HMM based approach for classifying protein structures
Journal
International Journal of Bio-Science and Bio-Technology
Date Issued
2012-12
Author(s)
Mirceva, Georgina
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 (HMM) for each class. In our approach
we align tertiary structures of proteins. Viterbi algorithm is used to find the most probable
path to the model. We have compared our approach against an existing approach named 3D
HMM, which also performs alignment of tertiary structures of proteins by using HMM. The
results show that our approach is more accurate than 3D HMM.
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 (HMM) for each class. In our approach
we align tertiary structures of proteins. Viterbi algorithm is used to find the most probable
path to the model. We have compared our approach against an existing approach named 3D
HMM, which also performs alignment of tertiary structures of proteins by using HMM. The
results show that our approach is more accurate than 3D HMM.
Subjects
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