Improving the Accuracy of Predicting protein–ligand binding-free Energy With Semiempirical Quantum Chemistry Charge

Cheng, Peng; Wang, Jinan; Yu, Yuqi; Wang, Guimin; Chen, Zhaoqiang; Xu, Zhijian; Cai, Tingting; Shao, Qiang; Shi, Jiye; Zhu, Weiliang

doi:10.4155/fmc-2018-0207

Cited by 13 publications

(11 citation statements)

References 71 publications

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“…The approach 2 (Figure 3c, SIE‐SQMPC) is a combination of the SIE and SQMPC charges. The SIE approach is a parametrized scoring function to estimate Δ G based on MD simulation trajectory, of which the parameters used were kept the same with our previous study 18 . The R 2 of the approach 2 is 0.54.…”

Section: Resultsmentioning

confidence: 99%

“…The Delphi program 51 is used to calculate surface charges as the background charges of each SQM/MM system. In this work, PM7 SQM method 52 was used in SQMPC approach, according to our previous investigations of 50 ligand–protein systems 18 …”

Section: Methodsmentioning

confidence: 99%

“…In this work, PM7 SQM method 52 was used in SQMPC approach, according to our previous investigations of 50 ligand-protein systems. 18…”

Section: Sqmpc Calculationmentioning

confidence: 99%

“…The SIE approach proposed by Purisima et al has been often ranked as one of the best scoring functions in CSAR and SAMPL challenges 14–17 . In addition, our previous contribution in the Δ G prediction revealed that more accurate protein–ligand complex charges calculated by semi‐empirical quantum mechanical methods (SQMPC) could improve the accuracy of scoring functions based on MD simulation, including MM/GBSA and SIE, compared with conventional Amber ff03 atomic charges 18 …”

Section: Introductionmentioning

confidence: 97%

“…[14][15][16][17] In addition, our previous contribution in the ΔG prediction revealed that more accurate protein-ligand complex charges calculated by semi-empirical quantum mechanical methods (SQMPC) could improve the accuracy of scoring functions based on MD simulation, including MM/GBSA and SIE, compared with conventional Amber ff03 atomic charges. 18 However, the real difficulty for ΔG prediction is far from over.…”

Section: Introductionmentioning

confidence: 99%

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Accurate prediction of relative binding affinities of a series of HIV‐1 protease inhibitors using semi‐empirical quantum mechanical charge

Cheng

Wang

et al. 2020

J Comput Chem

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A major challenge in computer‐aided drug design is the accurate estimation of ligand binding affinity. Here, a new approach that combines the adaptive steered molecular dynamics (ASMD) and partial atomic charges calculated by semi‐empirical quantum mechanics (SQMPC), namely ASMD‐SQMPC, is suggested to predict the ligand binding affinities, with 24 HIV‐1 protease inhibitors as testing examples. In the ASMD‐SQMPC, the relative binding free energy (ΔG) is reflected by the average maximum potential of mean force (max) between bound and unbound states. The correlation coefficient (R2) between the max and experimentally determined ΔG is 0.86, showing a significant improvement compared with the conventional ASMD (R2 = 0.52). Therefore, this study provides an efficient approach to predict the relative ΔG and reveals the significance of precise partial atomic charges in the theoretical simulations.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…In this work, PM7 SQM method 52 was used in SQMPC approach, according to our previous investigations of 50 ligand-protein systems. 18…”

Section: Sqmpc Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Accurate prediction of relative binding affinities of a series of HIV‐1 protease inhibitors using semi‐empirical quantum mechanical charge

Cheng

Wang

et al. 2020

J Comput Chem

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Improving the Performance of MM/PBSA in Protein–Protein Interactions via the Screening Electrostatic Energy

Sheng

Yin

et al. 2021

J. Chem. Inf. Model.

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Accurate calculation of protein−protein binding free energy is of great importance in biological and medical science, yet it remains a hugely challenging problem. In this work, we develop a new strategy in which a screened electrostatic energy (i.e., adding an exponential damping factor to the Coulombic interaction energy) is used within the framework of the molecular mechanics/Poisson−Boltzmann surface area (MM/PBSA) method. Our results show that the Pearson correlation coefficient in the modified MM/PBSA is over 0.70, which is much better than that in the standard MM/PBSA, especially in the Amber14SB force field. In particular, the performance of the standard MM/PBSA is very poor in a system where the proteins carry like charges. Moreover, we also calculated the mean absolute error (MAE) between the calculated and experimental ΔG values and found that the MAE in the modified MM/PBSA was indeed much smaller than that in the standard MM/PBSA. Furthermore, the effect of the dielectric constant of the proteins and the salt conditions on the results was also investigated. The present study highlights the potential power of the modified MM/PBSA for accurately predicting the binding energy in highly charged biosystems.

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Exploration of stilbenoid trimers as potential inhibitors of sirtuin1 enzyme using a molecular docking and molecular dynamics simulation approach

et al. 2021

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Improving the Accuracy of Predicting protein–ligand binding-free Energy With Semiempirical Quantum Chemistry Charge

Cited by 13 publications

References 71 publications

Accurate prediction of relative binding affinities of a series of HIV‐1 protease inhibitors using semi‐empirical quantum mechanical charge

Accurate prediction of relative binding affinities of a series of HIV‐1 protease inhibitors using semi‐empirical quantum mechanical charge

Improving the Performance of MM/PBSA in Protein–Protein Interactions via the Screening Electrostatic Energy

Exploration of stilbenoid trimers as potential inhibitors of sirtuin1 enzyme using a molecular docking and molecular dynamics simulation approach

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