Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity

The enzymatic depolymerization
of poly(ethylene terephthalate)
(PET) is emerging as a leading chemical recycling technology for waste
polyester. As part of this endeavor, new candidate enzymes identified
from natural diversity can serve as useful starting points for enzyme
evolution and engineering. In this study, we improved upon HMM searches
by applying an iterative machine learning strategy to identify 400
putative PET-degrading enzymes (PET hydrolases) from naturally occurring
homologs. Using high-throughput (HTP) experimental techniques, we
successfully expressed and purified >200 enzyme candidates and
assayed
them for PET hydrolysis activity as a function of pH, temperature,
and substrate crystallinity. From this library, we discovered 91 previously
unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline
material, which are conditions relevant to developing more efficient
commercial processes. Notably, four enzymes showed equal to or higher
activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging
condition in our screening assay, and 11 of which have pH optima <7.
Using these data, we identified regions of PETases statistically correlated
to activity at lower pH. We additionally investigated the effect of
condition-specific activity data on trained machine learning predictors
and found a precision (putative hit rate) improvement of up to 30%
compared to a Hidden Markov Model alone. Our findings show that by
pointing enzyme discovery toward conditions of interest with multiple
rounds of experimental and machine learning, we can discover large
sets of active enzymes and explore factors associated with activity
at those conditions.