Searching for drug targets in microbial genomes

Identification of repurposable drug targets in Mycoplasma pneumoniae using subtractive genomics, molecular docking and dynamics simulation

Mycoplasma pneumoniae is a significant causative agent of community-acquired pneumonia, causing acute inflammation in the upper and lower respiratory tract as well as extrapulmonary syndromes. In particular, the elderly and infants are at greater risk of developing severe, life-threatening pneumonia caused by M. pneumoniae. Yet, the global increase in antimicrobial resistance against antibiotics for the treatment of M. pneumoniae infection highlights the urgent need to explore novel drug targets. To this end, bioinformatics approaches, such as subtractive genomics, can be employed to identify specific metabolic pathways and essential proteins unique to the pathogen that could be potential targets for new drugs. In this study, we implemented a subtractive genomics approach to identify 61 metabolic pathways and 42 essential proteins that are unique to M. pneumoniae. A subsequent screening in the DrugBank database revealed three druggable proteins with similarity to FDA-approved small-molecule drugs, and finally, the compound CHEBI:97093 was identified as a promising novel putative drug target. These findings can provide crucial insights for the development of highly effective drugs that selectively inhibit the pathogen-specific metabolic pathways, leading to better management and treatment of M. pneumoniae infections.

Identification of potential novel therapeutic drug target against Elizabethkingia anophelis by integrative pan and subtractive genomic analysis: An in silico approach

Elizabethkingia anophelis is a human pathogen responsible for severe nosocomial infections in neonates and immunocompromised patients. The significantly higher mortality rate from E. anophelis infections and the lack of available regimens highlight the critical need to explore novel drug targets. The current study investigated effective novel drug targets by employing a comprehensive in silico subtractive genomic approach integrated with pangenomic analysis of E. anophelis strains. A total of 2809 core genomic proteins were found by pangenomic analysis of non-paralogous proteins. Subsequently, 156 pathogen-specific, 442 choke point, 202 virulence factor, 53 antibiotic resistant and 119 host-pathogen interacting proteins were identified in E. anophelis. By subtractive genomic approach, at first 791 proteins were found to be indispensable for the survival of E. anophelis. 558 and 315 proteins were detected as non-homologous to human and gut microflora respectively. Following that 245 cytoplasmic, 245 novel, and 23 broad-spectrum targets were selected and finally four proteins were considered as potential therapeutic targets of E. anophelis based on highest degree score in PPI network. Among those, three proteins were subjected to molecular docking and subsequent MD simulation as one protein did not contain a plausible binding pocket with sufficient surface area and volume. All the complexes were found to be stable and compact in 100 ns molecular dynamics simulation studies as measured by RMSD, RMSF, and Rg. These three short-listed targets identified in this study may lead to the development of novel antimicrobials capable of curing infections and pave the way to prevent and control the disease progression caused by the deadly agent E. anophelis.

Role of hinge motion and ATP dynamics in factors for inversion stimulation FIS protein deduced while targeting drug resistant Orientia tsutsugamushi

Orientia tsutsugamushi, the causative agent of scrub typhus has been found resistant to various classes of antibiotics such as penicillins, gentamycin and cephalosporins. Review of current literature suggests that the prevalence of scrub typhus has increased globally. Therefore, the current study has aimed at exploring the genome of O. tsutsugamushi to identify potential drug target proteins that can be used for developing novel antibiotics against the pathogen. Subtractive proteomics approach has revealed FIS as a potential drug target protein involved in two component system (TCS), a signaling pathway crucial for bacteria to survive and adjust in changing environmental conditions. Molecular docking studies have revealed compound-356 (CHEMBRIDGE-10040641-3710.356) as a potential inhibitor in both chains A and B of the FIS protein. Simulation results suggest that the docked complex has remained stable and compact throughout the 200 ns run. Significant conformational changes including the hinge motion was observed in the DNA binding domain. Furthermore, the presence of salt bridge between GLU910 and ARG417, rearrangement of interaction residues and displacement of ATP in the central AAA + domain upon binding to the inhibitor were also observed playing a role in stabilizing the protein structure.

mRNA Sequencing Reveals Upregulation of Glutathione S-Transferase Genes during Acanthamoeba Encystation

Some members of the genus Acanthamoeba are facultative pathogens typically with a biphasic lifestyle: trophozoites and cysts. Acanthamoeba is capable of infecting the cornea, resulting in Acanthamoeba keratitis. The cyst is one of the key components for the persistence of infection. Gene expression during Acanthamoeba encystation showed an upregulation of glutathione S-transferase (GST) genes and other closely related proteins. mRNA sequencing showed GST, and five genes with similar sequences were upregulated after 24 h of inducing encystation. GST overexpression was verified with qPCR using the HPRT and the cyst-specific protein 21 genes as controls. The GST inhibitor ethacrynic acid was found to decrease cell viability by 70%. These results indicate a role of GST in successful encystation, possibly by maintaining redox balance. GST and associated processes could be targets for potential treatments alongside regular therapies to reduce relapses of Acanthamoeba infection.

Position preference of essential genes in prokaryotic operons

Essential genes, which form the basis of life activities, are crucial for the survival of organisms. Essential genes tend to be located in operons, but how they are distributed in operons is still unclear for most prokaryotes. In order to clarify the general rule of position preference of essential genes in operons, an index of the average position of genes in an operon was proposed, and the distributions of essential and non-essential genes in operons in 51 bacterial genomes and two archaeal genomes were analyzed based on this new index. Consequently, essential genes were found to preferentially occupy the front positions of the operons, which tend to be expressed at higher levels.

A Comparative Genomics Approach for Shortlisting Broad-Spectrum Drug Targets in Nontuberculous Mycobacteria

Many members of nontuberculous mycobacteria (NTM) are opportunistic pathogens causing several infections in animals. The incidence of NTM infections and emergence of drug-resistant NTM strains are rising worldwide, emphasizing the need to develop novel anti-NTM drugs. The present study is aimed to identify broad-spectrum drug targets in NTM using a comparative genomics approach. The study identified 537 core proteins in NTM of which 45 were pathogen specific and essential for the survival of pathogens. Furthermore, druggability analysis indicated that 15 were druggable among those 45 proteins. These 15 proteins, which were core proteins, pathogen-specific, essential, and druggable, were considered as potential broad-spectrum candidates. Based on their locations in cytoplasm and membrane, targets were classified as drug and vaccine targets. The identified 15 targets were different enzymes, carrier proteins, transcriptional regulator, two-component system protein, ribosomal, and binding proteins. The identified targets could further be utilized by researchers to design inhibitors for the discovery of antimicrobial agents.

