In vivo structural characterization of the SARS-CoV-2 RNA genome identifies host proteins vulnerable to repurposed drugs.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding of the RNA virus and its interactions with host proteins could improve therapeutic interventions for COVID-19. By using icSHAPE, we determined the structural landscape of SARS-CoV-2 RNA in infected human cells and from refolded RNAs, as well as the regulatory untranslated regions of SARS-CoV-2 and six other coronaviruses. We validated several structural elements predicted in silico and discovered structural features that affect the translation and abundance of subgenomic viral RNAs in cells. The structural data informed a deep-learning tool to predict 42 host proteins that bind to SARS-CoV-2 RNA. Strikingly, antisense oligonucleotides targeting the structural elements and FDA-approved drugs inhibiting the SARS-CoV-2 RNA binding proteins dramatically reduced SARS-CoV-2 infection in cells derived from human liver and lung tumors. Our findings thus shed light on coronavirus and reveal multiple candidate therapeutics for COVID-19 treatment.

Cold Shock Response in Bacteria.

Bacteria often encounter temperature fluctuations in their natural habitats and must adapt to survive. The molecular response of bacteria to sudden temperature upshift or downshift is termed the heat shock response (HSR) or the cold shock response (CSR), respectively. Unlike the HSR, which activates a dedicated transcription factor that predominantly copes with heat-induced protein folding stress, the CSR is mediated by a diverse set of inputs. This review provides a picture of our current understanding of the CSR across bacteria. The fundamental aspects of CSR involved in sensing and adapting to temperature drop, including regulation of membrane fluidity, protein folding, DNA topology, RNA metabolism, and protein translation, are discussed. Special emphasis is placed on recent findings of a CSR circuitry in Escherichia coli mediated by cold shock family proteins and RNase R that monitors and modulates messenger RNA structure to facilitate global translation recovery during acclimation.

Stepwise sRNA targeting of structured bacterial mRNAs leads to abortive annealing.

Bacterial small RNAs (sRNAs) regulate the expression of hundreds of transcripts via base pairing mediated by the Hfq chaperone protein. sRNAs and the mRNA sites they target are heterogeneous in sequence, length, and secondary structure. To understand how Hfq can flexibly match diverse sRNA and mRNA pairs, we developed a single-molecule Förster resonance energy transfer (smFRET) platform that visualizes the target search on timescales relevant in cells. Here we show that unfolding of target secondary structure on Hfq creates a kinetic energy barrier that determines whether target recognition succeeds or aborts before a stable anti-sense complex is achieved. Premature dissociation of the sRNA can be alleviated by strong RNA-Hfq interactions, explaining why sRNAs have different target recognition profiles. We propose that the diverse sequences and structures of Hfq substrates create an additional layer of information that tunes the efficiency and selectivity of non-coding RNA regulation in bacteria.

Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms.

Severe-acute-respiratory-syndrome-related coronavirus 2 (SARS-CoV-2) is the positive-sense RNA virus that causes coronavirus disease 2019 (COVID-19). The genome of SARS-CoV-2 is unique among viral RNAs in its vast potential to form RNA structures, yet as much as 97% of its 30 kilobases have not been structurally explored. Here, we apply a novel long amplicon strategy to determine the secondary structure of the SARS-CoV-2 RNA genome at single-nucleotide resolution in infected cells. Our in-depth structural analysis reveals networks of well-folded RNA structures throughout Orf1ab and reveals aspects of SARS-CoV-2 genome architecture that distinguish it from other RNA viruses. Evolutionary analysis shows that several features of the SARS-CoV-2 genomic structure are conserved across ß-coronaviruses, and we pinpoint regions of well-folded RNA structure that merit downstream functional analysis. The native, secondary structure of SARS-CoV-2 presented here is a roadmap that will facilitate focused studies on the viral life cycle, facilitate primer design, and guide the identification of RNA drug targets against COVID-19.

Footprinting SHAPE-eCLIP Reveals Transcriptome-wide Hydrogen Bonds at RNA-Protein Interfaces.

