Higher-order representations of protein structure space

Fragment-based representations of protein structure have recently been proposed to identify remote homologs with reasonable accuracy. The representations have also been shown through PCA to elucidate low-dimensional maps of protein structure space. In this work we conduct further analysis of these representations, showing that the low-dimensional maps preserve functional co-localization. Moreover, we employ Latent Dirichlet Allocation to investigate a new, topic-based representation. We show through various techniques adapted from text mining that the topics have unique signatures over structural classes and allow a complementary yet informative organization of protein structure space.