Prediction of Polyketide Product from Module Organization of Enzymes Using Cumulative Tanimoto Fragment Scores

Abstract

Polyketide is a major class of natural products possessing several pharmacological properties. Performing wet laboratory experiments to discover a functional polyketide is costly and difficult because of its trial-and-error nature. However, the analogous biosynthesis of these metabolites to fatty acids makes the resulting compound predictable. Through the use of information technology, a stand-alone computational tool –Predyketide – is created to observe the resulting structure per elongation, and to allow prediction and visualization of the most possible natural product compound. The list of all known building blocks (starter and extender) used in the system is gathered from ASMPKS, another polyketide-related system. With these functionalities, this application can help in the discovery of new drugs requiring lesser time and effort.