In silico identification of common putative drug targets in <Emphasis Type="Italic">Leptospira interrogans</Emphasis>

Infectious diseases are the leading causes of death worldwide. Hence, there is a need to develop new antimicrobial agents. Traditional method of drug discovery is time consuming and yields a few drug targets with little intracellular information for guiding target selection. Thus, focus in drug development has been shifted to computational comparative genomics for identifying novel drug targets. Leptospirosis is a worldwide zoonosis of global concern caused by Leptospira interrogans. Availability of L. interrogans serovars and human genome sequences facilitated to search for novel drug targets using bioinformatics tools. The genome sequence of L. interrogans serovar Copenhageni has 5,124 genes while that of serovar Lai has 4,727 genes. Through subtractive genomic approach 218 genes in serovar Copenhageni and 158 genes in serovar Lai have been identified as putative drug targets. Comparative genomic approach had revealed that 88 drug targets were common to both the serovars. Pathway analysis using the Kyoto Encyclopaedia of Genes and Genomes revealed that 66 targets are enzymes and 22 are non-enzymes. Sixty two common drug targets were predicted to be localized in cytoplasm and 16 were surface proteins. The identified potential drug targets form a platform for further investigation in discovery of novel therapeutic compounds against Leptospira.     

Seeing is believing: the impact of structural genomics on antimicrobial drug discovery

Over the past decade, the availability of complete microbial genome sequences has led to changes in the strategies that are used to search for novel anti-infectives. However, despite the identification of many new potential drug targets, novel antimicrobial agents have been slow to emerge from these efforts. In part, this reflects the long discovery and development times that are needed to bring new drugs to market and the bottlenecks at the stages of identifying good lead compounds and optimizing these leads into drug candidates. Structural genomics will hopefully provide opportunities to overcome these bottlenecks and populate the antimicrobial pipeline.     

Functional genomics: identifying drug targets for parasitic diseases

The genomic sequences of parasitic diseases are rapidly becoming available and, recently, the full sequence of Plasmodium falciparum has been published. Much has been promised from this genomic revolution including the identification of new drug targets and novel chemotherapeutic treatments for the control of parasitic diseases. The challenge to use this information efficiently will require functional genomics tools such as bioinformatics, microarrays, proteomics and chemical genomics to identify potential drug targets, and to allow the development of optimized lead compounds. The information generated from these tools will provide a crucial link from genomic analysis to drug discovery.     

Using display technologies to identify macrocyclic peptide antibiotics

Antibiotic resistant bacterial infections are now a leading cause of global mortality. While drug resistance continues to spread, the clinical antibiotic pipeline has become bare. This discord has focused attention on developing new strategies for antimicrobial discovery. Natural macrocyclic peptide-based products have provided novel antibiotics and antibiotic scaffolds targeting several essential bacterial cell envelope processes, but discovery of such natural products remains a slow and inefficient process. Synthetic strategies employing peptide display technologies can quickly screen large libraries of macrocyclic sequences for specific target binding and general antibacterial potential providing alternative approaches for new antibiotic discovery. Here we review cell envelope processes that can be targeted with macrocyclic peptide therapeutics, outline important macrocyclic peptide display technologies, and discuss future strategies for both library design and screening.     

Biotechnology match making: screening orphan ligands and receptors

To date there has been a considerable amount of interest and success in the pharmaceutical industry in the discovery of drug targets and diagnostics via genomic technologies, namely DNA sequencing, mutation/polymorphism detection and expression monitoring of mRNA. As the ultimate targets for the majority of these methods are actually proteins, more and more emphasis has been placed upon protein-based methods in an effort to define the function of proteins discovered by genomic technologies. One of the most challenging areas of drug target discovery facing researchers today is the search for novel receptor-ligand pairs. Database mining techniques in conjunction with other computational methods are able to identify many novel sequences of putative receptors, but the ability to similarly identify the receptor's natural ligand is not possible by these methods. The past few years have seen an increase in methodology and instrumentation focused on the ability to discover and characterize protein-protein interactions, as well as receptor-ligand pairs. Significant advances have been made in the areas of instrumentation (biosensors and fluorescent plate readers) as well as methodologies relating to phage/ribosome display and library construction.     

