RNA 3D Research Articles

GRNAde: Geometric Deep Learning for 3D RNA inverse design.

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Open source code: github.com/chaitjo/geometric-rna-design.

GRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.

LociPARSE: a locality-aware invariant point attention model for scoring RNA 3D structures.

A scoring function that can reliably assess the accuracy of a 3D RNA structural model in the absence of experimental structure is not only important for model evaluation and selection but also useful for scoring-guided conformational sampling. However, high-fidelity RNA scoring has proven to be difficult using conventional knowledge-based statistical potentials and currently-available machine learning-based approaches. Here we present lociPARSE, a locality-aware invariant point attention architecture for scoring RNA 3D structures. Unlike existing machine learning methods that estimate superposition-based root mean square deviation (RMSD), lociPARSE estimates Local Distance Difference Test (lDDT) scores capturing the accuracy of each nucleotide and its surrounding local atomic environment in a superposition-free manner, before aggregating information to predict global structural accuracy. Tested on multiple datasets including CASP15, lociPARSE significantly outperforms existing statistical potentials (rsRNASP, cgRNASP, DFIRE-RNA, and RASP) and machine learning methods (ARES and RNA3DCNN) across complementary assessment metrics. lociPARSE is freely available at https://github.com/Bhattacharya-Lab/lociPARSE.

FURNA: A database for functional annotations of RNA structures.

Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by noncoding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (Functions of RNAs), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand-binding sites, RNA families, protein-binding motifs, and cross-references to related databases. FURNA is available at https://seq2fun.dcmb.med.umich.edu/furna/ to enable quick discovery of RNA functions from their structures and sequences.

RiboDiffusion: tertiary structure-based RNA inverse folding with generative diffusion models.

RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge is to find functional RNA sequences that satisfy given structural constraints, known as the inverse folding problem. Computational approaches have emerged to address this problem based on secondary structures. However, designing RNA sequences directly from 3D structures is still challenging, due to the scarcity of data, the nonunique structure-sequence mapping, and the flexibility of RNA conformation. In this study, we propose RiboDiffusion, a generative diffusion model for RNA inverse folding that can learn the conditional distribution of RNA sequences given 3D backbone structures. Our model consists of a graph neural network-based structure module and a Transformer-based sequence module, which iteratively transforms random sequences into desired sequences. By tuning the sampling weight, our model allows for a trade-off between sequence recovery and diversity to explore more candidates. We split test sets based on RNA clustering with different cut-offs for sequence or structure similarity. Our model outperforms baselines in sequence recovery, with an average relative improvement of 11% for sequence similarity splits and 16% for structure similarity splits. Moreover, RiboDiffusion performs consistently well across various RNA length categories and RNA types. We also apply in silico folding to validate whether the generated sequences can fold into the given 3D RNA backbones. Our method could be a powerful tool for RNA design that explores the vast sequence space and finds novel solutions to 3D structural constraints. The source code is available at https://github.com/ml4bio/RiboDiffusion.

Knotted artifacts in predicted 3D RNA structures.

Unlike proteins, RNAs deposited in the Protein Data Bank do not contain topological knots. Recently, admittedly, the first trefoil knot and some lasso-type conformations have been found in experimental RNA structures, but these are still exceptional cases. Meanwhile, algorithms predicting 3D RNA models have happened to form knotted structures not so rarely. Interestingly, machine learning-based predictors seem to be more prone to generate knotted RNA folds than traditional methods. A similar situation is observed for the entanglements of structural elements. In this paper, we analyze all models submitted to the CASP15 competition in the 3D RNA structure prediction category. We show what types of topological knots and structure element entanglements appear in the submitted models and highlight what methods are behind the generation of such conformations. We also study the structural aspect of susceptibility to entanglement. We suggest that predictors take care of an evaluation of RNA models to avoid publishing structures with artifacts, such as unusual entanglements, that result from hallucinations of predictive algorithms.

Prediction of 3D RNA Structures from Sequence Using Energy Landscapes of RNA Dimers: Application to RNA Tetraloops.

