Doctoral Seminar: Munira Alballa

Speaker: Munira Alballa

Supervisor: Dr. G. Butler

Supervisory Committee: Drs. L. Kossein, A. Krzyzak, J. Y. Yu

Title: On Predicting Transporter Proteins

Date: Thursday, March 28, 2019

Time: 11:40 a.m.

Place: EV 3.309

ABSTRACT

The publication of numerous genome projects has produced an abundance of proteins sequences, many of which remain unannotated. Membrane proteins, which include transporters, receptors, enzymes, and others, are among the least characterized proteins, owing to their hydrophobic surfaces and their lack of conformational stability. This research aims to build a proteome-wide system that can determine the transporter substrate specificity. This involves distinguishing membrane proteins, differentiating transporters from other functional types of membrane proteins and detecting the substrate specificity of the transporters.

To distinguish membrane from non-membrane proteins, we evaluated the performance of various feature extraction techniques in combination with different learning algorithms. Experimental results show that incorporating evolution information consistently performs better than using traditional amino acid compositions. The highest prediction outcome was achieved by an ensemble classifier that fuses the results of OET-KNN (Optimized Evidence-Theoretic K-Nearest Neighbor) classifiers where protein samples are represented by Pseudo Position-Specific Score Matrix (Pse-PSSM) vectors. We also found that incorporating transmembrane topology prediction tools can further boost the overall accuracy by 2.17%.