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Bioinformatics 2005 21(Suppl 2):ii243-ii244; doi:10.1093/bioinformatics/bti1140
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© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oxfordjournals.org

Fast maximum-likelihood refinement of electron microscopy images

Sjors H.W. Scheres , Mikel Valle and José-María Carazo *

Centro Nacional de Biotecnología—CSIC, Campus Universidad Autónoma 28049, Madrid, Spain

*To whom correspondence should be addressed.

Motivation: Maximum-likelihood (ML) image refinement is a promising candidate to improve attainable resolution limits in 3D-EM. However, its large CPU requirements may prohibit application to 3D-structure optimization.

Results: We speeded up ML image refinement by reducing its search space over the alignment parameters. Application of this reduced-search approach to a cryo-EM dataset yielded practically identical results as the original approach, but in approximately one day instead of one week of CPU.

Availability: This work has been implemented in the public domain package Xmipp. Documentation and download instructions may be found at: http://www.cnb.uam.es/~bioinfo

Contact: carazo{at}cnb.uam.es


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