Literature DB >> 17189644

Prediction of the subcellular location of apoptosis proteins.

Ying-Li Chen1, Qian-Zhong Li.   

Abstract

Apoptosis proteins have a central role in the development and the homeostasis of an organism. These proteins are very important for understanding the mechanism of programmed cell death. The function of an apoptosis protein is closely related to its subcellular location. Based on the concept that the subcellular location of an apoptosis protein is mainly determined by its amino acid sequence, a new algorithm for prediction of the subcellular location of an apoptosis protein is proposed. By using of a distinctive set of information parameters derived from the primary sequence of 317 apoptosis proteins, the increment of diversity (ID), the sole prediction parameter, is calculated. The higher predictive success rates than the previous other algorithms is obtained by the jackknife tests using the expanded dataset. Our prediction results show that the local compositions of twin amino acids and hydropathy distribution are very useful to predict subcellular location of protein.

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Year:  2006        PMID: 17189644     DOI: 10.1016/j.jtbi.2006.11.010

Source DB:  PubMed          Journal:  J Theor Biol        ISSN: 0022-5193            Impact factor:   2.691


  22 in total

1.  Prediction of subcellular location of mycobacterial protein using feature selection techniques.

Authors:  Hao Lin; Hui Ding; Feng-Biao Guo; Jian Huang
Journal:  Mol Divers       Date:  2009-11-12       Impact factor: 2.943

2.  A new multi-label classifier in identifying the functional types of human membrane proteins.

Authors:  Hong-Liang Zou; Xuan Xiao
Journal:  J Membr Biol       Date:  2014-11-30       Impact factor: 1.843

3.  Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA.

Authors:  Lei Du; Qingfang Meng; Yuehui Chen; Peng Wu
Journal:  BMC Bioinformatics       Date:  2020-05-24       Impact factor: 3.169

4.  Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.

Authors:  Jianjun He; Hong Gu; Wenqi Liu
Journal:  PLoS One       Date:  2012-06-08       Impact factor: 3.240

5.  The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements.

Authors:  Zhenxing Feng; Xiuzhen Hu; Zhuo Jiang; Hangyu Song; Muhammad Aqeel Ashraf
Journal:  Saudi J Biol Sci       Date:  2015-12-11       Impact factor: 4.219

6.  Accurate prediction of subcellular location of apoptosis proteins combining Chou's PseAAC and PsePSSM based on wavelet denoising.

Authors:  Bin Yu; Shan Li; Wen-Ying Qiu; Cheng Chen; Rui-Xin Chen; Lei Wang; Ming-Hui Wang; Yan Zhang
Journal:  Oncotarget       Date:  2017-11-21

7.  Protein domain boundary predictions: a structural biology perspective.

Authors:  Svetlana Kirillova; Suresh Kumar; Oliviero Carugo
Journal:  Open Biochem J       Date:  2009-01-21

8.  Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier.

Authors:  Xiao Wang; Hui Li; Qiuwen Zhang; Rong Wang
Journal:  Biomed Res Int       Date:  2016-04-24       Impact factor: 3.411

9.  Identifying anticancer peptides by using improved hybrid compositions.

Authors:  Feng-Min Li; Xiao-Qian Wang
Journal:  Sci Rep       Date:  2016-09-27       Impact factor: 4.379

10.  Identification of metal ion binding sites based on amino acid sequences.

Authors:  Xiaoyong Cao; Xiuzhen Hu; Xiaojin Zhang; Sujuan Gao; Changjiang Ding; Yonge Feng; Weihua Bao
Journal:  PLoS One       Date:  2017-08-30       Impact factor: 3.240

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