and EFL were performed to examine the hypothesis that the divergent EF-1α in the dual-EF-containing eukaryotes does not strongly interact with aa-tRNA compared to the canonical EF-1α and EFL. The subsequent molecular dynamics simulations were carried out to confirm the validity of modeled structures and to analyze their stability. It was found that the molecular surfaces of the divergent EF-1α proteins were negatively charged partly, and thus they might not interact with negatively charged aa-tRNA as strongly as the canonical ones. The molecular docking simulations between EF-1α/EFL and aa-tRNA also support the hypothesis.

Identification and Analysis of Key Residues in Protein-RNA Complexes

Protein-RNA complexes play important roles in various biological processes. The functions of protein-RNA complexes are dictated by their interactions, binding, stability, and affinity. In this work, we have identified the key residues (KRs), which are involved in both stability and binding. We found that 42 percent of considered proteins share common binding and stabilizing residues, whereas these residues are distinct in 58 percent of the proteins. Overall, 5 percent of stabilizing and 3 percent of binding residues serve as key residues. These residues are enriched with the combination of polar, charged, aliphatic, and aromatic residues. Analysis on subclasses of protein-RNA complexes based on protein structural class, function and RNA type showed that regulatory proteins, and complexes with single stranded RNA and rRNA have appreciable number of key residues. Specifically, Arg, Tyr, and Thr are preferred in most of the subclasses of protein-RNA complexes. In addition, residues with similar chemical behavior have different preferences to be KRs, such that Arg, Tyr, Val, and Thr are preferred over Lys, Trp, Ile, and Ser, respectively. Atomic level contacts revealed that charged and polar-nonpolar contacts are dominant in enzymes, polar in structural, and nonpolar in regulatory proteins. On the other hand, polar-nonpolar contacts are enriched in all these classes of protein-RNA complexes. Further, the influence of sequence and structural features such as conservation score, surrounding hydrophobicity, solvent accessibility, secondary structure, and long-range order in key residues are also discussed. We envisage that the present study provides insights to understand the structural and functional aspects of protein-RNA complexes.

Predicting Cotranscriptional Folding Kinetics For Riboswitch

On the basis of a helix-based transition rate model, we developed a new method for sampling cotranscriptional RNA conformational ensemble and the prediction of cotranscriptional folding kinetics. Applications to E. coli. SRP RNA and pbuE riboswitch indicate that the model may provide reliable predictions for the cotranscriptional folding pathways and population kinetics. For E. coli. SRP RNA, the predicted population kinetics and the folding pathway are consistent with the SHAPE profiles in the recent cotranscriptional SHAPE-seq experiments. For the pbuE riboswitch, the model predicts the transcriptional termination efficiency as a function of the force. The theoretical results show (a) a force-induced transition from the aptamer (antiterminator) to the terminator structure and (b) the different folding pathways for the riboswitch with and without the ligand (adenine). More specifically, without adenine, the aptamer structure emerges as a short-lived kinetic transient state instead of a thermodynamically stable intermediate state. Furthermore, from the predicted extension-time curves, the model identifies a series of conformational switches in the pulling process, where the predicted relative residence times for the different structures are in accordance with the experimental data. The model may provide a new tool for quantitative predictions of cotranscriptional folding kinetics, and results can offer useful insights into cotranscriptional folding-related RNA functions such as regulation of gene expression with riboswitches.

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine

RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.

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.

Effects of mRNA secondary structure on the expression of HEV ORF2 proteins in Escherichia coli

Background

Viral protein expression in Escherichia coli (E. coli) is a powerful tool for structural/functional studies as well as for vaccine and diagnostics development. However, numerous factors such as codon bias, mRNA secondary structure and nucleotides distribution, have been indentified to hamper this heterologous expression.

Results

In this study, we combined computational and biochemical methods to analyze the influence of these factors on the expression of different segments of hepatitis E virus (HEV) ORF 2 protein and hepatitis B virus surface antigen (HBsAg). Three out of five HEV antigens were expressed while all three HBsAg fragments were not. The computational analysis revealed a significant difference in nucleotide distribution between expressed and non-expressed genes; and all these non-expressing constructs shared similar stable 5′-end mRNA secondary structures that affected the accessibility of both Shine-Dalgarno (SD) sequence and start codon AUG. By modifying the 5′-end of HEV and HBV non-expressed genes, there was a significant increase in the total free energy of the mRNA secondary structures that permitted the exposure of the SD sequence and the start codon, which in turn, led to the successful expression of these genes in E. coli.

