Properties and identification of antibiotic drug targets

Unrealized targets in the discovery of antibiotics for Gram-negative bacterial infections

Advances in areas that include genomics, systems biology, protein structure determination and artificial intelligence provide new opportunities for target-based antibacterial drug discovery. The selection of a 'good' new target for direct-acting antibacterial compounds is the first decision, for which multiple criteria must be explored, integrated and re-evaluated as drug discovery programmes progress. Criteria include essentiality of the target for bacterial survival, its conservation across different strains of the same species, bacterial species and growth conditions (which determines the spectrum of activity of a potential antibiotic) and the level of homology with human genes (which influences the potential for selective inhibition). Additionally, a bacterial target should have the potential to bind to drug-like molecules, and its subcellular location will govern the need for inhibitors to penetrate one or two bacterial membranes, which is a key challenge in targeting Gram-negative bacteria. The risk of the emergence of target-based drug resistance for drugs with single targets also requires consideration. This Review describes promising but as-yet-unrealized targets for antibacterial drugs against Gram-negative bacteria and examples of cognate inhibitors, and highlights lessons learned from past drug discovery programmes.

Predicting congenital renal tract malformation genes using machine learning

Congenital renal tract malformations (RTMs) are the major cause of severe kidney failure in children. Studies to date have identified defined genetic causes for only a minority of human RTMs. While some RTMs may be caused by poorly defined environmental perturbations affecting organogenesis, it is likely that numerous causative genetic variants have yet to be identified. Unfortunately, the speed of discovering further genetic causes for RTMs is limited by challenges in prioritising candidate genes harbouring sequence variants. Here, we exploited the computer-based artificial intelligence methodology of supervised machine learning to identify genes with a high probability of being involved in renal development. These genes, when mutated, are promising candidates for causing RTMs. With this methodology, the machine learning classifier determines which attributes are common to renal development genes and identifies genes possessing these attributes. Here we report the validation of an RTM gene classifier and provide predictions of the RTM association status for all protein-coding genes in the mouse genome. Overall, our predictions, whilst not definitive, can inform the prioritisation of genes when evaluating patient sequence data for genetic diagnosis. This knowledge of renal developmental genes will accelerate the processes of reaching a genetic diagnosis for patients born with RTMs.

Covalent Proteomimetic Inhibitor of the Bacterial FtsQB Divisome Complex

The use of antibiotics is threatened by the emergence and spread of multidrug-resistant strains of bacteria. Thus, there is a need to develop antibiotics that address new targets. In this respect, the bacterial divisome, a multi-protein complex central to cell division, represents a potentially attractive target. Of particular interest is the FtsQB subcomplex that plays a decisive role in divisome assembly and peptidoglycan biogenesis in E. coli. Here, we report the structure-based design of a macrocyclic covalent inhibitor derived from a periplasmic region of FtsB that mediates its binding to FtsQ. The bioactive conformation of this motif was stabilized by a customized cross-link resulting in a tertiary structure mimetic with increased affinity for FtsQ. To increase activity, a covalent handle was incorporated, providing an inhibitor that impedes the interaction between FtsQ and FtsB irreversibly. The covalent inhibitor reduced the growth of an outer membrane-permeable E. coli strain, concurrent with the expected loss of FtsB localization, and also affected the infection of zebrafish larvae by a clinical E. coli strain. This first-in-class inhibitor of a divisome protein–protein interaction highlights the potential of proteomimetic molecules as inhibitors of challenging targets. In particular, the covalent mode-of-action can serve as an inspiration for future antibiotics that target protein–protein interactions.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Core-Proteomics-Based Annotation of Antigenic Targets and Reverse-Vaccinology-Assisted Design of Ensemble Immunogen against the Emerging Nosocomial Infection-Causing Bacterium Elizabethkingia meningoseptica

Elizabethkingia meningoseptica is a ubiquitous Gram-negative emerging pathogen that causes hospital-acquired infection in both immunocompromised and immunocompetent patients. It is a multi-drug-resistant bacterium; therefore, an effective subunit immunogenic candidate is of great interest to encounter the pathogenesis of this pathogen. A protein-wide annotation of immunogenic targets was performed to fast-track the vaccine development against this pathogen, and structural-vaccinology-assisted epitopes were predicted. Among the total proteins, only three, A0A1T3FLU2, A0A1T3INK9, and A0A1V3U124, were shortlisted, which are the essential vaccine targets and were subjected to immune epitope mapping. The linkers EAAK, AAY, and GPGPG were used to link CTL, HTL, and B-cell epitopes and an adjuvant was also added at the N-terminal to design a multi-epitope immunogenic construct (MEIC). The computationally predicted physiochemical properties of the ensemble immunogen reported a highly antigenic nature and produced multiple interactions with immune receptors. In addition, the molecular dynamics simulation confirmed stable binding and good dynamic properties. Furthermore, the computationally modeled immune response proposed that the immunogen triggered a strong immune response after several doses at different intervals. Neutralization of the antigen was observed on the 3rd day of injection. Conclusively, the immunogenic construct produces protection against Elizabethkingia meningoseptica; however, further immunological testing is needed to unveil its real efficacy.

