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

Elucidating OASL-RNA Interactions: Structural and energetic insights into vault RNAs binding

Oligoadenylate synthetase-like (OASL) proteins play an essential role in the innate immune response by detecting RNA molecules and modulating antiviral signalling pathways. This study investigated the structural and functional dynamics of OASL in its interaction with endogenous vault RNAs (vtRNAs) through computational analyses, including molecular docking and molecular dynamics simulations. Predicted 3D structures of vtRNAs revealed key interactions within the positively charged RNA-binding groove of OASL, involving residues such as Arg45, Lys63, and Arg196. Among the vtRNAs analysed, vtRNA 1-1 exhibited the highest binding affinity and stability, inducing conformational changes in OASL consistent with canonical activation mechanisms. In contrast, vtRNA 1-2 and 1-3 demonstrated moderate interactions, while vtRNA 2-1 had minimal impact on OASL conformation. Our results underscore the critical role of guanine- and cytosine-enriched regions in mediating binding stability and specificity, as corroborated by MM/GBSA calculations. The study highlights the molecular determinants of OASL-vtRNA interactions, offering structural insights into the mechanisms of nucleic acid recognition.

Predicting lncRNA-protein interactions using a hybrid deep learning model with dinucleotide-codon fusion feature encoding

Long non-coding RNAs (lncRNAs) play crucial roles in numerous biological processes and are involved in complex human diseases through interactions with proteins. Accurate identification of lncRNA-protein interactions (LPI) can help elucidate the functional mechanisms of lncRNAs and provide scientific insights into the molecular mechanisms underlying related diseases. While many sequence-based methods have been developed to predict LPIs, efficiently extracting and effectively integrating potential feature information that reflects functional attributes from lncRNA and protein sequences remains a significant challenge. This paper proposes a Dinucleotide-Codon Fusion Feature encoding (DNCFF) and constructs an LPI prediction model based on deep learning, termed LPI-DNCFF. The Dual Nucleotide Visual Fusion Feature encoding (DNVFF) incorporates positional information of single nucleotides with subsequent nucleotide connections, while Codon Fusion Feature encoding (CFF) considers the specificity, molecular weight, and physicochemical properties of each amino acid. These encoding methods encapsulate rich and intuitive sequence information in limited encoding dimensions. The model comprehensively predicts LPIs by integrating global, local, and structural features, and inputs them into BiLSTM and attention layers to form a hybrid deep learning model. Experimental results demonstrate that LPI-DNCFF effectively predicts LPIs. The BiLSTM layer and attention mechanism can learn long-term dependencies and identify weighted key features, enhancing model performance. Compared to one-hot encoding, DNCFF more efficiently and thoroughly extracts potential sequence features. Compared to other existing methods, LPI-DNCFF achieved the best performance on the RPI1847 and ATH948 datasets, with MCC values of approximately 97.84% and 84.58%, respectively, outperforming the state-of-the-art method by about 1.44% and 3.48%.

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.

The Arabidopsis SR45 splicing factor bridges the splicing machinery and the exon-exon junction complex

The Arabidopsis splicing factor serine/arginine-rich 45 (SR45) contributes to several biological processes. The sr45-1 loss-of-function mutant exhibits delayed root development, late flowering, unusual numbers of floral organs, shorter siliques with decreased seed sets, narrower leaves and petals, and altered metal distribution. SR45 bears a unique RNA recognition motif (RRM) flanked by one serine/arginine-rich (RS) domain on both sides. Here, we studied the function of each SR45 domains by examining their involvement in: (i) the spatial distribution of SR45; (ii) the establishment of a protein-protein interaction network including spliceosomal and exon-exon junction complex (EJC) components; and (iii) the RNA binding specificity. We report that the endogenous SR45 promoter is active during vegetative and reproductive growth, and that the SR45 protein localizes in the nucleus. We demonstrate that the C-terminal arginine/serine-rich domain is a determinant of nuclear localization. We show that the SR45 RRM domain specifically binds purine-rich RNA motifs via three residues (H101, H141, and Y143), and is also involved in protein-protein interactions. We further show that SR45 bridges both mRNA splicing and surveillance machineries as a partner of EJC core components and peripheral factors, which requires phosphoresidues probably phosphorylated by kinases from both the CLK and SRPK families. Our findings provide insights into the contribution of each SR45 domain to both spliceosome and EJC assemblies.

