Aug 2026· Frontiers in Biophysics· 0 citations· 43 references
Receptor Mechanisms and Signaling
TL;DR
Results show that current co-folding methods remain unreliable as stand-alone predictors of ion-channel ligand-binding modes and highlight pose sampling, pocket selection, ligand representation and independent structural validation as priorities for methodological development.
Abstract
All-atom protein-ligand co-folding offers a route to modelling biomolecular complexes without prespecifying the receptor conformation or binding site, but its reliability for large, state-dependent membrane proteins remains uncertain. Here, we established a training-cut-off-aware benchmark of experimentally resolved ligand-bound complexes of voltage-gated sodium (Nav), calcium (Cav) and potassium (Kv) channels to evaluate AlphaFold 3, Boltz-2 and Protenix-v2 across multiple ligand representations. The final production set comprised 301 completed method-input jobs and 7,525 predicted structures generated from RCSB/CACTVS SMILES, Protein Data Bank Chemical Component Dictionary identifiers and matched PubChem and ChEMBL representations. Executable coverage differed markedly among methods, particularly for Kv-family systems, necessitating restriction of the balanced pose-accuracy comparison to 22 Nav and Cav complexes completed by all three methods. Median top-ranked ligand heavy-atom root-mean-square deviations were 19.85 Å for AlphaFold 3, 22.12 Å for Boltz-2 and 20.25 Å for Protenix-v2. Best-of-25 selection improved pose accuracy, but recovered a ligand pose within 5 Å of the experimental reference in only two, three and three systems, respectively, indicating that insufficient sampling was the predominant limitation, with ranking errors contributing to a smaller subset. Same-pocket recovery remained below 50% for all methods, despite broad preservation of receptor architecture, and method-native confidence measures showed limited discrimination of ligand-pose accuracy. Alternative ligand representations produced small aggregate changes but substantial system-specific effects on execution, pose category and ranking. Approximate close-contact screening identified additional geometric concerns in a subset of predictions. Collectively, these results show that current co-folding methods remain unreliable as stand-alone predictors of ion-channel ligand-binding modes and highlight pose sampling, pocket selection, ligand representation and independent structural validation as priorities for methodological development.
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.