Combinatorial assembly and design of enzymes

The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.

Automated summary

An atomistic and machine learning strategy called CADENZ was used to design enzyme fragments that combine to form diverse, low-energy structures with stable catalytic configurations. This strategy was applied to endoxylanases and resulted in the recovery of thousands of structurally diverse enzymes using activity-based protein profiling. Functional designs showed high active-site preorganization and more stable and compact packing outside the active site. Implementing these findings into CADENZ led to a 10-fold improved hit rate and over 10,000 recovered enzymes. This design-test-learn loop can be applied to any modular protein family, providing vast diversity and general principles for protein design.