Conclusions

This study demonstrates that the mRNA secondary structure near the start codon is the key limiting factor for an efficient expression of HEV ORF2 proteins in E. coli. It describes also a simple and effective strategy for the production of viral proteins of different lengths for immunogenicity/antigenicity comparative studies during vaccine and diagnostics development.

Electronic supplementary material

The online version of this article (10.1186/s12934-017-0812-8) contains supplementary material, which is available to authorized users.

Comparative analyses of the thermodynamic RNA binding signatures of different types of RNA recognition motifs

RNA recognition motifs (RRMs) are structurally versatile domains important in regulation of alternative splicing. Structural mechanisms of sequence-specific recognition of single-stranded RNAs (ssRNAs) by RRMs are well understood. The thermodynamic strategies are however unclear. Therefore, we utilized microcalorimetry and semi-empirical analyses to comparatively analyze the cognate ssRNA binding thermodynamics of four different RRM domains, each with a different RNA binding mode. The different binding modes are: canonical binding to the β-sheet surface; canonical binding with involvement of N- and C-termini; binding to conserved loops; and binding to an α-helix. Our results identify enthalpy as the sole and general force driving association at physiological temperatures. Also, networks of weak interactions are a general feature regulating stability of the different RRM–ssRNA complexes. In agreement, non-polyelectrolyte effects contributed between ∼75 and 90% of the overall free energy of binding in the considered complexes. The various RNA binding modes also displayed enormous heat capacity differences, that upon dissection revealed large differential changes in hydration, conformations and dynamics upon binding RNA. Altogether, different modes employed by RRMs to bind cognate ssRNAs utilize various thermodynamics strategies during the association process.

Identification of a novel frameshift mutation in the ILDR1 gene in a UAE family, mutations review and phenotype genotype correlation

Autosomal recessive non-syndromic hearing loss is one of the most common monogenic diseases. It is characterized by high allelic and locus heterogeneities that make a precise diagnosis difficult. In this study, whole-exome sequencing was performed for an affected patient allowing us to identify a new frameshift mutation (c.804delG) in the Immunoglobulin-Like Domain containing Receptor-1 (ILDR1) gene. Direct Sanger sequencing and segregation analysis were performed for the family pedigree. The mutation was homozygous in all affected siblings but heterozygous in the normal consanguineous parents. The present study reports a first ILDR1 gene mutation in the UAE population and confirms that the whole-exome sequencing approach is a robust tool for the diagnosis of monogenic diseases with high levels of allelic and locus heterogeneity. In addition, by reviewing all reported ILDR1 mutations, we attempt to establish a genotype phenotype correlation to explain the phenotypic variability observed at low frequencies.

Enzyme classification using multiclass support vector machine and feature subset selection

Proteins are the macromolecules responsible for almost all biological processes in a cell. With the availability of large number of protein sequences from different sequencing projects, the challenge with the scientist is to characterize their functions. As the wet lab methods are time consuming and expensive, many computational methods such as FASTA, PSI-BLAST, DNA microarray clustering, and Nearest Neighborhood classification on protein-protein interaction network have been proposed. Support vector machine is one such method that has been used successfully for several problems such as protein fold recognition, protein structure prediction etc. Cai et al. in 2003 have used SVM for classifying proteins into different functional classes and to predict their function. They used the physico-chemical properties of proteins to represent the protein sequences. In this paper a model comprising of feature subset selection followed by multiclass Support Vector Machine is proposed to determine the functional class of a newly generated protein sequence. To train and test the model for its performance, 32 physico-chemical properties of enzymes from 6 enzyme classes are considered. To determine the features that contribute significantly for functional classification, Sequential Forward Floating Selection (SFFS), Orthogonal Forward Selection (OFS), and SVM Recursive Feature Elimination (SVM-RFE) algorithms are used and it is observed that out of 32 properties considered initially, only 20 features are sufficient to classify the proteins into its functional classes with an accuracy ranging from 91% to 94%. On comparison it is seen that, OFS followed by SVM performs better than other methods. Our model generalizes the existing model to include multiclass classification and to identify most significant features affecting the protein function.

