In order to identify the locations of metal ions in the binding sites of proteins, we have developed a method named the MADE (MAcromolecular DEnsity and Structure Analysis) approach. The MADE approach represents an evolution of our previous toolset, the ProBiS H₂O (MD) methodology, for the identification of conserved water molecules. Our method uses experimental structures of proteins homologous to a query, which are subsequently superimposed upon it. Areas with a particular species present in a similar location among many homologous protein structures are identified using a clustering algorithm. Dense clusters likely represent positions containing species important to the query protein structure or function. We analyze well-characterized apo protein structures and show that the MADE approach can identify clusters corresponding to the expected positions of metal ions in their binding sites. The greatest advantage of our method lies in its generality. It can in principle be applied to any species found in protein records; it is not only limited to metal ions. We additionally demonstrate that the MADE approach can be successfully applied to predict the location of cofactors in computer-modeled structures, e.g., via AlphaFold. We also conduct a careful protein superposition method comparison and find our methodology robust and the results largely independent of the selected protein superposition algorithm. We postulate that with increasing structural data availability, additional applications of the MADE approach will be possible such as non-protein systems, water network identification, protein binding site elaboration, and analysis of binding events, all in a dynamic manner. We have implemented the MADE approach as a plugin for the PyMOL molecular visualization tool. The MADE plugin is available free of charge at https://gitlab.com/Jukic/made_software.

Background: Proteolytic cleavage of inactive pathogen proteins by furin is critical for their entry into human cells, and thus furin cleavage of the SARS-CoV-2 spike protein was identified as a prerequisite for virus binding and the subsequent infection of human cells in the recent COVID-19 pandemic. We report a water-aware structure-based protease inhibitor design study. Methods: Our efforts focused on the biological evaluation of small molecule inhibitors that emerged from a conserved water-aware virtual screening campaign of a library of compounds that shared structural or physicochemical properties with known furin inhibitors exhibiting newly recognized binding modes. Results: We identified a novel small-molecule furin protease inhibitor with a 1,3-thiazol-2-ylaminosulfonyl scaffold. Namely, the compound N-[4-(1,3-thiazol-2-ylaminosulfonyl)phenyl]-3-{(E)-5-[(2-methoxyphenyl)methylene]-4-oxo-2-thioxo-1,3-thiazolidin-3-yl}propionamide showed an IC₅₀ value of 17.58 μM, comparable to other published inhibitors. Conclusions: This compound could represent a starting point for the further design and development of non-peptidic, small-molecule furin inhibitors that could assist in furin cleavage studies and coronaviral pathogenesis.

Background/Objectives: Natural compounds occupy a pharmacologically rich chemical space, characterized by abundant scaffolds, extensive functional group elaboration, and defined stereochemistry. In this context, Helichrysum italicum, a Mediterranean medicinal plant, represents a valuable source of polyphenols with multiple biological and pharmacological activities. Methods: Here, we introduce an inverse molecular docking fingerprint approach to systematically investigate eight major Helichrysum italicum polyphenols, including $\alpha$-pyrones (arzanol, ethylpyrone), flavonols (gnaphaliin, kaempferol, quercetin), and flavanones (naringenin, pinocembrin, hesperetin). More than 40,000 human protein structures from the Protein Data Bank were screened to generate target-based inverse docking score fingerprints for each compound. Results: Hierarchical clustering of these fingerprints revealed shared binding patterns among structurally related polyphenols and enabled hypothesis generation regarding potential synergistic effects. Notably, favorable interactions were identified with PPARG and CARM1, supporting therapeutic relevance in inflammation and cancer, alongside additional targets associated with neurodegeneration and bone metabolism. Conclusions: This study establishes inverse docking fingerprints as a robust, mechanism-oriented method for natural product research and highlights Helichrysum italicum polyphenols as starting points for medicinal chemistry and drug discovery.

Monte Carlo computer simulations in the canonical and grand canonical statistical ensemble were used to explore the properties of the central force (CF1) water model. The intramolecular structure of the H2O molecule is well reproduced by the model. Emphasis was made on hydrogen bonding, and on the tehrahedral, q, and translational, tau, order parameters. An energetic definition of the hydrogen bond gives more consistent results for the average number of hydrogen bonds compared to the one-parameter distance criterion. At 300 K, an average value of 3.8 was obtained. The q and tau metrics were used to elucidate the water-like anomalous behaviour of the CF1 model. The structural anomalies lead to the density anomaly, with a good agreement of the model's density with the experimental rho(T) trends. The chemical potential-density projection of the model's equation of state was explored. Vapour-liquid coexistence was observed at suWciently low temperatures.