Sampling Native-like Structures of RNA-Protein Complexes through Rosetta Folding and Docking

HIPPO: HIstogram-based Pseudo-POtential for scoring protein-ssRNA fragment-based docking poses

BACKGROUND

The RNA-Recognition motif (RRM) is a protein domain that binds single-stranded RNA (ssRNA) and is present in as much as 2% of the human genome. Despite this important role in biology, RRM-ssRNA interactions are very challenging to study on the structural level because of the remarkable flexibility of ssRNA. In the absence of atomic-level experimental data, the only method able to predict the 3D structure of protein-ssRNA complexes with any degree of accuracy is ssRNA'TTRACT, an ssRNA fragment-based docking approach using ATTRACT. However, since ATTRACT parameters are not ssRNA-specific and were determined in 2010, there is substantial opportunity for enhancement.

RESULTS

Here we present HIPPO, a composite RRM-ssRNA scoring potential derived analytically from contact frequencies in near-native versus non-native docking models. HIPPO consists of a consensus of four distinct potentials, each extracted from a distinct reference pool of protein-trinucleotide docking decoys. To score a docking pose with one potential, for each pair of RNA-protein coarse-grained bead types, each contact is awarded or penalised according to the relative frequencies of this contact distance range among the correct and incorrect poses of the reference pool. Validated on a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, HIPPO achieved a threefold or higher enrichment for half of the fragments, versus only a quarter with the ATTRACT scoring function. In particular, HIPPO drastically improved the chance of very high enrichment (12-fold or higher), a scenario where the incremental modelling of entire ssRNA chains from fragments becomes viable. However, for the latter result, more research is needed to make it directly practically applicable. Regardless, our approach already improves upon the state of the art in RRM-ssRNA modelling and is in principle extendable to other types of protein-nucleic acid interactions.

Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation

Non-coding RNAs (ncRNAs), generated from nonprotein coding DNA sequences, constitute 98-99% of the human genome. Non-coding RNAs encompass diverse functional classes, including microRNAs, small interfering RNAs, PIWI-interacting RNAs, small nuclear RNAs, small nucleolar RNAs, and long non-coding RNAs. With critical involvement in gene expression and regulation across various biological and physiopathological contexts, such as neuronal disorders, immune responses, cardiovascular diseases, and cancer, non-coding RNAs are emerging as disease biomarkers and therapeutic targets. In this review, after providing an overview of non-coding RNAs' role in cell homeostasis, we illustrate the potential and the challenges of state-of-the-art computational methods exploited to study non-coding RNAs biogenesis, function, and modulation. This can be done by directly targeting them with small molecules or by altering their expression by targeting the cellular engines underlying their biosynthesis. Drawing from applications, also taken from our work, we showcase the significance and role of computer simulations in uncovering fundamental facets of ncRNA mechanisms and modulation. This information may set the basis to advance gene modulation tools and therapeutic strategies to address unmet medical needs.

Computational generation and characterization of IsdA-binding aptamers with single-molecule FRET analysis

Staphylococcus aureus is a major foodborne bacterial pathogen. Early detection of S. aureus is crucial to prevent infections and ensure food quality. The iron-regulated surface determinant protein A (IsdA) of S. aureus is a unique surface protein necessary for sourcing vital iron from host cells for the survival and colonization of the bacteria. The function, structure, and location of the IsdA protein make it an important protein for biosensing applications relating to the pathogen. Here, we report an in-silico approach to develop and validate high-affinity binding aptamers for the IsdA protein detection using custom-designed in-silico tools and single-molecule Fluorescence Resonance Energy Transfer (smFRET) measurements. We utilized in-silico oligonucleotide screening methods and metadynamics-based methods to generate 10 aptamer candidates and characterized them based on the Dissociation Free Energy (DFE) of the IsdA-aptamer complexes. Three of the aptamer candidates were shortlisted for smFRET experimental analysis of binding properties. Limits of detection in the low picomolar range were observed for the aptamers, and the results correlated well with the DFE calculations, indicating the potential of the in-silico approach to support aptamer discovery. This study showcases a computational SELEX method in combination with single-molecule binding studies deciphering effective aptamers against S. aureus IsdA, protein. The established approach demonstrates the ability to expedite aptamer discovery that has the potential to cut costs and predict binding efficacy. The application can be extended to designing aptamers for various protein targets, enhancing molecular recognition, and facilitating the development of high-affinity aptamers for multiple uses.