Bioinformatics comparisons of RNA-binding proteins of pathogenic and non-pathogenic Escherichia coli strains reveal novel virulence factors

Background

Pathogenic bacteria have evolved various strategies to counteract host defences. They are also exposed to environments that are undergoing constant changes. Hence, in order to survive, bacteria must adapt themselves to the changing environmental conditions by performing regulations at the transcriptional and/or post-transcriptional levels. Roles of RNA-binding proteins (RBPs) as virulence factors have been very well studied. Here, we have used a sequence search-based method to compare and contrast the proteomes of 16 pathogenic and three non-pathogenic E. coli strains as well as to obtain a global picture of the RBP landscape (RBPome) in E. coli.

Results

Our results show that there are no significant differences in the percentage of RBPs encoded by the pathogenic and the non-pathogenic E. coli strains. The differences in the types of Pfam domains as well as Pfam RNA-binding domains, encoded by these two classes of E. coli strains, are also insignificant. The complete and distinct RBPome of E. coli has been established by studying all known E. coli strains till date. We have also identified RBPs that are exclusive to pathogenic strains, and most of them can be exploited as drug targets since they appear to be non-homologous to their human host proteins. Many of these pathogen-specific proteins were uncharacterised and their identities could be resolved on the basis of sequence homology searches with known proteins. Detailed structural modelling, molecular dynamics simulations and sequence comparisons have been pursued for selected examples to understand differences in stability and RNA-binding.

Conclusions

The approach used in this paper to cross-compare proteomes of pathogenic and non-pathogenic strains may also be extended to other bacterial or even eukaryotic proteomes to understand interesting differences in their RBPomes. The pathogen-specific RBPs reported in this study, may also be taken up further for clinical trials and/or experimental validations.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4045-3) contains supplementary material, which is available to authorized users.

A combinatorial scoring function for protein-RNA docking

Protein-RNA docking is still an open question. One of the main challenges is to develop an effective scoring function that can discriminate near-native structures from the incorrect ones. To solve the problem, we have constructed a knowledge-based residue-nucleotide pairwise potential with secondary structure information considered for nonribosomal protein-RNA docking. Here we developed a weighted combined scoring function RpveScore that consists of the pairwise potential and six physics-based energy terms. The weights were optimized using the multiple linear regression method by fitting the scoring function to L_rmsd for the bound docking decoys from Benchmark II. The scoring functions were tested on 35 unbound docking cases. The results show that the scoring function RpveScore including all terms performs best. Also RpveScore was compared with the statistical mechanics-based method derived potential ITScore-PR, and the united atom-based statistical potentials QUASI-RNP and DARS-RNP. The success rate of RpveScore is 71.6% for the top 1000 structures and the number of cases where a near-native structure is ranked in top 30 is 25 out of 35 cases. For 32 systems (91.4%), RpveScore can find the binding mode in top 5 that has no lower than 50% native interface residues on protein and nucleotides on RNA. Additionally, it was found that the long-range electrostatic attractive energy plays an important role in distinguishing near-native structures from the incorrect ones. This work can be helpful for the development of protein-RNA docking methods and for the understanding of protein-RNA interactions. RpveScore program is available to the public at http://life.bjut.edu.cn/kxyj/kycg/2017116/14845362285362368_1.html Proteins 2017; 85:741-752. © 2016 Wiley Periodicals, Inc.

Ultraslow Domain Motions in HIV-1 TAR RNA Revealed by Solid-State Deuterium NMR

Intrinsic motions may allow HIV-1 transactivation response (TAR) RNA to change its conformation to form a functional complex with the Tat protein, which is essential for viral replication. Understanding the dynamic properties of TAR necessitates determining motion on the intermediate nanosecond-to-microsecond time scale. To this end, we performed solid-state deuterium NMR line-shape and T_1Z relaxation-time experiments to measure intermediate motions for two uridine residues, U40 and U42, within the lower helix of TAR. We infer global motions at rates of ∼10⁵ s^-1 in the lower helix, which are much slower than those in the upper helix (∼10⁶ s^-1), indicating that the two helical domains reorient independently of one another in the solid-state sample. These results contribute to the aim of fully describing the properties of functional motions in TAR RNA.

