Wienecke Anastacia, Laederach Alain
Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Biophys J. 2022 Jan 4;121(1):7-10. doi: 10.1016/j.bpj.2021.12.004. Epub 2021 Dec 10.
RNA research is advancing at an ever increasing pace. The newest and most state-of-the-art instruments and techniques have made possible the discoveries of new RNAs, and they have carried the field to new frontiers of disease research, vaccine development, therapeutics, and architectonics. Like proteins, RNAs show a marked relationship between structure and function. A deeper grasp of RNAs requires a finer understanding of their elaborate structures. In pursuit of this, cutting-edge experimental and computational structure-probing techniques output several candidate geometries for a given RNA, each of which is perfectly aligned with experimentally determined parameters. Identifying which structure is the most accurate, however, remains a major obstacle. In recent years, several algorithms have been developed for ranking candidate RNA structures in order from most to least probable, though their levels of accuracy and transparency leave room for improvement. Most recently, advances in both areas are demonstrated by rsRNASP, a novel algorithm proposed by Tan et al. rsRNASP is a residue-separation-based statistical potential for three-dimensional structure evaluation, and it outperforms the leading algorithms in the field.
RNA研究正以前所未有的速度向前发展。最新且最先进的仪器和技术使得新RNA的发现成为可能，并且将该领域带入了疾病研究、疫苗开发、治疗学和结构学的新前沿。与蛋白质一样，RNA在结构和功能之间表现出显著的关系。要更深入地理解RNA，需要更精细地了解其复杂的结构。为此，前沿的实验和计算结构探测技术为给定的RNA输出了几种候选几何结构，每一种都与实验确定的参数完美匹配。然而，确定哪种结构最准确仍然是一个主要障碍。近年来，已经开发了几种算法来对候选RNA结构按可能性从高到低进行排序，尽管它们的准确性和透明度还有提升空间。最近，Tan等人提出的一种新算法rsRNASP展示了这两个领域的进展。rsRNASP是一种基于残基分离的三维结构评估统计势能，它在该领域领先算法中表现更优。
