Literature DB >> 21466835

Predicting homo-oligomers and hetero-oligomers by pseudo-amino acid composition: an approach from discrete wavelet transformation.

Jian-Ding Qiu1, Xing-Yu Sun, Sheng-Bao Suo, Shao-Ping Shi, Shu-Yun Huang, Ru-Ping Liang, Li Zhang.   

Abstract

Many proteins exist in vivo as oligomers with different quaternary structural attributes rather than as individual chains. These proteins are the structural components of various biological functions, including cooperative effects, allosteric mechanisms and ion-channel gating. With the dramatic increase in the number of protein sequences submitted to the public databank, it is important for both basic research and drug discovery research to acquire the knowledge about possible quaternary structural attributes of their interested proteins in a timely manner. A high-throughput method (DWT_SVM), fusing discrete wavelet transform (DWT) and support vector machine (SVM) classifier algorithm with various physicochemical features, has been developed to predict protein quaternary structure. The accuracy in distinguishing candidate proteins as homo-oligomer or hetero-oligomer using the dataset R(2720) was 85.95% and 85.49% respectively by jackknife, showing that DWT_SVM is guide promising in predicting protein quaternary structures. The online service is available at http://bioinfo.ncu.edu.cn/Services.aspx . Protein sequences in FASTA format can be directly fed to the system OligoPred. The processed results will be presented in a diagram that includes the information of feature extraction and the classification error rate.
Copyright © 2011 Elsevier Masson SAS. All rights reserved.

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Year:  2011        PMID: 21466835     DOI: 10.1016/j.biochi.2011.03.010

Source DB:  PubMed          Journal:  Biochimie        ISSN: 0300-9084            Impact factor:   4.079


  2 in total

1.  The role of electrostatic energy in prediction of obligate protein-protein interactions.

Authors:  Mina Maleki; Gokul Vasudev; Luis Rueda
Journal:  Proteome Sci       Date:  2013-11-07       Impact factor: 2.480

2.  GLTM: A Global-Local Attention LSTM Model to Locate Dimer Motif of Single-Pass Membrane Proteins.

Authors:  Quanchao Ma; Kai Zou; Zhihai Zhang; Fan Yang
Journal:  Front Genet       Date:  2022-03-15       Impact factor: 4.599

  2 in total

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