Structural Bioinformatics, Modeling and Biological Mechanisms (Model3DBio)

The group has a clear multidisciplinary character, and its main research activity is the development and application of computational methods for the structural modeling of biomolecules and biological mechanisms of interest in grapevine and wine sciences.  The ultimate goal is to contribute to understand and rationally manipulate biomolecular processes related with wine, from the biosynthesis of metabolites in grapevine and further modification in vinification microorganisms, to their effects in human.

The specific aims are grouped in these major research lines:

• 1. Development of new computational methods for multi-scale modeling of biological processes. 
• 1.1. Modeling of biomolecular interactions based on energetic functions and artificial intelligence, focused to current challenges: antibody-antigen complexes, multi-protein assemblies and association dynamics.
• 1.2. Modeling impact of mutations on protein binding based on energetic potentials and artificial intelligence techniques.
• 1.3. Analysis of biological data and probabilistic modeling of cell and population dynamics.
• 1.4. Validation of developed methods, distribution and implementation as web servers.

• 2. Application to the study of molecular mechanisms of interest in wine sciences.
• 2.1. Identification and modeling of disease-related molecular targets of wine polyphenols.
• 2.2. Analysis of molecular data of wine, quality determinants and modeling of sensory perception mechanisms.
• 2.3. Modeling and stabilization of enzymes and other biomolecules in yeasts and winemaking processes.
• 2.4. Structural modeling of grapevine proteins and molecular-level interpretation of genetic variants.

• 3. Experimental validation and identification of new compounds against aging-related diseases.
• 3.1. In vitro validation of anthocyanins and other dietary polyphenols.
• 3.2. Structural and biochemical analysis of molecular mechanisms of polyphenols and other compounds.

https://orcid.org/0000-0002-3986-7686
https://scholar.google.es/citations?user=p7uqDecAAAAJ&hl=en&oi=ao
http://www.ncbi.nlm.nih.gov/pubmed/?term=Fernandez-Recio+J%5BAuthor%5D

https://www.scopus.com/authid/detail.uri?authorId=55909661300

• Jiménez-Panizo, A., Alegre-Martí, A., Tettey, T.T., Fettweis, G., Abella, M., Antón, R., Johnson, T.A., Kim, S., Schiltz, R.L., Nuñez-Barrios, I., Font-Díaz, J., Caelles, C., Valledor, A., Perez, P., Rojas, A.M., Fernandez-Recio, J., Presman, D.M., Hager, G.L., Fuentes-Prior, P., Estébanez-Perpiñá, E. (2022) The multivalency of the glucocorticoid receptor ligand-binding domain explains its manifold physiological activities. Nucl. Acid. Res. 50,13063-13082.
• Rodríguez-Lumbreras, L.A., Jiménez-García, B., Giménez-Santamarina, S., Fernández-Recio, J. (2022) pyDockDNA: A new web server for energy-based protein-DNA docking and scoring. Front. Mol. Biosci. 9:988996.
• Landras, A., Reger de Moura, C., Villoutreix, B.O., Battistella, M., Sadoux, A., Dumaz, N., Menashi, S., Fernández-Recio, J., Lebbé, C., Mourah, S. (2022) Novel treatment strategy for NRAS-mutated melanoma through a selective inhibitor of CD147/VEGFR-2 interaction. Oncogene 41, 2254-2264.
• Castell, C., Rodríguez-Lumbreras, L.A., Hervás, M., Fernandez-Recio, J., Navarro, J.A. (2021) New Insights Into the Evolution of the Electron Transfer from Cytochrome f to Photosystem I in the Green and Red Branches of Photosynthetic Eukaryotes. Plant Cell Physiol. 63, 1082-1093.
• Fernández-Recio, J. (2020) Modelling the Evolution of COVID-19 in High-Incidence European Countries and Regions: Estimated Number of Infections and Impact of Past and Future Intervention Measures. J.Clin.Med 9, 1825.
• Rosell, M., Fernández-Recio, J. (2020) Docking approaches for modeling multi-molecular assemblies. Curr. Opin. Struct. Biol. 64, 59-65.
• Errasti-Murugarren, E., Fort, J., Bartoccioni, P., Díaz, L., Pardon, E., Carpena, X., Espino-Guarch, M., Zorzano, A., Ziegler, C., Steyaert, J., Fernández-Recio, J., Fita, I., Palacin, M. (2019) L-amino acid transporter structure and molecular bases for the asymmetry of substrate interaction. Nat. Comm. 10, 1807.
• Jankauskaité, J., Jiménez-García, B., Dapkūnas, J., Fernández-Recio, J., Moal, I.H. (2019) SKEMPI 2.0: An updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation. Bioinformatics 35, 462-469.
• Rosell, M., Fernández-Recio, J. (2018) Hot-spot analysis for drug discovery targeting protein-protein interactions. Exp. Opin. Drug Discov. 13, 327-338.
• Jiménez-García, B., Roel-Touris, J., Romero-Durana, M., Vidal, M., Jiménez-González, D., Fernández-Recio, J. (2018) LightDock: A new multi-scale approach to protein-protein docking. Bioinformatics 34, 49-55.