Computer-aided synthesis planning and molecular design for molecules made with enzymes
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levin-itail-phd-be-2024-thesis_updated.pdf
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Author(s)
Levin, Itai
Advisor(s)
Voigt, Christopher A.
Coley, Connor W.
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
Abstract
Enzyme-catalyzed chemistry is unique. As catalysts, enzymes are selective, biodegradable, evolvable, and often function under mild conditions. Further, the possibility of genetically encoding enzymes and producing them in living organisms makes them wholly distinct from other chemical catalysts. This thesis presents algorithms to automate planning synthetic routes with enzymatic and nonenzymatic chemistry, scoring proposed syntheses based on the space of chemicals they lead to, and identifying valuable molecules that can be synthesized in cells, applied to the design of reporter molecules for remote readout of living biosensors.
We report a retrosynthetic search algorithm using two neural network models for retrosynthesis–one covering 7,984 enzymatic transformations and one 163,723 synthetic transformations– that balances the exploration of enzymatic and synthetic reactions to identify hybrid synthesis plans. This approach extends the space of retrosynthetic moves by thousands of uniquely enzymatic one-step transformations, discovers routes to molecules for which synthetic or enzymatic searches find none, and designs shorter routes for others.
We demonstrate how we can estimate route “diversifiability” and use it as a criterion during route selection. We illustrate how the chemical space of synthetically accessible analogs is influenced by properties of alternative starting materials or constraints on their cost. We integrate these analyses with a synthesizability-constrained hit expansion workflow in a virtual screening pipeline for focused library expansion around putative hits to support molecular optimization.
Finally, we introduce “hyperspectral reporters” (HSRs) designed for hyperspectral imaging (HSI) cameras that are commonly mounted on unmanned aerial vehicles (UAVs) and satellites. The HSR gene(s) encode enzymes that make a molecule with a unique spectral signature. Candidates were identified by quantum mechanical simulations of 20,170 metabolites. These calculations led to putative HSRs, from which biliverdin IXα and bacteriochlorophyll a were selected. These were connected to small molecule sensors in soil (Pseudomonas putida) and aquatic (Rubrivivax gelatinosus) bacteria. These bacteria could be seen outdoors under ambient light from up to 90 m in a single image covering 4000 m² (1 acre), taken in 0.4 min. HSRs could be applied to study large-scale problems in basic biology and ecology and enable applications spanning agriculture, environmental monitoring, forensics, and defense.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
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