RNA Complex Research Articles

Post-Docking Refinement of Peptide or Protein-RNA Complexes Using Thermal Titration Molecular Dynamics (TTMD): A Stability Insight.

RNA-protein interactions drive and regulate fundamental cellular processes like transcription and translation. Despite being still limited, the growing body of structural data significantly contributes to the characterization of these interactions. However, RNA complexes involving proteins or peptides are not always available due to the structural determination challenges that this biopolymer entails. Consequently, modeling approaches like molecular docking are exploited to generate complexes relevant to structural and pharmaceutical purposes, including analysis of putative drug targets. Docking methods, despite their widespread adoption, are often hindered by limitations in scoring accuracy, which affects the ranking of the generated poses. Postdocking refining methods, including molecular dynamics (MD) approaches, have been developed to tackle this issue. Thermal Titration Molecular Dynamics (TTMD) is an enhanced sampling molecular dynamics technique that has been previously effectively applied to refine protein or RNA-small-molecule docking poses. This study presents the first application of TTMD to RNA-peptide complexes, validating this method on more complex systems and extending its applicability domain. Our findings showcase the capability of this technique to refine peptide-RNA docking poses, correctly identifying native binding modes among decoys for different pharmaceutically relevant targets.

Advances and Mechanisms of RNA–Ligand Interaction Predictions

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA’s specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA–ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA–ligand complexes by examining RNA’s sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA–ligand interaction prediction tools.

A Simple Protocol for Visualization of RNA-Protein Complexes by Atomic Force Microscopy.

Atomic force microscopy (AFM) has recently received increasing interest in molecular biology. This technique allows quick and reliable detection of biomolecules. However, studying RNA-protein complexes using AFM poses significant challenges. Here, we describe a simple and reliable method to visualize positively charged proteins bound to RNA that does not require metallic cations. This method allowed us to effectively detect and visualize Staufen-RNA complexes by height or logarithmic stiffness. The study of the mechanical properties is particularly important in the case of protein-coated RNA complexes, where RNA cannot be detected by height channel. In any case, it is necessary to compare AFM data with the data derived from other techniques like nuclear magnetic resonance, X-ray crystallography, cryogenic electron microscopy, and small-angle X-ray scattering. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Preparation and visualization of RNA-protein complex.

Recognition of Noncanonical RNA Base Pairs Using Triplex-Forming Peptide Nucleic Acids.

Noncanonical base pairs play an important role in enabling the structural and functional complexity of RNA. Molecular recognition of such motifs is challenging because of their diversity, significant deviation from the Watson-Crick structures, and dynamic behavior, resulting in alternative conformations of similar stability. Triplex-forming peptide nucleic acids (PNAs) have emerged as excellent ligands for the recognition of Watson-Crick base-paired double helical RNA. The present study extends the recognition potential of PNA to RNA helices having noncanonical GoU, AoC, and tandem GoA/AoG base pairs. The purines of the noncanonical base pairs formed M+·GoU, T·AoC, M+·GoA, and T·AoG Hoogsteen triples of similar or slightly reduced stability compared to the canonical M+·G-C and T·A-U triples. Recognition of pyrimidines was more challenging. While the P·CoA triple was only slightly less stable than P·C-G, the E nucleobase did not form a stable triple with U of the UoG wobble pair. Molecular dynamics simulations suggested the formation of expected Hoogsteen hydrogen bonds for all of the stable triples. Collectively, these results expand the scope of triple helical recognition to noncanonical structures and sequence motifs common in biologically relevant RNAs.

Robust, Scalable Microfluidic Manufacturing of RNA-Lipid Nanoparticles Using Immobilized Antifouling Lubricant Coating.

Despite the numerous advantages demonstrated by microfluidic mixing for RNA-loaded lipid nanoparticle (RNA-LNP) production over bulk methods, such as precise size control, homogeneous distributions, higher encapsulation efficiencies, and improved reproducibility, their translation from research to commercial manufacturing remains elusive. A persistent challenge hindering the adoption of microfluidics for LNP production is the fouling of device surfaces during prolonged operation, which significantly diminishes performance and reliability. The complexity of LNP constituents, including lipids, cholesterol, RNA, and solvent mixtures, makes it difficult to find a single coating that can prevent fouling. To address this challenge, we propose using an immobilized liquid lubricant layer of perfluorodecalin (PFD) to create an antifouling surface that can repel the multiple LNP constituents. We apply this technology to a staggered herringbone microfluidic (SHM) mixing chip and achieve >3 h of stable operation, a >15× increase relative to gold standard approaches. We also demonstrate the compatibility of this approach with a parallelized microfluidic platform that incorporates 256 SHM mixers, with which we demonstrate scale up, stable production at L/h production rates suitable for commercial scale applications. We verify that the LNPs produced on our chip match both the physiochemical properties and performance for both in vitro and in vivo mRNA delivery as those made on chips without the coating. By suppressing surface fouling with an immobilized liquid lubricant layer, this technology not only enhances RNA-LNP production but also promises to transform the microfluidic manufacturing of diverse materials, ensuring more reliable and robust processes.

