protein-RNA Interface Research Articles

N-terminal domain of polypyrimidine-tract binding protein is a dynamic folding platform for adaptive RNA recognition.

The N-terminal RNA recognition motif domain (RRM1) of polypyrimidine tract binding protein (PTB) forms an additional C-terminal helix α3, which docks to one edge of the β-sheet upon binding to a stem-loop RNA containing a UCUUU pentaloop. Importantly, α3 does not contact the RNA. The α3 helix therefore represents an allosteric means to regulate the conformation of adjacent domains in PTB upon binding structured RNAs. Here we investigate the process of dynamic adaptation by stem-loop RNA and RRM1 using NMR and MD in order to obtain mechanistic insights on how this allostery is achieved. Relaxation data and NMR structure determination of the free protein show that α3 is partially ordered and interacts with the domain transiently. Stem-loop RNA binding quenches fast time scale dynamics and α3 becomes ordered, however microsecond dynamics at the protein-RNA interface is observed. MD shows how RRM1 binding to the stem-loop RNA is coupled to the stabilization of the C-terminal helix and helps to transduce differences in RNA loop sequence into changes in α3 length and order. IRES assays of full length PTB and a mutant with altered dynamics in the α3 region show that this dynamic allostery influences PTB function in cultured HEK293T cells.

Dynamics of Protein-RNA Interfaces Using All-Atom Molecular Dynamics Simulations.

Facing the current challenges posed by human health diseases requires the understanding of cell machinery at a molecular level. The interplay between proteins and RNA is key for any physiological phenomenon, as well protein-RNA interactions. To understand these interactions, many experimental techniques have been developed, spanning a very wide range of spatial and temporal resolutions. In particular, the knowledge of tridimensional structures of protein-RNA complexes provides structural, mechanical, and dynamical pieces of information essential to understand their functions. To get insights into the dynamics of protein-RNA complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on nine different protein-RNA complexes with different functions and interface size by taking into account the bound and unbound forms. First, we characterized structural changes upon binding and, for the RNA part, the change in the puckering. Second, we extensively analyzed the interfaces, their dynamics and structural properties, and the structural waters involved in the binding, as well as the contacts mediated by them. Based on our analysis, the interfaces rearranged during the simulation time showing alternative and stable residue-residue contacts with respect to the experimental structure.

Efficient mapping of RNA-binding residues in RNA-binding proteins using local sequence features of binding site residues in protein-RNA complexes.

Protein-RNA interactions play vital roles in plethora of biological processes such as regulation of gene expression, protein synthesis, mRNA processing and biogenesis. Identification of RNA-binding residues (RBRs) in proteins is essential to understand RNA-mediated protein functioning, to perform site-directed mutagenesis and to develop novel targeted drug therapies. Moreover, the extensive gap between sequence and structural data restricts the identification of binding sites in unsolved structures. However, efficient use of computational methods demanding only sequence to identify binding residues can bridge this huge sequence-structure gap. In this study, we have extensively studied protein-RNA interface in known RNA-binding proteins (RBPs). We find that the interface is highly enriched in basic and polar residues with Gly being the most common interface neighbor. We investigated several amino acid features and developed a method to predict putative RBRs from amino acid sequence. We have implemented balanced random forest (BRF) classifier with local residue features of protein sequences for prediction. With 5-fold cross-validations, the sequence pattern derived dipeptide composition based BRF model (DCP-BRF) resulted in an accuracy of 87.9%, specificity of 88.8%, sensitivity of 82.2%, Mathew's correlation coefficient of 0.60 and AUC of 0.93, performing better than few existing methods. We further validated our prediction model on known human RBPs through RBR prediction and could map ~54% of them. Further, knowledge of binding site preferences obtained from computational predictions combined with experimental validations of potential RNA binding sites can enhance our understanding of protein-RNA interactions. This may serve to accelerate investigations on functional roles of many novel RBPs.

Specific Signal Enhancement on an RNA-Protein Interface by Dynamic Nuclear Polarization.

Invited for the cover of this issue are the groups of Alexander Marchanka at the Leibniz University of Hannover and Björn Corzilius at the University of Rostock. The image depicts the local generation of nuclear spin hyperpolarization, which selectively "illuminates" the interaction surface of a ribonuclear protein complex for solid-state NMR spectroscopy. Read the full text of the article at 10.1002/chem.202203443.

