The week's pick
Random Articles
Reseach Article
Soft Computational Framework for Tertiary Protein Structure Prediction
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 168 - Number 10 |
| Year of Publication: 2017 |
| Authors: Arundhati Deka |
10.5120/ijca2017914523
|
Arundhati Deka . Soft Computational Framework for Tertiary Protein Structure Prediction. International Journal of Computer Applications. 168, 10 ( Jun 2017), 45-49. DOI=10.5120/ijca2017914523
Abstract
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.
References
- S.Kushwaha and M.Shakya, "A machine learning technique for Tertiary Structure Prediction of proteins from peptide sequences", in Proceedings of International Conference on Advances in Recent Technologies in Communication and Computing, 2009.
- C.Branden and J.Tooze, "Introduction to protein structure", 2nd Ed.,Garland Pub.,1999
- Protein Structure. Wikipedia: http://en.wikipedia.org/wiki/Protein_structure
- D. L.Nelson and Michael M.Cox,"Lehninger's principles of Biochemistry",4th Edition, 2009.
- G. Pok, C. H. Jin and K. H. Ryu, "Correlation of Amino Acid Physicochemical Properties with Protein Secondary Structure Conformation", in Proceedings of International Conference on BioMedical Engineering and Informatics, 2008.
- Cornell and Scripps researchers cite evidence supporting theory of how proteins fold into their critical shapes. By Krishna Ramanujan http://www.news.cornell.edu/stories/Aug06/ProteinFoldingScheraga.kr.html
- S. Malkov, M. V. Zivkovic, M.s V. Beljanski, S. D. Zaric: ``Correlation of amion acids with secondary structure types,connection with amino acid structure", paper preview, May 24, 2005.
- G. Pollastri, D. Przybylski, B. Rost and P. Baldi," Improving the Prediction of Protein Secondary Structure in Three and Eight Classes Using Recurrent Neural Networks and Profiles", Department of Information and Computer Science Institute for Genomics and Bioinformatics University of California, Irvine
- S. Haykins,"Neural Networks, A Comprehensive Foundation", 2nd Ed., Pearson Education, New Delhi,2003.
- Image: Artificial Neural Network: http://en.wikipedia.org/wiki/File:Artificial_neural_network.svg
Index Terms
Keywords