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

Some of the metrics are blocked by your
consent settings
Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from ESMFold-Predicted Tertiary Structures
(MDPI AG, 2026)
Cordoves-Delgado, Greneter
;
García-Jacas, César R.
;
Marrero Ponce, Yovani
;
Aguila, Sergio A.
;
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.
Some of the metrics are blocked by your
consent settings
Mapping the Chemical Space of Antiviral Peptides with Half-Space Proximal and Metadata Networks Through Interactive Data Mining
(MDPI AG, 2025)
Llano García, Daniela de
;
Marrero Ponce, Yovani
;
Agüero-Chapin, Guillermin
;
Rodríguez, Hortensia
;
Ferri, Francesc J.
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. ©The authors ©MDPI.
Some of the metrics are blocked by your
consent settings
Half-Space Proximal Networks (HSPNs): A Proxy for Multi-Query Similarity Searching Models Predicting Tumor-Homing Peptides
(American Chemical Society (ACS), 2025)
Romero, Maylin
;
Marrero Ponce, Yovani
;
Martínez Ríos, Félix Orlando
;
Agüero-Chapin, Guillermin
;
Aguilera-Mendoza, Longendri
Tumor-homing peptides (THPs) have emerged as promising agents in cancer treatments. These short sequences can specifically target tumor cells and vasculature. Here, a nontrained machine learning (ML) method based on network science and multiquery similarity searching to predict THPs is presented. We leverage the network-based representation of THPs’ chemical space to extract valuable information by employing a novel similarity-based, yet sparse, network known as the halfspace proximal network (HSPN). The HSPN of the THPs’ giant component is composed of 12 communities that represent distinct modes of action and/or targets, as well as sequence templates (scaffolds). In the HSPN analysis, various centrality measures were employed to identify the most significant and nonredundant THPs. These central THPs were then used as queries (Qs) in group fusion similarity-based searches against an established collection of known THPs. The performance of the resulting multiquery similarity-based search models (MQSSMs) was assessed using three benchmarking datasets of THPs/non-THPs. The MQSSMs derived from the HSPNs (THP2) demonstrated superior discrimination performance compared to the classical chemical space networks (CSNs, namely THP1) when applied to the THPs/non-THPs datasets Remarkably, exceptional MCC values (>0.887) were achieved when utilizing Qs from both CSN and HSPN networks to construct MQSSMs (THP3), employing a similarity threshold of 0.6, in external datasets. Next, we conducted a statistical comparison between the performance of our top-performing MQSSM, THP3, and several THP prediction servers, including TumorHPD, THPep, SCMTHP, and NEPTUNE. Our proposed model demonstrated its superiority by surpassing the state-of-the-art supervised and trained ML methods for THP prediction with statistically significant differences. These results provide strong evidence that network-based similarity searches are highly effective and reliable for identifying THPs. ©The authors ©American Chemical Society (ACS).
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
A 2026 Update on Computational Approaches to the Discovery and Design of Antimicrobial Peptides
(MDPI AG, 2026)
Agüero-Chapin, Guillermin
;
Antunes, Agostinho
;
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.
Some of the metrics are blocked by your
consent settings
Ligand-Based Virtual Screening Workflow for Antimalarial Repositioning from Known Drugs and Chemical Libraries
(Springer Nature Switzerland, 2025-10-11)
Machado-Tugores,Yanetsy
;
Meneses-Marcel, Alfredo
;
Marrero Ponce, Yovani
;
Martínez Ríos, Félix Orlando
;
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.
Some of the metrics are blocked by your
consent settings
Potential Bioactive Function of Microbial Metabolites as Inhibitors of Tyrosinase: A Systematic Review
(MDPI AG, 2026)
Barcenas-Giraldo, Sofia
;
Baez-Leguizamon, Vanessa
;
Barbosa-Gonzalez, Laura
;
Leon-Rodriguez, Angelica
;
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.
Some of the metrics are blocked by your
consent settings
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.
;
Marrero Ponce, Yovani
;
García-Jacas, César R.
;
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.
Some of the metrics are blocked by your
consent settings
Relationship between Hydration and Catalytic Activity of Endonucleases: The Case of Cas9 and Its Evolutionary Variants
(Pleiades Publishing Ltd, 2025)
Alvarado, Ysaías J.
;
Vivas, Alejandro
;
Méndez, Anibal
;
Rodríguez-Lugo, Patricia
;
Troconis, María Elena
This study examined the structures of SpCas9 endonuclease of Streptococcus pyogenes and their evolutionary variants using different computational biophysical models to investigate the behavior of hydration in these endonucleases. Although the mechanism of SpCas9 is well understood from an evolutionary perspective, its hydration has not been thoroughly explored. The study found that all endonucleases tended to compact together and expose less surface area to water as a solvent, resulting in a significant loss of water molecules from the hydration layer, as occurs in the folding of many globular proteins. A comparative analysis revealed that the distribution of water molecules in the hydration shell and PI domain, which is responsible for the biological recognition function of ligand, differed between each endonuclease. All endonucleases have a higher density in their hydration shell in relation to the density of water as a solvent, with SpCas9 having the highest density in the hydration shell (19%) and the lowest being the primitive endonuclease SCA (4%) in relation to the bulk water. The previously reported catalytic activity of these endonucleases toward the OCA2 and TYR genes increased nonlinearly with both maximum of probability density of the number of water molecules and the degree of hydration in the evolutionary direction from the oldest to the current. These findings suggest that water molecules in the hydration shell play an important role in the conformational changes, biological recognition, and activity of this endonuclease of great biotechnological interest. ©The authors ©Springer.
Some of the metrics are blocked by your
consent settings
StarPepWeb: an integrative, graph-based resource for bioactive peptides
(Oxford University Press, 2024)
López, Christian
;
Cárdenas, Roberto
;
Aguilera-Mendoza, Longendri
;
Agüero-Chapin, Guillermin
;
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.

Marrero Ponce, Yovani

Filters

Settings

Sort By

Results per page