Feasible-metabolic-pathway-exploration technique using chemical latent space

Combining Chemical Catalysis with Enzymatic Steps for the Synthesis of the Artemisinin Precursor Dihydroartemisinic Acid

The supply of artemisinin, the primary antimalarial drug recommended by the World Health Organization (WHO), is limited due to synthesis cost and supply constraints. This study explores novel chemo-enzymatic pathways for the efficient synthesis of dihydroartemisinic acid (DHAA), the penultimate precursor to artemisinin. The key concept here is to leverage the seamless integration of chemical and enzymatic steps for more thoroughly exploring synthesis alternatives. Using novoStoic, a biosynthetic pathway design tool, we identified previously unexplored carbon- and energy-balanced pathways for converting amorpha-4,11-diene (AMPD) to DHAA. For some of the enzymatically catalyzed steps lacking efficient enzymes, chemical catalysis alternatives were proposed and implemented, leading to a hybrid chemo-enzymatic pathway design. The proposed pathway converts AMPD directly to DHAA without going through artemisinic acid (AA), making it a shorter pathway compared with the existing synthesis routes for artemisinin. This effort paves the way for the systematic design of chemo-enzymatic pathways and provides insight into decision strategies between chemical synthesis and enzymatic synthesis steps. It serves as an example of how synthesis pathway design tools can be integrated with human intuition for accelerating retrosynthesis and how AI-based tools can identify and replace human intuitions to automate the decision processes. This can help reduce human-machine interventions and improve the development of future tools for synthesis planning.

Deep learning for metabolic pathway design

The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.

Designing Microbial Cell Factories for the Production of Chemicals

The sustainable production of chemicals from renewable, nonedible biomass has emerged as an essential alternative to address pressing environmental issues arising from our heavy dependence on fossil resources. Microbial cell factories are engineered microorganisms harboring biosynthetic pathways streamlined to produce chemicals of interests from renewable carbon sources. The biosynthetic pathways for the production of chemicals can be defined into three categories with reference to the microbial host selected for engineering: native-existing pathways, nonnative-existing pathways, and nonnative-created pathways. Recent trends in leveraging native-existing pathways, discovering nonnative-existing pathways, and designing de novo pathways (as nonnative-created pathways) are discussed in this Perspective. We highlight key approaches and successful case studies that exemplify these concepts. Once these pathways are designed and constructed in the microbial cell factory, systems metabolic engineering strategies can be used to improve the performance of the strain to meet industrial production standards. In the second part of the Perspective, current trends in design tools and strategies for systems metabolic engineering are discussed with an eye toward the future. Finally, we survey current and future challenges that need to be addressed to advance microbial cell factories for the sustainable production of chemicals.

Applications of artificial intelligence to enzyme and pathway design for metabolic engineering

Metabolic engineering for developing industrial strains capable of overproducing bioproducts requires good understanding of cellular metabolism, including metabolic reactions and enzymes. However, metabolic pathways and enzymes involved are still unknown for many products of interest, which presents a key challenge in their biological production. This challenge can be partly overcome by constructing novel biosynthetic pathways through enzyme and pathway design approaches. With the increase in bio-big data, data-driven approaches using artificial intelligence (AI) techniques are allowing more advanced protein and pathway design. In this paper, we review recent studies on AI-aided protein engineering and design, focusing on directed evolution that uses AI approaches to efficiently construct mutant libraries. Also, recent works of AI-aided pathway design strategies, including template-based and template-free approaches, are discussed.