Extracting Physicochemical Features to Predict Protein Secondary Structure

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal paramet...

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Main Authors: Yin-Fu Huang, Shu-Ying Chen
Format: Article
Language:English
Published: Wiley 2013-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1155/2013/347106
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author Yin-Fu Huang
Shu-Ying Chen
author_facet Yin-Fu Huang
Shu-Ying Chen
author_sort Yin-Fu Huang
collection DOAJ
description We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.
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series The Scientific World Journal
spelling doaj-art-eabcc70c95cf479698af8e044413cb642025-02-03T05:44:26ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/347106347106Extracting Physicochemical Features to Predict Protein Secondary StructureYin-Fu Huang0Shu-Ying Chen1Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliu, Yunlin 640, TaiwanDepartment of Computer Science and Information Engineering, National Yunlin University of Science and Technology, 123 University Road, Section 3, Touliu, Yunlin 640, TaiwanWe propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.http://dx.doi.org/10.1155/2013/347106
spellingShingle Yin-Fu Huang
Shu-Ying Chen
Extracting Physicochemical Features to Predict Protein Secondary Structure
The Scientific World Journal
title Extracting Physicochemical Features to Predict Protein Secondary Structure
title_full Extracting Physicochemical Features to Predict Protein Secondary Structure
title_fullStr Extracting Physicochemical Features to Predict Protein Secondary Structure
title_full_unstemmed Extracting Physicochemical Features to Predict Protein Secondary Structure
title_short Extracting Physicochemical Features to Predict Protein Secondary Structure
title_sort extracting physicochemical features to predict protein secondary structure
url http://dx.doi.org/10.1155/2013/347106
work_keys_str_mv AT yinfuhuang extractingphysicochemicalfeaturestopredictproteinsecondarystructure
AT shuyingchen extractingphysicochemicalfeaturestopredictproteinsecondarystructure