Histidine-Based Supramolecular Nanoassembly Exhibiting Dual Enzyme-Mimetic Functions: Altering the Tautomeric Preference of Histidine to Tailor Oxidative/Hydrolytic Catalysis.

Challenges persist in replicating enzyme-like active sites with functional group arrangements in supramolecular catalysis. In this study, we present a supramolecular material comprising Fmoc-modified histidine and copper. We also investigated the impact of noncanonical amino acids (δmH and εmH), isomers of histidine, on the catalytic process. The Fmoc-δmH-based nanoassembly exhibits an approximately 15-fold increase in oxidative activity and an ∼50-fold increase in hydrolytic activity compared to Fmoc-εmH (kcat/Km). This distinction arises from differences in basicity and ligation properties between the ε- and δ-nitrogen of histidine. The addition of guanosine monophosphate further enhances the oxidative activity of the histidine- and methylated histidine-based catalysts. The Fmoc-δmH/Cu2+-based nanoassembly catalyzes the oxidation/hydrolysis cascade of 2',7'-dichlorofluorescein diacetate, benefiting from the synergistic effect between the copper center and the nonligating ε-nitrogen of histidine. These findings advance the biomimetic catalyst design and provide insights into the mechanistic role of essential residues in natural systems.

A H2 O2 -Supplied Supramolecular Material for Post-irradiated Infected Wound Treatment.

Photodynamic therapy (PDT) is a light triggered therapy by producing reactive oxygen species (ROS), but traditional PDT may suffer from the real-time illumination that reduces the compliance of treatment and cause phototoxicity. A supramolecular photoactive G-quartet based material is reported, which is self-assembled from guanosine (G) and 4-formylphenylboronic acid/1,8-diaminooctane, with incorporation of riboflavin as a photocatalyst to the G4 nanowire, for post-irradiation photodynamic antibacterial therapy. The G4-materials, which exhibit hydrogel-like properties, provide a scaffold for loading riboflavin, and the reductant guanosine for the riboflavin for phototriggered production of the therapeutic H2 O2 . The photocatalytic activity shows great tolerance against room temperature storage and heating/cooling treatments. The riboflavin-loaded G4 hydrogels, after photo-irradiation, are capable of killing gram-positive bacteria (e.g., Staphylococcus aureus), gram-negative bacteria (e.g., Escherichia coli), and multidrug resistant bacteria (methicillin-resistant Staphylococcus aureus) with sterilization ratio over 99.999%. The post-irradiated hydrogels also exhibit great antibacterial activity in the infected wound of the rats, revealing the potential of this novel concept in the light therapy.

A nucleotide-copper(II) complex possessing a monooxygenase-like catalytic function.

The de novo design of artificial biocatalysts with enzyme-like active sites and catalytic functions has long been an attractive yet challenging goal. In this study, we present a nucleotide-Cu2+ complex, synthesized through a one-pot approach, capable of catalyzing ortho-hydroxylation reactions resembling those of minimalist monooxygenases. Both experimental and theoretical findings demonstrate that the catalyst, in which Cu2+ coordinates with both the nucleobase and phosphate moieties, forms a ternary-complex intermediate with H2O2 and tyramine substrates through multiple weak interactions. The subsequent electron transfer and hydrogen (or proton) transfer steps lead to the ortho-hydroxylation of tyramine, where the single copper center exhibits a similar function to natural dicopper sites. Moreover, Cu2+ bound to nucleotides or oligonucleotides exhibits thermophilic catalytic properties within the temperature range of 25 °C to 75 °C, while native enzymes are fully deactivated above 35 °C. This study may provide insights for the future design of oxidase-mimetic catalysts and serve as a guide for the design of primitive metallocentre-dependent enzymes.

A supramolecular metalloenzyme possessing robust oxidase-mimetic catalytic function.

Enzymes fold into unique three-dimensional structures to distribute their reactive amino acid residues, but environmental changes can disrupt their essential folding and lead to irreversible activity loss. The de novo synthesis of enzyme-like active sites is challenging due to the difficulty of replicating the spatial arrangement of functional groups. Here, we present a supramolecular mimetic enzyme formed by self-assembling nucleotides with fluorenylmethyloxycarbonyl (Fmoc)-modified amino acids and copper. This catalyst exhibits catalytic functions akin those of copper cluster-dependent oxidases, and catalytic performance surpasses to date-reported artificial complexes. Our experimental and theoretical results reveal the crucial role of periodic arrangement of amino acid components, enabled by fluorenyl stacking, in forming oxidase-mimetic copper clusters. Nucleotides provide coordination atoms that enhance copper activity by facilitating the formation of a copper-peroxide intermediate. The catalyst shows thermophilic behavior, remaining active up to 95 °C in an aqueous environment. These findings may aid the design of advanced biomimetic catalysts and offer insights into primordial redox enzymes.

Denoising Sparse Autoencoder-Based Ictal EEG Classification.

Automatic seizure detection technology can automatically mark the EEG by using the epileptic detection algorithm, which is helpful to the diagnosis and treatment of epileptic diseases. This paper presents an EEG classification framework based on the denoising sparse autoencoder. The denoising sparse autoencoder (DSAE) is an improved unsupervised deep neural network over sparse autoencoder and denoising autoencoder, which can learn the closest representation of the data. The sparsity constraint applied in the hidden layer of the network makes the expression of data as sparse as possible so as to obtain a more efficient representation of EEG signals. In addition, corrupting operation used in input data help to enhance the robustness of the system and make it suitable for the analysis of non-stationary epileptic EEG signals. In this paper, we first imported the pre-processed training data to the DSAE network and trained the network. A logistic regression classifier was connected to the top of the DSAE. Then, put the test data into the system for classification. Finally, the output results of the overall network were post-processed to obtain the final epilepsy detection results. In the two-class (nonseizure and seizure EEGs) problem, the system has achieved effective results with the average sensitivity of 100%, specificity of 100%, and recognition of 100%, showing that the proposed framework can be efficient for the classification of epileptic EEGs.