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Espinosa-Urgel M.,CSIC - Experimental Station of El Zaidin | Serrano L.,Systems Biology Research Unit | Ramos J.L.,CSIC - Experimental Station of El Zaidin | Fernandez-Escamilla A.M.,CSIC - Experimental Station of El Zaidin
Molecular Biotechnology | Year: 2015

Environmental contamination by toxic organic compounds and antimicrobials is one of the causes for the recent surge of multidrug-resistant pathogenic bacteria. Monitoring contamination is therefore the first step in containment of antimicrobial resistance and requires the development of simple, sensitive, and quantitative tools that detect a broad spectrum of toxic compounds. In this study, we have engineered a new microbial biosensor based on the ttgR-regulated promoter that controls expression of the TtgABC extrusion efflux pump of Pseudomonas putida, coupled to a gfp reporter. The system was introduced in P. putida DOT-T1E, a strain characterized by its ability to survive in the presence of high concentrations of diverse toxic organic compounds. This whole-cell biosensor is capable to detect a wide range of structurally diverse antibiotics, as well as compounds such as toluene or flavonoids. © 2015, Springer Science+Business Media New York. Source

Schaefer M.H.,Systems Biology Research Unit | Schaefer M.H.,University Pompeu Fabra | Serrano L.,Systems Biology Research Unit | Serrano L.,University Pompeu Fabra | And 3 more authors.
Frontiers in Genetics | Year: 2015

Protein-protein interaction (PPI) networks are associated with multiple types of biases partly rooted in technical limitations of the experimental techniques. Another source of bias are the different frequencies with which proteins have been studied for interaction partners. It is generally believed that proteins with a large number of interaction partners tend to be essential, evolutionarily conserved, and involved in disease. It has been repeatedly reported that proteins driving tumor formation have a higher number of PPI partners. However, it has been noticed before that the degree distribution of PPI networks is biased toward disease proteins, which tend to have been studied more often than non-disease proteins. At the same time, for many poorly characterized proteins no interactions have been reported yet. It is unclear to which extent this study bias affects the observation that cancer proteins tend to have more PPI partners. Here, we show that the degree of a protein is a function of the number of times it has been screened for interaction partners. We present a randomization-based method that controls for this bias to decide whether a group of proteins is associated with significantly more PPI partners than the proteomic background. We apply our method to cancer proteins and observe, in contrast to previous studies, no conclusive evidence for a significantly higher degree distribution associated with cancer proteins as compared to non-cancer proteins when we compare them to proteins that have been equally often studied as bait proteins. Comparing proteins from different tumor types, a more complex picture emerges in which proteins of certain cancer classes have significantly more interaction partners while others are associated with a smaller degree. For example, proteins of several hematological cancers tend to be associated with a higher number of interaction partners as expected by chance. Solid tumors, in contrast, are usually associated with a degree distribution similar to those of equally often studied random protein sets. We discuss the biological implications of these findings. Our work shows that accounting for biases in the PPI network is possible and increases the value of PPI data. © 2015 Schaefer, Serrano and Andrade-Navarro. Source

Hasenhindl C.,Christian Doppler Laboratory | Hasenhindl C.,University of Natural Resources and Life Sciences, Vienna | Lai B.,University of Natural Resources and Life Sciences, Vienna | Delgado J.,Systems Biology Research Unit | And 10 more authors.
Biochimica et Biophysica Acta - Proteins and Proteomics | Year: 2014

Fcabs (Fc antigen binding) are crystallizable fragments of IgG where the C-terminal structural loops of the CH3 domain are engineered for antigen binding. For the design of libraries it is beneficial to know positions that will permit loop elongation to increase the potential interaction surface with antigen. However, the insertion of additional loop residues might impair the immunoglobulin fold. In the present work we have probed whether stabilizing mutations flanking the randomized and elongated loop region improve the quality of Fcab libraries. In detail, 13 libraries were constructed having the C-terminal part of the EF loop randomized and carrying additional residues (1, 2, 3, 5 or 10, respectively) in the absence and presence of two flanking mutations. The latter have been demonstrated to increase the thermal stability of the CH3 domain of the respective solubly expressed proteins. Assessment of the stability of the libraries expressed on the surface of yeast cells by flow cytometry demonstrated that loop elongation was considerably better tolerated in the stabilized libraries. By using in silico loop reconstruction and mimicking randomization together with MD simulations the underlying molecular dynamics were investigated. In the presence of stabilizing stem residues the backbone flexibility of the engineered EF loop as well as the fluctuation between its accessible conformations were decreased. In addition the CD loop (but not the AB loop) and most of the framework regions were rigidified. The obtained data are discussed with respect to the design of Fcabs and available data on the relation between flexibility and affinity of CDR loops in Ig-like molecules. © 2014 The Authors. Published by Elsevier B.V. Source

Yang J.-S.,Systems Biology Research Unit | Campagna A.,Systems Biology Research Unit | Delgado J.,Systems Biology Research Unit | Vanhee P.,Systems Biology Research Unit | And 3 more authors.
Bioinformatics | Year: 2012

Protein interaction networks are widely used to depict the relationships between proteins. These networks often lack the information on physical binary interactions, and they do not inform whether there is incompatibility of structure between binding partners. Here, we introduce Sapin, a framework dedicated to the structural analysis of protein interaction networks. Sapin first identifies the protein parts that could be involved in the interaction and provides template structures. Next, SAPIN performs structural superimpositions to identify compatible and mutually exclusive interactions. Finally, the results are displayed using Cytoscape Web. © 2012 The Author. Source

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