Sep 2026· Zenodo (CERN European Organization for Nuclear Research)
Abstract
The archive contains the processed ¹⁵N CEST profiles of WT Im7 recorded at 600, 800 and 1200 MHz at 10 °C and at 800 MHz at 15 and 20 °C, the ¹⁵N TROSY-CPMG profiles recorded at 600 and 800 MHz at 10 °C, and the ChemEx experiment, method and parameter files together with the shell scripts that launched every fit reported in the paper. Also included are the scripts that extract the fitted parameters and draw each figure and table, and the self-contained simulations behind Figures 1, S1 and S7.
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.
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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.
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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.
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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.
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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.
