Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations.
Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations.
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热力学参数扰动下 RNA 玻尔兹曼采样的调节和鲁棒性。
DOI：
10.1016/j.bpj.2017.05.026
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发表时间：
2017
影响因子：
3.4
通讯作者：
Christine E. Heitsch
中科院分区：
文献类型：
--
作者：
E. Rogers;D. Murrugarra;Christine E. Heitsch
理解RNA二级结构预测方法如何依赖于底层的最近邻热力学模型仍然是该领域的一个基本挑战。已知最小自由能（MFE）预测是“病态的”，因为对热力学模型的微小改变可导致显著不同的最佳结构。因此，现在的最佳实践是从玻尔兹曼分布中进行采样，这会生成一组次优结构。虽然这个玻尔兹曼样本的结构信号是已知的随机噪声是强大的，调节和热力学扰动下的鲁棒性还有待解决。在这里，我们提出了一个严格的数学模型的空调启发的数值分析，也是一个生物学启发的定义下的热力学扰动的鲁棒性。我们证明了空调和鲁棒性之间的强相关性，并使用其紧密的关系来定义定量阈值以及不良空调。这些得到的阈值表明，大多数序列至少是样本鲁棒的，这验证了采样的MFE预测的改进条件的假设。此外，因为我们没有发现空调和MFE准确性之间的相关性，以及病态序列的存在表明热力学模型的改进和替代的RNA结构预测方法超出了基于物理的方法的持续需要。
Understanding how RNA secondary structure prediction methods depend on the underlying nearest-neighbor thermodynamic model remains a fundamental challenge in the field. Minimum free energy (MFE) predictions are known to be "ill conditioned" in that small changes to the thermodynamic model can result in significantly different optimal structures. Hence, the best practice is now to sample from the Boltzmann distribution, which generates a set of suboptimal structures. Although the structural signal of this Boltzmann sample is known to be robust to stochastic noise, the conditioning and robustness under thermodynamic perturbations have yet to be addressed. We present here a mathematically rigorous model for conditioning inspired by numerical analysis, and also a biologically inspired definition for robustness under thermodynamic perturbation. We demonstrate the strong correlation between conditioning and robustness and use its tight relationship to define quantitative thresholds for well versus ill conditioning. These resulting thresholds demonstrate that the majority of the sequences are at least sample robust, which verifies the assumption of sampling's improved conditioning over the MFE prediction. Furthermore, because we find no correlation between conditioning and MFE accuracy, the presence of both well- and ill-conditioned sequences indicates the continued need for both thermodynamic model refinements and alternate RNA structure prediction methods beyond the physics-based ones.
DOI：
10.1073/pnas.91.20.9218
发表时间：
1994-09-27
影响因子：
11.1
作者：
WALTER, AE;TURNER, DH;ZUKER, M
通讯作者：
ZUKER, M
影响因子：
4.8
作者：
Ge, Ping;Zhang, Shaojie
通讯作者：
Zhang, Shaojie
影响因子：
5.6
作者：
Mathews, DH;Turner, DH
通讯作者：
Turner, DH