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Soft Computational Framework for Tertiary Protein Structure Prediction

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Arundhati Deka

Arundhati Deka. Soft Computational Framework for Tertiary Protein Structure Prediction. International Journal of Computer Applications 168(10):45-49, June 2017. BibTeX

	author = {Arundhati Deka},
	title = {Soft Computational Framework for Tertiary Protein Structure Prediction},
	journal = {International Journal of Computer Applications},
	issue_date = {June 2017},
	volume = {168},
	number = {10},
	month = {Jun},
	year = {2017},
	issn = {0975-8887},
	pages = {45-49},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017914523},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Protein structure prediction is turning out to be one of the major challenges in the field of bio-informatics. It is highly important in medicine, especially in drug design and biotechnology. Proteins, being the basic building unit of all organisms, require experimental techniques for prediction of related structures. Among available methods, soft-computational tools provide readily available solutions for making predictions with less complexity, higher reliability and less time. The Artificial Neural Network (ANN) is one such tool which is used for structure prediction of proteins. This method is a machine learning approach in which ANNs are trained to make them capable of recognizing the 8-level subclasses of secondary structure. After the subclasses are recognized in a given sequence, their association with 3-level secondary protein structures is derived. The final structure is obtained from a majority selection from the protein structure. The work is also done in the reverse way, by predicting the 3-level secondary structure from the primary structure .This is done to confirm the accuracy of the prediction. In this work, ANNs are used as classifier to predict the secondary structure.


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Protein structure prediction, proteinogenic amino acids, DSSP codes