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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Multitarget Design of Steroidal Inhibitors Against Hormone-Dependent Breast Cancer: An Integrated In Silico Approach
(MDPI AG, 2025)
Rodríguez-Macías, Juan
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Saurith-Coronell, Oscar
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Vargas-Echeverria, Carlos
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Insuasty Delgado, Daniel
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Márquez Brazón, Edgar A.
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. ©The authors ©MDPI.
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Molecular Modeling of Vasodilatory Activity : Unveiling Novel Candidates Through Density Functional Theory, QSAR, and Molecular Dynamics
(MDPI, 2024)
Bernal, Anthony
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Márquez, Edgar A.
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Flores-Sumoza, Máryury
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Cuesta, Sebastián A.
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Mora, José Ramón
Cardiovascular diseases (CVD) pose a significant global health challenge, requiring innovative therapeutic strategies. Vasodilators, which are central to vasodilation and blood pressure reduction, play a crucial role in cardiovascular treatment. This study integrates quantitative structure– (QSAR) modeling and molecular dynamics (MD) simulations to predict the biological activity and interactions of vasodilatory compounds with the aim to repurpose drugs already known and estimateing their potential use as vasodilators. By exploring molecular descriptors, such as electronegativity, softness, and highest occupied molecular orbital (HOMO) energy, this study identifies key structural features influencing vasodilatory effects, as it seems molecules with the same mechanism of actions present similar frontier orbitals pattern. The QSAR model was built using fifty-four Food Drugs Administration-approved (FDA-approved) compounds used in cardiovascular treatment and their activities in rat thoracic aortic rings; several molecular descriptors, such as electronic, thermodynamics, and topographic were used. The best QSAR model was validated through robust training and test dataset split, demonstrating high predictive accuracy in drug design. The validated model was applied on the FDA dataset and molecules in the application domain with high predicted activity were retrieved and filtered. Thirty molecules with the best-predicted pKI50 were further analyzed employing molecular orbital frontiers and classified as angiotensin-I or β1-adrenergic inhibitors; then, the best scoring values obtained from molecular docking were used to perform a molecular dynamics simulation, providing insight into the dynamic interactions between vasodilatory compounds and their targets, elucidating the strength and stability of these interactions over time. According to the binding energies results, this study identifies novel vasodilatory candidates where Dasabuvir and Sertindole seem to have potent and selective activity, offering promising avenues for the development of next-generation cardiovascular therapies. Finally, this research bridges computational modelling with experimental validation, providing valuable insight for the design of optimized vasodilatory agents to address critical unmet needs in cardiovascular medicine. ©The authors ©International Journal of Molecular Sciences ©MDPI
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Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures
(MDPI AG, 2026)
Cordoves-Delgado, Greneter
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García-Jacas, César R.
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Marrero Ponce, Yovani
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Aguila, Sergio A.
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Lizama-Uc, Gabriel
Background: Machine learning models have been shown to be a time-saving and cost-effective tool for peptide-based drug discovery. In this regard, different graph learning-driven frameworks have been introduced to exploit graph representations derived from predicted peptide structures. Such graphs are always derived by applying a Euclidean distance threshold between amino acid pairs, despite the fact that there is no evidence other than intuitive reasoning that supports the Euclidean distance as the most suitable. Objective: In this work, we examined the use of different distance functions to derive graph representations from predicted peptide structures to train deep graph learning-based models to predict antiviral peptides. Methods: To this end, we first analyzed how differently the closeness of the amino acids is characterized by different distance functions. Then, we studied the similarity between the graphs derived with several distance functions, as well as between them and random graphs. Finally, we trained several models with the best graph representations and analyzed how different they are regarding their predictions. Comparisons regarding state-of-the-art models were also performed. Results and Conclusion: We demonstrated that only using Euclidean distance thresholds is not sufficient criterion to build graphs representing structural features of predicted peptide structures, since other distance functions enabled building dissimilar graphs codifying different chemical spaces, which were useful in the construction of better discriminative models. ©The authors ©MDPI.
