The tuning mechanism of pH can be extremely challenging to model computationally in complex biological systems, especially with respect to the photochemical properties. This article reports a protocol aimed at modeling pH-dependent photodynamics using a combination of constant-pH molecular dynamics and semiclassical nonadiabatic molecular dynamics simulations. With retinal photoisomerization in Anabaena sensory rhodopsin (ASR) as a testbed, we show that our protocol produces pH-dependent photochemical properties, such as the isomerization quantum yield or decay rates. We decompose our results into single-titrated residue contributions, identifying some key tuning amino acids. Additionally, we assess the validity of the single protonation state picture to represent the system at a given pH and propose the most populated protein charge state as a compromise between cost and accuracy.

Pieri, E., Weingart, O., Huix-Rotllant, M., Ledentu, V., Garavelli, M., Ferré, N. (2024). Modeling pH-Dependent Biomolecular Photochemistry. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 20, 842-855 [10.1021/acs.jctc.3c00980].

Modeling pH-Dependent Biomolecular Photochemistry

Garavelli M.;
2024

Abstract

The tuning mechanism of pH can be extremely challenging to model computationally in complex biological systems, especially with respect to the photochemical properties. This article reports a protocol aimed at modeling pH-dependent photodynamics using a combination of constant-pH molecular dynamics and semiclassical nonadiabatic molecular dynamics simulations. With retinal photoisomerization in Anabaena sensory rhodopsin (ASR) as a testbed, we show that our protocol produces pH-dependent photochemical properties, such as the isomerization quantum yield or decay rates. We decompose our results into single-titrated residue contributions, identifying some key tuning amino acids. Additionally, we assess the validity of the single protonation state picture to represent the system at a given pH and propose the most populated protein charge state as a compromise between cost and accuracy.
2024
Pieri, E., Weingart, O., Huix-Rotllant, M., Ledentu, V., Garavelli, M., Ferré, N. (2024). Modeling pH-Dependent Biomolecular Photochemistry. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 20, 842-855 [10.1021/acs.jctc.3c00980].
Pieri, E.; Weingart, O.; Huix-Rotllant, M.; Ledentu, V.; Garavelli, M.; Ferré, N.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/998068
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