-
Nature Communications Dec 2023Zika virus (ZIKV) has emerged as a global health issue, yet neither antiviral therapy nor a vaccine are available. ZIKV is an enveloped RNA virus, replicating in the...
Zika virus (ZIKV) has emerged as a global health issue, yet neither antiviral therapy nor a vaccine are available. ZIKV is an enveloped RNA virus, replicating in the cytoplasm in close association with ER membranes. Here, we isolate ER membranes from ZIKV-infected cells and determine their proteome. Forty-six host cell factors are enriched in ZIKV remodeled membranes, several of these having a role in redox and methylation pathways. Four proteins are characterized in detail: thioredoxin reductase 1 (TXNRD1) contributing to folding of disulfide bond containing proteins and modulating ZIKV secretion; aldo-keto reductase family 1 member C3 (AKR1C3), regulating capsid protein abundance and thus, ZIKV assembly; biliverdin reductase B (BLVRB) involved in ZIKV induced lipid peroxidation and increasing stability of viral transmembrane proteins; adenosylhomocysteinase (AHCY) indirectly promoting mA methylation of ZIKV RNA by decreasing the level of S- adenosyl homocysteine and thus, immune evasion. These results highlight the involvement of redox and methylation enzymes in the ZIKV life cycle and their accumulation at virally remodeled ER membranes.
Topics: Humans; Zika Virus; Zika Virus Infection; Methylation; Proviruses; Virus Replication; Viral Proteins; Oxidation-Reduction
PubMed: 38052817
DOI: 10.1038/s41467-023-43665-6 -
The Journal of Biological Chemistry Sep 2023Recent discoveries establish tRNAs as central regulators of mRNA translation dynamics, and therefore cotranslational folding and function of the encoded protein. The... (Review)
Review
Recent discoveries establish tRNAs as central regulators of mRNA translation dynamics, and therefore cotranslational folding and function of the encoded protein. The tRNA pool, whose composition and abundance change in a cell- and tissue-dependent manner, is the main factor which determines mRNA translation velocity. In this review, we discuss a group of pathogenic mutations, in the coding sequences of either protein-coding genes or in tRNA genes, that alter mRNA translation dynamics. We also summarize advances in tRNA biology that have uncovered how variations in tRNA levels on account of genetic mutations affect protein folding and function, and thereby contribute to phenotypic diversity in clinical manifestations.
Topics: Humans; Codon; Mutation; Protein Biosynthesis; RNA, Messenger; RNA, Transfer; Polymorphism, Single Nucleotide; Time Factors
PubMed: 37495112
DOI: 10.1016/j.jbc.2023.105089 -
Briefings in Bioinformatics Jul 2023The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic... (Review)
Review
The three-dimensional structure of RNA molecules plays a critical role in a wide range of cellular processes encompassing functions from riboswitches to epigenetic regulation. These RNA structures are incredibly dynamic and can indeed be described aptly as an ensemble of structures that shifts in distribution depending on different cellular conditions. Thus, the computational prediction of RNA structure poses a unique challenge, even as computational protein folding has seen great advances. In this review, we focus on a variety of machine learning-based methods that have been developed to predict RNA molecules' secondary structure, as well as more complex tertiary structures. We survey commonly used modeling strategies, and how many are inspired by or incorporate thermodynamic principles. We discuss the shortcomings that various design decisions entail and propose future directions that could build off these methods to yield more robust, accurate RNA structure predictions.
Topics: RNA; Epigenesis, Genetic; Machine Learning; Protein Structure, Secondary; Computational Biology
PubMed: 37280185
DOI: 10.1093/bib/bbad210 -
Bioinformatics (Oxford, England) Jun 2024RNA design shows growing applications in synthetic biology and therapeutics, driven by the crucial role of RNA in various biological processes. A fundamental challenge...
MOTIVATION
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.
RESULTS
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.
AVAILABILITY AND IMPLEMENTATION
The source code is available at https://github.com/ml4bio/RiboDiffusion.
