Protein Organization with Manifold Exploration and Spectral Clustering

Abstract

We present a method to provide a biologically meaningful representation of the space of protein sequences. While billions of protein sequences are available, organizing this vast amount of information into functional categories is daunting, time-consuming and incomplete. We present our unsupervised approach that combines Transformer protein language models, UMAP graphs, and spectral clustering to create meaningful clusters in the protein spaces. To demonstrate the meaningfulness of the clusters, we show that they preserve most of the signal present in a dataset of manually curated enzyme protein families.