Tissue Usage Preference and Intrinsically Disordered Region Remodeling of Alternative Splicing Derived Proteoforms in the Heart.

A computational analysis of mass spectrometry data was performed to uncover alternative splicing derived protein variants across chambers of the human heart. Evidence for 216 non-canonical isoforms was apparent in the atrium and the ventricle, including 52 isoforms not documented on SwissProt and recovered using an RNA sequencing derived database. Among non-canonical isoforms, 29 show signs of regulation based on statistically significant preferences in tissue usage, including a ventricular enriched protein isoform of tensin-1 (TNS1) and an atrium-enriched PDZ and LIM Domain 3 (PDLIM3) isoform 2 (PDLIM3-2/ALP-H). Examined variant regions that differ between alternative and canonical isoforms are highly enriched with intrinsically disordered regions. Moreover, over two-thirds of such regions are predicted to function in protein binding and RNA binding. The analysis here lends further credence to the notion that alternative splicing diversifies the proteome by rewiring intrinsically disordered regions, which are increasingly recognized to play important roles in the generation of biological function from protein sequences.

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.

A comprehensive comparative review of sequence-based predictors of DNA- and RNA-binding residues.

Motivated by the pressing need to characterize protein-DNA and protein-RNA interactions on large scale, we review a comprehensive set of 30 computational methods for high-throughput prediction of RNA- or DNA-binding residues from protein sequences. We summarize these predictors from several significant perspectives including their design, outputs and availability. We perform empirical assessment of methods that offer web servers using a new benchmark data set characterized by a more complete annotation that includes binding residues transferred from the same or similar proteins. We show that predictors of DNA-binding (RNA-binding) residues offer relatively strong predictive performance but they are unable to properly separate DNA- from RNA-binding residues. We design and empirically assess several types of consensuses and demonstrate that machine learning (ML)-based approaches provide improved predictive performance when compared with the individual predictors of DNA-binding residues or RNA-binding residues. We also formulate and execute first-of-its-kind study that targets combined prediction of DNA- and RNA-binding residues. We design and test three types of consensuses for this prediction and conclude that this novel approach that relies on ML design provides better predictive quality than individual predictors when tested on prediction of DNA- and RNA-binding residues individually. It also substantially improves discrimination between these two types of nucleic acids. Our results suggest that development of a new generation of predictors would benefit from using training data sets that combine both RNA- and DNA-binding proteins, designing new inputs that specifically target either DNA- or RNA-binding residues and pursuing combined prediction of DNA- and RNA-binding residues.

Computational modeling of protein-RNA complex structures.

Protein-RNA interactions play fundamental roles in many biological processes, such as regulation of gene expression, RNA splicing, and protein synthesis. The understanding of these processes improves as new structures of protein-RNA complexes are solved and the molecular details of interactions analyzed. However, experimental determination of protein-RNA complex structures by high-resolution methods is tedious and difficult. Therefore, studies on protein-RNA recognition and complex formation present major technical challenges for macromolecular structural biology. Alternatively, protein-RNA interactions can be predicted by computational methods. Although less accurate than experimental measurements, theoretical models of macromolecular structures can be sufficiently accurate to prompt functional hypotheses and guide e.g. identification of important amino acid or nucleotide residues. In this article we present an overview of strategies and methods for computational modeling of protein-RNA complexes, including software developed in our laboratory, and illustrate it with practical examples of structural predictions.

Structural determinants for tRNA selective cleavage by RNase 2/EDN.

tRNA-derived fragments (tRFs) have emerged as key players of immunoregulation. Some RNase A superfamily members participate in the shaping of the tRFs population. By comparing wild-type and knockout macrophage cell lines, our previous work revealed that RNase 2 can selectively cleave tRNAs. Here, we confirm the in vitro protein cleavage pattern by screening of synthetic tRNAs, single-mutant variants, and anticodon-loop DNA/RNA hairpins. By sequencing of tRF products, we identified the cleavage selectivity of recombinant RNase 2 with base specificity at B1 (U/C) and B2 (A) sites, consistent with a previous cellular study. Lastly, protein-hairpin complexes were predicted by MD simulations. Results reveal the contribution of the α1, loop 3 and loop 4, and ß6 RNase 2 regions, where residues Arg36/Asn39/Gln40/Asn65/Arg68/Arg132 provide interactions, spanning from P-1 to P2 sites that are essential for anticodon loop recognition. Knowledge of RNase 2-specific tRFs generation might guide new therapeutic approaches for infectious and immune-related diseases.

Profiling the in vivo RNA interactome associated with the endoribonuclease RNase III in Staphylococcus aureus.

