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Urease is a metalloenzyme that catalyzes the hydrolysis of urea, and its modulation has an important role in both the agricultural and medical industry. Even though numerous molecules have been tested against ureases of different species, their clinical translation has been limited due to chemical and metabolic stability as well as side effects. Therefore, screening new compounds against urease would be of interest in part due to rising concerns regarding antibiotic resistance. In this work, we collected and curated a diverse set of 2640 publicly available small-molecule inhibitors of jack bean urease and developed a classifier using a random forest machine learning method with high predictive performance. In addition, the physicochemical features of compounds were paired with molecular docking and protein–ligand fingerprint analysis to gather insight into the current activity landscape. We observed that the docking score could not differentiate active from inactive compounds within each chemical family, but scores were correlated with compound activity when all compounds were considered. Additionally, a decision tree model was built based on 2D and 3D Morgan fingerprints to mine patterns of the known active-class compounds. The final machine learning model showed good prediction performance against the test set (81% and 77% precision for active and inactive compounds, respectively). Finally, this model was employed, as a proof-of-concept, on an in-house library to predict new hits that were then tested against urease and found to be active. This is, to date, the largest, most diverse dataset of compounds used to develop predictive in silico models. Overall, the results highlight the usefulness of using machine learning classifiers and molecular docking to predict novel urease inhibitors.

HIV-2 infection is frequently neglected in HIV/AIDS campaigns. However, a special emphasis must be given to HIV-2 as an untreated infection that also leads to AIDS and death, and for which the efficacy of most available drugs is limited against HIV-2. HIV envelope glycoproteins mediate binding to the receptor CD4 and co-receptors at the surface of the target cell, enabling fusion with the cell membrane and viral entry. Here, we developed and optimized a computer-assisted drug design approach of an important HIV-2 glycoprotein that allows us to explore and gain further insights at the molecular level into protein structures and interactions crucial for the inhibition of HIV-2 cell entry. The 3D structure of a key HIV-2ROD gp125 region was generated by a homology modeling campaign. To disclose the importance of the main structural features and compare them with experimental results, 3D-models of six mutants were also generated. These mutations revealed the selective impact on the behavior of the protein. Furthermore, molecular dynamics simulations were performed to optimize the models, and the dynamic behavior was tackled to account for structure flexibility and interactions network formation. Structurally, the mutations studied lead to a loss of aromatic features, which is very important for the establishment of π-π interactions and could induce a structural preference by a specific coreceptor. These new insights into the structure-function relationship of HIV-2 gp125 V3 and surrounding regions will help in the design of better models and the design of new small molecules capable to inhibit the attachment and binding of HIV with host cells.

The role of metalloproteinases (MMPs) on the migration and invasion of cancer cells has been correlated with tumor aggressiveness, namely with the up-regulation of MMP-2 and 9. Herein, two pyridine-containing macrocyclic compounds, [15]pyN5 and [16]pyN5, were synthesized, chemically characterized and evaluated as potential MMP inhibitors for breast cancer therapy using 3D and 2D cellular models. [15]pyN5 and [16]pyN5 (5&ndash, 20 &micro, M) showed a marked inhibition of MMPs activity (100% at concentrations &ge, 7.5 &mu, M) when compared to ARP-100, a known MMP inhibitor. The inhibitory activity of [15]pyN5 and [16]pyN5 was further supported through in silico docking studies using Goldscore and ChemPLP scoring functions. Moreover, although no significant differences were observed in the invasion studies in the presence of all MMPs inhibitors, cell migration was significantly inhibited by both pyridine-containing macrocycles at concentrations above 5 &mu, M in 2D cells (p &lt, 0.05). In spheroids, the same effect was observed, but only with [16]pyN5 at 20 &mu, M and ARP-100 at 40 &mu, M. Overall, [15]pyN5 and [16]pyN5 led to impaired breast cancer cell migration and revealed to be potential inhibitors of MMPs 2 and 9.