Current status and future prospects for ion-mobility mass spectrometry in the biopharmaceutical industry

Detailed characterization of protein reagents and biopharmaceuticals is key in defining successful drug discovery campaigns, aimed at bringing molecules through different discovery stages up to development and commercialization. There are many challenges in this process, with complex and detailed analyses playing paramount roles in modern industry.Mass spectrometry (MS) has become an essential tool for characterization of proteins ever since the onset of soft ionization techniques and has taken the lead in quality assessment of biopharmaceutical molecules, and protein reagents, used in the drug discovery pipeline. MS use spans from identification of correct sequences, to intact molecule analyses, protein complexes and more recently epitope and paratope identification.MS toolkits could be incredibly diverse and with ever evolving instrumentation, increasingly novel MS-based techniques are becoming indispensable tools in the biopharmaceutical industry. Here we discuss application of Ion Mobility MS (IMMS) in an industrial setting, and what the current applications and outlook are for making IMMS more mainstream.     

Structure-based lead identification of ATP-competitive MK2 inhibitors

MK2 kinase is a promising drug discovery target for the treatment of inflammatory diseases. Here, we describe the discovery of novel MK2 inhibitors using X-ray crystallography and structure-based drug design. The lead has in vivo efficacy in a short-term preclinical model.     

Small Molecule Screening in Human Induced Pluripotent Stem Cell-derived Terminal Cell Types

A need for better clinical outcomes has heightened interest in the use of physiologically relevant human cells in the drug discovery process. Patient-specific human induced pluripotent stem cells may offer a relevant, robust, scalable, and cost-effective model of human disease physiology. Small molecule high throughput screening in human induced pluripotent stem cell-derived cells with the intent of identifying novel therapeutic compounds is starting to influence the drug discovery process; however, the use of these cells presents many high throughput screening development challenges. This technology has the potential to transform the way drug discovery is performed.     

Recombinant environmental libraries provide access to microbial diversity for drug discovery from natural products

To further explore possible avenues for accessing microbial biodiversity for drug discovery from natural products, we constructed and screened a 5,000-clone "shotgun" environmental DNA library by using an Escherichia coli-Streptomyces lividans shuttle cosmid vector and DNA inserts from microbes derived directly (without cultivation) from soil. The library was analyzed by several means to assess diversity, genetic content, and expression of heterologous genes in both expression hosts. We found that the phylogenetic content of the DNA library was extremely diverse, representing mostly microorganisms that have not been described previously. The library was screened by PCR for sequences similar to parts of type I polyketide synthase genes and tested for the expression of new molecules by screening of live colonies and cell extracts. The results revealed new polyketide synthase genes in at least eight clones. In addition, at least five additional clones were confirmed by high-pressure liquid chromatography analysis and/or biological activity to produce heterologous molecules. These data reinforce the idea that exploiting previously unknown or uncultivated microorganisms for the discovery of novel natural products has potential value and, most importantly, suggest a strategy for developing this technology into a realistic and effective drug discovery tool.     

Drugs from slugs—Past,present and future perspectives of ω-conotoxin research

Peptides from the venom of carnivorous cone shells have provided six decades of intense research, which has led to the discovery and development of novel analgesic peptide therapeutics. Our understanding of this unique natural marine resource is however somewhat limited. Given the past pharmacological record, future investigations into the toxinology of these highly venomous tropical marine snails will undoubtedly yield other highly selective ion channel inhibitors and modulators. With over a thousand conotoxin-derived sequences identified to date, those identified as ion channel inhibitors represent only a small fraction of the total. Here we discuss our present understanding of conotoxins, focusing on the ω-conotoxin peptide family, and illustrate how such a seemingly simple snail has yielded a highly effective clinical drug.     

