STAFF

Dr. Ana María Fernández Escamilla

Associate Professor, Universidad Miguel Hernandez

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Unit
Molecular and Cellular Design

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Subunit
Molecular Recognition and Protein Engineering

Group
Protein Architecture

ana.fernandeze@umh.es

+ 34 965 222 088

RESEARCH FIELDS 

  • Protein engineering.
  • Protein-ligand binding interactions and protein-protein interaction.
  • Molecular modeling.
  • Biochemical, biophysical and structural characterization of proteins.
  • New amyloids: exploitation in biomedicine, food security (food allergy) and sustainable agriculture.
  • Bacterial biosensor development for detection of toxic and antimicrobial compounds.
  • Characterization of the molecular mechanisms involved in microorganism-environment interaction.

PROFESSIONAL BACKGROUND

  • Previous positions:
    1. Senior Researcher. National Programme for the Promotion of Talent and Its Employability, Ramón y Cajal National Sub-Programme. Environmental Protection Department. Estación Experimental del Zaidín (EEZ), Spanish National Research Council (CSIC). 2010-2016.
    2. Research Associate. Environmental Protection Department. EEZ (CSIC). 2009-2010.
    3. Research Associate. Biochemistry and Molecular Parasitology Group. Institute of Biotechnology. University of Granada (UGR). Mar 2009.
    4. Research Associate. Microbial Antagonists Substances Group. Microbiology Department. Faculty of Sciences. UGR. 2007-2008.
    5. Postdoctoral Research Fellow. Medicinal Chemistry Unit. Structural Biology Laboratory. Felipe Prince Research Centre (CIPF). 2005-2006.
    6. Postdoctoral Research Fellow. Structure and Molecular Spectroscopy Unit. Physical Chemistry Rocasolano Institute. Spanish National Research Council (CSIC). 2003.
    7. Postdoctoral Research Fellow. Structural and Computational Biology Unit. European Molecular Biology Laboratory (EMBL). Heidelberg (Germany). 2001-2005.
    8. European Molecular Biology Organization (EMBO) predoctoral-fellow. Agricultural University of Norway. Molecular Genetic Laboratory (LMG), Department of Biotechnologies Sciences. 1998.
    9. European Community Fellow. Department of Physical Chemistry. Faculty of Sciences. University of Granada (UGR). 1997-2000.
  • Postdoctoral positions:
  • PhD programme: Methodology and Treatment of the Chemical Phenomena.
  • Degree in Chemical Sciences.

REPRESENTATIVE PUBLICATIONS

Fernandez-Escamilla AM.*, Sánchez-Hidalgo M.*, et al. (2010)

Protein and Peptide Letters, DOI: 10.2174/092986610791190390

Conformational stability and activity of circular enterocin AS-48 derivatives.

Fernandez-Escamilla A.M., et al. (2006)

Protein Science, DOI: 10.1110/ps.062186506

Design and NMR conformational study of a beta-sheet peptide based on Betanova and WW domains.

Fernandez-Escamilla A. M., et al. (2004)

Nature Biotechnology, DOI: 10.1038/nbt1012

Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins.

Fernandez-Escamilla A. M., et al. (2004)

Proceedings of the National Academy of Science of the United States of America: PNAS, DOI: 10.1073/pnas.0304180101

Solvation in protein folding analysis, combination of theoretical and experimental approaches..

Fernandez A. M., et al. (2000)

European Journal of Biochemistry, DOI: 10.1046/j.1432-1327.2000.01638.x

Thermodynamic analysis of helix-engineered forms of the activation domain of human procarboxypeptidase A2.

PATENTS AND AVAILABLE TECHNOLOGIES

  • TANGO. A computer algorithm designed to predict aggregation-nucleating regions in proteins, as well as the effect of mutations and environmental conditions on the aggregation propensity of these regions. We derived a statistical mechanics algorithm, TANGO, based on simple physico-chemical principles of secondary structure formation extended by the assumption that the core regions of an aggregate are fully buried. TANGO was benchmarked against 175 peptides of over 20 proteins and was able to predict the sequences experimentally observed to contribute to the aggregation of these proteins. Further TANGO correctly predicts the aggregation propensities of several disease-related mutations in the Alzheimer’s b-peptide. Our algorithm, therefore, opens the possibility to screen large databases for potentially disease-related aggregation motifs as well as to optimize recombinant protein yields by rationally out-designing protein aggregation.

COMPANY AGREEMENTS

  • Detection of toxic compounds by the bacterial biosensor based on the TtgR repressor of Pseudomonas putida DOT -T1E. Financial Entities: GRONTAL Biotechnological Solutions S.L. and Spanish National Research Council (CSIC). Principal Investigator: Ana María Fernández Escamilla