Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes
The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed...
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doaj-3106484f074f4e8395eb208eaaf30a802020-11-24T21:01:11ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182014-01-01201410.1155/2014/715494715494Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure AttributesYajun Sheng0Xingye Qiu1Chen Zhang2Jun Xu3Yanping Zhang4Wei Zheng5Ke Chen6School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaSchool of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, ChinaSchool of Computer Science and Software Engineering, Tianjin Polytechnic University, No. 399 Binshui Road, Tianjin 300387, ChinaThe protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively.http://dx.doi.org/10.1155/2014/715494 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yajun Sheng Xingye Qiu Chen Zhang Jun Xu Yanping Zhang Wei Zheng Ke Chen |
spellingShingle |
Yajun Sheng Xingye Qiu Chen Zhang Jun Xu Yanping Zhang Wei Zheng Ke Chen Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes Computational and Mathematical Methods in Medicine |
author_facet |
Yajun Sheng Xingye Qiu Chen Zhang Jun Xu Yanping Zhang Wei Zheng Ke Chen |
author_sort |
Yajun Sheng |
title |
Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes |
title_short |
Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes |
title_full |
Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes |
title_fullStr |
Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes |
title_full_unstemmed |
Quad-PRE: A Hybrid Method to Predict Protein Quaternary Structure Attributes |
title_sort |
quad-pre: a hybrid method to predict protein quaternary structure attributes |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2014-01-01 |
description |
The protein quaternary structure is very important to the biological process. Predicting their attributes is an essential task in computational biology for the advancement of the proteomics. However, the existing methods did not consider sufficient properties of amino acid. To end this, we proposed a hybrid method Quad-PRE to predict protein quaternary structure attributes using the properties of amino acid, predicted secondary structure, predicted relative solvent accessibility, and position-specific scoring matrix profiles and motifs. Empirical evaluation on independent dataset shows that Quad-PRE achieved higher overall accuracy 81.7%, especially higher accuracy 92.8%, 93.3%, and 90.6% on discrimination for trimer, hexamer, and octamer, respectively. Our model also reveals that six features sets are all important to the prediction, and a hybrid method is an optimal strategy by now. The results indicate that the proposed method can classify protein quaternary structure attributes effectively. |
url |
http://dx.doi.org/10.1155/2014/715494 |
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