Phylogenetic Analysis of the vesicular fusion SNARE machinery revealing its functional divergence across Eukaryotes.

Proteins of the SNARE (Soluble N-ethylmaleimide-sensitive factor attachment protein receptors) family play a significant role in all vesicular fusion events involved in endocytic and exocytic pathways. These proteins act as molecular machines that assemble into tight four-helix bundle complex, bridging the opposing membranes into close proximity forming membrane fusion. Almost all SNARE proteins share a 53 amino acid coiled-coil domain, which is mostly linked to the transmembrane domain at the C-terminal end. Despite significant variations between SNARE sequences across species, the SNARE mediated membrane fusion is evolutionary conserved in all eukaryotes. It is of interest to compare the functional divergence of SNARE proteins across various eukaryotic groups during evolution. Here, we report an exhaustive phylogeny of the SNARE proteins retrieved from SNARE database including plants, animals, fungi and protists. The Initial phylogeny segregated SNARE protein sequences into five well-supported clades Qa, Qb, Qc, Qbc and R reflective of their positions in the four-helix SNARE complex. Further to improve resolution the Qa, Qb, Qc and R family specific trees were reconstructed, each of these were further segregated into organelle specific clades at first and later diverged into lineage specific subgroups. This revealed that most of the SNARE orthologs are conserved at subcellular locations or at trafficking pathways across various species during eukaryotic evolution. The paralogous expansion in SNARE repertoire was observed at metazoans (animals) and plants independently during eukaryotic evolution. However, results also show that the multi-cellular and saprophytic fungi have limited SNAREs.

Functional analysis of TPM domain containing Rv2345 of Mycobacterium tuberculosis identifies its phosphatase activity.

Mycobacterium tuberculosis (Mtb) is the causal agent of tuberculosis, the second largest infectious disease. With the rise of multi-drug resistant strains of M. tuberculosis, serious challenge lies ahead of us in treating the disease. The availability of complete genome sequence of Mtb has improved the scope for identifying new proteins that would not only further our understanding of biology of the organism but could also serve to discover new drug targets. In this study, Rv2345, a hypothetical membrane protein of M. tuberculosis H37Rv, which is reported to be a putative ortholog of ZipA cell division protein has been assigned function through functional annotation using bioinformatics tools followed by experimental validation. Sequence analysis showed Rv2345 to have a TPM domain at its N-terminal region and predicted it to have phosphatase activity. The TPM domain containing region of Rv2345 was cloned and expressed using pET28a vector in Escherichia coli and purified by Nickel affinity chromatography. The purified TPM domain was tested in vitro and our results confirmed it to have phosphatase activity. The enzyme activity was first checked and optimized with pNPP as substrate, followed by using ATP, which was also found to be used as substrate by the purified protein. Hence sequence analysis followed by in vitro studies characterizes TPM domain of Rv2345 to contain phosphatase activity.

Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina.

UNLABELLED: The Mur E enzyme of Mur pathway of Mycobacterium tuberculosis is an attractive drug target as it is unique to bacteria and is absent in mammalian cells. The virtual screening of large libraries of drug like molecules against a protein target is a common strategy used to identify novel inhibitors. However, the method has a large number of pitfalls, with large variations in accuracy caused in part by inaccurate protocols, use of improper standards and libraries, and system dependencies such as the potential for nonspecific docking from large active-site cavities. The screening of drug-like small molecules from diversity sets can, however, be used to short-list potential fragments as building blocks to generate leads with improved specificity. We describe a protocol to implement this strategy, which involves an analysis of the active site and known inhibitors to identify orthospecific determinants, virtual screening of a drug-like diversity library to identify potential drug primitives, and inspection of the potential docked fragments for both binding potential and toxicity. The protocol is implemented on the M.tb Mur E protein which has a large active site with poor enrichment of known positives and a set of drug-like molecules that meets this criteria is presented for further analysis. ABBREVIATIONS: MTB - Mycobacterium tuberculosis, NCI - National Cancer Institute, PDB - Protein Databank.

HMM-ModE: implementation, benchmarking and validation with HMMER3.