Challenges in structural modeling of RNA-protein interactions

In the past few years, the number of RNA-binding proteins (RBP) and RNA-RBP interactions has increased significantly. Here, we review recent developments in the methodology for protein-RNA and protein-protein complex structure modeling with deep learning and co-evolution, as well as discuss the challenges and opportunities for building a reliable approach for protein-RNA complex structure modelling. Protein Data bank (PDB) and Cross-linking immunoprecipitation (CLIP) data could be combined together and used to infer 2D geometry of protein-RNA interactions by deep learning.

DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design

Typical drug discovery and development processes are costly, time consuming and often biased by expert opinion. Aptamers are short, single-stranded oligonucleotides (RNA/DNA) that bind to target proteins and other types of biomolecules. Compared with small-molecule drugs, aptamers can bind to their targets with high affinity (binding strength) and specificity (uniquely interacting with the target only). The conventional development process for aptamers utilizes a manual process known as Systematic Evolution of Ligands by Exponential Enrichment (SELEX), which is costly, slow, dependent on library choice and often produces aptamers that are not optimized. To address these challenges, in this research, we create an intelligent approach, named DAPTEV, for generating and evolving aptamer sequences to support aptamer-based drug discovery and development. Using the COVID-19 spike protein as a target, our computational results suggest that DAPTEV is able to produce structurally complex aptamers with strong binding affinities.

RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study

RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.

Adaptation of HIV-1/HIV-2 Chimeras with Defects in Genome Packaging and Viral Replication

Frequent recombination is a hallmark of retrovirus replication. In rare cases, recombination occurs between distantly related retroviruses, generating novel viruses that may significantly impact viral evolution and public health. These recombinants may initially have substantial replication defects due to impaired interactions between proteins and/or nucleic acids from the two parental viruses. However, given the high mutation rates of retroviruses, these recombinants may be able to evolve improved compatibility of these viral elements. To test this hypothesis, we examined the adaptation of chimeras between two distantly related human pathogens: HIV-1 and HIV-2. We constructed HIV-1-based chimeras containing the HIV-2 nucleocapsid (NC) domain of Gag or the two zinc fingers of HIV-2 NC, which are critical for specific recognition of viral RNA. These chimeras exhibited significant defects in RNA genome packaging and replication kinetics in T cells. However, in some experiments, the chimeric viruses replicated with faster kinetics when repassaged, indicating that viral adaptation had occurred. Sequence analysis revealed the acquisition of a single amino acid substitution, S18L, in the first zinc finger of HIV-2 NC. This substitution, which represents a switch from a conserved HIV-2 residue to a conserved HIV-1 residue at this position, partially rescued RNA packaging and replication kinetics. Further analysis revealed that the combination of two substitutions in HIV-2 NC, W10F and S18L, almost completely restored RNA packaging and replication kinetics. Our study demonstrates that chimeras of distantly related retroviruses can adapt and significantly enhance their replication by acquiring a single substitution. IMPORTANCE Novel retroviruses can emerge from recombination between distantly related retroviruses. Most notably, HIV-1 originated from zoonotic transmission of a novel recombinant (SIV_cpz) into humans. Newly generated recombinants may initially have significant replication defects due to impaired interactions between viral proteins and/or nucleic acids, such as between cis- and trans-acting elements from the two parental viruses. However, provided that the recombinants retain some ability to replicate, they may be able to adapt and repair the defective interactions. Here, we used HIV-1 and HIV-2 Gag chimeras as a model system for studying the adaptation of recombinant viruses. We found that only two substitutions in the HIV-2 NC domain, W10F and S18L, were required to almost fully restore RNA genome packaging and replication kinetics. These results illustrate the extremely flexible nature of retroviruses and highlight the possible emergence of novel recombinants in the future that could pose a significant threat to public health.

