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ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa


Background: Apicomplexan parasites contain a relict chloroplast known as the apicoplast. This organelle is essential for parasite survival and thus serves as a promising target for drug treatment. As the gatekeepers of this important organelle, apicoplast membrane proteins are potentially excellent drug target candidates and therefore their identification is important. A limited number of apicoplast membrane proteins have been identified experimentally, but it is impractical to identify them all in vitro. Thus, there is a strong need for identification of apicoplast membrane proteins by means of a computational approach. Unfortunately, no such computational method exists.

Methodology/Principal Findings: In this work, we develop a method for predicting apicoplast-targeted transmembrane proteins for multiple species of Apicomplexa, whereby several classifiers trained on different feature sets and based on different algorithms are evaluated and combined in an ensemble classification model to obtain the best expected performance. The feature sets considered are the hydrophobicity and composition characteristics of amino acids over transmembrane domains, the existence of short sequence motifs over cytosolically disposed regions, and Gene Ontology (GO) terms associated with given proteins. Our model, ApicoAMP, is an ensemble classification model that combines decisions of classifiers following the majority vote principle. ApicoAMP is trained on a set of proteins from 11 apicomplexan species and achieves 91% overall expected accuracy.

Conclusions/Significance: ApicoAMP is the first computational model capable of identifying apicoplast-targeted transmembrane proteins in Apicomplexa. The ApicoAMP prediction software is available at and

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Cilingir, Gokcen, Audrey OT Lau, and Shira L. Broschat. "ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa." Journal of microbiological methods 95.3 (2013): 313-319.