Advances and Mechanisms of RNA–Ligand Interaction Predictions
The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA’s specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Life |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1729/15/1/104 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832588156154675200 |
|---|---|
| author | Chen Zhuo Chengwei Zeng Haoquan Liu Huiwen Wang Yunhui Peng Yunjie Zhao |
| author_facet | Chen Zhuo Chengwei Zeng Haoquan Liu Huiwen Wang Yunhui Peng Yunjie Zhao |
| author_sort | Chen Zhuo |
| collection | DOAJ |
| description | The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA’s specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA–ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA–ligand complexes by examining RNA’s sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA–ligand interaction prediction tools. |
| format | Article |
| id | doaj-art-b3f1dda8da0a476fa8e0499d2e55861e |
| institution | Kabale University |
| issn | 2075-1729 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Life |
| spelling | doaj-art-b3f1dda8da0a476fa8e0499d2e55861e2025-01-24T13:38:48ZengMDPI AGLife2075-17292025-01-0115110410.3390/life15010104Advances and Mechanisms of RNA–Ligand Interaction PredictionsChen Zhuo0Chengwei Zeng1Haoquan Liu2Huiwen Wang3Yunhui Peng4Yunjie Zhao5Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, ChinaInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, ChinaInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, ChinaSchool of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, ChinaInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, ChinaInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, ChinaThe diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA’s specific recognition of other molecules. With advancements in biotechnology, RNA–ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA–ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA–ligand complexes by examining RNA’s sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA–ligand interaction prediction tools.https://www.mdpi.com/2075-1729/15/1/104RNA secondary structure motifsRNA pocket geometric featureRNA–ligand interaction mechanismstructure prediction |
| spellingShingle | Chen Zhuo Chengwei Zeng Haoquan Liu Huiwen Wang Yunhui Peng Yunjie Zhao Advances and Mechanisms of RNA–Ligand Interaction Predictions Life RNA secondary structure motifs RNA pocket geometric feature RNA–ligand interaction mechanism structure prediction |
| title | Advances and Mechanisms of RNA–Ligand Interaction Predictions |
| title_full | Advances and Mechanisms of RNA–Ligand Interaction Predictions |
| title_fullStr | Advances and Mechanisms of RNA–Ligand Interaction Predictions |
| title_full_unstemmed | Advances and Mechanisms of RNA–Ligand Interaction Predictions |
| title_short | Advances and Mechanisms of RNA–Ligand Interaction Predictions |
| title_sort | advances and mechanisms of rna ligand interaction predictions |
| topic | RNA secondary structure motifs RNA pocket geometric feature RNA–ligand interaction mechanism structure prediction |
| url | https://www.mdpi.com/2075-1729/15/1/104 |
| work_keys_str_mv | AT chenzhuo advancesandmechanismsofrnaligandinteractionpredictions AT chengweizeng advancesandmechanismsofrnaligandinteractionpredictions AT haoquanliu advancesandmechanismsofrnaligandinteractionpredictions AT huiwenwang advancesandmechanismsofrnaligandinteractionpredictions AT yunhuipeng advancesandmechanismsofrnaligandinteractionpredictions AT yunjiezhao advancesandmechanismsofrnaligandinteractionpredictions |