Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins

• Main Authors: Mallika V, Sivakumar Kc, Jaichand S, Soniya Ev
• Format: Article
• Language: English
• Published: De Gruyter 2010-03-01
• Series: Journal of Integrative Bioinformatics
• Online Access: http://www.degruyter.com/view/j/jib.2010.7.issue-1/biecoll-jib-2010-143/biecoll-jib-2010-143.xml?format=INT

Type III Polyketide synthases (PKS) are family of proteins considered to have significant role in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins show positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence, being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machine (SVM). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.