Free Energy Computation for an Isomerizing Chromophore in a Molecular Cavity via the Average Solvent Electrostatic Configuration Model: Applications in Rhodopsin and Rhodopsin-Mimicking Systems

The Photophysical Path to the Triplet State in Light-Oxygen-Voltage (LOV) Domains

Upon blue-light absorption, LOV domains efficiently undergo intersystem crossing (ISC) to the triplet state. Several factors potentially contribute to this efficiency. One often proposed in the literature is the heavy atom effect of the nearby (and eventually adduct-forming) cysteine. However, some LOV domain derivatives that lack the cysteine residue also undergo ISC efficiently. Using hybrid multireference quantum mechanical/molecular mechanical (QM / MM) models, we investigated the effect of the electrostatic environment in a prototypal LOV domain, Arabidopsis thaliana Phototropin 1 LOV2 (AtLOV2), compared to the effect of the dielectric of an aqueous solution. We find that the electrostatic environment of AtLOV2 is especially well tuned to stabilize a triplet( n N , π * ) ${(n_{\rm{N}}, \pi ^{\ast} )}$ state, which we posit is the state involved in the ISC step. Other low-lying triplet states that have( π , π * ) ${(\pi, \pi ^{\ast} )}$ and( n O , π * ) ${(n_{\rm{O}}, \pi ^{\ast} )}$ character are ruled out on the basis of energetics and/or their orbital character. The mechanistic picture that emerges from the calculations is one that involves the ISC of photoexcited flavin to a triplet( n N , π * ) ${(n_{\rm{N}}, \pi ^{\ast} )}$ state followed by rapid internal conversion to a triplet( π , π * ) ${(\pi, \pi ^{\ast} )}$ state, which is the state detected spectroscopically. This insight into the ISC mechanism can provide guidelines for tuning flavin's photophysics through mutations that alter the protein electrostatic environment and potentially helps to explain why ISC (and subsequent flavin photochemistry) does not occur readily in many classes of flavin-binding enzymes.

Rational Design of Far-Red Archaerhodopsin-3-Based Fluorescent Genetically Encoded Voltage Indicators: from Elucidation of the Fluorescence Mechanism in Archers to Novel Red-Shifted Variants

Genetically encoded voltage indicators (GEVIs) have found wide applications as molecular tools for visualization of changes in cell membrane potential. Among others, several classes of archaerhodopsin-3-based GEVIs have been developed and have proved themselves promising in various molecular imaging studies. To expand the application range for this type of GEVIs, new variants with absorption band maxima shifted toward the first biological window and enhanced fluorescence signal are required. Here, we integrate computational and experimental strategies to reveal structural factors that distinguish far-red bright archaerhodopsin-3-based GEVIs, Archers, obtained by directed evolution in a previous study (McIsaac et al., PNAS, 2014) and the wild-type archaerhodopsin-3 with an extremely dim fluorescence signal, aiming to use the obtained information in subsequent rational design. We found that the fluorescence can be enhanced by stabilization of a certain conformation of the protein, which, in turn, can be achieved by tuning the pK a value of two titratable residues. These findings were supported further by introducing mutations into wild-type archeorhodopsin-3 and detecting the enhancement of the fluorescence signal. Finally, we came up with a rational design and proposed previously unknown Archers variants with red-shifted absorption bands (λmax up to 640 nm) and potential-dependent bright fluorescence (quantum yield up to 0.97%).

A general graph neural network based implicit solvation model for organic molecules in water

The dynamical behavior of small molecules in their environment can be studied with classical molecular dynamics (MD) simulations to gain deeper insight on an atomic level and thus complement and rationalize the interpretation of experimental findings. Such approaches are of great value in various areas of research, e.g., in the development of new therapeutics. The accurate description of solvation effects in such simulations is thereby key and has in consequence been an active field of research since the introduction of MD. So far, the most accurate approaches involve computationally expensive explicit solvent simulations, while widely applied models using an implicit solvent description suffer from reduced accuracy. Recently, machine learning (ML) approaches that provide a probabilistic representation of solvation effects have been proposed as potential alternatives. However, the associated computational costs and minimal or lack of transferability render them unusable in practice. Here, we report the first example of a transferable ML-based implicit solvent model trained on a diverse set of 3 000 000 molecular structures that can be applied to organic small molecules for simulations in water. Extensive testing against reference calculations demonstrated that the model delivers on par accuracy with explicit solvent simulations while providing an up to 18-fold increase in sampling rate.