Genome Subtraction and Comparison for the Identification of Novel Drug Targets against Mycobacterium avium subsp. hominissuis

Mycobacterium avium complex (MAC) is a major cause of non-tuberculous pulmonary and disseminated diseases worldwide, inducing bronchiectasis, and affects HIV and immunocompromised patients. In MAC, Mycobacterium avium subsp. hominissuis is a pathogen that infects humans and mammals, and that is why it is a focus of this study. It is crucial to find essential drug targets to eradicate the infections caused by these virulent microorganisms. The application of bioinformatics and proteomics has made a significant impact on discovering unique drug targets against the deadly pathogens. One successful bioinformatics methodology is the use of in silico subtractive genomics. In this study, the aim was to identify the unique, non-host and essential protein-based drug targets of Mycobacterium avium subsp. hominissuis via in silico a subtractive genomics approach. Therefore, an in silico subtractive genomics approach was applied in which complete proteome is subtracted systematically to shortlist potential drug targets. For this, the complete dataset of proteins of Mycobacterium avium subsp. hominissuis was retrieved. The applied subtractive genomics method, which involves the homology search between the host and the pathogen to subtract the non-druggable proteins, resulted in the identification of a few prioritized potential drug targets against the three strains of M. avium subsp. Hominissuis, i.e., MAH-TH135, OCU466 and A5. In conclusion, the current study resulted in the prioritization of vital drug targets, which opens future avenues to perform structural as well as biochemical studies on predicted drug targets against M. avium subsp. hominissuis.

Pharmacodynamics of Ceftiofur Selected by Genomic and Proteomic Approaches of Streptococcus parauberis Isolated from the Flounder, Paralichthys olivaceus

We employed an integrative strategy to present subtractive and comparative metabolic and genomic-based findings of therapeutic targets against Streptococcus parauberis. For the first time, we not only identified potential targets based on genomic and proteomic database analyses but also recommend a new antimicrobial drug for the treatment of olive flounder (Paralichthys olivaceus) infected with S. parauberis. To do that, 102 total annotated metabolic pathways of this bacterial strain were extracted from computational comparative metabolic and genomic databases. Six druggable proteins were identified from these metabolic pathways from the DrugBank database with their respective genes as mtnN, penA, pbp2, murB, murA, coaA, and fni out of 112 essential nonhomologous proteins. Among these hits, 26 transmembrane proteins and 77 cytoplasmic proteins were extracted as potential vaccines and drug targets, respectively. From the FDA DrugBank, ceftiofur was selected to prevent antibiotic resistance as it inhibited our selected identified target. Florfenicol is used for treatment of S. parauberis infection in flounder and was chosen as a comparator drug. All tested strains of fish isolates with S. parauberis were susceptible to ceftiofur and florfenicol with minimum inhibitory concentrations (MIC) of 0.0039-1 μg/mL and 0.5-8 μg/mL, IC50 of 0.001-0.5 μg/mL and 0.7-2.7 μg/mL, and minimum biofilm eradication concentrations (MBEC) of 2-256 μg/mL and 4-64 μg/mL, respectively. Similar susceptibility profiles for ceftiofur and florfenicol were found, with ceftiofur observed as an effective and potent antimicrobial drug against both planktonic and biofilm-forming strains of the fish pathogen Streptococcus parauberis, and it can be applied in the aquaculture industry. Thus, our predictive approach not only showed novel therapeutic agents but also indicated that marketed drugs should also be tested for efficacy against newly identified targets of this important fish pathogen.

Species-specific transcriptional profiles of the gut and gut microbiome of Ceratitis quilicii and Ceratitis rosa sensu stricto

The fruit fly species, Ceratitis rosa sensu stricto and Ceratitis quilicii, are sibling species restricted to the lowland and highland regions, respectively. Until recently, these sibling species were considered as allopatric populations of C. rosa with distinct bionomics. We used deep Next Generation Sequencing (NGS) technology on intact guts of individuals from the two sibling species to compare their transcriptional profiles and simultaneously understand gut microbiome and host molecular processes and identify distinguishing genetic differences between the two species. Since the genomes of both species had not been published previously, the transcriptomes were assembled de novo into transcripts. Microbe-specific transcript orthologs were separated from the assembly by filtering searches of the transcripts against microbe databases using OrthoMCL. We then used differential expression analysis of host-specific transcripts (i.e. those remaining after the microbe-specific transcripts had been removed) and microbe-specific transcripts from the two-sibling species to identify defining species-specific transcripts that were present in only one fruit fly species or the other, but not in both. In C. quilicii females, bacterial transcripts of Pectobacterium spp., Enterobacterium buttiauxella, Enterobacter cloacae and Klebsiella variicola were upregulated compared to the C. rosa s.s. females. Comparison of expression levels of the host transcripts revealed a heavier investment by C. quilicii (compared with C. rosa s.s.) in: immunity; energy production; cell proliferation; insecticide resistance; reproduction and proliferation; and redox reactions that are usually associated with responses to stress and degradation of fruit metabolites.

New insights on human essential genes based on integrated analysis and the construction of the HEGIAP web-based platform

Essential genes are those whose loss of function compromises organism viability or results in profound loss of fitness. Recent gene-editing technologies have provided new opportunities to characterize essential genes. Here, we present an integrated analysis that comprehensively and systematically elucidates the genetic and regulatory characteristics of human essential genes. First, we found that essential genes act as ‘hubs’ in protein–protein interaction networks, chromatin structure and epigenetic modification. Second, essential genes represent conserved biological processes across species, although gene essentiality changes differently among species. Third, essential genes are important for cell development due to their discriminate transcription activity in embryo development and oncogenesis. In addition, we developed an interactive web server, the Human Essential Genes Interactive Analysis Platform (http://sysomics.com/HEGIAP/), which integrates abundant analytical tools to enable global, multidimensional interpretation of gene essentiality. Our study provides new insights that improve the understanding of human essential genes.