In silico optimization of RNA-protein interactions for CRISPR-Cas13-based antimicrobials

RNA–protein interactions are crucial for diverse biological processes. In prokaryotes, RNA–protein interactions enable adaptive immunity through CRISPR-Cas systems. These defence systems utilize CRISPR RNA (crRNA) templates acquired from past infections to destroy foreign genetic elements through crRNA-mediated nuclease activities of Cas proteins. Thanks to the programmability and specificity of CRISPR-Cas systems, CRISPR-based antimicrobials have the potential to be repurposed as new types of antibiotics. Unlike traditional antibiotics, these CRISPR-based antimicrobials can be designed to target specific bacteria and minimize detrimental effects on the human microbiome during antibacterial therapy. In this study, we explore the potential of CRISPR-based antimicrobials by optimizing the RNA–protein interactions of crRNAs and Cas13 proteins. CRISPR-Cas13 systems are unique as they degrade specific foreign RNAs using the crRNA template, which leads to non-specific RNase activities and cell cycle arrest. We show that a high proportion of the Cas13 systems have no colocalized CRISPR arrays, and the lack of direct association between crRNAs and Cas proteins may result in suboptimal RNA–protein interactions in the current tools. Here, we investigate the RNA–protein interactions of the Cas13-based systems by curating the validation dataset of Cas13 protein and CRISPR repeat pairs that are experimentally validated to interact, and the candidate dataset of CRISPR repeats that reside on the same genome as the currently known Cas13 proteins. To find optimal CRISPR-Cas13 interactions, we first validate the 3-D structure prediction of crRNAs based on their experimental structures. Next, we test a number of RNA–protein interaction programs to optimize the in silico docking of crRNAs with the Cas13 proteins. From this optimized pipeline, we find a number of candidate crRNAs that have comparable or better in silico docking with the Cas13 proteins of the current tools. This study fully automatizes the in silico optimization of RNA–protein interactions as an efficient preliminary step for designing effective CRISPR-Cas13-based antimicrobials.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Cross-linking and mass spectrometry as a tool for studying the structural biology of ribonucleoproteins

Cross-linking and mass spectrometry (XL-MS) workflows represent an increasingly popular technique for low-resolution structural studies of macromolecular complexes. Cross-linking reactions take place in the solution state, capturing contact sites between components of a complex that represent the native, functionally relevant structure. Protein-protein XL-MS protocols are widely adopted, providing precise localization of cross-linking sites to single amino acid positions within a pair of cross-linked peptides. In contrast, protein-RNA XL-MS workflows are evolving rapidly and differ in their ability to localize interaction regions within the RNA sequence. Here, we review protein-protein and protein-RNA XL-MS workflows, and discuss their applications in studies of protein-RNA complexes. The examples highlight the complementary value of XL-MS in structural studies of protein-RNA complexes, where more established high-resolution techniques might be unable to produce conclusive data.

Conformational variability in proteins bound to single-stranded DNA: A new benchmark for new docking perspectives

We explored the Protein Data Bank (PDB) to collect protein-ssDNA structures and create a multi-conformational docking benchmark including both bound and unbound protein structures. Due to ssDNA high flexibility when not bound, no ssDNA unbound structure is included in the benchmark. For the 91 sequence-identity groups identified as bound-unbound structures of the same protein, we studied the conformational changes in the protein induced by the ssDNA binding. Moreover, based on several bound or unbound protein structures in some groups, we also assessed the intrinsic conformational variability in either bound or unbound conditions and compared it to the supposedly binding-induced modifications. To illustrate a use case of this benchmark, we performed docking experiments using ATTRACT docking software. This benchmark is, to our knowledge, the first one made to peruse available structures of ssDNA-protein interactions to such an extent, aiming to improve computational docking tools dedicated to this kind of molecular interactions.

