FCCC LOGO Faculty Publications
Wickstrom L , Deng N , He P , Mentes A , Nguyen C , Gilson MK , Kurtzman T , Gallicchio E , Levy RM
Parameterization of an effective potential for protein-ligand binding from host-guest affinity data
J Mol Recognit. 2016 Jan;29(1) :10-21
PMID: 26256816    PMCID: PMC4715590   
Back to previous list
Force field accuracy is still one of the "stalemates" in biomolecular modeling. Model systems with high quality experimental data are valuable instruments for the validation and improvement of effective potentials. With respect to protein-ligand binding, organic host-guest complexes have long served as models for both experimental and computational studies because of the abundance of binding affinity data available for such systems. Binding affinity data collected for cyclodextrin (CD) inclusion complexes, a popular model for molecular recognition, is potentially a more reliable resource for tuning energy parameters than hydration free energy measurements. Convergence of binding free energy calculations on CD host-guest systems can also be obtained rapidly, thus offering the opportunity to assess the robustness of these parameters. In this work, we demonstrate how implicit solvent parameters can be developed using binding affinity experimental data and the binding energy distribution analysis method (BEDAM) and validated using the Grid Inhomogeneous Solvation Theory analysis. These new solvation parameters were used to study protein-ligand binding in two drug targets against the HIV-1 virus and improved the agreement between the calculated and the experimental binding affinities. This work illustrates how benchmark sets of high quality experimental binding affinity data and physics-based binding free energy models can be used to evaluate and optimize force fields for protein-ligand systems. Copyright (c) 2015 John Wiley & Sons, Ltd.
1099-1352 Wickstrom, Lauren Deng, Nanjie He, Peng Mentes, Ahmet Nguyen, Crystal Gilson, Michael K Kurtzman, Tom Gallicchio, Emilio Levy, Ronald M R01 GM061300/GM/NIGMS NIH HHS/United States GM613000/GM/NIGMS NIH HHS/United States GM095417/GM/NIGMS NIH HHS/United States SC3 GM095417/GM/NIGMS NIH HHS/United States GM30580/GM/NIGMS NIH HHS/United States GM100946/GM/NIGMS NIH HHS/United States R01 GM030580/GM/NIGMS NIH HHS/United States R01 GM100946/GM/NIGMS NIH HHS/United States Journal Article England J Mol Recognit. 2016 Jan;29(1):10-21. doi: 10.1002/jmr.2489. Epub 2015 Aug 10.