Marrero Ponce, Yovani

Main Affiliation

Facultad de Ingeniería - CampCM

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 35

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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
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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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Potential Bioactive Function of Microbial Metabolites as Inhibitors of Tyrosinase: A Systematic Review
(MDPI AG, 2026)
Barcenas-Giraldo, Sofia
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Baez-Leguizamon, Vanessa
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Barbosa-Gonzalez, Laura
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Leon-Rodriguez, Angelica
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Marrero Ponce, Yovani
Tyrosinase (EC 1.14.18.1) is a binuclear copper enzyme responsible for the rate-limiting steps of melanogenesis, catalyzing the hydroxylation of L-tyrosine and oxidation of L-DOPA into o-quinones that polymerize melanin. Beyond its physiological role in pigmentation, tyrosinase is also implicated in food browning and oxidative stress–related disorders, making it a key target in cosmetic, food, and biomedical industries. This systematic review, conducted following PRISMA guidelines, aimed to identify and analyze microbial metabolites with tyrosinase inhibitory potential as sustainable alternatives to conventional inhibitors such as hydroquinone and kojic acid. Literature searches in Scopus and Web of Science (March 2025) yielded 156 records; after screening and applying inclusion criteria, 11 studies were retained for analysis. The inhibitors identified include indole derivatives, phenolic acids, peptides, and triterpenoids, mainly produced by fungi (e.g., Ganoderma lucidum, Trichoderma sp.), actinobacteria (Streptomyces, Massilia), and microalgae (Spirulina, Synechococcus). Reported IC50 values ranged from micromolar to milli-molar levels, with methyl lucidenate F (32.23 µM) and p-coumaric acid (52.71 mM). Mechanisms involved competitive and non-competitive inhibition, as well as gene-level regulation. However, methodological heterogeneity, the predominance of mushroom tyrosinase assays, and limited human enzyme validation constrain translational relevance. Computational modeling, site-directed mutagenesis, and molecular dynamics are proposed to overcome these limitations. Overall, microbial metabolites exhibit promising efficacy, stability, and biocompatibility, positioning them as emerging preclinical candidates for the development of safer and more sustainable tyrosinase inhibitors. ©The authors ©MDPI.
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StarPepWeb: an integrative, graph-based resource for bioactive peptides
(Oxford University Press, 2024)
López, Christian
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Cárdenas, Roberto
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Aguilera-Mendoza, Longendri
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Agüero-Chapin, Guillermin
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Martínez Ríos, Félix Orlando
Motivation: 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. Results: 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. ©The authors ©Oxford University Press.
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Editorial: Harnessing marine biodiversity for novel antimicrobial agents against multidrug-resistant pathogens
(Frontiers Media SA, 2025)
Agüero-Chapin, Guillermin
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Domínguez-Pérez, Dany
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Marrero Ponce, Yovani
Antimicrobial resistance (AMR) is a defining challenge of our era, responsible for an alarming number of deaths that now surpass those caused by HIV and malaria. Projections estimate that by 2050, AMR could lead to 10 million deaths annually. The COVID19 pandemic has amplified this crisis, fueling the spread of multidrug-resistant (MDR) pathogens, particularly those associated with biofilms. In response, governments have begun adopting more agile investment models, while academia and emerging biotech initiatives play increasingly central roles in the discovery of next-generation antimicrobials. The ocean, covering over 70% of Earth’s surface, represents an extraordinary yet underexploited reservoir of chemical diversity. Marine ecosystems harbor a vast array of microorganisms and multicellular life forms adapted to extreme and varied habitats. These organisms, from actinomycetes to fish and fungi, produce structurally unique secondary metabolites as chemical defenses or communication tools—many of which exhibit promising antimicrobial activities. This Research Topic aims to showcase the potential of marine biodiversity in providing new solutions to counteract MDR pathogens. © The authors © Frontiers Media.
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A 2026 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides
(MDPI AG, 2026)
Agüero-Chapin, Guillermin
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Antunes, Agostinho
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Marrero Ponce, Yovani
Antimicrobial resistance (AMR) continues to stand as a critical global healthcare challenge. Current projections suggest that AMR could become the leading cause of death by 2050, potentially surpassing cancer mortality rates [1]. The urgency for novel therapeutic agents was further underscored by the 2020 pandemic, which shifted antibiotic consumption patterns and accelerated the emergence of multidrug-resistant (MDR) “superbugs” [2]. In this precarious landscape, Antimicrobial Peptides (AMPs) represent a transformative frontier. Unlike traditional antibiotics, AMPs typically target microbial membranes, a mechanism that significantly diminishes the likelihood of resistance development [3,4]. The conventional discovery of AMPs is a laboratory-intensive process often hampered by prohibitive costs and protracted timelines. This Special Issue, “A 2026 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides”, showcases the power of in silico methodologies to accelerate the identification and optimization of these therapeutic candidates [5]. ©The authors ©MDPI.
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Quantum Chemical Characterization of Urea Methanolysis: Mechanistic Pathways and Organotin-Catalyzed DMC Formation
(Wiley, 2025)
Martinez‐Arias, Daniel
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Mora, José R.
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Rodriguez, Vladimir
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Marquez, Edgar A.
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Espinoza‐Montero, Patricio J.
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 (ΔH‡ ≈ 42–52 kcal/mol). Entropy values indicated that mechanisms with two methanol molecules involve higher preorganization (ΔS‡ ≈ −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 ΔH‡ 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 Cδ+—Nδ-. 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. ©The authors ©Wiley.