Conformational Heterogeneity of RNA Stem-Loop Hairpins Bound to FUS-RNA Recognition Motif with Disordered RGG Tail Revealed by Unbiased Molecular Dynamics Simulations.

RNA-protein complexes use diverse binding strategies, ranging from structurally well-defined interfaces to completely disordered regions. Experimental characterization of flexible segments is challenging and can be aided by atomistic molecular dynamics (MD) simulations. Here, we used an extended set of microsecond-scale MD trajectories (400 μs in total) to study two FUS-RNA constructs previously characterized by nuclear magnetic resonance (NMR) spectroscopy. The FUS protein contains a well-structured RNA recognition motif domain followed by a presumably disordered RGG tail that binds RNA stem-loop hairpins. Our simulations not only provide several suggestions complementing the experiments but also reveal major methodological difficulties in studies of such complex RNA-protein interfaces. Despite efforts to stabilize the binding via system-specific force-field adjustments, we have observed progressive distortions of the RNA-protein interface inconsistent with experimental data. We propose that the dynamics is so rich that its converged description is not achievable even upon stabilizing the system. Still, after careful analysis of the trajectories, we have made several suggestions regarding the binding. We identify substates in the RNA loops, which can explain the NMR data. The RGG tail localized in the minor groove remains disordered, sampling countless transient interactions with the RNA. There are long-range couplings among the different elements contributing to the recognition, which can lead to allosteric communication throughout the system. Overall, the RNA-FUS systems form dynamical ensembles that cannot be fully represented by single static structures. Thus, albeit imperfect, MD simulations represent a viable tool to investigate dynamic RNA-protein complexes.

An atlas of posttranslational modifications on RNA binding proteins

RNA structure and function are intimately tied to RNA binding protein recognition and regulation. Posttranslational modifications are chemical modifications which can control protein biology. The role of PTMs in the regulation RBPs is not well understood, in part due to a lacking analysis of PTM deposition on RBPs. Herein, we present an analysis of posttranslational modifications (PTMs) on RNA binding proteins (RBPs; a PTM RBP Atlas). We curate published datasets and primary literature to understand the landscape of PTMs and use protein–protein interaction data to understand and potentially provide a framework for understanding which enzymes are controlling PTM deposition and removal on the RBP landscape. Intersection of our data with The Cancer Genome Atlas also provides researchers understanding of mutations that would alter PTM deposition. Additional characterization of the RNA–protein interface provided from in-cell UV crosslinking experiments provides a framework for hypotheses about which PTMs could be regulating RNA binding and thus RBP function. Finally, we provide an online database for our data that is easy to use for the community. It is our hope our efforts will provide researchers will an invaluable tool to test the function of PTMs controlling RBP function and thus RNA biology.

PRIP: A Protein-RNA Interface Predictor Based on Semantics of Sequences

RNA–protein interactions play an indispensable role in many biological processes. Growing evidence has indicated that aberration of the RNA–protein interaction is associated with many serious human diseases. The precise and quick detection of RNA–protein interactions is crucial to finding new functions and to uncovering the mechanism of interactions. Although many methods have been presented to recognize RNA-binding sites, there is much room left for the improvement of predictive accuracy. We present a sequence semantics-based method (called PRIP) for predicting RNA-binding interfaces. The PRIP extracted semantic embedding by pre-training the Word2vec with the corpus. Extreme gradient boosting was employed to train a classifier. The PRIP obtained a SN of 0.73 over the five-fold cross validation and a SN of 0.67 over the independent test, outperforming the state-of-the-art methods. Compared with other methods, this PRIP learned the hidden relations between words in the context. The analysis of the semantics relationship implied that the semantics of some words were specific to RNA-binding interfaces. This method is helpful to explore the mechanism of RNA–protein interactions from a semantics point of view.