Rational design of antithrombotic peptides to target the von Willebrand Factor (vWf) - GPIb integrin interaction

Conventional antithrombotic drug discovery requires testing of large numbers of drug candidates. We used computer-aided macromolecular interaction assessment (MIAX) to select antithrombotic molecules that mimic and therefore block platelet GPIb’s binding to von Willebrand factor (vWf), an early step in thrombus formation. We screened a random array of 15-mer D-amino acid peptides for binding vWf. Structures of 4 candidate peptides were inferred by comparison to sequences in protein databases, conversion from the L to D conformations and molecular dynamics (MD) determinations of those most energetically stable. By MIAX, we deduced the amino acids and intermolecular hydrogen bonds contributing to the GPIb-vWf interaction interface. We docked the peptides onto vWf in silico to localize their binding sites and consequent potential for preventing GPIb-vWf binding. In vitro inhibition of ristocetin-initiated platelet agglutination confirmed peptide function and suitability for antithrombotic development, thereby validating this novel approach to drug discovery.     

Identifying G-protein coupled receptors using weighted Levenshtein distance and the nearest neighbor method

G-protein coupled receptors （GPCRs） are a class of seven-helix transmembrane proteins that have been used in bioinformatics as the targets to facilitate drug discovery for human diseases. Although thousands of GPCR sequences have been collected, the ligand specificity of many GPCRs is still unknown and only one crystal structure of the rhodopsin-like family has been solved. Therefore, identifying GPCR types only from sequence data has become an important research issue. In this study, a novel technique for identifying GPCR types based on the weighted Levenshtein distance between two receptor sequences and the nearest neighbor method （NNM） is introduced, which can deal with receptor sequences with different lengths directly. In our experiments for classifying four classes （acetylcholine, adrenoceptor, dopamine, and serotonin） of the rhodopsin-like family of GPCRs, the error rates from the leave-one-out procedure and the leave-half-out procedure were 0.62% and 1.24%, respectively. These results are prior to those of the covariant discriminant algorithm, the support vector machine method, and the NNM with Euclidean distance.     

Flow NMR applications in combinatorial chemistry

Flow NMR techniques are now well accepted and widely used in many areas of drug discovery. Although natural-product-, rational-drug-design-, and NMR-screening-programs have begun to use flow NMR more routinely, flow NMR has not yet gained widespread acceptance in combinatorial chemistry, even though it has been shown to be a potentially useful tool. Recent developments in DI-NMR, FIA-NMR, and LC-NMR will help flow NMR eventually gain a wider acceptance within combinatorial chemistry. These developments include LC-NMR-MS instrumentation, flow probe improvements, new pulse sequences, improved automation of NMR data analysis, and the application of flow NMR to related fields in drug discovery.     

Will genetics really revolutionize the drug discovery process?

Genetics has played only a modest role in drug discovery, but new technologies will radically change this. Whole genome sequencing will identify new drug discovery targets, and emerging methods for the determination of gene function will increase the ability to select robust targets. Detection of single nucleotide polymorphisms and common polymorphisms will enhance the investigation of polygenic diseases and the use of genetics in drug development. Oligonucleotide arraying technologies will allow analysis of gene expression patterns in novel ways.     

Proteomics in the post-genome age.

The genome sequencing effort has helped spawn the burgeoning field of proteomics. This review article examines state-of-the-art proteomics methods that are helping change the discovery paradigm in a variety of biological disciplines and, in particular, protein biochemistry. The review discusses both classical and novel methods to perform high-throughput qualitative and quantitative "global" as well as targeted proteome analysis of complex biological systems. From a drug discovery standpoint, the synergy between genomics and proteomics will help elucidate disease mechanisms, identify novel drug targets, and identify surrogate biomarkers that could be used to conduct clinical trials.     

