Abstract
Electrostatic interactions involving proteins depend on not only the ionic charges involved but also their chemical identities. Here we examine the origins of incompletely understood differences in the strength of association of different pairs of monovalent molecular ions that are relevant to protein-protein and protein-ligand interactions. Cationic analogues of the basic amino acid side chains are simulated, along with oxyanionic analogues of cation-exchange ligands and acidic amino acids. Experimentally observed association trends with respect to the cations, but not anions, are captured by a nonpolarizable model. An effective continuum correction to account for electronic polarizability can capture both trends better but at the expense of fidelity to the underlying free energy landscape for ion-pair association. A polarizable model proves decisive in capturing experimentally suggested trends with respect to both cations and anions; critically, the free energy landscape for ion-pair association is itself altered, thus altering configurational sampling.
| Original language | English |
|---|---|
| Pages (from-to) | 7020-7026 |
| Number of pages | 7 |
| Journal | Journal of Physical Chemistry Letters |
| Volume | 14 |
| Issue number | 31 |
| DOIs | |
| State | Published - Aug 10 2023 |
Funding
The authors thank Tom Beck (Oak Ridge National Laboratory) for helpful discussions. This research was supported in part through the use of the DARWIN computing system, DARWIN - A Resource for Computational and Data-intensive Research at the University of Delaware and in the Delaware Region, which is supported by National Science Foundation Grant 1919839, Rudolf Eigenmann, Benjamin E. Bagozzi, Arthi Jayaraman, William Totten, and Cathy H. Wu, University of Delaware, 2021, https://udspace.udel.edu/handle/19716/29071 . This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC05-00OR22725. This manuscript has been authored by UTBattelle, LLC, under Contract DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The U.S. government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for U.S. government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan ( http://energy.gov/downloads/doe-public-access-plan ). The authors thank Tom Beck (Oak Ridge National Laboratory) for helpful discussions. This research was supported in part through the use of the DARWIN computing system, DARWIN - A Resource for Computational and Data-intensive Research at the University of Delaware and in the Delaware Region, which is supported by National Science Foundation Grant 1919839, Rudolf Eigenmann, Benjamin E. Bagozzi, Arthi Jayaraman, William Totten, and Cathy H. Wu, University of Delaware, 2021, https://udspace.udel.edu/handle/19716/29071. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract DE-AC05-00OR22725.
| Funders | Funder number |
|---|---|
| National Science Foundation | 1919839 |
| U.S. Department of Energy | DE-AC05-00OR22725 |
| University of Delaware | |
| Office of Science | |
| Oak Ridge National Laboratory |