Protein-RNA interactions: a structural analysis

Electrostatic modulation of multiple binding events between loquacious-PD and double-stranded RNA

Electrostatics can alter the RNA-binding properties of proteins that display structure selectivity without sequence specificity. Loquacious-PD relies on this broad scope response to mediate the interaction of endonucleases with double stranded RNAs. Multimodal spectroscopic probes with in situ perturbations reveal an efficient and stable binding mechanism that disfavors high protein density complexes and is sensitive to local electrostatics.

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

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

Graphene Multiplexed Sensor for Point-of-Need Viral Wastewater-Based Epidemiology

Wastewater-based epidemiology (WBE) can help mitigate the spread of respiratory infections through the early detection of viruses, pathogens, and other biomarkers in human waste. The need for sample collection, shipping, and testing facilities drives up the cost of WBE and hinders its use for rapid detection and isolation in environments with small populations and in low-resource settings. Given the ubiquitousness and regular outbreaks of respiratory syncytial virus, SARS-CoV-2, and various influenza strains, there is a rising need for a low-cost and easy-to-use biosensing platform to detect these viruses locally before outbreaks can occur and monitor their progression. To this end, we have developed an easy-to-use, cost-effective, multiplexed platform able to detect viral loads in wastewater with several orders of magnitude lower limit of detection than that of mass spectrometry. This is enabled by wafer-scale production and aptamers preattached with linker molecules, producing 44 chips at once. Each chip can simultaneously detect four target analytes using 20 transistors segregated into four sets of five for each analyte to allow for immediate statistical analysis. We show our platform's ability to rapidly detect three virus proteins (SARS-CoV-2, RSV, and Influenza A) and a population normalization molecule (caffeine) in wastewater. Going forward, turning these devices into hand-held systems would enable wastewater epidemiology in low-resource settings and be instrumental for rapid, local outbreak prevention.

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

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

PRMT5-mediated arginine methylation of FXR1 is essential for RNA binding in cancer cells

Emerging evidence indicates that arginine methylation promotes the stability of arginine-glycine-rich (RGG) motif-containing RNA-binding proteins (RBPs) and regulates gene expression. Here, we report that post-translational modification of FXR1 enhances the binding with mRNAs and is involved in cancer cell growth and proliferation. Independent point mutations in arginine residues of FXR1's nuclear export signal (R386 and R388) and RGG (R453, R455 and R459) domains prevent it from binding to RNAs that form G-quadruplex (G4) RNA structures. Disruption of G4-RNA structures by lithium chloride failed to bind with FXR1, indicating its preference for G4-RNA structure containing mRNAs. Furthermore, loss-of-function of PRMT5 inhibited FXR1 methylation both in vivo and in vitro, affecting FXR1 protein stability, inhibiting RNA-binding activity and cancer cell growth and proliferation. Finally, the enhanced crosslinking and immunoprecipitation (eCLIP) analyses reveal that FXR1 binds with the G4-enriched mRNA targets such as AHNAK, MAP1B, AHNAK2, HUWE1, DYNC1H1 and UBR4 and controls its mRNA expression in cancer cells. Our findings suggest that PRMT5-mediated FXR1 methylation is required for RNA/G4-RNA binding, which promotes gene expression in cancer cells. Thus, FXR1's structural characteristics and affinity for RNAs preferentially G4 regions provide new insights into the molecular mechanism of FXR1 in oral cancer cells.

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

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

Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.

MucLiPred: Multi-Level Contrastive Learning for Predicting Nucleic Acid Binding Residues of Proteins

