Structure based computational RNA design towards MafA transcriptional repressor implicated in multiple myeloma

IF 2.7 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Güneş Yıldırım Akdeniz , Ahmet Can Timuçin
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Abstract

Multiple myeloma is recognized as the second most common hematological cancer. MafA transcriptional repressor is an established mediator of myelomagenesis. While there are multitude of drugs available for targeting various effectors in multiple myeloma, current literature lacks a candidate RNA based MafA modulator. Thus, using the structure of MafA homodimer-consensus target DNA, a computational effort was implemented to design a novel RNA based chemical modulator against MafA. First, available MafA-consensus DNA structure was employed to generate an RNA library. This library was further subjected to global docking to select the most plausible RNA candidates, preferring to bind DNA binding region of MafA. Following global docking, MD-ready complexes that were prepared via local docking program, were subjected to 500 ns of MD simulations. First, each of these MD simulations were analyzed for relative binding free energy through MM-PBSA method, which pointed towards a strong RNA based MafA binder, RNA1. Second, through a detailed MD analysis, RNA1 was shown to prefer binding to a single monomer of the dimeric DNA binding domain of MafA using higher number of hydrophobic interactions compared with positive control MafA-DNA complex. At the final phase, a principal component analyses was conducted, which led us to identify the actual interaction region of RNA1 and MafA monomer. Overall, to our knowledge, this is the first computational study that presents an RNA molecule capable of potentially targeting MafA protein. Furthermore, limitations of our study together with possible future implications of RNA1 in multiple myeloma were also discussed.

Abstract Image

针对与多发性骨髓瘤有关的 MafA 转录抑制因子的基于结构的计算 RNA 设计。
多发性骨髓瘤是公认的第二大常见血液肿瘤。MafA 转录抑制因子是骨髓瘤发生的公认介质。虽然有多种药物可用于靶向多发性骨髓瘤中的各种效应因子,但目前的文献缺乏基于 RNA 的候选 MafA 调节剂。因此,我们利用 MafA 同源二聚体-共识靶 DNA 的结构,通过计算设计出了一种新型的基于 RNA 的 MafA 化学调节剂。首先,利用现有的 MafA 共识 DNA 结构生成一个 RNA 文库。进一步对该库进行全局对接,以选择最合理的 RNA 候选者,这些候选者更倾向于结合 MafA 的 DNA 结合区。全局对接之后,对通过局部对接程序制备的 MD 就绪复合物进行了 500 ns 的 MD 模拟。首先,通过 MM-PBSA 方法分析了每个 MD 模拟的相对结合自由能,结果发现了一个基于 RNA 的 MafA 强结合体 RNA1。其次,通过详细的 MD 分析表明,与阳性对照 MafA-DNA 复合物相比,RNA1 更倾向于利用更多的疏水相互作用与 MafA 的二聚 DNA 结合域的单个单体结合。在最后阶段,我们进行了主成分分析,从而确定了 RNA1 与 MafA 单体的实际相互作用区域。总之,据我们所知,这是首次通过计算研究发现一种 RNA 分子有可能靶向 MafA 蛋白。此外,我们还讨论了研究的局限性以及 RNA1 未来在多发性骨髓瘤中可能产生的影响。
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来源期刊
Journal of molecular graphics & modelling
Journal of molecular graphics & modelling 生物-计算机:跨学科应用
CiteScore
5.50
自引率
6.90%
发文量
216
审稿时长
35 days
期刊介绍: The Journal of Molecular Graphics and Modelling is devoted to the publication of papers on the uses of computers in theoretical investigations of molecular structure, function, interaction, and design. The scope of the journal includes all aspects of molecular modeling and computational chemistry, including, for instance, the study of molecular shape and properties, molecular simulations, protein and polymer engineering, drug design, materials design, structure-activity and structure-property relationships, database mining, and compound library design. As a primary research journal, JMGM seeks to bring new knowledge to the attention of our readers. As such, submissions to the journal need to not only report results, but must draw conclusions and explore implications of the work presented. Authors are strongly encouraged to bear this in mind when preparing manuscripts. Routine applications of standard modelling approaches, providing only very limited new scientific insight, will not meet our criteria for publication. Reproducibility of reported calculations is an important issue. Wherever possible, we urge authors to enhance their papers with Supplementary Data, for example, in QSAR studies machine-readable versions of molecular datasets or in the development of new force-field parameters versions of the topology and force field parameter files. Routine applications of existing methods that do not lead to genuinely new insight will not be considered.
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