REFinder: Mining new enzymes from metagenomes

Abstract

Microbial metagenomes encode vast catalytic diversity, but recovering enzymes of novel, yet-undescribed functionality typically requires whole-community assembly plus a means of identifying active proteins ab initio. The first step of the task is compute-intensive and misses low-abundance sequences. The second is complicated by our inability to predict novel functions. REBEAN, our DNA language model, sidesteps the latter by assigning each sequencing read a high-level Enzyme Commission (EC) class or a non-enzyme label without alignment. Here we build REFinder, a pipeline that addresses both steps by routing REBEAN-annotated reads to assemble only the putative enzymatic reads that have identified catalytic signatures. In our evaluation of 50 microbiome metagenomes, REFinder identified 1.2 to 2.1 fold more enzymes than could be recovered via homology-based annotation of the proteins from the corresponding full assemblies. Moreover, it was as much as 6.4-fold cheaper computationally than full assembly. Across all samples, REFinder identified at least three fourths and as many as 90% of the homology-accessible enzymes identified via full assembly of the complete metagenomes. Notably, a fraction of these, 22% to 45% per EC class, carried no similarity to Swiss-Prot proteins, i.e. a set of enzymes homology cannot annotate. For roughly two fifths of the over thirteen thousand such novel oxidoreductases from two saliva samples, ESMFold predicted structures aligned with TM-score[≥]0.7 to a characterized enzyme structure in the PDB - a substantial structural similarity without sequence homology. These results illustrate that targeted, alignment-free assembly turns even well-mapped microbiomes into a source of thousands of previously invisible but credible novel enzymes, raising our expectations for exploration of environmental microbiomes.