RPI-EDLCN: An Ensemble Deep Learning Framework Based on Capsule Network for ncRNA-Protein Interaction Prediction

Noncoding RNAs (ncRNAs) play crucial roles in many cellular life activities by interacting with proteins. Identification of ncRNA-protein interactions (ncRPIs) is key to understanding the function of ncRNAs. Although a number of computational methods for predicting ncRPIs have been developed, the problem of predicting ncRPIs remains challenging. It has always been the focus of ncRPIs research to select suitable feature extraction methods and develop a deep learning architecture with better recognition performance. In this work, we proposed an ensemble deep learning framework, RPI-EDLCN, based on a capsule network (CapsuleNet) to predict ncRPIs. In terms of feature input, we extracted the sequence features, secondary structure sequence features, motif information, and physicochemical properties of ncRNA/protein. The sequence and secondary structure sequence features of ncRNA/protein are encoded by the conjoint k-mer method and then input into an ensemble deep learning model based on CapsuleNet by combining the motif information and physicochemical properties. In this model, the encoding features are processed by convolution neural network (CNN), deep neural network (DNN), and stacked autoencoder (SAE). Then the advanced features obtained from the processing are input into the CapsuleNet for further feature learning. Compared with other state-of-the-art methods under 5-fold cross-validation, the performance of RPI-EDLCN is the best, and the accuracy of RPI-EDLCN on RPI1807, RPI2241, and NPInter v2.0 data sets was 93.8%, 88.2%, and 91.9%, respectively. The results of the independent test indicated that RPI-EDLCN can effectively predict potential ncRPIs in different organisms. In addition, RPI-EDLCN successfully predicted hub ncRNAs and proteins in Mus musculus ncRNA-protein networks. Overall, our model can be used as an effective tool to predict ncRPIs and provides some useful guidance for future biological studies.

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.

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.

Upstream of N-Ras C-terminal cold shock domains mediate poly(A) specificity in a novel RNA recognition mode and bind poly(A) binding protein

RNA binding proteins (RBPs) often engage multiple RNA binding domains (RBDs) to increase target specificity and affinity. However, the complexity of target recognition of multiple RBDs remains largely unexplored. Here we use Upstream of N-Ras (Unr), a multidomain RBP, to demonstrate how multiple RBDs orchestrate target specificity. A crystal structure of the three C-terminal RNA binding cold-shock domains (CSD) of Unr bound to a poly(A) sequence exemplifies how recognition goes beyond the classical ππ-stacking in CSDs. Further structural studies reveal several interaction surfaces between the N-terminal and C-terminal part of Unr with the poly(A)-binding protein (pAbp). All interactions are validated by mutational analyses and the high-resolution structures presented here will guide further studies to understand how both proteins act together in cellular processes.

Pab1 acetylation at K131 decreases stress granule formation in Saccharomyces cerevisiae

Under environmental stress, such as glucose deprivation, cells form stress granules-the accumulation of cytoplasmic aggregates of repressed translational initiation complexes, proteins, and stalled mRNAs. Recent research implicates stress granules in various diseases, such as neurodegenerative diseases, but the exact regulators responsible for the assembly and disassembly of stress granules are unknown. An important aspect of stress granule formation is the presence of posttranslational modifications on core proteins. One of those modifications is lysine acetylation, which is regulated by either a lysine acetyltransferase or a lysine deacetylase enzyme. This work deciphers the impact of lysine acetylation on an essential protein found in Saccharomyces cerevisiae stress granules, poly(A)-binding protein (Pab1). We demonstrated that an acetylation mimic of the lysine residue in position 131 reduces stress granule formation upon glucose deprivation and other stressors such as ethanol, raffinose, and vanillin. We present genetic evidence that the enzyme Rpd3 is the primary candidate for the deacetylation of Pab1-K131. Further, our electromobility shift assay studies suggest that the acetylation of Pab1-K131 negatively impacts poly(A) RNA binding. Due to the conserved nature of stress granules, therapeutics targeting the activity of lysine acetyltransferases and lysine deacetylase enzymes may be a promising route to modulate stress granule dynamics in the disease state.

