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Ning S, Sun M, Dong X, Li A, Zeng C, Liu M, Gong Z, Zhao Y. Dynamic geometry design of cyclic peptide architectures for RNA structure. Phys Chem Chem Phys 2023; 25:27967-27980. [PMID: 37768078 DOI: 10.1039/d3cp03384h] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Designing inhibitors for RNA is still challenging due to the bottleneck of maintaining the binding interaction of inhibitor-RNA accompanied by subtle RNA flexibility. Thus, the current approach usually needs to screen thousands of candidate inhibitors for binding. Here, we propose a dynamic geometry design approach to enrich the hits with only a tiny pool of designed geometrically compatible scaffold candidates. First, our method uses graph-based tree decomposition to explore the complementarity rigid binding cyclic peptide and design the amino acid side chain length and charge to fit the RNA pocket. Then, we perform an energy-based dynamical network algorithm to optimize the inhibitor-RNA hydrogen bonds. Dynamic geometry-guided design yields successful inhibitors with low micromolar binding affinity scaffolds and experimentally competes with the natural RNA chaperone. The results indicate that the dynamic geometry method yields higher efficiency and accuracy than traditional methods. The strategy could be further optimized to design the length and chirality by adopting nonstandard amino acids and facilitating RNA engineering for biological or medical applications.
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Affiliation(s)
- Shangbo Ning
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
| | - Min Sun
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
| | - Xu Dong
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
| | - Anbang Li
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
| | - Chen Zeng
- Department of Physics, The George Washington University, Washington, DC 20052, USA
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
| | - Zhou Gong
- State Key Laboratory of Magnetic Resonance and Atomic Molecular Physics, Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences, Wuhan, Hubei 430071, China.
| | - Yunjie Zhao
- Institute of Biophysics and Department of Physics, Central China Normal University, Wuhan, 430079, China.
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RPflex: A Coarse-Grained Network Model for RNA Pocket Flexibility Study. Int J Mol Sci 2023; 24:ijms24065497. [PMID: 36982570 PMCID: PMC10058308 DOI: 10.3390/ijms24065497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 03/18/2023] Open
Abstract
RNA regulates various biological processes, such as gene regulation, RNA splicing, and intracellular signal transduction. RNA’s conformational dynamics play crucial roles in performing its diverse functions. Thus, it is essential to explore the flexibility characteristics of RNA, especially pocket flexibility. Here, we propose a computational approach, RPflex, to analyze pocket flexibility using the coarse-grained network model. We first clustered 3154 pockets into 297 groups by similarity calculation based on the coarse-grained lattice model. Then, we introduced the flexibility score to quantify the flexibility by global pocket features. The results show strong correlations between the flexibility scores and root-mean-square fluctuation (RMSF) values, with Pearson correlation coefficients of 0.60, 0.76, and 0.53 in Testing Sets I–III. Considering both flexibility score and network calculations, the Pearson correlation coefficient was increased to 0.71 in flexible pockets on Testing Set IV. The network calculations reveal that the long-range interaction changes contributed most to flexibility. In addition, the hydrogen bonds in the base–base interactions greatly stabilize the RNA structure, while backbone interactions determine RNA folding. The computational analysis of pocket flexibility could facilitate RNA engineering for biological or medical applications.
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