Biocatalysis has proven its value in enabling new sustainable synthetic routes for complex chemistry, first and foremost for API manufacturing but also in the context of industrial chemicals. Its widespread use, however, has been hampered by two distinct challenges. The first one is our ability to rapidly and reliably identify and improve the required enzymes. To this end, Arzeda has been developing high-throughput computational methodologies to design <i>de novo</i> activity or exploit natural enzyme latent promiscuity to rapidly design new biocatalysts; these catalysts are further improved when needed using traditional directed evolution techniques or with Machine Learning approaches that Arzeda has been developing. Successful academic and industrial applications of this methodology will be presented.<br/> <br/>The second challenge is that of finding optimal ways to arrange natural and designed enzymes to biosynthetize a small-molecule of interest. To illustrate our progress towards solving this challenge, we will discuss a dedicated software (Scylax™) that Arzeda has been developing for the automated design of novel biosynthetic pathways. Inspired by retrosynthetic methods in organic chemistry, Scylax™ draws on databases of known natural enzymatic reactions as well as reactions that can be catalyzed by computationally designed enzymes to exhaustively enumerate biosynthetic routes from a set of desired metabolites down to any small molecule of interest. Such biosynthetic routes can then be implemented in bioreactor or in the context of designer fermentation strains (designer cell factories).

Computational pathway and enzyme design for synthetic biochemistry

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.