In silico approaches for predicting natural compounds with therapeutic potential and vaccine candidates against Streptococcus equi

Equine strangles is a prevalent disease that affects the upper respiratory in horses and is caused by the Gram-positive bacterium Streptococcus equi. In addition to strangles, other clinical conditions are caused by the two S. equi subspecies, equi and zooepidemicus, which present relevant zoonotic potential. Treatment of infections caused by S. equi has become challenging due to the worldwide spreading of infected horses and the unavailability of effective therapeutics and vaccines. Penicillin treatment is often recommended, but multidrug resistance issues arised. We explored the whole genome sequence of 18 S. equi isolates to identify candidate proteins to be targeted by natural drug-like compounds or explored as immunogens. We considered only proteins shared among the sequenced strains of subspecies equi and zooepidemicus, absent in the equine host and predicted to be essential and involved in virulence. Of these, 4 proteins with cytoplasmic subcellular location were selected for molecular docking with a library of 5008 compounds, while 6 proteins were proposed as prominent immunogens against S. equi due to their probabilities of behaving as adhesins. The molecular docking analyses revealed the best ten ligands for each of the 4 drug target candidates, and they were ranked according to their binding affinities and the number of hydrogen bonds for complex stability. Finally, the natural 5-ring compound C25H20F3N5O3 excelled in molecular dynamics simulations for the increased stability in the interaction with UDP-N-acetylenolpyruvoylglucosamine reductase (MurB). This research paves the way to developing new therapeutics to minimize the impacts caused by S. equi infections.Communicated by Ramaswamy H. Sarma.

Combined antimicrobial effect of two peptide nucleic acids against Staphylococcus aureus and S. pseudintermedius veterinary isolates

BACKGROUND

Staphylococcus aureus and S. pseudintermedius are the major etiological agents of staphylococcal infections in humans, livestock, and companion animals. The misuse of antimicrobial drugs has led to the emergence of antimicrobial-resistant Staphylococcus spp., including methicillin-resistant S. aureus (MRSA) and methicillin-resistant S. pseudintermedius (MRSP). One novel therapeutic approach against MRSA and MRSP is a peptide nucleic acid (PNA) that can bind to the target nucleotide strands and block expression. Previously, two PNAs conjugated with cell-penetrating peptides (P-PNAs), antisense PNA (ASP)-cmk and ASP-deoD, targeting two essential genes in S. aureus, were constructed, and their antibacterial activities were analyzed.

OBJECTIVES

This study analyzed the combined antibacterial effects of P-PNAs on S. aureus and S. pseudintermedius clinical isolates.

METHODS

S. aureus ATCC 29740 cells were treated simultaneously with serially diluted ASP-cmk and ASP-deoD, and the minimal inhibitory concentrations (MICs) were measured. The combined P-PNA mixture was then treated with S. aureus and S. pseudintermedius veterinary isolates at the determined MIC, and the antibacterial effect was examined.

RESULTS

The combined treatment of two P-PNAs showed higher antibacterial activity than the individual treatments. The MICs of two individual P-PNAs were 20 and 25 μM, whereas that of the combined treatment was 10 μM. The application of a combined treatment to clinical Staphylococcus spp. revealed S. aureus isolates to be resistant to P-PNAs and S. pseudintermedius isolates to be susceptible.

CONCLUSIONS

These observations highlight the complexity of designing ASPs with high efficacy for potential applications in treating staphylococcal infections in humans and animals.

New putative therapeutic targets against Serratia marcescens using reverse vaccinology and subtractive genomics

The Gram-negative bacillus Serratia marcescens, a member of Enterobacteriaceae family, is an opportunistic nosocomial pathogen commonly found in hospital outbreaks that can cause infections in the urinary tract, bloodstream, central nervous system and pneumonia. Because S. marcescens strains are resistant to several antibiotics, it is critical the need for effective treatments, including new drugs and vaccines. Here, we applied reverse vaccinology and subtractive genomic approaches for the in silico prediction of potential vaccine and drug targets against 59 strains of S. marcescens. We found 759 core non-host homologous proteins, of which 87 are putative surface-exposed proteins, 183 secreted proteins, and 80 membrane proteins. From these proteins, we predicted seven candidates vaccine targets: a sn-glycerol-3-phosphate-binding periplasmic protein UgpB, a vitamin B12 TonB-dependent receptor, a ferrichrome porin FhuA, a divisome-associated lipoprotein YraP, a membrane-bound lytic murein transglycosylase A, a peptidoglycan lytic exotransglycosylase, and a DUF481 domain-containing protein. We also predicted two drug targets: a N(4)-acetylcytidine amidohydrolase, and a DUF1428 family protein. Using the molecular docking approach for each drug target, we identified and selected ZINC04259491 and ZINC04235390 molecules as the most favorable interactions with the target active site residues. Our findings may contribute to the development of vaccines and new drug targets against S. marcescens. Communicated by Ramaswamy H. Sarma.

