Updates to Protex for Simulating Proton Transfers in an Ionic Liquid.

Márta Gődény, Florian Joerg, Maximilian P-P Kovar, Christian Schröder
Author Information
  1. Márta Gődény: Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Währinger Straße 17, Vienna 1090, Austria. ORCID
  2. Florian Joerg: Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Währinger Straße 17, Vienna 1090, Austria.
  3. Maximilian P-P Kovar: Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Währinger Straße 17, Vienna 1090, Austria. ORCID
  4. Christian Schröder: Faculty of Chemistry, Department of Computational Biological Chemistry, University of Vienna, Währinger Straße 17, Vienna 1090, Austria. ORCID

Abstract

The Python-based program Protex was initially developed for simulating proton transfers in a pure protic ionic liquid via polarizable molecular dynamics simulations. This method employs a single topology approach wherein deprotonated species retain a dummy atom, which is transformed into a real hydrogen atom during the protonation process. In this work, we extended Protex to include more intricate systems and to facilitate the simulation of the Grotthuss mechanism to enhance alignment with the empirical findings. The handling of proton transfer events within Protex was further refined for increased flexibility. In the original model, each deprotonated molecule contained a single dummy atom connected to the hydrogen acceptor atom. This model posed limitations for molecules with multiple atoms that could undergo protonation. To mitigate this issue, Protex was extended to execute a proton transfer when one of these potential atoms was within a suitable proximity for the transfer event. For the purpose of maintaining simplicity, Protex continues to utilize only a single dummy atom per deprotonated molecule. Another new feature pertains to the determination of the eligibility for a proton transfer event. A range of acceptable distances can now be defined within which the transfer probability is gradually turned off. These modifications allow for a more nuanced approach to simulating proton transfer events, offering greater accuracy and control of the modeling process.

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