Discovering the interaction mechanism and location of RNA-binding proteins (RBPs) on RNA is critical for understanding gene expression regulation. Here, we apply selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) on in vivo transcripts compared to protein-absent transcripts in four human cell lines to identify transcriptome-wide footprints (fSHAPE) on RNA. Structural analyses indicate that fSHAPE precisely detects nucleobases that hydrogen bond with protein. We demonstrate that fSHAPE patterns predict binding sites of known RBPs, such as iron response elements in both known loci and previously unknown loci in CDC34, SLC2A4RG, COASY, and H19. Furthermore, by integrating SHAPE and fSHAPE with crosslinking and immunoprecipitation (eCLIP) of desired RBPs, we interrogate specific RNA-protein complexes, such as histone stem-loop elements and their nucleotides that hydrogen bond with stem-loop-binding proteins. Together, these technologies greatly expand our ability to study and understand specific cellular RNA interactions in RNA-protein complexes.

Hydrogen bonding heterogeneity correlates with protein folding transition state passage time as revealed by data sonification.

Protein-protein and protein-water hydrogen bonding interactions play essential roles in the way a protein passes through the transition state during folding or unfolding, but the large number of these interactions in molecular dynamics (MD) simulations makes them difficult to analyze. Here, we introduce a state space representation and associated "rarity" measure to identify and quantify transition state passage (transit) events. Applying this representation to a long MD simulation trajectory that captured multiple folding and unfolding events of the GTT WW domain, a small protein often used as a model for the folding process, we identified three transition categories: Highway (faster), Meander (slower), and Ambiguous (intermediate). We developed data sonification and visualization tools to analyze hydrogen bond dynamics before, during, and after these transition events. By means of these tools, we were able to identify characteristic hydrogen bonding patterns associated with "Highway" versus "Meander" versus "Ambiguous" transitions and to design algorithms that can identify these same folding pathways and critical protein-water interactions directly from the data. Highly cooperative hydrogen bonding can either slow down or speed up transit. Furthermore, an analysis of protein-water hydrogen bond dynamics at the surface of WW domain shows an increase in hydrogen bond lifetime from folded to unfolded conformations with Ambiguous transitions as an outlier. In summary, hydrogen bond dynamics provide a direct window into the heterogeneity of transits, which can vary widely in duration (by a factor of 10) due to a complex energy landscape.

Secondary structure determines electron transport in peptides.

Proteins play a key role in biological electron transport, but the structure-function relationships governing the electronic properties of peptides are not fully understood. Despite recent progress, understanding the link between peptide conformational flexibility, hierarchical structures, and electron transport pathways has been challenging. Here, we use single-molecule experiments, molecular dynamics (MD) simulations, nonequilibrium Green's function-density functional theory (NEGF-DFT), and unsupervised machine learning to understand the role of secondary structure on electron transport in peptides. Our results reveal a two-state molecular conductance behavior for peptides across several different amino acid sequences. MD simulations and Gaussian mixture modeling are used to show that this two-state molecular conductance behavior arises due to the conformational flexibility of peptide backbones, with a high-conductance state arising due to a more defined secondary structure (beta turn or 310 helices) and a low-conductance state occurring for extended peptide structures. These results highlight the importance of helical conformations on electron transport in peptides. Conformer selection for the peptide structures is rationalized using principal component analysis of intramolecular hydrogen bonding distances along peptide backbones. Molecular conformations from MD simulations are used to model charge transport in NEGF-DFT calculations, and the results are in reasonable qualitative agreement with experiments. Projected density of states calculations and molecular orbital visualizations are further used to understand the role of amino acid side chains on transport. Overall, our results show that secondary structure plays a key role in electron transport in peptides, which provides broad avenues for understanding the electronic properties of proteins.

sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

MOTIVATION: Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure from a raw RNA sequence is a long-standing unsolved problem, which after decades of almost unchanged performance has now re-emerged due to deep learning. Traditional RNA secondary structure prediction algorithms have been mostly based on thermodynamic models and dynamic programming for free energy minimization. More recently deep learning methods have shown competitive performance compared with the classical ones, but there is still a wide margin for improvement. RESULTS: In this work we present sincFold, an end-to-end deep learning approach, that predicts the nucleotides contact matrix using only the RNA sequence as input. The model is based on 1D and 2D residual neural networks that can learn short- and long-range interaction patterns. We show that structures can be accurately predicted with minimal physical assumptions. Extensive experiments were conducted on several benchmark datasets, considering sequence homology and cross-family validation. sincFold was compared with classical methods and recent deep learning models, showing that it can outperform the state-of-the-art methods.

