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RNA. 2018 Sep;24(9):1183-1194. doi: 10.1261/rna.065896.118. Epub 2018 Jun 21.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

Chen F1,2,3, Sun H1, Wang J4, Zhu F1, Liu H1, Wang Z1,2, Lei T1, Li Y5, Hou T1,2.

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College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
State Key Lab of CAD&CG, Zhejiang University, Hangzhou, Zhejiang 310058, China.
College of Life and Environmental Sciences, Shanghai Normal University, Shanghai 200234, China.
Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania 15261, USA.
Institute of Functional Nano and Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.


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.


MM/GBSA; MM/PBSA; binding free energy; docking; protein–RNA interactions

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