Computing Prediction and Functional Analysis of Prokaryotic Propionylation

• Published in: Journal of chemical information and modeling Vol. 57; no. 11; pp. 2896 - 2904
• Main Authors: Wang, Li-Na, Shi, Shao-Ping, Wen, Ping-Ping, Zhou, Zhi-You, Qiu, Jian-Ding
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 27.11.2017
• Subjects: Binding Sites; Computational Biology - methods; Encyclopedias; Evolution, Molecular; Experiments; Functional analysis; Gene Ontology; Genomes; Genomics; Lysine; Lysine - metabolism; Metabolism; Predictions; Prokaryotes; Prokaryotic Cells - metabolism; Protein Interaction Mapping; Protein Processing, Post-Translational; Protein synthesis; Proteins; Substrates; Support vector machines; Validation studies
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/acs.jcim.7b00482

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.