Protein-molecule interactions play a crucial role in various biological functions, with their accurate prediction being pivotal for drug discovery and design processes. Traditional methods for predicting protein-molecule interactions are limited. Some can only predict interactions with a specific molecule, restricting their applicability, while others target multiple molecule types but fail to efficiently process diverse interaction information, leading to complexity and inefficiency. This study presents a novel deep learning model, MucLiPred, equipped with a dual contrastive learning mechanism aimed at improving the prediction of multiple molecule-protein interactions and the identification of potential molecule-binding residues. The residue-level paradigm focuses on differentiating binding from non-binding residues, illuminating detailed local interactions. The type-level paradigm, meanwhile, analyzes overarching contexts of molecule types, like DNA or RNA, ensuring that representations of identical molecule types gravitate closer in the representational space, bolstering the model's proficiency in discerning interaction motifs. This dual approach enables comprehensive multi-molecule predictions, elucidating the relationships among different molecule types and strengthening precise protein-molecule interaction predictions. Empirical evidence demonstrates MucLiPred's superiority over existing models in robustness and prediction accuracy. The integration of dual contrastive learning techniques amplifies its capability to detect potential molecule-binding residues with precision. Further optimization, separating representational and classification tasks, has markedly improved its performance. MucLiPred thus represents a significant advancement in protein-molecule interaction prediction, setting a new precedent for future research in this field.

PRA-Pred: Structure-based prediction of protein-RNA binding affinity

Understanding crucial factors that affect the binding affinity of protein-RNA complexes is vital for comprehending their recognition mechanisms. This study involved compiling experimentally measured binding affinity (ΔG) values of 217 protein-RNA complexes and extracting numerous structure-based features, considering RNA, protein, and interactions between protein and RNA. Our findings indicate the significance of RNA base-step parameters, interaction energies, number of atomic contacts in the complex, hydrogen bonds, and contact potentials in understanding the binding affinity. Further, we observed that these factors are influenced by the type of RNA strand and the function of the protein in a protein-RNA complex. Multiple regression equations were developed for different classes of complexes to perform the prediction of the binding affinity between the protein and RNA. We evaluated the models using the jack-knife test and achieved an overall correlation 0.77 between the experimental and predicted binding affinities with a mean absolute error of 1.02 kcal/mol. Furthermore, we introduced a web server, PRA-Pred, intended for the prediction of protein-RNA binding affinity, and it is freely accessible through https://web.iitm.ac.in/bioinfo2/prapred/. We propose that our approach could function as a potential resource for investigating protein-RNA recognitions and developing therapeutic strategies.

Structural Insights into Protein-Aptamer Recognitions Emerged from Experimental and Computational Studies

Aptamers are synthetic nucleic acids that are developed to target with high affinity and specificity chemical entities ranging from single ions to macromolecules and present a wide range of chemical and physical properties. Their ability to selectively bind proteins has made these compounds very attractive and versatile tools, in both basic and applied sciences, to such an extent that they are considered an appealing alternative to antibodies. Here, by exhaustively surveying the content of the Protein Data Bank (PDB), we review the structural aspects of the protein-aptamer recognition process. As a result of three decades of structural studies, we identified 144 PDB entries containing atomic-level information on protein-aptamer complexes. Interestingly, we found a remarkable increase in the number of determined structures in the last two years as a consequence of the effective application of the cryo-electron microscopy technique to these systems. In the present paper, particular attention is devoted to the articulated architectures that protein-aptamer complexes may exhibit. Moreover, the molecular mechanism of the binding process was analyzed by collecting all available information on the structural transitions that aptamers undergo, from their protein-unbound to the protein-bound state. The contribution of computational approaches in this area is also highlighted.

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.

Dynamically regulated two-site interaction of viral RNA to capture host translation initiation factor

Many RNA viruses employ internal ribosome entry sites (IRESs) in their genomic RNA to commandeer the host's translational machinery for replication. The IRES from encephalomyocarditis virus (EMCV) interacts with eukaryotic translation initiation factor 4 G (eIF4G), recruiting the ribosomal subunit for translation. Here, we analyze the three-dimensional structure of the complex composed of EMCV IRES, the HEAT1 domain fragment of eIF4G, and eIF4A, by cryo-electron microscopy. Two distinct eIF4G-interacting domains on the IRES are identified, and complex formation changes the angle therebetween. Further, we explore the dynamics of these domains by using solution NMR spectroscopy, revealing conformational equilibria in the microsecond to millisecond timescale. In the lowly-populated conformations, the base-pairing register of one domain is shifted with the structural transition of the three-way junction, as in the complex structure. Our study provides insights into the viral RNA's sophisticated strategy for optimal docking to hijack the host protein.

