Similarity based enzymatic retrosynthesis

Karthik Sankaranarayanan; Esther Heid; Connor W Coley; Deeptak Verma; William H Green; Klavs F Jensen

doi:10.1039/d2sc01588a

Similarity based enzymatic retrosynthesis

Chem Sci. 2022 Apr 26;13(20):6039-6053. doi: 10.1039/d2sc01588a. eCollection 2022 May 25.

Authors

Karthik Sankaranarayanan¹, Esther Heid^{1

2}, Connor W Coley¹, Deeptak Verma³, William H Green¹, Klavs F Jensen¹

Affiliations

¹ Department of Chemical Engineering, Massachusetts Institute of Technology 77 Massachusetts Avenue Cambridge Massachusetts 02139 USA kfjensen@mit.edu.
² Institute of Materials Chemistry, TU Wien 1060 Vienna Austria.
³ Computational and Structural Chemistry, Discovery Chemistry, Merck & Co., Inc. Kenilworth NJ 07033 USA.

Abstract

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 ∼5500 reactions from RHEA as a knowledge base, the recorded reactants to the product are among the top 10 proposed suggestions in 71% of ∼700 test reactions. Second, we trained a statistical model capable of discriminating between reaction pairs belonging to homologous enzymes and evolutionarily distant enzymes using ∼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.