Protein structure abstraction and automatic clustering using secondary structure element sequences
- Authors
- Park, SH; Park, Chang Yun; Kim, DH; Park, SH; Sim, JS
- Issue Date
- May-2005
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2005, PT 2, v.3481, pp 1284 - 1292
- Pages
- 9
- Journal Title
- COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2005, PT 2
- Volume
- 3481
- Start Page
- 1284
- End Page
- 1292
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60664
- DOI
- 10.1007/11424826_136
- ISSN
- 0302-9743
1611-3349
- Abstract
- To study protein clustering is very important in diverse fields such as drug design and environmental industry. For a meaningful clustering, protein structure must be considered. But, protein structures are very complicated and have so much information such as angles, 3-dimensional coordinates. Thus, it is not easy to efficiently compute their relations. In this paper, we present a method to efficiently abstract and cluster protein structures using secondary structure element sequences. Since a secondary structure element sequence is an abstract representation of protein structure, it can be regarded as a useful descriptor to cluster a set of proteins at the abstraction level. Using secondary structure element sequences and their distances, we implemented an automatic protein clustering system and verify their efficiency by experimental results.
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Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
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