A Deeply Glimpse into Protein Fold Recognition

Author(s): Marwa Mohammed M. Ghareeb, Ahmed Sharaf Eldin, Taysir Hassan A. Soliman, Mohammed Ebrahim Marie

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.

Protein fold recognition, Neural network, Evolutionary algorithms, Meta Servers.

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