Predicting non-classical secretory proteins by using Gene Ontology terms and physicochemical properties

Wen Lin Huang*, Chyn Liaw, Shinn-Ying Ho

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Eukaryotic secretory proteins that traverse classical ER-Golgi pathway are usually characterized by short N-terminal signal peptides. However, several secretory proteins lacking the signal peptides are found to be exported by a non-classical secretion pathway. Therefore, predicting non-classical secretory proteins regardless of the N-terminal signal peptides is necessary for developing a critical computational approach. Several prediction methods have been proposed by using various types of features to predict secretory proteins. However, prediction performance seems not acceptable. This study proposes an SVM-based prediction method, namely ProSec-iGOX, which uses a major set of informative Gene Ontology (GO) terms and a minor set of assistance features. Physicochemical properties as the assistance features are useful when a query protein sequence without homologous protein with annotated GO terms. Two data sets, S25 and S40, having the identity 25% and 40%, respectively, are adopted for performance comparisons. The ProSec-iGOX yields test accuracies of 95.1% and 96.8% when adopting on the data sets S25 and S40 respectively. The latter accuracy (96.8%) is significantly higher than that of SPRED (82.2%), which uses frequency of tri-peptides and short peptides, secondary structure, physicochemical properties as input features to a random forest classifier. The experimental results show that GO terms are effective features for predicting non-classical secretory proteins.

Original languageEnglish
Title of host publicationProceedings - 2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011
Pages234-237
Number of pages4
DOIs
StatePublished - 25 Aug 2011
Event2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011 - Shanghai, China
Duration: 10 Jun 201112 Jun 2011

Publication series

NameProceedings - 2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011
Volume4

Conference

Conference2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011
CountryChina
CityShanghai
Period10/06/1112/06/11

Keywords

  • Gene Ontology
  • non-classical secretion
  • physicochemical properties
  • secretory
  • signal peptides

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    Huang, W. L., Liaw, C., & Ho, S-Y. (2011). Predicting non-classical secretory proteins by using Gene Ontology terms and physicochemical properties. In Proceedings - 2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011 (pp. 234-237). [5952841] (Proceedings - 2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011; Vol. 4). https://doi.org/10.1109/CSAE.2011.5952841