Targeting RNA-binding proteins with small molecules: Perspectives, pitfalls and bifunctional molecules

RNA-binding proteins (RBPs) play vital roles in organisms through binding with RNAs to regulate their functions. Small molecules affecting the function of RBPs have been developed, providing new avenues for drug discovery. Herein, we describe the perspectives on developing small molecule regulators of RBPs. The following types of small molecule modulators are of great interest in drug discovery: small molecules binding to RBPs to affect interactions with RNA molecules, bifunctional molecules binding to RNA or RBP to influence their interactions, and other types of molecules that affect the stability of RNA or RBPs. Moreover, we emphasize that the bifunctional molecules may play important roles in small molecule development to overcome the challenges encountered in the process of drug discovery.

Broad anti-pathogen potential of DEAD box RNA helicase eIF4A-targeting rocaglates

Inhibition of eukaryotic initiation factor 4A has been proposed as a strategy to fight pathogens. Rocaglates exhibit the highest specificities among eIF4A inhibitors, but their anti-pathogenic potential has not been comprehensively assessed across eukaryotes. In silico analysis of the substitution patterns of six eIF4A1 aa residues critical to rocaglate binding, uncovered 35 variants. Molecular docking of eIF4A:RNA:rocaglate complexes, and in vitro thermal shift assays with select recombinantly expressed eIF4A variants, revealed that sensitivity correlated with low inferred binding energies and high melting temperature shifts. In vitro testing with silvestrol validated predicted resistance in Caenorhabditis elegans and Leishmania amazonensis and predicted sensitivity in Aedes sp., Schistosoma mansoni, Trypanosoma brucei, Plasmodium falciparum, and Toxoplasma gondii. Our analysis further revealed the possibility of targeting important insect, plant, animal, and human pathogens with rocaglates. Finally, our findings might help design novel synthetic rocaglate derivatives or alternative eIF4A inhibitors to fight pathogens.

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

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

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

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

Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile

This paper describes a method Pprint2, which is an improved version of Pprint developed for predicting RNA-interacting residues in a protein. Training and independent/validation datasets used in this study comprises of 545 and 161 non-redundant RNA-binding proteins, respectively. All models were trained on training dataset and evaluated on the validation dataset. The preliminary analysis reveals that positively charged amino acids such as H, R and K, are more prominent in the RNA-interacting residues. Initially, machine learning based models have been developed using binary profile and obtain maximum area under curve (AUC) 0.68 on validation dataset. The performance of this model improved significantly from AUC 0.68 to 0.76, when evolutionary profile is used instead of binary profile. The performance of our evolutionary profile-based model improved further from AUC 0.76 to 0.82, when convolutional neural network has been used for developing model. Our final model based on convolutional neural network using evolutionary information achieved AUC 0.82 with Matthews correlation coefficient of 0.49 on the validation dataset. Our best model outperforms existing methods when evaluated on the independent/validation dataset. A user-friendly standalone software and web-based server named 'Pprint2' has been developed for predicting RNA-interacting residues (https://webs.iiitd.edu.in/raghava/pprint2 and https://github.com/raghavagps/pprint2).

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

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

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.

Emergence of Compensatory Mutations Reveals the Importance of Electrostatic Interactions between HIV-1 Integrase and Genomic RNA

