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

Abstract

Polyketide is a major class of natural product 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 –was created to observe the resulting structure per elongation, and to allow
prediction and visualization of the most possible natural product compound on the
molecular level. Such design can provide the researchers the structures of some novel
compounds, which can be used for in-silico drug research and screening. With these
functionalities, Predyketide can help in the discovery of new drugs – requiring lesser time
and effort.

Key words: polyketide, polyketide synthase, Tanimoto coefficient, modular synthesis,
domain sequence