Deciphering the RRM-RNA recognition code: A computational analysis

RRMScorer: A web server for predicting RNA recognition motif binding preferences

RRMScorer is a web server designed to predict RNA binding preferences for proteins containing RNA recognition motifs (RRMs), the most prevalent RNA binding domain in eukaryotes. By carefully analysing a dataset of 187 RRM-RNA structural complexes, we calculated residue-level binding scores using a probabilistic model derived from amino acid-nucleotide interaction propensities, which are the basis of RRMScorer. The server accepts protein sequences and optional RNA sequences as input, providing detailed outputs, including bar plots, sequence logos, and downloadable CSV/JSON files, to visualize and interpret RNA binding preferences. RRMScorer is particularly useful for studying the impact of single-point mutations and comparing binding preferences across multiple RRM domains. The web server, freely accessible at https://bio2byte.be/rrmscorer without login requirements, offers a user-friendly interface and integrates precomputed predictions for over 1400 RRM-containing proteins. With its ability to provide residue-level insights and accurate predictions, RRMScorer serves as a valuable tool for researchers exploring the functional landscape of RRM-RNA interactions.

Regulatory Effects of RNA-Protein Interactions Revealed by Reporter Assays of Bacteria Grown on Solid Media

Reporter systems are widely used to study biomolecular interactions and processes in vivo, representing one of the basic tools used to characterize synthetic regulatory circuits. Here, we developed a method that enables the monitoring of RNA-protein interactions through a reporter system in bacteria with high temporal resolution. For this, we used a Real-Time Protein Expression Assay (RT-PEA) technology for real-time monitoring of a fluorescent reporter protein, while having bacteria growing on solid media. Experimental results were analyzed by fitting a three-variable Gompertz growth model. To validate the method, the interactions between a set of RNA sequences and the RNA-binding protein (RBP) Musashi-1 (MSI1) were evaluated, as well as the allosteric modulation of the interaction by a small molecule (oleic acid). This new approach proved to be suitable to quantitatively characterize RNA-RBP interactions, thereby expanding the toolbox to study molecular interactions in living bacteria, including allosteric modulation, with special relevance for systems that are not suitable to be studied in liquid media.

Deep mutational scanning of the Trypanosoma brucei developmental regulator RBP6 reveals an essential disordered region influenced by positive residues

To regain infectivity, Trypanosoma brucei, the pathogen causing Human and Animal African trypanosomiasis, undergoes a complex developmental program within the tsetse fly known as metacyclogenesis. RNA-binding protein 6 (RBP6) is a potent orchestrator of this process, however, an understanding of its functionally important domains and their mutational constraints is lacking. Here, we perform deep mutational scanning of the entire RBP6 primary structure. Expression of libraries containing all single-point variants of RBP6 in non-infectious procyclic forms and subsequent purification of infectious metacyclics supports the existence of an RNA-recognition motif (RRM) and reveal an N-terminal intrinsically disordered region (N-IDR). In contrast to the RRM, the N-IDR is more tolerant to substitutions; however, a handful of positions contain a third of all deleterious mutations found in the N-IDR. Introduction of positively charged residues in the N-IDR dramatically alters the normal metacyclogenesis pattern. Our results reveal an essential N-IDR, possibly playing a regulatory role, and an RRM likely involved in protein-RNA interactions.

From genes to traits: Trends in RNA-binding proteins and their role in plant trait development: A review

RNA-binding proteins (RBPs) are essential for cellular functions by attaching to RNAs, creating dynamic ribonucleoprotein complexes (RNPs) essential for managing RNA throughout its life cycle. These proteins are critical to all post-transcriptional processes, impacting vital cellular functions during development and adaptation to environmental changes. Notably, in plants, RBPs are critical for adjusting to inconsistent environmental conditions, with recent studies revealing that plants possess, more prominent, and both novel and conserved RBP families compared to other eukaryotes. This comprehensive review delves into the varied RBPs covering their structural attributes, domain base function, and their interactions with RNA in metabolism, spotlighting their role in regulating post-transcription and splicing and their reaction to internal and external stimuli. It highlights the complex regulatory roles of RBPs, focusing on plant trait regulation and the unique functions they facilitate, establishing a foundation for appreciating RBPs' significance in plant growth and environmental response strategies.

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.

Large-scale structure-informed multiple sequence alignment of proteins with SIMSApiper

SUMMARY

SIMSApiper is a Nextflow pipeline that creates reliable, structure-informed MSAs of thousands of protein sequences faster than standard structure-based alignment methods. Structural information can be provided by the user or collected by the pipeline from online resources. Parallelization with sequence identity-based subsets can be activated to significantly speed up the alignment process. Finally, the number of gaps in the final alignment can be reduced by leveraging the position of conserved secondary structure elements.

