a-ARM: Automatic Rhodopsin Modeling with Chromophore Cavity Generation, Ionization State Selection, and External Counterion Placement

Abstract

The Automatic Rhodopsin Modeling (ARM) protocol has recently been proposed as a tool for the fast and parallel generation of basic hybrid quantum mechanics/molecular mechanics (QM/MM) models of wild type and mutant rhodopsins. However, in its present version, input preparation requires a few hours long user's manipulation of the template protein structure, which also impairs the reproducibility of the generated models. This limitation, which makes model building semiautomatic rather than fully automatic, comprises four tasks: definition of the retinal chromophore cavity, assignment of protonation states of the ionizable residues, neutralization of the protein with external counterions, and finally congruous generation of single or multiple mutations. In this work, we show that the automation of the original ARM protocol can be extended to a level suitable for performing the above tasks without user's manipulation and with an input preparation time of minutes. The new protocol, called a-ARM, delivers fully reproducible (i.e., user independent) rhodopsin QM/MM models as well as an improved model quality. More

Molecular Photochemistry: Recent Developments in Theory

“…For further computational efficiency, QM/MM can also be combined with the acceleration techniques mentioned above, for example GPU implementations of electronic structure methods . Another trend is the automatization of the laborious setup of QM/MM models in photobiological investigations, as shown, for example, in a specialized model for the family of rhodopsin proteins . For completeness, we note that it is also possible to calculate excited states in solution by combining QM with an implicit description of the solvent using a continuum model and this constitutes the easiest and by far most employed method to treat electronic states in solution and assist experiments …”

Exploration of natural red-shifted rhodopsins using a machine learning-based Bayesian experimental design

“…Then, we demonstrated the prediction performance of the ML-based prediction model using a data-splitting approach, i.e., the data set was randomly divided into a training set and a test set; the former was used to construct the prediction model, and the latter was used to estimate the prediction ability. The results of this “proof-of-concept” study suggested that the absorption wavelengths of an unknown family of rhodopsins could be predicted with an average error of ±7.8 nm, which is comparable to the mean absolute error of λ max estimated by the hybrid quantum mechanics/molecular mechanics (QM/MM) 21 method. Considering the computational cost of both approaches, the ML-based approach is much more efficient than QM/MM approach, while the latter provides insights on the physical origin controlling λ max .…”

Molekulare Photochemie: Moderne Entwicklungen in der theoretischen Chemie

“…Ein Beispiel dafüri st ein spezialisiertes QM/MM-Modell zur Beschreibung der Familie der Rhodopsin-Proteine. [129] Der Vollständigkeit halber sei an dieser Stelle noch erwähnt, dass angeregte Zustände von Molekülen in Lçsung auch mit der Kombination aus QM-Methoden und impliziten Lçsungsmittelmodellen (beispielsweise Kontinuum-Modellen) beschrieben werden kçnnen. [130] Dies ist vermutlich die einfachste und am weitesten verbreitete Methode fürd ie Behandlung von angeregten Zuständen in Lçsung,w elche häufig zur Unterstützung experimenteller Untersuchungen eingesetzt wird.…”

“…Ein davon unabhängiger, derzeitiger Trend ist die Automatisierung der sehr aufwendigen Erstellung von QM/MM‐Modellen für photobiologische Untersuchungen. Ein Beispiel dafür ist ein spezialisiertes QM/MM‐Modell zur Beschreibung der Familie der Rhodopsin‐Proteine . Der Vollständigkeit halber sei an dieser Stelle noch erwähnt, dass angeregte Zustände von Molekülen in Lösung auch mit der Kombination aus QM‐Methoden und impliziten Lösungsmittelmodellen (beispielsweise Kontinuum‐Modellen) beschrieben werden können .…”