Flavoprotein oxidoreductases are members of a large protein family of specialized dehydrogenases, which include type II NADH dehydrogenase, pyridine nucleotide-disulphide oxidoreductases, ferredoxin-NAD+ reductases, NADH oxidases, and NADH peroxidases, playing a crucial role in the metabolism of several prokaryotes and eukaryotes. Although several studies have been performed on single members or protein subgroups of flavoprotein oxidoreductases, a comprehensive analysis on structure–function relationships among the different members and subgroups of this great dehydrogenase family is still missing. Here, we present a structural comparative analysis showing that the investigated flavoprotein oxidoreductases have a highly similar overall structure, although the investigated dehydrogenases are quite different in functional annotations and global amino acid composition. The different functional annotation is ascribed to their participation in species-specific metabolic pathways based on the same biochemical reaction, i.e., the oxidation of specific cofactors, like NADH and FADH2. Notably, the performed comparative analysis sheds light on conserved sequence features that reflect very similar oxidation mechanisms, conserved among flavoprotein oxidoreductases belonging to phylogenetically distant species, as the bacterial type II NADH dehydrogenases and the mammalian apoptosis-inducing factor protein, until now retained as unique protein entities in Bacteria/Fungi or Animals, respectively. Furthermore, the presented computational analyses will allow consideration of FAD/NADH oxidoreductases as a possible target of new small molecules to be used as modulators of mitochondrial respiration for patients affected by rare diseases or cancer showing mitochondrial dysfunction, or antibiotics for treating bacterial/fungal/protista infections.

Eleven piperazine-containing 1,3-diphenylprop-2-en-1-one derivatives (PC1-PC11) were evaluated for their inhibitory activities against monoamine oxidases (MAOs), cholinesterases (ChEs), and β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) with a view toward developing new treatments for neurological disorders. Compounds PC10 and PC11 remarkably inhibited MAO-B with IC 50 values of 0.65 and 0.71 μM, respectively. Ten of the eleven compounds weakly inhibited AChE and BChE with > 50% of residual activities at 10 μM, although PC4 inhibited AChE by 56.6% (IC 50 = 8.77 μM). Compound PC3 effectively inhibited BACE-1 (IC 50 = 6.72 μM), and PC10 and PC11 moderately inhibited BACE-1 (IC 50 =14.9 and 15.3 μM, respectively). Reversibility and kinetic studies showed that PC10 and PC11 were reversible and competitive inhibitors of MAO-B with K i values of 0.63 ± 0.13 and 0.53 ± 0.068 μM, respectively. ADME predictions for lead compounds revealed that PC10 and PC11 have central nervous system (CNS) drug-likeness. Molecular docking simulations showed that fluorine atom and trifluoromethyl group on PC10 and PC11, respectively, interacted with the substrate cavity of the MAO-B active site. Our results suggested that PC10 and PC11 can be considered potential candidates for the treatment of neurological disorders such as Alzheimer's disease and Parkinson's disease.

Previously synthesized novel chalcone oxime ethers (COEs) were evaluated for inhibitory activities against monoamine oxidases (MAOs) and acetylcholinesterase (AChE). Twenty-two of the 24 COEs synthesized, except COE-17 and COE-24, had potent and/or significant selective inhibitory effects on MAO-B. COE-6 potently inhibited MAO-B with an IC50 value of 0.018 µM, which was 105, 2.3, and 1.1 times more potent than clorgyline, lazabemide, and pargyline (reference drugs), respectively. COE-7, and COE-22 were also active against MAO-B, both had an IC50 value of 0.028 µM, which was 67 and 1.5 times lower than those of clorgyline and lazabemide, respectively. Most of the COEs exhibited weak inhibitory effects on MAO-A and AChE. COE-13 most potently inhibited MAO-A (IC50 = 0.88 µM) and also significantly inhibited MAO-B (IC50 = 0.13 µM), and it could be considered as a potential nonselective MAO inhibitor. COE-19 and COE-22 inhibited AChE with IC50 values of 5.35 and 4.39 µM, respectively. The selectivity index (SI) of COE-22 for MAO-B was higher than that of COE-6 (SI = 778.6 vs. 222.2), but the IC50 value (0.028 µM) was slightly lower than that of COE-6 (0.018 µM). In reversibility experiments, inhibitions of MAO-B by COE-6 and COE-22 were recovered to the levels of reference reversible inhibitors and both competitively inhibited MAO-B, with Ki values of 0.0075 and 0.010 µM, respectively. Our results show that COE-6 and COE-22 are potent, selective MAO-B inhibitors, and COE-22 is a candidate of dual-targeting molecule for MAO-B and AChE.

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Artificial intelligence and multiobjective optimization represent promising solutions to bridge chemical and biological landscapes by addressing the automated de novo design of compounds as a result of a humanlike creative process. In the present study, we conceived a novel pair-based multiobjective approach implemented in an adapted SMILES generative algorithm based on recurrent neural networks for the automated de novo design of new molecules whose overall features are optimized by finding the best trade-offs among relevant physicochemical properties (MW, logP, HBA, HBD) and additional similarity-based constraints biasing specific biological targets. In this respect, we carried out the de novo design of chemical libraries targeting neuraminidase, acetylcholinesterase, and the main protease of severe acute respiratory syndrome coronavirus 2. Several quality metrics were employed to assess drug-likeness, chemical feasibility, diversity content, and validity. Molecular docking was finally carried out to better evaluate the scoring and posing of the de novo generated molecules with respect to X-ray cognate ligands of the corresponding molecular counterparts. Our results indicate that artificial intelligence and multiobjective optimization allow us to capture the latent links joining chemical and biological aspects, thus providing easy-to-use options for customizable design strategies, which are especially effective for both lead generation and lead optimization. The algorithm is freely downloadable at and all of the data are available as Supporting Information.