基于伪氨基酸组成预测膜蛋白类型的密度相似性应用。
Application of density similarities to predict membrane protein types based on pseudo-amino acid composition.
机构信息
Department of Statistics, Faculty of Science, Shiraz University, Shiraz, Iran.
出版信息
J Theor Biol. 2011 May 7;276(1):132-7. doi: 10.1016/j.jtbi.2011.01.048. Epub 2011 Feb 4.
Abstract
Cell membranes provide integrity of living cells. Although the stability of biological membrane is maintained by the lipid bilayer, membrane proteins perform most of the specific functions such as signal transduction, transmembrane transport, etc. Then it is plausible membrane proteins being attractive drug targets. In this article, based on the concept of using the pseudo-amino acid composition to define a protein, three different density similarities are developed for predicting the membrane protein type. The predicted results showed that the proposed approach can remarkably improve the accuracy, and might become a useful tool for predicting the other attributes of proteins as well.
摘要
细胞膜为活细胞提供了完整性。尽管生物膜的稳定性是由脂质双层维持的，但膜蛋白执行着大多数特定的功能，如信号转导、跨膜运输等。因此，膜蛋白成为有吸引力的药物靶点是合理的。在本文中，基于使用伪氨基酸组成来定义蛋白质的概念，开发了三种不同的密度相似度来预测膜蛋白类型。预测结果表明，所提出的方法可以显著提高准确性，并且可能成为预测蛋白质其他属性的有用工具。
相似文献
1
Application of density similarities to predict membrane protein types based on pseudo-amino acid composition.
J Theor Biol. 2011 May 7;276(1):132-7. doi: 10.1016/j.jtbi.2011.01.048. Epub 2011 Feb 4.
2
Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition.
Protein Eng Des Sel. 2004 Jun;17(6):509-16. doi: 10.1093/protein/gzh061. Epub 2004 Aug 16.
3
Improving discrimination of outer membrane proteins by fusing different forms of pseudo amino acid composition.
Anal Biochem. 2010 Mar 1;398(1):52-9. doi: 10.1016/j.ab.2009.10.040. Epub 2009 Oct 27.
4
Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition.
J Theor Biol. 2006 May 7;240(1):9-13. doi: 10.1016/j.jtbi.2005.08.016. Epub 2005 Sep 28.
5
Predict mycobacterial proteins subcellular locations by incorporating pseudo-average chemical shift into the general form of Chou's pseudo amino acid composition.
J Theor Biol. 2012 Jul 7;304:88-95. doi: 10.1016/j.jtbi.2012.03.017. Epub 2012 Mar 23.
6
Predicting membrane protein types by the LLDA algorithm.
Protein Pept Lett. 2008;15(9):915-21. doi: 10.2174/092986608785849308.
7
A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou's PseAAC.
J Theor Biol. 2014 Mar 7;344:31-9. doi: 10.1016/j.jtbi.2013.11.017. Epub 2013 Dec 4.
8
Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou's pseudo amino acid composition.
J Theor Biol. 2013 Feb 7;318:1-12. doi: 10.1016/j.jtbi.2012.10.033. Epub 2012 Nov 5.
9
SLLE for predicting membrane protein types.
J Theor Biol. 2005 Jan 7;232(1):7-15. doi: 10.1016/j.jtbi.2004.07.023.
10
Prediction of multi-type membrane proteins in human by an integrated approach.
PLoS One. 2014 Mar 27;9(3):e93553. doi: 10.1371/journal.pone.0093553. eCollection 2014.
引用本文的文献
1
Applications of Molecular Dynamics Simulation in Protein Study.
Membranes (Basel). 2022 Aug 29;12(9):844. doi: 10.3390/membranes12090844.
2
iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.
Comput Math Methods Med. 2021 Oct 11;2021:7681497. doi: 10.1155/2021/7681497. eCollection 2021.
3
Employing a novel 2-gram subgroup intra pattern (2GSIP) with stacked auto encoder for membrane protein classification.
Mol Biol Rep. 2019 Apr;46(2):2259-2272. doi: 10.1007/s11033-019-04680-3. Epub 2019 Feb 18.
4
Prediction of multi-type membrane proteins in human by an integrated approach.
PLoS One. 2014 Mar 27;9(3):e93553. doi: 10.1371/journal.pone.0093553. eCollection 2014.
本文引用的文献
1
Some remarks on protein attribute prediction and pseudo amino acid composition.
J Theor Biol. 2011 Mar 21;273(1):236-47. doi: 10.1016/j.jtbi.2010.12.024. Epub 2010 Dec 17.
2
GPCR-GIA: a web-server for identifying G-protein coupled receptors and their families with grey incidence analysis.
Protein Eng Des Sel. 2009 Nov;22(11):699-705. doi: 10.1093/protein/gzp057. Epub 2009 Sep 22.
3
GPCR-CA: A cellular automaton image approach for predicting G-protein-coupled receptor functional classes.
J Comput Chem. 2009 Jul 15;30(9):1414-23. doi: 10.1002/jcc.21163.
4
Probing the interaction between the coiled coil leucine zipper of cGMP-dependent protein kinase Ialpha and the C terminus of the myosin binding subunit of the myosin light chain phosphatase.
J Biol Chem. 2008 Nov 21;283(47):32860-9. doi: 10.1074/jbc.M804916200. Epub 2008 Sep 9.
5
Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image.
J Theor Biol. 2008 Oct 7;254(3):691-6. doi: 10.1016/j.jtbi.2008.06.016. Epub 2008 Jun 24.
6
Using grey dynamic modeling and pseudo amino acid composition to predict protein structural classes.
J Comput Chem. 2008 Sep;29(12):2018-24. doi: 10.1002/jcc.20955.
7
MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM.
Biochem Biophys Res Commun. 2007 Aug 24;360(2):339-45. doi: 10.1016/j.bbrc.2007.06.027. Epub 2007 Jun 15.
8
A dynamic model for the p53 stress response networks under ion radiation.
Amino Acids. 2007 Jul;33(1):75-83. doi: 10.1007/s00726-006-0454-3. Epub 2006 Oct 31.
9
Abundance of intrinsically unstructured proteins in P. falciparum and other apicomplexan parasite proteomes.
Mol Biochem Parasitol. 2006 Dec;150(2):256-67. doi: 10.1016/j.molbiopara.2006.08.011. Epub 2006 Sep 20.
10
A set of glycosylphosphatidyl inositol-anchored membrane proteins of Plasmodium falciparum is refractory to genetic deletion.
Infect Immun. 2006 Jul;74(7):4330-8. doi: 10.1128/IAI.00054-06.
Zotero 插件