Crystal structure of human RPP20-RPP25 proteins in complex with the P3 domain of lncRNA RMRP

Human RNase MRP ribonucleoprotein complex is an essential endoribonuclease involved in the processing of ribosomal RNAs, mitochondrial RNAs and certain messenger RNAs. Its RNA subunit RMRP catalyzes the cleavage of substrate RNAs, and the protein components of RNase MRP are required for activity. RMRP mutations are associated with several types of inherited developmental disorders, but the pathogenic mechanism is largely unknown. Recent structural studies shed lights on the catalytic mechanism of yeast RNase MRP and the closely related RNase P; however, the structural and catalytic mechanism of RMRP in human RNase MRP complex remains unclear. Here we report the crystal structure of the P3 domain of RMRP in complex with the RPP20 and RPP25 proteins of human RNase MRP, which shows that the P3 RNA binds to a conserved positively-charged surface of the RPP20-RPP25 heterodimer through its distal stem and internal loop regions. The disease-related mutations of RMRPP3 are mostly located at the protein-RNA interface and are likely to weaken the binding of P3 to RPP20-RPP25. Moreover, the structure reveals a homodimeric organization of the entire RPP20-RPP25-RMRPP3 complex, which might mediate the dimerization of human RNase MRP complex in cells. These findings provide structural clues to the assembly and pathogenesis of human RNase MRP complex and also reveal a tetrameric feature of RPP20-RPP25 evolutionarily conserved with that of the archaeal Alba proteins.

Heat flow random walks in biomolecular systems using symbolic transfer entropy and graph theory

This study combines the information- and graph-theoretic measures to investigate the cluster modulation of the amino acid residues and nucleotides at complex biomolecular interfaces. The symbolic transfer entropy is used as an information-theoretic measure. I also used graph theory to obtain information and heat flow weighted digraph models used to study the topology of information and heat flow paths at complex biomolecular interfaces. I introduce the graph-theoretic measures, such as the influence score and betweenness centrality, to identify the most influential amino acid and nucleotide sequences as sources of the information and absorb centers of the structure’s heat flow. PageRank-like random walks algorithm is used to analyze the network of amino acid and nucleotide sequences at the protein-RNA interface combined with weighted digraph models. The cluster analysis using graph-theoretic measures revealed the modular molecular structure and the mechanism of the binding interface. In this study, the first benchmark system is an intuitive directed information flow network used to test the algorithms, and the second benchmark is a protein-RNA complex system. The approach was able to identify the most influential amino acid residues and nucleotides. Furthermore, the statistical cluster analysis using graph-theoretic measures revealed the modular molecular structure and the binding mechanism at the interface.

Co-crystal structures of HIV TAR RNA bound to lab-evolved proteins show key roles for arginine relevant to the design of cyclic peptide TAR inhibitors

RNA-protein interfaces control key replication events during the HIV-1 life cycle. The viral trans-activator of transcription (Tat) protein uses an archetypal arginine-rich motif (ARM) to recruit the host positive transcription elongation factor b (pTEFb) complex onto the viral trans-activation response (TAR) RNA, leading to activation of HIV transcription. Efforts to block this interaction have stimulated production of biologics designed to disrupt this essential RNA-protein interface. Here, we present four co-crystal structures of lab-evolved TAR-binding proteins (TBPs) in complex with HIV-1 TAR. Our results reveal that high-affinity binding requires a distinct sequence and spacing of arginines within a specific β2-β3 hairpin loop that arose during selection. Although loops with as many as five arginines were analyzed, only three arginines could bind simultaneously with major-groove guanines. Amino acids that promote backbone interactions within the β2-β3 loop were also observed to be important for high-affinity interactions. Based on structural and affinity analyses, we designed two cyclic peptide mimics of the TAR-binding β2-β3 loop sequences present in two high-affinity TBPs (KD values of 4.2 ± 0.3 and 3.0 ± 0.3 nm). Our efforts yielded low-molecular weight compounds that bind TAR with low micromolar affinity (KD values ranging from 3.6 to 22 μm). Significantly, one cyclic compound within this series blocked binding of the Tat-ARM peptide to TAR in solution assays, whereas its linear counterpart did not. Overall, this work provides insight into protein-mediated TAR recognition and lays the ground for the development of cyclic peptide inhibitors of a vital HIV-1 RNA-protein interaction.

APRBind: protein-RNA interface prediction by combining sequence and I-TASSER model-based structural features learned with convolutional neural networks.

