Explore the vast range of titles available.

Protein‐RNA interactions play a critical role in various biological processes and disease development. Proteins interact with RNAs through dynamic binding sites that exhibit specific structural patterns under various cellular conditions. While current computational methods take RNA structures in vivo into account, they fall short in capturing the structure contextual association of nucleotides, limiting predictive accuracy. Here, diPaRIS, a deep learning method, is proposed to predict dynamic protein‐RNA interactions with improved accuracy and enhanced interpretability by integrating RNA structures in vivo. diPaRIS introduces a novel encoding scheme for SHAPE‐seq to encode nucleotide correlations, providing a more comprehensive representation of RNA structures. Leveraging a U‐shaped network architecture, diPaRIS not only improves prediction performance but also enables interpretable analysis by learning sequence binding motifs and generating attribution maps. Benchmarking diPaRIS across 44 datasets shows its superiority over existing methods. The model consistently achieves the highest accuracy, AUC, AUPR, and F1‐score across all datasets. Additionally, diPaRIS excels in cross‐cell line predictions, consistently outperforming the second‐best method across all datasets. Predictions by diPaRIS reflect the conservation of protein‐RNA binding and facilitate further functional interpretation of genetic variants in complex diseases. The findings highlight that diPaRIS effectively predicts protein‐RNA interactions and interprets potential gene‐disease associations. This study presents diPaRIS, a deep learning method designed to predict dynamic protein‐ RNA interactions with enhanced accuracy. By integrating RNA structures in vivo through a novel encoding scheme for nucleotide correlations, diPaRIS improves the interpretability of binding motifs and outperforms current state‐of‐the‐art methods across multiple datasets. The results provide new insights into protein‐RNA binding preferences and their role in disease development.

The double-stranded RNA binding domain (dsRBD) is a small protein domain of 65–70 amino acids adopting an αβββα fold, whose central property is to bind to double-stranded RNA (dsRNA). This domain is present in proteins implicated in many aspects of cellular life, including antiviral response, RNA editing, RNA processing, RNA transport and, last but not least, RNA silencing. Even though proteins containing dsRBDs can bind to very specific dsRNA targets in vivo, the binding of dsRBDs to dsRNA is commonly believed to be shape-dependent rather than sequence-specific. Interestingly, recent structural information on dsRNA recognition by dsRBDs opens the possibility that this domain performs a direct readout of RNA sequence in the minor groove, allowing a global reconsideration of the principles describing dsRNA recognition by dsRBDs. We review in this article the current structural and molecular knowledge on dsRBDs, emphasizing the intricate relationship between the amino acid sequence, the structure of the domain and its RNA recognition capacity. We especially focus on the molecular determinants of dsRNA recognition and describe how sequence discrimination can be achieved by this type of domain.

The iCLIP and eCLIP techniques facilitate the detection of protein–RNA interaction sites at high resolution, based on diagnostic events at crosslink sites. However, previous methods do not explicitly model the specifics of iCLIP and eCLIP truncation patterns and possible biases. We developed PureCLIP ( https://github.com/skrakau/PureCLIP ), a hidden Markov model based approach, which simultaneously performs peak-calling and individual crosslink site detection. It explicitly incorporates a non-specific background signal and, for the first time, non-specific sequence biases. On both simulated and real data, PureCLIP is more accurate in calling crosslink sites than other state-of-the-art methods and has a higher agreement across replicates.

Interferon (IFN) responses play key roles in cellular defense against pathogens. Highly expressed IFN-induced proteins with tetratricopeptide repeats (IFITs) are proposed to function as RNA binding proteins, but the RNA binding and discrimination specificities of IFIT proteins remain unclear. Here we show that human IFIT5 has comparable affinity for RNAs with diverse phosphate-containing 5′-ends, excluding the higher eukaryotic mRNA cap. Systematic mutagenesis revealed that sequence substitutions in IFIT5 can alternatively expand or introduce bias in protein binding to RNAs with 5′ monophosphate, triphosphate, cap0 (triphosphate-bridged N7-methylguanosine), or cap1 (cap0 with RNA 2′- O -methylation). We defined the breadth of cellular ligands for IFIT5 by using a thermostable group II intron reverse transcriptase for RNA sequencing. We show that IFIT5 binds precursor and processed tRNAs, as well as other RNA polymerase III transcripts. Our findings establish the RNA recognition specificity of the human innate immune response protein IFIT5.

