Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Model reconstruction from Small angle X-ray Scattering data using deep learning methods

Hao He, Can Liu, Haiguang Liu
doi: https://doi.org/10.1101/691832
Hao He
aComplex Systems Division, Beijing Computational Science Research Center, 8 E Xibeiwang Rd, Haidian, Beijing, 100193, People’s Republic of China
bSchool of Software Engineering, University of Science and Technology China, Suzhou, Jiang Su, 215123, People’s Republic of China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Can Liu
aComplex Systems Division, Beijing Computational Science Research Center, 8 E Xibeiwang Rd, Haidian, Beijing, 100193, People’s Republic of China
bSchool of Software Engineering, University of Science and Technology China, Suzhou, Jiang Su, 215123, People’s Republic of China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Haiguang Liu
aComplex Systems Division, Beijing Computational Science Research Center, 8 E Xibeiwang Rd, Haidian, Beijing, 100193, People’s Republic of China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: hgliu@csrc.ac.cn
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

We present an algorithm based on a deep learning method for model reconstruction from small angle X-ray scattering (SAXS) data. An auto-encoder for protein 3D models was trained to compress 3D shape information into vectors of a 200-dimensional latent space, and the vectors are optimized using genetic algorithms to build 3D models that are consistent with the scattering data. The algorithm was implemented using Python with the TensorFlow framework and tested with experimental data, demonstrating capacity and robustness of accurate model reconstruction even without using prior model size information.

Synopsis A deep learning method based on the auto-encoder framework for model reconstruction from small angle scattering data

Footnotes

  • Funding information National Natural Science Foundation of China (grant No. 11575021, U1530401, U1430237 to Haiguang Liu).

  • http://liulab.csrc.ac.cn/decodeSAXS

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Back to top
PreviousNext
Posted July 03, 2019.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Model reconstruction from Small angle X-ray Scattering data using deep learning methods
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Model reconstruction from Small angle X-ray Scattering data using deep learning methods
Hao He, Can Liu, Haiguang Liu
bioRxiv 691832; doi: https://doi.org/10.1101/691832
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Model reconstruction from Small angle X-ray Scattering data using deep learning methods
Hao He, Can Liu, Haiguang Liu
bioRxiv 691832; doi: https://doi.org/10.1101/691832

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3698)
  • Biochemistry (7809)
  • Bioengineering (5689)
  • Bioinformatics (21330)
  • Biophysics (10595)
  • Cancer Biology (8199)
  • Cell Biology (11961)
  • Clinical Trials (138)
  • Developmental Biology (6777)
  • Ecology (10419)
  • Epidemiology (2065)
  • Evolutionary Biology (13900)
  • Genetics (9726)
  • Genomics (13094)
  • Immunology (8164)
  • Microbiology (20058)
  • Molecular Biology (7871)
  • Neuroscience (43147)
  • Paleontology (321)
  • Pathology (1280)
  • Pharmacology and Toxicology (2264)
  • Physiology (3362)
  • Plant Biology (7246)
  • Scientific Communication and Education (1315)
  • Synthetic Biology (2010)
  • Systems Biology (5547)
  • Zoology (1132)