posted on 2024-02-15, 13:09authored byAnders Lønstrup Hansen, Frederik Friis Theisen, Ramon Crehuet, Enrique Marcos, Nushin Aghajari, Martin Willemoës
Enzymes
are indispensable biocatalysts for numerous industrial
applications, yet stability, selectivity, and restricted substrate
recognition present limitations for their use. Despite the importance
of enzyme engineering in overcoming these limitations, success is
often challenged by the intricate architecture of enzymes derived
from natural sources. Recent advances in computational methods have
enabled the de novo design of simplified scaffolds with specific functional
sites. Such scaffolds may be advantageous as platforms for enzyme
engineering. Here, we present a strategy for the de novo design of
a simplified scaffold of an endo-α-N-acetylgalactosaminidase
active site, a glycoside hydrolase from the GH101 enzyme family. Using
a combination of trRosetta hallucination, iterative cycles of deep-learning-based
structure prediction, and ProteinMPNN sequence design, we designed
proteins with 290 amino acids incorporating the active site while
reducing the molecular weight by over 100 kDa compared to the initial
endo-α-N-acetylgalactosaminidase. Of 11 tested
designs, six were expressed as soluble monomers, displaying similar
or increased thermostabilities compared to the natural enzyme. Despite
lacking detectable enzymatic activity, the experimentally determined
crystal structures of a representative design closely matched the
design with a root-mean-square deviation of 1.0 Å, with most
catalytically important side chains within 2.0 Å. The results
highlight the potential of scaffold hallucination in designing proteins
that may serve as a foundation for subsequent enzyme engineering.