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

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

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

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

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/

Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP). Publishing Authors By Initials

ZQ Ye,SQ Zhao,G Gao,XQ Liu,RE Langlois,H Lu,L Wei,

For similar biochemical phenomena, metabolism, and nutrition: biochemical phenomena: sequence homology: sequence homology, amino acid research abstracts see: biochemical phenomena, metabolism, and nutrition: biochemical phenomena: sequence homology: sequence homology, amino acid research

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

PUBLICATION TYPE: Research Support, Non-U.S. Gov

Journal: Bioinformatics (Oxford, England)

VOLUME: 23

Page Numbers: 1444-50

Journal Abbreviation: Bioinformatics

ISSN: 1460-2059

DAY: 24

MONTH: 03

YEAR: 2007

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

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LANGUAGE: eng

NlmUniqueID: 9808944

Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP). Keywords Mesh Terms:

KEYWORDS: Sequence Homology, Amino Acid

MESH TERMS: chemistry

Chemical & Substance for Abstract: Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP). Information

Substance Name: Disulfides

Registry Number: 0

Grant and Affiliation Information for Finding new structural and sequence attributes to predict possible disease association of single amino acid polymorphism (SAP).

AFFILIATION: Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing, PR China.

Country: England

AGENCY: United States NIAID

GRANT: P01 AI060915

ACRONYM: AI

MEDLINETA: Bioinformatics

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