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Improved Performance in Protein Secondary Structure Prediction by Combining Multiple Predictions

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Abstract: In this paper1 we present a novel framework for protein secondary structure prediction. In this prediction framework, firstly we propose a novel parameterized semi-probability profile, which combines single sequence with evolutionary information effectively. Secondly, different semi-probability profiles are respectively applied as network input to predict protein secondary structure. Then a comparison among these different predictions is discussed in this article. Finally, naïve Bayes approaches are used to combine these predictions in order to obtain a better prediction performance than individual prediction. The experimental results show that our proposed framework can indeed improve the prediction accuracy.

Keywords: Protein secondary structure prediction; multiple sequence alignment; naive bayes; reliability score; semi-probability profile; two-level network

Document Type: Research Article


Affiliations: Intelligent Computing Lab.,Hefei Institute of Intelligent Machines, CAS, P.O. Box 1130, Hefei, Anhui,China.

Publication date: October 1, 2006

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.

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