Sep 2026· Protein Science· Vol 35· 0 citations· 58 references
Medicine
Abstract
Autoantibodies against tumor‐associated autoantigens are clinically valuable biomarkers for cancer diagnosis; however, the structural determinants governing epitope selectivity remain unknown. Here, we investigated whether the intrinsic conformational stability of target autoantigens regulates the epitope propensity of tumor‐associated autoantibodies in non‐small cell lung cancer. Using a dual‐antigen Luminex bead‐based assay that presents each autoantigen in both native and S‐cationized denatured forms, we directly compared IgG autoantibody reactivity toward conformational and linear epitopes across 11 autoantigens. Intrinsically disordered aggregation‐prone autoantigens, including cancer/testis antigens, NY‐ESO‐1, and XAGE‐1b, predominantly elicit linear epitope‐directed responses, whereas thermodynamically stable soluble autoantigens generally drive conformational epitope recognition. Intriguingly, for autoantigens harboring both ordered and disordered segments, the immune response is strictly guided by domain‐specific biophysics: while p53‐specific antibodies preferentially target their flanking disordered regions, the Wilms' tumor protein 1 exceptionally drives conformational recognition directed toward its structured zinc‐finger domains. Recombinant solubility in Escherichia coli broadly correlated with epitope class across all 11 autoantigens, confirming that prokaryotic folding efficiency generally reflects intrinsic conformational stability in vivo for autonomously folding monomeric cytosolic proteins. Independent computational validation was provided by the concordance between the experimental solubility ratios and AlphaFold3‐derived Rosetta energy unit/solvent‐accessible surface area values, demonstrating the convergence of thermodynamic estimates and patient‐derived immune data within a unifying biophysical framework. These findings establish that the autoantibody epitope propensity is closely associated with the thermodynamic stability of the target autoantigen and provide a rational basis for tailoring antigen preparation strategies for autoantibody‐based cancer diagnosis.
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.