Investigating RNA-protein recognition mechanisms through supervised molecular dynamics (SuMD) simulations

Ribonucleic acid (RNA) plays a key regulatory role within the cell, cooperating with proteins to control the genome expression and several biological processes. Due to its characteristic structural features, this polymer can mold itself into different three-dimensional structures able to recognize target biomolecules with high affinity and specificity, thereby attracting the interest of drug developers and medicinal chemists. One successful example of the exploitation of RNA's structural and functional peculiarities is represented by aptamers, a class of therapeutic and diagnostic tools that can recognize and tightly bind several pharmaceutically relevant targets, ranging from small molecules to proteins, making use of the available structural and conformational freedom to maximize the complementarity with their interacting counterparts. In this scientific work, we present the first application of Supervised Molecular Dynamics (SuMD), an enhanced sampling Molecular Dynamics-based method for the study of receptor-ligand association processes in the nanoseconds timescale, to the study of recognition pathways between RNA aptamers and proteins, elucidating the main advantages and limitations of the technique while discussing its possible role in the rational design of RNA-based therapeutics.

LION: an integrated R package for effective prediction of ncRNA-protein interaction

Understanding ncRNA-protein interaction is of critical importance to unveil ncRNAs' functions. Here, we propose an integrated package LION which comprises a new method for predicting ncRNA/lncRNA-protein interaction as well as a comprehensive strategy to meet the requirement of customisable prediction. Experimental results demonstrate that our method outperforms its competitors on multiple benchmark datasets. LION can also improve the performance of some widely used tools and build adaptable models for species- and tissue-specific prediction. We expect that LION will be a powerful and efficient tool for the prediction and analysis of ncRNA/lncRNA-protein interaction. The R Package LION is available on GitHub at https://github.com/HAN-Siyu/LION/.

Base-Stacking Heterogeneity in RNA Resolved by Fluorescence-Detected Circular Dichroism Spectroscopy

RNA plays a critical role in many biological processes, and the structures it adopts are intimately linked to those functions. Among many factors that contribute to RNA folding, van der Waals interactions between adjacent nucleobases stabilize structures in which the bases are stacked on top of one another. Here, we utilize fluorescence-detected circular dichroism spectroscopy (FDCD) to investigate base-stacking heterogeneity in RNA labeled with the fluorescent adenine analogue 2-aminopurine (2-AP). Comparison of standard (transmission-detected) CD and FDCD spectra reveals that in dinucleotides, 2-AP fluorescence is emitted almost exclusively by unstacked molecules. In a trinucleotide, some fluorescence is emitted by a population of stacked and highly quenched molecules, but more than half originates from a minor ∼10% population of unstacked molecules. The combination of FDCD and standard CD measurements reveals the prevalence of stacked and unstacked conformational subpopulations as well as their relative fluorescence quantum yields.

Common sequence motifs of nascent chains engage the ribosome surface and trigger factor

Proteins are produced by ribosomes in the cell, and during this process, can begin to adopt their biologically active forms assisted by molecular chaperones such as trigger factor. This fundamental cellular mechanism is crucial to maintaining a functional proteome and avoiding deleterious misfolding. Here, we study how disordered nascent chains emerge from the ribosome exit tunnel, and find that interactions with the ribosome surface dominate their dynamics in vitro and in vivo. Moreover, we show that the types of amino acids that mediate such interactions are also those that recruit trigger factor. This lays the foundation to describe how nascent chains are handed over from the ribosome surface to chaperones during biosynthesis within the crowded cytosol.

In the cell, the conformations of nascent polypeptide chains during translation are modulated by both the ribosome and its associated molecular chaperone, trigger factor. The specific interactions that underlie these modulations, however, are still not known in detail. Here, we combine protein engineering, in-cell and in vitro NMR spectroscopy, and molecular dynamics simulations to explore how proteins interact with the ribosome during their biosynthesis before folding occurs. Our observations of α-synuclein nascent chains in living Escherichia coli cells reveal that ribosome surface interactions dictate the dynamics of emerging disordered polypeptides in the crowded cytosol. We show that specific basic and aromatic motifs drive such interactions and directly compete with trigger factor binding while biasing the direction of the nascent chain during its exit out of the tunnel. These results reveal a structural basis for the functional role of the ribosome as a scaffold with holdase characteristics and explain how handover of the nascent chain to specific auxiliary proteins occurs among a host of other factors in the cytosol.

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.