CRISPR-Cas12a bends DNA to destabilize base pairs during target interrogation.

RNA-guided endonucleases are involved in processes ranging from adaptive immunity to site-specific transposition and have revolutionized genome editing. CRISPR-Cas9, -Cas12 and related proteins use guide RNAs to recognize ∼20-nucleotide target sites within genomic DNA by mechanisms that are not yet fully understood. We used structural and biochemical methods to assess early steps in DNA recognition by Cas12a protein-guide RNA complexes. We show here that Cas12a initiates DNA target recognition by bending DNA to induce transient nucleotide flipping that exposes nucleobases for DNA-RNA hybridization. Cryo-EM structural analysis of a trapped Cas12a-RNA-DNA surveillance complex and fluorescence-based conformational probing show that Cas12a-induced DNA helix destabilization enables target discovery and engagement. This mechanism of initial DNA interrogation resembles that of CRISPR-Cas9 despite distinct evolutionary origins and different RNA-DNA hybridization directionality of these enzyme families. Our findings support a model in which RNA-mediated DNA interference begins with local helix distortion by transient CRISPR-Cas protein binding.

Essential Considerations for Free Energy Calculations of RNA-Small Molecule Complexes: Lessons from the Theophylline-Binding RNA Aptamer.

Alchemical free energy calculations are widely used to predict the binding affinity of small molecule ligands to protein targets; however, the application of these methods to RNA targets has not been deeply explored. We systematically investigated how modeling decisions affect the performance of absolute binding free energy calculations for a relatively simple RNA model system: theophylline-binding RNA aptamer with theophylline and five analogs. The goal of this investigation was 2-fold: (1) understanding the performance levels we can expect from absolute free energy calculations for a simple RNA complex and (2) learning about practical modeling considerations that impact the success of RNA-binding predictions, which may be different from the best practices established for protein targets. We learned that magnesium ion (Mg2+) placement is a critical decision that impacts affinity predictions. When information regarding Mg2+ positions is lacking, implementing RNA backbone restraints is an alternative way of stabilizing the RNA structure that recapitulates prediction accuracy. Since mistakes in Mg2+ placement can be detrimental, omitting magnesium ions entirely and using RNA backbone restraints are attractive as a risk-mitigating approach. We found that predictions are sensitive to modeling experimental buffer conditions correctly, including salt type and ionic strength. We explored the effects of sampling in the alchemical protocol, choice of the ligand force field (GAFF2/OpenFF Sage), and water model (TIP3P/OPC) on predictions, which allowed us to give practical advice for the application of free energy methods to RNA targets. By capturing experimental buffer conditions and implementing RNA backbone restraints, we were able to compute binding affinities accurately (mean absolute error (MAE) = 2.2 kcal/mol, Pearson's correlation coefficient = 0.9, Kendall's τ = 0.7). We believe there is much to learn about how to apply free energy calculations for RNA targets and how to enhance their performance in prospective predictions. This study is an important first step for learning best practices and special considerations for RNA-ligand free energy calculations. Future studies will consider increasingly complicated ligands and diverse RNA systems and help the development of general protocols for therapeutically relevant RNA targets.

Hybrid RNA/DNA Concatemers and Self-Limited Complexes: Structure and Prospects for Therapeutic Applications.

The development of new convenient tools for the design of multicomponent nucleic acid (NA) complexes is one of the challenges in biomedicine and NA nanotechnology. In this paper, we analyzed the formation of hybrid RNA/DNA concatemers and self-limited complexes by a pair of oligonucleotides using UV melting, circular dichroism spectroscopy, and a gel shift assay. Effects of the size of the linker between duplex-forming segments of the oligonucleotides on complexes' shape and number of subunits were compared and systematized for RNA/DNA, DNA/DNA, and RNA/RNA assemblies. The data on complex types summarized here as heat maps offer a convenient tool for the design of NA constructs. General rules found for RNA/DNA, DNA/DNA, and RNA/RNA complexes allow not only designing complexes with desired structures but also purposefully transforming their geometry. The A-form of the double helix of the studied RNA/DNA complexes was confirmed by circular dichroism analysis. Moreover, we show for the first time efficient degradation of RNA in hybrid self-limited complexes by RNase H and imidazole. The results open up new prospects for the design of supramolecular complexes as tools for nanotechnology, nanomachinery, and biomedical applications.

