Martínez Ríos, Félix Orlando

Main Affiliation

Facultad de Ingeniería - CampCM

Preferred name

Official Name

Martínez Ríos, Félix Orlando

ORCID

0000-0002-2346-5922

Researcher ID

N-7502-2018

Scopus Author ID

24339038500

Now showing 1 - 10 of 46

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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.
16Scopus© Citations 8
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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)
Romero, Maylin
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Marrero Ponce, Yovani
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Martínez Ríos, Félix Orlando
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Agüero-Chapin, Guillermin
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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).
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Theoretical study of the hydrolysis mechanism of β-lactam antibiotics catalysed by a Zn(II) dinuclear biomimetic organometallic complex
(Taylor and Francis Group, 2024)
Zurita, Juan E.
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Mora, José R.
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Barigye, Stephen J.
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Espinoza-Montero, Patricio J.
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Martínez Ríos, Félix Orlando
The degradation and metabolism of antibiotics have attracted the scientific community's attention due to the environmental problems caused by the inappropriate use and disposal of these drugs. Therefore, it is crucial to understand the reaction mechanism involved in the degradation of these compounds. We studied the hydrolysis of nitrocefin and benzylpenicillin, two β-lactam ring antibiotics, mediated by an enzymatic mimetic Zn organometallic compound, using DFT methods. The electronic effects of functional groups adjacent to the β-lactam ring were analysed and good agreement between experimental and theoretical results was found. The reaction involves the nucleophilic attack of the bridging hydroxide group on the Zn atoms towards the β-lactam ring. A stepwise mechanism was found to agree with the experimental results. Chemical hardness profiles were affected by the solvent and reaction synchronicity. Geometric rearrangements dominated the activation barrier, and both antibiotics exhibited late transition states. © Taylor and Francis Group.
12Scopus© Citations 2
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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
9Scopus© Citations 3
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Smoothed Spherical Truncation based on Fuzzy Membership Functions: Application to the Molecular Encoding
(2019)
García‐Jacas, César R.
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Marrero Ponce, Yovani
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Brizuela, Carlos A.
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Suárez-Lezcano, José
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Martínez Ríos, Félix Orlando
A novel spherical truncation method, based on fuzzy membership functions, is introduced to truncate interatomic (or interaminoacid) relations according to smoothing values computed from fuzzy membership degrees. In this method, the molecules are circumscribed into a sphere, so that the geometric centers of the molecules are the centers of the spheres. The fuzzy membership degree of each atom (or aminoacid) is computed from its distance with respect to the geometric center of the molecule, by using a fuzzy membership function. So, the smoothing value to be applied in the truncation of a relation (or interaction) is computed by averaging the fuzzy membership degrees of the atoms (or aminoacids) involved in the relation. This truncation method is rather different from the existing ones, at considering the geometric center for the whole molecule and not only for atom-groups, as well as for using fuzzy membership functions to compute the smoothing values. A variability study on a set comprised of 20,469 compounds (15,050 drug-like compounds, 2994 drugs approved, 880 natural products from African sources, and 1545 plant-derived natural compounds exhibiting anti-cancerous activity) demonstrated that the truncation method proposed allows to determine molecular encodings with better ability for discriminating among structurally different molecules than the encodings obtained without applying truncation or applying non-fuzzy truncation functions. Moreover, a principal component analysis revealed that orthogonal chemical information of the molecules is encoded by using the method proposed. Lastly, a modeling study proved that the truncation method improves the modeling ability of existing geometric molecular descriptors, at allowing to develop more robust models than the ones built only using non-truncated descriptors. In this sense, a comparison and statistical assessment were performed on eight chemical datasets. As a result, the models based on the truncated molecular encodings yielded statistically better results than 12 procedures considered from the literature. It can thus be stated that the proposed truncation method is a relevant strategy for obtaining better molecular encodings, which will be ultimately useful in enhancing the modeling ability of existing encodings both on small-to-medium size molecules and biomacromolecules. © 2019 Wiley Periodicals, Inc. © 2019 Wiley Periodicals, Inc.
1 25Scopus© Citations 4
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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
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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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Strategy, Power and CSR: Practices and Challenges in Organizational Management
(Emerald, 2020)
Meza-Mejía, Mónica del Carmen
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García Casas, Claudia María
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Ortega-Barba, Claudia-Fabiola
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Galbán-Lozano, Sara-Elvira
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Roux, Sofia
Organizational management, along with strategy, is the most important part of business administration. Directors must know how to manage people, make decisions and, above all, have the ability to create strategies that help organizations achieve their objectives, greater strategic competitiveness, and above-average returns. In today’s global and complex environment, traditional views towards organizational management are not enough for businesses to thrive. It’s only by bringing together different approaches can management styles develop fast enough to keep pace with the ever-changing big picture. In this innovative new look at organizational management, expert authors Santiago García-Álvarez and Connie Atristain-Suárez explore how looking through lenses of philosophy, health, communication, law, engineering, pedagogy and policy can affect a modern organization’s prospects. Built through the collective and collaborative work of the research professors at the Universidad Panamericana, this work includes interdisciplinary approaches to real-world problems. For students and researchers of business and management, this is an unmissable read. ©2020 Emerald Publishing Limited.
Scopus© Citations 1 24 11
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A new hybridized algorithm based on Population-Based Simulated Annealing with an experimental study of phase transition in 3-SAT
(2017)
Martínez Ríos, Félix Orlando
This paper is about experiments for satisfiability problem using a new algorithm (GR-MM-PBSA) that improves the algorithm Population-Based Simulated Annealing (PBSA). GR-MM-PBSA runs in a parallel way Simulated Annealing (SA) and Threshold Annealing (TA) algorithms with a Golden Ratio space search strategy and Markovian Model to select initial and final temperature. In this paper we execute differents hybridized Simulated Annealing (or Threshold Accepting) algorithms and compares the efficiency of these, using a metric based on transition phase effect. Simulated Annealing Algorithms (SAA) theoretically can reach the optimum if the control parameters and cooling scheme are chosen correctly. All algorithms are compared with a metric based on transition phase obtained for 3-SAT instances. This paper shows the results of SAA hybridizations are more efficient than the original algorithm, without increasing their computational complexity. We also show the experimental data about runs with 820 3-SAT instances with ratio clauses-variables between 2.0 to 6.0.
1 3Scopus© Citations 7
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Rethinking the applicability domain analysis in QSAR models
(Springer, 2024)
Mora, Jose R.
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Marquez, Edgar A.
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Pérez-Pérez, Noel
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Contreras-Torres, Ernesto
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Perez-Castillo, Yunierkis
Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in “rational” model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates. © 2024 Springer Nature
Scopus© Citations 3 1 11

Martínez Ríos, Félix Orlando

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