AIMC Topic: Nucleic Acid Conformation

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MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

IEEE journal of biomedical and health informatics
This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-mo...

Exploring protein-mediated compaction of DNA by coarse-grained simulations and unsupervised learning.

Biophysical journal
Protein-DNA interactions and protein-mediated DNA compaction play key roles in a range of biological processes. The length scales typically involved in DNA bending, bridging, looping, and compaction (≥1 kbp) are challenging to address experimentally ...

Machine learning-based classification reveals distinct clusters of non-coding genomic allelic variations associated with Erm-mediated antibiotic resistance.

mSystems
UNLABELLED: The erythromycin resistance RNA methyltransferase () confers cross-resistance to all therapeutically important macrolides, lincosamides, and streptogramins (MLS phenotype). The expression of is often induced by the macrolide-mediated rib...

Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead.

Genes
RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations al...

DEBFold: Computational Identification of RNA Secondary Structures for Sequences across Structural Families Using Deep Learning.

Journal of chemical information and modeling
It is now known that RNAs play more active roles in cellular pathways beyond simply serving as transcription templates. These biological mechanisms might be mediated by higher RNA stereo conformations, triggering the need to understand RNA secondary ...

DNA shape features improve prediction of CRISPR/Cas9 activity.

Methods (San Diego, Calif.)
The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine ...

RNA3DB: A structurally-dissimilar dataset split for training and benchmarking deep learning models for RNA structure prediction.

Journal of molecular biology
With advances in protein structure prediction thanks to deep learning models like AlphaFold, RNA structure prediction has recently received increased attention from deep learning researchers. RNAs introduce substantial challenges due to the sparser a...

An RNA origami robot that traps and releases a fluorescent aptamer.

Science advances
RNA nanotechnology aims to use RNA as a programmable material to create self-assembling nanodevices for application in medicine and synthetic biology. The main challenge is to develop advanced RNA robotic devices that both sense, compute, and actuate...

Prediction of DNA origami shape using graph neural network.

Nature materials
Unlike proteins, which have a wealth of validated structural data, experimentally or computationally validated DNA origami datasets are limited. Here we present a graph neural network that can predict the three-dimensional conformation of DNA origami...

Machine learning in RNA structure prediction: Advances and challenges.

Biophysical journal
RNA molecules play a crucial role in various biological processes, with their functionality closely tied to their structures. The remarkable advancements in machine learning techniques for protein structure prediction have shown promise in the field ...