Cas9-Mediated Poxvirus Recombinant Recovery (CASPRR) for Fast Recovery of Recombinant Vaccinia Viruses.

Generation of recombinant vaccinia viruses opens many avenues for poxvirus research; however current methods for virus production can be laborious. Traditional methods rely on recombination strategies that produce engineered viruses at a low frequency, which then need to be identified and isolated from a large background of parent virus. For this reason, marker and reporter genes are often included, but in many cases these require removal in subsequent steps and the entire process is relatively inefficient. Cas9-mediated selection is a technique that repurposes Cas9/guide RNA complexes to amplify chosen subsets of vaccinia viruses. We refer to this approach as Cas9-mediated poxvirus recombinant recovery (CASPRR). Transient introduction of appropriately guided Cas9 allows for recovery of marker-free recombinant viruses in just 5days, and requires no additional virus modification. Following three rounds of purification, pure virus stocks are obtained. A newer method, stable expression of Cas9 and guide RNAs in a permissive cell line, allows for additional process streamlining, removing cell type-specific concerns related to transient transfection of Cas9. Within this chapter, the protocol for CASPRR is described in both a transient and stable expression model. These methods can be utilized to accelerate recovery of recombinant vaccinia viruses and be applied to generation of vaccinia libraries or novel therapeutic agents.

Targeted epigenome engineering

Cellular differentiation and homeostasis rely on complex mechanisms to control gene expression, enabling the different cell lineages of an organism to establish and then "memorize" different epigenetic states. The processes that control gene expression are centered on chromatin, a complex of DNA, histone proteins and RNA, whose structure is finely regulated. Targeted epigenomic engineering tools make it possible to interfere with and study these processes, revealing the logic of epigenetic memory mechanisms. This article reviews the main classes of targeted epigenome modification tools and illustrates how they can be used to better understand and modify the epigenome of cells, paving the way for potentially revolutionary therapeutic prospects.

Molecular basis of mRNA delivery to the bacterial ribosome.

Protein synthesis begins with the formation of a ribosome-messenger RNA (mRNA) complex. In bacteria, the small ribosomal subunit (30S) is recruited to many mRNAs through base pairing with the Shine-Dalgarno (SD) sequence and RNA binding by ribosomal protein bS1. Translation can initiate on nascent mRNAs, and RNA polymerase (RNAP) can promote the recruitment of the pioneering 30S. Here, we examined 30S recruitment to nascent mRNAs using cryo-electron microscopy, single-molecule fluorescence colocalization, and in-cell cross-linking mass spectrometry. We show that bS1 delivers the mRNA to the ribosome for SD duplex formation and 30S activation. Additionally, bS1 and RNAP stimulate translation initiation. Our work provides a mechanistic framework for how the SD duplex, ribosomal proteins, and RNAP cooperate in 30S recruitment to mRNAs and establish transcription-translation coupling.

RADIP technology comprehensively identifies H3K27me3-associated RNA-chromatin interactions.

Many RNAs associate with chromatin, either directly or indirectly. Several technologies for mapping regions where RNAs interact across the genome have been developed to investigate the function of these RNAs. Obtaining information on the proteins involved in these RNA-chromatin interactions is critical for further analysis. Here, we developed RADIP [RNA and DNA interacting complexes ligated and sequenced (RADICL-seq) with immunoprecipitation], a novel technology that combines RADICL-seq technology with chromatin immunoprecipitation to characterize RNA-chromatin interactions mediated by individual proteins. Building upon the foundational principles of RADICL-seq, RADIP extends its advantages by increasing genomic coverage and unique mapping rate efficiency compared to existing methods. To demonstrate its effectiveness, we applied an anti-H3K27me3 antibody to the RADIP technology and generated libraries from mouse embryonic stem cells (mESCs). We identified a multitude of RNAs, including RNAs from protein-coding genes and non-coding RNAs, that are associated with chromatin via H3K27me3 and that likely facilitate the spread of Polycomb repressive complexes over broad regions of the mammalian genome, thereby affecting gene expression, chromatin structures and pluripotency of mESCs. Our study demonstrates the applicability of RADIP to investigations of the functions of chromatin-associated RNAs.

