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.

Protein misfolding and deposition underlie an increasing number of debilitating human disorders. We have shown that model proteins unrelated to disease, such as the Src homology 3 (SH3) domain of the p58α subunit of bovine phosphatidyl-inositol-3′-kinase (PI3-SH3), can be converted in vitro into assemblies with structural and cytotoxic properties similar to those of pathological aggregates. By contrast, homologous proteins, such as α-spectrin-SH3, lack the capability of forming amyloid fibrils at a measurable rate under any of the conditions we have so far examined. However, transplanting a small sequence stretch (6 aa) from PI3-SH3 to α-spectrin-SH3, comprising residues of the diverging turn and adjacent RT loop, creates an amyloidogenic protein closely similar in its behavior to the original PI3-SH3. Analysis of specific PI3-SH3 mutants further confirms the involvement of this region in conferring amyloidogenic properties to this domain. Moreover, the inclusion in this stretch of two consensus residues favored in SH3 sequences substantially inhibits aggregation. These findings show that short specific amino acid stretches can act as mediators or facilitators in the incorporation of globular proteins into amyloid structures, and they support the suggestion that natural protein sequences have evolved in part to code for structural characteristics other than those included in the native fold, such as avoidance of aggregation.

Background: The polypeptides involved in amyloidogenesis may be globular proteins with a defined 3D-structure or natively unfolded proteins. The first class includes polypeptides such as β2-microglobulin, lysozyme, transthyretin or the prion protein, whereas β-amyloid peptide, amylin or α-synuclein all belong to the second class. Recent studies suggest that specific regions in the proteins act as "hot spots" driving aggregation. This should be especially relevant for natively unfolded proteins or unfolded states of globular proteins as they lack significant secondary and tertiary structure and specific intra-chain interactions that can mask these aggregation-prone regions. Prediction of such sequence stretches is important since they are potential therapeutic targets. Results: In this study we exploited the experimental data obtained in an in vivo system using β-amyloid peptide as a model to derive the individual aggregation propensities of natural amino acids. These data are used to generate aggregation profiles for different disease-related polypeptides. The approach detects the presence of "hot spots" which have been already validated experimentally in the literature and provides insights into the effect of disease-linked mutations in these polypeptides. Conclusion: The proposed method might become a useful tool for the future development of sequence-targeted anti-aggregation pharmaceuticals.