Abstract
The reliability of molecular mechanics (MM) simulations in describing biomolecular ion-driven processes depends on their ability to accurately model interactions of ions simultaneously with water and other biochemical groups. In these models, ion descriptors are calibrated against reference data on ion-water interactions, and it is then assumed that these descriptors will also satisfactorily describe interactions of ions with other biochemical ligands. The comparison against the experiment and high-level quantum mechanical data show that this transferability assumption can break down severely. One approach to improve transferability is to assign cross terms or separate sets of non-bonded descriptors for every distinct pair of ion type and its coordinating ligand. Here, we propose an alternative solution that targets an error-source directly and corrects misrepresented physics. In standard model development, ligand descriptors are never calibrated or benchmarked in the high electric fields present near ions. We demonstrate for a representative MM model that when the polarization descriptors of its ligands are improved to respond to both low and high fields, ligand interactions with ions also improve, and transferability errors reduce substantially. In our case, the overall transferability error reduces from 3.3 kcal/mol to 1.8 kcal/mol. These improvements are observed without compromising on the accuracy of low-field interactions of ligands in gas and condensed phases. Reference data for calibration and performance evaluation are taken from the experiment and also obtained systematically from "gold-standard"CCSD(T) in the complete basis set limit, followed by benchmarked vdW-inclusive density functional theory.
Original language | English |
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Article number | 0022058 |
Journal | Journal of Chemical Physics |
Volume | 153 |
Issue number | 9 |
DOIs | |
State | Published - 7 Sep 2020 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2020 Author(s).
Funding
The authors acknowledge the use of computer time from Research Computing at USF and UL. V.W.-F., Y.A.-H., A.T., and S.V. acknowledge funding from NIH (Grant No. R01GM118697). P.R.N. is grateful for financial support of NKFIH (Grant No. KKP126451), the New National Excellence Program of the Ministry for Innovation and Technology (No. ÚNKP-19-4-BME-418), and the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.
Funders | Funder number |
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Ministry for Innovation and Technology | |
National Institutes of Health | |
Nemzeti Kutatási Fejlesztési és Innovációs Hivatal | KKP126451 |