Metal ions modulate the folding and stability of the tumor suppressor protein S100A2

Febs Journal (2009)276:1776-1786

A minimal version of a Protein Tyrosine Phosphatase

Phosphatase and tensin homologue (PTEN) protein belongs to the family of protein tyrosine phos-phatase. Mutations on the phosphatase and tensin homologue (PTEN) protein are highly observed in diverse types of human tumors, being mostly identified on the phosphatase domain of the protein. Although PTEN is a modular protein composed by a phosphatase domain and a C2 domain for mem-brane anchoring, this work aimed at developing a minimal version of PTEN´s phosphatase domain. The minimal version (Small Domain) comprises a 28 residue peptide, with the PTEN 8-mer catalytic peptide accommodated between a α-helix and β-turn as observed in PTEN native structure. Firstly, a de novo prediction of the Small Domain´s secondary structure was carried out by molecular modeling tools. The stability of the predicted structures were then evaluated by Molecular Dynamics. Automated molecular docking of PTEN natural substrate PIP3, its analogue (Inositol) and a PTEN inhibitor (L-tar-tare) were performed with the modeled structure, and PTEN used as a positive control. The gene en-coding for Small Domain was designed and cloned into an expression vector at N-terminal of Green Fluorescence Protein (GFP) encoding gene. The fusion protein was then expressed in Escherichia coli cells. Different expression conditions have been explored for the production of the fusion protein to minimize the formation of inclusion bodies.

UAV - based imagery processing using structure from motion and remote sensing technology

With the recent advances in technology and miniaturization of devices such as GPS or IMU, Unmanned Aerial Vehicles became a feasible platform for a Remote Sensing applications. The use of UAVs compared to the conventional aerial platforms provides a set of advantages such as higher spatial resolution of the derived products. UAV - based imagery obtained by a user grade cameras introduces a set of problems which have to be solved, e. g. rotational or angular differences or unknown or insufficiently precise IO and EO camera parameters. In this work, UAV - based imagery of RGB and CIR type was processed using two different workflows based on PhotoScan and VisualSfM software solutions resulting in the DSM and orthophoto products. Feature detection and matching parameters influence on the result quality as well as a processing time was examined and the optimal parameter setup was presented. Products of the both workflows were compared in terms of a quality and a spatial accuracy. Both workflows were compared by presenting the processing times and quality of the results. Finally, the obtained products were used in order to demonstrate vegetation classification. Contribution of the IHS transformations was examined with respect to the classification accuracy.