Topics: RNA; Nucleic Acid Conformation; RNA Folding; Computational Biology; Algorithms; Software; Neural Networks, Computer; Sequence Analysis, RNA
PubMed: 38940178
DOI: 10.1093/bioinformatics/btae259 -
RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias.Biophysical Journal Aug 2023Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic...
Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. As a step forward in tackling these issues, we extend to RNA an enhanced path-sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force promoting the generation of productive folding trajectories in an all-atom force field with explicit solvent. A rigorous variational principle is then applied to minimize the effect of the bias. Here, we report on an application of this method to RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. By comparison with analog simulations performed on small proteins with similar size and architecture, we show that the RNA folding landscape is significantly more frustrated, even for relatively small chains with a simple topology. The predicted RNA folding mechanisms are found to be consistent with the available experiments and some of the existing coarse-grained models. Due to its computational performance, this scheme provides a promising platform to efficiently gather atomistic RNA folding trajectories, thus retain the information about the chemical composition of the sequence.
Topics: Protein Folding; RNA Folding; Proteins; Molecular Conformation; RNA; Molecular Dynamics Simulation; Thermodynamics
PubMed: 37355771
DOI: 10.1016/j.bpj.2023.06.012 -
The Journal of Biological Chemistry Aug 2023tRNAs are short noncoding RNAs responsible for decoding mRNA codon triplets, delivering correct amino acids to the ribosome, and mediating polypeptide chain formation.... (Review)
Review
tRNAs are short noncoding RNAs responsible for decoding mRNA codon triplets, delivering correct amino acids to the ribosome, and mediating polypeptide chain formation. Due to their key roles during translation, tRNAs have a highly conserved shape and large sets of tRNAs are present in all living organisms. Regardless of sequence variability, all tRNAs fold into a relatively rigid three-dimensional L-shaped structure. The conserved tertiary organization of canonical tRNA arises through the formation of two orthogonal helices, consisting of the acceptor and anticodon domains. Both elements fold independently to stabilize the overall structure of tRNAs through intramolecular interactions between the D- and T-arm. During tRNA maturation, different modifying enzymes posttranscriptionally attach chemical groups to specific nucleotides, which not only affect translation elongation rates but also restrict local folding processes and confer local flexibility when required. The characteristic structural features of tRNAs are also employed by various maturation factors and modification enzymes to assure the selection, recognition, and positioning of specific sites within the substrate tRNAs. The cellular functional repertoire of tRNAs continues to extend well beyond their role in translation, partly, due to the expanding pool of tRNA-derived fragments. Here, we aim to summarize the most recent developments in the field to understand how three-dimensional structure affects the canonical and noncanonical functions of tRNA.
Topics: Nucleic Acid Conformation; RNA, Transfer; Anticodon; Protein Biosynthesis; Ribosomes
PubMed: 37380076
DOI: 10.1016/j.jbc.2023.104966 -
Drug Discovery Today Aug 2023The novel coronavirus crisis caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) was a global pandemic. Although various therapeutic approaches were... (Review)
Review
The novel coronavirus crisis caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) was a global pandemic. Although various therapeutic approaches were developed over the past 2 years, novel strategies with more efficient applicability are required to target new variants. Aptamers are single-stranded (ss)RNA or DNA oligonucleotides capable of folding into unique 3D structures with robust binding affinity to a wide variety of targets following structural recognition. Aptamer-based theranostics have proven excellent capability for diagnosing and treating various viral infections. Herein, we review the current status and future perspective of the potential of aptamers as COVID-19 therapies.
Topics: Humans; COVID-19; SARS-CoV-2; Oligonucleotides; DNA; RNA; Aptamers, Nucleotide
PubMed: 37315763
DOI: 10.1016/j.drudis.2023.103663 -
Journal of Computational Biology : a... Oct 2023RNA secondary structures are essential abstractions for understanding spacial folding behaviors of those macromolecules. Many secondary structure algorithms involve a...
