Protein structure estimation from NMR data by matrix completion

Protein structure estimation from NMR data by matrix completion Knowledge of protein structures is very important to understand their corresponding physical and chemical properties. Nuclear Magnetic Resonance (NMR) spectroscopy is one of the main methods to measure protein structure. In this paper, we propose a two-stage approach to calculate the structure of a protein from a highly incomplete distance matrix, where most data are obtained from NMR. We first randomly “guess” a small part of unobservable distances by utilizing the triangle inequality, which is crucial for the second stage. Then we use matrix completion to calculate the protein structure from the obtained incomplete distance matrix. We apply the accelerated proximal gradient algorithm to solve the corresponding optimization problem. Furthermore, the recovery error of our method is analyzed, and its efficiency is demonstrated by several practical examples. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Biophysics Journal Springer Journals

Protein structure estimation from NMR data by matrix completion

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by European Biophysical Societies' Association
Subject
Life Sciences; Biochemistry, general; Biological and Medical Physics, Biophysics; Cell Biology; Neurobiology; Membrane Biology; Nanotechnology
ISSN
0175-7571
eISSN
1432-1017
D.O.I.
10.1007/s00249-017-1198-6
Publisher site
See Article on Publisher Site

Abstract

Knowledge of protein structures is very important to understand their corresponding physical and chemical properties. Nuclear Magnetic Resonance (NMR) spectroscopy is one of the main methods to measure protein structure. In this paper, we propose a two-stage approach to calculate the structure of a protein from a highly incomplete distance matrix, where most data are obtained from NMR. We first randomly “guess” a small part of unobservable distances by utilizing the triangle inequality, which is crucial for the second stage. Then we use matrix completion to calculate the protein structure from the obtained incomplete distance matrix. We apply the accelerated proximal gradient algorithm to solve the corresponding optimization problem. Furthermore, the recovery error of our method is analyzed, and its efficiency is demonstrated by several practical examples.

Journal

European Biophysics JournalSpringer Journals

Published: Feb 6, 2017

References

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