Hassan, Fahad and Minhas, Fayyaz ul Amir Afsar (2017) Deep and self-taught learning for protein accessible surface area prediction. In: 2017 International Conference on Frontiers of Information Technology (FIT), Islamabad, Pakistan, 18-20 Dec 2017. Published in: 2017 International Conference on Frontiers of Information Technology (FIT) pp. 264-269. ISBN 9781538635674. doi:10.1109/FIT.2017.00054

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Abstract

ASA captures the degree of burial or surface accessibility of a protein residue. It is a very important indicator of the behavior of amino acids within a protein as well. It can be used to find protein interactions, interfaces, folding states, etc. Calculation of the ASA requires the presence of the structure of the protein. However, structure determination for proteins is expensive and requires significant technical effort. As a consequence, the prediction of ASA is a very important and fundamental problem in Bioinformatics and Proteomics. In this work, we have investigated self-taught machine learning methods along with deep neural network to predict the residue level accessible surface area (ASA) of a protein. We have found that deep learning neural networks can predict the ASA of the residues in a protein accurately. Furthermore, the proposed deep learning based method does not require the use of computationally demanding features such as the position specific scoring matrix (PSSM) which have been used in previous works. A simple Blosum62 matrix based position dependent representation of amino acids in a sequence window gives comparable performance. This is particularly attractive for proteome wide prediction of ASA. We have used various self-taught learning schemes for obtaining an optimal feature representation from unlabeled data. These include a sparse and regularized autoencoder neural network and a dictionary based learning scheme. We have used unlabeled data from the protein universe in an attempt to improve the feature representation. We have also evaluated the performance of a stochastic gradient based predictor of accessible surface area for different feature representations.

Item Type:	Conference Item (Paper)
Subjects:	Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QP Physiology
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH):	Protein-protein interactions, Protein-protein interactions -- Data processing, Quadratic programming -- Computer programs, Machine learning
Journal or Publication Title:	2017 International Conference on Frontiers of Information Technology (FIT)
Publisher:	IEEE
ISBN:	9781538635674
Official Date:	18 January 2017
Dates:	Date Event 18 January 2017 Published 1 November 2017 Accepted
Page Range:	pp. 264-269
DOI:	10.1109/FIT.2017.00054
Status:	Peer Reviewed
Publication Status:	Published
Reuse Statement (publisher, data, author rights):	© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version:	Restricted or Subscription Access
Date of first compliant deposit:	5 November 2019
Date of first compliant Open Access:	7 November 2019
Conference Paper Type:	Paper
Title of Event:	2017 International Conference on Frontiers of Information Technology (FIT)
Type of Event:	Conference
Location of Event:	Islamabad, Pakistan
Date(s) of Event:	18-20 Dec 2017
Related URLs:	Organisation

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