DeepSS2GO: protein function prediction from secondary structure.

Predicting protein function is crucial for understanding biological life processes, preventing diseases and developing new drug targets. In recent years, methods based on sequence, structure and biological networks for protein function annotation have been extensively researched. Although obtaining a protein in three-dimensional structure through experimental or computational methods enhances the accuracy of function prediction, the sheer volume of proteins sequenced by high-throughput technologies presents a significant challenge. To address this issue, we introduce a deep neural network model DeepSS2GO (Secondary Structure to Gene Ontology). It is a predictor incorporating secondary structure features along with primary sequence and homology information. The algorithm expertly combines the speed of sequence-based information with the accuracy of structure-based features while streamlining the redundant data in primary sequences and bypassing the time-consuming challenges of tertiary structure analysis. The results show that the prediction performance surpasses state-of-the-art algorithms. It has the ability to predict key functions by effectively utilizing secondary structure information, rather than broadly predicting general Gene Ontology terms. Additionally, DeepSS2GO predicts five times faster than advanced algorithms, making it highly applicable to massive sequencing data. The source code and trained models are available at https://github.com/orca233/DeepSS2GO.

A Stress Response that Monitors and Regulates mRNA Structure Is Central to Cold Shock Adaptation.

Temperature influences the structural and functional properties of cellular components, necessitating stress responses to restore homeostasis following temperature shift. Whereas the circuitry controlling the heat shock response is well understood, that controlling the E. coli cold shock adaptation program is not. We found that during the growth arrest phase (acclimation) that follows shift to low temperature, protein synthesis increases, and open reading frame (ORF)-wide mRNA secondary structure decreases. To identify the regulatory system controlling this process, we screened for players required for increased translation. We identified a two-member mRNA surveillance system that enables recovery of translation during acclimation: RNase R assures appropriate mRNA degradation and the Csps dynamically adjust mRNA secondary structure to globally modulate protein expression level. An autoregulatory switch in which Csps tune their own expression to cellular demand enables dynamic control of global translation. The universality of Csps in bacteria suggests broad utilization of this control mechanism.

Sequence, Structure, and Context Preferences of Human RNA Binding Proteins.

RNA binding proteins (RBPs) orchestrate the production, processing, and function of mRNAs. Here, we present the affinity landscapes of 78 human RBPs using an unbiased assay that determines the sequence, structure, and context preferences of these proteins in vitro by deep sequencing of bound RNAs. These data enable construction of "RNA maps" of RBP activity without requiring crosslinking-based assays. We found an unexpectedly low diversity of RNA motifs, implying frequent convergence of binding specificity toward a relatively small set of RNA motifs, many with low compositional complexity. Offsetting this trend, however, we observed extensive preferences for contextual features distinct from short linear RNA motifs, including spaced "bipartite" motifs, biased flanking nucleotide composition, and bias away from or toward RNA structure. Our results emphasize the importance of contextual features in RNA recognition, which likely enable targeting of distinct subsets of transcripts by different RBPs that recognize the same linear motif.

An amino-domino model described by a cross-peptide-bond Ramachandran plot defines amino acid pairs as local structural units.

Protein structure, both at the global and local level, dictates function. Proteins fold from chains of amino acids, forming secondary structures, α-helices and ß-strands, that, at least for globular proteins, subsequently fold into a three-dimensional structure. Here, we show that a Ramachandran-type plot focusing on the two dihedral angles separated by the peptide bond, and entirely contained within an amino acid pair, defines a local structural unit. We further demonstrate the usefulness of this cross-peptide-bond Ramachandran plot by showing that it captures ß-turn conformations in coil regions, that traditional Ramachandran plot outliers fall into occupied regions of our plot, and that thermophilic proteins prefer specific amino acid pair conformations. Further, we demonstrate experimentally that the effect of a point mutation on backbone conformation and protein stability depends on the amino acid pair context, i.e., the identity of the adjacent amino acid, in a manner predictable by our method.

Generation of antibodies to an extracellular region of the transporters Glut1/Glut4 by immunization with a designed antigen.

