Alignment-free (AF) methodologies have increased in popularity in the last decades as alternative tools to alignment-based (AB) algorithms for performing comparative sequence analyses. They have been especially useful to detect remote homologs within the twilight zone of highly diverse gene/protein families and superfamilies. The most popular alignment-free methodologies, as well as their applications to classification problems, have been described in previous reviews. Despite a new set of graph theory-derived sequence/structural descriptors that have been gaining relevance in the detection of remote homology, they have been omitted as AF predictors when the topic is addressed. Here, we first go over the most popular AF approaches used for detecting homology signals within the twilight zone and then bring out the state-of-the-art tools encoding graph theory-derived sequence/structure descriptors and their success for identifying remote homologs. We also highlight the tendency of integrating AF features/measures with the AB ones, either into the same prediction model or by assembling the predictions from different algorithms using voting/weighting strategies, for improving the detection of remote signals. Lastly, we briefly discuss the efforts made to scale up AB and AF features/measures for the comparison of multiple genomes and proteomes. Alongside the achieved experiences in remote homology detection by both the most popular AF tools and other less known ones, we provide our own using the graphical–numerical methodologies, MARCH-INSIDE, TI2BioP, and ProtDCal. We also present a new Python-based tool (SeqDivA) with a friendly graphical user interface (GUI) for delimiting the twilight zone by using several similar criteria.

Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles) are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs:Saccharomyces cerevisiae-Kluyveromyces lactis,Saccharomyces cerevisiae-Candida glabrata, andSaccharomyces cerevisiae-Schizosaccharomyces pombeas benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

Background: Virtual Screening methodologies have emerged as efficient alternatives for the discovery of new drug candidates. At the same time, ensemble methods are nowadays frequently used to overcome the limitations of employing a single model in ligand-based drug design. However, many applications of ensemble methods to this area do not consider important aspects related to both virtual screening and the modeling process. During the application of ensemble methods to virtual screening the proper validation of the models in virtual screening conditions is often neglected. No analysis of the diversity of the ensemble members is performed frequently or no considerations regarding the applicability domain of the base models are being made. Methods: In this research, we review basic concepts and definitions related to virtual screening. We comment recent applications of ensemble methods to ligand-based virtual screening and highlight their advantages and limitations. Results: Next, we propose a method based on genetic algorithms optimization for the generation of virtual screening tailored ensembles which address the previously identified problems in the current applications of ensemble methods to virtual screening. Conclusion: Finally, the proposed methodology is successfully applied to the generation of ensemble models for the ligand-based virtual screening of dual target A2A adenosine receptor antagonists and MAO-B inhibitors as potential Parkinson’s disease therapeutics.

A new Schiff base, (E)-2-(((1,10-phenanthrolin-5-yl)imino)methyl)-4,6-di-tert-butylphenol (Fen-IHB), was designed to incorporate an intramolecular hydrogen bond (IHB) between the phenolic OH and the azomethine nitrogen with the goal of modulating its physicochemical and biological properties. Fen-IHB was synthesized by condensation of 5-amino-1,10-phenanthroline with 3,5-di-tert-butyl-2-hydroxybenzaldehyde and exhaustively characterized by HR-ESI-MS, FTIR, 1D/2D NMR (¹H, ¹³C, DEPT-45, HH-COSY, CH-COSY, D₂O exchange), and UV–Vis spectroscopy. Cyclic voltammetry in anhydrous CH₃CN revealed a single irreversible cathodic peak at −1.43 V (vs. Ag/Ag⁺), which is consistent with the intramolecular reductive coupling of the azomethine moiety. Density functional theory (DFT) calculations, including MEP mapping, Fukui functions, dual descriptor analysis, and Fukui potentials with dual descriptor potential, identified the exocyclic azomethine carbon as the principal nucleophilic site and the phenolic ring (hydroxyl oxygen and adjacent carbons) as the main electrophilic region. Noncovalent interaction (NCI) analysis further confirmed the strength and geometry of the intramolecular hydrogen bond (IHB). In vitro antimicrobial assays indicated that Fen-IHB was inactive against Gram-negative facultative anaerobes (Salmonella enterica serovar Typhimurium and Typhi, Escherichia coli) and strictly anaerobic Gram-positive species (Clostridioides difficile, Roseburia inulinivorans, Blautia coccoides), as any growth inhibition was indistinguishable from the DMSO control. Conversely, Fen-IHB displayed measurable activity against Gram-positive aerobes and aerotolerant anaerobes, including Bacillus subtilis, Streptococcus pyogenes, Enterococcus faecalis, Staphylococcus aureus, and Staphylococcus haemolyticus. Overall, these comprehensive characterization results confirm the distinctive chemical and electronic properties of Fen-IHB, underlining the crucial role of the intramolecular hydrogen bond and electronic descriptors in defining its reactivity profile and selective biological activity.

Bacterial outer membrane vesicles (OMVs) are nanoscale extracellular structures produced by Gram-negative bacteria that are critical for microbial biology and host-pathogen interactions and have great potential in biotechnological applications. Despite the availability of fluorescent dyes for OMV studies, many are repurposed from eukaryotic extracellular vesicle research and are not explicitly optimized for OMVs, leading to challenges in achieving consistent labeling, minimizing background noise, and preserving vesicle integrity during analyses. This study evaluates B2, a benzimidazole-derived fluorophore, for OMV labeling in advanced techniques like flow cytometry and confocal microscopy. OMVs were isolated from Escherichia coli strains BL21 and O157, and their integrity was confirmed using transmission electron microscopy (TEM). B2 staining protocols were optimized for OMVs, and fluorescence analyses revealed specific interactions with the vesicle membranes, reducing aggregation and enhancing signal uniformity. Flow cytometry indicated near-complete labeling efficiency (98–100%) with minimal background interference. Confocal microscopy further validated B2’s effectiveness, showing evident OMV internalization into epithelial HT-29 cells and compatibility with other fluorophores. Density functional theory (DFT) calculations, including Fukui function analysis, identified key electrophilic and nucleophilic regions in B2 that facilitate specific hydrogen bonding and polar interactions with membrane components. Non-covalent interaction (NCI) analysis revealed pronounced intramolecular hydrogen bonding along with discrete regions of weak van der Waals interactions. Molecular dynamics simulations suggest that B2 exhibits an affinity for both the hydrophobic core of the lipid bilayer and the core oligosaccharide region of the LPS layer, which collectively ensures sustained retention of the dye. The findings presented in this study position B2 as a valuable fluorophore for OMV research.