Oct 2026· Acta Crystallographica Section F Structural Biology Communications· 0 citations
TL;DR
Crystal structures provided a framework for further investigation of NTD–RNA recognition, the possible role of the CTD positively charged groove in nucleic acid interactions and N-protein assembly and suggest that RNA binding may be accommodated by local side-chain rearrangements within a pre-existing positively charged cleft.
Abstract
The nucleocapsid (N) protein of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) functions in viral RNA binding, genome packaging and ribonucleoprotein (RNP) assembly. Crystal structures were determined of the N-terminal domain (NTD) from crystals grown at pH 8.0 to a resolution of 1.94 Å and of the C-terminal domain (CTD) from crystals grown at pH 5.0 and 8.5 to resolutions of 1.59 and 1.30 Å, respectively. The NTD adopts the canonical coronavirus NTD fold and contains a positively charged cleft corresponding to a previously proposed RNA-binding region. Comparison of the apo NTD structure determined in this study with a previously reported RNA-bound NTD structure revealed local differences in the side-chain conformations of RNA-contacting residues, including Thr49, Arg88, Arg92, Arg107, Tyr109 and Tyr111, whereas the overall domain fold remained essentially unchanged. These observations suggest that RNA binding may be accommodated by local side-chain rearrangements within a pre-existing positively charged cleft rather than by a large-scale conformational change. The CTD forms a domain-swapped dimer mediated by β-hairpins, with a positively charged groove extending across the dimer interface. Superposition of the two CTD structures yielded a root-mean-square deviation (r.m.s.d.) of 0.129 Å, and 11 intersubunit hydrogen bonds were observed at the dimer interface in each structure, indicating that the dimer architecture is conserved under the crystallization conditions examined. Together, these structures provide a framework for further investigation of NTD–RNA recognition, the possible role of the CTD positively charged groove in nucleic acid interactions and N-protein assembly.
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.