Monoclonal antibodies are one of the fastest growing class of drugs. Nevertheless, relatively few biologics target multispanning membrane proteins because of technical challenges. To target relatively small extracellular regions of multiple membrane-spanning proteins, synthetic peptides, which are composed of amino acids corresponding to an extracellular region of a membrane protein, are often utilized in antibody discovery. However, antibodies to these peptides often do not recognize parental membrane proteins. In this study, we designed fusion proteins in which an extracellular helix of the membrane protein glucose transporter 1 (Glut1) was grafted onto the scaffold protein Adhiron. In the initial design, the grafted fragment did not form a helical conformation. Molecular dynamics simulations of full-length Glut1 suggested the importance of intramolecular interactions formed by surrounding residues in the formation of the helical conformation. A fusion protein designed to maintain such intramolecular interactions did form the desired helical conformation in the grafted region. We then immunized an alpaca with the designed fusion protein and obtained VHH (variable region of heavy-chain antibodies) using the phage display method. The binding of these VHH antibodies to the recombinant Glut1 protein was evaluated by surface plasmon resonance, and their binding to Glut1 on the cell membrane was further validated by flow cytometry. Furthermore, we also succeeded in the generation of a VHH against another integral membrane protein, glucose transporter 4 (Glut4) with the same strategy. These illustrates that our combined biochemical and computational approach can be applied to designing other novel fusion proteins for generating site-specific antibodies.

RNA:RNA interaction in ternary complexes resolved by chemical probing.

RNA regulation can be performed by a second targeting RNA molecule, such as in the microRNA regulation mechanism. Selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) probes the structure of RNA molecules and can resolve RNA:protein interactions, but RNA:RNA interactions have not yet been addressed with this technique. Here, we apply SHAPE to investigate RNA-mediated binding processes in RNA:RNA and RNA:RNA-RBP complexes. We use RNA:RNA binding by SHAPE (RABS) to investigate microRNA-34a (miR-34a) binding its mRNA target, the silent information regulator 1 (mSIRT1), both with and without the Argonaute protein, constituting the RNA-induced silencing complex (RISC). We show that the seed of the mRNA target must be bound to the microRNA loaded into RISC to enable further binding of the compensatory region by RISC, while the naked miR-34a is able to bind the compensatory region without seed interaction. The method presented here provides complementary structural evidence for the commonly performed luciferase-assay-based evaluation of microRNA binding-site efficiency and specificity on the mRNA target site and could therefore be used in conjunction with it. The method can be applied to any nucleic acid-mediated RNA- or RBP-binding process, such as splicing, antisense RNA binding, or regulation by RISC, providing important insight into the targeted RNA structure.

bpRNA-align: improved RNA secondary structure global alignment for comparing and clustering RNA structures.

Ribonucleic acid (RNA) is a polymeric molecule that is fundamental to biological processes, with structure being more highly conserved than primary sequence and often key to its function. Advances in RNA structure characterization have resulted in an increase in the number of accurate secondary structures. The task of uncovering common RNA structural motifs with a collective function through structural comparison, providing a level of similarity, remains challenging and could be used to improve RNA secondary structure databases and discover new RNA families. In this work, we present a novel secondary structure alignment method, bpRNA-align. bpRNA-align is a customized global structural alignment method, utilizing an inverted (gap extend costs more than gap open) and context-specific affine gap penalty along with a structural, feature-specific substitution matrix to provide similarity scores. We evaluate our similarity scores in comparison to other methods, using affinity propagation clustering, applied to a benchmarking data set of known structure types. bpRNA-align shows improvement in clustering performance over a broad range of structure types.

Computational design and experimental verification of pseudoknotted ribozymes.

The design of new RNA sequences that retain the function of a model RNA structure is a challenge in bioinformatics because of the structural complexity of these molecules. RNA can fold into its secondary and tertiary structures by forming stem-loops and pseudoknots. A pseudoknot is a set of base pairs between a region within a stem-loop and nucleotides outside of this stem-loop; this motif is very important for numerous functional structures. It is important for any computational design algorithm to take into account these interactions to give a reliable result for any structures that include pseudoknots. In our study, we experimentally validated synthetic ribozymes designed by Enzymer, which implements algorithms allowing for the design of pseudoknots. Enzymer is a program that uses an inverse folding approach to design pseudoknotted RNAs; we used it in this study to design two types of ribozymes. The ribozymes tested were the hammerhead and the glmS, which have a self-cleaving activity that allows them to liberate the new RNA genome copy during rolling-circle replication or to control the expression of the downstream genes, respectively. We demonstrated the efficiency of Enzymer by showing that the pseudoknotted hammerhead and glmS ribozymes sequences it designed were extensively modified compared to wild-type sequences and were still active.

