Protein-RNA interactions: structural biology and computational modeling techniques

Structural comparison of homologous protein-RNA interfaces reveals widespread overall conservation contrasted with versatility in polar contacts

Protein-RNA interactions play a critical role in many cellular processes and pathologies. However, experimental determination of protein-RNA structures is still challenging, therefore computational tools are needed for the prediction of protein-RNA interfaces. Although evolutionary pressures can be exploited for structural prediction of protein-protein interfaces, and recent deep learning methods using protein multiple sequence alignments have radically improved the performance of protein-protein interface structural prediction, protein-RNA structural prediction is lagging behind, due to the scarcity of structural data and the flexibility involved in these complexes. To study the evolution of protein-RNA interface structures, we first identified a large and diverse dataset of 2,022 pairs of structurally homologous interfaces (termed structural interologs). We leveraged this unique dataset to analyze the conservation of interface contacts among structural interologs based on the properties of involved amino acids and nucleotides. We uncovered that 73% of distance-based contacts and 68% of apolar contacts are conserved on average, and the strong conservation of these contacts occurs even in distant homologs with sequence identity below 20%. Distance-based contacts are also much more conserved compared to what we had found in a previous study of homologous protein-protein interfaces. In contrast, hydrogen bonds, salt bridges, and π-stacking interactions are very versatile in pairs of protein-RNA interologs, even for close homologs with high interface sequence identity. We found that almost half of the non-conserved distance-based contacts are linked to a small proportion of interface residues that no longer make interface contacts in the interolog, a phenomenon we term "interface switching out". We also examined possible recovery mechanisms for non-conserved hydrogen bonds and salt bridges, uncovering diverse scenarios of switching out, change in amino acid chemical nature, intermolecular and intramolecular compensations. Our findings provide insights for integrating evolutionary signals into predictive protein-RNA structural modeling methods.

SEISMICgraph: a web-based tool for RNA structure data visualization

In recent years, RNA has been increasingly recognized for its essential roles in biology, functioning not only as a carrier of genetic information but also as a dynamic regulator of gene expression through its interactions with other RNAs, proteins, and itself. Advances in chemical probing techniques have significantly enhanced our ability to identify RNA secondary structures and understand their regulatory roles. These developments, alongside improvements in experimental design and data processing, have greatly increased the resolution and throughput of structural analyses. Here, we introduce SEISMICgraph, a web-based tool designed to support RNA structure research by offering data visualization and analysis capabilities for a variety of chemical probing modalities. SEISMICgraph enables simultaneous comparison of data across different sequences and experimental conditions through a user-friendly interface that requires no programming expertise. We demonstrate its utility by investigating known and putative riboswitches and exploring how RNA modifications influence their structure and binding. SEISMICgraph's ability to rapidly visualize adenine-dependent structural changes and assess the impact of pseudouridylation on these transitions provides novel insights and establishes a roadmap for numerous future applications.

RNA-binding domain of SARS-CoV2 nucleocapsid: MD simulation study of the effect of the proline substitutions P67S and P80R on the structure of the protein

The nucleocapsid component of SARS-CoV2 is involved in the viral genome packaging. GammaP.1(Brazil) and the 20 C-US(USA) variants had a high frequency of the P80R and P67S mutations respectively in the RNA-binding domain of the nucleocapsid. Since RNA-binding domain participates in the electrostatic interactions with the viral genome, the study of the effects of proline substitutions on the flexibility of the protein will be meaningful. It evinced that the trajectory of the wildtype and mutants was stable during the simulation and exhibited distinct changes in the flexibility of the protein. Moreover, the beta-hairpin loop region of the protein structures exhibited high amplitude fluctuations and dominant motions. Additionally, modulations were detected in the drug binding site. Besides, the extent of correlation and anti-correlation motions involving the protruding region, helix, and the other RNA binding sites differed between the wildtype and mutants. The secondary structure analysis disclosed the variation in the occurrence pattern of the secondary structure elements between the proteins. Protein-ssRNA interaction analysis was also done to detect the amino acid contacts with ssRNA. R44, R59, and Y61 residues of the wildtype and P80R mutant exhibited different duration contacts with the ssRNA. It was also noticed that R44, R59, and Y61 of the wildtype and P80R formed hydrogen bonds with the ssRNA. However in P67S, residues T43, R44, R45, R40, R59, and R41 displayed contacts and formed hydrogen bonds with ssRNA. Binding free energy was also calculated and was lowest for P67S than wildtype andP80R. Thus, proline substitutions influence the structure of the RNA-binding domain and may modulate viral genome packaging besides the host-immune response.Communicated by Ramaswamy H. Sarma.

