School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, P.R. China.
Nucleic Acids Res. 2018 May 18;46(9):e56. doi: 10.1093/nar/gky113.
RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA-RNA interactions based on a training set of 97 diverse RNA-RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA-RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA-RNA complexes.
RNA-RNA 相互作用在基因和细胞调控中发挥着基础性作用。因此，准确预测 RNA-RNA 相互作用对于确定其复杂结构和理解相互作用的分子机制至关重要。在这里，我们开发了一种基于物理的双重迭代策略，根据 97 个不同的 RNA-RNA 复合物的训练集来确定 RNA-RNA 相互作用的有效势。该双重迭代策略通过迭代更新势来规避基于知识的评分函数中的参考状态问题，并且通过迭代构建诱饵来克服了以前迭代方法中的诱饵依赖性限制。所得评分函数称为 DITScoreRR，在 60 个测试案例的 RNA-RNA 对接基准测试中进行了评估，并与其他三个评分函数进行了比较。结果表明，对于结合对接，当考虑前 1 个和前 10 个预测时，我们的评分函数 DITScoreRR 在结合模式预测中的成功率分别为 90%和 98.3%，而范德华相互作用的成功率分别为 63.3%和 71.7%，ITScorePP 的成功率分别为 45.0%和 65.0%，ZDOCK 2.1 的成功率分别为 11.7%和 26.7%。对于非结合对接，当考虑前 1 个和前 10 个预测时，DITScoreRR 在结合模式预测中的成功率分别为 53.3%和 71.7%，而范德华相互作用的成功率分别为 13.3%和 28.3%，ITScorePP 的成功率分别为 11.7%和 26.7%，ZDOCK 2.1 的成功率分别为 3.3%和 6.7%。DITScoreRR 在对诱饵进行排序方面也表现出色，并且与其他三个评分函数相比，它具有更高的得分-RMSD 相关性。DITScoreRR 将对 RNA 结构和 RNA-RNA 复合物的预测和设计具有重要价值。
