Abstract Cell-free synthetic biology offers a rapid prototyping environment for molecular diagnostics, yet the utility of these systems is often limited by the slow kinetics and restricted multiplexing capabilities of traditional protein reporters. Here, we report the BINOCULAR (BIspecific Novel Orthogonal Coiled-coil-Using LAteral flow Reporter) system based on supramolecular coiled-coil peptide conjugates that dramatically accelerates signal generation and enables robust multiplexing in cell-free transcription-translation reactions. Using BINOCULARs as toehold switch outputs, we achieve visible readout times of less than 5 min in lateral flow assays, achieving a ≥12-fold increase in speed compared to standard cell-free reporters like β-galactosidase or fluorescent proteins. To demonstrate the utility of this platform, we integrated the bispecific reporters with loop-mediated isothermal amplification and lateral flow assays for the detection of mycoplasma, a common adventitious agent in biomanufacturing. The integrated system demonstrates exceptional sensitivity, achieving a detection limit of 50 genomic copies/mL for mycoplasma and a mammalian internal control. Cell-free assay results were available within 5 min following a 20 min amplification step, and we successfully achieved the simultaneous detection of three distinct targets in a single reaction. Crucially, the platform maintains reliable performance in complex matrices and after lyophilization, making it more accessible for point-of-use applications. Our findings suggest that the modular BINOCULAR architecture can overcome current bottlenecks in cell-free sensing systems, providing a versatile and scalable framework for rapid, point-of-use screening across clinical and industrial applications.
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.