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.

RNA binding proteins in senescence: A potential common linker for age-related diseases?

Aging represents the major risk factor for the onset and/or progression of various disorders including neurodegenerative diseases, metabolic disorders, and bone-related defects. As the average age of the population is predicted to exponentially increase in the coming years, understanding the molecular mechanisms underlying the development of aging-related diseases and the discovery of new therapeutic approaches remain pivotal. Well-reported hallmarks of aging are cellular senescence, genome instability, autophagy impairment, mitochondria dysfunction, dysbiosis, telomere attrition, metabolic dysregulation, epigenetic alterations, low-grade chronic inflammation, stem cell exhaustion, altered cell-to-cell communication and impaired proteostasis. With few exceptions, however, many of the molecular players implicated within these processes as well as their role in disease development remain largely unknown. RNA binding proteins (RBPs) are known to regulate gene expression by dictating at post-transcriptional level the fate of nascent transcripts. Their activity ranges from directing primary mRNA maturation and trafficking to modulation of transcript stability and/or translation. Accumulating evidence has shown that RBPs are emerging as key regulators of aging and aging-related diseases, with the potential to become new diagnostic and therapeutic tools to prevent or delay aging processes. In this review, we summarize the role of RBPs in promoting cellular senescence and we highlight their dysregulation in the pathogenesis and progression of the main aging-related diseases, with the aim of encouraging further investigations that will help to better disclose this novel and captivating molecular scenario.

ProtNAff: protein-bound Nucleic Acid filters and fragment libraries

MOTIVATION

Atomistic models of nucleic acids (NA) fragments can be used to model the 3D structures of specific protein-NA interactions and address the problem of great NA flexibility, especially in their single-stranded regions. One way to obtain relevant NA fragments is to extract them from existing 3D structures corresponding to the targeted context (e.g. specific 2D structures, protein families, sequences) and to learn from them. Several databases exist for specific NA 3D motifs, especially in RNA, but none can handle the variety of possible contexts.

RESULTS

This article presents protNAff (protein-bound Nucleic Acids filters and fragments), a new pipeline for the conception of searchable databases on the 2D and 3D structures of protein-bound NA, the selection of context-specific (regions of) NA structures by combinations of filters, and the creation of context-specific NA fragment libraries. The strength of this pipeline is its modularity, allowing users to adapt it to many specific modeling problems. As examples, the pipeline is applied to the quantitative analysis of (i) the sequence-specificity of trinucleotide conformations, (ii) the conformational diversity of RNA at several levels of resolution, (iii) the effect of protein binding on RNA local conformations and (iv) the protein-binding propensity of RNA hairpin loops of various lengths.

AVAILABILITY AND IMPLEMENTATION

The source code is freely available for download at URL https://github.com/isaureCdB/protNAff. The database and the trinucleotide fragment library are downloadable at URL https://zenodo.org/record/6483823#.YmbVhFxByV4.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Environmentally sensitive fluorescent nucleoside analogues as probes for nucleic acid - protein interactions: molecular design and biosensing applications

Fluorescent nucleoside analogues (FNAs) are indispensable in studying the interactions of nucleic acids with nucleic acid-binding proteins. By replacing one of the poorly emissive natural nucleosides, FNAs enable real-time optical monitoring of the binding interactions in solutions, under physiologically relevant conditions, with high sensitivity. Besides that, FNAs are widely used to probe conformational dynamics of biomolecular complexes using time-resolved fluorescence methods. Because of that, FNAs are tools of high utility for fundamental biological research, with potential applications in molecular diagnostics and drug discovery. Here I review the structural and physical factors that can be used for the conversion of the molecular binding events into a detectable fluorescence output. Typical environmentally sensitive FNAs, their properties and applications, and future challenges in the field are discussed.

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.

