2018/05/29 by Z. Li, Li, Z., S. Li +7
Biochemistry, Genetics and Molecular Biology · Materials Science · #Biological Physics (physics.bio-ph) #Biomolecules (q-bio.BM) #Enzyme Structure and Function #FOS: Biological sciences #FOS: Physical sciences #Machine Learning in Bioinformatics #Protein Structure and Dynamics
paper · pdf · doi:10.48550/arxiv.1805.11312
openalex publication_date 2018/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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