Prediction of β-Hairpins in Proteins Using Physicochemical Properties and Structure Information
In this study, we propose a new method to predict β-Hairpins in proteins and its evaluation based on the support vector machine. Different from previous methods, new feature representation scheme based on auto covariance is adopted. We also investigate two structure properties of proteins (protein secondary structure and residue conformation propensity), and examine their effects on prediction. Moreover, we employ an ensemble classifier approach based on the majority voting to improve prediction accuracy on hairpins. Experimental results on a dataset of 1926 protein chains show that our approach outperforms those previously published in the literature, which demonstrates the effectiveness of the proposed method.
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Document Type: Research Article
Publication date: 2010-09-01
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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.