Carbon monoxide (CO) is a well-known inhibitor of nitrogenase activity. Under turnover conditions, CO binds to FeMoco, the active site of Mo nitrogenase. Time-resolved IR measurements suggest an initial terminal CO at 1904 cm –1 that converts to a bridging CO at 1715 cm –1 , and an X-ray structure shows that CO can displace one of the bridging belt sulfides of FeMoco. However, the CO-binding redox state(s) of FeMoco (E n ) and the role of the protein environment in stabilizing specific CO-bound intermediates remain elusive. In this work, we carry out an in-depth analysis of the CO–FeMoco interaction based on quantum chemical calculations addressing different aspects of the electronic structure. (1) The local electronic structure of the Fe–CO bond is studied through diamagnetically substituted FeMoco. (2) A cluster model of FeMoco within a polarizable continuum illustrates how CO binding may affect the spin-coupling between the metal centers. (3) A QM/MM model incorporates the explicit influence of the amino acid residues surrounding FeMoco in the MoFe protein. The QM/MM model predicts both a terminal and a bridging CO in the E 1 redox state. The scaled calculated CO frequencies (1922 and 1716 cm –1 , respectively) are in good agreement with the experimentally observed IR bands supporting CO binding to the E 1 state. Alternatively, an E 2 state QM/MM model, which has the same atomic structure as the CO-bound X-ray structure, features a semi-bridging CO with a scaled calculated frequency (1718 cm –1 ) similar to the bridging CO in the E 1 model.

The replacement of S with Se is a useful technique for studying iron‐sulfur clusters. The substitution is typically considered a small perturbation to the electronic structure of the cluster. The advantage is that element specific techniques, such as X‐ray absorption and emission spectroscopy, can be used to selectively investigate the environment of the Se atoms in the cluster. In this work, the effect of this perturbation has been studied quantitatively with the help of high‐level electronic structure calculations. We present a systematic comparison of iron‐sulfur monomers and dimers and their Se analogs using wave function‐based ab initio methods. First, the local electronic structure of the Fe–S and Fe–Se bonds is studied using ab initio ligand field theory (AILFT) in conjunction with the angular overlap model (AOM). Second, the effect of Se substitution on the low‐energy spectrum in homo‐valent (Fe3+Fe3+) and the mixed‐valent (Fe2+Fe3+) iron‐sulfur dimers is investigated in detail. We find that Se‐based ligands generally induce a weaker ligand field, possess a smaller donor strength, and reduce the coupling between the iron centers compared to their S counterparts. Furthermore, the differences between S and Se can affect the energy ordering of electronic states in cases with close‐lying electronic states.

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

A mixed valence tellurium bridged Fe(ii)–Fe(iii) complex was studied using correlated ab initio methods. Spectroscopic and magnetic properties have been rationalized considering coupling between spins and vibrations.