  Protein structure reconstruction from Nuclear Magnetic Resonance (NMR)
experiments largely relies on computational algorithms. Recently, some
effective low-rank matrix completion (MC) methods, such as ASD and ScaledASD,
have been successfully applied to image processing, which inspires us to apply
the methods to reconstruct protein structures. In this paper, we present an
efficient method to determine protein structures based on experimental NMR
NOESY distances. ScaledASD algorithm is used in the method with several
post-procedures including chirality refinement, distance lower (upper) bound
refinement, force field-based energy minimization (EM) and water refinement. By
comparing several metrics in the conformation evaluation on our results with
Protein Data Bank (PDB) structures, we conclude that our method is consistent
with the popularly used methods. In particular, our results show higher
validities in Procheck dihedral angles G-factor. Furthermore, we compare our
calculation results with PDB structures by examining the structural similarity
to X-ray crystallographic structures in a special dataset. The software and its
MATLAB source codes are available in https://github.com/xubiaopeng/PRASD
