Title

PREDICTION OF ENZYMATIC PROPERTIES OF PROTEIN SEQUENCES BASED ON THE ENZYME COMMISION NOMENCLATURE

Abstract

Abstract

The volume of expert manual annotation of biomolecules is steady due to high costs associated with it, although the number of sequenced genomes continues to grow exponentially. Computational methods have been proposed in order to predict the attributes of gene products. The prediction of Enzyme Commission (EC) numbers is a challenging issue in this area. Enzymes have crucial roles in metabolic pathways, therefore they are widely employed in biotechnological and biomedical applications. EC numbers are numerical representations of enzymatic functions based on chemical reactions that they catalyze. Due to the cost and labor extensiveness of in vitro experiments EC classification annotation of catalytically active proteins are limited. Therefore, computational tools have been proposed to classify these proteins to annotate them with EC nomenclature. However, the performance results of existing tools indicate that EC number prediction field still requires improvement. Here, we present an EC number prediction tool, ECPred, to obtain predictions for large-scale protein sets. In ECPred, we employed hierarchical data preparation and evaluation steps by utilizing the functional relations among the four levels of EC annotation system. The main features that distinguish our approach from existing studies are the use of a combination of independent classifiers, and novel data preparation and evaluation methods

Biography:

Supervisor(s)

Supervisor(s)

ALPEREN DALKIRAN

Date and Location

Date and Location

2017-09-07;11:00:00-A101

Category

Category

MSc_Thesis