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Reseach Article

Artificial Neural Network aided Protein Structure Prediction

by Arundhati Deka, Kandarpa Kr. Sarma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 48 - Number 18
Year of Publication: 2012
Authors: Arundhati Deka, Kandarpa Kr. Sarma
10.5120/7450-0494

Arundhati Deka, Kandarpa Kr. Sarma . Artificial Neural Network aided Protein Structure Prediction. International Journal of Computer Applications. 48, 18 ( June 2012), 33-37. DOI=10.5120/7450-0494

@article{ 10.5120/7450-0494,
author = { Arundhati Deka, Kandarpa Kr. Sarma },
title = { Artificial Neural Network aided Protein Structure Prediction },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 48 },
number = { 18 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume48/number18/7450-0494/ },
doi = { 10.5120/7450-0494 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:44:26.593670+05:30
%A Arundhati Deka
%A Kandarpa Kr. Sarma
%T Artificial Neural Network aided Protein Structure Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 48
%N 18
%P 33-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Protein structure prediction plays a vital role in drug design and biotechnology. Understanding protein structures is necessary to determine the function of a protein and its interaction with DNA, RNA and Enzymes. Experimental techniques such as NMR Spectroscopy and X-ray Crystallography have been the main source of information about protein structures. But these conventional methods are now replaced by Machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM)s. In this paper, ANNs are used as a two level classifier to estimate the tertiary structure of proteins. ANNs are trained to make them capable of recognizing the primary sequences and DSSP codes of protein structures and their association with the secondary structure is derived. Based on majority selection, the final secondary structure is evaluated. These secondary structures can be further used as inputs to classify between the basic tertiary folds and subclasses of tertiary folds.

References
  1. H. Bordoloi and K. K. Sarma, ``Protein Structure Prediction Using Multiple Artificial Neural Network Classifier'', as a Chapter of a volume titled Soft Computing Techniques in Vision Science, Studies in Computational Intelligence, 2012, Volume 395/2012, pp. 137-146, DOI: 10. 1007/978-3-642-25507-6_12 , 2012.
  2. H. Bordoloi and K. K. Sarma, ``Protein Structure Prediction using Artificial Neural Network'', IJCA Special Issue on Electronics, Information and Communication Engineering ICEICE (3), pp. 24-26, December 2011. Published by Foundation of Computer Science, New York, USA.
  3. A. Deka, H. Bordoloi and K. K. Sarma, "ANN-aided Tertiary Protein Structure Prediction using Certain Coding Techniques and Known Secondary Structures", in Proceedings of International Conference on Electronics and Communication Engineering(ECE), 2012.
  4. A. Deka and K. K. Sarma,"Soft Computational Framework for Tertiary Protein Structure Prediction", International Journal of Electronics Signals and Systems (IJESS), ISSN:2231-5969, Vol. 1, Issue 3
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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Intelligence