Marrero Ponce, Yovani

Main Affiliation

Preferred name

Official Name

Marrero Ponce, Yovani

ORCID

0000-0003-2721-1142

Researcher ID

H-5724-2011

Scopus Author ID

55665599200

Now showing 1 - 10 of 28

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Quantum Chemical Characterization of Urea Methanolysis: Mechanistic Pathways and Organotin‐Catalyzed
<scp>DMC</scp>
Formation
(Wiley, 2025-12-18)
Daniel Martinez‐Arias
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José R. Mora
;
Vladimir Rodriguez
;
Edgar A. Marquez
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Patricio J. Espinoza‐Montero
<jats:title>ABSTRACT</jats:title> <jats:p> The methanolysis of urea represents a promising green route for the synthesis of dimethyl carbonate (DMC), a versatile compound with applications in sustainable chemistry and energy storage. In this work, a comprehensive quantum chemical investigation of the reaction mechanism is presented using density functional theory (DFT), focusing on both uncatalyzed and organotin‐catalyzed systems, considering both stepwise and concerted pathways. For MC production, both the stepwise and the concerted mechanisms mediated by a methanol dimer exhibit the lowest activation enthalpies. Consequently, an effective activation enthalpy of 24.0 kcal/mol was determined, in excellent agreement with the experimental value of 23.45 kcal/mol. In contrast, the bimolecular stepwise and concerted models exhibited higher barriers (Δ <jats:italic>H</jats:italic> <jats:sup>‡</jats:sup> ≈ 42–52 kcal/mol). Entropy values indicated that mechanisms with two methanol molecules involve higher preorganization (Δ <jats:italic>S</jats:italic> <jats:sup>‡</jats:sup> ≈ −60 cal/mol K), compared to −30 cal/mol K in single‐molecule pathways. For DMC production from the methyl carbamate intermediate, the rate‐limiting step, it was analyzed with and without an organotin catalyst. Catalysis lowers the activation enthalpy by approximately 10 kcal/mol, yielding a value of 24.9 kcal/mol for the methanol monomer catalyzed system, in good agreement with the experimental Δ <jats:italic>H</jats:italic> <jats:sup>‡</jats:sup> of 24.3 kcal/mol. To deepen mechanistic understanding, we employed advanced quantum descriptors including reaction force analysis, reaction electronic flux (REF), and natural bond orbital (NBO) charge evolution. These tools revealed synchronous bond rearrangements and electronic polarization effects that govern transition state stability, mainly by the electronic charges of the carbon atom in the carbonyl group and the amine group in the sense C <jats:sup>δ+</jats:sup> —N <jats:sup>δ‐</jats:sup> . This study provides novel mechanistic insights into the dual role of hydrogen bonding and Lewis acid catalysis in DMC synthesis and demonstrates the utility of quantum chemical tools in elucidating complex reaction pathways, offering a foundation for rational catalyst design. </jats:p>
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Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides
(American Chemical Society (ACS), 2025-11-05)
Maylin Romero
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;
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Guillermin Agüero-Chapin
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Longendri Aguilera-Mendoza
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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
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Saurith-Coronell, Oscar
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Rodríguez-Macías, Juan
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Márquez, Edgar
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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.
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Mapping the Chemical Space of Antiviral Peptides with Half-Space Proximal and Metadata Networks Through Interactive Data Mining
(MDPI AG, 2025-10-03)
Daniela de Llano García
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Guillermin Agüero-Chapin
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Hortensia Rodríguez
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Francesc J. Ferri
<jats:p>Antiviral peptides (AVPs) are promising therapeutic candidates, yet the rapid growth of sequence data and the field’s emphasis on predictors have left a gap: the lack of an integrated view linking peptide chemistry with biological context. Here, we map the AVP landscape through interactive data mining using Half-Space Proximal Networks (HSPNs) and Metadata Networks (MNs) in the StarPep toolbox. HSPNs minimize edges and avoid fixed thresholds, reducing computational cost while enabling high-resolution analysis. A threshold-free HSPN resolved eight chemically and biologically distinct communities, while MNs contextualized AVPs by source, function, and target, revealing structural–functional relationships. To capture diversity compactly, we applied centrality-guided scaffold extraction with redundancy removal (90–50% identity), producing four representative subsets suitable for modeling and similarity searches. Alignment-free motif discovery yielded 33 validated motifs, including 10 overlapping with reported AVP signatures and 23 apparently novel. Motifs displayed category-specific enrichment across antimicrobial classes, and sequences carrying multiple motifs (≥4–5) consistently showed higher predicted antiviral probabilities. Beyond computational insights, scaffolds provide representative “entry points” into AVP chemical space, while motifs serve as modular building blocks for rational design. Together, these resources provide an integrated framework that may inform AVP discovery and support scaffold- and motif-guided therapeutic design.</jats:p>
