Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson‐Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1‐BCC‐GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1‐BCC (Austin Model 1‐bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types,
Prediction of anisotropic NMR data without knowledge of alignment medium structure by surface decomposition
Prediction of anisotropic NMR data directly from solute-medium interaction is of significant theoretical and practical interest, particularly for structure elucidation, configurational analysis and conformational studies of complex organic molecules and natural products. Current prediction methods require an explicit structural model of the alignment medium: a requirement either impossible or impractical on a scale necessary for small organic molecules. Here we formulate a comprehensive mathematical framework for a parametrization protocol that deconvolutes an arbitrary surface of the medium into several simple local landscapes that are distributed over the medium's surface by specific orientational order parameters. The shapes and order parameters of these local landscapes are determined via fitting that maximizes the congruence between experimentally determined anisotropic NMR measurables and their predicted counterparts, thus avoiding the need for an a priori knowledge of the global medium morphology. This method achieves substantial improvements in the accuracy of predicted anisotropic NMR values compared to current methods, as demonstrated herein with sixteen natural products. Furthermore, because this formalism extracts structural commonalities of the medium by combining anisotropic NMR data from different compounds, its robustness and accuracy are expected to improve as more experimental data become available for further re-optimization of fitting parameters.
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- Award ID(s):
- 2116395
- NSF-PAR ID:
- 10416701
- Date Published:
- Journal Name:
- Physical Chemistry Chemical Physics
- Volume:
- 24
- Issue:
- 34
- ISSN:
- 1463-9076
- Page Range / eLocation ID:
- 20164 to 20182
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1039/D2CP02621J
