AIMC Topic: Nucleic Acid Conformation

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

Nucleic acids research Jul 7, 2025
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...
Software Deep Learning Proteins Protein Conformation Nucleic Acid Conformation Cryoelectron Microscopy Models, Molecular Nucleic Acids
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CGeNArateWeb: a web server for the atomistic study of the structure and dynamics of chromatin fibers.

Nucleic acids research Jul 7, 2025
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 ...
DNA Algorithms Internet Nucleic Acid Conformation Machine Learning Molecular Dynamics Simulation Chromatin User-Computer Interface Software
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DeepRNA-Twist: language-model-guided RNA torsion angle prediction with attention-inception network.

Briefings in bioinformatics May 1, 2025
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...
RNA Nucleic Acid Conformation Deep Learning Computational Biology Software
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DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.

Nucleic acids research Mar 20, 2025
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...
Humans Machine Learning Software RNA Ligands Nucleic Acid Conformation Algorithms Molecular Docking Simulation Binding Sites
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DRAG: design RNAs as hierarchical graphs with reinforcement learning.

Briefings in bioinformatics Mar 4, 2025
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...
RNA Machine Learning Algorithms Nucleic Acid Conformation Computational Biology Neural Networks, Computer
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Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2025
In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies in...
Machine Learning Software Riboswitch Models, Molecular Algorithms RNA Computational Biology Nucleic Acid Conformation
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gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2025
Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse p...
Nucleic Acid Conformation RNA, Catalytic Models, Molecular RNA Neural Networks, Computer Deep Learning Computational Biology Software
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Machine Learning for RNA Design: LEARNA.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2025
Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to remarkable results. In this chapter, we describe machine learning approaches specifically developed for...
RNA Nucleic Acid Conformation Machine Learning Software Deep Learning Computational Biology Algorithms
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Grand canonical Monte Carlo and deep learning assisted enhanced sampling to characterize the distribution of Mg2+ and influence of the Drude polarizable force field on the stability of folded states of the twister ribozyme.

The Journal of chemical physics Dec 14, 2024
Molecular dynamics simulations are crucial for understanding the structural and dynamical behavior of biomolecular systems, including the impact of their environment. However, there is a gap between the time scale of these simulations and that of rea...
Nucleic Acid Conformation Monte Carlo Method Magnesium Deep Learning Molecular Dynamics Simulation RNA, Catalytic
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Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics Sep 23, 2024
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...
Protein Binding Binding Sites DNA Bacteria Computational Biology Machine Learning Bacterial Proteins Nucleic Acid Conformation DNA, Bacterial Algorithms Transcription Factors
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