Modular architecture and functional annotation of human RNA-binding proteins containing RNA recognition motif

Oligomerization regulates the interaction of Gemin5 with members of the SMN complex and the translation machinery

RNA-binding proteins are multifunctional molecules impacting on multiple steps of gene regulation. Gemin5 was initially identified as a member of the survival of motor neurons (SMN) complex. The protein is organized in structural and functional domains, including a WD40 repeats domain at the N-terminal region, a tetratricopeptide repeat (TPR) dimerization module at the central region, and a non-canonical RNA-binding site at the C-terminal end. The TPR module allows the recruitment of the endogenous Gemin5 protein in living cells and the assembly of a dimer in vitro. However, the biological relevance of Gemin5 oligomerization is not known. Here we interrogated the Gemin5 interactome focusing on oligomerization-dependent or independent regions. We show that the interactors associated with oligomerization-proficient domains were primarily annotated to ribosome, splicing, translation regulation, SMN complex, and RNA stability. The presence of distinct Gemin5 protein regions in polysomes highlighted differences in translation regulation based on their oligomerization capacity. Furthermore, the association with native ribosomes and negative regulation of translation was strictly dependent on both the WD40 repeats domain and the TPR dimerization moiety, while binding with the majority of the interacting proteins, including SMN, Gemin2, and Gemin4, was determined by the dimerization module. The loss of oligomerization did not perturb the predominant cytoplasmic localization of Gemin5, reinforcing the cytoplasmic functions of this essential protein. Our work highlights a distinctive role of the Gemin5 domains for its functions in the interaction with members of the SMN complex, ribosome association, and RBP interactome.

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.

Protein-based delivery systems for RNA delivery

RNA-based therapeutics have emerged as promising approaches to modulate gene expression and generate therapeutic proteins or antigens capable of inducing immune responses to treat a variety of diseases, such as infectious diseases, cancers, immunologic disorders, and genetic disorders. However, the efficient delivery of RNA molecules into cells poses significant challenges due to their large molecular weight, negative charge, and susceptibility to degradation by RNase enzymes. To overcome these obstacles, viral and non-viral vectors have been developed, including lipid nanoparticles, viral vectors, proteins, dendritic macromolecules, among others. Among these carriers, protein-based delivery systems have garnered considerable attention due to their potential to address specific issues associated with nanoparticle-based systems, such as liver accumulation and immunogenicity. This review provides an overview of currently marketed RNA drugs, underscores the significance of RNA delivery vector development, delineates the essential characteristics of an ideal RNA delivery vector, and introduces existing protein carriers for RNA delivery. By offering valuable insights, this review aims to serve as a reference for the future development of protein-based delivery vectors for RNA therapeutics.

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.