National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disease (NIH/NIDDK)
R01 DK123463-01
United States
National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disease (NIH/NIDDK)
R01 DK131118-01
United States
National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disease (NIH/NIDDK)
F32DK137477-01A1
United States
National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Disease (NIH/NIDDK)
TL1DK132769
United States
Citation
Journal: Acta Crystallogr D Struct Biol / Year: 2018 Title: ISOLDE: a physically realistic environment for model building into low-resolution electron-density maps. Authors: Tristan Ian Croll / Abstract: This paper introduces ISOLDE, a new software package designed to provide an intuitive environment for high-fidelity interactive remodelling/refinement of macromolecular models into electron-density ...This paper introduces ISOLDE, a new software package designed to provide an intuitive environment for high-fidelity interactive remodelling/refinement of macromolecular models into electron-density maps. ISOLDE combines interactive molecular-dynamics flexible fitting with modern molecular-graphics visualization and established structural biology libraries to provide an immersive interface wherein the model constantly acts to maintain physically realistic conformations as the user interacts with it by directly tugging atoms with a mouse or haptic interface or applying/removing restraints. In addition, common validation tasks are accelerated and visualized in real time. Using the recently described 3.8 Å resolution cryo-EM structure of the eukaryotic minichromosome maintenance (MCM) helicase complex as a case study, it is demonstrated how ISOLDE can be used alongside other modern refinement tools to avoid common pitfalls of low-resolution modelling and improve the quality of the final model. A detailed analysis of changes between the initial and final model provides a somewhat sobering insight into the dangers of relying on a small number of validation metrics to judge the quality of a low-resolution model.
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