3dRNA/DNA: 3D Structure Prediction from RNA to DNA

IF 4.7 2区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY Journal of Molecular Biology Pub Date : 2024-09-01 DOI:10.1016/j.jmb.2024.168742
Yi Zhang, Yiduo Xiong, Chenxi Yang, Yi Xiao
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Abstract

There is an increasing need for determining 3D structures of DNAs, e.g., for increasing the efficiency of DNA aptamer selection. Recently, we have proposed a computational method of 3D structure prediction of DNAs, called 3dDNA, which has been integrated into our original web server 3dRNA, now renamed 3dRNA/DNA (http://biophy.hust.edu.cn/new/3dRNA). Currently, 3dDNA can only output the predicted DNA 3D structures for users but cannot rank them as an energy function for assessing DNA 3D structures is still lacking. Here, we first provide a brief introduction to 3dDNA and then introduce a new energy function, 3dDNAscore, for the assessment of DNA 3D structures. 3dDNAscore is an all-atom knowledge-based potential by integrating 86 atomic types from nucleic acids. Benchmarks demonstrate that 3dDNAscore can effectively identify near-native structures from the decoys generated by 3dDNA, thus enhancing the completeness of 3dDNA.

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3dRNA/DNA:从 RNA 到 DNA 的三维结构预测
现在越来越需要确定 DNA 的三维结构,例如提高 DNA 合体选择的效率。最近,我们提出了一种名为 3dDNA 的 DNA 三维结构预测计算方法,并将其集成到了我们最初的网络服务器 3dRNA,现在更名为 3dRNA/DNA(http://biophy.hust.edu.cn/new/3dRNA)。目前,3dDNA 只能为用户输出预测的 DNA 3D 结构,但不能对其进行排序,因为还缺乏评估 DNA 3D 结构的能量函数。在此,我们首先简要介绍 3dDNA,然后介绍一种用于评估 DNA 3D 结构的新能量函数 3dDNAscore。3dDNAscore 是一种基于全原子知识的势能,它整合了核酸中的 86 种原子类型。基准测试表明,3dDNAscore 能从 3dDNA 生成的诱饵中有效识别近原生结构,从而提高 3dDNA 的完整性。
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来源期刊
Journal of Molecular Biology
Journal of Molecular Biology 生物-生化与分子生物学
CiteScore
11.30
自引率
1.80%
发文量
412
审稿时长
28 days
期刊介绍: Journal of Molecular Biology (JMB) provides high quality, comprehensive and broad coverage in all areas of molecular biology. The journal publishes original scientific research papers that provide mechanistic and functional insights and report a significant advance to the field. The journal encourages the submission of multidisciplinary studies that use complementary experimental and computational approaches to address challenging biological questions. Research areas include but are not limited to: Biomolecular interactions, signaling networks, systems biology; Cell cycle, cell growth, cell differentiation; Cell death, autophagy; Cell signaling and regulation; Chemical biology; Computational biology, in combination with experimental studies; DNA replication, repair, and recombination; Development, regenerative biology, mechanistic and functional studies of stem cells; Epigenetics, chromatin structure and function; Gene expression; Membrane processes, cell surface proteins and cell-cell interactions; Methodological advances, both experimental and theoretical, including databases; Microbiology, virology, and interactions with the host or environment; Microbiota mechanistic and functional studies; Nuclear organization; Post-translational modifications, proteomics; Processing and function of biologically important macromolecules and complexes; Molecular basis of disease; RNA processing, structure and functions of non-coding RNAs, transcription; Sorting, spatiotemporal organization, trafficking; Structural biology; Synthetic biology; Translation, protein folding, chaperones, protein degradation and quality control.
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Editorial Board Outside Front Cover Assembly of the human multi-tRNA synthetase complex through leucine zipper motifs. Corrigendum to “The Role of ATG9 Vesicles in Autophagosome Biogenesis” [J. Mol. Biol. 436(15) (2024) 168489] Structural studies on Mycobacterial NudC reveal a class of zinc independent NADH pyrophosphatase.
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