Computational pipeline for sustainable enzyme discovery through (re)use of metagenomic data

Abstract

Enzymes derived from extremophilic organisms, also known as extremozymes, offer sustainable and efficient solutions for industrial applications. Valued for their resilience and low environmental impact, extremozymes have found use as catalysts in various processes, ranging from dairy production to pharmaceutical manufacturing. However, discovery of novel extremozymes is often hindered by challenges such as culturing difficulties, underrepresentation of extreme environments in reference databases, and limitations of traditional sequence-based screening methods. In this work, we present a computational pipeline designed to discover novel enzymes from metagenomic data derived from extreme environments. This pipeline represents a versatile and sustainable approach that promotes reuse and recycling of existing datasets and minimises the need for additional environmental sampling. In its core, the algorithm integrates both traditional bioinformatic techniques and recent advances in structural prediction, enabling rapid and accurate identification of enzymes. However, due to its design, the algorithm relies heavily on existing databases, which can limit its effectiveness in situations where reference data is scarce or when encountering novel protein families. As a proof-of-concept, we applied the pipeline to metagenomic data from deep-sea hydrothermal vents, with a focus on β-galactosidases. The pipeline identified 11 potential candidate proteins, out of which 10 showed in vitro activity. One of the selected enzymes, βGal_UW07, showed strong potential for industrial applications. The enzyme exhibited optimal activity at 70 °C and was exceptionally resistant to high pH and the presence of metal ions and reducing agents. Overall, our results indicate that the pipeline is highly accurate and can play a key role in sustainable bioprospecting, leveraging existing metagenomic datasets and minimising in situ interventions in pristine regions.