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. 2024 Feb 6;25(4):1968.
doi: 10.3390/ijms25041968.

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

Paulina Valenzuela-Hormazabal  1 , Romina V Sepúlveda  2 , Melissa Alegría-Arcos  3 , Elizabeth Valdés-Muñoz  4 , Víctor Rojas-Pérez  4 , Ileana González-Bonet  5 , Reynier Suardíaz  6 , Christian Galarza  7 , Natalia Morales  8 , Verónica Leddermann  8 , Ricardo I Castro  9 , Bruna Benso  10 , Gabriela Urra  11 , Erix W Hernández-Rodríguez  11   12 , Daniel Bustos  11
Affiliations

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

Paulina Valenzuela-Hormazabal et al. Int J Mol Sci. .

Abstract

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.

Keywords: ADMET; Helicobacter pylori; computer-aided drug design; molecular dynamics simulations; pharmacophore-based virtual screening; structure-based virtual screening; urease.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Schematic workflow: Each stage is represented as a section of a funnel receiving as input the data processed by the output of the previous stage. The number of molecules processed in each stage is shown, as well as the percentage with respect to the total of the immediately previous stage.
Figure 2
Figure 2
Pharmacophore-based virtual screening: The best two pharmacophore hypotheses (A,B). The pink and red spheres represent HB donors and negatively charged groups, while the rings correspond to aromatic groups. The alignment of each molecule in the database (C,D) is shown against their respective pharmacophore hypotheses (A,B). The similarity measured is represented by the term PSS, the more positive is the score, the more similar. The dotted red lines represent the cutoff value used to differentiate the selected molecules from those discarded.
Figure 3
Figure 3
Binding free energy distribution of the compound subset: The distributions of free energy for the LYN_Only and LYN_Both subsets are visually represented by light purple and dark purple hues, respectively. Similarly, the free energy distributions for the KCX_Only and KCX_Both subsets are depicted using light and dark cyan colors, respectively. The free energy values for the control compounds (DJM, HAE, BME, and urea) are depicted as individual points within each violin plot. The inclusion of a dashed line at 0 kcal/mol serves to delineate differences in the proportions of complexes within each subset that exhibit unfavorable and favorable energy values.
Figure 4
Figure 4
Binding free energy of the urease inhibitor candidates: Each boxplot in the analysis was constructed using the free energy values of the protein–ligand complex over 200 frames, corresponding to the 100 ns simulation period. The binding free energy of the inhibitor DJM, HAE and BME is presented as control.
Figure 5
Figure 5
Intermolecular interaction patterns: Chemical structures of the inhibitor DJM (A) and candidate inhibitors CA5 (B), CA8 (C), and CA12 (D) are displayed, along with non-bonding interactions involving nickel ions and protein residues within the urease binding site. Residue colors signify the nature of the interaction: orange for negatively charged (ionic), light blue for positively charged (ionic), gray for nickel ions and carbamylated residue Lys219, green for hydrophobic, and light yellow for glycine. The numerical representation of interaction frequency is depicted in light gray, providing quantitative insights into the occurrence of specific interactions. Additionally, the distances of the nickel ions are visually represented in black.
Figure 6
Figure 6
Comparison between urease inhibitors (UIs) for Hp deposited in ChEMBL and our candidates. From the multidimensional analysis carried out in PCA, UMAP and t-SNE, two main similarity clusters were detected: (A) UIs similar to candidates 5 and 12 and (B) UIs similar to candidate 8.
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Grants and funding

D.B. would like to offer thanks to ANID FONDECYT de Iniciación #11220444 and FOVI230136. Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC (ECM-02). E.W.H.R would like to thank ANID FONDECYT Postdoctoral Project No. 3170107 and ANID FONDECYT Initiation Project No. 11230033. R.S. would like to thank RSC for Research Fund (Grants R20-6912 and R21-6448709305) and MCIN/AEI/10.13039/501100011033 (Grant PID2020-113147GA-I00 and PID2021-122839NB-I00). B.B. was supported by ANID FONDECYT de Iniciación #11230490.