Enhanced functional and structural domain assignments using remote similarity detection procedures for proteins encoded in the genome of Mycobacterium tuberculosis H37Rv

Master Blaster: an approach to sensitive identification of remotely related proteins

Genome sequencing projects unearth sequences of all the protein sequences encoded in a genome. As the first step, homology detection is employed to obtain clues to structure and function of these proteins. However, high evolutionary divergence between homologous proteins challenges our ability to detect distant relationships. In the past, an approach involving multiple Position Specific Scoring Matrices (PSSMs) was found to be more effective than traditional single PSSMs. Cascaded search is another successful approach where hits of a search are queried to detect more homologues. We propose a protocol, ‘Master Blaster’, which combines the principles adopted in these two approaches to enhance our ability to detect remote homologues even further. Assessment of the approach was performed using known relationships available in the SCOP70 database, and the results were compared against that of PSI-BLAST and HHblits, a hidden Markov model-based method. Compared to PSI-BLAST, Master Blaster resulted in 10% improvement with respect to detection of cross superfamily connections, nearly 35% improvement in cross family and more than 80% improvement in intra family connections. From the results it was observed that HHblits is more sensitive in detecting remote homologues compared to Master Blaster. However, there are true hits from 46-folds for which Master Blaster reported homologs that are not reported by HHblits even using the optimal parameters indicating that for detecting remote homologues, use of multiple methods employing a combination of different approaches can be more effective in detecting remote homologs. Master Blaster stand-alone code is available for download in the supplementary archive.

Functional Prediction and Assignment of Methanobrevibacter ruminantium M1 Operome Using a Combined Bioinformatics Approach

Methanobrevibacter ruminantium M1 (MRU) is a rod-shaped rumen methanogen with the ability to use H₂ and CO₂, and formate as substrates for methane formation in the ruminants. Enteric methane emitted from this organism can also be influential to the loss of dietary energy in ruminants and humans. To date, there is no successful technology to reduce methane due to a lack of knowledge on its molecular machinery and 73% conserved hypothetical proteins (HPs; operome) whose functions are still not ascertained perceptively. To address this issue, we have predicted and assigned a precise function to HPs and categorize them as metabolic enzymes, binding proteins, and transport proteins using a combined bioinformatics approach. The results of our study show that 257 (34%) HPs have well-defined functions and contributed essential roles in its growth physiology and host adaptation. The genome-neighborhood analysis identified 6 operon-like clusters such as hsp, TRAM, dsr, cbs and cas, which are responsible for protein folding, sudden heat-shock, host defense, and protection against the toxicities in the rumen. The functions predicted from MRU operome comprised of 96 metabolic enzymes with 17 metabolic subsystems, 31 transcriptional regulators, 23 transport, and 11 binding proteins. Functional annotation of its operome is thus more imperative to unravel the molecular and cellular machinery at the systems-level. The functional assignment of its operome would advance strategies to develop new anti-methanogenic targets to mitigate methane production. Hence, our approach provides new insight into the understanding of its growth physiology and lifestyle in the ruminants and also to reduce anthropogenic greenhouse gas emissions worldwide.

Proteomic analysis of streptomycin resistant and sensitive clinical isolates of Mycobacterium tuberculosis

Background

Streptomycin (SM) is a broad spectrum antibiotic and is an important component of any anti-tuberculosis therapy regimen. Several mechanisms have been proposed to explain the emergence of resistance but still our knowledge is inadequate. Proteins form a very complex network and drugs are countered by their modification/efflux or over expression/modification of targets. As proteins manifest most of the biological processes, these are attractive targets for developing drugs, immunodiagnostics or therapeutics. The aim of present study was to analyze and compare the protein profile of whole cell extracts from Mycobacterium tuberculosis clinical isolates susceptible and resistant to SM.

Results

Two-dimensional gel electrophoresis (2DE) and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry was employed for analyzing the protein profiles. Homology and in silico characterization for identified proteins was assessed using BLAST, InterProScan and KEGG database searches. Computational studies on the possible interactions between SM and identified proteins were carried out by a battery of online servers and softwares, namely, CLUSTALW (KEGG), I-TASSER, VMD, PatchDock and FireDock. On comparing 2DE patterns, nine proteins were found consistently overexpressed in SM resistant isolates and were identified as Rv0350, Rv0440, Rv1240, Rv3075c, Rv2971, Rv3028c, Rv2145c, Rv2031c and Rv0569. In silico docking analysis showed significant interactions of SM with essential (Rv0350, Rv0440 and Rv2971) and non essential (Rv1240, Rv3075c and Rv2031c) genes.

Conclusions

The computational results suggest high protein binding affinity of SM and suggested many possible interactions between identified proteins and the drug. Bioinformatic analysis proves attributive for analysis of diversity of proteins identified by whole proteome analysis. In-depth study of the these proteins will give an insight into probable sites of drug action other than established primary sites and hence may help in search of novel chemotherapeutic agents at these new sites as inhibitors.

Comparative genomics of emerging human ehrlichiosis agents

Anaplasma (formerly Ehrlichia) phagocytophilum, Ehrlichia chaffeensis, and Neorickettsia (formerly Ehrlichia) sennetsu are intracellular vector-borne pathogens that cause human ehrlichiosis, an emerging infectious disease. We present the complete genome sequences of these organisms along with comparisons to other organisms in the Rickettsiales order. Ehrlichia spp. and Anaplasma spp. display a unique large expansion of immunodominant outer membrane proteins facilitating antigenic variation. All Rickettsiales have a diminished ability to synthesize amino acids compared to their closest free-living relatives. Unlike members of the Rickettsiaceae family, these pathogenic Anaplasmataceae are capable of making all major vitamins, cofactors, and nucleotides, which could confer a beneficial role in the invertebrate vector or the vertebrate host. Further analysis identified proteins potentially involved in vacuole confinement of the Anaplasmataceae, a life cycle involving a hematophagous vector, vertebrate pathogenesis, human pathogenesis, and lack of transovarial transmission. These discoveries provide significant insights into the biology of these obligate intracellular pathogens.

Ehrlichiosis is an acute disease that triggers flu-like symptoms in both humans and animals. It is caused by a range of bacteria transmitted by ticks or flukes. Because these bacteria are difficult to culture, however, the organisms are poorly understood. The genomes of three emerging human pathogens causing ehrlichiosis were sequenced. A database was designed to allow the comparison of these three genomes to sixteen other bacteria with similar lifestyles. Analysis from this database reveals new species-specific and disease-specific genes indicating niche adaptations, pathogenic traits, and other features. In particular, one of the organisms contains more than 100 copies of a single gene involved in interactions with the host(s). These comparisons also enabled a reconstruction of the metabolic potential of five representative genomes from these bacteria and their close relatives. With this work, scientists can study these emerging pathogens in earnest.