Journal
CHEMICAL SCIENCE
Volume 13, Issue 20, Pages 6039-6053Publisher
ROYAL SOC CHEMISTRY
DOI: 10.1039/d2sc01588a
Keywords
-
Categories
Funding
- DARPA PANACEA program [HR0011-19-2-0022]
- consortium for Machine Learning in Pharmaceutical Discovery and Synthesis (MLPDS)
- Austrian Science Fund (FWF) [J-4415]
Ask authors/readers for more resources
Enzymatic synthesis of natural product analogs is facilitated by a single-step retrosynthesis search algorithm based on molecular similarity. The algorithm extracts and applies enzymatic reaction templates and uses a statistical model to predict evolution success. Successful enzymatic synthesis routes for active pharmaceutical ingredients and commodity chemicals are planned retrospectively using this approach.
Enzymes synthesize complex natural products effortlessly by catalyzing chemo-, regio-, and enantio-selective transformations. Further, biocatalytic processes are increasingly replacing conventional organic synthesis steps because they use mild solvents, avoid the use of metals, and reduce overall non-biodegradable waste. Here, we present a single-step retrosynthesis search algorithm to facilitate enzymatic synthesis of natural product analogs. First, we develop a tool, RDEnzyme, capable of extracting and applying stereochemically consistent enzymatic reaction templates, i.e., subgraph patterns that describe the changes in connectivity between a product molecule and its corresponding reactant(s). Using RDEnzyme, we demonstrate that molecular similarity is an effective metric to propose retrosynthetic disconnections based on analogy to precedent enzymatic reactions in UniProt/RHEA. Using similar to 5500 reactions from RHEA as a knowledge base, the recorded reactants to the product are among the top 10 proposed suggestions in 71% of similar to 700 test reactions. Second, we trained a statistical model capable of discriminating between reaction pairs belonging to homologous enzymes and evolutionarily distant enzymes using similar to 30 000 reaction pairs from SwissProt as a knowledge base. This model is capable of understanding patterns in enzyme promiscuity to evaluate the likelihood of experimental evolution success. By recursively applying the similarity-based single-step retrosynthesis and evolution prediction workflow, we successfully plan the enzymatic synthesis routes for both active pharmaceutical ingredients (e.g. Islatravir, Molnupiravir) and commodity chemicals (e.g. 1,4-butanediol, branched-chain higher alcohols/biofuels), in a retrospective fashion. Through the development and demonstration of the single-step enzymatic retrosynthesis strategy using natural transformations, our approach provides a first step towards solving the challenging problem of incorporating both enzyme- and organic-chemistry based transformations into a computer aided synthesis planning workflow.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available