Computational planning and experimental implementation of enzyme catalyzed routes towards small molecules

Biocatalysis uses enzymes to catalyze chemical transformations to yield a desired small molecule or natural product, e.g., agrochemicals, commodity chemicals and pharmaceutical agents. Enzymes catalyze stereo-, regio-, and enatio- specific reactions; as a result, biocatalysis enables more efficient, shorter, synthetic routes with less need for protection and deprotection reactions. Because of the many advantages of employing enzymes, biocatalysis has been synergistically used with organic chemistry- the workhorse of the modern chemical manufacturing industry- to produce small molecules. However, organic chemists’ educational experience has only a small overlap with that of enzymologists, making it challenging for some synthetic chemists to tap into benefits at the interface between organic- and enzyme chemistry. To support these chemists, Computer-Aided Synthesis Planning (CASP) tools employ retrosynthesis techniques to propose feasible synthetic routes to a target from available starting materials by starting with the target and choosing appropriate disconnections recursively. The methods predict enzymatic and chemo-enzymatic routes to a desired target using rule-based methods and machine learning to generalize known reactions. Notwithstanding these impressive advances, there remains a strong need for a suite of user-friendly enzymatic synthesis planning tools to produce realistic synthetic routes towards a target molecule. Further, these tools need to be tested experimentally to plan routes to medicinally relevant targets. Some relevant publications on this topic include:

• Sankaranarayanan, K. & Jensen, K. F. Computer-assisted multistep chemoenzymatic retrosynthesis using a chemical synthesis planner. Chemical Science 14, 6467–6475 (2023).

• Sankaranarayanan K., Heid E., Coley C.W., Verma D., Green W.H., Jensen K.F. Similarity based enzymatic retrosynthesis. Chemical Science 13, 6039–6053 (2022).

• Sankaranarayanan K.*, Antaris X.X.*, Palanski B.A., Gamal A.E., Kao C.M., Fitch W.L., Fischer C.R., Khosla C. Tunable enzymatic synthesis of the immunomodulator Lipid IVA to enable structure-activity analysis. J. Am. Chem. Soc. 141, 9474–9478 (2019) 

National Institute of Health Project

Enzymes enable the synthesis of complex pharmaceutical agents because they catalyze selective chemical reactions under mild conditions. We will develop and test computational tools capable of proposing synthesis routes (or “recipes”) that primarily use enzyme catalysis to make complex pharmaceutical agents. These computational tools would identify ways to cost-efficiently manufacture complex pharmaceutical agents and would improve our ability to synthesize a diverse library of complex chemical compounds for drug discovery.  

The structural and stereochemical complexity of natural product analogs and some synthetic small molecules can complicate the development of practical organic synthesis routes, leaving enzymatic, or ‘biocatalytic’, routes as the primary source for drug discovery and production. Proposing these enzymatic routes requires expert intuition and experience, but it is difficult for a single expert or a small group to be knowledgeable about the full substrate scope of every class of enzymatic reactions. Further, the need for experts complicates the scaling of the approach and hinders the development of automated, robotic platforms for enzymatic synthesis of small molecules to accelerate drug discovery and scale up. Computer-Aided Synthesis Planning (CASP) tools in enzyme chemistry use rule-based methods and machine learning to generalize known reactions to predict enzymatic and chemo-enzymatic routes to a desired target molecule. Notwithstanding their success, three key gaps remain that limit the practical use of these CASP tools for drug discovery and production. First, current CASP tools still require substantial expert intervention to propose enzymatic routes to complex molecules starting from simple building blocks. Second, enzymatic CASP tools have limited ability to synergistically use organic and enzyme chemistry to propose hybrid chemo-enzymatic routes. For example, a preliminary tool published by the PI and his co-authors is limited to proposing enzymatic drop-in replacements for organic steps. However, the full effectiveness of biocatalytic retrosynthesis is realized when the introduction of one or more enzymatic steps into a synthesis enables a major redesign of the synthesis route for drastically improved drug production (e.g., shorter routes). Third, the selection of enzymes to catalyze CASP proposed reactions relies heavily on the enzymologists’ knowledge, and with thousands of plausible enzymatic reactions generated in a few minutes by CASP tools, it would be impossible to rely on manual input for evaluating and recommending enzymes.  