Regulatory small RNA (sRNA) have been extensively studied in model Gram-negative bacteria, but the functional characterisation of these post-transcriptional gene regulators in Gram-positives remains a major challenge. Our previous work in enterohaemorrhagic E. coli utilised the proximity-dependant ligation technique termed CLASH (UV-crosslinking, ligation, and sequencing of hybrids) for direct high-throughput sequencing of the regulatory sRNA-RNA interactions within the cell. Recently, we adapted the CLASH technique and demonstrated that UV-crosslinking and RNA proximity-dependant ligation can be applied to Staphylococcus aureus, which uncovered the first RNA-RNA interaction network in a Gram-positive bacterium. In this chapter, we describe modifications to the CLASH technique that were developed to capture the RNA interactome associated with the double-stranded endoribonuclease RNase III in two clinical isolates of S. aureus. To briefly summarise our CLASH methodology, regulatory RNA-RNA interactions were first UV-crosslinked in vivo to the RNase III protein and protein-RNA complexes were affinity-purified using the His6-TEV-FLAG tags. Linkers were ligated to RNase III-bound RNA during library preparation and duplexed RNA-RNA species were ligated together to form a single contiguous RNA 'hybrid'. The RNase III-RNA binding sites and RNA-RNA interactions occurring on RNase III (RNA hybrids) were then identified by paired-end sequencing technology. RNase III-CLASH represents a step towards a systems-level understanding of regulatory RNA in Gram-positive bacteria.

Computational methods for predicting hotspots at protein-RNA interfaces.

Protein-RNA interactions play essential roles in many critical biological events. A comprehensive understanding of the mechanisms underlying these interactions is helpful when studying cellular activities and therapeutic applications. Hotspots are a small portion of residues contributing much toward protein-RNA binding affinity. In pharmaceutical research, the hotspot residues are seen as the best option for designing small molecules to target proteins of therapeutic interest. With the accumulation of experimental data about protein-RNA interactions, computational methods have been produced for hotspot prediction on a large scale. In this review, we first present an overview of the existing databases for protein-RNA binding data. Furthermore, we outline the most adopted computational methods for hotspots prediction in protein-RNA interactions. Finally, we discuss the applications of hotspot prediction. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Methods > RNA Analyses In Vitro and In Silico.

Deep structural insights into RNA-binding disordered protein regions.

Recent efforts to identify RNA binding proteins in various organisms and cellular contexts have yielded a large collection of proteins that are capable of RNA binding in the absence of conventional RNA recognition domains. Many of the recently identified RNA interaction motifs fall into intrinsically disordered protein regions (IDRs). While the recognition mode and specificity of globular RNA binding elements have been thoroughly investigated and described, much less is known about the way IDRs can recognize their RNA partners. Our aim was to summarize the current state of structural knowledge on the RNA binding modes of disordered protein regions and to propose a classification system based on their sequential and structural properties. Through a detailed structural analysis of the complexes that contain disordered protein regions binding to RNA, we found two major binding modes that represent different recognition strategies and, most likely, functions. We compared these examples with DNA binding disordered proteins and found key differences stemming from the nucleic acids as well as similar binding strategies, implying a broader substrate acceptance by these proteins. Due to the very limited number of known structures, we integrated molecular dynamics simulations in our study, whose results support the proposed structural preferences of specific RNA-binding IDRs. To broaden the scope of our review, we included a brief analysis of RNA-binding small molecules and compared their structural characteristics and RNA recognition strategies to the RNA-binding IDRs. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions.

Mechanism of RNA recognition by a Musashi RNA-binding protein.

The Musashi RNA-binding proteins (RBPs) regulate translation of target mRNAs and maintenance of cell stemness and tumorigenesis. Musashi-1 (MSI1), long considered as an intestinal and neural stem cell marker, has been more recently found to be over expressed in many cancers. It has served as an important drug target for treating acute myeloid leukemia and solid tumors such as ovarian, colorectal and bladder cancer. One of the reported binding targets of MSI1 is Numb, a negative regulator of the Notch signaling. However, the dynamic mechanism of Numb RNA binding to MSI1 remains unknown, largely hindering effective drug design targeting this critical interaction. Here, we have performed extensive all-atom microsecond-timescale simulations using a robust Gaussian accelerated molecular dynamics (GaMD) method, which successfully captured multiple times of spontaneous and highly accurate binding of the Numb RNA from bulk solvent to the MSI1 protein target site. GaMD simulations revealed that Numb RNA binding to MSI1 involved largely induced fit in both the RNA and protein. The simulations also identified important low-energy intermediate conformational states during RNA binding, in which Numb interacted mainly with the ß2-ß3 loop and C terminus of MSI1. The mechanistic understanding of RNA binding obtained from our GaMD simulations is expected to facilitate rational structure-based drug design targeting MSI1 and other RBPs.

