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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 674–681Cite as

Prediction of Protein Domains from Sequence Information Using Support Vector Machines

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

Abstract

Guessing the boundaries of structural domains has been an important and challenging problem in experimental and computational structural biology. Predictions were based on intuition, biochemical properties, statistics, sequence homology and other aspects of predicted protein structure. In this paper a promising method for detecting the domain structure of a protein from sequence information alone was presented. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using support vector machines. The overall accuracy of the method for a single protein chains dataset, is about 85%. The result demonstrates that the utility of the method can help not only in predicting the complete 3D structure of a protein but also in the study of proteins’ building blocks and for functional analysis.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Jilin University, 130012, China

    Shuxue Zou, Yanxin Huang, Yan Wang & Chunguang Zhou

Authors
  1. Shuxue Zou
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  2. Yanxin Huang
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  3. Yan Wang
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  4. Chunguang Zhou
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Zou, S., Huang, Y., Wang, Y., Zhou, C. (2006). Prediction of Protein Domains from Sequence Information Using Support Vector Machines. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_99

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  • DOI: https://doi.org/10.1007/11760191_99

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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