Designing a vaccine-based therapy against Epstein-Barr virus-associated tumors using immunoinformatics approach

Epstein-Barr virus (EBV) is widely known due to its role in the etiology of infectious mononucleosis. However, it is the first oncovirus that was identified and has been implicated in the etiology of several types of cancers. Globally, EBV infection is associated with more than 200, 000 new cancer cases and 150, 000 deaths yearly. A prophylactic or therapeutic vaccine targeting tumors associated with EBV infection is currently lacking. Therefore, this study aimed to develop a multiepitope-based polyvalent vaccine against EBV-associated tumors using immunoinformatics approach. The latency-associated proteins (LAP) of three strains of the virus were used in this study. Potential epitopes predicted from the proteins were analyzed and selected based on several predicted properties. Thirty viable B-cell and T-cell epitopes were selected and conjugated using various linkers alongside beta-defensin 3 as an adjuvant and pan HLA DR-binding epitope (PADRE) sequence to improve the immunogenicity of the vaccine construct. Molecular docking studies of the vaccine construct against toll-like receptors (TLRs) showed it is capable of inducing immune response via recognition by TLRs while immune simulation studies showed it could induce both cellular and humoral immune responses. Furthermore, molecular dynamics study of the complex formed by the vaccine candidate and TLR-4 showed that the complex was stable. Ultimately, the designed vaccine showed desirable properties based on in silico evaluation; however, experimental studies are needed to validate the efficacy of the vaccine against EBV-associated tumors.

Accurate Prediction of Human Essential Proteins Using Ensemble Deep Learning

Essential proteins are considered the foundation of life as they are indispensable for the survival of living organisms. Computational methods for essential protein discovery provide a fast way to identify essential proteins. But most of them heavily rely on various biological information, especially protein-protein interaction networks, which limits their practical applications. With the rapid development of high-throughput sequencing technology, sequencing data has become the most accessible biological data. However, using only protein sequence information to predict essential proteins has limited accuracy. In this paper, we propose EP-EDL, an ensemble deep learning model using only protein sequence information to predict human essential proteins. EP-EDL integrates multiple classifiers to alleviate the class imbalance problem and to improve prediction accuracy and robustness. In each base classifier, we employ multi-scale text convolutional neural networks to extract useful features from protein sequence feature matrices with evolutionary information. Our computational results show that EP-EDL outperforms the state-of-the-art sequence-based methods. Furthermore, EP-EDL provides a more practical and flexible way for biologists to accurately predict essential proteins. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/EP-EDL.

Utilizing Quantitative Proteomics to Identify Species-Specific Protein Therapeutic Targets for the Treatment of Leishmaniasis

Leishmaniasis is a tropical disease caused by Leishmania parasites, which are transmitted through the bites of infected sandflies. We focused on the emergence of leishmaniasis in Thailand caused by a species (Leishmania orientalis). Treatment by chemotherapy is not effective against L. orientalis. Hence, we intended to solve this issue using a proteomics approach to investigate protein profiles and in silico analysis for the identification of antigenic proteins from L. orientalis, Leishmania martiniquensis, and Leishmania donovani. Using principal component analysis (PCA), protein profile comparisons indicated that different species of Leishmania are different at the protein level. Proteomics analysis identified 6099 proteins. Among these proteins, 1065 proteins were used for further analysis. There were 16 proteins that were promising candidates for therapeutic aspects as they were abundantly expressed and common to all species. In silico analysis of protein's antigenicity revealed that eight proteins had the potential for the development of antigenic molecules. Protein profile information and these antigenic proteins may play key roles in the pathogeny of leishmaniasis and can be used as novel therapeutic targets against leishmaniasis in the future.

Identification of Putative Vaccine and Drug Targets against the Methicillin-Resistant Staphylococcus aureus by Reverse Vaccinology and Subtractive Genomics Approaches

Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen and responsible for causing life-threatening infections. The emergence of hypervirulent and multidrug-resistant (MDR) S. aureus strains led to challenging issues in antibiotic therapy. Consequently, the morbidity and mortality rates caused by S. aureus infections have a substantial impact on health concerns. The current worldwide prevalence of MRSA infections highlights the need for long-lasting preventive measures and strategies. Unfortunately, effective measures are limited. In this study, we focus on the identification of vaccine candidates and drug target proteins against the 16 strains of MRSA using reverse vaccinology and subtractive genomics approaches. Using the reverse vaccinology approach, 4 putative antigenic proteins were identified; among these, PrsA and EssA proteins were found to be more promising vaccine candidates. We applied a molecular docking approach of selected 8 drug target proteins with the drug-like molecules, revealing that the ZINC4235426 as potential drug molecule with favorable interactions with the target active site residues of 5 drug target proteins viz., biotin protein ligase, HPr kinase/phosphorylase, thymidylate kinase, UDP-N-acetylmuramoyl-L-alanyl-D-glutamate-L-lysine ligase, and pantothenate synthetase. Thus, the identified proteins can be used for further rational drug or vaccine design to identify novel therapeutic agents for the treatment of multidrug-resistant staphylococcal infection.