An Unusual U2AF2 Inhibits Splicing and Attenuates the Virulence of the Human Protozoan Parasite Entamoeba histolytica.

E. histolytica is the etiological agent of intestinal amebiasis and liver abscesses, which still poses public health threat globally. Metronidazole is the drug of choice against amebiasis. However, metronidazole-resistant amoebic clinical isolates and strains have been reported recently, challenging the efforts for amebiasis eradication. In search of alternative treatments, E. histolytica transcriptomes have shown the association of genes involved in RNA metabolism with the virulence of the parasite. Among the upregulated genes in amoebic liver abscesses are the splicing factors EhU2AF2 and a paralog of EhSF3B1. For this reason and because EhU2AF2 contains unusual KH-QUA2 (84KQ) motifs in its lengthened C-terminus domain, here we investigated how the role of EhU2AF2 in pre-mRNA processing impacts the virulence of the parasite. We found that 84KQ is involved in splicing inhibition/intron retention of several virulence and non-virulence-related genes. The 84KQ domain interacts with the same domain of the constitutive splicing factor SF1 (SF1KQ), both in solution and when SF1KQ is bound to branchpoint signal RNA probes. The 84KQ-SF1KQ interaction prevents splicing complex E to A transition, thus inhibiting splicing. Surprisingly, the deletion of the 84KQ domain in EhU2AF2 amoeba transformants increased splicing and enhanced the in vitro and in vivo virulence phenotypes. We conclude that the interaction of the 84KQ and SF1KQ domains, probably involving additional factors, tunes down Entamoeba virulence by favoring intron retention.

Sorting mRNA Molecules for Cytoplasmic Transport and Localization.

In eukaryotic cells, gene expression is highly regulated at many layers. Nascent RNA molecules are assembled into ribonucleoprotein complexes that are then released into the nucleoplasmic milieu and transferred to the nuclear pore complex for nuclear export. RNAs are then either translated or transported to the cellular periphery. Emerging evidence indicates that RNA-binding proteins play an essential role throughout RNA biogenesis, from the gene to polyribosomes. However, the sorting mechanisms that regulate whether an RNA molecule is immediately translated or sent to specialized locations for translation are unclear. This question is highly relevant during development and differentiation when cells acquire a specific identity. Here, we focus on the RNA-binding properties of heterogeneous nuclear ribonucleoproteins (hnRNPs) and how these mechanisms are believed to play an essential role in RNA trafficking in polarized cells. Further, by focusing on the specific hnRNP protein CBF-A/hnRNPab and its naturally occurring isoforms, we propose a model on how hnRNP proteins are capable of regulating gene expression both spatially and temporally throughout the RNA biogenesis pathway, impacting both healthy and diseased cells.

A tale of two paralogs: human Transformer2 proteins with differential RNA-binding affinities.

The Transformer2 (Tra2) proteins in humans are homologues of the Drosophila Tra2 protein. One of the two RNA-binding paralogs, Tra2ß, has been very well-studied over the past decade, but not much is known about Tra2α. It was very recently shown that the two proteins demonstrate the phenomenon of paralog compensation. Here, we provide a structural basis for this genetic backup circuit, using molecular modelling and dynamics studies. We show that the two proteins display similar binding specificities, but differential affinities to a short GAA-rich RNA stretch. Starting from the 6-nucleotide RNA in the solution structure, close to 4000 virtual mutations were modelled on RNA and the domain-RNA interactions were studied after energy minimisation to convergence. Separately, another known 13-nucleotide stretch was docked and the domain-RNA interactions were observed through a 100-ns dynamics trajectory. We have also demonstrated the 'compensatory' mechanism at the level of domains in one of the domain repeat-containing RNA-binding proteins.

Structure and intrinsic disorder of the proteins of the Trypanosoma brucei editosome.

Mitochondrial pre-mRNAs in trypanosomatids undergo RNA editing to be converted into translatable mRNAs. The reaction is characterized by the insertion and deletion of uridine residues and is catalyzed by a macromolecular protein complex called the editosome. Despite intensive research, structural information for the majority of editosome proteins is still missing and no high resolution structure for the editosome exists. Here we present a comprehensive structural bioinformatics analysis of all proteins of the Trypanosoma brucei editosome. We specifically focus on the interplay between intrinsic order and disorder. According to computational predictions, editosome proteins involved in the basal reaction steps of the processing cycle are mostly ordered. By contrast, thirty percent of the amino acid content of the editosome is intrinsically disordered, which includes most prominently proteins with OB-fold domains. Based on the data we suggest a functional model, in which the structurally disordered domains of the complex are correlated with the RNA binding and RNA unfolding activity of the T. brucei editosome.