Access to the three-dimensional structure of RNA enables an ability to gain a more profound understanding of its biological mechanisms, as well as the ability to design RNA-targeting drugs, which can take advantage of the unique chemical environment imposed by a folded RNA structure. Due to the dynamic and structurally complex properties of RNA, both experimental and traditional computational methods have difficulty in determining RNA's 3D structure. Herein, we introduce TAPERSS (Theoretical Analyses, Prediction, and Evaluation of RNA Structures from Sequence), a physics-based fragment assembly method for predicting 3D RNA structures from sequence. Using a fragment library created using discrete path sampling calculations of RNA dinucleoside monophosphates, TAPERSS can sample the physics-based energy landscapes of any RNA sequence with relatively low computational complexity. We have benchmarked TAPERSS on 21 RNA tetraloops, using a combinatorial algorithm as a proof-of-concept. We show that TAPERSS was successfully able to predict the apo-state structures of all 21 RNA hairpins, with 16 of those structures also having low predicted energies as well. We demonstrate that TAPERSS performs most accurately on GNRA-like tetraloops with mostly stacked loop-nucleotides, while having limited success with more dynamic UNCG and CUYG tetraloops, most likely due to the influence of the RNA force field used to create the fragment library. Moreover, we show that TAPERSS can successfully predict the majority of the experimental non-apo states, highlighting its potential in anticipating biologically significant yet unobserved states. This holds great promise for future applications in drug design and related studies. With discussed improvements and implementation of more efficient sampling algorithms, we believe TAPERSS may serve as a useful tool for a physics-based conformational sampling of large RNA structures.

RNAhugs web server for customized 3D RNA structure alignment.

Alignment of 3D molecular structures involves overlaying their sets of atoms in space in such a way as to minimize the distance between the corresponding atoms. The purpose of this procedure is usually to analyze and assess structural similarity on a global (e.g.evaluating predicted 3D models and clustering structures) or a local level (e.g.searching for common substructures). Although the idea of alignment is simple, combinatorial algorithms that implement it require considerable computational resources, even when processing relatively small structures. In this paper, we introduce RNAhugs, a web server for custom and flexible alignment of 3D RNA structures. Using two efficient heuristics, GEOS and GENS, it finds the longest corresponding fragments within 3D structures that may differ in sizes-given in the PDB or PDBx/mmCIF formats-that manage to align with user-specified accuracy (i.e.with an RMSD not exceeding a cutoff value given as an input parameter). A distinctive advantage of the system lies in its ability to process multi-model files and compare the results of 1-25 alignments in a single task. RNAhugs has an intuitive interface and is publicly available at https://rnahugs.cs.put.poznan.pl/.

Extracting 3D RNA structural information from dimethyl sulfate (DMS) chemical mapping using deep learning

Extracting 3D RNA structural information from dimethyl sulfate (DMS) chemical mapping using deep learning

Joint epigenome profiling reveals cell-type-specific gene regulatory programmes in human cortical organoids

Gene expression is regulated by multiple epigenetic mechanisms, which are coordinated in development and disease. However, current multiomics methods are frequently limited to one or two modalities at a time, making it challenging to obtain a comprehensive gene regulatory signature. Here, we describe a method—3D genome, RNA, accessibility and methylation sequencing (3DRAM-seq)—that simultaneously interrogates spatial genome organization, chromatin accessibility and DNA methylation genome-wide and at high resolution. We combine 3DRAM-seq with immunoFACS and RNA sequencing in cortical organoids to map the cell-type-specific regulatory landscape of human neural development across multiple epigenetic layers. Finally, we apply a massively parallel reporter assay to profile cell-type-specific enhancer activity in organoids and to functionally assess the role of key transcription factors for human enhancer activation and function. More broadly, 3DRAM-seq can be used to profile the multimodal epigenetic landscape in rare cell types and different tissues.

Predicting 3D RNA structure from the nucleotide sequence using Euclidean neural networks

Fast and accurate 3D RNA structure prediction remains a major challenge in structural biology, mostly due to the size and flexibility of RNA molecules, as well as the lack of diverse experimentally determined structures of RNA molecules. Unlike DNA structure, RNA structure is far less constrained by basepair hydrogen bonding, resulting in an explosion of potential stable states. Here, we propose a convolutional neural network that predicts all pairwise distances between residues in an RNA, using a recently described smooth parametrization of Euclidean distance matrices. We achieve high-accuracy predictions on RNAs up to 100 nt in length in fractions of a second, a factor of 107 faster than existing molecular dynamics-based methods. We also convert our coarse-grained machine learning output into an all-atom model using discrete molecular dynamics with constraints. Our proposed computational pipeline predicts all-atom RNA models solely from the nucleotide sequence. However, this method suffers from the same limitation as nucleic acid molecular dynamics: the scarcity of available RNA crystal structures for training.

