AMPpred: An On-Line Predictor Design for Automated AMP Recognition

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Antimicrobial peptides (AMP) are potent, broad spectrum antibiotics which demonstrate potential as novel therapeutic agents. Because it is both time-consuming and laborious to identify new AMPs by experiment, this paper tries to resolve this problem by pattern recognition. Two major contents included: Firstly, up to six kinds of physicochemical properties value are selected to code the AMP sequence as physical-chemical property matrix (PCM), then auto and cross covariance transformation is performed to extract features from the PCM for AMP sequence expression; Secondly, these feature vectors are input to a powerful Support Vector Machine (SVM) classifier for training and new query AMP recognition. For a newly constructed AMP benchmark dataset, the overall classification accuracy about 96% has been achieved through the rigorous Leave-One-Out cross-validation. For convenience, a user-friendly web server, AMPpred, has been established at http://icpr.jci.jx.cn/bioinfo/AMPpred. It is anticipated that this on-line predictor may become a useful bioinformatics tool for molecular biology and drug development. Also, its novel approach will further stimulate the development of predicting peptide attributes.

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Periodical:

Edited by:

Mohamed Othman

Pages:

2276-2279

Citation:

Y. A. Pan et al., "AMPpred: An On-Line Predictor Design for Automated AMP Recognition", Applied Mechanics and Materials, Vols. 229-231, pp. 2276-2279, 2012

Online since:

November 2012

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$38.00

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