HIV-1 integrase (IN) has a noncatalytic function in virion maturation through its binding to the viral RNA genome (gRNA). Class II IN substitutions inhibit IN-gRNA binding and result in the formation of virions with aberrant morphologies marked by mislocalization of the gRNA between the capsid lattice and the lipid envelope. These viruses are noninfectious due to a block at an early reverse transcription stage in target cells. HIV-1 IN utilizes basic residues within its C-terminal domain (CTD) to bind to the gRNA; however, the molecular nature of how these residues mediate gRNA binding and whether other regions of IN are involved remain unknown. To address this, we have isolated compensatory substitutions in the background of a class II IN mutant virus bearing R269A/K273A substitutions within the IN-CTD. We found that the nearby D256N and D270N compensatory substitutions restored the ability of IN to bind gRNA and led to the formation of mature infectious virions. Reinstating the local positive charge of the IN-CTD through individual D256R, D256K, D278R, and D279R substitutions was sufficient to specifically restore IN-gRNA binding and reverse transcription for the IN R269A/K273A as well as the IN R262A/R263A class II mutants. Structural modeling suggested that compensatory substitutions in the D256 residue created an additional interaction interface for gRNA binding, whereas other substitutions acted locally within the unstructured C-terminal tail of IN. Taken together, our findings highlight the essential role of CTD in gRNA binding and reveal the importance of pliable electrostatic interactions between the IN-CTD and the gRNA. IMPORTANCE In addition to its catalytic function, HIV-1 integrase (IN) binds to the viral RNA genome (gRNA) through positively charged residues (i.e., R262, R263, R269, K273) within its C-terminal domain (CTD) and regulates proper virion maturation. Mutation of these residues results in the formation of morphologically aberrant viruses blocked at an early reverse transcription stage in cells. Here we show that compensatory substitutions in nearby negatively charged aspartic acid residues (i.e., D256N, D270N) restore the ability of IN to bind gRNA for these mutant viruses and result in the formation of accurately matured infectious virions. Similarly, individual charge reversal substitutions at D256 as well as other nearby positions (i.e., D278, D279) are all sufficient to enable the respective IN mutants to bind gRNA, and subsequently restore reverse transcription and virion infectivity. Taken together, our findings reveal the importance of highly pliable electrostatic interactions in IN-gRNA binding.

SELEX: Critical factors and optimization strategies for successful aptamer selection

Within the last decade, the application range of aptamers in biochemistry and medicine has expanded rapidly. More than just a replacement for antibodies, these intrinsically structured RNA- or DNA-oligonucleotides show great potential for utilization in diagnostics, specific drug delivery, and treatment of certain medical conditions. However, what is analyzed less frequently is the process of aptamer identification known as systematic evolution of ligands by exponential enrichment (SELEX) and the functional mechanisms that lie at its core. SELEX involves numerous singular processes, each of which contributes to the success or failure of aptamer generation. In this review, critical steps during aptamer selection are discussed in-depth, and specific problems are presented along with potential solutions. The discussed aspects include the size and molecule type of the selected target, the nature and stringency of the selection process, the amplification step with its possible PCR bias, the efficient regeneration of RNA or single-stranded DNA, and the different sequencing procedures and screening assays currently available. Finally, useful quality control steps and their role within SELEX are presented. By understanding the mechanisms through which aptamer selection is influenced, the design of more efficient SELEX procedures leading to a higher success rate in aptamer identification is enabled.

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.

Role of Mutations in Differential Recognition of Viral RNA Molecules by Peptides

The conserved noncoding RNA elements in viral genomes interact with proteins to regulate various events during viral replication. We report studies on the recognition mechanisms of two helical peptides, namely, a native (Rev) peptide and a lab-evolved (RSG1.2) peptide, by a highly conserved viral RNA element from the human immunodeficiency virus 1 genome. Specifically, we investigated the physical interactions between the viral RNA molecule and helical peptides by computing free energy changes on mutating key amino acid residues involved in recognition of an internal loop in the viral RNA molecule.

TSNAPred: predicting type-specific nucleic acid binding residues via an ensemble approach

MOTIVATION

The interplay between protein and nucleic acid participates in diverse biological activities. Accurately identifying the interaction between protein and nucleic acid can strengthen the understanding of protein function. However, conventional methods are too time-consuming, and computational methods are type-agnostic predictions. We proposed an ensemble predictor termed TSNAPred and first used it to identify residues that bind to A-DNA, B-DNA, ssDNA, mRNA, tRNA and rRNA. TSNAPred combines LightGBM and capsule network, both learned on the feature derived from protein sequence. TSNAPred utilizes the sliding window technique to extract long-distance dependencies between residues and a weighted ensemble strategy to enhance the prediction performance. The results show that TSNAPred can effectively identify type-specific nucleic acid binding residues in our test set. What is more, it also can discriminate DNA-binding and RNA-binding residues, which has improved 5% to 10% on the AUC value compared with other state-of-the-art methods. The dataset and code of TSNAPred are available at: https://github.com/niewenjuan-csu/TSNAPred.

