Albany 2015:Book of Abstracts

Albany 2015
Conversation 19
June 9-13 2015
©Adenine Press (2012)

Designing Non-Toxic Ligands against TB using Data Intensive Genome Level Analysis

The current approaches to discover new and efficient drugs to treat multi drug resistant (MDR) and extreme drug resistant (XDR) Tuberculosis (TB) are prohibitively expensive leading to a dry pipeline for new chemical entities (NCE). Newer strategies need to be developed for reducing long and protracting procedures of clinical development of new drugs. To minimize the cost of TB drug discovery, innovative approaches of designing NCE need to be devised utilizing a systems biology approach. Previously from our lab, critical and low concentration proteins required for the growth and survival of Mycobacterium tuberculosis (Mtb) was identified using a novel Systems Biology Spindle Map (SBSM) (Vashisht, 2014). The result of the in silico single gene knock out analysis was carried out on a total of 890 metabolic genes and 961 metabolites involved in 1152 reactions to identify putative drug targets. Out of the 116 lethal genes, 75 of these protein/enzymes had a lower concentration than the mean protein concentration making them effective drug targets. In the present work, these genes are being studied for their potential as putative, non-toxic and metabolically critical drug targets. Based on the existing knowledge, we also hypothesize that the most critical targets in Mtb should be evolutionarily conserved.

Analysis of these targets for their conservation across the 1879 genomes of Mtb using GMTV database (Chernyaeva, 2014) was evaluated. Though, it is known that a large number of the sequences are conserved, only a small number actually remains invariant across the various strains of the bacteria. The initial analysis resulted in two genes, which showed no variation across the wide Mtb genome reported in the database. The two genes viz. Rv3607c (involved in folate biosynthesis) and Rv0321 (involved in interconversion of dCTP and dUTP) have an existing PDB structure. The former also has a reported GSK molecule proposed to be its inhibitor, generated as a result of a high-throughput screen (HTS) of two million compound library for anti-mycobacterial phenotypes further resulting into an open access list of 776 compounds with activity against tuberculosis (Jimenez, 2013). Based on the understanding of the active site of these proteins, and utilizing structure based drug design approaches as well as analyzing the SAR around the reported GSK molecule, we have generated a library of compounds from the existing chemical databases. The small molecules library has compounds, some of which are also the existing drugs for different diseases. Hence, together with recalibrating old drugs, utilizing small molecules databases and performing SAR on these, the generated library of compounds were docked (Glide tool of Schrodinger) against the two proteins of interest with improved binding affinities.

These studies are expected to lead to the generation of a new anti- TB drug candidate, primarily developed in silico. With these methodologies, it is proposed that, new ligands can be generated, with the success rate of 1/10 as compared to the existing 1/100 molecule entering clinical trials.

Authors thank TATA-OSDD, J. C. Bose National Fellowship and R. Vashisht for providing the target analysis data.

    R. Vashisht, et. al. (2014) Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high value durg targets J. Transl. Med., 12:263.

    E. N. Chernyaeva, et al. (2014) Genome-wide Mycobacterium tuberculosis variation (GMTV) database: a new tool for integrating sequence variations and epidemiology BMC Genomics, 15:308.

    F. M. Jimenez, et al. (2013) Target prediction of an open access set of compounds active against Mycobacterium tuberculosis Plos. Comp. Biol., 9, e100253.

Divneet Kaur 1, 2
S. K. Brahmachari 1, 2, 3*

1> CSIR- Institute of Genomics and Integrative Biology
New Delhi, India
New Delhi, India
3 Academy of Scientific and Innovative Research
New Delhi, India