AI-integrated network for RNA complex structure and dynamic prediction.

RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.

REDalign: accurate RNA structural alignment using residual encoder-decoder network

BackgroundRNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of O(L6)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(L^6)$$\\end{document} for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities.ResultsIn this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency.ConclusionREDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

Cationic and ionizable amphiphiles based on di-hexadecyl ester of <I>L</I>-glutamic acid for liposomal transport of RNA

Various RNAs are among the most promising and actively developed therapeutic agents for the treatment of tumors, infectious diseases and a number of other pathologies associated with the dysfunction of specific genes. Some nanocarriers are used for the effective delivery of RNAs to target cells, including liposomes based on cationic and/or ionizable amphiphiles. Cationic amphiphiles contain a protonated amino group and exist as salts in an aqueous environment. Ionizable amphiphiles are a new generation of cationic lipids that exhibit reduced toxicity and immunogenicity and undergo ionization only in the acidic environment of the cell. In this work we developed a scheme for the preparation and carried out the synthesis of new cationic and ionizable amphiphiles based on natural amino acids (L-glutamic acid, glycine, beta-alanine, and gamma-aminobutyric acid). Cationic and ionizable liposomes were formed based on the obtained compounds, mixed with natural lipids (phosphatidylcholine and cholesterol), and their physicochemical characteristics (particle size, zeta potential, and storage stability) were determined. Average diameter of particles stable for 5–7 days did not exceed 100 nm. Zeta potential of cationic and ionizable liposomes was about 30 and 1 mV, respectively. The liposomal particles were used to form complexes with RNA molecules. Such RNA complexes were characterized by atomic force microscopy and their applicability for nucleic acid transport was determined.

Integrated Biochemical and Computational Methods for Deciphering RNA-Processing Codes.

RNA processing involves steps such as capping, splicing, polyadenylation, modification, and nuclear export. These steps are essential for transforming genetic information in DNA into proteins and contribute to RNA diversity and complexity. Many biochemical methods have been developed to profile and quantify RNAs, as well as to identify the interactions between RNAs and RNA-binding proteins (RBPs), especially when coupled with high-throughput sequencing technologies. With the rapid accumulation of diverse data, it is crucial to develop computational methods to convert the big data into biological knowledge. In particular, machine learning and deep learning models are commonly utilized to learn the rules or codes governing the transformation from DNA sequences to intriguing RNAs based on manually designed or automatically extracted features. When precise enough, the RNA codes can be incredibly useful for predicting RNA products, decoding the molecular mechanisms, forecasting the impact of disease variants on RNA processing events, and identifying driver mutations. In this review, we systematically summarize the biochemical and computational methods for deciphering five important RNA codes related to alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RBP binding. For each code, we review the main types of experimental methods used to generate training data, as well as the key features, strategic model structures, and advantages of representative tools. We also discuss the challenges encountered in developing predictive models using large language models and extensive domain knowledge. Additionally, we highlight useful resources and propose ways to improve computational tools for studying RNA codes.

Pulsed EPR Methods in the Angstrom to Nanometre Scale Shed Light on the Conformational Flexibility of a Fluoride Riboswitch.

Riboswitches control gene regulation upon external stimuli such as environmental factors or ligand binding. The fluoride sensing riboswitch from Thermotoga petrophila is a complex regulatory RNA proposed to be involved in resistance to F- cytotoxicity. The details of structure and dynamics underpinning the regulatory mechanism are currently debated. Here we demonstrate that a combination of pulsed electron paramagnetic resonance (ESR/EPR) spectroscopies, detecting distances in the angstrom to nanometre range, can probe distinct regions of conformational flexibility in this riboswitch. PELDOR (pulsed electron-electron double resonance) revealed a similar preorganisation of the sensing domain in three forms, i.e. the free aptamer, the Mg2+-bound apo, and the F--bound holo form. 19F ENDOR (electron-nuclear double resonance) was used to investigate the active site structure of the F--bound holo form. Distance distributions without a priori structural information were compared with in silico modelling of spin label conformations based on the crystal structure. While PELDOR, probing the periphery of the RNA fold, revealed conformational flexibility of the RNA backbone, ENDOR indicated low structural heterogeneity at the ligand binding site. Overall, the combination of PELDOR and ENDOR with sub-angstrom precision gave insight into structural organisation and flexibility of a riboswitch, not easily attainable by other biophysical techniques.