Identification of putative non-host essential genes and novel drug targets against Acinetobacter baumannii by in silico comparative genome analysis

Acinetobacter baumannii, the gram-negative bacteria emerged as an extremely critical pathogen causing nosocomial and different kinds of infections. A. baumannii exhibit resistivity towards various classes of antibiotics that shows that there is a dire need to search more drug targets by exploiting the full genome of the bacteria. In doing so, a strategy is made with the combination of computational biology, pathogen informatics and cheminformatics. Comparative genomics analysis, modeling and docking studies have been performed for the prediction of non-host essential genes and novel drug candidates against A. baumannii. Among 37 unique and 82 common metabolic pathways, 92 genes were predicted as non-host genes. Similarly, using homology search between A. baumannii genome and essential genes of different bacteria, 293 genes were predicted as essential genes of A. baumannii. Among these predicted non-host and essential genes, 86 genes were predicted as non-host essential genes which could serve as potential novel drug and vaccine targets. Additional drug-target like physicochemical properties were estimated such as the molecular weight, subcellular localization and druggability potential. On the structural part, the crystal structures of all the non-host essential genes of A. baumannii were found except the three genes. Out of these three, a homology model of Undecaprenyl-diphosphatase was built using a PDB template by MODELLER [version 9.18]. The quality of the model was assessed by the ProSA and RAMPAGE. The built model was subjected as a receptor for the molecular docking with Adenosine diphosphate (ADP) as a ligand. The molecular docking was performed by AutoDock4 and the best conformation with lowest binding energy (-4.39 kcal/mol) was obtained. The LigPlot was used to identify the close interactions between the ligand the receptor's residues. This study will further aid for the selection of putative inhibitors against a novel drug target identified against A. baumannii and hence could lead to the better therapeutics.

What bacteria want

Bacterial signal transduction systems are responsible for sensing environmental cues and adjusting the cellular behaviour and/or metabolism in response to these cues. They also monitor the intracellular conditions and the status of the cell envelope and the cytoplasmic membrane and trigger various stress responses to counteract adverse changes. This surveillance involves several classes of sensor proteins: histidine kinases; chemoreceptors; membrane components of the sugar phosphotransferase system; adenylate, diadenylate and diguanylate cyclases and certain cAMP, c-di-AMP and c-di-GMP phosphodiesterases; extracytoplasmic function sigma factors and Ser/Thr/Tyr protein kinases and phosphoprotein phosphatases. We have compiled a detailed listing of sensor proteins that are encoded in the genomes of Escherichia coli, Bacillus subtilis and 10 widespread pathogens: Chlamydia trachomatis, Haemophilus influenzae, Helicobacter pylori, Mycobacterium tuberculosis, Mycoplasma pneumoniae, Neisseria gonorrhoeae, Porphyromonas gingivalis, Rickettsia typhi, Streptococcus pyogenes and Treponema pallidum, and checked what, if anything, is known about their functions. This listing shows significant gaps in the understanding of which environmental and intracellular cues are perceived by these bacteria and which cellular responses are triggered by the changes in the respective parameters. A better understanding of bacterial preferences may suggest new ways to modulate the expression of virulence factors and therefore decrease the reliance on antibiotics to fight infection.

Identification of potential drug targets and inhibitor of the pathogenic bacteria Shigella flexneri 2a through the subtractive genomic approach

Shigella flexneri 2a is one of the most pathogenic bacteria among the Shigella spp., which is responsible for dysentery and causes masses of deaths throughout the world per year. A proper identification of the potential drug targets and inhibitors is crucial for the treatment of the shigellosis due to their emerging multidrug resistance (MDR) patterns. In this study, a systematic subtractive approach was implemented for the identification of novel therapeutic targets of S. flexneri 2a (301) through genome-wide metabolic pathway analysis of the essential genes and proteins. Ligand-based virtual screening and ADMET analyses were also made for the identification of potential inhibitors as well. Initially, we found 70 essential unique proteins as novel targets. After subsequent prioritization, finally we got six unique targets as the potential therapeutic targets and their three-dimensional models were built thereafter. Aspartate-β-semialdehyde dehydrogenase (ASD), was the most potent target among them and used for docking analysis through ligand-based virtual screening. The compound 3 (PubChem CID: 11319750) suited well as the best inhibitor of the ASD through ADMET and enzyme inhibition capacity analysis. To end, we hope that our proposed therapeutic targets and its inhibitors might give some breakthrough to treat shigellosis efficiently in in vitro.

Subtractive Genomics, Molecular Docking and Molecular Dynamics Simulation Revealed LpxC as a Potential Drug Target Against Multi-Drug Resistant Klebsiella pneumoniae

The emergence and dissemination of pan drug resistant clones of Klebsiella pneumoniae are great threat to public health. In this regard new therapeutic targets must be highlighted to pave the path for novel drug discovery and development. Subtractive proteomic pipeline brought forth UDP-3-O-[3-hydroxymyristoyl] N-acetylglucosamine deacetylase (LpxC), a Zn⁺² dependent cytoplasmic metalloprotein and catalyze the rate limiting deacetylation step of lipid A biosynthesis pathway. Primary sequence analysis followed by 3-dimensional (3-D) structure elucidation of the protein led to the detection of K. pneumoniae LpxC (KpLpxC) topology distinct from its orthologous counterparts in other bacterial species. Molecular docking study of the protein recognized receptor antagonist compound 106, a uridine-based LpxC inhibitory compound, as a ligand best able to fit the binding pocket with a Gold Score of 67.53. Molecular dynamics simulation of docked KpLpxC revealed an alternate binding pattern of ligand in the active site. The ligand tail exhibited preferred binding to the domain I residues as opposed to the substrate binding hydrophobic channel of subdomain II, usually targeted by inhibitory compounds. Comparison with the undocked KpLpxC system demonstrated ligand induced high conformational changes in the hydrophobic channel of subdomain II in KpLpxC. Hence, ligand exerted its inhibitory potential by rendering the channel unstable for substrate binding.

A Comprehensive Overview of Online Resources to Identify and Predict Bacterial Essential Genes

Genes critical for the survival or reproduction of an organism in certain circumstances are classified as essential genes. Essential genes play a significant role in deciphering the survival mechanism of life. They may be greatly applied to pharmaceutics and synthetic biology. The continuous progress of experimental method for essential gene identification has accelerated the accumulation of gene essentiality data which facilitates the study of essential genes in silico. In this article, we present some available online resources related to gene essentiality, including bioinformatic software tools for transposon sequencing (Tn-seq) analysis, essential gene databases and online services to predict bacterial essential genes. We review several computational approaches that have been used to predict essential genes, and summarize the features used for gene essentiality prediction. In addition, we evaluate the available online bacterial essential gene prediction servers based on the experimentally validated essential gene sets of 30 bacteria from DEG. This article is intended to be a quick reference guide for the microbiologists interested in the essential genes.