The metaphorical swiss army knife: The multitude and diverse roles of HEAT domains in eukaryotic translation initiation

Biomolecular associations forged by specific interaction among structural scaffolds are fundamental to the control and regulation of cell processes. One such structural architecture, characterized by HEAT repeats, is involved in a multitude of cellular processes, including intracellular transport, signaling, and protein synthesis. Here, we review the multitude and versatility of HEAT domains in the regulation of mRNA translation initiation. Structural and cellular biology approaches, as well as several biophysical studies, have revealed that a number of HEAT domain-mediated interactions with a host of protein factors and RNAs coordinate translation initiation. We describe the basic structural architecture of HEAT domains and briefly introduce examples of the cellular processes they dictate, including nuclear transport by importin and RNA degradation. We then focus on proteins in the translation initiation system featuring HEAT domains, specifically the HEAT domains of eIF4G, DAP5, eIF5, and eIF2Bϵ. Comparative analysis of their remarkably versatile interactions, including protein–protein and protein–RNA recognition, reveal the functional importance of flexible regions within these HEAT domains. Here we outline how HEAT domains orchestrate fundamental aspects of translation initiation and highlight open mechanistic questions in the area.

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

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

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

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

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

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

Structural basis for tRNA methylthiolation by the radical SAM enzyme MiaB

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

Rapid structure-function insights via hairpin-centric analysis of big RNA structure probing datasets

The functions of RNA are often tied to its structure, hence analyzing structure is of significant interest when studying cellular processes. Recently, large-scale structure probing (SP) studies have enabled assessment of global structure-function relationships via standard data summarizations or local folding. Here, we approach structure quantification from a hairpin-centric perspective where putative hairpins are identified in SP datasets and used as a means to capture local structural effects. This has the advantage of rapid processing of big (e.g. transcriptome-wide) data as RNA folding is circumvented, yet it captures more information than simple data summarizations. We reformulate a statistical learning algorithm we previously developed to significantly improve precision of hairpin detection, then introduce a novel nucleotide-wise measure, termed the hairpin-derived structure level (HDSL), which captures local structuredness by accounting for the presence of likely hairpin elements. Applying HDSL to data from recent studies recapitulates, strengthens and expands on their findings which were obtained by more comprehensive folding algorithms, yet our analyses are orders of magnitude faster. These results demonstrate that hairpin detection is a promising avenue for global and rapid structure-function analysis, furthering our understanding of RNA biology and the principal features which drive biological insights from SP data.

Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins

MOTIVATION

Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account.

RESULTS

We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions.

AVAILABILITY AND IMPLEMENTATION

BMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The RNA structurome in the asexual blood stages of malaria pathogen plasmodium falciparum

Plasmodium falciparum is a deadly human pathogen responsible for the devastating disease called malaria. In this study, we measured the differential accumulation of RNA secondary structures in coding and non-coding transcripts from the asexual developmental cycle in P. falciparum in human red blood cells. Our comprehensive analysis that combined high-throughput nuclease mapping of RNA structures by duplex RNA-seq, SHAPE-directed RNA structure validation, immunoaffinity purification and characterization of antisense RNAs collectively measured differentially base-paired RNA regions throughout the parasite's asexual RBC cycle. Our mapping data not only aligned to a diverse pool of RNAs with known structures but also enabled us to identify new structural RNA regions in the malaria genome. On average, approximately 71% of the genes with secondary structures are found to be protein coding mRNAs. The mapping pattern of these base-paired RNAs corresponded to all regions of mRNAs, including the 5' UTR, CDS and 3' UTR as well as the start and stop codons. Histone family genes which are known to form secondary structures in their mRNAs and transcripts from genes which are important for transcriptional and post-transcriptional control, such as the unique plant-like transcription factor family, ApiAP2, DNA-/RNA-binding protein, Alba3 and proteins important for RBC invasion and malaria cytoadherence also showed strong accumulation of duplex RNA reads in various asexual stages in P. falciparum. Intriguingly, our study determined stage-specific, dynamic relationships between mRNA structural contents and translation efficiency in P. falciparum asexual blood stages, suggesting an essential role of RNA structural changes in malaria gene expression programs.