RNA secondary structures are essential abstractions for understanding spacial folding behaviors of those macromolecules. Many secondary structure algorithms involve a common dynamic programming setup to exploit the property that secondary structures can be decomposed into substructures. Dirks et al. noted that this setup cannot directly address an issue of distinguishability among secondary structures, which arises for classes of sequences that admit nontrivial symmetry. Circular sequences are among these. We examine the problem of counting distinguishable secondary structures. Drawing from elementary results in group theory, we identify useful subsets of secondary structures. We then extend an algorithm due to Hofacker et al. for computing the sizes of these subsets. This yields a cubic-time algorithm to count distinguishable structures compatible with a given circular sequence. Furthermore, this general approach may be used to solve similar problems for which the RNA structures of interest involve symmetries.
Topics: RNA; Nucleic Acid Conformation; Algorithms; Protein Structure, Secondary; RNA Folding; Sequence Analysis, RNA
PubMed: 37815558
DOI: 10.1089/cmb.2022.0501 -
The EMBO Journal Sep 2023Slower translation rates reduce protein misfolding. Such reductions in speed can be mediated by the presence of non-optimal codons, which allow time for proper folding...
Slower translation rates reduce protein misfolding. Such reductions in speed can be mediated by the presence of non-optimal codons, which allow time for proper folding to occur. Although this phenomenon is conserved from bacteria to humans, it is not known whether there are additional eukaryote-specific mechanisms which act in the same way. MicroRNAs (miRNAs), not present in prokaryotes, target both coding sequences (CDS) and 3' untranslated regions (UTR). Given their low suppressive efficiency, it has been unclear why miRNAs are equally likely to bind to a CDS. Here, we show that miRNAs transiently stall translating ribosomes, preventing protein misfolding with little negative effect on protein abundance. We first analyzed ribosome profiles and miRNA binding sites to examine whether miRNAs stall ribosomes. Furthermore, either global or specific miRNA deficiency accelerated ribosomes and induced aggregation of a misfolding-prone polypeptide reporter. These defects were rescued by slowing ribosomes using non-cleaving shRNAs as miRNA mimics. We finally show that proinsulin misfolding, associated with type II diabetes, was resolved by non-cleaving shRNAs. Our findings provide a eukaryote-specific mechanism of co-translational protein folding and a previously unknown mechanism of action to target protein misfolding diseases.
Topics: Humans; MicroRNAs; Protein Biosynthesis; Eukaryota; Diabetes Mellitus, Type 2; RNA, Messenger; Ribosomes; Proteins
PubMed: 37492926
DOI: 10.15252/embj.2022112469 -
Briefings in Bioinformatics Jul 2023Nucleic-acid G-quadruplexes (G4s) play vital roles in many cellular processes. Due to their importance, researchers have developed experimental assays to measure... (Review)
Review
Nucleic-acid G-quadruplexes (G4s) play vital roles in many cellular processes. Due to their importance, researchers have developed experimental assays to measure nucleic-acid G4s in high throughput. The generated high-throughput datasets gave rise to unique opportunities to develop machine-learning-based methods, and in particular deep neural networks, to predict G4s in any given nucleic-acid sequence and any species. In this paper, we review the success stories of deep-neural-network applications for G4 prediction. We first cover the experimental technologies that generated the most comprehensive nucleic-acid G4 high-throughput datasets in recent years. We then review classic rule-based methods for G4 prediction. We proceed by reviewing the major machine-learning and deep-neural-network applications to nucleic-acid G4 datasets and report a novel comparison between them. Next, we present the interpretability techniques used on the trained neural networks to learn key molecular principles underlying nucleic-acid G4 folding. As a new result, we calculate the overlap between measured DNA and RNA G4s and compare the performance of DNA- and RNA-G4 predictors on RNA- and DNA-G4 datasets, respectively, to demonstrate the potential of transfer learning from DNA G4s to RNA G4s. Last, we conclude with open questions in the field of nucleic-acid G4 prediction and computational modeling.
Topics: G-Quadruplexes; Nucleic Acids; DNA; RNA; Neural Networks, Computer
PubMed: 37438149
DOI: 10.1093/bib/bbad252