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This article is part of the supplement: Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

Open Access Open Badges Research

Improved Chou-Fasman method for protein secondary structure prediction

Hang Chen1*, Fei Gu2 and Zhengge Huang3

Author Affiliations

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

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

3 Department of Computer Science, Center for engineering and scientific computation, Zhejiang University, Hangzhou, 310027, China

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BMC Bioinformatics 2006, 7(Suppl 4):S14  doi:10.1186/1471-2105-7-S4-S14

Published: 12 December 2006



Protein secondary structure prediction is a fundamental and important component in the analytical study of protein structure and functions. The prediction technique has been developed for several decades. The Chou-Fasman algorithm, one of the earliest methods, has been successfully applied to the prediction. However, this method has its limitations due to low accuracy, unreliable parameters, and over prediction. Thanks to the recent development in protein folding type-specific structure propensities and wavelet transformation, the shortcomings in Chou-Fasman method are able to be overcome.


We improved Chou-Fasman method in three aspects. (a) Replace the nucleation regions with extreme values of coefficients calculated by the continuous wavelet transform. (b) Substitute the original secondary structure conformational parameters with folding type-specific secondary structure propensities. (c) Modify Chou-Fasman rules. The CB396 data set was tested by using improved Chou-Fasman method and three indices: Q3, Qpre, SOV were used to measure this method. We compared the indices with those obtained from the original Chou-Fasman method and other four popular methods. The results showed that our improved Chou-Fasman method performs better than the original one in all indices, about 10–18% improvement. It is also comparable to other currently popular methods considering all the indices.


Our method has greatly improved Chou-Fasman method. It is able to predict protein secondary structure as good as current popular methods. By locating nucleation regions with refined wavelet transform technology and by calculating propensity factors with larger size data set, it is likely to get a better result.