AIMC Topic: RNA

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Deep learning for RNA structure prediction.

Current opinion in structural biology Feb 10, 2025
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...
RNA Nucleic Acid Conformation Deep Learning Computational Biology
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ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.

Journal of molecular biology Feb 1, 2025
RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various dis...
Humans Neural Networks, Computer RNA Cytidine RNA Processing, Post-Transcriptional Deep Learning Computational Biology
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ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions.

Methods (San Diego, Calif.) Jan 30, 2025
RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indisp...
RNA-Binding Proteins RNA Protein Binding Computational Biology Graph Neural Networks Humans Neural Networks, Computer Machine Learning Algorithms
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The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning.

Computational biology and chemistry Jan 29, 2025
Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. Wit...
Binding Sites Small Molecule Libraries RNA Ligands Deep Learning
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Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Journal of chemical information and modeling Jan 7, 2025
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...
Deep Learning Protein Binding RNA, Viral Peptides Nucleic Acid Conformation Molecular Dynamics Simulation RNA
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SequenceCraft: machine learning-based resource for exploratory analysis of RNA-cleaving deoxyribozymes.

BMC bioinformatics Jan 6, 2025
BACKGROUND: Deoxyribozymes or DNAzymes represent artificial short DNA sequences bearing many catalytic properties. In particular, DNAzymes able to cleave RNA sequences have a huge potential in gene therapy and sequence-specific analytic detection of ...
Computational Biology RNA DNA, Catalytic Machine Learning
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Trans-m5C: A transformer-based model for predicting 5-methylcytosine (m5C) sites.

Methods (San Diego, Calif.) Dec 30, 2024
5-Methylcytosine (m5C) plays a pivotal role in various RNA metabolic processes, including RNA localization, stability, and translation. Current high-throughput sequencing technologies for m5C site identification are resource-intensive in terms of cos...
Deep Learning Mice Neural Networks, Computer Animals High-Throughput Nucleotide Sequencing Humans Computational Biology 5-Methylcytosine RNA
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Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

PLoS computational biology Dec 30, 2024
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...
RNA Sequence Analysis, RNA Nucleic Acid Conformation Sequence Alignment Deep Learning Benchmarking Computational Biology
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Predicting RNA structure and dynamics with deep learning and solution scattering.

Biophysical journal Dec 25, 2024
Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce confo...
Nucleic Acid Conformation Solutions Scattering, Small Angle RNA X-Ray Diffraction Models, Molecular Magnesium Deep Learning
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Determining structures of RNA conformers using AFM and deep neural networks.

Nature Dec 18, 2024
Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible...
Humans Unsupervised Machine Learning RNA Models, Molecular Neural Networks, Computer Microscopy, Atomic Force Signal-To-Noise Ratio Deep Learning Nucleic Acid Conformation
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