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DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks

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Abstract

Protein domains are the structural and functional units of proteins. The ability to parse protein chains into different domains is important for protein classification and for understanding protein structure, function, and evolution. Here we use machine learning algorithms, in the form of recursive neural networks, to develop a protein domain predictor called DOMpro. DOMpro predicts protein domains using a combination of evolutionary information in the form of profiles, predicted secondary structure, and predicted relative solvent accessibility. DOMpro is trained and tested on a curated dataset derived from the CATH database. DOMpro correctly predicts the number of domains for 69% of the combined dataset of single and multi-domain chains. DOMpro achieves a sensitivity of 76% and specificity of 85% with respect to the single-domain proteins and sensitivity of 59% and specificity of 38% with respect to the two-domain proteins. DOMpro also achieved a sensitivity and specificity of 71% and 71% respectively in the Critical Assessment of Fully Automated Structure Prediction 4 (CAFASP-4) (Fisher et al., 1999; Saini and Fischer, 2005) and was ranked among the top ab initio domain predictors. The DOMpro server, software, and dataset are available at http://www.igb.uci.edu/servers/psss.html.

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Acknowledgments

This work is supported by the Institute for Genomics and Bioinformatics at UCI, a Laurel Wilkening Faculty Innovation award, an NIH Biomedical Informatics Training grant (LM-07443-01), an NSF MRI grant (EIA-0321390), a Sun Microsystems award, a grant from the University of California Systemwide Biotechnology Research and Education Program (UC BREP) to PB.

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  1. Institute for Genomics and Bioinformatics, School of Information and Computer Sciences, University of California Irvine, Irvine, CA, 92697, USA

    Jianlin Cheng, Michael J. Sweredoski & Pierre Baldi

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  1. Jianlin Cheng
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  2. Michael J. Sweredoski
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  3. Pierre Baldi
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Correspondence to Jianlin Cheng.

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Cheng, J., Sweredoski, M.J. & Baldi, P. DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks. Data Min Knowl Disc 13, 1–10 (2006). https://doi.org/10.1007/s10618-005-0023-5

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  • DOI: https://doi.org/10.1007/s10618-005-0023-5

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