NDLI: SVM-Based Approach for Predicting DNA-Binding Residues in Proteins from Amino Acid Sequences

Content Provider	IEEE Xplore Digital Library
Author	Xin Ma Jian-Sheng Wu Hong-De Liu Xi-Nan Yang Jian-Ming Xie Xiao Sun
Copyright Year	2009
Abstract	Protein-DNA interactions are vitally important in a wide range of biological processes such as gene regulation and DNA replication and repair. We predict DNA-binding residues in proteins from amino acid sequences by support vector machine (SVM) with a novel hybrid feature which incorporates evolutionary information of amino acid sequences and four physical–chemical properties, including the side chain pKa value, hydrophobicity index, molecular mass and lone electron pairs of amino acids. The classifier achieves 79.12% total accuracy with 74.19% sensitivity and 79.20% specificity, respectively. Moreover, an alternative classifier using random forest (RF) is also constructed. Further analysis proves that the hybrid feature shows obvious contribution to our excellent prediction performance, and the evolutionary information contributes most to the prediction improvement.
Starting Page	225
Ending Page	229
File Size	1124546
Page Count	5
File Format	PDF
ISBN	9780769537399
DOI	10.1109/IJCBS.2009.33
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sequences Support vector machine(SVM) Amino acids position specific scoring matrices (PSSMs) Information analysis Proteins Support vector machines Radio frequency DNA-binding residues Support vector machine classification Biological processes DNA Electrons
Content Type	Text
Resource Type	Article

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