Analysis on the in-silico ensemble methods for 3D modelling of ssDNA aptamers

Understanding the 3-D structure of nucleic acid aptamers is important for the rational design of aptamer-based constructs in various applications, including for developing aptasensors. Herein, a simple approach for 3D modelling of ssDNA aptamers through an ensemble of web applications has been described. The procedure utilized 30 aptamers whose 3D XRD or NMR experimental structures are available for validation. As a first step, the primary sequences of ssDNA aptamers were transformed into 2D structures using six widely used web applications: RNA fold, Vector builder, RNA Structure, UNA fold, Centroid fold, and IP Knot. The generated 2D structures were then passed through the RNA composer web application to generate 3D RNA structure, which in turn was converted to 3D DNA structures using various Visual Molecular Dynamics web applications that also include conversion of ribose sugar into deoxyribose sugar backbone and uracil to thiamine. The energy-minimized generated 3D structures were matched well with high accuracy to their experimental counterparts. This study identified that the Guanine residues are crucial in the aptamer 3D structure prediction and in algorithms that generate secondary structures. Further, the GC content (<50%), GC bond percentage (<60%), and G:C ratio (<1.12) act as limiting factors in predicting the 2D structures of aptamers. There were variations in the 2D structure predictions by the web applications, even though all these applications were a combination of the MFE, MEA, and McCaskill functions. Processing these structures through the web applications described above produced best-fit 3D structures with the experimental one, thus offering the present ensemble approach to reliably predict the 3D structure of aptamers for various applications.

Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction

RNAs are fundamental in living cells and perform critical functions determined by their tertiary architectures. However, accurate modeling of 3D RNA structure remains a challenging problem. We present a novel method, DRfold, to predict RNA tertiary structures by simultaneous learning of local frame rotations and geometric restraints from experimentally solved RNA structures, where the learned knowledge is converted into a hybrid energy potential to guide RNA structure assembly. The method significantly outperforms previous approaches by >73.3% in TM-score on a sequence-nonredundant dataset containing recently released structures. Detailed analyses showed that the major contribution to the improvements arise from the deep end-to-end learning supervised with the atom coordinates and the composite energy function integrating complementary information from geometry restraints and end-to-end learning models. The open-source DRfold program with fast training protocol allows large-scale application of high-resolution RNA structure modeling and can be further improved with future expansion of RNA structure databases.

RNA tertiary structure prediction using RNAComposer in CASP15.

As CASP15 participants, in the new category of 3D RNA structure prediction, we applied expert modeling with the support of our proprietary system RNAComposer. Although RNAComposer is primarily known as an automated web server, its features allow it to be used interactively, for example, for homology-based modeling or assembling models from user-provided structural elements. In the paper, we present various scenarios of applying the system to predict the 3D RNA structures that we employed. Their combination with expert input, comparative analysis of models, and routines to select representative resultant structures form a ready-for-reuse workflow. With selected examples, we demonstrate its application for the in silico modeling of natural and synthetic RNA molecules targeted in CASP15.

The assessment of molecular dynamics results of three-dimensional RNA aptamer structure prediction.

Aptamers are single-stranded DNA or RNA that bind to specific targets such as proteins, thus having similar characteristics to antibodies. It can be synthesized at a lower cost, with no batch-to-batch variations, and is easier to modify chemically than antibodies, thus potentially being used as therapeutic and biosensing agents. The current method for RNA aptamer identification in vitro uses the SELEX method, which is considered inefficient due to its complex process. Computational models of aptamers have been used to predict and study the molecular interaction of modified aptamers to improve affinity. In this study, we generated three-dimensional models of five RNA aptamers from their sequence using mFold, RNAComposer web server, and molecular dynamics simulation. The model structures were then evaluated and compared with the experimentally determined structures. This study showed that the combination of mFold, RNAComposer, and molecular dynamics simulation could generate 14-16, 28, or 29 nucleotides length of 3D RNA aptamer with similar geometry and topology to the experimentally determined structures. The non-canonical basepair structure of the aptamer loop was formed through the MD simulation, which also improved the three-dimensional RNA aptamers model. Clustering analysis was recommended to choose the more representative model.

Hyper-chaotic color image encryption based on 3D orthogonal Latin cubes and RNA diffusion

Hyper-chaotic color image encryption based on 3D orthogonal Latin cubes and RNA diffusion

Single-cell detection of primary transcripts, their genomic loci and nuclear factors by 3D immuno-RNA/DNA FISH in T cells.

Over the past decades, it has become increasingly clear that higher order chromatin folding and organization within the nucleus is involved in the regulation of genome activity and serves as an additional epigenetic mechanism that modulates cellular functions and gene expression programs in diverse biological processes. In particular, dynamic allelic interactions and nuclear locations can be of functional importance during the process of lymphoid differentiation and the regulation of immune responses. Analyses of the proximity between chromatin and/or nuclear regions can be performed on populations of cells with high-throughput sequencing approaches such as chromatin conformation capture ("3C"-based) or DNA adenine methyltransferase identification (DamID) methods, or, in individual cells, by the simultaneous visualization of genomic loci, their primary transcripts and nuclear compartments within the 3-dimensional nuclear space using Fluorescence In Situ Hybridization (FISH) and immunostaining. Here, we present a detailed protocol to simultaneously detect nascent RNA transcripts (3D RNA FISH), their genomic loci (3D DNA FISH) and/or their chromosome territories (CT paint DNA FISH) combined with the antibody-based detection of various nuclear factors (immunofluorescence). We delineate the application and effectiveness of this robust and reproducible protocol in several murine T lymphocyte subtypes (from differentiating thymic T cells, to activated splenic and peripheral T cells) as well as other murine cells, including embryonic stem cells, B cells, megakaryocytes and macrophages.