Harnessing model organism genomics to underpin the machine learning-based prediction of essential genes in eukaryotes - Biotechnological implications

The availability of high-quality genomes and advances in functional genomics have enabled large-scale studies of essential genes in model eukaryotes, including the 'elegant worm' (Caenorhabditis elegans; Nematoda) and the 'vinegar fly' (Drosophila melanogaster; Arthropoda). However, this is not the case for other, much less-studied organisms, such as socioeconomically important parasites, for which functional genomic platforms usually do not exist. Thus, there is a need to develop innovative techniques or approaches for the prediction, identification and investigation of essential genes. A key approach that could enable the prediction of such genes is machine learning (ML). Here, we undertake an historical review of experimental and computational approaches employed for the characterisation of essential genes in eukaryotes, with a particular focus on model ecdysozoans (C. elegans and D. melanogaster), and discuss the possible applicability of ML-approaches to organisms such as socioeconomically important parasites. We highlight some recent results showing that high-performance ML, combined with feature engineering, allows a reliable prediction of essential genes from extensive, publicly available 'omic data sets, with major potential to prioritise such genes (with statistical confidence) for subsequent functional genomic validation. These findings could 'open the door' to fundamental and applied research areas. Evidence of some commonality in the essential gene-complement between these two organisms indicates that an ML-engineering approach could find broader applicability to ecdysozoans such as parasitic nematodes or arthropods, provided that suitably large and informative data sets become/are available for proper feature engineering, and for the robust training and validation of algorithms. This area warrants detailed exploration to, for example, facilitate the identification and characterisation of essential molecules as novel targets for drugs and vaccines against parasitic diseases. This focus is particularly important, given the substantial impact that such diseases have worldwide, and the current challenges associated with their prevention and control and with drug resistance in parasite populations.

In Silico Prediction and Prioritization of Novel Selective Antimicrobial Drug Targets in Escherichia coli

Novel antimicrobials interfering with pathogen-specific targets can minimize the risk of perturbations of the gut microbiota (dysbiosis) during therapy. We employed an in silico approach to identify essential proteins in Escherichia coli that are either absent or have low sequence identity in seven beneficial taxa of the gut microbiota: Faecalibacterium, Prevotella, Ruminococcus, Bacteroides, Lactobacillus, Lachnospiraceae and Bifidobacterium. We identified 36 essential proteins that are present in hyper-virulent E. coli ST131 and have low similarity (bitscore < 50 or identity < 30% and alignment length < 25%) to proteins in mammalian hosts and beneficial taxa. Of these, 35 are also present in Klebsiella pneumoniae. None of the proteins are targets of clinically used antibiotics, and 3D structure is available for 23 of them. Four proteins (LptD, LptE, LolB and BamD) are easily accessible as drug targets due to their location in the outer membrane, especially LptD, which contains extracellular domains. Our results indicate that it may be possible to selectively interfere with essential biological processes in Enterobacteriaceae that are absent or mediated by unrelated proteins in beneficial taxa residing in the gut. The identified targets can be used to discover antimicrobial drugs effective against these opportunistic pathogens with a decreased risk of causing dysbiosis.

Reverse vaccinology and subtractive genomics approaches for identifying common therapeutics against Mycobacterium leprae and Mycobacterium lepromatosis

BACKGROUND

Mycobacterium leprae and Mycobacterium lepromatosis are gram-positive bacterial pathogens and the causative agents of leprosy in humans across the world. The elimination of leprosy cannot be achieved by multidrug therapy alone, and highlights the need for new tools and drugs to prevent the emergence of new resistant strains.

METHODS

In this study, our contribution includes the prediction of vaccine targets and new putative drugs against leprosy, using reverse vaccinology and subtractive genomics. Six strains of Mycobacterium leprae and Mycobacterium lepromatosis (4 and 2 strains, respectively) were used for comparison taking Mycobacterium leprae strain TN as the reference genome. Briefly, we used a combined reverse vaccinology and subtractive genomics approach.

RESULTS

As a result, we identified 12 common putative antigenic proteins as vaccine targets and three common drug targets against Mycobacterium leprae and Mycobacterium lepromatosis. Furthermore, the docking analysis using 28 natural compounds with three drug targets was done.

CONCLUSIONS

The bis-naphthoquinone compound Diospyrin (CID 308140) obtained from indigenous plant Diospyros spp. showed the most favored binding affinity against predicted drug targets, which can be a candidate therapeutic target in the future against leprosy.

Discerning novel drug targets for treating Mycobacterium avium ss. paratuberculosis-associated autoimmune disorders: an in silico approach

Mycobacterium avium subspecies paratuberculosis (MAP) exhibits 'molecular mimicry' with the human host resulting in several autoimmune diseases such as multiple sclerosis, type 1 diabetes mellitus (T1DM), Hashimoto's thyroiditis, Crohn's disease (CD), etc. The conventional therapy for autoimmune diseases includes immunosuppressants or immunomodulators that treat the symptoms rather than the etiology and/or causative mechanism(s). Eliminating MAP-the etiopathological agent might be a better strategy to treat MAP-associated autoimmune diseases. In this case study, we conducted a systematic in silico analysis to identify the metabolic chokepoints of MAP's mimicry proteins and their interacting partners. The probable inhibitors of chokepoint proteins were identified using DrugBank. DrugBank molecules were stringently screened and molecular interactions were analyzed by molecular docking and 'off-target' binding. Thus, we identified 18 metabolic chokepoints of MAP mimicry proteins and 13 DrugBank molecules that could inhibit three chokepoint proteins viz. katG, rpoB and narH. On the basis of molecular interaction between drug and target proteins finally eight DrugBank molecules, viz. DB00609, DB00951, DB00615, DB01220, DB08638, DB08226, DB08266 and DB07349 were selected and are proposed for treatment of three MAP-associated autoimmune diseases namely, T1DM, CD and multiple sclerosis. Because these molecules are either approved by the Food and Drug Administration or these are experimental drugs that can be easily incorporated in clinical studies or tested in vitro. The proposed strategy may be used to repurpose drugs to treat autoimmune diseases induced by other pathogens.