LGFC-CNN: Prediction of lncRNA-Protein Interactions by Using Multiple Types of Features through Deep Learning

Long noncoding RNA (lncRNA) plays a crucial role in many critical biological processes and participates in complex human diseases through interaction with proteins. Considering that identifying lncRNA–protein interactions through experimental methods is expensive and time-consuming, we propose a novel method based on deep learning that combines raw sequence composition features, hand-designed features and structure features, called LGFC-CNN, to predict lncRNA–protein interactions. The two sequence preprocessing methods and CNN modules (GloCNN and LocCNN) are utilized to extract the raw sequence global and local features. Meanwhile, we select hand-designed features by comparing the predictive effect of different lncRNA and protein features combinations. Furthermore, we obtain the structure features and unifying the dimensions through Fourier transform. In the end, the four types of features are integrated to comprehensively predict the lncRNA–protein interactions. Compared with other state-of-the-art methods on three lncRNA–protein interaction datasets, LGFC-CNN achieves the best performance with an accuracy of 94.14%, on RPI21850; an accuracy of 92.94%, on RPI7317; and an accuracy of 98.19% on RPI1847. The results show that our LGFC-CNN can effectively predict the lncRNA–protein interactions by combining raw sequence composition features, hand-designed features and structure features.

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.

Capsule-LPI: a LncRNA-protein interaction predicting tool based on a capsule network

BACKGROUND

Long noncoding RNAs (lncRNAs) play important roles in multiple biological processes. Identifying LncRNA-protein interactions (LPIs) is key to understanding lncRNA functions. Although some LPIs computational methods have been developed, the LPIs prediction problem remains challenging. How to integrate multimodal features from more perspectives and build deep learning architectures with better recognition performance have always been the focus of research on LPIs.

RESULTS

We present a novel multichannel capsule network framework to integrate multimodal features for LPI prediction, Capsule-LPI. Capsule-LPI integrates four groups of multimodal features, including sequence features, motif information, physicochemical properties and secondary structure features. Capsule-LPI is composed of four feature-learning subnetworks and one capsule subnetwork. Through comprehensive experimental comparisons and evaluations, we demonstrate that both multimodal features and the architecture of the multichannel capsule network can significantly improve the performance of LPI prediction. The experimental results show that Capsule-LPI performs better than the existing state-of-the-art tools. The precision of Capsule-LPI is 87.3%, which represents a 1.7% improvement. The F-value of Capsule-LPI is 92.2%, which represents a 1.4% improvement.

CONCLUSIONS

This study provides a novel and feasible LPI prediction tool based on the integration of multimodal features and a capsule network. A webserver ( http://csbg-jlu.site/lpc/predict ) is developed to be convenient for users.

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.

Computational design of SARS-CoV-2 spike glycoproteins to increase immunogenicity by T cell epitope engineering

The development of effective and safe vaccines is the ultimate way to efficiently stop the ongoing COVID-19 pandemic, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Built on the fact that SARS-CoV-2 utilizes the association of its Spike (S) protein with the human angiotensin-converting enzyme 2 (ACE2) receptor to invade host cells, we computationally redesigned the S protein sequence to improve its immunogenicity and antigenicity. Toward this purpose, we extended an evolutionary protein design algorithm, EvoDesign, to create thousands of stable S protein variants that perturb the core protein sequence but keep the surface conformation and B cell epitopes. The T cell epitope content and similarity scores of the perturbed sequences were calculated and evaluated. Out of 22,914 designs with favorable stability energy, 301 candidates contained at least two pre-existing immunity-related epitopes and had promising immunogenic potential. The benchmark tests showed that, although the epitope restraints were not included in the scoring function of EvoDesign, the top S protein design successfully recovered 31 out of the 32 major histocompatibility complex (MHC)-II T cell promiscuous epitopes in the native S protein, where two epitopes were present in all seven human coronaviruses. Moreover, the newly designed S protein introduced nine new MHC-II T cell promiscuous epitopes that do not exist in the wildtype SARS-CoV-2. These results demonstrated a new and effective avenue to enhance a target protein's immunogenicity using rational protein design, which could be applied for new vaccine design against COVID-19 and other pathogens.

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.

