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Similarity based enzymatic retrosynthesis

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 no...

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Detalles Bibliográficos
Autores principales: Sankaranarayanan, Karthik, Heid, Esther, Coley, Connor W., Verma, Deeptak, Green, William H., Jensen, Klavs F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society of Chemistry 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132021/
https://www.ncbi.nlm.nih.gov/pubmed/35685792
http://dx.doi.org/10.1039/d2sc01588a
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author Sankaranarayanan, Karthik
Heid, Esther
Coley, Connor W.
Verma, Deeptak
Green, William H.
Jensen, Klavs F.
author_facet Sankaranarayanan, Karthik
Heid, Esther
Coley, Connor W.
Verma, Deeptak
Green, William H.
Jensen, Klavs F.
author_sort Sankaranarayanan, Karthik
collection PubMed
description 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.
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spelling pubmed-91320212022-06-08 Similarity based enzymatic retrosynthesis Sankaranarayanan, Karthik Heid, Esther Coley, Connor W. Verma, Deeptak Green, William H. Jensen, Klavs F. Chem Sci Chemistry 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. The Royal Society of Chemistry 2022-04-26 /pmc/articles/PMC9132021/ /pubmed/35685792 http://dx.doi.org/10.1039/d2sc01588a Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/
spellingShingle Chemistry
Sankaranarayanan, Karthik
Heid, Esther
Coley, Connor W.
Verma, Deeptak
Green, William H.
Jensen, Klavs F.
Similarity based enzymatic retrosynthesis
title Similarity based enzymatic retrosynthesis
title_full Similarity based enzymatic retrosynthesis
title_fullStr Similarity based enzymatic retrosynthesis
title_full_unstemmed Similarity based enzymatic retrosynthesis
title_short Similarity based enzymatic retrosynthesis
title_sort similarity based enzymatic retrosynthesis
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9132021/
https://www.ncbi.nlm.nih.gov/pubmed/35685792
http://dx.doi.org/10.1039/d2sc01588a
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