Inside our cells, some proteins remain flexible and dynamic rather than folding into rigid shapes. These intrinsically disordered proteins (IDPs) can assemble into liquid-like biomolecular condensates, like oil droplets in water solution, to organize cellular biochemistry without encapsulating lipid membranes while continuously exchanging components with their environment. Studying these ever-changing assemblies is challenging, but computer simulations act as a “computational microscope.” I first recalibrated the popular simulation tool Martini, and created Martini3-IDP, which correctly reproduces the flexibility and expanded shapes of IDPs, all while remaining compatible with the existing Martini toolkit. Using this tool, I uncovered three key insights. First, condensates containing both structured and disordered domains, as in real proteins, have a very different internal architecture and slower internal motions than simplified disordered-only models. Second, the condensate scaffold proteins can reshape the conformation of client proteins that enter the droplet, revealing design rules for how condensates might alter protein function. Third, I built a realistic computational model of cellular P-body, incorporating six key proteins and RNA mimics. This multicomponent condensate revealed a highly uneven interior with mobile solvent pockets and component-specific behavior. Together, this work provides a powerful simulation tool and new molecular insights into how cellular droplet based organization works, from internal architecture to client reprogramming.
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.