Cross-linking and mass spectrometry as a tool for studying the structural biology of ribonucleoproteins

Cross-linking and mass spectrometry (XL-MS) workflows represent an increasingly popular technique for low-resolution structural studies of macromolecular complexes. Cross-linking reactions take place in the solution state, capturing contact sites between components of a complex that represent the native, functionally relevant structure. Protein-protein XL-MS protocols are widely adopted, providing precise localization of cross-linking sites to single amino acid positions within a pair of cross-linked peptides. In contrast, protein-RNA XL-MS workflows are evolving rapidly and differ in their ability to localize interaction regions within the RNA sequence. Here, we review protein-protein and protein-RNA XL-MS workflows, and discuss their applications in studies of protein-RNA complexes. The examples highlight the complementary value of XL-MS in structural studies of protein-RNA complexes, where more established high-resolution techniques might be unable to produce conclusive data.

Protein-RNA interaction prediction with deep learning: structure matters

Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lack of protein structure data, most of the existing computational methods rely heavily on the sequence data, with only a small portion of the methods utilizing the structural information. Recently, AlphaFold has revolutionized the entire protein and biology field. Foreseeably, the protein-RNA interaction prediction will also be promoted significantly in the upcoming years. In this work, we give a thorough review of this field, surveying both the binding site and binding preference prediction problems and covering the commonly used datasets, features and models. We also point out the potential challenges and opportunities in this field. This survey summarizes the development of the RNA-binding protein-RNA interaction field in the past and foresees its future development in the post-AlphaFold era.

Probing the Conformational State of mRNPs Using smFISH and SIM

mRNAs and lncRNAs assemble with RNA-binding proteins (RBPs) to form ribonucleoprotein complexes (RNPs ). The assembly of RNPs initiates co-transcriptionally, and their composition and organization is thought to change during the different steps of an RNP life cycle. Modulation of RNP structural organization has been implicated in the regulation of different aspects of RNA metabolism, including establishing interactions between the 5' and 3' ends in regulating mRNA translation and turnover. In this chapter, we describe a single-molecule microscopy approach that combines fluorescent RNA in situ hybridization (smFISH) and structured illumination microscopy (SIM ) and allows to measure different aspects of RNP organization in cells, including distances between different regions within individual mRNAs, as well as the overall compaction state of RNAs in different subcellular compartments and environmental conditions. Moreover, we describe a detailed workflow required for image registration and analysis that allows determining distances at sub-diffraction resolution.

Zinc and Copper Ions Differentially Regulate Prion-Like Phase Separation Dynamics of Pan-Virus Nucleocapsid Biomolecular Condensates

Liquid-liquid phase separation (LLPS) is a rapidly growing research focus due to numerous demonstrations that many cellular proteins phase-separate to form biomolecular condensates (BMCs) that nucleate membraneless organelles (MLOs). A growing repertoire of mechanisms supporting BMC formation, composition, dynamics, and functions are becoming elucidated. BMCs are now appreciated as required for several steps of gene regulation, while their deregulation promotes pathological aggregates, such as stress granules (SGs) and insoluble irreversible plaques that are hallmarks of neurodegenerative diseases. Treatment of BMC-related diseases will greatly benefit from identification of therapeutics preventing pathological aggregates while sparing BMCs required for cellular functions. Numerous viruses that block SG assembly also utilize or engineer BMCs for their replication. While BMC formation first depends on prion-like disordered protein domains (PrLDs), metal ion-controlled RNA-binding domains (RBDs) also orchestrate their formation. Virus replication and viral genomic RNA (vRNA) packaging dynamics involving nucleocapsid (NC) proteins and their orthologs rely on Zinc (Zn) availability, while virus morphology and infectivity are negatively influenced by excess Copper (Cu). While virus infections modify physiological metal homeostasis towards an increased copper to zinc ratio (Cu/Zn), how and why they do this remains elusive. Following our recent finding that pan-retroviruses employ Zn for NC-mediated LLPS for virus assembly, we present a pan-virus bioinformatics and literature meta-analysis study identifying metal-based mechanisms linking virus-induced BMCs to neurodegenerative disease processes. We discover that conserved degree and placement of PrLDs juxtaposing metal-regulated RBDs are associated with disease-causing prion-like proteins and are common features of viral proteins responsible for virus capsid assembly and structure. Virus infections both modulate gene expression of metalloproteins and interfere with metal homeostasis, representing an additional virus strategy impeding physiological and cellular antiviral responses. Our analyses reveal that metal-coordinated virus NC protein PrLDs initiate LLPS that nucleate pan-virus assembly and contribute to their persistence as cell-free infectious aerosol droplets. Virus aerosol droplets and insoluble neurological disease aggregates should be eliminated by physiological or environmental metals that outcompete PrLD-bound metals. While environmental metals can control virus spreading via aerosol droplets, therapeutic interference with metals or metalloproteins represent additional attractive avenues against pan-virus infection and virus-exacerbated neurological diseases.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models