Modeling Novel Putative Drugs and Vaccine Candidates against Tick-Borne Pathogens: A Subtractive Proteomics Approach

Ticks and tick-borne pathogens (TBPs) continuously causing substantial losses to the public and veterinary health sectors. The identification of putative drug targets and vaccine candidates is crucial to control TBPs. No information has been recorded on designing novel drug targets and vaccine candidates based on proteins. Subtractive proteomics is an in silico approach that utilizes extensive screening for the identification of novel drug targets or vaccine candidates based on the determination of potential target proteins available in a pathogen proteome that may be used effectively to control diseases caused by these infectious agents. The present study aimed to investigate novel drug targets and vaccine candidates by utilizing subtractive proteomics to scan the available proteomes of TBPs and predict essential and non-host homologous proteins required for the survival of these diseases causing agents. Subtractive proteome analysis revealed a list of fifteen essential, non-host homologous, and unique metabolic proteins in the complete proteome of selected pathogens. Among these therapeutic target proteins, three were excluded due to the presence in host gut metagenome, eleven were found to be highly potential drug targets, while only one was found as a potential vaccine candidate against TBPs. The present study may provide a foundation to design potential drug targets and vaccine candidates for the effective control of infections caused by TBPs.

In silico Prediction of New Drug Candidates Against the Multidrug-Resistant and Potentially Zoonotic Fish Pathogen Serotype III Streptococcus agalactiae

Streptococcus agalactiae is an invasive multi-host pathogen that causes invasive diseases mainly in newborns, elderly, and individuals with underlying health complications. In fish, S. agalactiae causes streptococcosis, which is characterized by septicemia and neurological signs, and leads to great economic losses to the fish farming industry worldwide. These bacteria can be classified into different serotypes based on capsular antigens, and into different sequence types (ST) based on multilocus sequence typing (MLST). In 2015, serotype III ST283 was identified to be associated with a foodborne invasive disease in non-pregnant immunocompetent humans in Singapore, and the infection was related to raw fish consumption. In addition, a serotype III strain isolated from tilapia in Brazil has been reported to be resistant to five antibiotic classes. This specific serotype can serve as a reservoir of resistance genes and pose a serious threat to public health. Thus, new approaches for the control and treatment of S. agalactiae infections are needed. In the present study, 24 S. agalactiae serotype III complete genomes, isolated from human and fish hosts, were compared. The core genome was identified, and, using bioinformatics tools and subtractive criteria, five proteins were identified as potential drug targets. Furthermore, 5,008 drug-like natural compounds were virtually screened against the identified targets. The ligands with the best binding properties are suggested for further in vitro and in vivo analysis.

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.

A Novel Peptide Nucleic Acid against the Cytidine Monophosphate Kinase of S. aureus Inhibits Staphylococcal Infection In Vivo

Here, we report a novel bactericidal peptide nucleic acid (PNA) that can induce the antisense effect on the cytidine monophosphate kinase (Cmk) of Staphylococcus aureus, a putative essential component for bacterial species. Based on the genome sequence of S. aureus N315, a set of PNA conjugates with a bacterial penetration peptide, (KFF)₃K, were synthesized to target the seven potentially essential genes (cmk, deoD, ligA, smpB, glmU, pyrH, and ftsA) and further evaluated for their antibacterial properties in vitro as well as in vivo. The results demonstrated that two peptide-conjugated PNAs (P-PNAs), antisense P-PNA (ASP)-cmk1 and ASP-deoD1, targeting either the cmk or the deoD genes, had the strongest inhibitory effects on the growth of S. aureus ATCC 29740 (a bovine mastitic milk isolate) in a dose-dependent manner. In vivo application of ASP-cmk1 resulted in a significant reduction of bacterial loads in mice intraperitoneally infected with a sublethal dose of S. aureus. Moreover, ASP-cmk1 significantly increased the survival rate of the breast-fed infant mice after intramammary infection of the lactating CD-1 mice. Taken together, our characterization of ASP-cmk1 demonstrated its bactericidal activity against S. aureus as well as its effectiveness in vivo.

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.

Comprehensive review of the identification of essential genes using computational methods: focusing on feature implementation and assessment

Essential genes have attracted increasing attention in recent years due to the important functions of these genes in organisms. Among the methods used to identify the essential genes, accurate and efficient computational methods can make up for the deficiencies of expensive and time-consuming experimental technologies. In this review, we have collected researches on essential gene predictions in prokaryotes and eukaryotes and summarized the five predominant types of features used in these studies. The five types of features include evolutionary conservation, domain information, network topology, sequence component and expression level. We have described how to implement the useful forms of these features and evaluated their performance based on the data of Escherichia coli MG1655, Bacillus subtilis 168 and human. The prerequisite and applicable range of these features is described. In addition, we have investigated the techniques used to weight features in various models. To facilitate researchers in the field, two available online tools, which are accessible for free and can be directly used to predict gene essentiality in prokaryotes and humans, were referred. This article provides a simple guide for the identification of essential genes in prokaryotes and eukaryotes.

