Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.
Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.
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评估 MM/PBSA 和 MM/GBSA 方法的性能。
DOI：
10.1261/rna.065896.118
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发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
Hou T
中科院分区：
文献类型：
--
作者：
Chen F;Sun H;Wang J;Zhu F;Liu H;Wang Z;Lei T;Li Y;Hou T
分子对接为预测蛋白质-RNA相互作用（PRI）的原子结构细节提供了一种计算高效的方法，但准确预测PRI的三维结构和结合亲和力仍然非常困难，部分原因是现有的PRI评分函数不可靠。MM/PBSA和MM/GBSA在理论上比大多数蛋白质-RNA对接的评分函数更严格，但它们对蛋白质-RNA系统的预测性能仍不清楚。在此，我们系统地评估了MM/PBSA和MM/GBSA在不同溶剂模型和内部介电常数（εin）的蛋白质-RNA系统中预测结合亲和力和识别近天然结合结构的能力。对于结合亲和力的预测，MM/GBSA基于显式溶剂中的最小化结构和GBGBn 1模型（εin = 2）给出的预测与实验数据的相关性最高。此外，基于隐式溶剂中的最小化结构和GBGBn 1模型的MM/GBSA计算区分了148个蛋白质-RNA系统中的117个（79.1%）的前10个诱饵中的近天然结合结构。该性能优于这里研究的所有对接评分函数。因此，MM/GBSA重评分是提高蛋白质-RNA系统评分函数预测能力的有效途径。
Molecular docking provides a computationally efficient way to predict the atomic structural details of protein–RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein–RNA docking, but their prediction performance for protein–RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein–RNA systems with different solvent models and interior dielectric constants (εin). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with εin = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 117 out of the 148 protein–RNA systems (79.1%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein–RNA systems.
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影响因子：
3.7
作者：
Guilhot-Gaudeffroy A;Froidevaux C;Azé J;Bernauer J
通讯作者：
Bernauer J
影响因子：
64.5
作者：
Hafner M;Landthaler M;Burger L;Khorshid M;Hausser J;Berninger P;Rothballer A;Ascano M Jr;Jungkamp AC;Munschauer M;Ulrich A;Wardle GS;Dewell S;Zavolan M;Tuschl T
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Tuschl T
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5.6
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Gupta, Aditi;Gribskov, Michael
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Gribskov, Michael
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3
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Huang, Sheng-You;Zou, Xiaoqin
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Zou, Xiaoqin
影响因子：
6.3
作者：
Genheden S;Ryde U
通讯作者：
Ryde U