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Constrained cyclic coordinate descent for cryo-EM images at medium resolutions: beyond the protein loop closure problem

Published online by Cambridge University Press:  19 May 2016

Kamal Al Nasr  and
Jing He
Kamal Al Nasr
Affiliation:
Department of Computer Science, Tennessee State University, Nashville, TN 37209, USA
Jing He*
Affiliation:
Department of Computer Science, Old Dominion University, Norfolk, VA 23525, USA
*
*Corresponding author. E-mail: jhe@cs.odu.edu
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Summary

The cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91 Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76 Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.

Keywords

Inverse kinematicsCryo-electron microscopyImageSkeletonProtein structureLoop modelingCyclic coordinate descent
Type
Articles
Information
Robotica , Volume 34 , Issue 8 , August 2016 , pp. 1777 - 1790
Copyright
Copyright © Cambridge University Press 2016 

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