In silico analysis of putative drug and vaccine targets of the metabolic pathways of Actinobacillus pleuropneumoniae using a subtractive/comparative genomics approach

Actinobacillus pleuropneumoniae is a Gram-negative bacterium that resides in the respiratory tract of pigs and causes porcine respiratory disease complex, which leads to significant losses in the pig industry worldwide. The incidence of drug resistance in this bacterium is increasing; thus, identifying new protein/gene targets for drug and vaccine development is critical. In this study, we used an in silico approach, utilizing several databases including the Kyoto Encyclopedia of Genes and Genomes (KEGG), the Database of Essential Genes (DEG), DrugBank, and Swiss-Prot to identify non-homologous essential genes and prioritize these proteins for their druggability. The results showed 20 metabolic pathways that were unique and contained 273 non-homologous proteins, of which 122 were essential. Of the 122 essential proteins, there were 95 cytoplasmic proteins and 11 transmembrane proteins, which are potentially suitable for drug and vaccine targets, respectively. Among these, 25 had at least one hit in DrugBank, and three had similarity to metabolic proteins from Mycoplasma hyopneumoniae, another pathogen causing porcine respiratory disease complex; thus, they could serve as common therapeutic targets. In conclusion, we identified glyoxylate and dicarboxylate pathways as potential targets for antimicrobial therapy and tetra-acyldisaccharide 4'-kinase and 3-deoxy-D-manno-octulosonic-acid transferase as vaccine candidates against A. pleuropneumoniae.

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.

Developing genome-reduced Pseudomonas chlororaphis strains for the production of secondary metabolites

BACKGROUND

The current chassis organisms or various types of cell factories have considerable advantages and disadvantages. Therefore, it is necessary to develop various chassis for an efficient production of different bioproducts from renewable resources. In this context, synthetic biology offers unique potentialities to produce value-added products of interests. Microbial genome reduction and modification are important strategies for constructing cellular chassis and cell factories. Many genome-reduced strains from Escherichia coli, Bacillus subtilis, Corynebacterium glutamicum and Streptomyces, have been widely used for the production of amino acids, organic acids, and some enzymes. Some Pseudomonas strains could serve as good candidates for ideal chassis cells since they grow fast and can produce many valuable metabolites with low nutritional requirements and strong environmental adaptability. Pseudomonas chlororaphis GP72 is a non-pathogenic plant growth-promoting rhizobacterium that possesses capacities of tolerating various environmental stresses and synthesizing many kinds of bioactive compounds with high yield. These include phenazine-1-carboxylic acid (PCA) and 2-hydroxyphenazine (2-OH-PHZ), which exhibit strong bacteriostatic and antifungal activity toward some microbial pathogens.

RESULTS

We depleted 685 kb (10.3% of the genomic sequence) from the chromosome of P. chlororaphis GP72(rpeA-) by a markerless deletion method, which included five secondary metabolic gene clusters and 17 strain-specific regions (525 non-essential genes). Then we characterized the 22 multiple-deletion series (MDS) strains. Growth characteristics, production of phenazines and morphologies were changed greatly in mutants with large-fragment deletions. Some of the genome-reduced P. chlororaphis mutants exhibited more productivity than the parental strain GP72(rpeA-). For example, strain MDS22 had 4.4 times higher production of 2-OH-PHZ (99.1 mg/L) than strain GP72(rpeA-), and the specific 2-OH-PHZ production rate (mmol/g/h) increased 11.5-fold. Also and MDS10 had the highest phenazine production (852.0 mg/L) among all the studied strains with a relatively high specific total phenazine production rate (0.0056 g/g/h).

CONCLUSIONS

In conclusion, P. chlororaphis strains with reduced genome performed better in production of secondary metabolites than the parent strain. The newly developed mutants can be used for the further genetic manipulation to construct chassis cells with the less complex metabolic network, better regulation and more efficient productivity for diverse biotechnological applications.

Study of intra-inter species protein-protein interactions for potential drug targets identification and subsequent drug design for Escherichia coli O104:H4 C277-11

Protein-protein interaction (PPI) and host-pathogen interactions (HPI) proteomic analysis has been successfully practiced for potential drug target identification in pathogenic infections. In this research, we attempted to identify new drug target based on PPI and HPI computation approaches and subsequently design new drug against devastating enterohemorrhagic Escherichia coli O104:H4 C277-11 (Broad), which causes life-threatening food borne disease outbreak in Germany and other countries in Europe in 2011. Our systematic in silico analysis on PPI and HPI of E. coli O104:H4 was able to identify bacterial D-galactose-binding periplasmic and UDP-N-acetylglucosamine 1-carboxyvinyltransferase as attractive candidates for new drug targets. Furthermore, computational three-dimensional structure modeling and subsequent molecular docking finally proposed [3-(5-Amino-7-Hydroxy-[1,2,3]Triazolo[4,5-D]Pyrimidin-2-Yl)-N-(3,5-Dichlorobenzyl)-Benzamide)] and (6-amino-2-[(1-naphthylmethyl)amino]-3,7-dihydro-8H-imidazo[4,5-g]quinazolin-8-one) as promising candidate drugs for further evaluation and development for E. coli O104:H4 mediated diseases. Identification of new drug target would be of great utility for humanity as the demand for designing new drugs to fight infections is increasing due to the developing resistance and side effects of current treatments. This research provided the basis for computer aided drug design which might be useful for new drug target identification and subsequent drug design for other infectious organisms.