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Unveiling Encrypted Antimicrobial Peptides from Cephalopods' Salivary Glands: A Proteolysis-Driven Virtual Approach
(American Chemical Society, 2024)
Agüero-Chapin, Guillermin
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Domínguez-Pérez, Dany
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Marrero Ponce, Yovani
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Castillo-Mendieta, Kevin
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Antunes, Agostinho
Antimicrobial peptides (AMPs) have potential against antimicrobial resistance and serve as templates for novel therapeutic agents. While most AMP databases focus on terrestrial eukaryotes, marine cephalopods represent a promising yet underexplored source. This study reveals the putative reservoir of AMPs encrypted within the proteomes of cephalopod salivary glands via in silico proteolysis. A composite protein database comprising 5,412,039 canonical and noncanonical proteins from salivary apparatus of 14 cephalopod species was subjected to digestion by 5 proteases under three protocols, yielding over 9 million of nonredundant peptides. These peptides were effectively screened by a selection of 8 prediction and sequence comparative tools, including machine learning, deep learning, multiquery similarity-based models, and complex networks. The screening prioritized the antimicrobial activity while ensuring the absence of hemolytic and toxic properties, and structural uniqueness compared to known AMPs. Five relevant AMP datasets were released, ranging from a comprehensive collection of 542,485 AMPs to a refined dataset of 68,694 nonhemolytic and nontoxic AMPs. Further comparative analyses and application of network science principles helped identify 5466 unique and 808 representative nonhemolytic and nontoxic AMPs. These datasets, along with the selected mining tools, provide valuable resources for peptide drug developers. ©The authors ©ACS Omega.
Scopus© Citations 1 11
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Innovative Alignment-Based Method for Antiviral Peptide Prediction
(2024)
Daniela de Llano García
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Marrero Ponce, Yovani
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Guillermin Agüero-Chapin
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Francesc J. Ferri
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Agostinho Antunes
<jats:p>Antiviral peptides (AVPs) represent a promising strategy for addressing the global challenges of viral infections and their growing resistances to traditional drugs. Lab-based AVP discovery methods are resource-intensive, highlighting the need for efficient computational alternatives. In this study, we developed five non-trained but supervised multi-query similarity search models (MQSSMs) integrated into the StarPep toolbox. Rigorous testing and validation across diverse AVP datasets confirmed the models’ robustness and reliability. The top-performing model, M13+, demonstrated impressive results, with an accuracy of 0.969 and a Matthew’s correlation coefficient of 0.71. To assess their competitiveness, the top five models were benchmarked against 14 publicly available machine-learning and deep-learning AVP predictors. The MQSSMs outperformed these predictors, highlighting their efficiency in terms of resource demand and public accessibility. Another significant achievement of this study is the creation of the most comprehensive dataset of antiviral sequences to date. In general, these results suggest that MQSSMs are promissory tools to develop good alignment-based models that can be successfully applied in the screening of large datasets for new AVP discovery.</jats:p>
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Biological Implications of the Intrinsic Deformability of Human Acetylcholinesterase Induced by Diverse Compounds: A Computational Study
(MDPI, 2024)
Alvarado, Ysaías J.
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González-Paz, Lenin
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Paz , José L.
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Loroño-González, Marcos A.
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Santiago Contreras, Julio
The enzyme acetylcholinesterase (AChE) plays a crucial role in the termination of nerve impulses by hydrolyzing the neurotransmitter acetylcholine (ACh). The inhibition of AChE has emerged as a promising therapeutic approach for the management of neurological disorders such as Lewy body dementia and Alzheimer’s disease. The potential of various compounds as AChE inhibitors was investigated. In this study, we evaluated the impact of natural compounds of interest on the intrinsic deformability of human AChE using computational biophysical analysis. Our approach incorporates classical dynamics, elastic networks (ENM and NMA), statistical potentials (CUPSAT and SWOTein), energy frustration (Frustratometer), and volumetric cavity analyses (MOLE and PockDrug). The results revealed that cyanidin induced significant changes in the flexibility and rigidity of AChE, especially in the distribution and volume of internal cavities, compared to model inhibitors such as TZ2PA6, and through a distinct biophysical-molecular mechanism from the other inhibitors considered. These findings suggest that cyanidin could offer potential mechanistic pathways for future research and applications in the development of new treatments for neurodegenerative diseases. ©The authors ©MDPI
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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.
Scopus© Citations 2 17 5
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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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Marrero Ponce, Yovani
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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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Peptide hemolytic activity analysis using visual data mining of similarity-based complex networks
(2024)
Kevin Castillo-Mendieta
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Guillermin Agüero-Chapin
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Edgar A. Marquez
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Yunierkis Perez-Castillo
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Stephen J. Barigye
6Scopus© Citations 5
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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
10Scopus© Citations 3

Marrero Ponce, Yovani

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