The PI’s research program will address these gaps by investigating three challenges: (i) Development and experimental validation of a multi-step enzymatic synthesis planner that requires minimal expert intervention (ii) Computational planning of de novo chemo-enzymatic synthesis pathways for complex medicinal compounds and their analogs, and (iii) Enzyme sequence-function annotation using machine learning to recommend enzymes for reactions proposed by CASP tools. The PI will curate high-quality databases and combine them with state-of-the-art machine learning algorithms to predict experimentally testable enzymatic routes towards medicinal small molecules. These algorithm-predicted routes will be reconstituted in vitro using purified enzymes and simple building blocks to produce analytically pure samples of product molecules for mass spectrometry-based characterization. The overall goal for the 5-year grant duration is to develop innovative computational methods, bioinformatics tools, and experimental chemical-biology platforms to support the synthesis of complex synthetic molecules and natural product analogs using enzyme reaction catalysis. 

National Science Foundation Project

Worldwide production of plastics is a nearly $1T industry, topping 400 million metric tons in 2022. Yet, only about 10% of plastics are recycled. Compostable plastics could help reduce plastic pollution and address sustainability needs, but existing compostable plastics do not have many of the desirable properties of existing commodity plastics such as high toughness, solvent resistance, thermal stability, and processability while also being infinitely recyclable. We will develop bacteria that efficiently produce biodegradable and recyclable plastics that will withstand higher temperatures than is currently possible with existing compostable plastics and that are readily chemically recyclable to their building blocks (monomers). We will also develop novel (i) computational tools for proposing synthetic routes (or “recipes”) towards the desired bioplastics, (ii) computational protein design tools to design the biocatalysts for the proposed synthetic routes, and (iii) experimental design-build-test-learn cycles that will leverage both a newly developed DNA synthesis platform and the latest advances in cell-free technology to rapidly screen the computationally designed biocatalysts for desired catalytic performance. The experimental and computational methods together will accelerate future biodesign goals. To enable the biomanufacturing of other advanced materials, we will make our methods broadly available via designer-facing infrastructure and research publications. We have assembled a diverse group of investigators with expertise in computational protein design, synthetic route finding, cell-free technology, cell engineering, DNA synthesis, and sustainable polymers. The graduate students and postdoctoral fellows funded by this project will be trained in this interdisciplinary environment to apply protein engineering to a practical problem with global impact. We will broaden participation by involving high-school and undergraduate students from groups underrepresented in STEM and developing new workshops on use-inspired protein design and its real-world applications. By expanding the applications for which biodegradable/compostable plastics are possible, our work will enable replacement of unrecyclable petroleum plastics that currently do not have compostable/recyclable alternatives.  

Gem-dimethylated polyhydroxyalkanoates (gdPHAs) have been synthesized and shown to be biodegradable, chemically recyclable, and melt-processible. However, there are no known metabolic pathways to incorporate α,α−gem-dimethyl monomer units into polyhydroxyalkanoates (PHAs), and it is desirable to produce a wider range of unnatural PHAs with tunable properties comprising different gem-dimethyl monomer units that would be challenging to synthesize using existing polymerization methods. We will engineer polyketide synthases (PKSs) to produce the gem-dimethylated monomers (3-hydroxy-2,2-dimethylbutanoicacid, 3hdmBA) for PHAs. Although we will focus on producing gdPHAs, this technology will enable us to vary the functionality on the image- and image-carbons of the polymer monomers. Historically, engineering PKSs for programmable biosynthesis has been plagued by slow turnover numbers of the hybrid synthases, in part because of the difficulty in engineering interactions among the various domains of PKSs. This is a protein engineering problem that can now be addressed with recent advances in computational protein design fueled by deep learning. To achieve our goals, our proposal will develop methodologies to (i) identify PKS domains and modules that can be recombined into a hybrid PKS to produce nearly any 3-hydroxyacid, (ii) engineer the hybrid PKSs that we construct to have high turnover rates using advanced computational protein design methods, (iii) develop a platform for high-GC gene synthesis for encoding the PKSs, and (iv) use design-build-test-learn cycles to validate and optimize the designs. We will demonstrate the utility of these methods by integrating an engineered PKS with a PHA synthase into a bacterium to produce PHAs that are more resistant to high temperatures and are amenable to depolymerization so they can be either biodegraded if discarded in a landfill or recycled back to monomers. The proposed work will develop (i) retrosynthesis tools for PKSs (ii) protein design tools and workflows that will engineer the transient protein-protein interactions in PKSs that enable them to function, and reprogram active sites for new substrates, and (iii) a better understanding of solid-phase synthesis of high GC DNA.  The tools we develop, especially the protein design tools will be general, having applicability to future biosystems design efforts that enable applications of importance to the bioeconomy.   

Defense Advanced Research Projects Agency Project

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