Abstract
To understand the structure-to-function relationship, life sciences researchers and biologists need to retrieve similar structures from protein databases and classify them into the same protein fold. With the technology innovation the number of protein structures increases every day, so, retrieving structurally similar proteins using current structural alignment algorithms may take hours or even days. Therefore, improving the efficiency of protein structure retrieval and classification becomes an important research issue. In this paper we propose novel approach which provides faster classification (minutes) of protein structures. We build separate Hidden Markov Model for each class. In our approach we align tertiary structures of proteins. Additionally we have compared our approach against an existing approach named 3D HMM. The results show that our approach is more accurate than 3D HMM.
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Mirceva, G., Davcev, D. (2009). HMM Approach for Classifying Protein Structures. In: Lee, Yh., Kim, Th., Fang, Wc., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2009. Lecture Notes in Computer Science, vol 5899. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10509-8_5
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DOI: https://doi.org/10.1007/978-3-642-10509-8_5
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