Machine Learning for Prediction of Drug Targets in Microbe Associated Cardiovascular Diseases by Incorporating Host-pathogen Interaction Network Parameters

Host-pathogen interactions play a crucial role in invasion, infection, and induction of immune response in humans. In this work, four machine learning algorithms, namely Logistic regression, K-nearest neighbor, Support Vector Machine, and Random Forest were implemented for the classification of drug targets. The algorithms were trained using 3400 hosts and 3800 pathogen drug and non-drug target proteins as learning instances. For each protein, 68 pathogen and 73 host features were computed that included sequence, structure, biological and host-pathogen network centrality characteristics. The Random Forest classifier model achieved the best accuracy after 10-fold cross-validation. 99 % accuracy was achieved with a ROC-AUC score of 0.99±0.01 for both pathogen and host training sets. The Eigenvector Centrality of host-pathogen interactions and host-host interactions was the top feature in performing classification of pathogen and host targets respectively. Other features important for classification were the presence of catalytic and binding sites, low instability/aliphatic index, and cellular location. The Random Forest classifier was then used for prediction of drug targets involved in Microbe Associated Cardiovascular Diseases. 331 host and 743 pathogen proteins were predicted as drug targets by the random forest model and can be validated experimentally for therapeutic intervention in Microbe Associated Cardiovascular Diseases.

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.

Synergizing the potential of bacterial genomics and metabolomics to find novel antibiotics

Antibiotic development based on natural products has faced a long lasting decline since the 1970s, while both the speed and the extent of antimicrobial resistance (AMR) development have been severely underestimated. The discovery of antimicrobial natural products of bacterial and fungal origin featuring new chemistry and previously unknown mode of actions is increasingly challenged by rediscovery issues. Natural products that are abundantly produced by the corresponding wild type organisms often featuring strong UV signals have been extensively characterized, especially the ones produced by extensively screened microbial genera such as streptomycetes. Purely synthetic chemistry approaches aiming to replace the declining supply from natural products as starting materials to develop novel antibiotics largely failed to provide significant numbers of antibiotic drug leads. To cope with this fundamental issue, microbial natural products science is being transformed from a 'grind-and-find' study to an integrated approach based on bacterial genomics and metabolomics. Novel technologies in instrumental analytics are increasingly employed to lower detection limits and expand the space of detectable substance classes, while broadening the scope of accessible and potentially bioactive natural products. Furthermore, the almost exponential increase in publicly available bacterial genome data has shown that the biosynthetic potential of the investigated strains by far exceeds the amount of detected metabolites. This can be judged by the discrepancy between the number of biosynthetic gene clusters (BGC) encoded in the genome of each microbial strain and the number of secondary metabolites actually detected, even when considering the increased sensitivity provided by novel analytical instrumentation. In silico annotation tools for biosynthetic gene cluster classification and analysis allow fast prioritization in BGC-to-compound workflows, which is highly important to be able to process the enormous underlying data volumes. BGC prioritization is currently accompanied by novel molecular biology-based approaches to access the so-called orphan BGCs not yet correlated with a secondary metabolite. Integration of metabolomics, in silico genomics and molecular biology approaches into the mainstream of natural product research will critically influence future success and impact the natural product field in pharmaceutical, nutritional and agrochemical applications and especially in anti-infective research.

Most Promising Compounds for Treating COVID-19 and Recent Trends in Antimicrobial & Antifungal Agents

Multidrug resistance in microbes poses a major health crisis and demands for the discovery of novel antimicrobial agents. The recent pandemic of SARS-CoV-2 has raised a public health emergency in almost all the countries of the world. Unlike viruses, a bacterium plays a significant role in various environmental issues such as bioremediation. Furthermore, biosurfactants produced by various bacterial species have an edge over traditionally produced chemical surfactants for its biodegradability, low toxicity and better interfacial activity with various applications in agriculture and industry. This special issue focuses on the global perspective of drug discovery for various antimicrobial, antiviral, and antifungal agents for infectious diseases. The issue also emphasizes the ongoing developments and the role of microbes in environmental remediation. We wish the articles published in this issue will enhance the current understanding in microbiology among the readers, and serve as the "seed of an idea" for drug development for ongoing COVID-19 pandemic.