Recent advances in optical aptasensor technology for amplification strategies in cancer diagnostics

Aptamers are chemically synthetic single-stranded DNA or RNA molecules selected by molecular evolution. They have been widely used as attractive tools in biosensing and bioimaging because they can bind to a large variety of targets with high sensitivity and high affinity and specificity. As recognition elements, aptamers contribute in particular to cancer diagnostics by recognizing different cancer biomarkers, while they can also facilitate ultrasensitive detection by further employing signal amplification elements. Optical techniques have been widely used for direct and real-time monitoring of cancer-related biomolecules and bioprocesses due to the high sensitivity, quick response, and simple operation, which has greatly benefited cancer diagnostics. In this review, we highlight recent advances in optical platform-based sensing strategies for cancer diagnostics aided by aptamers. Limitations and current challenges are also discussed.

ppiGReMLIN: a graph mining based detection of conserved structural arrangements in protein-protein interfaces

Background

Protein-protein interactions (PPIs) are fundamental in many biological processes and understanding these interactions is key for a myriad of applications including drug development, peptide design and identification of drug targets. The biological data deluge demands efficient and scalable methods to characterize and understand protein-protein interfaces. In this paper, we present ppiGReMLIN, a graph based strategy to infer interaction patterns in a set of protein-protein complexes. Our method combines an unsupervised learning strategy with frequent subgraph mining in order to detect conserved structural arrangements (patterns) based on the physicochemical properties of atoms on protein interfaces. To assess the ability of ppiGReMLIN to point out relevant conserved substructures on protein-protein interfaces, we compared our results to experimentally determined patterns that are key for protein-protein interactions in 2 datasets of complexes, Serine-protease and BCL-2.

Results

ppiGReMLIN was able to detect, in an automatic fashion, conserved structural arrangements that represent highly conserved interactions at the specificity binding pocket of trypsin and trypsin-like proteins from Serine-protease dataset. Also, for the BCL-2 dataset, our method pointed out conserved arrangements that include critical residue interactions within the conserved motif LXXXXD, pivotal to the binding specificity of BH3 domains of pro-apoptotic BCL-2 proteins towards apoptotic suppressors. Quantitatively, ppiGReMLIN was able to find all of the most relevant residues described in literature for our datasets, showing precision of at least 69% up to 100% and recall of 100%.

Conclusions

ppiGReMLIN was able to find highly conserved structures on the interfaces of protein-protein complexes, with minimum support value of 60%, in datasets of similar proteins. We showed that the patterns automatically detected on protein interfaces by our method are in agreement with interaction patterns described in the literature.

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.

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.

Interaction of aspartic acid and asparagine with RNA nucleobases: a quantum chemical view

Communicated by Ramaswamy H. Sarma.

d-/l-Isothymidine incorporation in the core sequence of aptamer BC15 enhanced its binding affinity to the hnRNP A1 protein

The heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) was reported to participate in the development of a variety of tumors. BC15 is a DNA aptamer targeting hnRNP A1. Firstly, through sequence truncation, we identified 31-mer sequence BC15-31 as the core sequence of BC15 with a strong binding affinity and high selectivity to the hnRNP A1 protein. Isothymidine (isoT) modification was then applied for the structural optimization of BC15-31, systematic modification and biological evaluation were carried out. Incorporation of isoT in the 1,3 sites at the 5'-end of BC15-31 can significantly enhance the protein affinity. Chemical modifications close to the 3'-end can greatly improve the stability of the aptamer. Furthermore, BC15-31 modified with isoT at both the 5'-end and 3'-end displayed an additive effect with enhanced bioactivity and stability at the same time. Our study strategy on BC15 provides a useful guideline for chemical modification and optimization of the aptamer for further clinical application.

Assembly and cryo-EM structures of RNA-specific measles virus nucleocapsids provide mechanistic insight into paramyxoviral replication

Assembly of paramyxoviral nucleocapsids on the RNA genome is an essential step in the viral cycle. The structural basis of this process has remained obscure due to the inability to control encapsidation. We used a recently developed approach to assemble measles virus nucleocapsid-like particles on specific sequences of RNA hexamers (poly-Adenine and viral genomic 5') in vitro, and determined their cryoelectron microscopy maps to 3.3-Å resolution. The structures unambiguously determine 5' and 3' binding sites and thereby the binding-register of viral genomic RNA within nucleocapsids. This observation reveals that the 3' end of the genome is largely exposed in fully assembled measles nucleocapsids. In particular, the final three nucleotides of the genome are rendered accessible to the RNA-dependent RNA polymerase complex, possibly enabling efficient RNA processing. The structures also reveal local and global conformational changes in the nucleoprotein upon assembly, in particular involving helix α6 and helix α13 that form edges of the RNA binding groove. Disorder is observed in the bound RNA, localized at one of the two backbone conformational switch sites. The high-resolution structure allowed us to identify putative nucleobase interaction sites in the RNA-binding groove, whose impact on assembly kinetics was measured using real-time NMR. Mutation of one of these sites, R195, whose sidechain stabilizes both backbone and base of a bound nucleic acid, is thereby shown to be essential for nucleocapsid-like particle assembly.

