Prediction of rat protein subcellular localization with pseudo amino acid composition based on multiple sequential features.

Abstract:

:The study of rat proteins is an indispensable task in experimental medicine and drug development. The function of a rat protein is closely related to its subcellular location. Based on the above concept, we construct the benchmark rat proteins dataset and develop a combined approach for predicting the subcellular localization of rat proteins. From protein primary sequence, the multiple sequential features are obtained by using of discrete Fourier analysis, position conservation scoring function and increment of diversity, and these sequential features are selected as input parameters of the support vector machine. By the jackknife test, the overall success rate of prediction is 95.6% on the rat proteins dataset. Our method are performed on the apoptosis proteins dataset and the Gram-negative bacterial proteins dataset with the jackknife test, the overall success rates are 89.9% and 96.4%, respectively. The above results indicate that our proposed method is quite promising and may play a complementary role to the existing predictors in this area.

journal_name

Protein Pept Lett

authors

Shi R,Xu C

doi

10.2174/092986611795222768

subject

Has Abstract

pub_date

2011-06-01 00:00:00

pages

625-33

issue

6

eissn

0929-8665

issn

1875-5305

pii

BSP/ PPL/ E pub/0287

journal_volume

18

pub_type

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