A growing number of studies have detected nanoplastics, particularly polystyrene (PS) nanoparticles, in human blood and various critical tissues. Nevertheless, the molecular mechanisms by which their physical size modulates the aggregation of disease-related proteins remain poorly understood. Here, using well-tempered metadynamics simulations, we demonstrate that single-chain PS nanoparticles with a degree of polymerization N collapse into spherical globules in water, following a scaling law of radius of gyration Rg ∼ N1/3, and that their size critically determines the conformational fate of alpha-synuclein (αS), a key protein in Parkinson’s disease. Within our single-chain simulation framework, we observe a sensitive size-dependent transition regime around N = 75 to 80 (Rg = 1.14 to 1.16 nm), where PS nanoparticles transition from insufficient surface area to a threshold capable of encapsulating the αS chain. The partially exposed protein chain subsequently folds into stable intramolecular β-sheet domains, representing the initial nucleation step toward amyloid aggregation. Conversely, nanoparticles with N ≥ 80 (Rg ≥ 1.16 nm) present a sufficiently large hydrophobic surface to completely encapsulate αS through dominant hydrophobic and π-π interactions, effectively suppressing the intramolecular β-sheet nucleation. These findings reveal that smaller nanoplastics may inadvertently promote protein misfolding, which may implicitly lower the likelihood of downstream intermolecular aggregation. Our work establishes a quantitative, size-dependent hazard principle for nanoplastic neurotoxicity and provides a molecular foundation for assessing the risk of environmentally derived nanoparticles in synucleinopathies.
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.