AIMC Topic: Nucleic Acid Conformation

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Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

BMC systems biology
BACKGROUND: DNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome...

Machine Learning-Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands.

Angewandte Chemie (International ed. in English)
The challenge of targeting RNA with small molecules necessitates a better understanding of RNA-ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand-induced stabilization, and how conformational changes influence g...

Characterizing DNA Origami Nanostructures in TEM Images Using Convolutional Neural Networks.

Journal of chemical information and modeling
Artificial intelligence (AI) models remain an emerging strategy to accelerate materials design and development. We demonstrate that CNN models can characterize DNA origami nanostructures employed in programmable self-assembly, which is important in m...

A Hyperbolic Discrete Diffusion 3D RNA Inverse Folding Model for Functional RNA Design.

Journal of chemical information and modeling
Generative design of functional RNAs presents revolutionary opportunities for diverse RNA-based biotechnologies and biomedical applications. To this end, RNA inverse folding is a promising strategy for generatively designing new RNA sequences that ca...

Predicting rare DNA conformations via dynamical graphical models: a case study of the B→A transition.

Nucleic acids research
DNA exhibits local conformational preferences that affect its ability to adopt biologically relevant conformations, such as those required for binding proteins. Traditional methods, like Markov state models and molecular dynamics (MD) simulations, ha...

DEMO-EMol: modeling protein-nucleic acid complex structures from cryo-EM maps by coupling chain assembly with map segmentation.

Nucleic acids research
Atomic structure modeling is a crucial step in determining the structures of protein complexes using cryo-electron microscopy (cryo-EM). This work introduces DEMO-EMol, an improved server that integrates deep learning-based map segmentation and chain...

CGeNArateWeb: a web server for the atomistic study of the structure and dynamics of chromatin fibers.

Nucleic acids research
We present CGeNArateWeb, a new web tool for the three-dimensional simulation of naked DNA and protein-bound chromatin fibers. The server allows the user to obtain a dynamic representation of long segments of linear, circular, or protein-DNA segments ...

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...

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...

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...