High-quality, customizable heuristics for RNA 3D structure alignment.

Tertiary structure alignment is one of the main challenges in the computer-aided comparative study of molecular structures. Its aim is to optimally overlay the 3D shapes of two or more molecules in space to find the correspondence between their nucleotides. Alignment is the starting point for most algorithms that assess structural similarity or find common substructures. Thus, it has applications in solving a variety of bioinformatics problems, e.g. in the search for structural patterns, structure clustering, identifying structural redundancy, and evaluating the prediction accuracy of 3D models. To date, several tools have been developed to align 3D structures of RNA. However, most of them are not applicable to arbitrarily large structures and do not allow users to parameterize the optimization algorithm. We present two customizable heuristics for flexible alignment of 3D RNA structures, geometric search (GEOS), and genetic algorithm (GENS). They work in sequence-dependent/independent mode and find the suboptimal alignment of expected quality (below a predefined RMSD threshold). We compare their performance with those of state-of-the-art methods for aligning RNA structures. We show the results of quantitative and qualitative tests run for all of these algorithms on benchmark sets of RNA structures. Source codes for both heuristics are hosted at https://github.com/RNApolis/rnahugs.

AHNAK, regulated by the OSM/OSMR signaling, involved in the development of primary localized cutaneous amyloidosis

BackgroundPrimary localized cutaneous amyloidosis (PLCA) is a chronic skin disease characterized by aberrant keratinocyte differentiation, epidermal hyperproliferation, and amyloid deposits. Previously, we demonstrated OSMR loss-function mutants enhanced basal keratinocyte differentiation through the OSMR/STAT5/KLF7 signaling in PLCA patients. ObjectiveTo investigate the underlying mechanisms involved in basal keratinocyte proliferation in PLCA patients that remain unclear. MethodsPatients with pathologically confirmed PLCA visiting the dermatologic outpatient clinic were involved in the study. Laser capture microdissection and mass spectrometry analysis, gene-edited mice, 3D human epidermis culture, flow cytometry, western blot, qRT-PCR and RNA sequencing were used to explore the underlying molecular mechanisms. ResultsIn this study, we found that AHNAK peptide fragments were enriched in the lesions of PLCA patients, as detected by laser capture microdissection and mass spectrometry analysis. The upregulated expression of AHNAK was further confirmed using immunohistochemical staining. qRT-PCR and flow cytometry revealed that pre-treatment with OSM can inhibit AHNAK expression in HaCaT cells, NHEKs, and 3D human skin models, but OSMR knockout or OSMR mutations abolished this down-regulation trend. Similar results were obtained in wild-type and OSMR knockout mice. More importantly, EdU incorporation and FACS assays demonstrated the knockdown of AHNAK could induce G1 phase cell cycle arrest and inhibit keratinocyte proliferation. Furthermore, RNA sequencing revealed that AHNAK knockdown regulated keratinocyte differentiation. ConclusionTaken together, these data indicated that the elevated expression of AHNAK by OSMR mutations led to hyperproliferation and overdifferentiation of keratinocytes, and the discovered mechanism might provide insights into potential therapeutic targets for PLCA.

Capture RIC-seq reveals positional rules of PTBP1-associated RNA loops in splicing regulation

RNA-binding proteins (RBPs) bind at different positions of the pre-mRNA molecules to promote or reduce the usage of a particular exon. Seeking to understand the working principle of these positional effects, we develop a capture RIC-seq (CRIC-seq) method to enrich specific RBP-associated in situ proximal RNA-RNA fragments for deep sequencing. We determine hnRNPA1-, SRSF1-, and PTBP1-associated proximal RNA-RNA contacts and regulatory mechanisms in HeLa cells. Unexpectedly, the 3D RNA map analysis shows that PTBP1-associated loops in individual introns preferentially promote cassette exon splicing by accelerating asymmetric intron removal, whereas the loops spanning across cassette exon primarily repress splicing. These "positional rules" can faithfully predict PTBP1-regulated splicing outcomes. We further demonstrate that cancer-related splicing quantitative trait loci can disrupt RNA loops by reducing PTBP1 binding on pre-mRNAs to cause aberrant splicing in tumors. Our study presents a powerful method for exploring the functions of RBP-associated RNA-RNA proximal contacts in gene regulation and disease.