Measuring the Multiscale Dynamics, Structure, and Function of Biomolecules at Interfaces

The individual and collective structure and properties of biomolecules can change dramatically when they are localized at an interface. However, the small spatial extent of interfacial regions poses challenges to the detailed characterization of multiscale processes that dictate the structure and function of large biological units such as peptides, proteins, or nucleic acids. This Perspective surveys a broad set of tools that provide new opportunities to probe complex, dynamic interfaces across the vast range of temporal regimes that connect molecular-scale events to macroscopic observables. An emphasis is placed on the integration over multiple time scales, the use of complementary techniques, and the incorporation of external stimuli to control interfacial properties with spatial, temporal, and chemical specificity.

aPRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks

MOTIVATION

Protein-RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence-based ab-initio methods.

RESULTS

In this work, we propose an approach aPRBind, a convolutional neural network-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue-nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score≥0.5, and meanwhile since the structural features are not very sensitive to the refined 3D structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures.

AVAILABILITY AND IMPLEMENTATION

The source code is available at https://github.com/ChunhuaLiLab/aPRbind.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Role and Perspective of Molecular Simulation-Based Investigation of RNA-Ligand Interaction: From Small Molecules and Peptides to Photoswitchable RNA Binding

Aberrant RNA–protein complexes are formed in a variety of diseases. Identifying the ligands that interfere with their formation is a valuable therapeutic strategy. Molecular simulation, validated against experimental data, has recently emerged as a powerful tool to predict both the pose and energetics of such ligands. Thus, the use of molecular simulation may provide insight into aberrant molecular interactions in diseases and, from a drug design perspective, may allow for the employment of less wet lab resources than traditional in vitro compound screening approaches. With regard to basic research questions, molecular simulation can support the understanding of the exact molecular interaction and binding mode. Here, we focus on examples targeting RNA–protein complexes in neurodegenerative diseases and viral infections. These examples illustrate that the strategy is rather general and could be applied to different pharmacologically relevant approaches. We close this study by outlining one of these approaches, namely the light-controllable association of small molecules with RNA, as an emerging approach in RNA-targeting therapy.

A Comprehensive Analysis of the Role of hnRNP A1 Function and Dysfunction in the Pathogenesis of Neurodegenerative Disease

Heterogeneous nuclear ribonucleoprotein A1 (hnRNP A1) is a member of the hnRNP family of conserved proteins that is involved in RNA transcription, pre-mRNA splicing, mRNA transport, protein translation, microRNA processing, telomere maintenance and the regulation of transcription factor activity. HnRNP A1 is ubiquitously, yet differentially, expressed in many cell types, and due to post-translational modifications, can vary in its molecular function. While a plethora of knowledge is known about the function and dysfunction of hnRNP A1 in diseases other than neurodegenerative disease (e.g., cancer), numerous studies in amyotrophic lateral sclerosis, frontotemporal lobar degeneration, multiple sclerosis, spinal muscular atrophy, Alzheimer’s disease, and Huntington’s disease have found that the dysregulation of hnRNP A1 may contribute to disease pathogenesis. How hnRNP A1 mechanistically contributes to these diseases, and whether mutations and/or altered post-translational modifications contribute to pathogenesis, however, is currently under investigation. The aim of this comprehensive review is to first describe the background of hnRNP A1, including its structure, biological functions in RNA metabolism and the post-translational modifications known to modify its function. With this knowledge, the review then describes the influence of hnRNP A1 in neurodegenerative disease, and how its dysfunction may contribute the pathogenesis.

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

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

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

Simple Summary

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

Abstract

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

Dynamics of Ionic Interactions at Protein-Nucleic Acid Interfaces

Molecular association of proteins with nucleic acids is required for many biological processes essential to life. Electrostatic interactions via ion pairs (salt bridges) of nucleic acid phosphates and protein side chains are crucial for proteins to bind to DNA or RNA. Counterions around the macromolecules are also key constituents for the thermodynamics of protein–nucleic acid association. Until recently, there had been only a limited amount of experiment-based information about how ions and ionic moieties behave in biological macromolecular processes. In the past decade, there has been significant progress in quantitative experimental research on ionic interactions with nucleic acids and their complexes with proteins. The highly negatively charged surfaces of DNA and RNA electrostatically attract and condense cations, creating a zone called the ion atmosphere. Recent experimental studies were able to examine and validate theoretical models on ions and their mobility and interactions with macromolecules. The ionic interactions are highly dynamic. The counterions rapidly diffuse within the ion atmosphere. Some of the ions are released from the ion atmosphere when proteins bind to nucleic acids, balancing the charge via intermolecular ion pairs of positively charged side chains and negatively charged backbone phosphates. Previously, the release of counterions had been implicated indirectly by the salt-concentration dependence of the association constant.

