Author:

Keywords:

Algorithms, Amino Acid Sequence, Amyloid beta-Peptides, Computational Biology, Databases, Protein, Drug Design, Humans, Molecular Sequence Data, Mutation, Peptides, Prealbumin, Protein Structure, Quaternary, Proteins, 0399 Other Chemical Sciences, 0601 Biochemistry and Cell Biology, Developmental Biology, 3101 Biochemistry and cell biology, 3404 Medicinal and biomolecular chemistry

Abstract:

Protein aggregation underlies the development of an increasing number of conformational human diseases of growing incidence, such as Alzheimer's and Parkinson's diseases. Furthermore, the accumulation of recombinant proteins as intracellular aggregates represents a critical obstacle for the biotechnological production of polypeptides. Also, ordered protein aggregates constitute novel and versatile nanobiomaterials. Consequently, there is an increasing interest in the development of methods able to forecast the aggregation properties of polypeptides in order to modulate their intrinsic solubility. In this context, we have developed AGGRESCAN, a simple and fast algorithm that predicts aggregation-prone segments in protein sequences, compares the aggregation properties of different proteins or protein sets and analyses the effect of mutations on protein aggregation propensities.