Browsing by Author "Gozalbes, Rafael"

Now showing 1 - 4 of 4
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    Computational Strategies for the Discovery of Biological Functions of Health Foods, Nutraceuticals and Cosmeceuticals: A Review
    (Molecular Diversity, 2021) Carpio, Laureano E.; Sanz, Yolanda; Gozalbes, Rafael; Barigye, Stephen J.
    Scientific and consumer interest in healthy foods (also known as functional foods), nutraceuticals and cosmeceuticals has increased in the recent years, leading to an increased presence of these products in the market. However, the regulations across different countries that define the type of claims that may be made, and the degree of evidence required to support these claims, are rather inconsistent. Moreover, there is also controversy on the effectiveness and biological mode of action of many of these products, which should undergo an exhaustive approval process to guarantee the consumer rights. Computational approaches constitute invaluable tools to facilitate the discovery of bioactive molecules and provide biological plausibility on the mode of action of these products. Indeed, methodologies like QSAR, docking or molecular dynamics have been used in drug discovery protocols for decades and can now aid in the discovery of bioactive food components. Thanks to these approaches, it is possible to search for new functions in food constituents, which may be part of our daily diet, and help to prevent disorders like diabetes, hypercholesterolemia or obesity. In the present manuscript, computational studies applied to this field are reviewed to illustrate the potential of these approaches to guide the first screening steps and the mechanistic studies of nutraceutical, cosmeceutical and functional foods.
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    Development of Generalized QSAR Models for Predicting Cytotoxicity and Genotoxicity of Metal Oxides Nanoparticles
    (International Journal of Quantitative Structure-Property Relationships, 2020) Ambure, Pravin; Ballesteros, Arantxa; Barigye, Stephen J.; Gozalbes, Rafael
    In recent years, nanomaterials have gained tremendous attention due to their wide variety of industrial applications including food packaging, consumer products, nanomedicines, etc. The fascinating properties of nanoparticles which are responsible for creating several exciting opportunities, however, are also accountable for growing concerns of their toxic effects on humans as well as the environment. Thus, in the present study, the authors have developed generalized models for predicting the cytotoxicity and genotoxicity of seven metal oxide nanoparticles. The models not only take into account the structural features, but also the diverse experimental conditions under which the toxicity of nanoparticles was determined. The diverse experimental conditions were captured in the generalized models using the Box-Jenkins moving average approach. Here, two machine learning techniques, namely, linear discriminant analysis and random forest were utilized to build the final models. Importantly, the validation metrics showed that the developed models have significant discriminatory power.
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    PeptiDesCalculator: Software for computation of peptide descriptors. Definition, implementation and case studies for 9 bioactivity endpoints
    (Proteins: Structure, Function, and Bioinformatics, 2021) Barigye, Stephen J.; Gómez-Ganau, Sergi; Serrano-Candelas, Eva; Gozalbes, Rafael
    We present a novel Java-based program denominated PeptiDesCalculator for computing peptide descriptors. These descriptors include: redefinitions of known protein parameters to suite the peptide domain, generalization schemes for the global descriptions of peptide characteristics, as well as empirical descriptors based on experimental evidence on peptide stability and interaction propensity. The PeptiDesCalculator software provides a user-friendly Graphical User Interface (GUI) and is parallelized to maximize the use of computational resources available in current work stations. The PeptiDesCalculator indices are employed in modeling 8 peptide bioactivity endpoints demonstrating satisfactory behavior. Moreover, we compare the performance of a support vector machine (SVM) classifier built using 15 PeptiDesCalculator indices with that of a recently reported deep neural network (DNN) antimicrobial activity classifier, demonstrating comparable test set performance notwithstanding the remarkably lower degree of freedom for the former. This software will facilitate the development of in silico models for the prediction of peptide properties.
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    Targeting the Aryl Hydrocarbon Receptor with a Novel set of Triarylmethanes
    (European Journal of Medicinal Chemistry, 2020) Goya-Jorge, Elizabeth; Loones, Nicolas; Barigye, Stephen J.; Gozalbes, Rafael
    The aryl hydrocarbon receptor (AhR) is a chemical sensor upregulating the transcription of responsive genes associated with endocrine homeostasis, oxidative balance and diverse metabolic, immunological and inflammatory processes, which have raised the pharmacological interest on its modulation. Herein, a novel set of 32 unsymmetrical triarylmethane (TAM) class of structures has been synthesized, characterized and their AhR transcriptional activity evaluated using a cell-based assay. Eight of the assayed TAM compounds (14, 15, 18, 19, 21, 22, 25, 28) exhibited AhR agonism but none of them showed antagonist effects. TAMs bearing benzotrifluoride, naphthol or heteroaromatic (indole, quinoline or thiophene) rings seem to be prone to AhR activation unlike phenyl substituted or benzotriazole derivatives. A molecular docking analysis with the AhR ligand binding domain (LBD) showed similarities in the binding mode and in the interactions of the most potent TAM identified 4-(pyridin-2-yl (thiophen-2-yl)methyl)phenol (22) compared to the endogenous AhR agonist 5,11-dihydroindolo[3,2[2], [3]carbazole-12-carbaldehyde (FICZ). Finally, in silico predictions of physicochemical and biopharmaceutical properties for the most potent agonistic compounds were performed and these exhibited acceptable druglikeness and good ADME profiles. To our knowledge, this is the first study assessing the AhR modulatory effects of unsymmetrical TAM class of compounds.