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Numerous viruses utilize essential long-range RNA-RNA genome interactions, specifically flaviviruses. Using Japanese encephalitis virus (JEV) as a model system, we computationally predicted and then biophysically validated and characterized its long-range RNA-RNA genomic interaction. Using multiple RNA computation assessment programs, we determine the primary RNA-RNA interacting site among JEV isolates and numerous related viruses. Following in vitro transcription of RNA, we provide, for the first time, characterization of an RNA-RNA interaction using size-exclusion chromatography coupled with multi-angle light scattering and analytical ultracentrifugation. Next, we demonstrate that the 5' and 3' terminal regions of JEV interact with nM affinity using microscale thermophoresis, and this affinity is significantly reduced when the conserved cyclization sequence is not present. Furthermore, we perform computational kinetic analyses validating the cyclization sequence as the primary driver of this RNA-RNA interaction. Finally, we examined the 3D structure of the interaction using small-angle X-ray scattering, revealing a flexible yet stable interaction. This pathway can be adapted and utilized to study various viral and human long-non-coding RNA-RNA interactions and determine their binding affinities, a critical pharmacological property of designing potential therapeutics., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

DNA in cells is organized in negatively supercoiled loops. The resulting torsional and bending strain allows DNA to adopt a surprisingly wide variety of 3-D shapes. This interplay between negative supercoiling, looping, and shape influences how DNA is stored, replicated, transcribed, repaired, and likely every other aspect of DNA activity. To understand the consequences of negative supercoiling and curvature on the hydrodynamic properties of DNA, we submitted 336 bp and 672 bp DNA minicircles to analytical ultracentrifugation (AUC). We found that the diffusion coefficient, sedimentation coefficient, and the DNA hydrodynamic radius strongly depended on circularity, loop length, and degree of negative supercoiling. Because AUC cannot ascertain shape beyond degree of non-globularity, we applied linear elasticity theory to predict DNA shapes, and combined these with hydrodynamic calculations to interpret the AUC data, with reasonable agreement between theory and experiment. These complementary approaches, together with earlier electron cryotomography data, provide a framework for understanding and predicting the effects of supercoiling on the shape and hydrodynamic properties of DNA., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Human Long Intergenic Noncoding RNA-p21 (LincRNA-p21) is a regulatory noncoding RNA that plays an important role in promoting apoptosis. LincRNA-p21 is also critical in down-regulating many p53 target genes through its interaction with a p53 repressive complex. The interaction between LincRNA-p21 and the repressive complex is likely dependent on the RNA tertiary structure. Previous studies have determined the two-dimensional secondary structures of the sense and antisense human LincRNA-p21 AluSx1 IRs using SHAPE. However, there were no insights into its three-dimensional structure. Therefore, we in vitro transcribed the sense and antisense regions of LincRNA-p21 AluSx1 Inverted Repeats (IRs) and performed analytical ultracentrifugation, size exclusion chromatography, light scattering, and small angle X-ray scattering (SAXS) studies. Based on these studies, we determined low-resolution, three-dimensional structures of sense and antisense LincRNA-p21. By adapting previously known two-dimensional information, we calculated their sense and antisense high-resolution models and determined that they agree with the low-resolution structures determined using SAXS. Thus, our integrated approach provides insights into the structure of LincRNA-p21 Alu IRs. Our study also offers a viable pipeline for combining the secondary structure information with biophysical and computational studies to obtain high-resolution atomistic models for long noncoding RNAs., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Methods for optimizing the prediction of Escherichia coli RNA polymerase promoter sequences by neural networks are presented. A neural network was trained on a set of 80 known promoter sequences combined with different numbers of random sequences. The conserved -10 region and -35 region of the promoter sequences and a combination of these regions were used in three independent training sets. The prediction accuracy of the resulting weight matrix was tested against a separate set of 30 known promoter sequences and 1500 random sequences. The effects of the network's topology, the extent of training, the number of random sequences in the training set and the effects of different data representations were examined and optimized. Accuracies of 100% on the promoter test set and 98.4% on the random test set were achieved with the optimal parameters.