Spatially addressed combinatorial protein libraries for recombinant antibody discovery and optimization

Antibody discovery typically uses hybridoma- or display-based selection approaches, which lack the advantages of directly screening spatially addressed compound libraries as in small-molecule discovery. Here we apply the latter strategy to antibody discovery, using a library of ～10,000 human germline antibody Fabs created by de novo DNA synthesis and automated protein expression and purification. In multiplexed screening assays, we obtained specific hits against seven of nine antigens. Using sequence-activity relationships and iterative mutagenesis, we optimized the binding affinities of two hits to the low nanomolar range. The matured Fabs showed full and partial antagonism activities in cell-based assays. Thus, protein drug leads can be discovered using surprisingly small libraries of proteins with known sequences, questioning the requirement for billions of members in an antibody discovery library. This methodology also provides sequence, expression and specificity information at the first step of the discovery process, and could enable novel antibody discovery in functional screens.     

Recombinant Environmental Libraries Provide Access to Microbial Diversity for Drug Discovery from Natural Products

To further explore possible avenues for accessing microbial biodiversity for drug discovery from natural products, we constructed and screened a 5,000-clone “shotgun” environmental DNA library by using an Escherichia coli-Streptomyces lividans shuttle cosmid vector and DNA inserts from microbes derived directly (without cultivation) from soil. The library was analyzed by several means to assess diversity, genetic content, and expression of heterologous genes in both expression hosts. We found that the phylogenetic content of the DNA library was extremely diverse, representing mostly microorganisms that have not been described previously. The library was screened by PCR for sequences similar to parts of type I polyketide synthase genes and tested for the expression of new molecules by screening of live colonies and cell extracts. The results revealed new polyketide synthase genes in at least eight clones. In addition, at least five additional clones were confirmed by high-pressure liquid chromatography analysis and/or biological activity to produce heterologous molecules. These data reinforce the idea that exploiting previously unknown or uncultivated microorganisms for the discovery of novel natural products has potential value and, most importantly, suggest a strategy for developing this technology into a realistic and effective drug discovery tool.     

Recent Advances in Drug Repositioning for the Discovery of New Anticancer Drugs

Drug repositioning (also referred to as drug repurposing), the process of finding new uses of existing drugs, has been gaining popularity in recent years. The availability of several established clinical drug libraries and rapid advances in disease biology, genomics and bioinformatics has accelerated the pace of both activity-based and in silico drug repositioning. Drug repositioning has attracted particular attention from the communities engaged in anticancer drug discovery due to the combination of great demand for new anticancer drugs and the availability of a wide variety of cell- and target-based screening assays. With the successful clinical introduction of a number of non-cancer drugs for cancer treatment, drug repositioning now became a powerful alternative strategy to discover and develop novel anticancer drug candidates from the existing drug space. In this review, recent successful examples of drug repositioning for anticancer drug discovery from non-cancer drugs will be discussed.     

NMR techniques for characterization of ligand binding: utility for lead generation and optimization in drug discovery.

Over the last ten years, nmr spectroscopy has evolved into an important discipline in drug discovery. Initially, nmr was most useful as a technique to provide structural information regarding protein drug targets and target-ligand interactions. More recently, it has been shown that nmr may be used as an alternative method for identification of small molecule ligands that bind to protein drug targets. High throughput implementation of these experiments to screen small molecule libraries may lead to identification of potent and novel lead compounds. In this review, we will use examples from our own research to illustrate how nmr experiments to characterize ligand binding may be used to both screen for novel compounds during the process of lead generation, as well as provide structural information useful for lead optimization during the latter stages of a discovery program.     

A new approach for anthelmintic discovery for humans

Natural product-based drug discovery has been deemphasized by the pharmaceutical industry. This situation is discordant with the fact that most people in developing countries rely on traditional medicines derived from local biodiversity for healthcare. Despite economic growth in the past 10 years, Africa remains plagued by parasitic infections, out of reach of eradication. Limited regional funding for drug discovery complicates the situation. Novel models are needed to bring sustainability to local drug discovery programs. This Opinion describes an innovative partnership that promotes local leadership to harness a recombinant yeast-based assay to screen for novel anthelmintic candidates in collections of African natural products. Implementation of this strategy in biodiversity-rich but resource-constrained settings can help build sustainable local capacity for drug discovery.