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This article is part of the supplement: Selected articles from The 5th IEEE International Conference on Systems Biology (ISB 2011)

Open Access Research

A novel neural response algorithm for protein function prediction

Hari Krishna Yalamanchili12, Quan-Wu Xiao3 and Junwen Wang124*

Author affiliations

1 Department of Biochemistry, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

2 Shenzhen Institute of Research and Innovation, The University of Hong Kong, Shenzhen, China

3 Department of Mathematics, City University of Hong Kong, Hong Kong SAR, China

4 Centre for Genomic Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

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Citation and License

BMC Systems Biology 2012, 6(Suppl 1):S19  doi:10.1186/1752-0509-6-S1-S19

Published: 16 July 2012

Abstract

Background

Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOFigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction.

Results

We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%.

Conclusions

The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/ webcite.