A Deeply Glimpse into Protein Fold Recognition

Marwa Mohammed M. Ghareeb, Ahmed Sharaf Eldin, Taysir Hassan A. Soliman and Mohammed Ebrahim Marie

International Journal of Sciences, 2013, vol. 2, issue 06, 24-33

Abstract: The rapid growth in genomic and proteomic data causes a lot of challenges that are raised up and need powerful solutions. It is worth noting that UniProtKB/TrEMBL database Release 28-Nov-2012 contains 28,395,832 protein sequence entries, while the number of stored protein structures in Protein Data Bank (PDB, 4-12-2012) is 65,643. Thus, the need of extracting structural information through computational analysis of protein sequences has become very important, especially, the prediction of the fold of a query protein from its primary sequence has become very challenging. The traditional computational methods are not powerful enough to address theses challenges. Researchers have examined the use of a lot of techniques such as neural networks, Monte Carlo, support vector machine and data mining techniques. This paper puts a spot on this growing field and covers the main approaches and perspectives to handle this problem.

Keywords: Protein fold recognition; Neural network; Evolutionary algorithms; Meta Servers. (search for similar items in EconPapers)
Date: 2013
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