Next Article in Journal
Metabolomics in Radiation-Induced Biological Dosimetry: A Mini-Review and a Polyamine Study
Previous Article in Journal
FMSP-Nanoparticles Induced Cell Death on Human Breast Adenocarcinoma Cell Line (MCF-7 Cells): Morphometric Analysis
Article Menu
Issue 2 (June) cover image

Export Article

Open AccessArticle
Biomolecules 2018, 8(2), 33; https://doi.org/10.3390/biom8020033

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network

1
School of Computer Science and Technology, Soochow University, Suzhou 215006, China
2
School of Computer and Information, Anqing Normal University, Anqing 246011, China
*
Author to whom correspondence should be addressed.
Received: 21 April 2018 / Revised: 18 May 2018 / Accepted: 22 May 2018 / Published: 25 May 2018
Full-Text   |   PDF [729 KB, uploaded 25 May 2018]   |  

Abstract

Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson’s correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset. View Full-Text
Keywords: solvent-accessibility prediction; bidirectional recurrent network; sequence profile; merging operator solvent-accessibility prediction; bidirectional recurrent network; sequence profile; merging operator
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, B.; Li, L.; Lü, Q. Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network. Biomolecules 2018, 8, 33.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Biomolecules EISSN 2218-273X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top