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Author: search.filters.author.Mora, José R.Subject: search.filters.subject.Drug discovery

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    In silico discovery of thioglycoside analogues as donor-site inhibitors of glycosyltransferase LgtC
    (Springer Science and Business Media LLC, 2026-03-17)
    Sierra-Hernández, Olimpo
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    Saurith-Coronell, Oscar
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    Alcázar, Jackson J.
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    Cortés, Eliceo
    ;
    Flores-Sumoza, Maryury C.
    The growing prevalence of multidrug-resistant Gram-negative pathogens highlights the urgent need for therapeutic strategies that complement traditional antibiotics by targeting essential virulence pathways. Glycosyltransferase LgtC, a key enzyme in lipooligosaccharide (LOS) biosynthesis, represents an attractive target for antivirulence approaches because of its essential role in bacterial immune evasion and pathogenicity. In this work, we employed an integrated in silico pipeline to identify thioglycoside analogs structurally related to the metabolic decoys FucSBn and BacSBn, evaluating their potential to hit the UDP-galactose donor pocket of LgtC. A similarity-based screening in PubChem, followed by ADME–Tox filtering yield 18 candidate analogs. Molecular docking using AutoDock-GPU revealed several candidates, most notably C-5 (− 8.36 kcal/mol) and C-18 (− 8.13 kcal/mol), to bind favorably within the donor site, showing more negative mean scoring values than natural donor UDP-α-D-galactose (− 6.74 kcal/mol). Redocking of the natural ligand reproduced the crystallographic pose, supporting the reliability of the docking protocol. To assess dynamic behavior, 100 ns molecular dynamics simulations (AMBER14) were performed for each complex. The top-scoring analogs maintained stable binding poses, with RMSD values of ~ 2.0–3.0 Å and preserved donor-like hydrogen-bond networks complemented by π-stacking and sulfur-mediated contacts. These interaction patterns suggest that the thioglycoside analogs may occupy the donor site in a manner compatible with competitive binding. While docking and MD describe different aspects of ligand recognition, several trends observed in docking, such as the favorable binding scores of C-5 and C-18, are broadly consistent with their ability to maintain stable poses during MD. Based on this consistency and scoring, the thioglycoside scaffolds C-5, C-14, and C-18 emerge as computationally prioritized candidates for subsequent biochemical testing against LgtC. Furthermore, these scaffolds offer a mechanistic basis and putative starting points for future structure-based optimization of thioglycoside analogs aimed at disrupting LOS biosynthesis in multidrug-resistant Gram-negative bacteria. © The authors © Springer.
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    Unraveling the hemolytic toxicity tapestry of peptides using chemical space complex networks
    (Oxford University Press, 2024)
    Castillo-Mendieta, Kevin
    ;
    Agüero-Chapin, Guillermin
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    Mora, José R.
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    Pérez, Noel
    ;
    Contreras-Torres, Ernesto
    Peptides have emerged as promising therapeutic agents. However, their potential is hindered by hemotoxicity. Understanding the hemotoxicity of peptides is crucial for developing safe and effective peptide-based therapeutics. Here, we employed chemical space complex networks (CSNs) to unravel the hemotoxicity tapestry of peptides. CSNs are powerful tools for visualizing and analyzing the relationships between peptides based on their physicochemical properties and structural features. We constructed CSNs from the StarPepDB database, encompassing 2,004 hemolytic peptides, and explored the impact of seven different (dis)similarity measures on network topology and cluster (communities) distribution. Our findings revealed that each CSN extracts orthogonal information, enhancing the motif discovery and enrichment process. We identified 12 consensus hemolytic motifs, whose amino acid composition unveiled a high abundance of lysine, leucine, and valine residues, whereas aspartic acid, methionine, histidine, asparagine, and glutamine were depleted. Additionally, physicochemical properties were used to characterize clusters/communities of hemolytic peptides. To predict hemolytic activity directly from peptide sequences, we constructed multi-query similarity searching models, which outperformed cutting-edge machine learning-based models, demonstrating robust hemotoxicity prediction capabilities. Overall, this novel in silico approach uses complex network science as its central strategy to develop robust model classifiers, characterize the chemical space, and discover new motifs from hemolytic peptides. This will help to enhance the design/selection of peptides with potential therapeutic activity and low toxicity. ©2024 Toxicological Sciences ©2024 The authors.
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