Sun, Yuchen
(2022)
End-Point Free Energy Prediction using PB and MSSAS.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Solvation free energy (SFE) is a basic concept used in many areas. The accurate prediction of SFE lays the foundation for binding free energy prediction and it is also useful for the calculation of logarithm of 1-octanol-water partition coefficient (logP) which is a frequently used parameter in drug discovery. In this work, the performance of ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, i.e., RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections) on SFE prediction of 633 small molecules in water by MM-PB/GBSA was evaluated. AM1-BCC charge model has the best performance of SFE prediction using GB1, PB_DELPHI methods with root mean square error (RMSE) of 1.88 kcal/mol, and 2.70 kcal/mol, respectively. Meanwhile, ABCG2 charge model performed better using GB2 and GB5 methods with RMSE of 2.06 kcal/mol and 2.17 kcal/mol, respectively. We further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radii parameters suitable for more accurate SFE prediction using ABCG2 charge model by MM-PBSA method. Then, we tuned the nonpolar model for SFE calculation. Using our new model and parameters, for 544 training set molecules, the mean signed error (MSE) and RMSE of the SFE calculation decreased from -1.59 kcal/mol to 0 kcal/mol, and 2.38 kcal/mol to 1.05 kcal/mol, respectively. We then tested the new atom radii parameters on other charge models and found the new radii parameters also outperformed old ones in SFE prediction. Finally, the new radii parameters were adopted in the prediction of protein ligand binding free energy using MM-PBSA method. For the four systems tested, there is improved correlation between experiment and calculation results. And smaller error for absolute binding free energy were also observed, except for JNK1. We then applied the new radii parameters and adopted same approach to generate nonpolar SFE model for octanol SFE prediction. Based on that, a mix logP prediction model using physical method supported with empirical corrections was built. The superiority of our logP model was validated by smaller prediction error to drug-like molecules in ZINC database compared to other commonly used methods.
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| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
6 April 2022 |
| Date Type: |
Publication |
| Defense Date: |
25 March 2022 |
| Approval Date: |
6 April 2022 |
| Submission Date: |
3 April 2022 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Number of Pages: |
76 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Pharmacy > Pharmaceutical Sciences |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Free energy calculation, MM-PBSA |
| Date Deposited: |
06 Apr 2022 16:29 |
| Last Modified: |
06 Apr 2022 16:29 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42463 |
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