ArticleJournal of industrial microbiology & biotechnology2012
Prediction of ketoacyl synthase family using reduced amino acid alphabets.
Article in Journal of industrial microbiology & biotechnology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Research progress of reduced amino acid alphabets in protein analysis and prediction.Computational and structural biotechnology journal · 2022Review
- Predicting Ion Channels Genes and Their Types With Machine Learning Techniques.Frontiers in genetics · 2019Article
- SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides.BMC genomics · 2017Article
- IonchanPred 2.0: A Tool to Predict Ion Channels and Their Types.International journal of molecular sciences · 2017Article
- Identification of apolipoprotein using feature selection technique.Scientific reports · 2016Article
- Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.International journal of genomics · 2016Article
- Recombination Hotspot/Coldspot Identification Combining Three Different Pseudocomponents via an Ensemble Learning Approach.BioMed research international · 2016Article
- An Ensemble Method to Distinguish Bacteriophage Virion from Non-Virion Proteins Based on Protein Sequence Characteristics.International journal of molecular sciences · 2015Article
- Protein binding site prediction by combining hidden Markov support vector machine and profile-based propensities.TheScientificWorldJournal · 2014Article
- Predicting the types of J-proteins using clustered amino acids.BioMed research international · 2014Article
- Unearthing the root of amino acid similarity.Journal of molecular evolution · 2013Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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