Ali Reza Khanteymoori

Protein Function Prediction


Protein Function Prediction

The Protein-Protein Interactions Network (PPI-Net) is a new National Network for Protein-Protein Interactions starting April 2011. The Network was jointly funded by Engineering and Physical Science Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (BBSRC), and the Medical Research Council (MRC) from April 2011 to November 2013.

Multiple vital cellular processes are regulated by specific interactions between proteins and examples of pathways mediated by protein-protein interactions (PPIs) include gene expression, proliferation, intracellular communication and apoptosis. Numerous diseases occur because pathways involving particular PPIs malfunction. The challenge is to understand the process by which one protein recognizes another and binds to modulate its function. The rewards for a better understanding of particular PPIs include the potential to develop new molecular therapeutics and to develop probes to investigate systems biology. To date, only a few PPIs have been the subject of a drug discovery initiative.

Owing to the many opportunities presented by modulation of PPIs, this field has gained increasing scientific interest over the past decade and has seen many innovations from both academic and industrial research groups. Topics will include in PPI network, Protein Function and Protein Structure Prediction.

Protein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins.

Current status

To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation.

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