Embedding Nonrigid Solutes in an Averaged Environment: A Case Study on Rhodopsins

Many simulation methods concerning solvated molecules are based on the assumption that the solvated species and the solvent can be characterized by some representative structures of the solute and some embedding potential corresponding to this structure. While the averaging of the solvent configurations to obtain an embedding potential has been studied in great detail, this hinges on a single solute structure representation. This assumption is re-examined and generalized for conformationally flexible solutes and tested on 4 nonrigid systems. In this generalized approach, the solute is characterized by a set of representative structures and the corresponding embedding potentials. The representative structures are identified by means of subdividing the statistical ensemble, which in this work is generated by a constant-temperature molecular dynamics simulation. The embedding potential defined in the Frozen-Density Embedding Theory is used to characterize the average effect of the solvent in each subensemble. The numerical examples concern the vertical excitation energies of protonated retinal Schiff bases in protein environments. It is comprehensively shown that subensemble averaging leads to huge computational savings compared with explicit averaging of the excitation energies in the whole ensemble while introducing only minor errors in the case of the systems examined.

Machine learning based implicit solvent model for aqueous-solution alanine dipeptide molecular dynamics simulations

Inspired by the recent work from Noé and coworkers on the development of machine learning based implicit solvent model for the simulation of solvated peptides [Chen et al., J. Chem. Phys., 2021, 155, 084101], here we report another investigation of the possibility of using machine learning (ML) techniques to "derive" an implicit solvent model directly from explicit solvent molecular dynamics (MD) simulations. For alanine dipeptide, a machine learning potential (MLP) based on the DeepPot-SE representation of the molecule was trained to capture its interactions with its average solvent environment configuration (ASEC). The predicted forces on the solute deviated only by an RMSD of 0.4 kcal mol-1 Å-1 from the reference values, and the MLP-based free energy surface differed from that obtained from explicit solvent MD simulations by an RMSD of less than 0.9 kcal mol-1. Our MLP training protocol could also accurately reproduce combined quantum mechanical molecular mechanical (QM/MM) forces on the quantum mechanical (QM) solute in ASEC environment, thus enabling the development of accurate ML-based implicit solvent models for ab initio-QM MD simulations. Such ML-based implicit solvent models for QM calculations are cost-effective in both the training stage, where the use of ASEC reduces the number of data points to be labelled, and the inference stage, where the MLP can be evaluated at a relatively small additional cost on top of the QM calculation of the solute.

Rhodopsins: An Excitingly Versatile Protein Species for Research, Development and Creative Engineering

The first member and eponym of the rhodopsin family was identified in the 1930s as the visual pigment of the rod photoreceptor cell in the animal retina. It was found to be a membrane protein, owing its photosensitivity to the presence of a covalently bound chromophoric group. This group, derived from vitamin A, was appropriately dubbed retinal. In the 1970s a microbial counterpart of this species was discovered in an archaeon, being a membrane protein also harbouring retinal as a chromophore, and named bacteriorhodopsin. Since their discovery a photogenic panorama unfolded, where up to date new members and subspecies with a variety of light-driven functionality have been added to this family. The animal branch, meanwhile categorized as type-2 rhodopsins, turned out to form a large subclass in the superfamily of G protein-coupled receptors and are essential to multiple elements of light-dependent animal sensory physiology. The microbial branch, the type-1 rhodopsins, largely function as light-driven ion pumps or channels, but also contain sensory-active and enzyme-sustaining subspecies. In this review we will follow the development of this exciting membrane protein panorama in a representative number of highlights and will present a prospect of their extraordinary future potential.

Ionic Atmosphere Effect on the Absorption Spectrum of a Flavoprotein: A Reminder to Consider Solution Ions

This study utilizes the FMN-dependent NADH:quinone oxidoreductase from Pseudomonas aeruginosa PAO1 to investigate the effect of introducing an active site negative charge on the flavin absorption spectrum both in the absence and presence of a long-range electrostatic potential coming from solution ions. There were no observed changes in the flavin UV-visible spectrum when an active site tyrosine (Y277) becomes deprotonated in vitro. These results could only be reproduced computationally using average solvent electrostatic configuration (ASEC) QM/MM simulations that include both positive and negative solution ions. The same calculations performed with minimal ions to neutralize the total protein charge predicted that deprotonating Y277 would significantly alter the flavin absorption spectrum. Analyzing the distribution of solution ions indicated that the ions reorganize around the protein surface upon Y277 deprotonation to cancel the effect of the tyrosinate on the flavin absorption spectrum. Additional biochemical experiments were performed to test this hypothesis.