Determining Functional Aptamer-Protein Interaction by Biolayer Interferometry

Short single-stranded nucleic acids called aptamers are widely being explored as recognition molecules of high affinity and specificity for binding a wide range of target molecules, particularly protein targets. In biolayer interferometry (BLI), a simple Dip-and-Read approach in which the aptamer-coated biosensors are dipped into microplate wells is used to study the interactions between an aptamer and its target protein. Here we describe the protocol for the analysis of the interaction between a well-characterized anti-thrombin RNA aptamer with thrombin (Basic Protocol). We also report on the protocol for the affinity screening of a panel of anti-thrombin RNA aptamers with a single phosphorodithioate (PS2) modification, whereby the position of the modification along the RNA backbone is varied systematically (Support Protocol). The PS2 modification uses two sulfur atoms to replace two non-bridging oxygen atoms at an internucleotide phosphodiester backbone linkage. The PS2-modified RNAs are nuclease resistant and several in vitro and in vivo assays have demonstrated their biological activity. For example, combining the PS2 with the 2'-OMe modification affords increased loading of modified small interfering RNA (siRNA) duplexes into the RNA-induced silencing complex (RISC) as well as enhanced gene-silencing antitumor activity. © 2016 by John Wiley & Sons, Inc.

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.

In silico predicted structural and functional insights of all missense mutations on 2B domain of K1/K10 causing genodermatoses

The K1 and K10 associated genodermatoses are characterized by clinical symptoms of mild to severe redness, blistering and hypertrophy of the skin. In this paper, we set out to computationally investigate the structural and functional effects of missense mutations on the 2B domain of K1/K10 heterodimer and its consequences in disease phenotype. We modeled the structure of the K1/K10 heterodimer based on crystal structures for the human homolog K5/K14 heterodimer, and identified that the missense mutations exert their effects on stability and assembly competence of the heterodimer by altering physico-chemical properties, interatomic interactions, and inter-residue atomic contacts. Comparative structural analysis between all the missense mutations and SNPs showed that the location and physico-chemical properties of the substituted amino acid are significantly correlated with phenotypic variations. In particular, we find evidence that a particular SNP (K10, p.E443K) is a pathogenic nsSNP which disrupts formation of the hydrophobic core and destabilizes the heterodimer through the loss of interatomic interactions. Our study is the first comprehensive report analyzing the mutations located on 2B domain of K1/K10 heterodimeric coiled-coil complex.

γ-AApeptides: Design, Structure, and Applications

The development of sequence-specific peptidomimetics has led to a variety of fascinating discoveries in chemical biology. Many peptidomimetics can mimic primary, secondary, and even tertiary structure of peptides and proteins, and because of their unnatural backbones, they also possess significantly enhanced resistance to enzymatic hydrolysis, improved bioavailability, and chemodiversity. It is known that peptide nucleic acids (PNAs) are peptidic sequences developed for the mimicry of nucleic acids; however, their unique backbone as the molecular scaffold of peptidomimetics to mimic structure and function of bioactive peptides has not been investigated systematically. As such, we recently developed a new class of peptidomimetics, "γ-AApeptides", based on the chiral γ-PNA backbone. They are termed γ-AApeptides because they are the oligomers of γ-substituted-N-acylated-N-aminoethyl amino acids. Similar to other classes of peptidomimetics, γ-AApeptides are also resistant to proteolytic degradation and possess the potential to enhance chemodiversity. Moreover, in our scientific journey on the exploration of this class of peptidomimetics, we have discovered some intriguing structures and functions of γ-AApeptides. In this Account, we summarize the current development and application of γ-AApeptides with biological potential. Briefly, both linear and cyclic (either through head-to-tail or head-to-side-chain cyclization) γ-AApeptides with diverse functional groups can be synthesized easily on the solid phase using the synthetic protocol we developed. γ-AApeptides could mimic the primary structure of peptides, as they project the same number of side chains as peptides of the same lengths. For instance, they could mimic the Tat peptide to permeate cell membranes and bind to HIV RNA with high specificity and affinity. Certain γ-AApeptides show similar activity to the RGD peptide and target integrin specifically on the cell surface. γ-AApeptides with function akin to fMLF peptides are also identified. More importantly, we found that γ-AApeptides can fold into discrete secondary structures, such as helical and β-turn-like structures. Therefore, they could be rationally designed for a range of biological applications. For instance, γ-AApeptides can mimic host-defense peptides and display potent and broad-spectrum activity toward a panel of drug-resistant bacterial pathogens. Meanwhile, because of their stability against proteolysis and their chemodiversity, γ-AApeptides are also amenable for combinatorial screening. We demonstrate that, through combinatorial selection, certain γ-AApeptides are identified to inhibit Aβ40 peptide aggregation, suggesting their potential use as a molecular probe to intervene in Alzheimer's disease. In addition, a few γ-AApeptides identified from the γ-AApeptide library have been shown to bind to the DNA-binding domain of STAT3 and antagonize STAT3/DNA interactions. Our studies suggest that, with further studies and exploration on both structures and functions, γ-AApeptides may emerge to be a new class of peptidomimetics that play an important role in chemical biology and biomedical sciences.

