AIMC Topic: Nucleic Acid Conformation

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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...
Software Algorithms Computational Biology Machine Learning Nucleic Acid Conformation Riboswitch Models, Molecular RNA
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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...
RNA, Catalytic RNA Nucleic Acid Conformation Neural Networks, Computer Software Computational Biology Deep Learning Models, Molecular
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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...
Machine Learning Software Computational Biology RNA Nucleic Acid Conformation Algorithms Deep Learning
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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...
Molecular Dynamics Simulation Nucleic Acid Conformation Monte Carlo Method Deep Learning Magnesium 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 ...
Nucleic Acid Conformation Binding Sites Computational Biology Algorithms DNA, Bacterial Protein Binding DNA Transcription Factors Machine Learning Bacteria Bacterial Proteins
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NucleoFind: a deep-learning network for interpreting nucleic acid electron density.

Nucleic acids research Sep 23, 2024
Nucleic acid electron density interpretation after phasing by molecular replacement or other methods remains a difficult problem for computer programs to deal with. Programs tend to rely on time-consuming and computationally exhaustive searches to re...
Software Models, Molecular Deep Learning Electrons Crystallography, X-Ray Nucleic Acids Nucleic Acid Conformation
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Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

Bioinformatics (Oxford, England) Aug 2, 2024
MOTIVATION: The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular proce...
Neural Networks, Computer Software Computational Biology RNA, Messenger Deep Learning Humans Nucleic Acid Conformation
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Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Nucleic acids research Jul 22, 2024
Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web...
RNA Folding Models, Molecular Nucleic Acid Conformation RNA Binding Sites Ligands Machine Learning Software
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sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure.

Briefings in bioinformatics May 23, 2024
MOTIVATION: Coding and noncoding RNA molecules participate in many important biological processes. Noncoding RNAs fold into well-defined secondary structures to exert their functions. However, the computational prediction of the secondary structure f...
Nucleic Acid Conformation Algorithms Deep Learning Neural Networks, Computer Thermodynamics Computational Biology RNA
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UFold: fast and accurate RNA secondary structure prediction with deep learning.

Nucleic acids research Feb 22, 2022
For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a platea...
Nucleic Acid Conformation Base Pairing Deep Learning Humans RNA Algorithms
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