The amyloid conformation can be adopted by a variety of sequences, but the precise boundaries of amyloid sequence space are still unclear. The currently charted amyloid sequence space is strongly biased towards hydrophobic, beta-sheet prone sequences that form the core of globular proteins and by Q/N/Y rich yeast prions. Here, we took advantage of the increasing amount of high-resolution structural information on amyloid cores currently available in the protein databank to implement a machine learning approach, named Cordax (https://cordax.switchlab.org), that explores amyloid sequence beyond its current boundaries. Clustering by t-Distributed Stochastic Neighbour Embedding (t-SNE) shows how our approach resulted in an expansion away from hydrophobic amyloid sequences towards clusters of lower aliphatic content and higher charge, or regions of helical and disordered propensities. These clusters uncouple amyloid propensity from solubility representing sequence flavours compatible with surface-exposed patches in globular proteins, functional amyloids or sequences associated to liquid-liquid phase transitions.

淀粉样蛋白构象可以被多种序列采用，但是淀粉样蛋白序列空间的精确边界仍然不清楚。当前绘制的淀粉样蛋白序列空间强烈偏向疏水性，易形成β-折叠的序列，这些序列形成球状蛋白的核心，并富含Q / N / Y酵母病毒。在这里，我们利用蛋白质数据库中当前可用的有关淀粉样蛋白核心的高分辨率结构信息的增加，来实现一种名为Cordax（https://cordax.switchlab.org）的机器学习方法，该方法探索了除目前的边界。通过t分布随机邻居嵌入（t-SNE）进行的聚类表明，我们的方法是如何从疏水性淀粉样蛋白序列扩展为脂肪族含量较低和电荷较高的簇的，或螺旋和无序倾向区域。这些簇使淀粉样蛋白的倾向与溶解度脱钩，溶解度代表与球形蛋白，功能淀粉样蛋白或与液-液相转变相关的序列的表面暴露斑块兼容的序列风味。