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Multitarget Design of Steroidal Inhibitors Against Hormone-Dependent Breast Cancer: An Integrated In Silico Approach
(MDPI AG, 2025-08-02)
Juan Rodríguez-Macías
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Oscar Saurith-Coronell
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Carlos Vargas-Echeverria
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Daniel Insuasty Delgado
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Edgar A. Márquez Brazón
<jats:p>Hormone-dependent breast cancer, particularly in its treatment-resistant forms, remains a significant therapeutic challenge. In this study, we applied a fully computational strategy to design steroid-based compounds capable of simultaneously targeting three key receptors involved in disease progression: progesterone receptor (PR), estrogen receptor alpha (ER-α), and HER2. Using a robust 3D-QSAR model (R2 = 0.86; Q2_LOO = 0.86) built from 52 steroidal structures, we identified molecular features associated with high anticancer potential, specifically increased polarizability and reduced electronegativity. From a virtual library of 271 DFT-optimized analogs, 31 compounds were selected based on predicted potency (pIC50 > 7.0) and screened via molecular docking against PR (PDB 2W8Y), HER2 (PDB 7JXH), and ER-α (PDB 6VJD). Seven candidates showed strong binding affinities (ΔG ≤ −9 kcal/mol for at least two targets), with Estero-255 emerging as the most promising. This compound demonstrated excellent conformational stability, a robust hydrogen-bonding network, and consistent multitarget engagement. Molecular dynamics simulations over 100 nanoseconds confirmed the structural integrity of the top ligands, with low RMSD values, compact radii of gyration, and stable binding energy profiles. Key interactions included hydrophobic contacts, π–π stacking, halogen–π interactions, and classical hydrogen bonds with conserved residues across all three targets. These findings highlight Estero-255, alongside Estero-261 and Estero-264, as strong multitarget candidates for further development. By potentially disrupting the PI3K/AKT/mTOR signaling pathway, these compounds offer a promising strategy for overcoming resistance in hormone-driven breast cancer. Experimental validation, including cytotoxicity assays and ADME/Tox profiling, is recommended to confirm their therapeutic potential.</jats:p>
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Unlocking Antimicrobial Peptides: In Silico Proteolysis and Artificial Intelligence-Driven Discovery from Cnidarian Omics
(MDPI, 2025)
Barroso, Ricardo Alexandre
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Agüero-Chapin, Guillermin
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Sousa, Rita
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Antunes, Agostinho
Overcoming the growing challenge of antimicrobial resistance (AMR), which affects millions of people worldwide, has driven attention for the exploration of marine-derived antimicrobial peptides (AMPs) for innovative solutions. Cnidarians, such as corals, sea anemones, and jellyfish, are a promising valuable resource of these bioactive peptides due to their robust innate immune systems yet are still poorly explored. Hence, we employed an in silico proteolysis strategy to search for novel AMPs from omics data of 111 Cnidaria species. Millions of peptides were retrieved and screened using shallow- and deep-learning models, prioritizing AMPs with a reduced toxicity and with a structural distinctiveness from characterized AMPs. After complex network analysis, a final dataset of 3130 Cnidaria singular non-haemolytic and non-toxic AMPs were identified. Such unique AMPs were mined for their putative antibacterial activity, revealing 20 favourable candidates for in vitro testing against important ESKAPEE pathogens, offering potential new avenues for antibiotic development. ©The authors. ©MDPI.
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Editorial: Harnessing marine biodiversity for novel antimicrobial agents against multidrug-resistant pathogens
(Frontiers Media SA, 2025-04-24)
Guillermin Agüero-Chapin
;
Dany Domínguez-Pérez
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Optimal Descriptor Subset Search via Chemical Information and Target Activity-Guided Algorithm for Antimicrobial Peptide Prediction
(American Chemical Society (ACS), 2025)
García-González, Luis A.
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García-Jacas, César R.
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Aguila Puentes, Sergio A.