Finding Potential Therapeutic Targets against Shigella flexneri through Proteome Exploration

Background:Shigella flexneri is a gram negative bacteria that causes the infectious disease “shigellosis.” S. flexneri is responsible for developing diarrhea, fever, and stomach cramps in human. Antibiotics are mostly given to patients infected with shigella. Resistance to antibiotics can hinder its treatment significantly. Upon identification of essential therapeutic targets, vaccine and drug could be effective therapy for the treatment of shigellosis.

Methods: The study was designed for the identification and qualitative characterization for potential drug targets from S. flexneri by using the subtractive proteome analysis. A set of computational tools were used to identify essential proteins those are required for the survival of S. flexneri. Total proteome (13,503 proteins) of S. flexneri was retrieved from NCBI and further analyzed by subtractive channel analysis. After identification of the metabolic proteins we have also performed its qualitative characterization to pave the way for the identification of promising drug targets.

Results: Subtractive analysis revealed that a list of 53 targets of S. flexneri were human non-homologous essential metabolic proteins that might be used for potential drug targets. We have also found that 11 drug targets are involved in unique pathway. Most of these proteins are cytoplasmic, can be used as broad spectrum drug targets, can interact with other proteins and show the druggable properties. The functionality and drug binding site analysis suggest a promising effective way to design the new drugs against S. flexneri.

Conclusion: Among the 53 therapeutic targets identified through this study, 13 were found highly potential as drug targets based on their physicochemical properties whilst only one was found as vaccine target against S. flexneri. The outcome might also be used as module as well as circuit design in systems biology.

Design, synthesis, and evaluation of pyrazolo-pyrazole derivatives on Methylisocitratelyase of Pseudomonas aeruginosa: in silico and in vitro study

Pseudomonas aeruginosa is an opportunistic micro-organism causing diseases both in animals and humans. In case of human pathology, the role of P. aeruginosa is one of the major concerns in intensive care septicemia. Presently, the drug resistance strains of P. aeruginosa are arising mainly by developing multiple mechanisms due to its natural and acquired resistance to many of the antimicrobial agents commonly used in clinical practice. As a result, there is a direct need to invent new drugs so that they may restrict the outbreak of multidrug resistant strains. Virtual high-throughput insilico screening, which helps to identify the chemical ligands that bind to the enzymes, is an important tool in drug discovery and the drugs discovered in this way are clinically tested. In this study, Methylisocitratelyase (MICL), which is essential for the survival of the bacterium and which doesn't show any similarity with the humans, was selected to evaluate the functions of high-affinity inhibitors (PPI-analogs) that are identified using the virtual screening approach. By adopting the computational analysis tools, structural, functional, and inhibitor interactions of MICL against P. aeruginosa were identified. The PPIA-32 is found to be the best binding interactions with MICL. PPIA-32 reduces the binding affinity for substrate to residues required for MICL enzyme activity and also Root Mean Square Deviation simulations show the most stable nature of PPA32-MICL(complex) than that of MICL alone, thereby effectively inhibiting the growth of virulent P. aeruginosa. To our surprise, the same phenomenon is also identified with other gram-negative bacteria like Escherichia coli, Klebsiella pneumoniae, and Salmonella typhi.

Pangenome and immuno-proteomics analysis of Acinetobacter baumannii strains revealed the core peptide vaccine targets

BACKGROUND

Acinetobacter baumannii has emerged as a significant nosocomial pathogen during the last few years, exhibiting resistance to almost all major classes of antibiotics. Alternative treatment options such as vaccines tend to be most promising and cost effective approaches against this resistant pathogen. In the current study, we have explored the pan-genome of A. baumannii followed by immune-proteomics and reverse vaccinology approaches to identify potential core vaccine targets.

RESULTS

The pan-genome of all available A. baumannii strains (30 complete genomes) is estimated to contain 7,606 gene families and the core genome consists of 2,445 gene families (~32 % of the pan-genome). Phylogenetic tree, comparative genomic and proteomic analysis revealed both intra- and inter genomic similarities and evolutionary relationships. Among the conserved core genome, thirteen proteins, including P pilus assembly protein, pili assembly chaperone, AdeK, PonA, OmpA, general secretion pathway protein D, FhuE receptor, Type VI secretion system OmpA/MotB, TonB dependent siderophore receptor, general secretion pathway protein D, outer membrane protein, peptidoglycan associated lipoprotein and peptidyl-prolyl cis-trans isomerase are identified as highly antigenic. Epitope mapping of the target proteins revealed the presence of antigenic surface exposed 9-mer T-cell epitopes. Protein-protein interaction and functional annotation have shown their involvement in significant biological and molecular processes. The pipeline is validated by predicting already known immunogenic targets against Gram negative pathogen Helicobacter pylori as a positive control.

CONCLUSION

The study, based upon combinatorial approach of pan-genomics, core genomics, proteomics and reverse vaccinology led us to find out potential vaccine candidates against A. baumannii. The comprehensive analysis of all the completely sequenced genomes revealed thirteen putative antigens which could elicit substantial immune response. The integration of computational vaccinology strategies would facilitate in tackling the rapid dissemination of resistant A.baumannii strains. The scarcity of effective antibiotics and the global expansion of sequencing data making this approach desirable in the development of effective vaccines against A. baumannii and other bacterial pathogens.

An antibiotic target ranking and prioritization pipeline combining sequence, structure and network-based approaches exemplified for Serratia marcescens

We investigate a drug target screening pipeline comparing sequence, structure and network-based criteria for prioritization. Serratia marcescens, an opportunistic pathogen, serves as test case. We rank according to (i) availability of three dimensional structures and lead compounds, (ii) not occurring in man and general sequence conservation information, and (iii) network information on the importance of the protein (conserved protein-protein interactions; metabolism; reported to be an essential gene in other organisms). We identify 45 potential anti-microbial drug targets in S. marcescens with KdsA involved in LPS biosynthesis as top candidate drug target. LpxC and FlgB are further top-ranked targets identified by interactome analysis not suggested before for S. marcescens. Pipeline, targets and complementarity of the three approaches are evaluated by available experimental data and genetic evidence and against other antibiotic screening pipelines. This supports reliable drug target identification and prioritization for infectious agents (bacteria, parasites, fungi) by these bundled complementary criteria.