Protein-RNA interactions play a critical role in various biological processes. The accurate prediction of RNA-binding residues in proteins has been one of the most challenging and intriguing problems in the field of computational biology. The existing methods still have a relatively low accuracy especially for the sequence-based ab-initio methods. In this work, we propose an approach aPRBind, a convolutional neural network-based ab-initio method for RNA-binding residue prediction. aPRBind is trained with sequence features and structural ones (particularly including residue dynamics information and residue-nucleotide propensity developed by us) that are extracted from the predicted structures by I-TASSER. The analysis of feature contributions indicates the sequence features are most important, followed by dynamics information, and the sequence and structural features are complementary in binding site prediction. The performance comparison of our method with other peer ones on benchmark dataset shows that aPRBind outperforms some state-of-the-art ab-initio methods. Additionally, aPRBind can give a better prediction for the modeled structures with TM-score≥0.5, and meanwhile since the structural features are not very sensitive to the refined 3D structures, aPRBind has only a marginal dependence on the accuracy of the structure model, which allows aPRBind to be applied to the RNA-binding site prediction for the modeled or unbound structures. The source code is available at https://github.com/ChunhuaLiLab/aPRbind. Supplementary data are available at Bioinformatics online.

A quantum chemical view of the interaction of RNA nucleobases and base pairs with the side chains of polar amino acids

Hydrogen bonding between amino acids and nucleobases is important for RNA–protein recognition. As a first step toward understanding the physicochemical features of these contacts, the present work employs density functional theory calculations to critically analyze the intrinsic structures and strength of all theoretically possible model hydrogen-bonded complexes involving RNA nucleobase edges and polar amino acid side chains. Our geometry optimizations uncover a number of unique complexes that involve variable hydrogen-bonding characteristics, including conventional donor–acceptor interactions, bifurcated interactions and single hydrogen-bonded contacts. Further, significant strength of these complexes in the gas phase (−27 kJ mol−1 to −226 kJ mol−1) and solvent phase (−19 kJ mol−1 to −78 kJ mol−1) points toward the ability of associated contacts to provide stability to RNA–protein complexes. More importantly, for the first time, our study uncovers the features of complexes involving protonated nucleobases, as well as those involving the weakly polar cysteine side chain, and thereby highlights their potential importance in biological processes that involve RNA–protein interactions. Additional analysis on select base pair-amino acid complexes uncovers the ability of amino acid side chain to simultaneously interact with both nucleobases of the base pair, and highlights the greater strength of such interactions compared to base-amino acid interactions. Overall, our analysis provides a basic physicochemical framework for understanding the molecular basis of nucleic acid–protein interactions. Further, our quantum chemical data can be used to design better algorithms for automated search of these contacts at the RNA–protein interface. Communicated by Ramaswamy H. Sarma

Analyses on clustering of the conserved residues at protein-RNA interfaces and its application in binding site identification

BackgroundThe maintenance of protein structural stability requires the cooperativity among spatially neighboring residues. Previous studies have shown that conserved residues tend to occur clustered together within enzyme active sites and protein-protein/DNA interfaces. It is possible that conserved residues form one or more local clusters in protein tertiary structures as it can facilitate the formation of functional motifs. In this work, we systematically investigate the spatial distributions of conserved residues as well as hot spot ones within protein-RNA interfaces.ResultsThe analysis of 191 polypeptide chains from 160 complexes shows the polypeptides interacting with tRNAs evolve relatively rapidly. A statistical analysis of residues in different regions shows that the interface residues are often more conserved, while the most conserved ones are those occurring at protein interiors which maintain the stability of folded polypeptide chains. Additionally, we found that 77.8% of the interfaces have the conserved residues clustered within the entire interface regions. Appling the clustering characteristics to the identification of the real interface, there are 31.1% of cases where the real interfaces are ranked in top 10% of 1000 randomly generated surface patches. In the conserved clusters, the preferred residues are the hydrophobic (Leu, Ile, Met), aromatic (Tyr, Phe, Trp) and interestingly only one positively charged Arg residues. For the hot spot residues, 51.5% of them are situated in the conserved residue clusters, and they are largely consistent with the preferred residue types in the conserved clusters.ConclusionsThe protein-RNA interface residues are often more conserved than non-interface surface ones. The conserved interface residues occur more spatially clustered relative to the entire interface residues. The high consistence of hot spot residue types and the preferred residue types in the conserved clusters has important implications for the experimental alanine scanning mutagenesis study. This work deepens the understanding of the residual organization at protein-RNA interface and is of potential applications in the identification of binding site and hot spot residues.

