AIMC Topic: Nucleic Acid Conformation

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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...

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...

Deep learning for RNA structure prediction.

Current opinion in structural biology
Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure p...

Recipes and ingredients for deep learning models of 3D genome folding.

Current opinion in genetics & development
Three-dimensional genome folding plays roles in gene regulation and disease. In this review, we compare and contrast recent deep learning models for predicting genome contact maps. We survey preprocessing, architecture, training, evaluation, and inte...

Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

The journal of physical chemistry letters
Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hindered by spectral interference from citrate ions, which overlap with key DNA features. This study intro...

Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Journal of chemical information and modeling
Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulat...

ChromatinHD connects single-cell DNA accessibility and conformation to gene expression through scale-adaptive machine learning.

Nature communications
Gene regulation is inherently multiscale, but scale-adaptive machine learning methods that fully exploit this property in single-nucleus accessibility data are still lacking. Here, we develop ChromatinHD, a pair of scale-adaptive models that uses the...

Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

PLoS computational biology
The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computati...