Antimicrobial peptides (AMPs) have emerged as a promising alternative to conventional drugs due to their potential applications in combating multidrug-resistant pathogens. Various computational approaches have been developed for AMP prediction, ranging from shallow learning methods to advanced deep learning techniques. Additionally, the performance of shallow learning models based on self-learning features derived from protein language models has recently been studied. However, the performance of AMP models based on shallow learning strongly depends on the quality of descriptors derived via manual feature engineering, which may miss crucial information by assuming that the initial descriptor set fully captures relevant information. The AExOp-DCS algorithm was introduced as an automatic feature domain optimization method that identifies the “optimal” descriptor set driven by the chemical structure and biological activity of the compounds under study. QSAR models built on AExOp-DCS optimized descriptors outperform those using nonoptimized sets. In this study, we explore the use of AExOp-DCS to identify optimal descriptor subsets for AMP modeling. Experimental results show that the descriptors returned by AExOp-DCS contain information comparable to those used in top-performing models while exhibiting higher discriminative capacity. The generated models based on the descriptors returned by AExOp-DCS achieved performance metric values comparable to state-of-the-art approaches while utilizing fewer descriptors, suggesting a more efficient modeling process. By reducing dimensionality without sacrificing accuracy, this approach contributes to the development of more efficient computational pipelines for AMP discovery. Finally, a Java software called AExOp-DCS-SEQ is freely available, enabling researchers to leverage its capabilities for peptide descriptor search and AMP classification tasks. ©The authors ©American Chemical Society (ACS) © Journal of Chemical Information and Modeling.
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Ligand-Based Virtual Screening Workflow for Antimalarial Repositioning from Known Drugs and Chemical Libraries
(Springer Nature Switzerland, 2025-10-11)
Machado-Tugores,Yanetsy
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Meneses-Marcel, Alfredo
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Cristina Aguilar, Ana
The present report outlines a workflow integrating various virtual screening methods to identify potential antimalarial compounds. To develop QSAR models, a dataset of 2,314 compounds was analyzed using linear discriminant analysis and the QuBiLs-MAS software. 37 individual models were generated and subsequently combined into a fusion-based multiclassifier system (MCS), which achieved predictive performances of 91.35% for the training set and 92.06% for the test set. The MCS was further evaluated through a virtual screening simulation involving 13,410 compounds from GlaxoSmithKline, yielding an extrapolation rate of 91.43%. Following this, several drug-likeness filters, the finalized MCS, and chemical diversity analyses were applied to select candidate compounds from three datasets for parasitological assays. Using the proposed in silico pipeline, a total of 6,811 drugs, 15,000 chemical compounds, and 1,120 biologically active molecules from the DrugBank, PrintScreen15, and Tocriscreen collections, respectively, were virtually screened. From these, 80 compounds were shortlisted as potential antimalarial candidates. Ultimately, 15 compounds were purchased and tested in vitro against two Plasmodium falciparum strains (3D7 and Dd2). Of these, five drugs (ziprasidone, isradipine, amcinonide, triflupromazine, and anisotropine) and four chemical compounds (NGB 2904, A23187, Otava-7019050991, and Otava-1677649) demonstrated antimalarial activity, with values μ. This approach represents a promising computational tool for the early stages of antimalarial drug discovery. ©The authors ©Springer.
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StarPepWeb: an integrative, graph-based resource for bioactive peptides
(Oxford University Press (OUP), 2024-12-26)
Christian López
;
Roberto Cárdenas
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Longendri Aguilera-Mendoza
;
Guillermin Agüero-Chapin
;
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Motivation</jats:title> <jats:p>The rapid growth of bioactive peptide sequences presents challenges for organization and analysis. Existing repositories often specialize in functions, taxonomic origins, or structural classes, but most remain isolated, use heterogeneous metadata, and lack uniform descriptors or structural models. Few integrative web services exist, offering only partial coverage or depth. As a result, reproducible and comprehensive exploration of the bioactive peptide landscape remains limited, underscoring the need for a unified, source-tracked, extensible platform.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>We present StarPepWeb, a freely accessible web application that democratizes access to StarPepDB, one of the largest curated repositories of bioactive peptides. The platform integrates 45 120 non-redundant sequences from 40 public databases into a source-tracked graph enriched with metadata, physicochemical features, and predicted 3D structures from ESMFold. Each peptide is represented with ESM-2 embeddings and iFeature descriptors, while the interface supports metadata-aware filtering, alignment-based similarity searches with single and multiple queries, and interactive visualization. A microservice-oriented architecture ensures scalability, maintainability, and reproducible versioned downloads, including Neo4j exports. StarPepWeb thus overcomes deployment and expertise barriers of the standalone database, providing an extensible, cloud-hosted framework for integrative bioactive peptide analysis.</jats:p> </jats:sec> <jats:sec> <jats:title>Availability and implementation</jats:title> <jats:p>StarPepWeb is freely available at https://starpepweb.org. Source code and documentation are hosted at https://github.com/starpep-web.</jats:p> </jats:sec>

Marrero Ponce, Yovani

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