Cell-free protein synthesis (CFPS) is a powerful platform for rapid protein prototyping and biomanufacturing, but expression of complex proteins remains limited by labor-intensive extract preparation as well as the inability of conventional Escherichia coli extracts to efficiently support disulfide bond formation and prevent protein aggregation. We previously developed a Cell-Free Autoinduction (CFAI) workflow that simplifies extract preparation, however, CFAI-derived extracts retain the folding limitations of standard E. coli systems for producing traditionally difficult proteins. Here, we address these challenges by utilizing extracts enriched with molecular chaperones or Erv1p-based oxidative folding catalysts and combining them through a modular extract mixing strategy to enable independent tuning of the cell-free folding environment. This workflow supplies folding factors through enriched extracts, avoiding recurring purchases of purified supplements and enabling a modeled reagent-cost advantage. To evaluate real-world applications and utility of lysate mixing for protein production, we demonstrate a 10-fold increase in solubility of the aggregation-prone bacterial toxin Colicin M, achieving >95% soluble protein while retaining antimicrobial activity. We further used a Design of Experiments (DoE) framework to optimize combinations of chaperone-enriched and Erv1p-based oxidative folding extracts containing either protein disulfide isomerase (PDI) or DsbC. The optimized compositions produced >95% soluble truncated human tissue plasminogen activator (vtPA), comprising the kringle 2 and protease domains, while preserving enzymatic activity. Together, these results demonstrate that CFAI-derived modular extract mixing provides a tunable, high-throughput strategy with low modeled materials costs for tailoring cell-free biochemical environments for difficult-to-express proteins, expanding CFPS utility for protein engineering and biomanufacturing.
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.· Journal of Systems and Softw...· 111 citations· ⚡8
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.· Journal of Systems and Softw...· 78 citations· ⚡6
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· SSE@SIGSOFT FSE· 56 citations· ⚡4
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· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
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...
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.