Case-specific performance of MM-PBSA, MM-GBSA, and SIE in virtual screening

• Published in: Journal of molecular graphics & modelling 2015-11, Vol.62, p.303-318
• Main Authors: Virtanen, Salla I., Niinivehmas, Sanna P., Pentikäinen, Olli T.
• Format: Article
• Language: English
• Subjects: Accuracy; Aldehyde Reductase - chemistry; Aldose reductase 2; Ampc beta-lactamase; Area Under Curve; Bacterial Proteins - chemistry; beta-Lactamase Inhibitors - chemistry; beta-Lactamases - chemistry; Binding energy; Binding Sites; Computational efficiency; Docking; Drug Discovery - methods; Heat shock protein 90; HSP90 Heat-Shock Proteins - chemistry; Humans; Hydrogen Bonding; Hydrophobic and Hydrophilic Interactions; Mathematical models; Models, Chemical; Molecular Docking Simulation; Molecular dynamics; Molecular Dynamics Simulation; Molecular mechanics generalized Born surface area (MM-GBSA); Molecular mechanics Poisson–Boltzmann surface area (MM-PBSA); Phosphodiesterase; Phosphodiesterase Inhibitors - chemistry; Phosphoric Diester Hydrolases - chemistry; Progesterone receptor; Protein Binding; Receptors, Progesterone - chemistry; ROC Curve; Screening; Search methods; Shape comparison; Solvated interaction energy (SIE); Surface area; Virtual screening
• ISSN: 1093-3263; 1873-4243
• DOI: 10.1016/j.jmgm.2015.10.012

[Display omitted] •The results show significant differences between the different implicit water models.•Performance of different implicit water models is highly case-specific.•The length of the molecular dynamics simulation was not crucial for accuracy of results. In drug discovery the reliable prediction of binding free energies is of crucial importance. Methods that combine molecular mechanics force fields with continuum solvent models have become popular because of their high accuracy and relatively good computational efficiency. In this research we studied the performance of molecular mechanics generalized Born surface area (MM-GBSA), molecular mechanics Poisson–Boltzmann surface area (MM-PBSA), and solvated interaction energy (SIE) both in their virtual screening efficiency and their ability to predict experimentally determined binding affinities for five different protein targets. The protein-ligand complexes were derived with two different approaches important in virtual screening: molecular docking and ligand-based similarity search methods. The results show significant differences between the different binding energy calculation methods. However, the length of the molecular dynamics simulation was not of crucial importance for accuracy of results.