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Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

by Eman Alnabati 1 and Daisuke Kihara 1,2,*
1
Department of Computer Science, Purdue University, West Lafayette, IN 47907, USA
2
Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Molecules 2020, 25(1), 82; https://doi.org/10.3390/molecules25010082
Received: 11 December 2019 / Revised: 20 December 2019 / Accepted: 20 December 2019 / Published: 24 December 2019
(This article belongs to the Special Issue Protein Domains: Structures and Molecular Functions)
Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time. View Full-Text
Keywords: cryo-electron microscopy; cryo-EM; density map; protein modeling; structure fitting algorithms; de novo modeling; machine learning methods cryo-electron microscopy; cryo-EM; density map; protein modeling; structure fitting algorithms; de novo modeling; machine learning methods
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Alnabati, E.; Kihara, D. Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps. Molecules 2020, 25, 82.

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