BioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts.

BACKGROUND: Tuberculosis (TB) is the second leading cause of death from a single infectious organism, demanding attention towards discovery of novel anti-tubercular compounds. Natural products or their derivatives have provided more than 50% of all existing drugs, offering a chemically diverse space for discovery of novel drugs. DESCRIPTION: BioPhytMol has been designed to systematically curate and analyze the anti-mycobacterial natural product chemical space. BioPhytMol is developed as a drug-discovery community resource with anti-mycobacterial phytomolecules and plant extracts. Currently, it holds 2582 entries including 188 plant families (692 genera and 808 species) from global flora, manually curated from literature. In total, there are 633 phytomolecules (with structures) curated against 25 target mycobacteria. Multiple analysis approaches have been used to prioritize the library for drug-like compounds, for both whole cell screening and target-based approaches. In order to represent the multidimensional data on chemical diversity, physiochemical properties and biological activity data of the compound library, novel approaches such as the use of circular graphs have been employed. CONCLUSION: BioPhytMol has been designed to systematically represent and search for anti-mycobacterial phytochemical information. Extensive compound analyses can also be performed through web-application for prioritizing drug-like compounds. The resource is freely available online at http://ab-openlab.csir.res.in/biophytmol/. Graphical AbstractBioPhytMol: a drug discovery community resource on anti-mycobacterial phytomolecules and plant extracts generated using Crowdsourcing. The platform comprises of manually curated data on antimycobacterial natural products along with tools to perform structure similarity and visualization. The platform allows for prioritization of drug like natural products for antimycobacterial drug discovery.

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

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.

A reverse vaccinology approach for the identification of potential vaccine candidates from Leishmania spp.

Leishmaniasis is a group of diseases with a spectrum of clinical manifestations ranging from cutaneous ulcers to visceral leishmaniasis, which results from the bite of an infected sandfly to human. Attempts to develop an effective vaccine have been shown to be feasible but no vaccine is in active clinical use. This study adopts a Reverse Vaccinology approach to identify common vaccine candidates from both highly pathogenic species Leishmania major and Leishmania infantum. Total proteome of both species were compared to identify common proteins, which are further taken for sub-cellular localization and transmembrane helices prediction. Plasma membrane proteins having only one transmembrane helix were first identified and analyzed which are non-homologous in human and mouse in order to avoid molecular mimicry with other proteins. Selected proteins were analyzed for their binding efficiency to both major histocompatibility complex (MHC) class I and class II alleles. As a result, 19 potential epitopes are screened in this study using different approaches, which can be further verified through in vivo experiments in MHC compatible animal models. This study demonstrates that Reverse Vaccinology approach has potential in discovering various immunogenic antigens from in silico analysis of pathogen's genome or proteome instead of culturing the whole organism by conventional methods.