Comparative structural and mechanistic studies of 4-hydroxy-tetrahydrodipicolinate reductases from Mycobacterium tuberculosis and Vibrio vulnificus

BACKGROUND

The products of the lysine biosynthesis pathway, meso-diaminopimelate and lysine, are essential for bacterial survival. This paper focuses on the structural and mechanistic characterization of 4-hydroxy-tetrahydrodipicolinate reductase (DapB), which is one of the enzymes from the lysine biosynthesis pathway. DapB catalyzes the conversion of (2S, 4S)-4-hydroxy-2,3,4,5-tetrahydrodipicolinate (HTPA) to 2,3,4,5-tetrahydrodipicolinate in an NADH/NADPH dependent reaction. Genes coding for DapBs were identified as essential for many pathogenic bacteria, and therefore DapB is an interesting new target for the development of antibiotics.

METHODS

We have combined experimental and computational approaches to provide novel insights into mechanism of the DapB catalyzed reaction.

RESULTS

Structures of DapBs originating from Mycobacterium tuberculosis and Vibrio vulnificus in complexes with NAD⁺, NADP⁺, as well as with inhibitors, were determined and described. The structures determined by us, as well as currently available structures of DapBs from other bacterial species, were compared and used to elucidate a mechanism of reaction catalyzed by this group of enzymes. Several different computational methods were used to provide a detailed description of a plausible reaction mechanism.

CONCLUSIONS

This is the first report presenting the detailed mechanism of reaction catalyzed by DapB.

GENERAL SIGNIFICANCE

Structural data in combination with information on the reaction mechanism provide a background for development of DapB inhibitors, including transition-state analogues.

In silico identification of natural fungicide from Melia azedarach against isocitrate lyase of Fusarium graminearum

Isocitrate Lyase (ICL) is a crucial enzyme involved in the Glyoxylate pathway, essential for the virulence of several fungal pathogens including Fusarium graminearum. ICL is a novel target for the discovery of antifungal compounds and F. graminearum ICL inhibitors can be used to control the growth of this fungus. Although, several inhibitors of ICL have been identified, however, most of these inhibitors are not environment-friendly. Hence there is still a need to discover natural inhibitors of ICL that can be more effective. To identify a potential antifungal compound, we performed a structure-based screening of phytochemicals of Melia azedarach against the FgICL structure by molecular docking and 104 ligands were found to have a better docking score as compared to the reference molecule. These compounds were assessed for drug-likeness and ADMET prediction. After molecular docking, drug-likeness and toxicity analysis, six potential compounds (Melianoninol (-6.6 kcal/mol), Nimbinene (-7.7 kcal/mol), Vilasinin (-8.1 kcal/mol), Fraxinellone (-6.7 kcal/mol), Gedunin (-7.8 kcal/mol), and Meldenin (-7.8 kcal/mol)) were subjected for rescoring by X-Score. The structural stability and dynamics of screened compounds at the active site of FgICL were examined using MD simulation and MM-PBSA analysis. The result of MM-PBSA revealed that four phytochemicals viz. Melianoninol, Nimbinene, Vilasinin, and Fraxinellone had binding free energy of -17.25 kcal/mol, -59.35 kcal/mol, -64.79 kcal/mol, and -29.86 kcal/mol, respectively. Molecular dynamics simulation and MM-PBSA demonstrated that these four phytochemicals displayed considerable significant structural and pharmacological properties and could be probable antifungal drug candidates against F. graminearum. These phyotchemicals of M. azedarach may be suitable candidates for further experimental analysis. [Formula: see text]Communicated by Ramaswamy H. Sarma.

A systematic strategy for the investigation of vaccines and drugs targeting bacteria

Infectious and epidemic diseases induced by bacteria have historically caused great distress to people, and have even resulted in a large number of deaths worldwide. At present, many researchers are working on the discovery of viable drug and vaccine targets for bacteria through multiple methods, including the analyses of comparative subtractive genome, core genome, replication-related proteins, transcriptomics and riboswitches, which plays a significant part in the treatment of infectious and pandemic diseases. The 3D structures of the desired target proteins, drugs and epitopes can be predicted and modeled through target analysis. Meanwhile, molecular dynamics (MD) analysis of the constructed drug/epitope-protein complexes is an important standard for testing the suitability of these screened drugs and vaccines. Currently, target discovery, target analysis and MD analysis are integrated into a systematic set of drug and vaccine analysis strategy for bacteria. We hope that this comprehensive strategy will help in the design of high-performance vaccines and drugs.