Puls EPR Methoden im Angstöm‐ bis Nanometerbereich geben Aufschluss über die konformationelle Flexibilität eines Fluorid‐Riboschalters

AbstractRiboschalter steuern die Genregulation in Reaktion auf externe Reize wie Umweltfaktoren oder Ligandenbindung. Der fluoriderkennende Riboschalter aus Thermotoga petrophila ist eine komplexe regulatorische RNA, von der angenommen wird, an der Resistenz gegen die zytotoxische Wirkung von Fluorid beteiligt zu sein. Details der Struktur und Dynamik, die dem regulatorischen Mechanismus zugrunde liegen, werden derzeit diskutiert. Hier zeigen wir, dass mit einer Kombination aus Puls Elektronen‐Paramagnetischer‐Resonanz (ESR/EPR) Spektroskopien, die Abstände im Angström‐ bis Nanometerbereich detektiert, spezifische Bereiche konformationeller Flexibilität in diesem Riboschalter untersucht werden können. PELDOR (pulsed electron‐electron double resonance) zeigte eine ähnliche Präorganisation der Sensordomäne in drei Formen: der freien Aptamerform, der Mg2+‐gebundenen apo‐Form und der F−‐gebundenen holo‐Form. 19F ENDOR (electron‐nuclear double resonance) wurde verwendet, um die Struktur des aktiven Zentrums der F−‐gebundenen holo‐Form zu untersuchen. Abstandsverteilungen ohne vorherige strukturelle Informationen wurden mit auf der Kristallstruktur basierenden in silico Modellierungen der Spinmarkerkonformationen verglichen. Während PELDOR, welches die Peripherie der RNA Faltung untersucht, die konformationelle Flexibilität des RNA Rückgrats aufdeckte, zeigte ENDOR eine geringe strukturelle Heterogenität der Ligandenbindungstasche. Die Kombination aus PELDOR und ENDOR lieferte sub‐Angström‐präzise Einblicke in die strukturelle Organisation und Flexibilität eines Riboschalters, die mit anderen biophysikalischen Techniken nur schwer zu erreichen sind.

Alternative Splicing: A Potential Therapeutic Target in Hematological Malignancies.

Leukemia represents the most prevalent malignancy in children, constituting 30% of childhood cancer cases, with acute lymphoblastic leukemia (ALL) being particularly heterogeneous. This paper explores the role of alternative splicing in leukemia, highlighting its significance in cancer development and progression. Aberrant splicing is often driven by mutations in splicing-factor genes, which can lead to the production of variant proteins that contribute to oncogenesis. The spliceosome, a complex of small nuclear RNAs and proteins, facilitates RNA splicing, a process critical for generating diverse mRNA and protein products from single genes. Mutations in splicing factors, such as U2AF1, SF3B1, SRSF2, ZRSR2, and HNRNPH1, are frequently observed across various hematological malignancies and are associated with poor prognosis and treatment resistance. This research underscores the necessity of understanding the mechanisms of RNA splicing dysregulation in order to develop targeted therapies to correct these aberrant processes, thereby improving outcomes for patients with leukemia and related disorders.

Activity-assembled nBAF complex mediates rapid immediate early gene transcription by regulating RNA polymerase II productive elongation

Signal-dependent RNA polymerase II (RNA Pol II) productive elongation is an integral component of gene transcription, including that of immediate early genes (IEGs) induced by neuronal activity. However, it remains unclear how productively elongating RNA Pol II overcomes nucleosomal barriers. Using RNAi, three degraders, and several small-molecule inhibitors, we show that the mammalian switch/sucrose non-fermentable (SWI/SNF) complex of neurons (neuronal BRG1/BRM-associated factor or nBAF) is required for activity-induced transcription of neuronal IEGs, including Arc. The nBAF complex facilitates promoter-proximal RNA Pol II pausing and signal-dependent RNA Pol II recruitment (loading) and, importantly, mediates productive elongation in the gene body via interaction with the elongation complex and elongation-competent RNA Pol II. Mechanistically, RNA Pol II elongation is mediated by activity-induced nBAF assembly (especially ARID1A recruitment) and its ATPase activity. Together, our data demonstrate that the nBAF complex regulates several aspects of RNA Pol II transcription and reveal mechanisms underlying activity-induced RNA Pol II elongation. These findings may offer insights into human maladies etiologically associated with mutational interdiction of BAF functions.