Critical controllability in proteome-wide protein interaction network integrating transcriptome

Recently, the number of essential gene entries has considerably increased. However, little is known about the relationships between essential genes and their functional roles in critical network control at both the structural (protein interaction network) and dynamic (transcriptional) levels, in part because the large size of the network prevents extensive computational analysis. Here, we present an algorithm that identifies the critical control set of nodes by reducing the computational time by 180 times and by expanding the computable network size up to 25 times, from 1,000 to 25,000 nodes. The developed algorithm allows a critical controllability analysis of large integrated systems composed of a transcriptome- and proteome-wide protein interaction network for the first time. The data-driven analysis captures a direct triad association of the structural controllability of genes, lethality and dynamic synchronization of co-expression. We believe that the identified optimized critical network control subsets may be of interest as drug targets; thus, they may be useful for drug design and development.

Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting

Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite's protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented illustrating the close proximity of some indels to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. This study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns.

Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bio-informatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E. coli O157:H7 were prioritized and proved to have the eligibility to become novel broad-spectrum antibiotic targets and DNA polymerase III alpha (dnaE) was the top-ranked among these targets. Moreover, druggability of each of the identified drug targets was evaluated by the DrugBank database. In addition, 3D structure of the dnaE was modeled and explored further for in silico docking with ligands having potential druggability. Finally, we confirmed that the compounds N-coeleneterazine and N-(1,4-dihydro-5H-tetrazol-5-ylidene)-9-oxo-9H-xanthene-2-sulfon-amide were the most suitable ligands of dnaE and hence proposed as the potential inhibitors of this target protein. The results of this study could facilitate the discovery and release of new and effective drugs against E. coli O157:H7 and other deadly human bacterial pathogens.

Treponema pallidum putative novel drug target identification and validation: rethinking syphilis therapeutics with plant-derived terpenoids

Syphilis, a slow progressive and the third most common sexually transmitted disease found worldwide, is caused by a spirochete gram negative bacteria Treponema pallidum. Emergence of antibiotic resistant T. pallidum has led to a search for novel drugs and their targets. Subtractive genomics analyses of pathogen T. pallidum and host Homo sapiens resulted in identification of 126 proteins essential for survival and viability of the pathogen. Metabolic pathway analyses of these essential proteins led to discovery of nineteen proteins distributed among six metabolic pathways unique to T. pallidum. One hundred plant-derived terpenoids, as potential therapeutic molecules against T. pallidum, were screened for their drug likeness and ADMET (absorption, distribution, metabolism, and toxicity) properties. Subsequently the resulting nine terpenoids were docked with five unique T. pallidum targets through molecular modeling approaches. Out of five targets analyzed, D-alanine:D-alanine ligase was found to be the most promising target, while terpenoid salvicine was the most potent inhibitor. A comparison of the inhibitory potential of the best docked readily available natural compound, namely pomiferin (flavonoid) with that of the best docked terpenoid salvicine, revealed that salvicine was a more potent inhibitor than that of pomiferin. To the best of our knowledge, this is the first report of a terpenoid as a potential therapeutic molecule against T. pallidum with D-alanine:D-alanine ligase as a novel target. Further studies are warranted to evaluate and explore the potential clinical ramifications of these findings in relation to syphilis that has public health importance worldwide.

Protective Enterotoxigenic Escherichia coli Antigens in a Murine Intranasal Challenge Model

Enterotoxigenic Escherichia coli (ETEC) is an endemic health threat in underdeveloped nations. Despite the significant effort extended to vaccine trials using ETEC colonization factors, these approaches have generally not been especially effective in mediating cross-protective immunity. We used quantitative proteomics to identify 24 proteins that differed in abundance in membrane protein preparations derived from wild-type vs. a type II secretion system mutant of ETEC. We expressed and purified a subset of these proteins and identified nine antigens that generated significant immune responses in mice. Sera from mice immunized with either the MltA-interacting protein MipA, the periplasmic chaperone seventeen kilodalton protein, Skp, or a long-chain fatty acid outer membrane transporter, ETEC_2479, reduced the adherence of multiple ETEC strains differing in colonization factor expression to human intestinal epithelial cells. In intranasal challenge assays of mice, immunization with ETEC_2479 protected 88% of mice from an otherwise lethal challenge with ETEC H10407. Immunization with either Skp or MipA provided an intermediate degree of protection, 68 and 64%, respectively. Protection was significantly correlated with the induction of a secretory immunoglobulin A response. This study has identified several proteins that are conserved among heterologous ETEC strains and may thus potentially improve cross-protective efficacy if incorporated into future vaccine designs.

Genome-wide Analysis of Mycoplasma hominis for the Identification of Putative Therapeutic Targets

Ever increasing propensity of antibiotic resistance among pathogenic bacteria raises the demand for the development of novel therapeutic agents to control this grave problem. Advances in the field of bioinformatics, genomics, and proteomics have greatly facilitated the discovery of alternative drugs by swift identification of new drug targets. In the present study, we employed comparative genomics and metabolic pathway analysis with an aim of identifying therapeutic targets in Mycoplasma hominis. Our study has revealed 40 annotated metabolic pathways, including five unique pathways of M. hominis. Our study also identified 179 essential proteins, including 59 proteins having no similarity with human proteins. Further filtering by molecular weight, subcellular localization, functional analysis, and protein network interaction, we identified 57 putative candidates for which new drugs can be developed. Druggability analysis for each of the identified targets has prioritized 16 proteins as suitable for potential drug development.

Mycobacterium tuberculosis H37Rv: In Silico Drug Targets Identification by Metabolic Pathways Analysis

Mycobacterium tuberculosis (Mtb) is a pathogenic bacteria species in the genus Mycobacterium and the causative agent of most cases of tuberculosis. Tuberculosis (TB) is the leading cause of death in the world from a bacterial infectious disease. This antibiotic resistance strain lead to development of the new antibiotics or drug molecules which can kill or suppress the growth of Mycobacterium tuberculosis. We have performed an in silico comparative analysis of metabolic pathways of the host Homo sapiens and the pathogen Mycobacterium tuberculosis (H37Rv). Novel efforts in developing drugs that target the intracellular metabolism of M. tuberculosis often focus on metabolic pathways that are specific to M. tuberculosis. We have identified five unique pathways for Mycobacterium tuberculosis having a number of 60 enzymes, which are nonhomologous to Homo sapiens protein sequences, and among them there were 55 enzymes, which are nonhomologous to Homo sapiens protein sequences. These enzymes were also found to be essential for survival of the Mycobacterium tuberculosis according to the DEG database. Further, the functional analysis using Uniprot showed involvement of all the unique enzymes in the different cellular components.

