PASTA 2.0: an improved server for protein aggregation prediction

Abstract

The formation of amyloid aggregates upon protein misfolding is related to several devastating degenerative diseases. The propensities of different protein sequences to aggregate into amyloids, how they are enhanced by pathogenic mutations, the presence of aggregation hot spots stabilizing pathological interactions, the establishing of cross-amyloid interactions between co-aggregating proteins, all rely at the molecular level on the stability of the amyloid cross-beta structure. Our redesigned server, PASTA 2.0, provides a versatile platform where all of these different features can be easily predicted on a genomic scale given input sequences. The server provides other pieces of information, such as intrinsic disorder and secondary structure predictions, that complement the aggregation data. The PASTA 2.0 energy function evaluates the stability of putative cross-beta pairings between different sequence stretches. It was re-derived on a larger dataset of globular protein domains. The resulting algorithm was benchmarked on comprehensive peptide and protein test sets, leading to improved, state-of-the-art results with more amyloid forming regions correctly detected at high specificity. The PASTA 2.0 server can be accessed at http://protein.bio.unipd.it/pasta2/.

Functional amyloids in Streptococcus mutans, their use as targets of biofilm inhibition and initial characterization of SMU_63c

“…1c). Because an S. mutans sortase-deficient mutant is defective in amyloid formation [19], an additional sortase substrate, GbpC, which was not identified in our initial screen but demonstrated high amyloid prediction scores by computational analyses [34,35], was also evaluated in these experiments. Unlike the proteins listed above, for which little structural information is available at present, the complete tertiary structure of P1 has been modelled based on crystal structures of several partial polypeptides [36][37][38].…”

Transmission of Tau Pathology from Human to Rodent Brain: How to Humanise Animal Models for Alzheimer’s Disease Research

“…These regions are particularly enriched with valine (V), isoleucine (I), alanine (A) and serine (S). Aggregation of tau protein into PHF is driven by its transition to β sheet structure [59]. Based on "PASTA 2.0", an advanced algorithm for prediction of amyloid-like structural aggregation depending on aggregation-prone regions (http://protein.bio.unipd.it/pasta2/) [60], we observed that tau protein (2N4R; longest tau isoform, 441aa) has 17.69% of sequences with potential for beta-strand formation ( 311 (based on single letter amino acid code) in the third MTBR of tau is identical to a segment globally classified as having high beta-sheet aggregation propensity [58].…”

Prediction of amyloidogenicity based on the n-gram analysis

“…To compare the performance of AmyloGram and other predictors of amyloids, we used external data set pep424 (Walsh et al, 2014). Since some peptides were common for both pep424 and AmyLoad, we removed them from the training data set.…”

“…Several computational approaches have been proposed to model and predict both kinds of regions. Physics-and chemistry-based models used in FoldAmyloid (Garbuzynskiy et al, 2010) and PASTA2 (Walsh et al, 2014) utilize the density of the protein contact sites. Statistical approaches include production of frequency profiles, such as the WALTZ method (Maurer-Stroh et al, 2010) and machine learning methods, for example those developed in our group (Gasior and Kotulska, 2014) and a novel predictor APPNN based on neural networks (Família et al, 2015).…”