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    Metalloproteins widely exist in biology and play important roles in various processes. To accurately simulate metalloprotein systems, modeling polarization and charge transfer effects is vital. The fluctuating charge (FQ) model can efficiently generate atomic charges and simulate the charge transfer effect; it has been developed for a wide range of applications, but few models have been specifically tailored for metalloproteins. In this study, we present a fluctuating charge model specifically for zinc-containing metalloproteins based on the extended charge equilibration (EQeq) scheme. Our model was parametrized to reproduce CM5 charges instead of RESP/CHELPG charges because the former is less dependent on the conformation or basis set, does not suffer from unphysical charges for buried atoms, and is still being able to well reproduce the molecular dipoles. During our study, we found that adding the Pauling-bond-order-like term (referred to as the "+C term" in a previous study) between the zinc ion and ligating atoms significantly improves the model's performance. Although our model was trained for four-coordinated zinc sites only, our results indicated it can well describe the atomic charges in diverse zinc sites. Morever, our model was used to generate partial charges for the metal sites in three different zinc-containing metalloproteins (with four-, five-, and six-coordinated metal sites, respectively). These charges exhibited performance comparable to that of the RESP charges in molecular dynamics (MD) simulations. Additional tests indicated our model could also well reproduce the CM5 charges when geometric changes were involved. Those results indicate that our model can efficiently calculate the atomic charges for metal sites and well simulate the charge transfer effect, which marks an important step toward developing versatile polarizable force fields for metalloproteins.

    Citation

    Luke Landry, Pengfei Li. Development of a Fluctuating Charge Model for Zinc-Containing Metalloproteins. Journal of chemical information and modeling. 2024 Feb 12;64(3):812-824

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    PMID: 38198652

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