Binding Site Identification and Flexible Docking of Single Stranded RNA to Proteins Using a Fragment-Based Approach

Protein-RNA docking is hampered by the high flexibility of RNA, and particularly single-stranded RNA (ssRNA). Yet, ssRNA regions typically carry the specificity of protein recognition. The lack of methodology for modeling such regions limits the accuracy of current protein-RNA docking methods. We developed a fragment-based approach to model protein-bound ssRNA, based on the structure of the protein and the sequence of the RNA, without any prior knowledge of the RNA binding site or the RNA structure. The conformational diversity of each fragment is sampled by an exhaustive RNA fragment library that was created from all the existing experimental structures of protein-ssRNA complexes. A systematic and detailed analysis of fragment-based ssRNA docking was performed which constitutes a proof-of-principle for the fragment-based approach. The method was tested on two 8-homo-nucleotide ssRNA-protein complexes and was able to identify the binding site on the protein within 10 Å. Moreover, a structure of each bound ssRNA could be generated in close agreement with the crystal structure with a mean deviation of ~1.5 Å except for a terminal nucleotide. This is the first time a bound ssRNA could be modeled from sequence with high precision.

Analysis of the optimality of the standard genetic code

Many theories have been proposed attempting to explain the origin of the genetic code. In this work, we compare performance of the standard genetic code against millions of randomly generated codes. On left, ability of genetic codes to encode additional information and their robustness to frameshift mutations.

Detection of RNA-Protein Interactions in Living Cells with SHAPE

SHAPE-MaP is unique among RNA structure probing strategies in that it both measures flexibility at single-nucleotide resolution and quantifies the uncertainties in these measurements. We report a straightforward analytical framework that incorporates these uncertainties to allow detection of RNA structural differences between any two states, and we use it here to detect RNA-protein interactions in healthy mouse trophoblast stem cells. We validate this approach by analysis of three model cytoplasmic and nuclear ribonucleoprotein complexes, in 2 min in-cell probing experiments. In contrast, data produced by alternative in-cell SHAPE probing methods correlate poorly (r = 0.2) with those generated by SHAPE-MaP and do not yield accurate signals for RNA-protein interactions. We then examine RNA-protein and RNA-substrate interactions in the RNase MRP complex and, by comparing in-cell interaction sites with disease-associated mutations, characterize these noncoding mutations in terms of molecular phenotype. Together, these results reveal that SHAPE-MaP can define true interaction sites and infer RNA functions under native cellular conditions with limited preexisting knowledge of the proteins or RNAs involved.

