کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
2090097 1081474 2013 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی بیوتکنولوژی یا زیست‌فناوری
پیش نمایش صفحه اول مقاله
ApicoAMP: The first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa
چکیده انگلیسی


• An ensemble classifier for predicting apicoplast-targeted transmembrane proteins
• A training set comprised of 11 apicomplexan species was curated.
• The ensemble classifier achieves 91% overall expected accuracy.
• A user-friendly, Python-based program of the prediction tool is provided.
• Complete lists of predicted proteins for all 11 apicomplexan species are provided.

BackgroundComputational identification of apicoplast-targeted proteins is important in drug target determination for diseases such as malaria. While there are established methods for identifying proteins with a bipartite signal in multiple species of Apicomplexa, not all apicoplast-targeted proteins possess this bipartite signature. The publication of recent experimental findings of apicoplast membrane proteins, called transmembrane proteins, that do not possess a bipartite signal has made it feasible to devise a machine learning approach for identifying this new class of apicoplast-targeted proteins computationally.Methodology/principal findingsIn 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/significanceApicoAMP is the first computational model capable of identifying apicoplast-targeted transmembrane proteins in Apicomplexa. The ApicoAMP prediction software is available at http://code.google.com/p/apicoamp/ and http://bcb.eecs.wsu.edu.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Microbiological Methods - Volume 95, Issue 3, December 2013, Pages 313–319
نویسندگان
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