Abstract

Transporters make up a large proportion of proteins in a cell, and play important roles in metabolism, regulation, and signal transduction by mediating movement of compounds across membranes but they are among the least characterized proteins due to their hydrophobic surfaces and lack of conformational stability. There is a need for tools that predict the substrates which are transported at the level of substrate class and the level of specific substrate.

This work develops a predictor, TportHMM, using profile Hidden Markov Model (HMM) and Multiple Sequence Alignment (MSA). We explore the role of multiple sequence alignment (MSA) algorithms to utilise evolutionary information, specificity-determining site (SDS) algorithms to highlight positional information, and a profile Hidden Markov Model (HMM) classifier to utilise sequence information.

We study the impact of different MSA algorithms (ClustalW, Clustal Omega, MAFFT, MUSCLE, AQUA, T-Coffee and TM-Coffee), and different SDS algorithms (Speer Server, GroupSim, Xdet and TCS). We compare these approaches with the state-of-the-art, TrSSP and TranCEP.