PASTA2 Predicts Amyloid STructural Aggregation with
enhanced functionality and sequence based properties.
It predicts the most aggregation-prone portions and the corresponding β-strand
inter-molecular pairing for multiple input sequences. In addition, it predicts
intrinsic protein disorder
and secondary structure efficiently allowing analyses of complementary sequence properties.
The PASTA energy threshold was recalibrated on a larger set of peptides and is given as a function of sensitivity and specificity.
Finally, an option to perform intra-protein aggregation (i.e. protein-protein aggregation) is possible.
Large scale analysis: PASTA2 allows a large scale analysis on many proteins. However,
if this grows very large it is wise to turn on the large-scale option which will remove graphics and increase efficiency.
PASTA 2.0 implements a statistical energy function in order to determine fibril formation. For a detailed description of the potential one should
read the methods paper. Machine learning is used to predict the secondary structure and intrinsic disorder.
Graphs were generated with the R package. In addition, the server is executed on a state-of-art-cluster where many sequences are processed at once using
the Sun Grid Engine system.