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• W2766753586 abstract "Identification and systematic analysis of candidates for protein propionylation are crucial steps for understanding its molecular mechanisms and biological functions. Although several proteome-scale methods have been performed to delineate potential propionylated proteins, the majority of lysine-propionylated substrates and their role in pathological physiology still remain largely unknown. By gathering various databases and literatures, experimental prokaryotic propionylation data were collated to be trained in a support vector machine with various features via a three-step feature selection method. A novel online tool for seeking potential lysine-propionylated sites (PropSeek) ( http://bioinfo.ncu.edu.cn/PropSeek.aspx ) was built. Independent test results of leave-one-out and n-fold cross-validation were similar to each other, showing that PropSeek is a stable and robust predictor with satisfying performance. Meanwhile, analyses of Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathways, and protein-protein interactions implied a potential role of prokaryotic propionylation in protein synthesis and metabolism." @default.
• W2766753586 created "2017-11-10" @default.
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• W2766753586 date "2017-11-07" @default.
• W2766753586 modified "2023-10-17" @default.
• W2766753586 title "Computing Prediction and Functional Analysis of Prokaryotic Propionylation" @default.
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• W2766753586 doi "https://doi.org/10.1021/acs.jcim.7b00482" @default.
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• W2766753586 hasPublicationYear "2017" @default.
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