Rec-Y3H screening allows the detection of simultaneous RNA-protein interface mutations.

Understanding which proteins and RNAs directly interact is crucial for revealing cellular mechanisms of gene regulation. Efficient methods allowing to detect RNA-protein interactions and dissect the underlying molecular origin for RNA-binding protein (RBP) specificity are in high demand. The recently developed recombination-Y3H screening (rec-Y3H) enabled many-by-many detection of interactions between pools of proteins and RNA fragments for the first time. Here, we test different conditions for protein-RNA interaction selection during rec-Y3H screening and provide information on the screen performance in several selection media. We further show that rec-Y3H can detect the nucleotide and amino acid sequence determinants of protein-RNA interactions by mutating residues of interacting proteins and RNAs simultaneously. We envision that systematic RNA-protein interface mutation screening will be useful to understand the molecular origin of RBP selectivity and to engineer RBPs with targeted specificities in the future.

A conformational switch balances viral RNA accessibility and protection in a nucleocapsid ring model

Virus from the Mononegavirales order share common features ranging from virion structure arrangement to mechanisms of replication and transcription. One of them is the way the nucleoprotein (N) wraps and protects the RNA genome from degradation by forming a highly ordered helical nucleocapsid. However, crystal structures from numerous Mononegavirales reveal that binding to the nucleoprotein results in occluded nucleotides that hinder base pairing necessary for transcription and replication. This hints at the existence of alternative conformations of the N protein that would impact on the protein-RNA interface, allowing for transient exposure of the nucleotides without complete RNA release. Moreover, the regulation between the alternative conformations should be finely tuned. Recombinant expression of N from the respiratory syncytial virus form regular N/RNA common among all Mononegavirales, and these constitute an ideal minimal unit for investigating the mechanisms through which these structures protect RNA so efficiently while allowing for partial accessibility during transcription and replication. Neither pH nor high ionic strength could dissociate the RNA but led to irreversible aggregation of the nucleoprotein. Low concentrations of guanidine chloride dissociated the RNA moiety but leading to irreversible aggregation of the protein moiety. On the other hand, high concentrations of urea and long incubation periods were required to remove bound RNA. Both denaturants eventually led to unfolding but converged in the formation of an RNA-free β-enriched intermediate species that remained decameric even at high denaturant concentrations. Although the N-RNA rings interact with the phosphoprotein P, the scaffold of the RNA polymerase complex, this interaction did not lead to RNA dissociation from the rings in vitro. Thus, we have uncovered complex equilibria involving changes in secondary structure of N and RNA loosening, processes that must take place in the context of RNA transcription and replication, whose detailed mechanisms and cellular and viral participants need to be established.

A Step-by-Step Guide to Study Protein-RNA Interactions.

Protein-RNA complex formation is at the center of RNA metabolism and leads to the modulation of protein and RNA functions. We propose here a step-by-step guide to investigate these interactions including the identification of the protein and RNA parts involved in complex formation, the determination of the affinity of the complex and the characterization of the protein-RNA interface at amino acid and nucleotide level. Moreover, we briefly review the methods that are the most often used to obtain this information using primarily examples from our lab and finally mention what we perceive as the next challenges in the field.

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.

QM/MM Calculations on Protein-RNA Complexes: Understanding Limitations of Classical MD Simulations and Search for Reliable Cost-Effective QM Methods.