HK022 Nun Requires Arginine-Rich Motif Residues Distinct from λ N

Bacteriophage λ N protein binds boxB RNA hairpins in the nut (N utilization) sites of immediate early λ transcripts and interacts with host factors to suppress transcriptional termination at downstream terminators. In opposition to λ N, the Nun protein of HK022 binds the boxBs of coinfecting λ transcripts, interacts with a similar or identical set of host factors, and terminates transcription to suppress λ replication. Comparison of N-boxB and Nun-boxB nuclear magnetic resonance (NMR) structural models suggests similar interactions, though limited mutagenesis of Nun is available. Here, libraries of Nun's arginine-rich motif (ARM) were screened for the ability to exclude λ coinfection, and mutants were assayed for Nun termination with a boxB plasmid reporter system. Several Nun ARM residues appear to be immutable: Asp26, Arg28, Arg29, Arg32, Trp33, and Arg36. Asp26 and Trp33 appear to be unable to contact boxB and are not found at equivalent positions in λ N ARM. To understand if the requirement of Asp26, Trp33, and Arg36 indicated differences between HK022 Nun termination and λ N antitermination complexes, the same Nun libraries were fused to the activation domain of λ N and screened for clones able to complement N-deficient λ. Mutants were assayed for N antitermination. Surprisingly, Asp26 and Trp33 were still essential when Nun ARM was fused to N. Docking suggests that Nun ARM contacts a hydrophobic surface of the NusG carboxy-terminal domain containing residues necessary for Nun function. These findings indicate that Nun ARM relies on distinct contacts in its ternary complex and illustrate how protein-RNA recognition can evolve new regulatory functions.

IMPORTANCE

λ N protein interacts with host factors to allow λ nut-containing transcripts to elongate past termination signals. A competing bacteriophage, HK022, expresses Nun protein, which causes termination of λ nut transcripts. λ N and HK022 Nun use similar arginine-rich motifs (ARMs) to bind the same boxB RNAs in nut transcripts. Screening libraries of Nun ARM mutants, both in HK022 Nun and in a λ N fusion, revealed amino acids essential to Nun that could bind one or more host factors. Docking suggests that NusG, which is present in both Nun termination and N antitermination, is a plausible partner. These findings could help understand how transcription elongation is regulated and illustrate how subtle differences allow ARMs to evolve new regulatory functions.

A Distinct, Sequence-Induced Conformation Is Required for Recognition of the Constitutive Decay Element RNA by Roquin

The constitutive decay element (CDE) of tumor necrosis factor α (TNF-α) mRNA (Tnf) represents the prototype of a class of RNA motifs that mediate rapid degradation of mRNAs encoding regulators of the immune response and development. CDE-type RNAs are hairpin structures featuring a tri-nucleotide loop. The protein Roquin recognizes CDE-type stem loops and recruits the Ccr4-Caf1-Not deadenylase complex to the mRNA, thereby inducing its decay. Stem recognition does not involve nucleotide bases; however, there is a strong stem sequence requirement for functional CDEs. Here, we present the solution structures of the natural Tnf CDE and of a CDE mutant with impaired Roquin binding. We find that the two CDEs adopt unique and distinct structures in both the loop and the stem, which explains the ability of Roquin to recognize stem loops in a sequence-specific manner. Our findings result in a relaxed consensus motif for prediction of new CDE stem loops.

Dissection and prediction of RNA-binding sites on proteins

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

Molecular architecture of protein-RNA recognition sites

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

Can We Execute Stable Microsecond-Scale Atomistic Simulations of Protein-RNA Complexes?

We report over 30 μs of unrestrained molecular dynamics simulations of six protein-RNA complexes in explicit solvent. We utilize the AMBER ff99bsc0χ(OL3) RNA force field combined with the ff99SB protein force field and its more recent ff12SB version with reparametrized side-chain dihedrals. The simulations show variable behavior, ranging from systems that are essentially stable to systems with progressive deviations from the experimental structure, which we could not stabilize anywhere close to the starting experimental structure. For some systems, microsecond-scale simulations are necessary to achieve stabilization after initial sizable structural perturbations. The results show that simulations of protein-RNA complexes are challenging and every system should be treated individually. The simulations are affected by numerous factors, including properties of the starting structures (the initially high force field potential energy, resolution limits, conformational averaging, crystal packing, etc.), force field imbalances, and real flexibility of the studied systems. These factors, and thus the simulation behavior, differ from system to system. The structural stability of simulated systems does not correlate with the size of buried interaction surface or experimentally determined binding affinities but reflects the type of protein-RNA recognition. Protein-RNA interfaces involving shape-specific recognition of RNA are more stable than those relying on sequence-specific RNA recognition. The differences between the protein force fields are considerably smaller than the uncertainties caused by sampling and starting structures. The ff12SB improves description of the tyrosine side-chain group, which eliminates some problems associated with tyrosine dynamics.