Background RNA-protein 3D complex structure prediction is still challenging. Recently, a template-based approach PRIME is proposed in our team to build RNA-protein 3D complex structure models with a higher success rate than computational docking software. However, scoring function of RNA alignment algorithm SARA in PRIME is size-dependent, which limits its ability to detect templates in some cases. Results Herein, we developed a novel RNA 3D structural alignment approach RMalign, which is based on a size-independent scoring function RMscore. The parameter in RMscore is then optimized in randomly selected RNA pairs and phase transition points (from dissimilar to similar) are determined in another randomly selected RNA pairs. In tRNA benchmarking, the precision of RMscore is higher than that of SARAscore (0.88 and 0.78, respectively) with phase transition points. In balance-FSCOR benchmarking, RMalign performed as good as ESA-RNA with a non-normalized score measuring RNA structural similarity. In balance-x-FSCOR benchmarking, RMalign achieves much better than a state-of-the-art RNA 3D structural alignment approach SARA due to a size-independent scoring function. Take the advantage of RMalign, we update our RNA-protein modeling approach PRIME to version 2.0. The PRIME2.0 significantly improves about 10% success rate than PRIME. Conclusion Based on a size-independent scoring function RMscore, a novel RNA 3D structural alignment approach RMalign is developed and integrated into PRIME2.0, which could be useful for the biological community in modeling protein-RNA interaction.

During translation, a plethora of protein factors bind to the ribosome and regulate protein synthesis. Many of those factors are guanosine triphosphatases (GTPases), proteins that catalyze the hydrolysis of guanosine 5′-triphosphate (GTP) to promote conformational changes. Despite numerous studies, the function of elongation factor 4 (EF-4/LepA), a highly conserved translational GTPase, has remained elusive. Here, we present the crystal structure at 2.6-Å resolution of the Thermus thermophilus 70S ribosome bound to EF-4 with a nonhydrolyzable GTP analog and A-, P-, and E-site tRNAs. The structure reveals the interactions of EF-4 with the A-site tRNA, including contacts between the C-terminal domain (CTD) of EF-4 and the acceptor helical stem of the tRNA. Remarkably, EF-4 induces a distortion of the A-site tRNA, allowing it to interact simultaneously with EF-4 and the decoding center of the ribosome. The structure provides insights into the tRNA-remodeling function of EF-4 on the ribosome and suggests that the displacement of the CCA-end of the A-site tRNA away from the peptidyl transferase center (PTC) is functionally significant.

RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.

Puf proteins bind RNA sequence specifically and regulate translation and stability of target mRNAs. A \"code\" for RNA recognition has been deduced from crystal structures of the Puf protein, human Pumilio1, where each of eight repeats binds an RNA base via a combination of three side chains at conserved positions. Here, we report the creation of seven soluble mutant proteins with predictably altered sequence specificity, including one that binds tightly to adenosine-uracil-rich element RNA. These data show that Pumilio1 can be used as a scaffold to engineer RNA-binding proteins with designed sequence specificity.

The Sm‐like protein Hfq is involved in post‐transcriptional regulation by small, noncoding RNAs in Escherichia coli that act by base pairing. Hfq stabilises the small RNAs and mediates their interaction with the target mRNA by an as yet unknown mechanism. We show here a novel chaperoning use of Hfq in the regulation by small RNAs. We analysed in vitro and in vivo the role of Hfq in the interaction between the small RNA RyhB and its sodB (iron superoxide dismutase) mRNA target. Hfq bound strongly to sodB mRNA and altered the structure of the mRNA, partially opening a loop. This gives access to a sequence complementary to RyhB and encompassing the translation initiation codon. RyhB binding blocked the translation initiation codon of sodB and triggered the degradation of both RyhB and sodB mRNA. Thus, Hfq is a critical chaperone in vivo and in vitro , changing the folding of the target mRNA to make it subject to the small RNA regulator.

The recognition of cis-regulatory RNA motifs in human transcripts by RNA binding proteins (RBPs) is essential for gene regulation. The molecular features that determine RBP specificity are often poorly understood. Here, we combined NMR structural biology with high-throughput iCLIP approaches to identify a regulatory mechanism for U2AF2 RNA recognition. We found that the intrinsically disordered linker region connecting the two RNA recognition motif (RRM) domains of U2AF2 mediates autoinhibitory intramolecular interactions to reduce nonproductive binding to weak Py-tract RNAs. This proofreading favors binding of U2AF2 at stronger Py-tracts, as required to define 3′ splice sites at early stages of spliceosome assembly. Mutations that impair the linker autoinhibition enhance the affinity for weak Py-tracts result in promiscuous binding of U2AF2 along mRNAs and impact on splicing fidelity. Our findings highlight an important role of intrinsically disordered linkers to modulate RNA interactions of multidomain RBPs.