Abstract
Molecular containers such as cucurbit[7]uril (CB7) and the octa-acid (OA) host are ideal simplified model test systems for optimizing and analyzing methods for computing free energies of binding intended for use with biologically relevant protein–ligand complexes. To this end, we have performed initially blind free energy calculations to determine the free energies of binding for ligands of both the CB7 and OA hosts. A subset of the selected guest molecules were those included in the SAMPL4 prediction challenge. Using expanded ensemble simulations in the dimension of coupling host–guest intermolecular interactions, we are able to show that our estimates in most cases can be demonstrated to fully converge and that the errors in our estimates are due almost entirely to the assigned force field parameters and the choice of environmental conditions used to model experiment. We confirm the convergence through the use of alternative simulation methodologies and thermodynamic pathways, analyzing sampled conformations, and directly observing changes of the free energy with respect to simulation time. Our results demonstrate the benefits of enhanced sampling of multiple local free energy minima made possible by the use of expanded ensemble molecular dynamics and may indicate the presence of significant problems with current transferable force fields for organic molecules when used for calculating binding affinities, especially in non-protein chemistries.
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Acknowledgments
The authors would like to thank the NanoSTAR Institute at the University of Virginia for an undergraduate research grant, David Mobley (UC-Irvine) for his patience and strong leadership in the SAMPL4 competition, OpenEye for sponsorship of the SAMPL4 competition, and Lyle Isaacs (University of Maryland-College Park) and Mike Gilson (UC-San Diego) for their work in preparing the host–guest systems for SAMPL4.
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Monroe, J.I., Shirts, M.R. Converging free energies of binding in cucurbit[7]uril and octa-acid host–guest systems from SAMPL4 using expanded ensemble simulations. J Comput Aided Mol Des 28, 401–415 (2014). https://doi.org/10.1007/s10822-014-9716-4
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DOI: https://doi.org/10.1007/s10822-014-9716-4