AI-integrated network for RNA complex structure and dynamic prediction Article Swipe
Haoquan Liu
,
Zhuo Chen
,
Jiaming Gao
,
Chengwei Zeng
,
Yunjie Zhao
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0237319
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1063/5.0237319
RNA complexes are essential components in many cellular processes. The functions of these complexes are linked to their tertiary structures, which are shaped by detailed interface information, such as binding sites, interface contact, and dynamic conformational changes. Network-based approaches have been widely used to analyze RNA complex structures. With their roots in the graph theory, these methods have a long history of providing insight into the static and dynamic properties of RNA molecules. These approaches have been effective in identifying functional binding sites and analyzing the dynamic behavior of RNA complexes. Recently, the advent of artificial intelligence (AI) has brought transformative changes to the field. These technologies have been increasingly applied to studying RNA complex structures, providing new avenues for understanding the complex interactions within RNA complexes. By integrating AI with traditional network analysis methods, researchers can build more accurate models of RNA complex structures, predict their dynamic behaviors, and even design RNA-based inhibitors. In this review, we introduce the integration of network-based methodologies with AI techniques to enhance the understanding of RNA complex structures. We examine how these advanced computational tools can be used to model and analyze the detailed interface information and dynamic behaviors of RNA molecules. Additionally, we explore the potential future directions of how AI-integrated networks can aid in the modeling and analyzing RNA complex structures.
Related Topics
Concepts
RNA
Computer science
Complex network
Complex system
Interface (matter)
Computational biology
Transformative learning
Nucleic acid structure
Artificial intelligence
Theoretical computer science
Chemistry
Biology
World Wide Web
Bubble
Maximum bubble pressure method
Parallel computing
Pedagogy
Biochemistry
Psychology
Gene
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0237319
- OA Status
- green
- Cited By
- 7
- References
- 93
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1063/5.0237319Digital Object Identifier
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AI-integrated network for RNA complex structure and dynamic predictionWork title
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reviewOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-05Full publication date if available
- Authors
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Haoquan Liu, Zhuo Chen, Jiaming Gao, Chengwei Zeng, Yunjie ZhaoList of authors in order
- Landing page
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https://doi.org/10.1063/5.0237319Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://pmc.ncbi.nlm.nih.gov/articles/PMC11540444/Direct OA link when available
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7Total citation count in OpenAlex
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2025: 7Per-year citation counts (last 5 years)
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