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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB07)

Open Access Research

Protein structural class prediction based on an improved statistical strategy

Fei Gu1, Hang Chen2* and Jun Ni3

Author Affiliations

1 Department of Biotechnology, College of Life Sciences, Zhejiang University, Hangzhou, 310027, China

2 Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027, China

3 Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA 52242, USA

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BMC Bioinformatics 2008, 9(Suppl 6):S5  doi:10.1186/1471-2105-9-S6-S5

Published: 28 May 2008

Abstract

Background

A protein structural class (PSC) belongs to the most basic but important classification in protein structures. The prediction technique of protein structural class has been developing for decades. Two popular indices are the amino-acid-frequency (AAF) based, and amino-acid-arrangement (AAA) with long-term correlation (LTC) – based indices. They were proposed in many works. Both indices have its pros and cons. For example, the AAF index focuses on a statistical analysis, while the AAA-LTC emphasizes the long-term, biological significance. Unfortunately, the datasets used in previous work were not very reliable for a small number of sequences with a high-sequence similarity.

Results

By modifying a statistical strategy, we proposed a new index method that combines probability and information theory together with a long-term correlation. We also proposed a numerically and biologically reliable dataset included more than 5700 sequences with a low sequence similarity. The results showed that the proposed approach has its high accuracy. Comparing with amino acid composition (AAC) index using a distance method, the accuracy of our approach has a 16–20% improvement for re-substitution test and about 6–11% improvement for cross-validation test. The values were about 23% and 15% for the component coupled method (CCM).

Conclusion

A new index method, combining probability and information theory together with a long-term correlation was proposed in this paper. The statistical method was improved significantly based on our new index. The cross validation test was conducted, and the result show the proposed method has a great improvement.