MOTIVATION

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).

RESULTS

Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/mprdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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

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

Protein Interaction with Charged Macromolecules: From Model Polymers to Unfolded Proteins and Post-Translational Modifications

Interaction of proteins with charged macromolecules is involved in many processes in cells. Firstly, there are many naturally occurred charged polymers such as DNA and RNA, polyphosphates, sulfated glycosaminoglycans, etc., as well as pronouncedly charged proteins such as histones or actin. Electrostatic interactions are also important for "generic" proteins, which are not generally considered as polyanions or polycations. Finally, protein behavior can be altered due to post-translational modifications such as phosphorylation, sulfation, and glycation, which change a local charge of the protein region. Herein we review molecular modeling for the investigation of such interactions, from model polyanions and polycations to unfolded proteins. We will show that electrostatic interactions are ubiquitous, and molecular dynamics simulations provide an outstanding opportunity to look inside binding and reveal the contribution of electrostatic interactions. Since a molecular dynamics simulation is only a model, we will comprehensively consider its relationship with the experimental data.

Screening Approaches for Targeting Ribonucleoprotein Complexes: A New Dimension for Drug Discovery

RNA-binding proteins (RBPs) are pleiotropic factors that control the processing and functional compartmentalization of transcripts by binding primarily to mRNA untranslated regions (UTRs). The competitive and/or cooperative interplay between RBPs and an array of coding and noncoding RNAs (ncRNAs) determines the posttranscriptional control of gene expression, influencing protein production. Recently, a variety of well-recognized and noncanonical RBP domains have been revealed by modern system-wide analyses, underlying an evolving classification of ribonucleoproteins (RNPs) and their importance in governing physiological RNA metabolism. The possibility of targeting selected RNA-protein interactions with small molecules is now expanding the concept of protein "druggability," with new implications for medicinal chemistry and for a deeper characterization of the mechanism of action of bioactive compounds. Here, taking SF3B1, HuR, LIN28, and Musashi proteins as paradigmatic case studies, we review the strategies applied for targeting RBPs, with emphasis on the technological advancements to study protein-RNA interactions and on the requirements of appropriate validation strategies to parallel high-throughput screening (HTS) efforts.

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.

Ribonucleoprotein particles: advances and challenges in computational methods

RNA-binding proteins (RBPs) interact with RNA to form Ribonucleoprotein Particles (RNPs). The interaction between RBPs and their RNA partners are traditionally thought to be mediated by highly conserved RNA-binding domains (RBDs). Recently, high-throughput studies led to the discovery of hundreds of novel proteins and domains, of which many do not follow the classical definition of RNA-binding. Despite technological innovations, experimental screenings are currently limited to the detection of specific types of RNPs, underscoring the importance of computational methods for predicting novel RBPs and RNA interacting residues and interfaces. Here, we discuss major challenges in computational prediction of RBPs and RBDs and outline new strategies to circumvent current limitations of experimental techniques.

Structural Changes of RNA in Complex with Proteins in the SRP

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

Foreword to 'Quantitative and analytical relations in biochemistry'-a special issue in honour of Donald J. Winzor's 80th birthday

The purpose of this special issue is to honour Professor Donald J. Winzor's long career as a researcher and scientific mentor, and to celebrate the milestone of his 80th birthday. Throughout his career, Don has been renowned for his development of clever approximations to difficult quantitative relations governing a range of biophysical measurements. The theme of this special issue, 'Quantitative and analytical relations in biochemistry', was chosen to reflect this aspect of Don's scientific approach.