Partner-specific prediction of RNA-binding residues in proteins: A critical assessment

RNA-protein interactions play essential roles in regulating gene expression. While some RNA-protein interactions are "specific", that is, the RNA-binding proteins preferentially bind to particular RNA sequence or structural motifs, others are "non-RNA specific." Deciphering the protein-RNA recognition code is essential for comprehending the functional implications of these interactions and for developing new therapies for many diseases. Because of the high cost of experimental determination of protein-RNA interfaces, there is a need for computational methods to identify RNA-binding residues in proteins. While most of the existing computational methods for predicting RNA-binding residues in RNA-binding proteins are oblivious to the characteristics of the partner RNA, there is growing interest in methods for partner-specific prediction of RNA binding sites in proteins. In this work, we assess the performance of two recently published partner-specific protein-RNA interface prediction tools, PS-PRIP, and PRIdictor, along with our own new tools. Specifically, we introduce a novel metric, RNA-specificity metric (RSM), for quantifying the RNA-specificity of the RNA binding residues predicted by such tools. Our results show that the RNA-binding residues predicted by previously published methods are oblivious to the characteristics of the putative RNA binding partner. Moreover, when evaluated using partner-agnostic metrics, RNA partner-specific methods are outperformed by the state-of-the-art partner-agnostic methods. We conjecture that either (a) the protein-RNA complexes in PDB are not representative of the protein-RNA interactions in nature, or (b) the current methods for partner-specific prediction of RNA-binding residues in proteins fail to account for the differences in RNA partner-specific versus partner-agnostic protein-RNA interactions, or both.

Amino Acid Stabilization of Nucleic Acid Secondary Structure: Kinetic Insights from Single-Molecule Studies

Amino acid and nucleic acid interactions are central in biology and may have played a role in the evolutionary development of protein-based life from an early "RNA Universe." To explore the possible role of single amino acids in promoting nucleic acid folding, single-molecule Förster resonance energy transfer experiments have been implemented with a DNA hairpin construct (7 nucleotide double strand with a 40A loop) as a simple model for secondary structure formation. Exposure to positively charged amino acids (arginine and lysine) is found to clearly stabilize the secondary structure. Kinetically, each amino acid promotes folding by generating a large increase in the folding rate with little change in the unfolding rate. From analysis as a function of temperature, arginine and lysine are found to significantly increase the overall exothermicity of folding while imposing only a small entropic penalty on the folding process. Detailed investigations into the kinetics and thermodynamics of this amino acid-induced folding stability reveal arginine and lysine to interact with nucleic acids in a manner reminiscent of monovalent cations. Specifically, these observations are interpreted in the context of an ion atmosphere surrounding the nucleic acid, in which amino acid salts stabilize folding qualitatively like small monovalent cations but also exhibit differences because of the composition of their side chains.

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.

Structural basis for guanidine sensing by the ykkC family of riboswitches

Regulation of gene expression by cis-encoded riboswitches is a prevalent theme in bacteria. Of the hundreds of riboswitch families identified, the majority of them remain as orphans, without a clear ligand assignment. The ykkC orphan family was recently characterized as guanidine-sensing riboswitches. Herein we present a 2.3 Å crystal structure of the guanidine-bound ykkC riboswitch from Dickeya dadantii The riboswitch folds into a boot-shaped structure, with a coaxially stacked P1/P2 stem forming the boot, and a 3'-P3 stem-loop forming the heel. Sophisticated base-pairing and cross-helix tertiary contacts give rise to the ligand-binding pocket between the boot and the heel. The guanidine is recognized in its positively charged guanidinium form, in its sp2 hybridization state, through a network of coplanar hydrogen bonds and by a cation-π stacking contact on top of a conserved guanosine residue. Disruption of these contacts resulted in severe guanidinium-binding defects. These results provide the structural basis for specific guanidine sensing by ykkC riboswitches and pave the way for a deeper understanding of guanidine detoxification-a previously unappreciated aspect of bacterial physiology.

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.

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 ].

