Intramolecular bridging strategies to suppress two-phonon Raman spin relaxation in dysprosocenium single-molecule magnets

Dy(III) bis-cyclopentadienyl (Cp) sandwich compounds exhibit extremely strong single-ion magnetic anisotropy which imbues them with magnetic memory effects such as magnetic hysteresis, and has put them at the forefront of high-performance single-molecule magnets (SMMs). Owing to the great success of design principles focused on maximising the anisotropy barrier, ever higher Ueff values have been reported leading to significant slow down of single-phonon Orbach spin relaxation. However, anisotropy-based SMM design has largely ignored two-phonon Raman spin relaxation, which is still limiting the temperatures at which a memory effect can be observed. In this work, we study the suppression of Raman relaxation through covalent bridging of the Cp ligands by alkyl chains, testing the hypothesis that increasing the rigidity of the ligand framework results in a blue shift of low frequency vibrations in the first coordination sphere of the Dy(III) ion. This reshaping of the vibrational low-energy density of states (DOS) results in lower occupation of pseudo-acoustic phonons available to drive Raman relaxation at low temperatures. We simulate Orbach and Raman spin relaxation in a series of zero-, mono-, di- and tri-bridged [Dy(Cpttt)2]+ analogues fully ab initio, using a quantum mechanics (QM)/molecular mechanics (MM) condensed phase embedding protocol in a periodic solvent matrix as a generic and experimentally testable environment model that can include (pseudo-)acoustic phononic degrees of freedom. We show that this approach can simulate magnetic relaxation dynamics in the condensed phase for the existing non-bridged [Dy(Cpttt)2]+ compound with quantitative experimental accuracy. Subsequently, we find a significant slowing down of Raman relaxation can be achieved for the singly-bridged SMM, while the introduction of further bridges leads to faster relaxation. A key result being that we find the two-phonon Raman rates correlate with the purity of the first-excited Kramers doublet in terms of its mJ = ±13/2 content. Even though the bridging design principle is successful at progressively reshaping the low-energy DOS, the introduction of linker atoms in the equatorial plane successively degrades magnetic anisotropy, suggesting the importance of refined design of the linker chemistry. The accuracy of our results emphasises the value of a generic periodic solvent embedding model, such that it permits the modelling of molecular spin dynamics in the condensed phase without knowledge of a crystal structure. This allows the study of hypothetical molecules or aggregates under real-world conditions, which we expect to have utility beyond the field of molecular magnetism.

Water oxidation electrocatalysis using ruthenium coordination oligomers adsorbed on multiwalled carbon nanotubes

Photoelectrochemical cells that utilize water as a source of electrons are one of the most attractive solutions for the replacement of fossil fuels by clean and sustainable solar fuels. To achieve this, heterogeneous water oxidation catalysis needs to be mastered and properly understood. The search continues for a catalyst that is stable at the surface of electro(photo)anodes and can efficiently perform this reaction at the desired neutral pH. Here, we show how oligomeric Ru complexes can be anchored on the surfaces of graphitic materials through CH-π interactions between the auxiliary ligands bonded to Ru and the hexagonal rings of the graphitic surfaces, providing control of their molecular coverage. These hybrid molecular materials behave as molecular electroanodes that catalyse water oxidation to dioxygen at pH 7 with high current densities. This strategy for the anchoring of molecular catalysts on graphitic surfaces can potentially be extended to other transition metals and other catalytic reactions.

Computational insight into the interaction of oxaliplatin with insulin

In an organism, cisplatin and its derivatives are known to interact with proteins besides their principal DNA target. These off-target interactions have major therapeutic consequences including undesired side effects, loss of bioavailability and emergence of resistance. Insulin is one of the prototypical protein targets of platinum drugs as it has been seen to be involved in bioavailability reduction and might also determine resistance in certain cancer lines. However, despite the interest in understanding the nature of the oxaliplatin-insulin adducts, no 3D models have been achieved so far. In this study, we apply our recent computational multiscale protocol optimized for bioinorganic interactions to provide structural insights into these systems. To do so, the initial structures are predicted by blind protein-metalloligand docking calculations optimized to account for a metal-containing species, and then refined using a Molecular Dynamics (MD) and Quantum Mechanics/Molecular Mechanics (QM/MM) integrated protocol. The results are consistent with experimental information obtained from fragment analysis, and also provide novel structural information like conformational changes occurring upon binding and potential effects on the biological functions of the protein. This study opens an avenue towards applying similar strategies to a wide ensemble of metallodrug-protein/peptide systems for which no structural data are available.