Three critical regions of the erythromycin resistance methyltransferase, ErmE, are required for function supporting a model for the interaction of Erm family enzymes with substrate rRNA

6-Methyladenosine modification of DNA and RNA is widespread throughout the three domains of life and often accomplished by a Rossmann-fold methyltransferase domain which contains conserved sequence elements directing S-adenosylmethionine cofactor binding and placement of the target adenosine residue into the active site. Elaborations to the conserved Rossman-fold and appended domains direct methylation to diverse DNA and RNA sequences and structures. Recently, the first atomic-resolution structure of a ribosomal RNA adenine dimethylase (RRAD) family member bound to rRNA was solved, TFB1M bound to helix 45 of 12S rRNA. Since erythromycin resistance methyltransferases are also members of the RRAD family, and understanding how these enzymes recognize rRNA could be used to combat their role in antibiotic resistance, we constructed a model of ErmE bound to a 23S rRNA fragment based on the TFB1M-rRNA structure. We designed site-directed mutants of ErmE based on this model and assayed the mutants by in vivo phenotypic assays and in vitro assays with purified protein. Our results and additional bioinformatic analyses suggest our structural model captures key ErmE-rRNA interactions and indicate three regions of Erm proteins play a critical role in methylation: the target adenosine binding pocket, the basic ridge, and the α4-cleft.

Statistical potentials for RNA-protein interactions optimized by CMA-ES

Characterizing RNA-protein interactions remains an important endeavor, complicated by the difficulty in obtaining the relevant structures. Evaluating model structures via statistical potentials is in principle straight-forward and effective. However, given the relatively small size of the existing learning set of RNA-protein complexes optimization of such potentials continues to be problematic. Notably, interaction-based statistical potentials have problems in addressing large RNA-protein complexes. In this study, we adopted a novel strategy with covariance matrix adaptation (CMA-ES) to calculate statistical potentials, successfully identifying native docking poses.

Sequence- and structure-selective mRNA m5C methylation by NSUN6 in animals

mRNA m⁵C, which has recently been implicated in the regulation of mRNA mobility, metabolism and translation, plays important regulatory roles in various biological events. Two types of m⁵C sites are found in mRNAs. Type I m⁵C sites, which contain a downstream G-rich triplet motif and are computationally predicted to be located at the 5^′ end of putative hairpin structures, are methylated by NSUN2. Type II m⁵C sites contain a downstream UCCA motif and are computationally predicted to be located in the loops of putative hairpin structures. However, their biogenesis remains unknown. Here we identified NSUN6, a methyltransferase that is known to methylate C72 of tRNA^Thr and tRNA^Cys, as an mRNA methyltransferase that targets Type II m⁵C sites. Combining the RNA secondary structure prediction, miCLIP, and results from a high-throughput mutagenesis analysis, we determined the RNA sequence and structural features governing the specificity of NSUN6-mediated mRNA methylation. Integrating these features into an NSUN6-RNA structural model, we identified an NSUN6 variant that largely loses tRNA methylation but retains mRNA methylation ability. Finally, we revealed a weak negative correlation between m⁵C methylation and translation efficiency. Our findings uncover that mRNA m⁵C is tightly controlled by an elaborate two-enzyme system, and the protein-RNA structure analysis strategy established may be applied to other RNA modification writers to distinguish the functions of different RNA substrates of a writer protein.

Integrating single-molecule FRET and biomolecular simulations to study diverse interactions between nucleic acids and proteins

The conformations of biological macromolecules are intimately related to their cellular functions. Conveniently, the well-characterized dipole-dipole distance-dependence of Förster resonance energy transfer (FRET) makes it possible to measure and monitor the nanoscale spatial dimensions of these conformations using fluorescence spectroscopy. For this reason, FRET is often used in conjunction with single-molecule detection to study a wide range of conformationally dynamic biochemical processes. Written for those not yet familiar with the subject, this review aims to introduce biochemists to the methodology associated with single-molecule FRET, with a particular emphasis on how it can be combined with biomolecular simulations to study diverse interactions between nucleic acids and proteins. In the first section, we highlight several conceptual and practical considerations related to this integrative approach. In the second section, we review a few recent research efforts wherein various combinations of single-molecule FRET and biomolecular simulations were used to study the structural and dynamic properties of biochemical systems involving different types of nucleic acids (e.g., DNA and RNA) and proteins (e.g., folded and disordered).

Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Adenosine-to-inosine (A-to-I) RNA editing catalyzed by ADAR enzymes occurs in double-stranded RNAs. Despite a compelling need towards predictive understanding of natural and engineered editing events, how the RNA sequence and structure determine the editing efficiency and specificity (i.e., cis-regulation) is poorly understood. We apply a CRISPR/Cas9-mediated saturation mutagenesis approach to generate libraries of mutations near three natural editing substrates at their endogenous genomic loci. We use machine learning to integrate diverse RNA sequence and structure features to model editing levels measured by deep sequencing. We confirm known features and identify new features important for RNA editing. Training and testing XGBoost algorithm within the same substrate yield models that explain 68 to 86 percent of substrate-specific variation in editing levels. However, the models do not generalize across substrates, suggesting complex and context-dependent regulation patterns. Our integrative approach can be applied to larger scale experiments towards deciphering the RNA editing code.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

P3DOCK: a protein-RNA docking webserver based on template-based and template-free docking

MOTIVATION

The main function of protein-RNA interaction is to regulate the expression of genes. Therefore, studying protein-RNA interactions is of great significance. The information of three-dimensional (3D) structures reveals that atomic interactions are particularly important. The calculation method for modeling a 3D structure of a complex mainly includes two strategies: free docking and template-based docking. These two methods are complementary in protein-protein docking. Therefore, integrating these two methods may improve the prediction accuracy.

RESULTS

In this article, we compare the difference between the free docking and the template-based algorithm. Then we show the complementarity of these two methods. Based on the analysis of the calculation results, the transition point is confirmed and used to integrate two docking algorithms to develop P3DOCK. P3DOCK holds the advantages of both algorithms. The results of the three docking benchmarks show that P3DOCK is better than those two non-hybrid docking algorithms. The success rate of P3DOCK is also higher (3-20%) than state-of-the-art hybrid and non-hybrid methods. Finally, the hierarchical clustering algorithm is utilized to cluster the P3DOCK's decoys. The clustering algorithm improves the success rate of P3DOCK. For ease of use, we provide a P3DOCK webserver, which can be accessed at www.rnabinding.com/P3DOCK/P3DOCK.html. An integrated protein-RNA docking benchmark can be downloaded from http://rnabinding.com/P3DOCK/benchmark.html.

AVAILABILITY AND IMPLEMENTATION

www.rnabinding.com/P3DOCK/P3DOCK.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FARFAR2: Improved De Novo Rosetta Prediction of Complex Global RNA Folds

Predicting RNA three-dimensional structures from sequence could accelerate understanding of the growing number of RNA molecules being discovered across biology. Rosetta's Fragment Assembly of RNA with Full-Atom Refinement (FARFAR) has shown promise in community-wide blind RNA-Puzzle trials, but lack of a systematic and automated benchmark has left unclear what limits FARFAR performance. Here, we benchmark FARFAR2, an algorithm integrating RNA-Puzzle-inspired innovations with updated fragment libraries and helix modeling. In 16 of 21 RNA-Puzzles revisited without experimental data or expert intervention, FARFAR2 recovers native-like structures more accurate than models submitted during the RNA-Puzzles trials. Remaining bottlenecks include conformational sampling for >80-nucleotide problems and scoring function limitations more generally. Supporting these conclusions, preregistered blind models for adenovirus VA-I RNA and five riboswitch complexes predicted native-like folds with 3- to 14 Å root-mean-square deviation accuracies. We present a FARFAR2 webserver and three large model archives (FARFAR2-Classics, FARFAR2-Motifs, and FARFAR2-Puzzles) to guide future applications and advances.

Protein-ensemble-RNA docking by efficient consideration of protein flexibility through homology models

MOTIVATION

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).

RESULTS

Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/mprdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Macromolecular modeling and design in Rosetta: recent methods and frameworks

The Rosetta software for macromolecular modeling, docking and design is extensively used in laboratories worldwide. During two decades of development by a community of laboratories at more than 60 institutions, Rosetta has been continuously refactored and extended. Its advantages are its performance and interoperability between broad modeling capabilities. Here we review tools developed in the last 5 years, including over 80 methods. We discuss improvements to the score function, user interfaces and usability. Rosetta is available at http://www.rosettacommons.org.