An In Silico Identification of Common Putative Vaccine Candidates against Treponema pallidum: A Reverse Vaccinology and Subtractive Genomics Based Approach

Sexually transmitted infections (STIs) are caused by a wide variety of bacteria, viruses, and parasites that are transmitted from one person to another primarily by vaginal, anal, or oral sexual contact. Syphilis is a serious disease caused by a sexually transmitted infection. Syphilis is caused by the bacterium Treponema pallidum subspecies pallidum. Treponema pallidum (T. pallidum) is a motile, gram-negative spirochete, which can be transmitted both sexually and from mother to child, and can invade virtually any organ or structure in the human body. The current worldwide prevalence of syphilis emphasizes the need for continued preventive measures and strategies. Unfortunately, effective measures are limited. In this study, we focus on the identification of vaccine targets and putative drugs against syphilis disease using reverse vaccinology and subtractive genomics. We compared 13 strains of T. pallidum using T. pallidum Nichols as the reference genome. Using an in silicoapproach, four pathogenic islands were detected in the genome of T. pallidum Nichols. We identified 15 putative antigenic proteins and sixdrug targets through reverse vaccinology and subtractive genomics, respectively, which can be used as candidate therapeutic targets in the future.

Tapping into Salmonella typhimurium LT2 genome in a quest to explore its therapeutic arsenal: A metabolic network modeling approach

S. typhimurium, the classical broad-host-range serovar is a widely distributed cause of food-borne illness. Escalating antibiotic resistance and potential of conjugal transmission to other pathogens attributable to its broad spectrum host specificities have aided S. typhimurium to emerge as a global health threat. To keep pace with ever evolving bacterial defenses, there is dire need to restock the antibiotic pipeline. Genome scale metabolic reconstructions present immense possibilities to decipher physiological properties of an organism using constraint-based methods The systems-level approaches of genome scale metabolic networks interrogation open up new avenues of drug target identification against deadly infectious diseases. We performed flux balance analysis and minimization of metabolic adjustment studies of genome scale reconstruction model of S. typhimurium targeted at identifying large number of metabolites with a potential to be utilized as therapeutic drug targets. These constraint based approaches initially predict a set of genes indispensable to bacterial survival by performing gene knockout studies which are then prioritized through a multistep process. Metabolites involved in l-rhamnose biosynthesis, peptidoglycan biosynthesis, fatty acid biosynthesis, and folate biosynthesis pathways were prioritized as candidate drug targets. This study provides a general therapeutic approach which can be effectively applied to other pathogens as well.

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.

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.

Novel Drug Targets for Food-Borne Pathogen Campylobacter jejuni: An Integrated Subtractive Genomics and Comparative Metabolic Pathway Study

Campylobacters are a major global health burden and a cause of food-borne diarrheal illness and economic loss worldwide. In developing countries, Campylobacter infections are frequent in children under age two and may be associated with mortality. In developed countries, they are a common cause of bacterial diarrhea in early adulthood. In the United States, antibiotic resistance against Campylobacter is notably increased from 13% in 1997 to nearly 25% in 2011. Novel drug targets are urgently needed but remain a daunting task to accomplish. We suggest that omics-guided drug discovery is timely and worth considering in this context. The present study employed an integrated subtractive genomics and comparative metabolic pathway analysis approach. We identified 16 unique pathways from Campylobacter when compared against H. sapiens with 326 non-redundant proteins; 115 of these were found to be essential in the Database of Essential Genes. Sixty-six proteins among these were non-homologous to the human proteome. Six membrane proteins, of which four are transporters, have been proposed as potential vaccine candidates. Screening of 66 essential non-homologous proteins against DrugBank resulted in identification of 34 proteins with drug-ability potential, many of which play critical roles in bacterial growth and survival. Out of these, eight proteins had approved drug targets available in DrugBank, the majority serving crucial roles in cell wall synthesis and energy metabolism and therefore having the potential to be utilized as drug targets. We conclude by underscoring that screening against these proteins with inhibitors may aid in future discovery of novel therapeutics against campylobacteriosis in ways that will be pathogen specific, and thus have minimal toxic effect on host. Omics-guided drug discovery and bioinformatics analyses offer the broad potential for veritable advances in global health relevant novel therapeutics.

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.

Purification and biochemical characterisation of GlmU from Yersinia pestis

Antibiotic resistance has emerged as a real threat to mankind, rendering many compounds ineffective in the fight against bacterial infection, including for significant diseases such as plague caused by Yersinia pestis. Essential genes have been identified as promising targets for inhibiting with new classes of compounds. Previously, the gene encoding the bifunctional UDP-N-acetylglucosamine pyrophosphorylase/glucosamine-1-phosphate N-acetyltransferase enzyme GlmU was confirmed as an essential gene in Yersinia. As a step towards exploiting this target for antimicrobial screening, we undertook a biochemical characterisation of the Yersinia GlmU. Effects of pH and magnesium concentration on the acetyltransferase and uridyltransferase activities were analysed, and kinetic parameters were determined. The acetyltransferase activity, which is strongly increased in the presence of reducing agent, was shown to be susceptible to oxidation and thiol-specific reagents.

From microbial gene essentiality to novel antimicrobial drug targets

Background

Bacterial respiratory tract infections, mainly caused by Streptococcus pneumoniae, Haemophilus influenzae and Moraxella catarrhalis are among the leading causes of global mortality and morbidity. Increased resistance of these pathogens to existing antibiotics necessitates the search for novel targets to develop potent antimicrobials.