Recently, direct detection of counterion release by NMR spectroscopy has become possible and enabled more accurate and quantitative analysis of the counterion release and its entropic impact on the thermodynamics of protein–nucleic acid association. Recent studies also revealed the dynamic nature of ion pairs of protein side chains and nucleic acid phosphates. These ion pairs undergo transitions between two major states. In one of the major states, the cation and the anion are in direct contact and form hydrogen bonds. In the other major state, the cation and the anion are separated by water. Transitions between these states rapidly occur on a picosecond to nanosecond time scale. When proteins interact with nucleic acids, interfacial arginine (Arg) and lysine (Lys) side chains exhibit considerably different behaviors. Arg side chains show a higher propensity to form rigid contacts with nucleotide bases, whereas Lys side chains tend to be more mobile at the molecular interfaces. The dynamic ionic interactions may facilitate adaptive molecular recognition and play both thermodynamic and kinetic roles in protein–nucleic acid interactions.

Racemic phosphorothioate as a tool for NMR investigations of protein-DNA complexes

A major driving force for protein-nucleic acid association is electrostatic interactions via ion pairs of the positively charged basic side chains and negatively charged phosphates. For a better understanding of how proteins scan DNA and recognize particular signatures, it is important to gain atomic-level insight into the behavior of basic side chains at the protein-DNA interfaces. NMR spectroscopy is a powerful tool for investigating the structural, dynamic, and kinetic aspects of protein-DNA interactions. However, resonance assignment of basic side-chain cationic moieties at the molecular interfaces remains to be a major challenge. Here, we propose a fast, robust, and inexpensive approach that greatly facilitates resonance assignment of interfacial moieties and also allows for kinetic measurements of protein translocation between two DNA duplexes. This approach utilizes site-specific incorporation of racemic phosphorothioate at the position of a phosphate that interacts with a protein side chain. This modification retains the electric charge of phosphate and therefore is mild, but causes significant chemical shift perturbations for the proximal protein side chains, which facilitates resonance assignment. Due to the racemic nature of the modification, two different chemical shifts are observed for the species with different diastereomers R_P and S_P of the incorporated phosphorothioate group. Kinetic information on the exchange of the protein molecule between R_P and S_P DNA duplexes can be obtained by ¹⁵N_z exchange spectroscopy. We demonstrate the applications of this approach to the Antennapedia homeodomain-DNA complex and the CREB1 basic leucine-zipper (bZIP)-DNA complex.

PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions

Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol⁻¹, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.

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.

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.

Machine Learning Approaches for Quality Assessment of Protein Structures

Protein structures play a very important role in biomedical research, especially in drug discovery and design, which require accurate protein structures in advance. However, experimental determinations of protein structure are prohibitively costly and time-consuming, and computational predictions of protein structures have not been perfected. Methods that assess the quality of protein models can help in selecting the most accurate candidates for further work. Driven by this demand, many structural bioinformatics laboratories have developed methods for estimating model accuracy (EMA). In recent years, EMA by machine learning (ML) have consistently ranked among the top-performing methods in the community-wide CASP challenge. Accordingly, we systematically review all the major ML-based EMA methods developed within the past ten years. The methods are grouped by their employed ML approach-support vector machine, artificial neural networks, ensemble learning, or Bayesian learning-and their significances are discussed from a methodology viewpoint. To orient the reader, we also briefly describe the background of EMA, including the CASP challenge and its evaluation metrics, and introduce the major ML/DL techniques. Overall, this review provides an introductory guide to modern research on protein quality assessment and directions for future research in this area.

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.