AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides

Abstract

Background: Protein aggregation correlates with the development of several debilitating human disorders of growing incidence, such as Alzheimer's and Parkinson's diseases. On the biotechnological side, protein production is often hampered by the accumulation of recombinant proteins into aggregates. Thus, the development of methods to anticipate the aggregation properties of polypeptides is receiving increasing attention. AGGRESCAN is a web-based software for the prediction of aggregation-prone segments in protein sequences, the analysis of the effect of mutations on protein aggregation propensities and the comparison of the aggregation properties of different proteins or protein sets.

Results: AGGRESCAN is based on an aggregation-propensity scale for natural amino acids derived from in vivo experiments and on the assumption that short and specific sequence stretches modulate protein aggregation. The algorithm is shown to identify a series of protein fragments involved in the aggregation of disease-related proteins and to predict the effect of genetic mutations on their deposition propensities. It also provides new insights into the differential aggregation properties displayed by globular proteins, natively unfolded polypeptides, amyloidogenic proteins and proteins found in bacterial inclusion bodies.

Conclusion: By identifying aggregation-prone segments in proteins, AGGRESCAN http://bioinf.uab.es/aggrescan/ shall facilitate (i) the identification of possible therapeutic targets for anti-depositional strategies in conformational diseases and (ii) the anticipation of aggregation phenomena during storage or recombinant production of bioactive polypeptides or polypeptide sets.

Figure 1

Hot spot area graphics. Hot spot area plots for a) lung surfactant protein C, b) serum amyloid A protein and c) Tau protein.

Figure 2

Comparative prediction performance of AGGRESCAN and structure-based methods. Comparative predictions of AGGRESCAN (solid circles), packing density profile [31] (no symbols), 3D Profile [32] using the NNQQNY template (solid squares) and 3D Profile using an ensemble of templates (empty squares). Predictions were tested in a Database of Fibril Formers and Non-Formers hexa-peptides. Predictions are shown as receiver-operator characteristic curves.

Figure 3

Changes in the hot spot area plot caused by point mutations in amyloidogenic proteins. a) Aβ42 wild type (red) and Aβ42 F19T mutant (green). b) SH3 wild type (red), SH3 D48G (green) and SH3 N47G (blue). c) TAU wild type (red), TAU P301L (green) and TAU S320F (blue).

Figure 4

"Hot spots" distribution in different protein groups. Distribution of the number of "hot spots" relative to sequence length in the following protein datasets: natively globular proteins, intrinsically unstructured proteins, amyloidogenic proteins, soluble proteins when overexpressed in bacteria and proteins forming inclusion bodies when overexpressed in bacteria.

Figure 5

Modulation of hot spot nucleation specificity by global aggregation propensity. The black solid line represents a standard amyloidogenic protein aggregation profile, with only one "hot spot" and low global aggregation propensity. The pink discontinuous line corresponds to a typical aggregation profile from an inclusion-body-forming protein, with many "hot spots" and high global aggregation propensity. The horizontal lines represent the aggregation-propensity average thresholds for each sequence. The coloured regions indicate the area of each "hot spot" over the aggregation propensity threshold. It is proposed that a higher area over the threshold promotes a more specific aggregation reaction, resulting in highly ordered deposits.