Computational protocol: Effect of Signal Peptide on Stability and Folding of Escherichia coli Thioredoxin

Similar protocols

Protocol publication

[…] Aggregation propensity profiles for various signal sequences were calculated using three different servers, namely Zyggregator , PASTA , AGGRESCAN . The Zyggregator server outputs the aggregation propensity score, Zagg for every amino acid in the query protein sequence. All the calculations were done at pH 7.4. A stretch of amino acid sequence having Zagg>1 is considered to have high aggregation propensity. The region with Zagg<0 is considered to have low propensity to aggregate. The PASTA server gives the per-residue aggregation probability, h(k).The regions with high h(k) values are considered to be involved in intermolecular pairing, resulting in aggregation. The amyloidogenic regions of the human amyloid β-peptide (Aβ-40) possess h(k) values in the range of 0.05–0.06. AGGRESCAN predicts hot-spots of aggregation in the query amino acid sequence based on a propensity scale derived from in vivo aggregation experiments. The amino acids with propensity values greater than −0.02 are considered as hot-spots of aggregation. Window width of 5 was used for these calculations. In addition, average hydrophobicity of the query amino acids, as a possible probe for aggregation, was also calculated using the program PREDBUR . The width of the sliding window was set to 7. All the four algorithms were applied to the pelB and malE signal sequences. To assess the prediction accuracies of Zagg, a control set of phoA, treA, and pcoE signal peptides, previously studied as soluble Trx fusion systems, was also used. […]

Pipeline specifications

Software tools Zyggregator, PASTA, AGGRESCAN
Application Protein physicochemical analysis
Organisms Escherichia coli