In-silico Subtractive Proteomic Analysis Approach for Therapeutic Targets in MDR Salmonella enterica subsp. enterica serovar Typhi str. CT18

OBJECTIVE

In the present study, an attempt has been made for subtractive proteomic analysis approach for novel drug targets in Salmonella enterica subsp. enterica serover Typhi str.CT18 using computational tools.

METHODS

Paralogous, redundant and less than 100 amino acid protein sequences were removed by using CD-HIT. Further detection of bacterial proteins which are non-homologous to host and are essential for the survival of pathogens by using BLASTp against host proteome and DEG`s, respectively. Comparative Metabolic pathways analysis was performed to find unique and common metabolic pathways. The non-redundant, non-homologous and essential proteins were BLAST against approved drug targets for drug targets while Psortb and CELLO were used to predict subcellular localization.

RESULTS

There were 4473 protein sequences present in NCBI Database for Salmonella enterica subsp. enterica serover Typhi str. CT18 out of these 327 were essential proteins which were non-homologous to human. Among these essential proteins, 124 proteins were involved in 19 unique metabolic pathways. These proteins were further BLAST against approved drug targets in which 7 cytoplasmic proteins showed druggability and can be used as a therapeutic target.

CONCLUSION

Drug targets identification is the prime step towards drug discovery. We identified 7 cytoplasmic druggable proteins which are essential for the pathogen survival and non-homologous to human proteome. Further in vitro and in vivo validation is needed for the evaluation of these targets to combat against salmonellosis.

Proteome Mining for the Identification of Putative Drug Targets For Human Pathogen Clostridium Tetani

Background:

Clostridium tetani are rod-like, anaerobic types of pathogenic bacteria of the genus Clostridium. It is Gram-positive in nature and appears as a tennis racket or drumsticks on staining with the dye. Tetanus is a neuromuscular disease wherein the Clostridium tetani exotoxin produces muscle fits in the host. Tetanus is the second leading cause of worldwide deaths occurring from the family of immunization-preventable diseases.

Methods:

In this research, subtractive proteome analysis of C. tetani was performed to identify putative drug targets. The proteins were subjected to blast analysis against Homo sapiens to exclude homologous proteins. The database of Essential Genes was used to determine the essential proteins of the pathogen. These basic proteins were additionally analyzed to anticipate the corresponding metabolic pathways.

Results:

Cellular localization analysis was carried out to determine the possibility of the protein presence in the outer membrane. The study has recognized 29 essential genes and 20 unique pathways of 2314 proteins as potential drug targets. There are 29 essential proteins, out of which, 3 membrane proteins were also identified as putative drug targets.

Conclusion:

Virtual screening in contrast to these proteins can be valuable in the identification of novel clinical compounds for the C. tetani infections in Homo sapiens.

Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs

Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.

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.

Potential and use of bacterial small RNAs to combat drug resistance: a systematic review

Background

Over the decades, new antibacterial agents have been developed in an attempt to combat drug resistance, but they remain unsuccessful. Recently, a novel class of bacterial gene expression regulators, bacterial small RNAs (sRNAs), has received increasing attention toward their involvement in antibiotic resistance. This systematic review aimed to discuss the potential of these small molecules as antibacterial drug targets.

Methods

Two investigators performed a comprehensive search of MEDLINE, EmBase, and ISI Web of Knowledge from inception to October 2016, without restriction on language. We included all in vitro and in vivo studies investigating the role of bacterial sRNA in antibiotic resistance. Risk of bias of the included studies was assessed by a modified guideline of Systematic Review Center for Laboratory Animal Experimentation (SYRCLE).

Results

Initial search yielded 432 articles. After exclusion of non-original articles, 20 were included in this review. Of these, all studies examined bacterial-type strains only. There were neither relevant in vivo nor clinical studies. The SYRCLE scores ranged from to 5 to 7, with an average of 5.9. This implies a moderate risk of bias. sRNAs influenced the antibiotics susceptibility through modulation of gene expression relevant to efflux pumps, cell wall synthesis, and membrane proteins.

