A Computational Biology Approach in Function Annotation to Enzymes

Abstract

Although homologous proteins do not necessarily exhibitidentical biochemical functions, local and global sequence similarity is widely used as an indication of functional identity. Enzyme Commission (EC) classified hundreds of thousands of enzymes into six essential classes. Then in each class, enzymes are given four digits numbers such that enzymes with identical functions carry the same EC number. EC numbers provide a well-defined, non-ambiguous method for annotation of enzyme function. In this article, in each of six enzyme class, enzymes are classified according to their EC numbers into enzyme subclasses. Among enzymes and enzyme subclasses a new similarity measure is defined, and it is seen that, similar enzymes according to this new similarity measure exhibit identical biochemical functions in 94% of the cases. This similarity measure is used for function annotation to enzymes, and an average accuracy rate of 94% is achieved. The technique is also used for function annotation to unknown enzymes.