Structure to function prediction of hypothetical protein KPN_00953 (Ycbk) from Klebsiella pneumoniae MGH 78578 highlights possible role in cell wall metabolism

Background

Klebsiella pneumoniae plays a major role in causing nosocomial infection in immunocompromised patients. Medical inflictions by the pathogen can range from respiratory and urinary tract infections, septicemia and primarily, pneumonia. As more K. pneumoniae strains are becoming highly resistant to various antibiotics, treatment of this bacterium has been rendered more difficult. This situation, as a consequence, poses a threat to public health. Hence, identification of possible novel drug targets against this opportunistic pathogen need to be undertaken. In the complete genome sequence of K. pneumoniae MGH 78578, approximately one-fourth of the genome encodes for hypothetical proteins (HPs). Due to their low homology and relatedness to other known proteins, HPs may serve as potential, new drug targets.

Results

Sequence analysis on the HPs of K. pneumoniae MGH 78578 revealed that a particular HP termed KPN_00953 (YcbK) contains a M15_3 peptidases superfamily conserved domain. Some members of this superfamily are metalloproteases which are involved in cell wall metabolism. BLASTP similarity search on KPN_00953 (YcbK) revealed that majority of the hits were hypothetical proteins although two of the hits suggested that it may be a lipoprotein or related to twin-arginine translocation (Tat) pathway important for transport of proteins to the cell membrane and periplasmic space. As lipoproteins and other components of the cell wall are important pathogenic factors, homology modeling of KPN_00953 was attempted to predict the structure and function of this protein. Three-dimensional model of the protein showed that its secondary structure topology and active site are similar with those found among metalloproteases where two His residues, namely His169 and His209 and an Asp residue, Asp176 in KPN_00953 were found to be Zn-chelating residues. Interestingly, induced expression of the cloned KPN_00953 gene in lipoprotein-deficient E. coli JE5505 resulted in smoother cells with flattened edges. Some cells showed deposits of film-like material under scanning electron microscope.

Conclusions

We postulate that KPN_00953 is a Zn metalloprotease and may play a role in bacterial cell wall metabolism. Structural biology studies to understand its structure, function and mechanism of action pose the possibility of utilizing this protein as a new drug target against K. pneumoniae in the future.

DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements

The combination of high-density transposon-mediated mutagenesis and high-throughput sequencing has led to significant advancements in research on essential genes, resulting in a dramatic increase in the number of identified prokaryotic essential genes under diverse conditions and a revised essential-gene concept that includes all essential genomic elements, rather than focusing on protein-coding genes only. DEG 10, a new release of the Database of Essential Genes (available at http://www.essentialgene.org), has been developed to accommodate these quantitative and qualitative advancements. In addition to increasing the number of bacterial and archaeal essential genes determined by genome-wide gene essentiality screens, DEG 10 also harbors essential noncoding RNAs, promoters, regulatory sequences and replication origins. These essential genomic elements are determined not only in vitro, but also in vivo, under diverse conditions including those for survival, pathogenesis and antibiotic resistance. We have developed customizable BLAST tools that allow users to perform species- and experiment-specific BLAST searches for a single gene, a list of genes, annotated or unannotated genomes. Therefore, DEG 10 includes essential genomic elements under different conditions in three domains of life, with customizable BLAST tools.

Identification and characterization of potential therapeutic candidates in emerging human pathogen Mycobacterium abscessus: a novel hierarchical in silico approach

Mycobacterium abscessus, a non-tuberculous rapidly growing mycobacterium, is recognized as an emerging human pathogen causing a variety of infections ranging from skin and soft tissue infections to severe pulmonary infections. Lack of an optimal treatment regimen and emergence of multi-drug resistance in clinical isolates necessitate the development of better/new drugs against this pathogen. The present study aims at identification and qualitative characterization of promising drug targets in M. abscessus using a novel hierarchical in silico approach, encompassing three phases of analyses. In phase I, five sets of proteins were mined through chokepoint, plasmid, pathway, virulence factors, and resistance genes and protein network analysis. These were filtered in phase II, in order to find out promising drug target candidates through subtractive channel of analysis. The analysis resulted in 40 therapeutic candidates which are likely to be essential for the survival of the pathogen and non-homologous to host, human anti-targets, and gut flora. Many of the identified targets were found to be involved in different metabolisms (viz., amino acid, energy, carbohydrate, fatty acid, and nucleotide), xenobiotics degradation, and bacterial pathogenicity. Finally, in phase III, the candidate targets were qualitatively characterized through cellular localization, broad spectrum, interactome, functionality, and druggability analysis. The study explained their subcellular location identifying drug/vaccine targets, possibility of being broad spectrum target candidate, functional association with metabolically interacting proteins, cellular function (if hypothetical), and finally, druggable property. Outcome of the present study could facilitate the identification of novel antibacterial agents for better treatment of M. abscesses infections.

Clostridium-DT(DB): a comprehensive database for potential drug targets of Clostridium difficile

Clostridium difficile is considered to be one of the most important causes of health care-associated infections currently. The prevalence and severity of C. difficile infection have increased significantly worldwide in the past decade which has led to the increased research interest. Here, using comparative genomics strategy coupled with bioinformatics tools we have identified potential drug targets in C. difficile and determined their three-dimensional structures in order to develop a database, named Clostridium-DT(DB). Currently, the database comprises the potential drug targets with their structural information from three strains of C. difficile, namely hypervirulent PCR-ribotype 027 strain R20291, PCR-ribotype 012 strain 630, and PCR-ribotype 027 strain CD196.

In silico investigation and structural characterization of virulent factor and a metallo peptidase present in Helicobacter pylori strain J99

VacA is a high-molecular weight multimeric pore-forming protein encoded by the chromosomal gene vacA of Helicobacter pylori J99 strian. It plays a significant role in the development of gastric cancer in human by inducing the formation of vacuoles. Genomics and proteomics features of an organism have provided a plethora of potential drug targets. The crystal structure of VacA is not available in any structural database; hence a 3D structure is very essential for structural studies and discovery of potential inhibitors against proteins. In this study 3D structure of VacA is modelled a by using Bhageerath: an energy based web enabled computer software suite. According to our study VacA steriochemical validation shows 91.7% residues are in allowed region of Ramachandran plot. Further validation was done by WHAT CHECK to provide evidence that the distribution of the main chain bond lengths and omega bond angles were within limits with Z-score 1.0 and error values are negligible. The modelled protein was submitted to Protein Model Database and can be downloaded with PMDID PM0077963. Further we found that metallo peptidase "M3" cleaves VacA and helps in import mechanism in mitochondria. Structure of metallo peptidase is also not available in any structural database so we modelled and validated its structure. With the help of docking studies we blocked the active site of metallo peptidase by ligand LA3 and 294 with binding energy -5.9 and -5.2 KJ/mol respectively, thus prevented import mechanism of VacA in mitochondria. The inhibitors identified from our study were LA3 and 294 ligands. The investigation concluded that these drugs could be used as the potential inhibitors against the damage of stomach and duodenum, which ultimately reduces the likelihood of ulcer as well as gastric cancer.

