de Groot Natalia S, Castillo Virginia, Graña-Montes Ricardo, Ventura Salvador
Department de Bioquímica i Biologia Molecular and Institut de Biotecnologia i de Biomedicina, Universitat Autónoma de Barcelona, Barcelona, Spain.
Methods Mol Biol. 2012;819:199-220. doi: 10.1007/978-1-61779-465-0_14.
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.
蛋白质聚集是越来越多发病率不断上升的构象性人类疾病（如阿尔茨海默病和帕金森病）发展的基础。此外，重组蛋白作为细胞内聚集体的积累是多肽生物技术生产的一个关键障碍。而且，有序的蛋白质聚集体构成了新型且多功能的纳米生物材料。因此，人们越来越关注开发能够预测多肽聚集特性以调节其固有溶解度的方法。在此背景下，我们开发了AGGRESCAN，这是一种简单快速的算法，可预测蛋白质序列中易于聚集的片段，比较不同蛋白质或蛋白质组的聚集特性，并分析突变对蛋白质聚集倾向的影响。
