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Abstract

Proton transport (PT) is ubiquitous in aqueous and biomolecular systems. One possible and popular simulation method to model PT reactions is ab initio molecular dynamics (AIMD) which computes the electronic structure explicitly on the fly. The generalized gradient approximated (GGA) density functional theory (DFT) represents a widely used and computationally tractable electronic structure method in AIMD, but compromises the accuracy of modeling charge delocalization and hydrogen bond strength. A correction built upon the experimental directed simulation (EDS) technique to hydrogen bonds of water and hydrated proton was developed and used to interpret the long-lived proton anisotropy observed in two-dimension infra-red (2D-IR) experiments. Due to the light mass of protons, the nuclear quantum effects (NQEs) are commonly thought to be important for describing proton and water solvation structures and dynamics. The use of a machine learning (ML) potential in conjunction with the ring polymer contraction (RPC) scheme was presented, as a method for achieving accurate and efficient quantum simulation at the same computing cost as classical simulations. The method was used to demonstrate that a typical approach of increasing simulation temperature in classical AIMD is ineffective in simulating the missing NQEs.As an alternative approach, the multiscale reactive molecular dynamics (MS-RMD) method benefiting greatly from its computational efficiency serves as a powerful simulation tool for proton solvation and transport. Constrained DFT (CDFT), a diabatic electronic structure method, was used to establish a systematic procedure for parameterizing MS-RMD models. The pKa’s of residues in both water and protein environments are reliably predicted by the amino acid models that were parametrized to match the CDFT charge transfer behavior. Proton transport reactions usually involve high free energy barriers, and for sufficient sampling of such rare events, importance sampling techniques are often required to accelerate key collective motions relevant to PT processes. Commonly used enhanced sampling methods achieve the acceleration by applying bias potentials on one or more degrees of freedom (DOFs) of the system which are referred to as collective variables (CVs). In the context of PT processes, the most relevant CVs are indeed the position of the excess proton, as well as the associated hydration required for a proton to travel through hydrophobic confined systems. A definition of center of excess charge (CEC), the effective position of the excess proton, was proposed based on charge transfer calculations from CDFT and the encoded proton collective motions were revealed by its IR spectrum. In addition, a differentiable CV was derived to represent the water connectivity in confined space allowing for direct quantification of the connecting between PT and water networks. PT and how it is connected to related collective motions in various complicated systems were explored thanks to advancements in simulation models and sampling techniques. The case studies include the proton intake mechanism via Sacro/endoplasmic reticulum Ca2+-ATPase (SERCA), the proton-hydration coupling in a Cl-/H+ antiporter, ClC-ec1, and the proton-ligand-conformation coupling in a member of the proton-coupled oligo-peptide transporters (POTs), PepTSh.

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