Conclusion

Preclinical studies on bacterial-type strains suggest that modulation of sRNAs could enhance bacterial susceptibility to antibiotics. Further studies on clinical isolates and in vivo models are needed to elucidate the therapeutic value of sRNA modulation on treatment of multidrug-resistant bacterial infection.

Hybrid imbalanced data classifier models for computational discovery of antibiotic drug targets

Identification of drug candidates is an important but also difficult process. Given drug resistance bacteria that we face, this process has become more important to identify protein candidates that demonstrate antibacterial activity. The aim of this study is therefore to develop a bioinformatics approach that is more capable of identifying a small but effective set of proteins that are expected to show antibacterial activity, subsequently to be used as antibiotic drug targets. As this is regarded as an imbalanced data classification problem due to smaller number of antibiotic drugs available, a hybrid classification model was developed and applied to the identification of antibiotic drugs. The model was developed by taking into account of various statistical models leading to the development of six different hybrid models. The best model has reached the accuracy of as high as 50% compared to earlier study with the accuracy of less than 1% as far as the proportion of the candidates identified and actual antibiotics in the candidate list is concerned.

In silico prediction of drug targets in phytopathogenic Pseudomonas syringae pv. phaseolicola: charting a course for agrigenomics translation research

Pseudomonas syringae pv. phaseolicola is a major plant pathogen causing halo blight disease and has world-wide importance. The emerging post-genomics field of agrigenomics, together with the availability of whole genome sequences of a number of pathogens and host organisms, offer the promise for identification of potential drug targets using sequence comparison approaches. On the other hand, lack of gene expression data for most of the phytopathogenic microbes still remains a formidable barrier. The present study aimed at the prediction of drug targets in Pseudomonas syringae pv. phaseolicola by exploiting the knowledge of Codon Usage bias for gene expression subtractively, supported by gene expression analysis and sequence comparisons. Based on screening of the Database of Essential Genes using blastx, 158 of the total 5172 genes of P. syringae pv. phaseolicola were enlisted as vitally essential genes. Similarity search for these 158 essential genes against available host-plant sequences (Phaseolous vulgaris) led to the identification of homologues of 21 genes in the host genome, thus leaving behind a subset of 137 genes. Expression analysis of these 137 genes using RSCU(gene,) validated by microarray gene expression data suggested 22 genes had higher expression levels in the cell, and therefore their products have been identified as putative drug targets. The gene ontology analysis of these 22 genes revealed their indispensable roles in pivotal metabolic pathways of P. syringae pv. phaseolicola. Upon comparison of the sequences of these genes with other soil bacteria, we identified two genes that were unique to P. syringae pv. phaseolicola. The products of these genes can potentially be utilized for drug development so as to control the halo blight disease and thereby accelerate translation research in the nascent field of agrigenomics.

Antimicrobial nanotechnology: its potential for the effective management of microbial drug resistance and implications for research needs in microbial nanotoxicology

The development of antibiotics revolutionized human health, providing a simple cure for once dreaded diseases such as tuberculosis. However, widespread production, use, and mis-use of antibiotics have contributed to the next-generation concern for global public health: the emergence of multiple drug-resistant (MDR) infectious organisms (a.k.a. “superbugs”). Recently, nanotechnology, specifically the use of nanomaterials (NMs) with antimicrobial activity, has been presented as a new defense against MDR infectious organisms. We discuss the potential for NMs to either circumvent microbial resistance or induce its development in light of our current state of knowledge, finding that this question points to a need for fundamental research targeting the molecular mechanisms causing antimicrobial activity in NMs. In the context of current microbial nanotoxicology studies, particularly reductionist laboratory studies, we offer suggestions and considerations for future research, using an illustrative example from our work with silver nanoparticles.

A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer

BACKGROUND

Systematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.

METHODS

We integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets.

RESULTS

We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method.

CONCLUSIONS

Our method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.

Drug discovery and the use of computational approaches for infectious diseases

For centuries infectious diseases were the scourge of humanity, overcome only by the discovery of vaccination and penicillin. With an armamentarium of effective antibiotics, vaccines and drugs at hand, infectious diseases for many years were considered to be negligible. With the onset of the AIDS pandemic, the return of tuberculosis and influenza (e.g., swine influenza) this notion has changed in recent years. Drug discovery for infectious diseases, therefore, is again gaining increasing interest. This article discusses the drug-discovery process in this area and introduces major computational approaches used to identify suitable drug targets and to discover and optimize chemical lead compounds towards drug candidates using examples from antiparasitic drug discovery.