Entzian Gregor, Hofacker Ivo L, Ponty Yann, Lorenz Ronny, Tanzer Andrea
Department of Theoretical Chemistry, Faculty of Chemistry, University of Vienna, Vienna 1090, Austria.
Research Group Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna 1090, Austria.
Bioinformatics. 2021 Aug 9;37(15):2126-2133. doi: 10.1093/bioinformatics/btab066.
Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the folding space. Abstractions are therefore needed to simplify downstream analyses, and thus make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space.
We introduce RNAxplorer, a novel adaptive sampling method to efficiently explore the structure space of RNAs. RNAxplorer uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials, which are accumulated into pseudo-energy terms and reflect similarity to already well-sampled structures. This way, we effectively steer sampling toward underrepresented or unexplored regions of the structure space.
We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples, yields rare conformations that may be inaccessible to other sampling methods and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape, which is well suited to subsequently compute better approximations of RNA folding kinetics.
https://github.com/ViennaRNA/RNAxplorer/.
Supplementary data are available at Bioinformatics online.
预测RNA的折叠动力学是一个计算难题，首要原因是折叠空间中替代结构的组合爆炸。因此需要抽象来简化下游分析，从而使其在计算上易于处理。这可以通过各种结构采样算法来实现。然而，当前的采样方法仍然耗时，并且经常无法代表折叠空间的关键元素。
我们引入了RNAxplorer，一种新颖的自适应采样方法，用于高效探索RNA的结构空间。RNAxplorer使用动态规划在存在引导势的情况下进行高效的玻尔兹曼采样，引导势被累积为伪能量项，并反映与已充分采样结构的相似性。通过这种方式，我们有效地将采样导向结构空间中代表性不足或未探索的区域。
我们开发并应用了不同的度量标准来将我们的采样方法与竞争对手进行基准测试。大多数度量标准表明，RNAxplorer产生更多样化的结构样本，产生其他采样方法可能无法获得的罕见构象，并且在找到景观中最相关的动力学陷阱方面表现更好。因此，它产生了更具代表性的景观粗粒度，非常适合随后计算RNA折叠动力学的更好近似值。
https://github.com/ViennaRNA/RNAxplorer/。
补充数据可在《生物信息学》在线获取。
