Skip to content

Artificial Biosynthetic Pathways for Cell-Free Production of Raspberry Ketone and the Cardiotonic Drug Dobutamine

Sep 2026 · ACS Synthetic Biology · 0 citations · 39 references
Enzyme Catalysis and Immobilization

Abstract

Raspberry ketone (RK) is a high-value flavor and fragrance compound widely used in the food, beverage, and cosmetic industries. However, existing biosynthetic routes largely rely on native metabolic pathways that involve multiple enzymatic steps and exhibit limited production efficiency. Here, we designed and constructed a retrosynthesis-guided artificial biosynthetic pathway for cell-free RK production. The pathway combines an artificial aldolase (ALD) with the native benzalacetone reductase (BAR) to convert 4-hydroxybenzaldehyde and acetone into RK through only two enzymatic steps. Following optimization of enzyme architecture and reaction conditions, the system produced 1.33 mM RK with a 66% conversion yield. We further extended this platform to the synthesis of the cardiotonic drug dobutamine through a non-natural reductive amination cascade using an engineered short-chain dehydrogenase/reductase (SDR). Rational protein engineering and byproduct recycling increased dobutamine production by approximately 104-fold relative to the wild-type enzyme. Both biosynthetic modules remained robust upon scale-up from microliter to milliliter volumes, and preparative-scale synthesis enabled isolation of 19.2 mg of RK. Overall, this work establishes artificial biosynthetic pathways for the production of a plant-derived natural product and a clinically relevant drug molecule, highlighting the potential of cell-free biomanufacturing as a sustainable platform for the synthesis of value-added chemicals and pharmaceuticals.

View source

Similar papers

#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Book Open access Jul 2015

Understanding the affect of developers: theoretical background and guidelines for psychoempirical software engineering

This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.

D. Graziotin, Xiaofeng Wang, P. Abrahamsson · 56 citations · ⚡4
#machine learning Open access May 2017

What Influences the Speed of Prototyping? An Empirical Investigation of Twenty Software Startups

This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.

Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson · 44 citations · ⚡5
#protein folding Open access Sep 2026

Programmable design of functional proteins from natural language

Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...

Fengyuan Dai, Shiyang You, Yudian Zhu et al. · 31 citations · ⚡3

Related blog posts

Google DeepMind Blog Sep 30, 2026

Introducing SynthID Bio

Proof of concept for watermarking AI-generated proteins while preserving biological function.

MIT News · Artificial Intelligence Aug 27, 2026

Looking beyond natural sequences

A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.