INTERACTOME NETWORK ANALYSIS TO IDENTIFY DRUG TARGETS OF MYCOBACTERIUM TUBERCULOSIS H37RV

Abstract

The preliminary step in rational-based drug discovery is identifying the society’s unmet medical needs that are not properly addressed with the available treatments. After prioritizing the unmet medical needs, drug target identification is the first and key phase in the pipeline. In the previous target-based drug discovery the failure rate is very high. Many of these failures are attributed to improper target identification. Our inadequate knowledge about the disease and molecular mechanisms played a significant role. The wealth of data and information in this ‘omics’ era present immense of new opportunities to enhance our understanding about the disease dynamics at cellular and molecular level. With these advancements, the task of successful identification of therapeutic targets becomes more promising. However, there is no single sufficient-enough method due to the complexity of human diseases, heterogeneity of biological data and inherent limitations of each approach. Therefore, systematically integrated computational methods can be used to identify potential drug targets for high burden drug-resistance diseases like tuberculosis (TB). TB is an infectious disease caused by the infamous etiological agent Mycobacterium tuberculosis (Mtb). It is the cause of morbidity and mortality to millions every year. Mycobacterium tuberculosis H37Rv is the most studied strain of TB. The emergences and rise of drug-resistance is the main bottleneck for the management, control and eradication programs of TB. Various strategies have been implemented to counter the problem of resistance. However, available statistics indicates that resistance forms are still on the rise. Drugs used in the current treatment of drug-resistance TB are expensive, toxic, with adverse side effects and ineffective to act on the latent forms of bacillus. The stated shortcomings highlight the requirement of new therapeutic targets. In this thesis, comprehensive protein-protein interactome network analyses have been carried out to identify potential drug targets and co-targets of Mycobacterium tuberculosis H37Rv. Proteins involved in the same cellular processes often interact with each other. Protein–protein interaction network analysis is fundamental to understand the complexity of biological systems by revealing hidden relationships between drugs, genes, proteins and diseases. There is enormous amount of protein-protein interaction data in various repositories due to the advancement of techniques such as two-hybrid systems, mass spectrometry, and protein microarrays. These analyses have been carried out by aiming to obtain important system-level insights about TB and counter the challenges at the target identification phase of drug discovery ii process. In silco molecular modelling and structure analysis has also been carried out for protein translocase subunit SecY (Rv0732). The list of potential primary drug targets has been identified through analysis of comparative genome and network centrality measures on the protein-protein interaction network of the pathogen. The interaction dataset was retrieved from STRING. It is one of the main sources of protein-protein interaction data of TB. It acts as a meta-database by integrating interactions from numerous sources such as experimental repositories, computational prediction methods and public text collections. The protein-protein interaction dataset of Mycobacterium tuberculosis H37Rv in STRING has been shown that it is of low quality by containing false positives and false negatives. This can affect the results of any analysis which is based on this dataset. To minimize the impact, the portion of the dataset which is more reliable has been considered. The four centrality measures degree, closeness, betweenness and eigenvector have been used to identify the most central proteins in the interactome network. Only proteins that found at the centre of gravity of the interactome network were considered. BLAST search of protein coding genes has been carried out against DEG to filter out genes which are essential for the survival and growth of the pathogen. The corresponding protein sequences obtained after DEG search were subjected to BLASTp search against the non-redundant database with an e-value threshold cut off set to 0.005 and restricted to Homo sapiens to avoid the possible host toxicity at the sequence level. A list consisting of 137 proteins have been proposed as potential primary drug targets of Mycobacterium tuberculosis H37Rv. These proteins are believed to be reliable targets since they are reported as essential proteins for the growth and survival of the pathogen, have no detectable homology with human so as to prevent host toxicity and prioritized based on their network centrality measure values where all of them were found within the close neighbourhood of the centre of gravity of protein-protein interaction network. Many of the proteins in the list have been reported as drug targets by other methods. The potential primary drug targets have been further prioritized based on their influence to resistance genes using maximum flow approach on weighted proteome interaction network of the pathogen. The weighted protein-protein interaction network of the pathogen has been constructed using a dataset retrieved from STRING. The combined score values of the pair of interacting proteins has been assigned as weight of interactions. The potential drug targets and resistance genes have been taken as inputs. Then, the potential drug targets have been prioritized based on their maximum flow value to resistance genes. This approach does not iii suffer from biasness towards shortest paths since it is based on flow. More importantly, the inhibition of a protein which has more influence on the resistance genes of the existing drugs is expected to disrupt the communication to these genes. Hence, it can be considered as an additional druggablity assessment criteria for drug resistance diseases like TB. Our limited system-level knowledge about the possible routes of resistance is one of the causes of failure to strategies against drug-resistance TB. Detailed analysis has been carried out to explore these routes through which information required for triggering drug-resistance may be passed on in the cell. Proteins involved in the emergence of resistance by mediating information among drug target proteins of eight clinically used drugs in the current treatment regime of TB and resistance genes have been identified. These lists of proteins have been proposed as potential co-targets of each drug. The analysis has been carried out on weighted drug-specific protein-protein interaction networks of the pathogen. The validated drug targets and resistance genes have been taken as inputs. The maximum flow values of proteins in the flow from validated drug targets to resistance genes have been computed. Proteins have been prioritized based on their maximum flow value. Subsequent filters such as non-homologous assessment to avoid host toxicity, identification of proteins that interact with the host and essentiality analysis have been carried out. The final refined lists of proteins have strong involvement in the emergence of drug resistance and targeting them with systematic combination of existing drugs is believed to be effective to prevent the emergence of drug resistance. In silco structural analysis of protein translocase subunit SecY (Rv0732) has been carried out to get descriptive three-dimensional structure. Rv0732 has been selected because it is highly ranked potential drug target without solved three-dimensional structure. The active site has been identified for protein-ligand or protein-inhibitor binding.