Result

Here, we report a proof of concept study for the reliable identification of potential drug targets in these human respiratory pathogens by combining high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics. Approximately 20% of all genes in these three species were essential for growth and viability, including 128 essential and conserved genes, part of 47 metabolic pathways. By comparing these essential genes to the human genome, and a database of genes from commensal human gut microbiota, we identified and excluded potential drug targets in respiratory tract pathogens that will have off-target effects in the host, or disrupt the natural host microbiota. We propose 249 potential drug targets, 67 of which are targets for 75 FDA-approved antimicrobials and 35 other researched small molecule inhibitors. Two out of four selected novel targets were experimentally validated, proofing the concept.

Conclusion

Here we have pioneered an attempt in systematically combining the power of high-density transposon mutagenesis, high-throughput sequencing, and integrative genomics to discover potential drug targets at genome-scale. By circumventing the time-consuming and expensive laboratory screens traditionally used to select potential drug targets, our approach provides an attractive alternative that could accelerate the much needed discovery of novel antimicrobials.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-958) contains supplementary material, which is available to authorized users.

Wolbachia transcription elongation factor "Wol GreA" interacts with α2ββ'σ subunits of RNA polymerase through its dimeric C-terminal domain

OBJECTIVES

Wolbachia, an endosymbiont of filarial nematode, is considered a promising target for therapy against lymphatic filariasis. Transcription elongation factor GreA is an essential factor that mediates transcriptional transition from abortive initiation to productive elongation by stimulating the escape of RNA polymerase (RNAP) from native prokaryotic promoters. Upon screening of 6257 essential bacterial genes, 57 were suggested as potential future drug targets, and GreA is among these. The current study emphasized the characterization of Wol GreA with its domains.

METHODOLOGY/PRINCIPAL FINDINGS

Biophysical characterization of Wol GreA with its N-terminal domain (NTD) and C-terminal domain (CTD) was performed with fluorimetry, size exclusion chromatography, and chemical cross-linking. Filter trap and far western blotting were used to determine the domain responsible for the interaction with α2ββ'σ subunits of RNAP. Protein-protein docking studies were done to explore residual interaction of RNAP with Wol GreA. The factor and its domains were found to be biochemically active. Size exclusion and chemical cross-linking studies revealed that Wol GreA and CTD exist in a dimeric conformation while NTD subsists in monomeric conformation. Asp120, Val121, Ser122, Lys123, and Ser134 are the residues of CTD through which monomers of Wol GreA interact and shape into a dimeric conformation. Filter trap, far western blotting, and protein-protein docking studies revealed that dimeric CTD of Wol GreA through Lys82, Ser98, Asp104, Ser105, Glu106, Tyr109, Glu116, Asp120, Val121, Ser122, Ser127, Ser129, Lys140, Glu143, Val147, Ser151, Glu153, and Phe163 residues exclusively participates in binding with α2ββ'σ subunits of polymerase.

CONCLUSIONS/SIGNIFICANCE

To the best of our knowledge, this research is the first documentation of the residual mode of action in wolbachial mutualist. Therefore, findings may be crucial to understanding the transcription mechanism of this α-proteobacteria and in deciphering the role of Wol GreA in filarial development.

Prophylaxis and therapy of plague

Plague has been a scourge of mankind for centuries, and outbreaks continue to the present day. The virulence mechanisms employed by the etiological agent Yersinia pestis are reviewed in the context of the available prophylactic and therapeutic strategies for plague. Although antibiotics are available, resistance is emerging in this dangerous pathogen. Therapeutics used in the clinic are discussed and innovative approaches to the design and development of new therapeutic compounds are reviewed. Currently there is no licensed vaccine available for prevention of plague in the USA or western Europe, although both live attenuated strains and killed whole-cell extracts have been used historically. Live strains are still approved for human use in some parts of the world, such as the former Soviet Union, but poor safety profiles render them unacceptable to many countries. The development of safe, effective next-generation vaccines, including the recombinant subunit vaccine currently used in clinical trials is discussed.

Construction of an inducible system for the analysis of essential genes in Yersinia pestis

Yersinia pestis, a Gram negative bacterium, causes bubonic and pneumonic plague. Emerging antibiotic resistance in clinical isolates is driving a need to develop novel antibiotics to treat infection by this transmissible and highly virulent pathogen. Proteins required for viability, so called essential genes, are attractive potential therapeutic targets, however, confirmation of essentiality is problematic. For the first time, we report the development of a system that allows the rapid determination of Y. pestis gene essentiality through mutagenesis and inducible expression of a plasmid borne copy of the target gene. Using this approach, we have confirmed the uridine monophosphate kinase PyrH as an essential protein in Y. pestis. This methodology and the tools we have developed will allow the confirmation of other putative essential genes in this dangerous pathogen, and facilitate the identification of novel targets for antimicrobial development.

Comparative genomics study for identification of drug and vaccine targets in Vibrio cholerae: MurA ligase as a case study

A systematic workflow consisting of comparative genomics, metabolic pathways analysis and additional drug prioritization parameters identified 264 proteins of Vibrio cholerae which were predicted to be absent in Homo sapiens. Among these, 40 proteins were identified as essential proteins that could serve as potential drug and vaccine targets. Additional prioritization parameters characterized 11 proteins as vaccine candidates while druggability of each of the identified proteins as evaluated by the Drug Bank database which prioritized 16 proteins suitable for drug targets. As a case study, we built a homology model of one of the potential drug targets, MurA ligase, using MODELLER (9v12) software. The model has been further explored for in silico docking with inhibitors having druggability potential from the Drug Bank database. Results from this study could facilitate selecting V. cholerae proteins for drug design and vaccine production pipelines in future.

