CRIS

Permanent URI for this communityhttps://scripta.up.edu.mx/handle/20.500.12552/1

Browse

Search Results

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
In Silico Identification of Potential Clovibactin-like Antibiotics Binding to Unique Cell Wall Precursors in Diverse Gram-Positive Bacterial Strains
(MDPI, 2025-02-18)
Sierra-Hernandez, Olimpo
;
Saurith-Coronell, Oscar
;
Rodríguez-Macías, Juan
;
Márquez, Edgar
;
Ramón Mora, José
The rise in multidrug-resistant bacteria highlights the critical need for novel antibiotics. This study explores clovibactin-like compounds as potential therapeutic agents targeting lipid II, a crucial component in bacterial cell wall synthesis, using in silico techniques. A total of 2624 clovibactin analogs were sourced from the PubChem database and screened using ProTox 3.0 software based on their ADME-Tox properties, prioritizing candidates with favorable pharmacokinetic profiles and minimal toxicity. Molecular docking protocols were then employed to assess the binding interactions of the selected compounds with lipid II. Our analysis identified Compound 22 as a particularly promising candidate, exhibiting strong binding affinity, stable complex formation, and high selectivity for the target. Binding energy analysis, conducted via molecular dynamics simulations, revealed a highly negative value of −25.50 kcal/mol for Compound 22, surpassing that of clovibactin and underscoring its potential efficacy. In addition, Compound 22 was prioritized due to its exceptional binding affinity to lipid II and its favorable ADME-Tox properties, suggesting a lower likelihood of adverse effects. These characteristics position Compound 22 as a promising candidate for further pharmacological development. While our computational results are encouraging, experimental validation is essential to confirm the efficacy and safety of these compounds. This study not only advances our understanding of clovibactin analogs but also contributes to the ongoing efforts to combat antimicrobial resistance through innovative antibiotic development. ©The authors ©International Journal of Molecular Sciences ©MDPI.
Some of the metrics are blocked by your
consent settings
Theoretical study of the hydrolysis mechanism of β-lactam antibiotics catalysed by a Zn(II) dinuclear biomimetic organometallic complex
(Taylor and Francis Group, 2024)
Zurita, Juan E.
;
Mora, José R.
;
Barigye, Stephen J.
;
Espinoza-Montero, Patricio J.
;
Martínez Ríos, Félix Orlando
The degradation and metabolism of antibiotics have attracted the scientific community's attention due to the environmental problems caused by the inappropriate use and disposal of these drugs. Therefore, it is crucial to understand the reaction mechanism involved in the degradation of these compounds. We studied the hydrolysis of nitrocefin and benzylpenicillin, two β-lactam ring antibiotics, mediated by an enzymatic mimetic Zn organometallic compound, using DFT methods. The electronic effects of functional groups adjacent to the β-lactam ring were analysed and good agreement between experimental and theoretical results was found. The reaction involves the nucleophilic attack of the bridging hydroxide group on the Zn atoms towards the β-lactam ring. A stepwise mechanism was found to agree with the experimental results. Chemical hardness profiles were affected by the solvent and reaction synchronicity. Geometric rearrangements dominated the activation barrier, and both antibiotics exhibited late transition states. © Taylor and Francis Group.
23
Some of the metrics are blocked by your
consent settings
Multiquery Similarity Searching Models: An Alternative Approach for Predicting Hemolytic Activity from Peptide Sequence
(American Chemical Society, 2024)
Castillo-Mendieta, Kevin
;
Agüero-Chapin, Guillermin
;
Marquez, Edgar
;
Perez-Castillo, Yunierkis
;
Barigye, Stephen J.
The desirable pharmacological properties and a broad number of therapeutic activities have made peptides promising drugs over small organic molecules and antibody drugs. Nevertheless, toxic effects, such as hemolysis, have hampered the development of such promising drugs. Hence, a reliable computational tool to predict peptide hemolytic toxicity is enormously useful before synthesis and experimental evaluation. Currently, four web servers that predict hemolytic activity using machine learning (ML) algorithms are available; however, they exhibit some limitations, such as the need for a reliable negative set and limited application domain. Hence, we developed a robust model based on a novel theoretical approach that combines network science and a multiquery similarity searching (MQSS) method. A total of 1152 initial models were constructed from 144 scaffolds generated in a previous report. These were evaluated on external data sets, and the best models were fused and improved. Our best MQSS model I1 outperformed all state-of-the-art ML-based models and was used to characterize the prevalence of hemolytic toxicity on therapeutic peptides. Based on our model’s estimation, the number of hemolytic peptides might be 3.9-fold higher than the reported.© 2024 American Chemical Society
Scopus© Citations 3 33 1
Some of the metrics are blocked by your
consent settings
Rethinking the applicability domain analysis in QSAR models
(Springer, 2024)
Mora, Jose R.
;
Marquez, Edgar A.
;
Pérez-Pérez, Noel
;
Contreras-Torres, Ernesto
;
Perez-Castillo, Yunierkis
Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in “rational” model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates. © 2024 Springer Nature
Scopus© Citations 3 14 1

CRIS

Browse

Filters

Settings

Sort By

Results per page

Search Results