AIMC Topic: Nucleic Acid Conformation

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Deep generalizable prediction of RNA secondary structure via base pair motif energy.

Nature communications
Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and r...

GMFOLD: Subgraph matching for high-throughput DNA-aptamer secondary structure classification and machine learning interpretability.

Mathematical biosciences
Aptamers are oligonucleotide receptors that bind to their targets with high affinity. Here, we consider aptamers comprised of single-stranded DNA that undergo target-binding-induced conformational changes, giving rise to unique secondary and tertiary...

AffiGrapher: Contrastive Heterogeneous Graph Learning with Aromatic Virtual Nodes for RNA-Small Molecule Binding Affinity Prediction.

Journal of chemical information and modeling
RNA molecules exhibit diverse structures and functions, making them promising drug targets. However, predicting RNA-small molecule binding affinity remains challenging due to limited experimental data and the structural variability introduced by mult...

Melting Profile of DNA in Crowded Solution: Model-Based Study.

International journal of molecular sciences
Recent advances in molecular dynamics (MD) simulations and the introduction of artificial intelligence (AI) have resulted in a significant increase in accuracy for structure prediction. However, the cell is a highly crowded environment consisting of ...

Loop Nucleotide Chemical Shifts as a Tool to Characterize DNA G-Quadruplexes.

Chemphyschem : a European journal of chemical physics and physical chemistry
DNA G-quadruplexes are known to play myriad functional roles in the cellular context and their structural diversity has diverse applications in various fields of science. Solution-state NMR spectroscopy has been instrumental in characterization of DN...

Generative and predictive neural networks for the design of functional RNA molecules.

Nature communications
RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence, structure, and function of RNA often ne...

GINClus: RNA structural motif clustering using graph isomorphism network.

NAR genomics and bioinformatics
Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming...

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.

PloS one
The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches ofte...

Identification of potential riboswitch elements in Homo sapiens mRNA 5'UTR sequences using positive-unlabeled machine learning.

PloS one
Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a conformational change that can then execute some regulatory purpose within the cell. Riboswitches are ubiquitous and well characterized in bacteria and ...

The prediction of RNA-small molecule binding sites in RNA structures based on geometric deep learning.

International journal of biological macromolecules
Biological interactions between RNA and small-molecule ligands play a crucial role in determining the specific functions of RNA, such as catalysis and folding, and are essential for guiding drug design in the medical field. Accurately predicting the ...