DNA aptamers are widely used in the construction of fluorescent sensors, typically employing labelled fluorophores as signaling indicators. However, this covalent labeling approach suffers from several limitations, including complex and costly chemical modification, incompatibility with long aptamer sequences, and susceptibility to false-positive signals. To address these limitations, we constructed a novel aptamer-based fluorescent sensor using Lettuce indicator, a light-up DNA aptamer that adopts a precise three-dimensional structure, which enables it to selectively bind and activate the otherwise non-fluorescent small-molecule fluorophores. In our design, we destabilized Lettuce by fusing it with a target-binding aptamer via a transducer sequence. Upon target binding, structural rearrangement is triggered through the transducer, leading to the folding of Lettuce and restoration of its ability to activate the fluorophore, generating a target-dependent fluorescent signal. Through systematic optimization of the transducer and target-binding aptamer sequences, we created sensors for diverse targets, including small molecules, proteins, and metal ions. These sensors exhibit high signal-to-noise ratios, sensitivity and selectivity, and a wide dynamic range. As a proof-of-concept demonstration, a paper-based test strip for cost-effective and rapid detection of small molecule mycotoxins was developed. This versatile design provides a generalizable platform for the development of aptamer-based sensors, opening the way for future detection of diverse analytes.
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.