Properties of site-specifically incorporated 3-Aminotyrosine in proteins to study redox-active tyrosines: E. coli ribonucleotide reductase as a paradigm.

3-Aminotyrosine (NH2Y) has been a useful probe to study the role of redox active tyrosines in enzymes. This report describes properties of NH2Y of key importance for its application in mechanistic studies. By combining the tRNA/NH2Y-RS suppression technology with a model protein tailored for amino acid redox studies (α3X, X = NH2Y), the formal reduction potential of NH2Y32(O•/OH) (E°’ = 395 ± 7 mV at pH 7.08 ± 0.05) could be determined using protein film voltammetry. We find that the ΔE°’ between NH2Y32(O•/OH) and Y32(O•/OH) when measured under reversible conditions is ~300 – 400 mV larger than earlier estimates based on irreversible voltammograms obtained on aqueous NH2Y and Y. We have also generated D6-NH2Y731-α2 of RNR, which when incubated with β2/CDP/ATP generates the D6-NH2Y731•-α2/β2 complex. By multi-frequency EPR (35, 94 and 263 GHz) and 34 GHz 1H ENDOR spectroscopies, we determined the hyperfine coupling (hfc) constants of the amino protons that establishes RNH2• planarity and thus minimal perturbation of the reduction potential by the protein environment. The amount of Y in the isolated NH2Y-RNR incorporated by infidelity of the tRNA/NH2Y-RS pair was determined by a generally useful LC-MS method. This information is essential to the utility of this NH2Y probe to study any protein of interest and is employed to address our previously reported activity associated with NH2Y-substituted RNR

Pseudocapacitive and Ion‐Insertion Materials: A Bridge between Energy Storage, Electronics and Neuromorphic Computing

There is considerable interest in new solid-state materials for many applications, from energy storage to electronics and neuromorphic computing. This concept paper highlights how pseudocapacitive and ion-insertion materials, for their inherent capability of storing charge and modulate electron conduction, represent a bridge between energy storage, electronics and neuromorphic computing and enable the design of new device architectures