BACKGROUND: HMM-ModE is a computational method that generates family specific profile HMMs using negative training sequences. The method optimizes the discrimination threshold using 10 fold cross validation and modifies the emission probabilities of profiles to reduce common fold based signals shared with other sub-families. The protocol depends on the program HMMER for HMM profile building and sequence database searching. The recent release of HMMER3 has improved database search speed by several orders of magnitude, allowing for the large scale deployment of the method in sequence annotation projects. We have rewritten our existing scripts both at the level of parsing the HMM profiles and modifying emission probabilities to upgrade HMM-ModE using HMMER3 that takes advantage of its probabilistic inference with high computational speed. The method is benchmarked and tested on GPCR dataset as an accurate and fast method for functional annotation. RESULTS: The implementation of this method, which now works with HMMER3, is benchmarked with the earlier version of HMMER, to show that the effect of local-local alignments is marked only in the case of profiles containing a large number of discontinuous match states. The method is tested on a gold standard set of families and we have reported a significant reduction in the number of false positive hits over the default HMM profiles. When implemented on GPCR sequences, the results showed an improvement in the accuracy of classification compared with other methods used to classify the familyat different levels of their classification hierarchy. CONCLUSIONS: The present findings show that the new version of HMM-ModE is a highly specific method used to differentiate between fold (superfamily) and function (family) specific signals, which helps in the functional annotation of protein sequences. The use of modified profile HMMs of GPCR sequences provides a simple yet highly specific method for classification of the family, being able to predict the sub-family specific sequences with high accuracy even though sequences share common physicochemical characteristics between sub-families.

MetaNET--a web-accessible interactive platform for biological metabolic network analysis.

BACKGROUND: Metabolic reactions have been extensively studied and compiled over the last century. These have provided a theoretical base to implement models, simulations of which are used to identify drug targets and optimize metabolic throughput at a systemic level. While tools for the perturbation of metabolic networks are available, their applications are limited and restricted as they require varied dependencies and often a commercial platform for full functionality. We have developed MetaNET, an open source user-friendly platform-independent and web-accessible resource consisting of several pre-defined workflows for metabolic network analysis. RESULT: MetaNET is a web-accessible platform that incorporates a range of functions which can be combined to produce different simulations related to metabolic networks. These include (i) optimization of an objective function for wild type strain, gene/catalyst/reaction knock-out/knock-down analysis using flux balance analysis. (ii) flux variability analysis (iii) chemical species participation (iv) cycles and extreme paths identification and (v) choke point reaction analysis to facilitate identification of potential drug targets. The platform is built using custom scripts along with the open-source Galaxy workflow and Systems Biology Research Tool as components. Pre-defined workflows are available for common processes, and an exhaustive list of over 50 functions are provided for user defined workflows. CONCLUSION: MetaNET, available at http://metanet.osdd.net , provides a user-friendly rich interface allowing the analysis of genome-scale metabolic networks under various genetic and environmental conditions. The framework permits the storage of previous results, the ability to repeat analysis and share results with other users over the internet as well as run different tools simultaneously using pre-defined workflows, and user-created custom workflows.

Open source drug discovery--a new paradigm of collaborative research in tuberculosis drug development.

It is being realized that the traditional closed-door and market driven approaches for drug discovery may not be the best suited model for the diseases of the developing world such as tuberculosis and malaria, because most patients suffering from these diseases have poor paying capacity. To ensure that new drugs are created for patients suffering from these diseases, it is necessary to formulate an alternate paradigm of drug discovery process. The current model constrained by limitations for collaboration and for sharing of resources with confidentiality hampers the opportunities for bringing expertise from diverse fields. These limitations hinder the possibilities of lowering the cost of drug discovery. The Open Source Drug Discovery project initiated by Council of Scientific and Industrial Research, India has adopted an open source model to power wide participation across geographical borders. Open Source Drug Discovery emphasizes integrative science through collaboration, open-sharing, taking up multi-faceted approaches and accruing benefits from advances on different fronts of new drug discovery. Because the open source model is based on community participation, it has the potential to self-sustain continuous development by generating a storehouse of alternatives towards continued pursuit for new drug discovery. Since the inventions are community generated, the new chemical entities developed by Open Source Drug Discovery will be taken up for clinical trial in a non-exclusive manner by participation of multiple companies with majority funding from Open Source Drug Discovery. This will ensure availability of drugs through a lower cost community driven drug discovery process for diseases afflicting people with poor paying capacity. Hopefully what LINUX the World Wide Web have done for the information technology, Open Source Drug Discovery will do for drug discovery.