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Bioinformatics Advance Access originally published online on March 24, 2007
Bioinformatics 2007 23(12):1444-1450; doi:10.1093/bioinformatics/btm119
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP)

Zhi-Qiang Ye 1, Shu-Qi Zhao 1, Ge Gao 1, Xiao-Qiao Liu 1, Robert E. Langlois 2, Hui Lu 2 and Liping Wei 1,*

1Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing 100871, P. R. China and 2Bioinformatics Program, Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: The rapid accumulation of single amino acid polymorphisms (SAPs), also known as non-synonymous single nucleotide polymorphisms (nsSNPs), brings the opportunities and needs to understand and predict their disease association. Currently published attributes are limited, the detailed mechanisms governing the disease association of a SAP remain unclear and thus, further investigation of new attributes and improvement of the prediction are desired.

Results: A SAP dataset was compiled from the Swiss-Prot variant pages. We extracted and demonstrated the effectiveness of several new biologically informative attributes including the structural neighbor profiles that describe the SAP's microenvironment, nearby functional sites that measure the structure-based and sequence-based distances between the SAP site and its nearby functional sites, aggregation properties that measure the likelihood of protein aggregation and disordered regions that consider whether the SAP is located in structurally disordered regions. The new attributes provided insights into the mechanisms of the disease association of SAPs. We built a support vector machines (SVMs) classifier employing a carefully selected set of new and previously published attributes. Through a strict protein-level 5-fold cross-validation, we attained an overall accuracy of 82.61%, and an MCC of 0.60. Moreover, a web server was developed to provide a user-friendly interface for biologists.

Availability: The web server is available at http://sapred.cbi.pku.edu.cn/

Contact: sapred{at}mail.cbi.pku.edu.cn

Supplementary information: Supplementary data are available at http://sapred.cbi.pku.edu.cn/supp.do

Associate Editor: Alfonso Valencia

Received on December 21, 2006; revised on February 25, 2007; accepted on March 16, 2007

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