Abstract

A COMPUTATIONAL METHOD FOR IDENTIFICATION OF PATHOGENIC CANDIDATES AMONGST THE HYPOTHETICAL PROTEINS IN CHLAMYDIA TRACHOMATIS D/UW-3/CX

Shahid Ali*, Laishram Singh, Reetika Arora, Yonghua Wang

ABSTRACT

Chlamydia trachomatis is the most usual manifestation of curable bacterial sexually transmitted infection worldwide. It is primarily present as urethritis in males and end-cervicitis in females. Traditionally, tissue culture was the gold standard for diagnosis. However, with the advent of advanced molecular methods of diagnosis, which are not only highly sensitive and specific but are cost-effective too. However, due to generous size of genome analysis becomes an arduous task. This problem can be overcome by using bioinformatics tools which makes it possible to analyze large molecular data within a short period of time and cost effective. The size of C. trachomatis genome (AC: NC_000117.1) is relatively smaller in comparison to other bacteria and contains 887 functional proteins, out of which 266 are classified as hypothetical proteins (HPs) because of the unavailability of experimentally validated functions. The function of the HPs were predicted by integrating a variety of protein classification systems, motif discovery tools as well as methods that are based on characteristic features obtained from the protein sequence. The probable virulence factor proteins of HPs were predicted successfully. Furthermore, the virulent HPs present in the set of 18 functionally annotated proteins were predicted by using the Bioinformatics tools and the conformational behaviours of the proteins with highest virulence scores amongst the effector-1 protein were studied by using the molecular dynamics simulations. This study will facilitate in a better understanding of various drug resistance and pathogenesis mechanisms present in the C. trachomatis which can be utilized in designing improved therapeutic agents.

Keywords: Hypothetical proteins (HPs).