Integrating Coarse-Grained Simulations and Deep Learning to Unveil Entropy-Driven dsRNA Unwinding by DDX3X
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| Format: | Preprint |
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2025
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| _version_ | 1866909951019974656 |
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| author | Wang, Kang Ren, Chun-Lai Ma, Yu-Qiang |
| author_facet | Wang, Kang Ren, Chun-Lai Ma, Yu-Qiang |
| contents | DEAD-box RNA helicases (DDXs) are essential RNA metabolism regulators that typically unwind dsRNA in an ATP-dependent manner. However, recent studies show some DDXs can also unwind dsRNA without ATP, a phenomenon that remains poorly understood. Here, we developed HelixTriad coarse-grained RNA model, incorporating Watson-Crick base pairing, base stacking, and electrostatics within a three-bead-per-nucleotide scheme to accurately reproduce experimental RNA melting curves. Molecular dynamics simulations showed that weak, specific DDX3X-dsRNA interactions drive stochastic strand separation without ATP. Free energy analysis revealed that successful unwinding via high-entropy, stand-displacing intermediates. Furthermore, we introduced Entropy-Unet, a deep learning framework for entropy prediction, which corroborated theoretical estimates and uncovered a hierarchical pattern of entropy contributions. Together, our findings suggest that ATP-independent dsRNA unwinding by DDXs is predominantly entropy-driven, offering new mechanistic insights into RNA helicases versatility. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_08367 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Integrating Coarse-Grained Simulations and Deep Learning to Unveil Entropy-Driven dsRNA Unwinding by DDX3X Wang, Kang Ren, Chun-Lai Ma, Yu-Qiang Biomolecules Soft Condensed Matter DEAD-box RNA helicases (DDXs) are essential RNA metabolism regulators that typically unwind dsRNA in an ATP-dependent manner. However, recent studies show some DDXs can also unwind dsRNA without ATP, a phenomenon that remains poorly understood. Here, we developed HelixTriad coarse-grained RNA model, incorporating Watson-Crick base pairing, base stacking, and electrostatics within a three-bead-per-nucleotide scheme to accurately reproduce experimental RNA melting curves. Molecular dynamics simulations showed that weak, specific DDX3X-dsRNA interactions drive stochastic strand separation without ATP. Free energy analysis revealed that successful unwinding via high-entropy, stand-displacing intermediates. Furthermore, we introduced Entropy-Unet, a deep learning framework for entropy prediction, which corroborated theoretical estimates and uncovered a hierarchical pattern of entropy contributions. Together, our findings suggest that ATP-independent dsRNA unwinding by DDXs is predominantly entropy-driven, offering new mechanistic insights into RNA helicases versatility. |
| title | Integrating Coarse-Grained Simulations and Deep Learning to Unveil Entropy-Driven dsRNA Unwinding by DDX3X |
| topic | Biomolecules Soft Condensed Matter |
| url | https://arxiv.org/abs/2512.08367 |