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Molecular and Descriptor Spaces for Predicting Initial Rate of Catalytic Homogeneous Quinoline Hydrogenation with Ru, Rh, Os, and Ir Catalysts
(American Chemical Society (ACS), 2025)
Izquierdo, Rodolfo
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Zadorosny, Rafael
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Rosales, Merlín
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Marrero Ponce, Yovani
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Cubillan, Néstor
Developing highly active catalysts for quinoline hydrogenation is crucial for efficient hydrogen carrier technologies and clean fossil fuel hydrodenitrogenation. In this work, we employed Tensor Algebra-based 3D-Geometrical Molecular Descriptors (QuBiLS-MIDAS) to develop Quantitative Structure–Property Relationship (QSPR) models predicting the initial rate of homogeneous quinoline hydrogenation catalyzed by transition metal complexes of Ru, Rh, Os, and Ir. A data set of 32 catalytic precursors was used: 25 for model training (training set) and 7 for external validation (testing set). Multiple linear regression analysis yielded a model with good predictive ability for the training set (R2 = 0.90) and satisfactory external validation for the testing set (QEXT2 = 0.86). The model’s descriptors highlighted the importance of hardness, softness, electrophilicity, and mass in predicting catalytic activity. The virtual screening revealed that Rh and Ir complexes with π-acidic ligands (e.g., olefins, diolefins, and η5-Cp) and nitrile ligands exhibited the highest predicted catalytic activity, suggesting potential for further improvement through ligand structural modification. Notably, iridium complexes, particularly those with tri(cyclopropyl)phosphine ligands, demonstrated significant potential for hydrogen storage, transport, and production, underscoring their relevance in sustainable energy systems. These findings demonstrate the potential of the QuBiLS-MIDAS approach for in silico design of efficient catalysts for quinoline hydrogenation processes. ©The authors ©ACS Omega ©American Chemical Society (ACS).
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Unraveling the hemolytic toxicity tapestry of peptides using chemical space complex networks
(Oxford University Press, 2024)
Castillo-Mendieta, Kevin
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Agüero-Chapin, Guillermin
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Mora, José R.
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Pérez, Noel
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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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MD-LAIs Software: Computing Whole-Sequence and Amino Acid-Level “Embeddings” for Peptides and Proteins
(American Chemical Society, 2024)
Torres García, José Ernesto
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Marrero Ponce, Yovani
Several computational tools have been developed to calculate sequence-based molecular descriptors (MDs) for peptides and proteins. However, these tools have certain limitations: 1) They generally lack capabilities for curating input data. 2) Their outputs often exhibit significant overlap. 3) There is limited availability of MDs at the amino acid (aa) level. 4) They lack flexibility in computing specific MDs. To address these issues, we developed MD-LAIs (Molecular Descriptors from Local Amino acid Invariants), Java-based software designed to compute both whole-sequence and aa-level MDs for peptides and proteins. These MDs are generated by applying aggregation operators (AOs) to macromolecular vectors containing the chemical-physical and structural properties of aas. The set of AOs includes both nonclassical (e.g., Minkowski norms) and classical AOs (e.g., Radial Distribution Function). Classical AOs capture neighborhood structural information at different k levels, while nonclassical AOs are applied using a sliding window to generalize the aa-level output. A weighting system based on fuzzy membership functions is also included to account for the contributions of individual aas. MD-LAIs features: 1) a module for data curation tasks, 2) a feature selection module, 3) projects of highly relevant MDs, and 4) low-dimensional lists of informative global and aa-level MDs. Overall, we expect that MD-LAIs will be a valuable tool for encoding protein or peptide sequences. The software is freely available as a stand-alone system on GitHub (https://github.com/Grupo-Medicina-Molecular-y-Traslacional/MD_LAIS). ©The authors © American Chemical Society ©Journal of Chemical Information and Modeling.
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Macromolecular interaction mechanism of the bacteriocin EntDD14 with the receptor binding domain (RBD) for the inhibition of SARS-CoV-2 and the JN.1 variant: Biomedical study based on elastic networks, stochastic Markov models, and macromolecular volumetric analysis
(Elsevier, 2025)
Moncayo Molina, Luis
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Aguaiza Pichazaca, María Erlinda
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Yamasqui Padilla, José Isidro
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Pinos Calle, María Eufemia
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Yamasqui Pinos, Karla Maribel
Bacteriocins, a class of molecules produced by bacteria, exhibit potent antimicrobial properties, including antiviral activities. The urgent need for treatments against SARS-CoV-2 has proposed bacteriocins such as enterocin DD14 (EntDD14) as potential therapeutic agents. However, the mechanism of macromolecular interaction of EntDD14 for the inhibition of SARS-CoV-2 is not yet fully understood, and its efficacy against variants like JN.1 has not been completely established. To address these knowledge gaps, biocomputational analyses were employed using a diverse set of tools, including Markov state models and volumetric analyses. This analysis revealed a favorable interaction between EntDD14 and the receptor-binding domain (RBD) of SARS-CoV-2. Furthermore, it was found that EntDD14 induces changes in the flexibility of the RBD and alters the distribution and size of its internal cavities, particularly in the JN.1 variant. These findings align with experimental observations and support the inhibitory mechanism of EntDD14 against SARS-CoV-2. Additionally, they suggest that EntDD14 may possess a broader spectrum of action, encompassing the JN.1 variant. This study paves the way for future investigations and therapeutic applications, encouraging further exploration of the antiviral activity of bacteriocins like EntDD14 against variants of concern like JN.1. However, additional experimental demonstrations are warranted to substantiate these findings. ©The authors ©Elsevier
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