On the Automatic Construction of QM/MM Models for Biological Photoreceptors: Rhodopsins as Model Systems

Pedraza-González, Laura; Marín, María del Carmen; Vico, Luca De; Yang, Xuchun; Olivucci, Massimo

doi:10.1007/978-3-030-57721-6_1

Cited by 7 publications

(38 citation statements)

References 138 publications

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“…The modeling of primary photoproducts, photoisomerization reaction paths and bistable states in animal and microbial rhodopsins is of great interest for engineering photoswitchable fluorescent probes [ 41 – 43 ]. Bistable rhodopsins are rhodopsins featuring two stable isomeric forms (i.e., characterized by two chromophore isomers such and ) and thus require the sequential and independent absorption of two photons (often of different wavelengths) to complete the photocycle [ 43 ]. Certain bistable rhodopsins can be interconverted using light of different wavelengths (type-2 or P-type photochromism 3 ) [ 44 ].…”

Section: Rhodopsins: a Family Of Biological Photoreceptorsmentioning

confidence: 99%

“…Applications of bistable rhodopsins are related to different properties/functions of the DA and LA states and the use of light irradiation to change the rhodopsin isomeric composition passing from a -dominated to a -dominated equilibrium. Since the efficiency of such conversion is proportional to the difference between the two values, it is apparent that achieving bistable rhodopsins featuring one form with a value significantly shifted to the red, may facilitate applications where it is important to switch on-and-off (i.e., control) the rhodopsin biological function using light irradiation [ 43 ].…”

Section: Rhodopsins: a Family Of Biological Photoreceptorsmentioning

confidence: 99%

“…[ 43 ], a basic model for a biological photoreceptor (e.g., rhodopsin) should feature at least three subsystems: (1) the reactive part of the system, or prosthetic group, that carries the photochemical process (i.e., chromophore), treated with a suitable, and usually computationally expensive, method (see Section 2.1.1 in Ref. [ 43 ]); (2) the residues that, due to either steric or electrostatic interactions with the prosthetic group, directly influence the role of the reactive part (i.e., amino acids and water molecules forming the chromophore cavity), treated classically with a less expensive (optionally polarizable), force field (see Section 2.1.2 in Ref. [ 43 ]); and (3) the residues without an evident role on the photochemical process (i.e., protein environment) structurally fixed during the calculation and treated as point charges (see Fig.…”

Section: The Original Version Of Arm: a Pioneer Te...mentioning

confidence: 99%

“…[ 43 ]); (2) the residues that, due to either steric or electrostatic interactions with the prosthetic group, directly influence the role of the reactive part (i.e., amino acids and water molecules forming the chromophore cavity), treated classically with a less expensive (optionally polarizable), force field (see Section 2.1.2 in Ref. [ 43 ]); and (3) the residues without an evident role on the photochemical process (i.e., protein environment) structurally fixed during the calculation and treated as point charges (see Fig. 5 ).…”

Section: The Original Version Of Arm: a Pioneer Te...mentioning

confidence: 99%

“…Usually, models are complex and, unfortunately, not univocally defined, thus Ref. [ 43 ] collates the different approaches to modeling the photochemical properties of , a rhodopsin for which the X-ray structure is available [ 2 , 53 , 54 ] and that, also for this reason, is often taken as a reference for the benchmarking of different models. Differences in construction protocols of the setup lead to variations in computed up to 41 nm [ 29 – 31 , 55 – 58 ].…”

Section: The Original Version Of Arm: a Pioneer Te...mentioning

confidence: 99%

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Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

et al. 2022

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In recent years, photoactive proteins such as rhodopsins have become a common target for cutting-edge research in the field of optogenetics. Alongside wet-lab research, computational methods are also developing rapidly to provide the necessary tools to analyze and rationalize experimental results and, most of all, drive the design of novel systems. The Automatic Rhodopsin Modeling (ARM) protocol is focused on providing exactly the necessary computational tools to study rhodopsins, those being either natural or resulting from mutations. The code has evolved along the years to finally provide results that are reproducible by any user, accurate and reliable so as to replicate experimental trends. Furthermore, the code is efficient in terms of necessary computing resources and time, and scalable in terms of both number of concurrent calculations as well as features. In this review, we will show how the code underlying ARM achieved each of these properties.

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Section: Rhodopsins: a Family Of Biological Photoreceptorsmentioning

confidence: 99%

Section: Rhodopsins: a Family Of Biological Photoreceptorsmentioning

confidence: 99%

Section: The Original Version Of Arm: a Pioneer Te...mentioning

confidence: 99%

Section: The Original Version Of Arm: a Pioneer Te...mentioning

confidence: 99%

Section: The Original Version Of Arm: a Pioneer Te...mentioning

confidence: 99%

See 3 more Smart Citations

Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

et al. 2022

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Quantum mechanical/molecular mechanical studies of photophysical properties of fluorescent proteins

Rossano-Tapia

Brown

2021

WIREs Comput Mol Sci

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Light‐responsive proteins are widely employed in bioimaging, for example, fluorescent proteins (FPs), which are comprised of a chromophore centered within a barrel‐shaped protein. FPs exhibit remarkable one‐ and multi‐photon absorption (1PA and MPA, respectively) in addition to their emissive properties. Over the last two decades, many types of quantum mechanical, molecular dynamics, and combined quantum mechanical/molecular‐mechanical (QM/MM) approaches have been employed in the study of the photophysics of FPs. Among the latter, QM/MM approaches have proven to be capable of capturing the strong correlation between FPs' light‐responsive properties and their chromophore–environment interactions. In particular, polarizable embedding QM/MM methods are gaining attention by reason of their outstanding performance in the computation of MPA in FPs. Herein, we discuss the outcomes of some of the investigations performed on the 1PA, MPA, and emissive features of FPs using QM/MM approaches. In addition, critical aspects of the use of QM/MM approaches to study FPs' 1PA and MPA features are described. To those researchers interested in starting to perform MPA computations for FPs using QM/MM methods, this review aims to be a compass to navigate among the relevant available literature. This article is categorized under: Electronic Structure Theory > Combined QM/MM Methods Structure and Mechanism > Computational Biochemistry and Biophysics

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UniversalQM/MMapproaches for general nanoscale applications

Csizi

Reiher

2023

WIREs Comput Mol Sci

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Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black‐box‐type manner. Although diverse best‐practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom‐economical physically‐motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build de novo models based on first‐principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations. This article is categorized under: Electronic Structure Theory > Combined QM/MM Methods

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On the Automatic Construction of QM/MM Models for Biological Photoreceptors: Rhodopsins as Model Systems

Cited by 7 publications

References 138 publications

Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

Evolution of the Automatic Rhodopsin Modeling (ARM) Protocol

Quantum mechanical/molecular mechanical studies of photophysical properties of fluorescent proteins

UniversalQM/MMapproaches for general nanoscale applications

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