Robust scoring functions for protein-ligand interactions with quantum chemical charge models.
Open accessAbstract
Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein-ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein-ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean-squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean-squared deviation of less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed).
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Rebuilding the AutoDock4 scoring function using ligand partial charges calculated with QM methods and protein charges from Amber99SB instead of the Gasteiger method improved both pose and affinity predictions.
“Wang et al. (2011) rebuild the AutoDock4 scoring function using ligand partial charges calculated with QM methods and protein charges from the Amber99SB instead of the Gasteiger method, improving both pose and affinity predictions.”
From Empirical scoring functions for structure-based virtual screening: applications, critical aspects, and challenges (2018) · cites this paper as Wang et al., 2011
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- Authors
- Wang, J.-C., Lin, J.-H., Chen, C.-M., Perryman, A. L., & Olson, A. J.
- Affiliations
- National Taiwan University (TW)
- Scripps Research Institute (US)
- Corresponding author
- Lin, Jung-Hsin
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2011-10-07
- Volume
- 51
- Issue
- 10
- Pages
- 2528-2537
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- PMC
- PMC4639406
- OpenAlex
- W1977056833
- Cited by
- 54
- References
- 54
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 4 · 2023: 1 · 2022: 2 · 2021: 2 · 2020: 2 · 2019: 6 · 2018: 5 · 2017: 3 · 2016: 5 · 2015: 6 · 2014: 5 · 2013: 5 · 2012: 2
- FWCI
- 3.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of Chemical Information and Modeling (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of General Medical Sciences (R01GM069832)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein-ligand binding free energy, scoring functions, robust regression, outlier exclusion, binding pose prediction, protein-ligand docking, electrostatic interactions
- MeSH terms
- Algorithms, Ligands, Molecular Conformation, Protein Binding, Proteins, Quantum Theory, Thermodynamics, Least-Squares Analysis, Quantitative Structure-Activity Relationship, Static Electricity, Molecular Dynamics Simulation
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