Geptop: a gene essentiality prediction tool for sequenced bacterial genomes based on orthology and phylogeny

Integrative genomics predictors, which score highly in predicting bacterial essential genes, would be unfeasible in most species because the data sources are limited. We developed a universal approach and tool designated Geptop, based on orthology and phylogeny, to offer gene essentiality annotations. In a series of tests, our Geptop method yielded higher area under curve (AUC) scores in the receiver operating curves than the integrative approaches. In the ten-fold cross-validations among randomly upset samples, Geptop yielded an AUC of 0.918, and in the cross-organism predictions for 19 organisms Geptop yielded AUC scores between 0.569 and 0.959. A test applied to the very recently determined essential gene dataset from the Porphyromonas gingivalis, which belongs to a phylum different with all of the above 19 bacterial genomes, gave an AUC of 0.77. Therefore, Geptop can be applied to any bacterial species whose genome has been sequenced. Compared with the essential genes uniquely identified by the lethal screening, the essential genes predicted only by Gepop are associated with more protein-protein interactions, especially in the three bacteria with lower AUC scores (<0.7). This may further illustrate the reliability and feasibility of our method in some sense. The web server and standalone version of Geptop are available at http://cefg.uestc.edu.cn/geptop/ free of charge. The tool has been run on 968 bacterial genomes and the results are accessible at the website.

Exoproteome and secretome derived broad spectrum novel drug and vaccine candidates in Vibrio cholerae targeted by Piper betel derived compounds

Vibrio cholerae is the causal organism of the cholera epidemic, which is mostly prevalent in developing and underdeveloped countries. However, incidences of cholera in developed countries are also alarming. Because of the emergence of new drug-resistant strains, even though several generic drugs and vaccines have been developed over time, Vibrio infections remain a global health problem that appeals for the development of novel drugs and vaccines against the pathogen. Here, applying comparative proteomic and reverse vaccinology approaches to the exoproteome and secretome of the pathogen, we have identified three candidate targets (ompU, uppP and yajC) for most of the pathogenic Vibrio strains. Two targets (uppP and yajC) are novel to Vibrio, and two targets (uppP and ompU) can be used to develop both drugs and vaccines (dual targets) against broad spectrum Vibrio serotypes. Using our novel computational approach, we have identified three peptide vaccine candidates that have high potential to induce both B- and T-cell-mediated immune responses from our identified two dual targets. These two targets were modeled and subjected to virtual screening against natural compounds derived from Piper betel. Seven compounds were identified first time from Piper betel to be highly effective to render the function of these targets to identify them as emerging potential drugs against Vibrio. Our preliminary validation suggests that these identified peptide vaccines and betel compounds are highly effective against Vibrio cholerae. Currently we are exhaustively validating these targets, candidate peptide vaccines, and betel derived lead compounds against a number of Vibrio species.

Structure and function of cytidine monophosphate kinase from Yersinia pseudotuberculosis, essential for virulence but not for survival

The need for new antibiotics has become pressing in light of the emergence of antibiotic-resistant strains of human pathogens. Yersinia pestis, the causative agent of plague, is a public health threat and also an agent of concern in biodefence. It is a recently emerged clonal derivative of the enteric pathogen Yersinia pseudotuberculosis. Previously, we developed a bioinformatic approach to identify proteins that may be suitable targets for antimicrobial therapy and in particular for the treatment of plague. One such target was cytidine monophosphate (CMP) kinase, which is an essential gene in some organisms. Previously, we had thought CMP kinase was essential for Y. pseudotuberculosis, but by modification of the mutagenesis approach, we report here the production and characterization of a Δcmk mutant. The isogenic mutant had a growth defect relative to the parental strain, and was highly attenuated in mice. We have also elucidated the structure of the CMP kinase to 2.32 Å, and identified three key residues in the active site that are essential for activity of the enzyme. These findings will have implications for the development of novel CMP kinase inhibitors for therapeutic use.

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.

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.

Antimicrobials for bacterial bioterrorism agents

The limitations of current antimicrobials for highly virulent pathogens considered as potential bioterrorism agents drives the requirement for new antimicrobials that are suitable for use in populations in the event of a deliberate release. Strategies targeting bacterial virulence offer the potential for new countermeasures to combat bacterial bioterrorism agents, including those active against a broad spectrum of pathogens. Although early in the development of antivirulence approaches, inhibitors of bacterial type III secretion systems and cell division mechanisms show promise for the future.

Putative essential and core-essential genes in Mycoplasma genomes

Mycoplasma, which was used to create the first “synthetic life”, has been an important species in the emerging field, synthetic biology. However, essential genes, an important concept of synthetic biology, for both M. mycoides and M. capricolum, as well as 14 other Mycoplasma with available genomes, are still unknown. We have developed a gene essentiality prediction algorithm that incorporates information of biased gene strand distribution, homologous search and codon adaptation index. The algorithm, which achieved an accuracy of 80.8% and 78.9% in self-consistence and cross-validation tests, respectively, predicted 5880 essential genes in the 16 Mycoplasma genomes. The intersection set of essential genes in available Mycoplasma genomes consists of 153 core essential genes. The predicted essential genes (available from pDEG, tubic.tju.edu.cn/pdeg) and the proposed algorithm can be helpful for studying minimal Mycoplasma genomes as well as essential genes in other genomes.