Prediction of amyloid aggregation in vivo

Abstract

Many human diseases owe their pathology, to some degree, to the erroneous conversion of proteins from their soluble state into fibrillar, β-structured aggregates, often referred to as amyloid fibrils. Neurodegenerative diseases, such as Alzheimer and spongiform encephalopathies, as well as type 2 diabetes and both localized and systemic amyloid osis, are among the conditions that are associated with the formation of amyloid fibrils. Several mathe matical tools can rationalize and even predict important para meters of amyloid fibril formation. It is not clear, however, whether such algorithms have predictive powers for in vivo systems, in which protein aggregation is affected by the presence of other biological factors. In this review, we briefly describe the existing algorithms and use them to predict the effects of mutations on the aggregation of specific proteins, for which in vivo experimental data are available. The comparison between the theoretical predictions and the experimental data obtained in vivo is shown for each algorithm and experimental data set, and statistically significant correlations are found in most cases.

Amyloidogenic peptides of yeast cell wall glucantransferase Bgl2p as a model for the investigation of its pH-dependent fibril formation

“…Furthermore, a sequence only selected by FoldAmyloid and AGGRESCAN was also selected, because these two programs had earlier demonstrated a high level of correlation between the change in aggregation propensity observed in vivo and the change in aggregation propensity predicted in silico. 17 Our prediction of amyloidogenic determinants in the Bgl2p sequence correlated well with the data obtained on the fibrillation ability of the peptides (Tables 1 and 2; Fig. 2).…”

“…Several potential amyloidogenic determinants (PADs) were predicted at least by four or even by five methods out of six. The predicted PADs were TALFFTAS (aa [12][13][14][15][16][17][18][19], FTIFVGV (aa 83-89), NAFS (aa 190-193) and GVNVIVFEA (aa 268-276). The rest of the protein sequence was presented by areas, which none of the programs used predicted as potentially amyloid ones, as well as by those that were predicted to be potentially amyloid, but by less than four of the programs.…”

“…It should be noted that upon comparison of predicted aggregation propensity results by different methods and experimental data obtained in vivo the programs FoldAmyloid and AGGRESCAN demonstrated better results than in the case of the TANGO, PASTA and WALTZ algorithms. 17 To verify whether the predicted PADs of Bgl2p really had the pronounced propensity to form amyloid structures, we synthesized PADcontaining peptides with a length of 10 aa.…”

“…17 Also it was suggested that the key elements of the fibril formation may be common to different proteins and simple model systems could help to clarify many general aspects of this process. 18 In the present work, the Bgl2p fibrillation process was investigated more thoroughly using Bgl2p synthetic peptides as model systems.…”

Amyloids or prions? That is the question

“…8,9 The short stretch model led to the development of over 20 algorithms that more or less successfully predict protein aggregation and amyloid formation based on the identification of specific b-aggregation and amyloid-prone regions in the polypeptide sequences. [10][11][12] In disease-associated amyloids these regions are generally between 5 and 10 residues in length. 13 Prions are considered a subclass of amyloids in which protein aggregation becomes self-perpetuating and infectious.…”