Abstract
To understand the structure-to-function relationship, life sciences researchers and biologists need to retrieve similar structures and classify them into the same protein fold. In this paper, we propose a 3D structure-based approach for efficient classification of protein molecules. Classification is performed in three phases. In the first phase, we apply fractal descriptor matching as a filter. Then, protein structures which satisfy the fractal and radius tolerance are classified in the second phase. In this phase, 3D Fourier Transform is applied in order to produce rotation invariant descriptors. Additionally, some properties of primary and secondary structure are taken. In the third phase we use k nearest neighbor classifier. Our approach achieves 86% classification accuracy with applying fractal filter, and 92% without fractal filter. It is shown that fractal filter significantly shorten the classification time. Our system is faster (seconds) than DALI system (minutes, hours, days), and we still get satisfactory results.
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Mirceva, G., Dimov, Z., Kalajdziski, S., Davcev, D. (2010). Protein Classification Based on 3D Structures and Fractal Features. In: Davcev, D., Gómez, J.M. (eds) ICT Innovations 2009. ICT Innovations 2009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10781-8_13
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DOI: https://doi.org/10.1007/978-3-642-10781-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10780-1
Online ISBN: 978-3-642-10781-8
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