Toehold-Mediated Displacement of an Adenosine-Binding Aptamer from a DNA Duplex by its Ligand

DNA is increasingly used to engineer dynamic nanoscale circuits, structures, and motors, many of which rely on DNA strand-displacement reactions. The use of functional DNA sequences (e.g., aptamers, which bind to a wide range of ligands) in these reactions would potentially confer responsiveness on such devices, and integrate DNA computation with highly varied molecular stimuli. By using high-throughput single-molecule FRET methods, we compared the kinetics of a putative aptamer-ligand and aptamer-complement strand-displacement reaction. We found that the ligands actively disrupted the DNA duplex in the presence of a DNA toehold in a similar manner to complementary DNA, with kinetic details specific to the aptamer structure, thus suggesting that the DNA strand-displacement concept can be extended to functional DNA-ligand systems.

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

BACKGROUND

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

RESULTS

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

CONCLUSIONS

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

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

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

Fragment-based modelling of single stranded RNA bound to RNA recognition motif containing proteins

Protein-RNA complexes are important for many biological processes. However, structural modeling of such complexes is hampered by the high flexibility of RNA. Particularly challenging is the docking of single-stranded RNA (ssRNA). We have developed a fragment-based approach to model the structure of ssRNA bound to a protein, based on only the protein structure, the RNA sequence and conserved contacts. The conformational diversity of each RNA fragment is sampled by an exhaustive library of trinucleotides extracted from all known experimental protein–RNA complexes. The method was applied to ssRNA with up to 12 nucleotides which bind to dimers of the RNA recognition motifs (RRMs), a highly abundant eukaryotic RNA-binding domain. The fragment based docking allows a precise de novo atomic modeling of protein-bound ssRNA chains. On a benchmark of seven experimental ssRNA–RRM complexes, near-native models (with a mean heavy-atom deviation of <3 Å from experiment) were generated for six out of seven bound RNA chains, and even more precise models (deviation < 2 Å) were obtained for five out of seven cases, a significant improvement compared to the state of the art. The method is not restricted to RRMs but was also successfully applied to Pumilio RNA binding proteins.

Assessing a peptidylic inhibitor-based therapeutic approach that simultaneously suppresses polyglutamine RNA- and protein-mediated toxicities in patient cells and Drosophila

Polyglutamine (polyQ) diseases represent a group of progressive neurodegenerative disorders that are caused by abnormal expansion of CAG triplet nucleotides in disease genes. Recent evidence indicates that not only mutant polyQ proteins, but also their corresponding mutant RNAs, contribute to the pathogenesis of polyQ diseases. Here, we describe the identification of a 13-amino-acid peptide, P3, which binds directly and preferentially to long-CAG RNA within the pathogenic range. When administered to cell and Drosophila disease models, as well as to patient-derived fibroblasts, P3 inhibited expanded-CAG-RNA-induced nucleolar stress and suppressed neurotoxicity. We further examined the combined therapeutic effect of P3 and polyQ-binding peptide 1 (QBP1), a well-characterized polyQ protein toxicity inhibitor, on neurodegeneration. When P3 and QBP1 were co-administered to disease models, both RNA and protein toxicities were effectively mitigated, resulting in a notable improvement of neurotoxicity suppression compared with the P3 and QBP1 single-treatment controls. Our findings indicate that targeting toxic RNAs and/or simultaneous targeting of toxic RNAs and their corresponding proteins could open up a new therapeutic strategy for treating polyQ degeneration.

Phase transition of a disordered nuage protein generates environmentally responsive membraneless organelles

Cells chemically isolate molecules in compartments to both facilitate and regulate their interactions. In addition to membrane-encapsulated compartments, cells can form proteinaceous and membraneless organelles, including nucleoli, Cajal and PML bodies, and stress granules. The principles that determine when and why these structures form have remained elusive. Here, we demonstrate that the disordered tails of Ddx4, a primary constituent of nuage or germ granules, form phase-separated organelles both in live cells and in vitro. These bodies are stabilized by patterned electrostatic interactions that are highly sensitive to temperature, ionic strength, arginine methylation, and splicing. Sequence determinants are used to identify proteins found in both membraneless organelles and cell adhesion. Moreover, the bodies provide an alternative solvent environment that can concentrate single-stranded DNA but largely exclude double-stranded DNA. We propose that phase separation of disordered proteins containing weakly interacting blocks is a general mechanism for forming regulated, membraneless organelles.

•

Intrinsically disordered N terminus of Ddx4 forms organelles in cells and in vitro

•

Phase transition to form organelles is driven by electrostatic interactions

•

Methylation, ionic strength, and temperature changes can dissolve the organelles

•

Sequence determinants of formation are common in membraneless organelle proteins