Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg-University, Staudingerweg 5, 55128 Mainz, Germany.
Institute for Quantitative and Computational Biosciences, Johannes Gutenberg-University, BioZentrum I, Hanns-Dieter-Hüsch.Weg 15, 55128 Mainz, Germany.
J Chem Inf Model. 2024 Jun 10;64(11):4485-4499. doi: 10.1021/acs.jcim.4c00520. Epub 2024 May 20.
With increasing interest in RNA as a therapeutic and a potential target, the role of RNA structures has become more important. Even slight changes in nucleobases, such as modifications or protomeric and tautomeric states, can have a large impact on RNA structure and function, while local environments in turn affect protonation and tautomerization. In this work, the application of empirical tools for p and tautomer prediction for RNA modifications was elucidated and compared with ab initio quantum mechanics (QM) methods and expanded toward macromolecular RNA structures, where QM is no longer feasible. In this regard, the Protonate3D functionality within the molecular operating environment (MOE) was expanded for nucleobase protomer and tautomer predictions and applied to reported examples of altered protonation states depending on the local environment. Overall, observations of nonstandard protomers and tautomers were well reproduced, including structural CG:C(A) and AGG motifs, several mismatches, and protonation of adenosine or cytidine as the general acid in nucleolytic ribozymes. Special cases, such as cobalt hexamine-soaked complexes or the deprotonation of guanosine as the general base in nucleolytic ribozymes, proved to be challenging. The collected set of examples shall serve as a starting point for the development of further RNA protonation prediction tools, while the presented Protonate3D implementation already delivers reasonable protonation predictions for RNA and DNA macromolecules. For cases where higher accuracy is needed, like following catalytic pathways of ribozymes, incorporation of QM-based methods can build upon the Protonate3D-generated starting structures. Likewise, this protonation prediction can be used for structure-based RNA-ligand design approaches.
随着人们对 RNA 作为治疗靶点和潜在靶标的兴趣日益增加,RNA 结构的作用变得越来越重要。即使碱基核苷稍有变化,如修饰或前体和互变异构态,也会对 RNA 结构和功能产生重大影响,而局部环境反过来又会影响质子化和互变异构化。在这项工作中,阐明了将经验工具应用于 RNA 修饰的 p 和互变异构预测,并将其与从头量子力学 (QM) 方法进行了比较,并扩展到不再可行 QM 的大分子 RNA 结构中。在这方面,扩展了分子操作环境 (MOE) 中的 Protonate3D 功能,以进行碱基前体和互变异构预测,并将其应用于报告的根据局部环境改变质子化状态的示例。总体而言,观察到非标准前体和互变异构体得到了很好的重现,包括结构 CG:C(A) 和 AGG 基序、几个错配以及核苷酸作为核酶中的广义酸的腺苷或胞嘧啶的质子化。特殊情况,如六氨合钴浸泡复合物或核酶中鸟嘌呤作为广义碱的去质子化,被证明具有挑战性。收集的示例集将作为进一步开发 RNA 质子化预测工具的起点,而呈现的 Protonate3D 实现已经为 RNA 和 DNA 大分子提供了合理的质子化预测。对于需要更高准确性的情况,例如追踪核酶的催化途径,可以在 Protonate3D 生成的起始结构上构建基于 QM 的方法。同样,这种质子化预测可用于基于结构的 RNA-配体设计方法。