title: Prediction of ketoacyl synthase family using reduced amino acid alphabets.
abstract: Ketoacyl synthases are enzymes involved in fatty acid synthesis and can be classified into five families based on primary sequence similarity. Different families have different catalytic mechanisms. Developing cost-effective computational models to identify the family of ketoacyl synthases will be helpful for enzyme engineering and in knowing individual enzymes' catalytic mechanisms. In this work, a support vector machine-based method was developed to predict ketoacyl synthase family using the n-peptide composition of reduced amino acid alphabets. In jackknife cross-validation, the model based on the 2-peptide composition of a reduced amino acid alphabet of size 13 yielded the best overall accuracy of 96.44% with average accuracy of 93.36%, which is superior to other state-of-the-art methods. This result suggests that the information provided by n-peptide compositions of reduced amino acid alphabets provides efficient means for enzyme family classification and that the proposed model can be efficiently used for ketoacyl synthase family annotation.
authors: Department of Physics, College of Sciences, Center for Genomics and Computational Biology, Hebei United University, Tangshan 063000, China. chenwei_imu@yahoo.com.cn
authors: Wei
authors: W
authors: Chen
authors: Pengmian
authors: P
authors: Feng
authors: Hao
authors: H
authors: Lin
mesh_terms: D000217:Acyltransferases / Q000032:analysis* / Q000737:chemistry; D000595:Amino Acid Sequence; D000596:Amino Acids / Q000032:analysis; D000818:Animals; D002384:Catalysis; D006801:Humans; D008956:Models, Chemical; D008969:Molecular Sequence Data; D010455:Peptides / Q000737:chemistry; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D010455:Peptides; D000217:Acyltransferases