Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.

Urease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the "chemical family type" attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.

Unveiling Novel Urease Inhibitors for Helicobacter pylori: A Multi-Methodological Approach from Virtual Screening and ADME to Molecular Dynamics Simulations.

Helicobacter pylori (Hp) infections pose a global health challenge demanding innovative therapeutic strategies by which to eradicate them. Urease, a key Hp virulence factor hydrolyzes urea, facilitating bacterial survival in the acidic gastric environment. In this study, a multi-methodological approach combining pharmacophore- and structure-based virtual screening, molecular dynamics simulations, and MM-GBSA calculations was employed to identify novel inhibitors for Hp urease (HpU). A refined dataset of 8,271,505 small molecules from the ZINC15 database underwent pharmacokinetic and physicochemical filtering, resulting in 16% of compounds for pharmacophore-based virtual screening. Molecular docking simulations were performed in successive stages, utilizing HTVS, SP, and XP algorithms. Subsequent energetic re-scoring with MM-GBSA identified promising candidates interacting with distinct urease variants. Lys219, a residue critical for urea catalysis at the urease binding site, can manifest in two forms, neutral (LYN) or carbamylated (KCX). Notably, the evaluated molecules demonstrated different interaction and energetic patterns in both protein variants. Further evaluation through ADMET predictions highlighted compounds with favorable pharmacological profiles, leading to the identification of 15 candidates. Molecular dynamics simulations revealed comparable structural stability to the control DJM, with candidates 5, 8 and 12 (CA5, CA8, and CA12, respectively) exhibiting the lowest binding free energies. These inhibitors suggest a chelating capacity that is crucial for urease inhibition. The analysis underscores the potential of CA5, CA8, and CA12 as novel HpU inhibitors. Finally, we compare our candidates with the chemical space of urease inhibitors finding physicochemical similarities with potent agents such as thiourea.

Rational Design of Hydrogels for Cationic Antimicrobial Peptide Delivery: A Molecular Modeling Approach.

In light of the growing bacterial resistance to antibiotics and in the absence of the development of new antimicrobial agents, numerous antimicrobial delivery systems over the past decades have been developed with the aim to provide new alternatives to the antimicrobial treatment of infections. However, there are few studies that focus on the development of a rational design that is accurate based on a set of theoretical-computational methods that permit the prediction and the understanding of hydrogels regarding their interaction with cationic antimicrobial peptides (cAMPs) as potential sustained and localized delivery nanoplatforms of cAMP. To this aim, we employed docking and Molecular Dynamics simulations (MDs) that allowed us to propose a rational selection of hydrogel candidates based on the propensity to form intermolecular interactions with two types of cAMPs (MP-L and NCP-3a). For the design of the hydrogels, specific building blocks were considered, named monomers (MN), co-monomers (CM), and cross-linkers (CL). These building blocks were ranked by considering the interaction with two peptides (MP-L and NCP-3a) as receptors. The better proposed hydrogel candidates were composed of MN3-CM7-CL1 and MN4-CM5-CL1 termed HG1 and HG2, respectively. The results obtained by MDs show that the biggest differences between the hydrogels are in the CM, where HG2 has two carboxylic acids that allow the forming of greater amounts of hydrogen bonds (HBs) and salt bridges (SBs) with both cAMPs. Therefore, using theoretical-computational methods allowed for the obtaining of the best virtual hydrogel candidates according to affinity with the specific cAMP. In conclusion, this study showed that HG2 is the better candidate for future in vitro or in vivo experiments due to its possible capacity as a depot system and its potential sustained and localized delivery system of cAMP.