Abstract
RNA folding prediction is very meaningful and challenging. The molecular dynamics simulation (MDS) of all atoms (AA) is limited to the folding of small RNA molecules. At present, most of the practical models are coarse grained (CG) model, and the coarse-grained force field (CGFF) parameters usually depend on known RNA structures. However, the limitation of the CGFF is obvious that it is difficult to study the modified RNA. Based on the 3 beads model (AIMS_RNA_B3), we proposed the AIMS_RNA_B5 model with three beads representing a base and two beads representing the main chain (sugar group and phosphate group). We first run the all atom molecular dynamic simulation (AAMDS), and fit the CGFF parameter with the AA trajectory. Then perform the coarse-grained molecular dynamic simulation (CGMDS). AAMDS is the foundation of CGMDS. CGMDS is mainly to carry out the conformation sampling based on the current AAMDS state and improve the folding speed. We simulated the folding of three RNAs, which belong to hairpin, pseudoknot and tRNA respectively. Compared to the AIMS_RNA_B3 model, the AIMS_RNA_B5 model is more reasonable and performs better.
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The data used to support the findings of this study are available from the corresponding author upon request.
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Acknowledgements
This work is supported by grants from the National Key R&D Program of China (2019YFA0709400), the National Natural Science Foundation of China (21933010), the Strategic Priority Research Program of Chinese Academy of Sciences (XDB 37000000), China Postdoctoral Science Foundation (No. 2021M703153) and the Foundation of Liaoning Province Education Administration (LJKZ0983).
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12539_2023_561_MOESM1_ESM.docx
Supplementary file1 (DOCX 1698 KB)—Fig.S1 Targeted MDS folding process of the three RNA. Fig.S2 Multiscale MDS of the folding process of 2a43. Fig.S3 Multiscale MDS of the folding process of 2zm5. Fig.S4 Multiscale MDS of the folding process of 2l2j.
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Zhang, D., Gong, L., Weng, J. et al. RNA Folding Based on 5 Beads Model and Multiscale Simulation. Interdiscip Sci Comput Life Sci 15, 393–404 (2023). https://doi.org/10.1007/s12539-023-00561-3
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DOI: https://doi.org/10.1007/s12539-023-00561-3