Accurate Models of Substrate Preferences of Post-Translational Modification Enzymes from a Combination of mRNA Display and Deep Learning

Promiscuous post-translational
modification (PTM) enzymes often
display nonobvious substrate preferences by acting on diverse yet
well-defined sets of peptides and/or proteins. Understanding of substrate
fitness landscapes for PTM enzymes is important in many areas of contemporary
science, including natural product biosynthesis, molecular biology,
and biotechnology. Here, we report an integrated platform for accurate
profiling of substrate preferences for PTM enzymes. The platform features
(i) a combination of mRNA display with next-generation sequencing
as an ultrahigh throughput technique for data acquisition and (ii)
deep learning for data analysis. The high accuracy (>0.99 in each
of two studies) of the resulting deep learning models enables comprehensive
analysis of enzymatic substrate preferences. The models can quantify
fitness across sequence space, map modification sites, and identify
important amino acids in the substrate. To benchmark the platform,
we performed profiling of a Ser dehydratase (LazBF) and a Cys/Ser
cyclodehydratase (LazDEF), two enzymes from the lactazole biosynthesis
pathway. In both studies, our results point to complex enzymatic preferences,
which, particularly for LazBF, cannot be reduced to a set of simple
rules. The ability of the constructed models to dissect such complexity
suggests that the developed platform can facilitate a wider study
of PTM enzymes.