Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

Making and breaking carbon-carbon bonds in class C radical SAM methyltransferases

Radical S-adenosylmethionine (SAM) enzymes utilize a [4Fe-4S]¹⁺ cluster and S-(5'-adenosyl)-L-methionine, (SAM), to generate a highly reactive radical and catalyze what is arguably the most diverse set of chemical reactions for any known enzyme family. At the heart of radical SAM catalysis is a highly reactive 5'-deoxyadenosyl radical intermediate (5'-dAdo●) generated through reductive cleavage of SAM or nucleophilic attack of the unique iron of the [4Fe-4S]⁺ cluster on the 5' C atom of SAM. Spectroscopic studies reveal the 5'-dAdo● is transiently captured in an FeC bond (Ω species). In the presence of substrate, homolytic scission of this metal‑carbon bond regenerates the 5'-dAdo● for catalytic hydrogen atom abstraction. While reminiscent of the adenosylcobalamin mechanism, radical SAM enzymes appear to encompass greater catalytic diversity. In this review we discuss recent developments for radical SAM enzymes involved in unique chemical rearrangements, specifically regarding class C radical SAM methyltransferases. Illuminating this class of radical SAM enzymes is especially significant as many enzymes have been shown to play critical roles in pathogenesis and the synthesis of novel antimicrobial compounds.