Molecular Modeling for Artificial Metalloenzyme Design and Optimization

Artificial metalloenzymes (ArMs) are obtained by inserting homogeneous catalysts into biological scaffolds and are among the most promising strategies in the quest for new-to-nature biocatalysts. The quality of their design strongly depends on how three partners interact: the biological host, the "artificial cofactor," and the substrate. However, structural characterization of functional artificial metalloenzymes by X-ray or NMR is often partial, elusive, or absent. How the cofactor binds to the protein, how the receptor reorganizes upon the binding of the cofactor and the substrate, and which are the binding mode(s) of the substrate for the reaction to proceed are key questions that are frequently unresolved yet crucial for ArM design. Such questions may eventually be solved by molecular modeling but require a step change beyond the current state-of-the-art methodologies.Here, we summarize our efforts in the study of ArMs, presenting both the development of computational strategies and their application. We first focus on our integrative computational framework that incorporates a variety of methods such as protein-ligand docking, classical molecular dynamics (MD), and pure quantum mechanical (QM) methods, which, when properly combined, are able to depict questions that range from host-cofactor binding predictions to simulations of entire catalytic mechanisms. We also pay particular attention to the protein-ligand docking strategies that we have developed to accurately predict the binding of transition metal-containing molecules to proteins. While this aspect is fundamental to many bioinorganic fields beyond ArMs, it has been disregarded from the molecular modeling landscape until very recently.Next we describe how to apply this computational framework to particular ArMs including systems previously characterized experimentally as well as others where computation served to guide the design. We start with the prediction of the interactions between homogeneous catalysts and biological hosts. Protein-ligand docking is pivotal at that stage, but it needs to be combined with QM/MM or MD approaches when the binding of the cofactor implies significant conformational changes of the protein or involve changes of the electronic state of the metal.Then, we summarize molecular modeling studies aimed at identifying cofactor-substrate arrangements inside the ArM active pocket that are consistent with its reactivity. These calculations stand on "Theozyme"-like dockings, MD-refined or not, which provide molecular rationale of the catalytic profiles of the artificial systems.In the third section, we present case studies to decode the entire catalytic mechanism of two ArMs: (1) an iridium based asymmetric transfer hydrogenase obtained by insertion of Noyori's catalyst into streptavidin and (2) a metallohydrolase achieved by including a receptor. Transition states, second coordination sphere effects, as well as motions of the cofactors are identified as drivers of the enantiomeric profiles.Finally, we report computer-aided designs of ArMs to guide experiments toward chemical and mutational changes that improve their activity and/or enantioselective profiles and expand toward future directions.

fromage: A library for the study of molecular crystal excited states at the aggregate scale

The study of photoexcitations in molecular aggregates faces the twofold problem of the increased computational cost associated with excited states and the complexity of the interactions among the constituent monomers. A mechanistic investigation of these processes requires the analysis of the intermolecular interactions, the effect of the environment, and 3D arrangements or crystal packing on the excited states. A considerable number of techniques have been tailored to navigate these obstacles; however, they are usually restricted to in‐house codes and thus require a disproportionate effort to adopt by researchers approaching the field. Herein, we present the FRamewOrk for Molecular AGgregate Excitations (fromage), which implements a collection of such techniques in a Python library complemented with ready‐to‐use scripts. The program structure is presented and the principal features available to the user are described: geometrical analysis, exciton characterization, and a variety of ONIOM schemes. Each is illustrated by examples of diverse organic molecules in condensed phase settings. The program is available at https://github.com/Crespo-Otero-group/fromage.