Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 32 Vassar Street, Cambridge, MA 02139, USA.
Nucleic Acids Res. 2012 May;40(10):4261-72. doi: 10.1093/nar/gks009. Epub 2012 Jan 28.
Thermodynamic folding algorithms and structure probing experiments are commonly used to determine the secondary structure of RNAs. Here we propose a formal framework to reconcile information from both prediction algorithms and probing experiments. The thermodynamic energy parameters are adjusted using 'pseudo-energies' to minimize the discrepancy between prediction and experiment. Our framework differs from related approaches that used pseudo-energies in several key aspects. (i) The energy model is only changed when necessary and no adjustments are made if prediction and experiment are consistent. (ii) Pseudo-energies remain biophysically interpretable and hold positional information where experiment and model disagree. (iii) The whole thermodynamic ensemble of structures is considered thus allowing to reconstruct mixtures of suboptimal structures from seemingly contradicting data. (iv) The noise of the energy model and the experimental data is explicitly modeled leading to an intuitive weighting factor through which the problem can be seen as folding with 'soft' constraints of different strength. We present an efficient algorithm to iteratively calculate pseudo-energies within this framework and demonstrate how this approach can be used in combination with SHAPE chemical probing data to improve secondary structure prediction. We further demonstrate that the pseudo-energies correlate with biophysical effects that are known to affect RNA folding such as chemical nucleotide modifications and protein binding.
热力学折叠算法和结构探测实验常用于确定 RNA 的二级结构。在这里，我们提出了一个正式的框架来协调来自预测算法和探测实验的信息。通过使用“伪能”来调整热力学能量参数，以最小化预测和实验之间的差异。我们的框架与使用伪能的相关方法在几个关键方面有所不同。（i）仅在必要时更改能量模型，如果预测和实验一致，则不进行任何调整。（ii）伪能仍然具有生物物理可解释性，并保留实验和模型不一致的位置信息。（iii）考虑整个热力学结构 ensemble，从而允许从看似矛盾的数据中重建次优结构的混合物。（iv）明确地对能量模型和实验数据的噪声进行建模，从而通过直观的加权因子来解决问题，将其视为具有不同强度的“软”约束的折叠。我们提出了一种在该框架内迭代计算伪能的有效算法，并演示了如何将这种方法与 SHAPE 化学探测数据结合使用，以提高二级结构预测。我们进一步证明，伪能与已知会影响 RNA 折叠的生物物理效应相关，例如化学核苷酸修饰和蛋白质结合。
