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DomSVR: domain boundary prediction with support vector regression from sequence information alone

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Abstract

Protein domains are structural and fundamental functional units of proteins. The information of protein domain boundaries is helpful in understanding the evolution, structures and functions of proteins, and also plays an important role in protein classification. In this paper, we propose a support vector regression-based method to address the problem of protein domain boundary identification based on novel input profiles extracted from AAindex database. As a result, our method achieves an average sensitivity of ∼36.5% and an average specificity of ∼81% for multi-domain protein chains, which is overall better than the performance of published approaches to identify domain boundary. As our method used sequence information alone, our method is simpler and faster.

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Acknowledgments

This work was supported in part by grant 2 G12 RR003048 from the RCMI program, Division of Research Infrastructure, National Center for Research Resources, NIH and the Mordecai Wyatt Johnson program of Howard University. This work was also supported in part by the Singapore MOE ARC Tier-2 funding grant T208B2203 and the National Science Foundation of China (No. 60803107). CL’s work was supported by NSF (CCF-0845888).

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Authors and Affiliations

  1. Department of Systems and Computer Science, Howard University, 2400 Sixth Street, NW, Washington, DC, 20059, USA

    Peng Chen, Chunmei Liu & Legand Burge

  2. Bioinformatics Research Center, School of Computer Engineering, Nanyang Technological University, Singapore, 639798, Singapore

    Peng Chen & Jinyan Li

  3. Department of Mathematics, Howard University, 2400 Sixth Street, NW, Washington, DC, 20059, USA

    Mahmood Mohammad

  4. Department of Biochemistry, Howard University, 2400 Sixth Street, NW, Washington, DC, 20059, USA

    William Southerland

  5. Department of Electrical and Computer Engineering, Howard University, 2400 Sixth Street, NW, Washington, DC, 20059, USA

    Clay Gloster

  6. School of Electrical Engineering and Information, Anhui University of Technology, Hudong Road 59, Ma’anshan, 243002, Anhui, People’s Republic of China

    Bing Wang

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  1. Peng Chen
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  2. Chunmei Liu
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  3. Legand Burge
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  4. Jinyan Li
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  5. Mahmood Mohammad
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  7. Clay Gloster
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  8. Bing Wang
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Correspondence to Peng Chen.

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Chen, P., Liu, C., Burge, L. et al. DomSVR: domain boundary prediction with support vector regression from sequence information alone. Amino Acids 39, 713–726 (2010). https://doi.org/10.1007/s00726-010-0506-6

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  • DOI: https://doi.org/10.1007/s00726-010-0506-6

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