Genome comparisons as a tool for antimicrobial target discovery

Essential genes are frequently conserved among bacterial species and thus microbial and eukaryote genome comparisons can be used to compile datasets of homologous proteins and families that can be utilized to identify attractive targets for the design of antimicrobial agents and other drugs. These searches can now often be conducted using Web tools. A number of such resources that provide sequence information and comparative software as well as computational tools for convenient analysis of the data are summarized here and their step-by-step use explained.

Comparative genomics analysis of Mycobacterium ulcerans for the identification of putative essential genes and therapeutic candidates

Mycobacterium ulcerans, the causative agent of Buruli ulcer, is the third most common mycobacterial disease after tuberculosis and leprosy. The present treatment options are limited and emergence of treatment resistant isolates represents a serious concern and a need for better therapeutics. Conventional drug discovery methods are time consuming and labor-intensive. Unfortunately, the slow growing nature of M. ulcerans in experimental conditions is also a barrier for drug discovery and development. In contrast, recent advancements in complete genome sequencing, in combination with cheminformatics and computational biology, represent an attractive alternative approach for the identification of therapeutic candidates worthy of experimental research. A computational, comparative genomics workflow was defined for the identification of novel therapeutic candidates against M. ulcerans, with the aim that a selected target should be essential to the pathogen, and have no homology in the human host. Initially, a total of 424 genes were predicted as essential from the M. ulcerans genome, via homology searching of essential genome content from 20 different bacteria. Metabolic pathway analysis showed that the most essential genes are associated with carbohydrate and amino acid metabolism. Among these, 236 proteins were identified as non-host and essential, and could serve as potential drug and vaccine candidates. Several drug target prioritization parameters including druggability were also calculated. Enzymes from several pathways are discussed as potential drug targets, including those from cell wall synthesis, thiamine biosynthesis, protein biosynthesis, and histidine biosynthesis. It is expected that our data will facilitate selection of M. ulcerans proteins for successful entry into drug design pipelines.

Subtractive genomics approach to identify putative drug targets and identification of drug-like molecules for beta subunit of DNA polymerase III in Streptococcus species

The prolonged use of the antibiotics over the years has transformed many organisms resistant to multiple drugs. This has made the field of drug discovery of vital importance in curing various infections and diseases. The drugs act by binding to a specific target protein of prime importance for the cell's survival. Streptococcus agalactiae, Streptococcus pneumoniae, and Streptococcus pyogenes are the few gram positive organisms that have developed resistance to drugs. It causes pneumonia, meningitis, pharyngitis, otitis media, sinusitis, bacteremia, pericarditis, and arthritis infections. The present study was carried out to identify potential drug targets and inhibitors for beta subunit of DNA polymerase III in these three Streptococcus species that might facilitate the discovery of novel drugs in near future. Various steps were adopted to find out novel drug targets. And finally 3D structure of DNA polymerase III subunit beta was modeled. The ligand library was generated from various databases to find the most suitable ligands. All the ligands were docked using Molegro Virtual Docker and the lead molecules were investigated for ADME and toxicity.

Generation and Analysis of Large-Scale Data-Driven Mycobacterium tuberculosis Functional Networks for Drug Target Identification

Technological developments in large-scale biological experiments, coupled with bioinformatics tools, have opened the doors to computational approaches for the global analysis of whole genomes. This has provided the opportunity to look at genes within their context in the cell. The integration of vast amounts of data generated by these technologies provides a strategy for identifying potential drug targets within microbial pathogens, the causative agents of infectious diseases. As proteins are druggable targets, functional interaction networks between proteins are used to identify proteins essential to the survival, growth, and virulence of these microbial pathogens. Here we have integrated functional genomics data to generate functional interaction networks between Mycobacterium tuberculosis proteins and carried out computational analyses to dissect the functional interaction network produced for identifying drug targets using network topological properties. This study has provided the opportunity to expand the range of potential drug targets and to move towards optimal target-based strategies.

Mycoplasma genitalium: a comparative genomics study of metabolic pathways for the identification of drug and vaccine targets

Increasing emergence of antibiotic-resistant pathogenic microorganisms is one of the biggest challenges for biomedical research and drug development. Traditional drug discovery methods are time-consuming, expensive and often yield few drug targets. In contrast, advances in complete genome sequencing, bioinformatics and cheminformatics represent an attractive alternative approach to identify drug targets worthy of experimental follow-up. Mycoplasma genitalium is a human parasitic pathogen that is associated with several sexually transmitted diseases. Recently, emergence of treatment-resistant isolates has been reported, which raises serious concern and a need for identification of additional drug targets. In the present study, a systematic workflow consisting of comparative genomics, metabolic pathways analysis and additional drug prioritizing parameters was defined for the identification of novel drug and vaccine targets that are essential for M. genitalium, but absent in its human host. In silico analyses and manual mining identified 79 proteins of M. genitalium, which showed no similarity to human proteins. Among these, 67 proteins were identified as non-homologous essential proteins that could serve as potential drug and vaccine targets. Subcellular localization, molecular weight, and three-dimensional structural characteristics that could facilitate filtering of attractive drug targets were also calculated for the non-homologous essential proteins. Enzymes from thiamine biosynthesis, protein biosynthesis, and folate biosynthesis were identified as attractive candidates for drug development. Furthermore, druggability of each of the identified drug targets was also evaluated by the DrugBank database. Results from this study could facilitate selection of M. genitalium proteins for entry into drug design and vaccine production pipelines.

