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000600655 1001_ $$0P:(DE-H253)PIP1095943$$aBeton, Joseph$$b0
000600655 245__ $$aIntegrating model simulation tools and cryo‐electron microscopy
000600655 260__ $$aMalden, MA$$bWiley-Blackwell$$c2023
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000600655 520__ $$aThe power of computer simulations, including machine-learning, has become an inseparable part of scientific analysis of biological data. This has significantly impacted the field of cryogenic electron microscopy (cryo-EM), which has grown dramatically since the “resolution-revolution.” Many maps are now solved at 3–4 Å or better resolution, although a significant proportion of maps deposited in the Electron Microscopy Data Bank are still at lower resolution, where the positions of atoms cannot be determined unambiguously. Additionally, cryo-EM maps are often characterized by a varying local resolution, partly due to conformational heterogeneity of the imaged molecule. To address such problems, many computational methods have been developed for cryo-EM map reconstruction and atomistic model building. Here, we review the development in algorithms and tools for building models in cryo-EM maps at different resolutions. We describe methods for model building, including rigid and flexible fitting of known models, model validation, small-molecule fitting, and model visualization. We provide examples of how these methods have been used to elucidate the structure and function of dynamic macromolecular machines.This article is categorized under:    Structure and Mechanism > Molecular Structures    Structure and Mechanism > Computational Biochemistry and Biophysics    Software > Molecular Modeling 
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000600655 7001_ $$0P:(DE-H253)PIP1096737$$aCragnolini, Tristan$$b1
000600655 7001_ $$0P:(DE-H253)PIP1094807$$aKaleel, Manaz$$b2
000600655 7001_ $$0P:(DE-H253)PIP1098630$$aMulvaney, Thomas$$b3
000600655 7001_ $$0P:(DE-H253)PIP1100083$$aSweeney, Aaron$$b4
000600655 7001_ $$0P:(DE-H253)PIP1094132$$aTopf, Maya$$b5$$eCorresponding author
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