Modeling ligand docking to RNA in the design of RNA-based nanostructures

Quantifying RNA structures and interactions with a unified reduced chain representation model

RNA is a pivotal molecule that plays critical roles in various cellular processes. Quantifying RNA structures and interactions is essential to understanding RNA function and developing RNA-based therapeutics. Using a unified five-bead model and a non-redundant database, this paper investigates the structural features and interactions of five commonly occurring RNA motifs, i.e., double-stranded helices, hairpin loops, internal/bulge loops, multi-branched junctions, and single-stranded terminal tails. Analyzing detailed distributions of RNA local structural features and base-base interactions reveals a preference for helical structures in both local backbone structures and base orientations. The interactions between adjacent bases exhibit motif-specific and sequence-dependent characteristics, reflecting the distinct topological constraints imposed by different loop-helix connection modes and the varying pairing and stacking interactions among different sequences. These findings shed light on the stability of RNA helices, emphasizing their significance in providing dominant base pairing and stacking interactions for RNA structures and stability. The four non-helix motifs encompass unpaired nucleotide loops and exhibit diverse base-base interactions, contributing to the structural diversity observed in RNA. Overall, the complexity of RNA structure arises from the intricate interplay of base-base interactions.

An Overview of the Potential Medicinal and Pharmaceutical Properties of Ru(II)/(III) Complexes

This review examines the existing knowledge about Ru(II)/(III) ion complexes with a potential application in medicine or pharmacy, which may offer greater potential in cancer chemotherapy than Pt(II) complexes, which are known to cause many side effects. Hence, much attention has been paid to research on cancer cell lines and clinical trials have been undertaken on ruthenium complexes. In addition to their antitumor activity, ruthenium complexes are under evaluation for other diseases, such as type 2 diabetes, Alzheimer's disease and HIV. Attempts are also being made to evaluate ruthenium complexes as potential photosensitizers with polypyridine ligands for use in cancer chemotherapy. The review also briefly examines theoretical approaches to studying the interactions of Ru(II)/Ru(III) complexes with biological receptors, which can facilitate the rational design of ruthenium-based drugs.

Application of Molecular Dynamics to Expand Docking Program's Exploratory Capabilities and to Evaluate Its Predictions

Recognition of the growing importance of RNA as a target for therapeutic or diagnostic ligands brings the importance of computational predictions of docking poses to such receptors to the forefront. Most docking programs have been optimized for protein targets, based on a relatively rich pool of known docked protein structures. Unfortunately, despite progress, numbers of known docked RNA complexes are low and the accuracy of the computational predictions trained on those inadequate samples lags behind that achieved for proteins. Compared to proteins, RNA structures generally have fewer docking pockets, have less diverse electrostatic surfaces, and are more flexible, raising the possibility of producing only transiently available good docking targets. We are presenting a docking prediction protocol that adds molecular dynamics simulations before and after the actual docking in order to explore the conformational space of the target RNA and then to reevaluate the stability of the predicted RNA-ligand complex. In this way we are attempting to overcome important limitations of the docking programs: the rigid (fully or mostly) target structure and imperfect nature of the docking scoring functions.

Computer-Assisted Design and Characterization of RNA Nanostructures

Molecular dynamics (MD) simulations can aid in the design and characterization of RNA nanomaterials, providing details about structural and dynamical properties as a function of sequence and environment. Here, we describe how to perform explicit and implicit solvent all-atom MD simulations for RNA nanoring systems.

Efficient DNA-ligand interaction framework using fuzzy C-means clustering based glowworm swarm optimization (FCMGSO) method

Assessment of DNA and ligand interaction is a great challenge to the medical researchers and drug industries since the accurate mapping of DNA and ligand plays an important role in associating drugs for suitable diseases. The primary objective of this research work is to develop an efficient model for predicting the best DNA and Ligand mapping. In this research work, 500 instances of DNA and drugs used for cancer and non-cancer diseases from the National Centre for Biotechnology Information (NCBI) were considered for analysis. Binding energy is one of the important measures to predict and finalize the best DNA and ligand interaction. Existing methods used for the docking process such as Simulated Annealing (SA), Lamarckian Genetic Algorithm (LGA), Genetic Clustering (GC), Fuzzy C-means clustering (FCM), and Genetic Clustering with Multi swarm Optimization (GCMSO) were applied for all 500 instances. These algorithms failed to produce better binding energy due to a lack of optimization in the existing approaches. Optimization methods play a major role in predicting accurate DNA ligand docking. Hence, this research proposes an efficient architecture using Fuzzy C-Means Clustering with Glowworm Swarm (FCMGSO) optimization method for accurate analysis of the DNA-ligand docking process. Results are proving that the proposed FCMGSO algorithm shows less binding energy than other existing methods in all instances of samples considered from the NCBI dataset.Communicated by Ramaswamy H. Sarma.

The enhancer RNA ADCY10P1 is associated with the progression of ovarian cancer

BACKGROUND

Emerging evidence identifies enhancer RNAs (eRNAs) as a class of regulatory ncRNAs that can contribute to the transcription of target genes. In this study, we used an integrated data analysis method to identify the important role of eRNAs in ovarian cancer (OC).

METHODS

Gene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) database were used for this study. Based on expression analysis using GEPIA2 gene and Kaplan-Meier survival was performed to ensure the significance of the selected enhancer RNA ADCY10P1 in OC. Next, we explored the correlation and clinical significance between ADCY10P1 and target gene NFYA. Furthermore, we evaluated the effects of overexpression of ADCY10P1 on the proliferation, migration, invasion and epithelial-mesenchymal transformation (EMT) of OC cell lines. We also investigated the biological function enrichment score of ADCY10P1 and verified it with OC cell lines. Finally, external validation was conducted, and the prognostic value of the ADCY10P1 in different tumors was demonstrated.

RESULTS

We selected the eRNA ADCY10P1 associated with OC prognosis, with NFYA as its predicted target gene. Low ADCY10P1 expression was found to be associated with poor overall survival, high histological grade, and advanced stage of OC. Additionally, overexpression of ADCY10P1 inhibited the proliferation, migration, invasion and EMT phenotype of OC cell lines. Furthermore, ADCY10P1 was observed to inhibit glycolysis and fatty acid metabolism, thereby affecting OC progression. Meanwhile, OC tissue samples were externally validated. In addition, the pan-cancer analysis revealed that ADCY10P1 had prognostic value in other cancers.

CONCLUSIONS

This study showed that ADCY10P1 plays a key role in OC progression and may facilitate prognosis prediction.

A simple screening strategy for complex RNA-DNA hybrid nanoshapes

The design of hybrid nucleic acid nanomaterials capitalizes on the partitioning of architectural and functional roles between structurally diverse RNA modules and chemically robust DNA components. Selecting optimal combinations of RNA and DNA building blocks is the key to preparing stable polygonal RNA-DNA hybrid nanoshapes. Here, we outline a simple screening strategy by gel electrophoresis under native folding conditions to identify combinations of RNA and DNA modules that self-assemble to robust polygonal hybrid nanoshapes. As a proof of concept, we outline the preparation of RNA-DNA hybrid nanoshapes containing a set of different RNA architectural joints, including internal loop motifs and three-way junction (3WJ) folds. For each hybrid nanoshape, we demonstrate the selection process used to identify optimal DNA modules from a library of DNA connectors. The simple screening strategy outlined here provides a general robust method to identify and prepare RNA-DNA hybrid nanoshapes from diverse libraries of discrete nucleic acid building blocks.