Impact on splicing in Saccharomyces cerevisiae of random 50-base sequences inserted into an intron.

Intron splicing is a key regulatory step in gene expression in eukaryotes. Three sequence elements required for splicing-5' and 3' splice sites and a branchpoint-are especially well-characterized in Saccharomyces cerevisiae, but our understanding of additional intron features that impact splicing in this organism is incomplete, due largely to its small number of introns. To overcome this limitation, we constructed a library in S. cerevisiae of random 50-nt (N50) elements individually inserted into the intron of a reporter gene and quantified canonical splicing and the use of cryptic splice sites by sequencing analysis. More than 70% of approximately 140,000 N50 elements reduced splicing by at least 20%. N50 features, including higher GC content, presence of GU repeats, and stronger predicted secondary structure of its pre-mRNA, correlated with reduced splicing efficiency. A likely basis for the reduced splicing of such a large proportion of variants is the formation of RNA structures that pair N50 bases-such as the GU repeats-with other bases specifically within the reporter pre-mRNA analyzed. However, multiple models were unable to explain more than a small fraction of the variance in splicing efficiency across the library, suggesting that complex nonlinear interactions in RNA structures are not accurately captured by RNA structure prediction methods. Our results imply that the specific context of a pre-mRNA may determine the bases allowable in an intron to prevent secondary structures that reduce splicing. This large data set can serve as a resource for further exploration of splicing mechanisms.

Identification of a putative Gag binding site critical for feline immunodeficiency virus genomic RNA packaging.

The retroviral Gag precursor plays a central role in the selection and packaging of viral genomic RNA (gRNA) by binding to virus-specific packaging signal(s) (psi or ψ). Previously, we mapped the feline immunodeficiency virus (FIV) ψ to two discontinuous regions within the 5' end of the gRNA that assumes a higher order structure harboring several structural motifs. To better define the region and structural elements important for gRNA packaging, we methodically investigated these FIV ψ sequences using genetic, biochemical, and structure-function relationship approaches. Our mutational analysis revealed that the unpaired U85CUG88 stretch within FIV ψ is crucial for gRNA encapsidation into nascent virions. High-throughput selective 2' hydroxyl acylation analyzed by primer extension (hSHAPE) performed on wild type (WT) and mutant FIV ψ sequences, with substitutions in the U85CUG88 stretch, revealed that these mutations had limited structural impact and maintained nucleotides 80-92 unpaired, as in the WT structure. Since these mutations dramatically affected packaging, our data suggest that the single-stranded U85CUG88 sequence is important during FIV RNA packaging. Filter-binding assays performed using purified FIV Pr50Gag on WT and mutant U85CUG88 ψ RNAs led to reduced levels of Pr50Gag binding to mutant U85CUG88 ψ RNAs, indicating that the U85CUG88 stretch is crucial for ψ RNA-Pr50Gag interactions. Delineating sequences important for FIV gRNA encapsidation should enhance our understanding of both gRNA packaging and virion assembly, making them potential targets for novel retroviral therapeutic interventions, as well as the development of FIV-based vectors for human gene therapy.

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gained significant traction in recent years. Historically, thermodynamics-based methods were widely employed for the prediction of RNA secondary structures; however, machine learning-based approaches have demonstrated remarkable advancements in recent years, enabling more accurate predictions. Consequently, the precision of sequence analysis pertaining to RNA secondary structures, such as RNA-protein interactions, has also been enhanced, making a substantial contribution to the field of RNA biology. Additionally, artificial intelligence and machine learning are also introducing technical innovations in the analysis of RNA-small molecule interactions for RNA-targeted drug discovery and in the design of RNA aptamers, where RNA serves as its own ligand. This review will highlight recent trends in the prediction of RNA secondary structure, RNA aptamers and RNA drug discovery using machine learning, deep learning and related technologies, and will also discuss potential future avenues in the field of RNA informatics.

Machine learning modeling of RNA structures: methods, challenges and future perspectives.

The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.