In silico prediction of drug targets in Vibrio cholerae

Identification of potential drug targets is the first step in the process of modern drug discovery, subjected to their validation and drug development. Whole genome sequences of a number of organisms allow prediction of potential drug targets using sequence comparison approaches. Here, we present a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets more efficiently. Based on the knowledge of 155 known virulence and their coexpressed genes mined from microarray database in the public domain, 357 coexpressed probable virulence genes for Vibrio cholerae were predicted. Based on screening of Database of Essential Genes using blastn, a total of 102 genes out of these 357 were enlisted as vitally essential genes, and hence good putative drug targets. As the effective drug target is a protein which is only present in the pathogen, similarity search of these 102 essential genes against human genome sequence led to subtraction of 66 genes, thus leaving behind a subset of 36 genes whose products have been called as potential drug targets. The gene ontology analysis using Blast2GO of these 36 genes revealed their roles in important metabolic pathways of V. cholerae or on the surface of the pathogen. Thus, we propose that the products of these genes be evaluated as target sites of drugs against V. cholerae in future investigations.

In silico quest for putative drug targets in Helicobacter pylori HPAG1: molecular modeling of candidate enzymes from lipopolysaccharide biosynthesis pathway

Aimed at identification and structural characterization of novel putative therapeutic targets in H. pylori, the etiological agent of numerous gastrointestinal diseases including peptic ulcer and gastric cancer, the present study comprised of three phases. First, through subtractive analysis of metabolic pathways of Helicobacter pylori HPAG1 and human, as documented in the KEGG database, 11 pathogen-specific pathways were identified. Next, all proteins involved in these pathogen-specific pathways were scrutinized in search of promising targets and the study yielded 25 candidate target proteins that are likely to be essential for the pathogen viability, but have no homolog in human. The lipopolysaccharide (LPS) biosynthesis pathway was found to be the largest contributor (nine proteins) to this list of candidate proteins. Considering the importance of LPS in H. pylori virulence, 3D structural models of three predicted target enzymes of this pathway, namely 2-dehydro-3-deoxy-phosphooctonate aldolase, UDP-3-O-[3-hydroxymyristoyl] N-acetylglucosamine deacetylase and Phosphoheptose isomerase, were then built up using the homology modeling approaches. Binding site analysis and docking of the known biological substrate PEP to 2-dehydro-3-deoxyphosphooctonate aldolase revealed the potential binding pocket present in the single monomeric form of the enzyme and identified 11 amino acid residues that might play the key roles in this protein-ligand interaction.

A novel in silico approach to identify potential therapeutic targets in human bacterial pathogens

In recent years, genome-sequencing projects of pathogens and humans have revolutionized microbial drug target identification. Of the several known genomic strategies, subtractive genomics has been successfully utilized for identifying microbial drug targets. The present work demonstrates a novel genomics approach in which codon adaptation index (CAI), a measure used to predict the translational efficiency of a gene based on synonymous codon usage, is coupled with subtractive genomics approach for mining potential drug targets. The strategy adopted is demonstrated using respiratory pathogens, namely, Streptococcus pneumoniae and Haemophilus influenzae as examples. Our approach identified 8 potent target genes (Streptococcus pneumoniae-2, H. influenzae-6), which are functionally significant and also play key role in host-pathogen interactions. This approach facilitates swift identification of potential drug targets, thereby enabling the search for new inhibitors. These results underscore the utility of CAI for enhanced in silico drug target identification.

ELECTRONIC SUPPLEMENTARY MATERIAL

The online version of this article (doi:10.1007/s11568-011-9152-7) contains supplementary material, which is available to authorized users.

ModEnzA: Accurate Identification of Metabolic Enzymes Using Function Specific Profile HMMs with Optimised Discrimination Threshold and Modified Emission Probabilities

Various enzyme identification protocols involving homology transfer by sequence-sequence or profile-sequence comparisons have been devised which utilise Swiss-Prot sequences associated with EC numbers as the training set. A profile HMM constructed for a particular EC number might select sequences which perform a different enzymatic function due to the presence of certain fold-specific residues which are conserved in enzymes sharing a common fold. We describe a protocol, ModEnzA (HMM-ModE Enzyme Annotation), which generates profile HMMs highly specific at a functional level as defined by the EC numbers by incorporating information from negative training sequences. We enrich the training dataset by mining sequences from the NCBI Non-Redundant database for increased sensitivity. We compare our method with other enzyme identification methods, both for assigning EC numbers to a genome as well as identifying protein sequences associated with an enzymatic activity. We report a sensitivity of 88% and specificity of 95% in identifying EC numbers and annotating enzymatic sequences from the E. coli genome which is higher than any other method. With the next-generation sequencing methods producing a huge amount of sequence data, the development and use of fully automated yet accurate protocols such as ModEnzA is warranted for rapid annotation of newly sequenced genomes and metagenomic sequences.

Evolution of bacterial phosphoglycerate mutases: non-homologous isofunctional enzymes undergoing gene losses, gains and lateral transfers

Background

The glycolytic phosphoglycerate mutases exist as non-homologous isofunctional enzymes (NISE) having independent evolutionary origins and no similarity in primary sequence, 3D structure, or catalytic mechanism. Cofactor-dependent PGM (dPGM) requires 2,3-bisphosphoglycerate for activity; cofactor-independent PGM (iPGM) does not. The PGM profile of any given bacterium is unpredictable and some organisms such as Escherichia coli encode both forms.

Methods/Principal Findings

To examine the distribution of PGM NISE throughout the Bacteria, and gain insight into the evolutionary processes that shape their phyletic profiles, we searched bacterial genome sequences for the presence of dPGM and iPGM. Both forms exhibited patchy distributions throughout the bacterial domain. Species within the same genus, or even strains of the same species, frequently differ in their PGM repertoire. The distribution is further complicated by the common occurrence of dPGM paralogs, while iPGM paralogs are rare. Larger genomes are more likely to accommodate PGM paralogs or both NISE forms. Lateral gene transfers have shaped the PGM profiles with intradomain and interdomain transfers apparent. Archaeal-type iPGM was identified in many bacteria, often as the sole PGM. To address the function of PGM NISE in an organism encoding both forms, we analyzed recombinant enzymes from E. coli. Both NISE were active mutases, but the specific activity of dPGM greatly exceeded that of iPGM, which showed highest activity in the presence of manganese. We created PGM null mutants in E. coli and discovered the ΔdPGM mutant grew slowly due to a delay in exiting stationary phase. Overexpression of dPGM or iPGM overcame this defect.

Conclusions/Significance

Our biochemical and genetic analyses in E. coli firmly establish dPGM and iPGM as NISE. Metabolic redundancy is indicated since only larger genomes encode both forms. Non-orthologous gene displacement can fully account for the non-uniform PGM distribution we report across the bacterial domain.