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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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Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance
(MDPI AG, 2026)
Saurith-Coronell, Oscar
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Sierra-Hernandez, Olimpo
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Rodríguez-Macías, Juan David
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Mora, José R.
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Perez-Perez, Noel
The rapid spread of antibiotic resistance through plasmid-mediated conjugation remains a primary global health concern. Despite its critical role in horizontal gene transfer, no approved drugs currently target this process, leaving a critical therapeutic gap. Integration Host Factor (IHF), a DNA-binding protein essential for plasmid replication and mobilization, emerges as a promising yet underexplored target for anti-conjugation strategies. This work aimed to develop a predictive computational model and identify small molecules that disrupt IHF function, thereby reducing plasmid transfer and limiting resistance gene dissemination. A curated dataset of 65 compounds with reported anti-plasmid activity was analyzed using a 3D-QSAR model based on algebraic descriptors computed with QuBiLS-MIDAS. The model was validated through leave-one-out cross-validation (Q2 = 0.82), Tropsha’s criteria, and Y-scrambling. Representative compounds were selected via pharmacophore clustering and evaluated through molecular docking at both the DNA-binding site and a predicted allosteric pocket of IHF. The most promising complexes underwent 200 ns molecular dynamics simulations to assess stability and interaction patterns. The QSAR model demonstrated strong predictive performance (R2 = 0.90). Docking simulations revealed more favorable binding energies at the allosteric site (up to −12.15 kcal/mol) compared to the DNA-binding site. Molecular dynamics confirmed the stability of these interactions, with allosteric complexes showing lower RMSD fluctuations and consistent binding energy profiles. Dynamic cross-correlation analysis revealed that allosteric ligand binding induces conformational changes in key catalytic residues, including Pro65, Pro61, and Leu66. These alterations may compromise DNA recognition and disrupt the initiation of replication. To our knowledge, this is the first computational study proposing allosteric inhibition of IHF as an anti-conjugation strategy. These findings provide a foundation for experimental validation and the development of novel agents to prevent horizontal gene transfer, offering a promising approach to restoring antibiotic efficacy against multidrug-resistant pathogens. ©The authors ©MDPI.
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Relationship between Hydration and Catalytic Activity of Endonucleases: The Case of Cas9 and Its Evolutionary Variants
(Pleiades Publishing Ltd, 2025)
Alvarado, Ysaías J.
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Vivas, Alejandro
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Méndez, Anibal
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Rodríguez-Lugo, Patricia
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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.
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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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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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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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Modeling the functional impact of CPEB3 and CPEB4 dysregulation in autism: A theoretical–computational framework
(Elsevier BV, 2026)
González-Paz, Lenin
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Vivas, Alejandro
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Cardozo-Urdaneta, Arlene
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Lossada, Carla
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Mendez, Anibal
Autism spectrum disorder (ASD) involves impaired synaptic plasticity tightly coupled to local mRNA translation. Cytoplasmic polyadenylation element-binding proteins 3 and 4 (CPEB3 and CPEB4) are post-transcriptional regulators of neuronal mRNA translation that may contribute to ASD-related molecular alterations. In this theoretical–computational study, we develop a weighted functional impact model that integrates transcriptomic expression with intrinsic molecular constraints of CPEB3 and CPEB4 to estimate regional and cell type–specific vulnerability in ASD. Coarse-grained molecular dynamics (MD) simulations were quantitatively analyzed to assess aggregation, diffusion, and cluster stability under cell type–specific cytoplasmic conditions, with statistical uncertainty explicitly evaluated. The anterior cingulate cortex and thalamus emerged as primary vulnerability sites. Despite higher CPEB4 expression—mainly in glial cells—our weighted functional impact model predicted greater theoretical susceptibility linked to CPEB3 dysfunction, particularly in inhibitory and excitatory neurons. MD simulations revealed that CPEB3 forms transient diffusion-permissive aggregates, whereas CPEB4 tends to assemble into more stable condensates. These complementary behaviors suggest differential but interdependent regulation of neuronal and glial functions. Importantly, the proposed framework provides experimentally testable predictions on how protein–protein interactions, microexon loss, and cytoplasmic crowding influence translational control in ASD. This integrative approach provides a quantitative and biologically grounded framework to investigate how post-transcriptional regulators contribute to ASD-relevant molecular vulnerability. ©The authors ©Sciencedirect ©Elsevier.
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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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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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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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