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.

In this continuing work, we have updated our recently proposed Multi-fingerprint Similarity Search algorithm (MuSSel) by enabling the generation of dominant ionized species at a physiological pH and the exploration of a larger data domain, which included more than half a million high-quality small molecules extracted from the latest release of ChEMBL (version 24.1, at the time of writing). Provided with a high biological assay confidence score, these selected compounds explored up to 2822 protein drug targets. To improve the data accuracy, samples marked as prodrugs or with equivocal biological annotations were not considered. Notably, MuSSel performances were overall improved by using an object-relational database management system based on PostgreSQL. In order to challenge the real effectiveness of MuSSel in predicting relevant therapeutic drug targets, we analyzed a pool of 36 external bioactive compounds published in the Journal of Medicinal Chemistry from October to December 2018. This study demonstrates that the use of highly curated chemical and biological experimental data on one side, and a powerful multi-fingerprint search algorithm on the other, can be of the utmost importance in addressing the fate of newly conceived small molecules, by strongly reducing the attrition of early phases of drug discovery programs.