Although atomistic explicit-solvent Molecular Dynamics (MD) is a popular tool to study protein-RNA recognition, satisfactory MD description of protein-RNA complexes is not always achieved. Unfortunately, it is often difficult to separate MD simulation instabilities primarily caused by the simple point-charge molecular mechanics (MM) force fields from problems related to the notorious uncertainties in the starting structures. Herein, we report a series of large-scale QM/MM calculations on the U1A protein-RNA complex. This experimentally well-characterized system has an intricate protein-RNA interface, which is very unstable in MD simulations. The QM/MM calculations identify several H-bonds poorly described by the MM method and thus indicate the sources of instabilities of the U1A interface in MD simulations. The results suggest that advanced QM/MM computations could be used to indirectly rationalize problems seen in MM-based MD simulations of protein-RNA complexes. As the most accurate QM method, we employ the computationally demanding meta-GGA density functional TPSS-D3(BJ)/def2-TZVP level of theory. Because considerably faster methods would be needed to extend sampling and to study even larger protein-RNA interfaces, a set of low-cost QM/MM methods is compared to the TPSS-D3(BJ)/def2-TZVP data. The PBEh-3c and B97-3c density functional composite methods appear to be suitable for protein-RNA interfaces. In contrast, HF-3c and the tight-binding Hamiltonians DFTB3-D3 and GFN-xTB perform unsatisfactorily and do not provide any advantage over the MM description. These conclusions are supported also by similar analysis of a simple HutP protein-RNA interface, which is well-described by MD with the exception of just one H-bond. Some other methodological aspects of QM/MM calculations on protein-RNA interfaces are discussed.

A Chemical Biology Approach to Model Pontocerebellar Hypoplasia Type 1B (PCH1B).

Mutations of EXOSC3 have been linked to the rare neurological disorder known as Pontocerebellar Hypoplasia type 1B (PCH1B). EXOSC3 is one of three putative RNA-binding structural cap proteins that guide RNA into the RNA exosome, the cellular machinery that degrades RNA. Using RNAcompete, we identified a G-rich RNA motif binding to EXOSC3. Surface plasmon resonance (SPR) and microscale thermophoresis (MST) indicated an affinity in the low micromolar range of EXOSC3 for long and short G-rich RNA sequences. Although several PCH1B-causing mutations in EXOSC3 did not engage a specific RNA motif as shown by RNAcompete, they exhibited lower binding affinity to G-rich RNA as demonstrated by MST. To test the hypothesis that modification of the RNA-protein interface in EXOSC3 mutants may be phenocopied by small molecules, we performed an in-silico screen of 50 000 small molecules and used enzyme-linked immunosorbant assays (ELISAs) and MST to assess the ability of the molecules to inhibit RNA-binding by EXOSC3. We identified a small molecule, EXOSC3-RNA disrupting (ERD) compound 3 (ERD03), which ( i) bound specifically to EXOSC3 in saturation transfer difference nuclear magnetic resonance (STD-NMR), ( ii) disrupted the EXOSC3-RNA interaction in a concentration-dependent manner, and ( iii) produced a PCH1B-like phenotype with a 50% reduction in the cerebellum and an abnormally curved spine in zebrafish embryos. This compound also induced modification of zebrafish RNA expression levels similar to that observed with a morpholino against EXOSC3. To our knowledge, this is the first example of a small molecule obtained by rational design that models the abnormal developmental effects of a neurodegenerative disease in a whole organism.

Structural accommodations accompanying splicing of a group II intron RNP

Group II introns, the putative progenitors of spliceosomal introns and retrotransposons, are ribozymes that are capable of self-splicing and DNA invasion. In the cell, group II introns form ribonucleoprotein (RNP) complexes with an intron-encoded protein, which is essential to folding, splicing and retromobility of the intron. To understand the structural accommodations underlying splicing, in preparation for retromobility, we probed the endogenously expressed Lactococcus lactis Ll.LtrB group II intron RNP using SHAPE. The results, which are consistent in vivo and in vitro, provide insights into the dynamics of the intron RNP as well as RNA–RNA and RNA–protein interactions. By comparing the excised intron RNP with mutant RNPs in the precursor state, confined SHAPE profile differences were observed, indicative of rearrangements at the active site as well as disengagement at the functional RNA–protein interface in transition between the two states. The exon-binding sequences in the intron RNA, which interact with the 5′ exon and the target DNA, show increased flexibility after splicing. In contrast, stability of major tertiary and protein interactions maintains the scaffold of the RNA through the splicing transition, while the active site is realigned in preparation for retromobility.