Dataset Information

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

ABSTRACT: A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

SUBMITTER: Vashisht R

PROVIDER: S-EPMC3395720 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

Vashisht Rohit R Mondal Anupam Kumar AK Jain Akanksha A Shah Anup A Vishnoi Priti P Priyadarshini Priyanka P Bhattacharyya Kausik K Rohira Harsha H Bhat Ashwini G AG Passi Anurag A Mukherjee Keya K Choudhary Kumari Sonal KS Kumar Vikas V Arora Anshula A Munusamy Prabhakaran P Subramanian Ahalyaa A Venkatachalam Aparna A Gayathri S S Raj Sweety S Chitra Vijaya V Verma Kaveri K Zaheer Salman S Balaganesh J J Gurusamy Malarvizhi M Razeeth Mohammed M Raja Ilamathi I Thandapani Madhumohan M Mevada Vishal V Soni Raviraj R Rana Shruti S Ramanna Girish Muthagadhalli GM Raghavan Swetha S Subramanya Sunil N SN Kholia Trupti T Patel Rajesh R Bhavnani Varsha V Chiranjeevi Lakavath L Sengupta Soumi S Singh Pankaj Kumar PK Atray Naresh N Gandhi Swati S Avasthi Tiruvayipati Suma TS Nisthar Shefin S Anurag Meenakshi M Sharma Pratibha P Hasija Yasha Y Dash Debasis D Sharma Arun A Scaria Vinod V Thomas Zakir Z Chandra Nagasuma N Brahmachari Samir K SK Bhardwaj Anshu A

PloS one 20120711 7

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourci ...[more]

PMID: 22808064

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Project description:BackgroundEmergence of drug resistant varieties of tuberculosis is posing a major threat to global tuberculosis eradication programmes. Although several approaches have been explored to counter resistance, there has been limited success due to a lack of understanding of how resistance emerges in bacteria upon drug treatment. A systems level analysis of the proteins involved is essential to gain insights into the routes required for emergence of drug resistance.ResultsWe derive a genome-scale protein-protein interaction network for Mycobacterium tuberculosis H37Rv from the STRING database, with proteins as nodes and interactions as edges. A set of proteins involved in both intrinsic and extrinsic drug resistance mechanisms are identified from literature. We then compute shortest paths from different drug targets to the set of resistance proteins in the protein-protein interactome, to derive a sub-network relevant to study emergence of drug resistance. The shortest paths are then scored and ranked based on a new scheme that considers (a) drug-induced gene upregulation data, from microarray experiments reported in literature, for the individual nodes and (b) edge-hubness, a network parameter which signifies centrality of a given edge in the network. High-scoring paths identified from this analysis indicate most plausible pathways for the emergence of drug resistance. Different targets appear to have different propensities for four drug resistance mechanisms. A new concept of 'co-targets' has been proposed to counter drug resistance, co-targets being defined as protein(s) that need to be simultaneously inhibited along with the intended target(s), to check emergence of resistance to a given drug.ConclusionThe study leads to the identification of possible pathways for drug resistance, providing novel insights into the problem of resistance. Knowledge of important proteins in such pathways enables identification of appropriate 'co-targets', best examples being RecA, Rv0823c, Rv0892 and DnaE1, for drugs targeting the mycolic acid pathway. Insights obtained about the propensity of a drug to trigger resistance will be useful both for more careful identification of drug targets as well as to identify target-co-target pairs, both implementable in early stages of drug discovery itself. This approach is also inherently generic, likely to significantly impact drug discovery.

Project description:BackgroundTuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation.ResultsWe report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed.ConclusionThe pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Dataset Information

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

Publications

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

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