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Nucleic Acid Conformation

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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 ...

RNA structure prediction using deep learning - A comprehensive review.

Computers in biology and medicine
In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led ...

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models.

Journal of molecular biology
RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule bind...

DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.

Nucleic acids research
Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential dr...

Critical Assessment of RNA and DNA Structure Predictions via Artificial Intelligence: The Imitation Game.

Journal of chemical information and modeling
Computational predictions of biomolecular structure via artificial intelligence (AI) based approaches, as exemplified by AlphaFold software, have the potential to model of all life's biomolecules. We performed oligonucleotide structure prediction and...

RNAmigos2: accelerated structure-based RNA virtual screening with deep graph learning.

Nature communications
RNAs are a vast reservoir of untapped drug targets. Structure-based virtual screening (VS) identifies candidate molecules by leveraging binding site information, traditionally using molecular docking simulations. However, docking struggles to scale w...

DRAG: design RNAs as hierarchical graphs with reinforcement learning.

Briefings in bioinformatics
The rapid development of RNA vaccines and therapeutics puts forward intensive requirements on the sequence design of RNAs. RNA sequence design, or RNA inverse folding, aims to generate RNA sequences that can fold into specific target structures. To d...

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...

DeepRNA-Twist: language-model-guided RNA torsion angle prediction with attention-inception network.

Briefings in bioinformatics
RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNA's structural complexity as well as fl...