Detecting target molecules in complex samples with both high sensitivity and selectivity remains a central challenge in biosensing, owing to low analyte abundance and interference from coexisting substances. Here, we present a membrane scaffold based on a ZZ-tag-displaying hepatitis B virus-derived L protein (ZZ-L membrane) that enables the oriented immobilization of sensing molecules. On quartz crystal microbalance sensor chips, the ZZ-L membrane enables controlled orientation of immunoglobulin G (IgG), leading to up to ~200-fold improvements in detection sensitivity and markedly enhanced binding capacity for purified food allergens, including ovomucoid, lectin, and tropomyosin, compared with direct immobilization. Whereas conventional methods failed to detect targets in complex matrices, the ZZ-L membrane enabled sensitive and selective detection of gliadin in wheat gluten and tropomyosin in heated shrimp extracts. Oriented immobilization of anti- hemagglutinin IgG further reduced the detectable amount of UV-inactivated influenza A virus by approximately 12-fold. The approach also extends beyond antibodies: Fc-fused leptin receptors were similarly immobilized, resulting in an approximately 15-fold increase in detection sensitivity. Across all targets, the number of bound analyte molecules per sensing molecule was consistently increased, indicating an increased active fraction at the molecular recognition interface. Together, these results demonstrate that membrane-mediated orientation control improves molecular accessibility and increases the active fraction, establishing the ZZ-L membrane as a robust platform for biosensing in complex samples with broad applicability in food safety, viral diagnostics, and biomarker detection.
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 sequence constraints.
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.