How RNA-Binding Proteins Interact with RNA: Molecules and Mechanisms

RNA-binding proteins (RBPs) comprise a large class of over 2,000 proteins that interact with transcripts in all manner of RNA-driven processes. The structures and mechanisms that RBPs use to bind and regulate RNA are incredibly diverse. In this review, we take a look at the components of protein-RNA interaction, from the molecular level to multi-component interaction. We first summarize what is known about protein-RNA molecular interactions based on analyses of solved structures. We additionally describe software currently available for predicting protein-RNA interaction and other resources useful for the study of RBPs. We then review the structure and function of seventeen known RNA-binding domains and analyze the hydrogen bonds adopted by protein-RNA structures on a domain-by-domain basis. We conclude with a summary of the higher-level mechanisms that regulate protein-RNA interactions.

A unified mechanism for intron and exon definition and back-splicing

The molecular mechanisms of exon definition and back-splicing are fundamental unanswered questions in pre-mRNA splicing. Here we report cryo-electron microscopy structures of the yeast spliceosomal E complex assembled on introns, providing a view of the earliest event in the splicing cycle that commits pre-mRNAs to splicing. The E complex architecture suggests that the same spliceosome can assemble across an exon, and that it either remodels to span an intron for canonical linear splicing (typically on short exons) or catalyses back-splicing to generate circular RNA (on long exons). The model is supported by our experiments, which show that an E complex assembled on the middle exon of yeast EFM5 or HMRA1 can be chased into circular RNA when the exon is sufficiently long. This simple model unifies intron definition, exon definition, and back-splicing through the same spliceosome in all eukaryotes and should inspire experiments in many other systems to understand the mechanism and regulation of these processes.

Blind tests of RNA-protein binding affinity prediction

Interactions between RNA and proteins are pervasive in biology, driving fundamental processes such as protein translation and participating in the regulation of gene expression. Modeling the energies of RNA-protein interactions is therefore critical for understanding and repurposing living systems but has been hindered by complexities unique to RNA-protein binding. Here, we bring together several advances to complete a calculation framework for RNA-protein binding affinities, including a unified free energy function for bound complexes, automated Rosetta modeling of mutations, and use of secondary structure-based energetic calculations to model unbound RNA states. The resulting Rosetta-Vienna RNP-ΔΔG method achieves root-mean-squared errors (RMSEs) of 1.3 kcal/mol on high-throughput MS2 coat protein-RNA measurements and 1.5 kcal/mol on an independent test set involving the signal recognition particle, human U1A, PUM1, and FOX-1. As a stringent test, the method achieves RMSE accuracy of 1.4 kcal/mol in blind predictions of hundreds of human PUM2-RNA relative binding affinities. Overall, these RMSE accuracies are significantly better than those attained by prior structure-based approaches applied to the same systems. Importantly, Rosetta-Vienna RNP-ΔΔG establishes a framework for further improvements in modeling RNA-protein binding that can be tested by prospective high-throughput measurements on new systems.

De novo computational RNA modeling into cryo-EM maps of large ribonucleoprotein complexes

Increasingly, cryo-electron microscopy (cryo-EM) is used to determine the structures of RNA-protein assemblies, but nearly all maps determined with this method have biologically important regions where the local resolution does not permit RNA coordinate tracing. To address these omissions, we present de novo ribonucleoprotein modeling in real space through assembly of fragments together with experimental density in Rosetta (DRRAFTER). We show that DRRAFTER recovers near-native models for a diverse benchmark set of RNA-protein complexes including the spliceosome, mitochondrial ribosome, and CRISPR-Cas9-sgRNA complexes; rigorous blind tests include yeast U1 snRNP and spliceosomal P complex maps. Additionally, to aid in model interpretation, we present a method for reliable in situ estimation of DRRAFTER model accuracy. Finally, we apply DRRAFTER to recently determined maps of telomerase, the HIV-1 reverse transcriptase initiation complex, and the packaged MS2 genome, demonstrating the acceleration of accurate model building in challenging cases.