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

Kamal Al Nasr, Jing He

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1777-1790
Number of pages14
JournalRobotica
Volume34
Issue number8
DOIs
StatePublished - Aug 1 2016
Externally publishedYes

Keywords

  • Cryo-electron microscopy
  • Cyclic coordinate descent
  • Image
  • Inverse kinematics
  • Loop modeling
  • Protein structure
  • Skeleton

ASJC Scopus subject areas

  • Software
  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Rehabilitation
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computational Mechanics
  • General Mathematics
  • Modeling and Simulation

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