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.
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Acknowldgements
This work was supported by grants BFU2010-14901 from Ministerio de Ciencia e Innovación (Spain) and 2009-SGR 760 from AGAUR (Generalitat de Catalunya). SV has been granted an ICREA ACADEMIA award (ICREA).
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de Groot, N.S., Castillo, V., Graña-Montes, R., Ventura, S. (2012). AGGRESCAN: Method, Application, and Perspectives for Drug Design. In: Baron, R. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 819. Springer, New York, NY. https://doi.org/10.1007/978-1-61779-465-0_14
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