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http://dx.doi.org/10.6109/jkiice.2018.22.1.26

A Performance Comparison of Protein Profiles for the Prediction of Protein Secondary Structures  

Chi, Sang-Mun (Department of Computer Science, Kyungsung University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.22, no.1, 2018 , pp. 26-32 More about this Journal
Abstract
The protein secondary structures are important information for studying the evolution, structure and function of proteins. Recently, deep learning methods have been actively applied to predict the secondary structure of proteins using only protein sequence information. In these methods, widely used input features are protein profiles transformed from protein sequences. In this paper, to obtain an effective protein profiles, protein profiles were constructed using protein sequence search methods such as PSI-BLAST and HHblits. We adjust the similarity threshold for determining the homologous protein sequence used in constructing the protein profile and the number of iterations of the profile construction using the homologous sequence information. We used the protein profiles as inputs to convolutional neural networks and recurrent neural networks to predict the secondary structures. The protein profile that was created by adding evolutionary information only once was effective.
Keywords
Protein secondary structure; Protein profile; Protein sequence search; PSI-BLAST; HHblits;
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