AVAILABILITY AND IMPLEMENTATION

The pipeline is implemented using Nextflow, Python3, and Bash. It is publicly available on github.com/Bio2Byte/simsapiper.

Analysis of the inter-domain orientation of tandem RRM domains with diverse linkers: connecting experimental with AlphaFold2 predicted models

The RNA recognition motif (RRM) is the most prevalent RNA binding domain in eukaryotes and is involved in most RNA metabolism processes. Single RRM domains have a limited RNA specificity and affinity and tend to be accompanied by other RNA binding domains, frequently additional RRMs that contribute to an avidity effect. Within multi-RRM proteins, the most common arrangement are tandem RRMs, with two domains connected by a variable linker. Despite their prevalence, little is known about the features that lead to specific arrangements, and especially the role of the connecting linker. In this work, we present a novel and robust way to investigate the relative domain orientation in multi-domain proteins using inter-domain vectors referenced to a stable secondary structure element. We apply this method to tandem RRM domains and cluster experimental tandem RRM structures according to their inter-domain and linker-domain contacts, and report how this correlates with their orientation. By extending our analysis to AlphaFold2 predicted structures, with particular attention to the inter-domain predicted aligned error, we identify new orientations not reported experimentally. Our analysis provides novel insights across a range of tandem RRM orientations that may help for the design of proteins with a specific RNA binding mode.

Origin of ribonucleotide recognition motifs through ligand mimicry at early earth

In an RNA world, the emergence of template-specific self-replication and catalysis necessitated the presence of motifs facilitating reliable recognition between RNA molecules. What did these motifs entail, and how did they evolve into the proteinaceous RNA recognition entities observed today? Direct observation of these primordial entities is hindered by rapid degradation over geological time scales. To overcome this challenge, researchers employ diverse approaches, including scrutiny of conserved sequences and structural motifs across extant organisms and employing directed evolution experiments to generate RNA molecules with specific catalytic abilities. In this review, we delve into the theme of ribonucleotide recognition across key periods of early Earth's evolution. We explore scenarios of RNA interacting with small molecules and examine hypotheses regarding the role of minerals and metal ions in enabling structured ribonucleotide recognition and catalysis. Additionally, we highlight instances of RNA-protein mimicry in interactions with other RNA molecules. We propose a hypothesis where RNA initially recognizes small molecules and metal ions/minerals, with subsequent mimicry by proteins leading to the emergence of proteinaceous RNA binding domains.

Research Progress on the Structural and Functional Roles of hnRNPs in Muscle Development

Heterogeneous nuclear ribonucleoproteins (hnRNPs) are a superfamily of RNA-binding proteins consisting of more than 20 members. These proteins play a crucial role in various biological processes by regulating RNA splicing, transcription, and translation through their binding to RNA. In the context of muscle development and regeneration, hnRNPs are involved in a wide range of regulatory mechanisms, including alternative splicing, transcription regulation, miRNA regulation, and mRNA stability regulation. Recent studies have also suggested a potential association between hnRNPs and muscle-related diseases. In this report, we provide an overview of our current understanding of how hnRNPs regulate RNA metabolism and emphasize the significance of the key members of the hnRNP family in muscle development. Furthermore, we explore the relationship between the hnRNP family and muscle-related diseases.

CroMaSt: a workflow for assessing protein domain classification by cross-mapping of structural instances between domain databases and structural alignment

Motivation

Protein domains can be viewed as building blocks, essential for understanding structure-function relationships in proteins. However, each domain database classifies protein domains using its own methodology. Thus, in many cases, domain models and boundaries differ from one domain database to the other, raising the question of domain definition and enumeration of true domain instances.

Results

We propose an automated iterative workflow to assess protein domain classification by cross-mapping domain structural instances between domain databases and by evaluating structural alignments. CroMaSt (for Cross-Mapper of domain Structural instances) will classify all experimental structural instances of a given domain type into four different categories ('Core', 'True', 'Domain-like' and 'Failed'). CroMast is developed in Common Workflow Language and takes advantage of two well-known domain databases with wide coverage: Pfam and CATH. It uses the Kpax structural alignment tool with expert-adjusted parameters. CroMaSt was tested with the RNA Recognition Motif domain type and identifies 962 'True' and 541 'Domain-like' structural instances for this domain type. This method solves a crucial issue in domain-centric research and can generate essential information that could be used for synthetic biology and machine-learning approaches of protein domain engineering.

Availability and implementation

The workflow and the Results archive for the CroMaSt runs presented in this article are available from WorkflowHub (doi: 10.48546/workflowhub.workflow.390.2).

Supplementary information

Supplementary data are available at Bioinformatics Advances online.