Chapter title |
Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information
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Chapter number | 4 |
Book title |
Prediction of Protein Secondary Structure
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Published in |
Methods in molecular biology, January 2017
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DOI | 10.1007/978-1-4939-6406-2_4 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6404-8, 978-1-4939-6406-2
|
Authors |
Gaurav Kandoi, Sumudu P. Leelananda, Robert L. Jernigan, Taner Z. Sen |
Abstract |
Predicting the secondary structure of a protein from its sequence still remains a challenging problem. The prediction accuracies remain around 80 %, and for very diverse methods. Using evolutionary information and machine learning algorithms in particular has had the most impact. In this chapter, we will first define secondary structures, then we will review the Consensus Data Mining (CDM) technique based on the robust GOR algorithm and Fragment Database Mining (FDM) approach. GOR V is an empirical method utilizing a sliding window approach to model the secondary structural elements of a protein by making use of generalized evolutionary information. FDM uses data mining from experimental structure fragments, and is able to successfully predict the secondary structure of a protein by combining experimentally determined structural fragments based on sequence similarities of the fragments. The CDM method combines predictions from GOR V and FDM in a hierarchical manner to produce consensus predictions for secondary structure. In other words, if sequence fragment are not available, then it uses GOR V to make the secondary structure prediction. The online server of CDM is available at http://gor.bb.iastate.edu/cdm/ . |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 11 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 5 | 45% |
Researcher | 1 | 9% |
Student > Master | 1 | 9% |
Unknown | 4 | 36% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 3 | 27% |
Agricultural and Biological Sciences | 2 | 18% |
Unknown | 6 | 55% |