Functional genomics to new drug targets

Dynamic Changes in Ion Channels during Myocardial Infarction and Therapeutic Challenges

In different areas of the heart, action potential waveforms differ due to differences in the expressions of sodium, calcium, and potassium channels. One of the characteristics of myocardial infarction (MI) is an imbalance in oxygen supply and demand, leading to ion imbalance. After MI, the regulation and expression levels of K+, Ca2+, and Na+ ion channels in cardiomyocytes are altered, which affects the regularity of cardiac rhythm and leads to myocardial injury. Myocardial fibroblasts are the main effector cells in the process of MI repair. The ion channels of myocardial fibroblasts play an important role in the process of MI. At the same time, a large number of ion channels are expressed in immune cells, which play an important role by regulating the in- and outflow of ions to complete intracellular signal transduction. Ion channels are widely distributed in a variety of cells and are attractive targets for drug development. This article reviews the changes in different ion channels after MI and the therapeutic drugs for these channels. We analyze the complex molecular mechanisms behind myocardial ion channel regulation and the challenges in ion channel drug therapy.

Inhibitors of 3-Hydroxy-3-methylglutaryl Coenzyme A Reductase Decrease the Growth, Ergosterol Synthesis and Generation of petite Mutants in Candida glabrata and Candida albicans

Candida glabrata and Candida albicans, the most frequently isolated candidiasis species in the world, have developed mechanisms of resistance to treatment with azoles. Among the clinically used antifungal drugs are statins and other compounds that inhibit 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGR), resulting in decreased growth and ergosterol levels in yeasts. Ergosterol is a key element for the formation of the yeast cell membrane. However, statins often cause DNA damage to yeast cells, facilitating mutation and drug resistance. The aim of the current contribution was to synthesize seven series of compounds as inhibitors of the HMGR enzyme of Candida ssp., and to evaluate their effect on cellular growth, ergosterol synthesis and generation of petite mutants of C. glabrata and C. albicans. Compared to the reference drugs (fluconazole and simvastatin), some HMGR inhibitors caused lower growth and ergosterol synthesis in the yeast species and generated fewer petite mutants. Moreover, heterologous expression was achieved in Pichia pastoris, and compounds 1a, 1b, 6g and 7a inhibited the activity of recombinant CgHMGR and showed better binding energy values than for α-asarone and simvastatin. Thus, we believe these are good candidates for future antifungal drug development.

Focus on the molecular mechanisms of cisplatin resistance based on multi-omics approaches

Cisplatin is commonly used in combination with other cytotoxic agents as a standard treatment regimen for a variety of solid tumors, such as lung, ovarian, testicular, and head and neck cancers. However, the effectiveness of cisplatin is accompanied by toxic side effects, for instance, nephrotoxicity and neurotoxicity. The response of tumors to cisplatin treatment involves multiple physiological processes, and the efficacy of chemotherapy is limited by the intrinsic and acquired resistance of tumor cells. Although enormous efforts have been made toward molecular mechanisms of cisplatin resistance, the development of omics provides new insights into the understanding of cisplatin resistance at genome, transcriptome, proteome, metabolome and epigenome levels. Mechanism studies using different omics approaches revealed the necessity of multi-omics applications, which provide information at different cellular function levels and expand our recognition of the peculiar genetic and phenotypic heterogeneity of cancer. The present work systematically describes the underlying mechanisms of cisplatin resistance in different tumor types using multi-omics approaches. In addition to the classical mechanisms such as enhanced drug efflux, increased DNA damage repair and changes in the cell cycle and apoptotic pathways, other changes like increased protein damage clearance, increased protein glycosylation, enhanced glycolytic process, dysregulation of the oxidative phosphorylation pathway, ferroptosis suppression and mRNA m6A methylation modification can also induce cisplatin resistance. Therefore, utilizing the integrated omics to identify key signaling pathways, target genes and biomarkers that regulate chemoresistance are essential for the development of new drugs or strategies to restore tumor sensitivity to cisplatin.

A direct-to-biology high-throughput chemistry approach to reactive fragment screening

Methods for rapid identification of chemical tools are essential for the validation of emerging targets and to provide medicinal chemistry starting points for the development of new medicines. Here, we report a screening platform that combines 'direct-to-biology' high-throughput chemistry (D2B-HTC) with photoreactive fragments. The platform enabled the rapid synthesis of >1000 PhotoAffinity Bits (HTC-PhABits) in 384-well plates in 24 h and their subsequent screening as crude reaction products with a protein target without purification. Screening the HTC-PhABit library with carbonic anhydrase I (CAI) afforded 7 hits (0.7% hit rate), which were found to covalently crosslink in the Zn2+ binding pocket. A powerful advantage of the D2B-HTC screening platform is the ability to rapidly perform iterative design-make-test cycles, accelerating the development and optimisation of chemical tools and medicinal chemistry starting points with little investment of resource.

Seeds of their own destruction: Dominant-negative peptide screening yields functional insight and therapeutic leads

Systematic, high-throughput screening for "dominant-negative" protein fragments is an emerging method for mapping functional regions of the parental protein in vivo. In this issue of Cell Systems, Ford et al. apply this approach to 65 cancer drivers, providing functional insights and demonstrating therapeutic potential for several dominant-negative peptides.

Systematic Target Screening Revealed That Tif302 Could Be an Off-Target of the Antifungal Terbinafine in Fission Yeast

We used a heterozygous gene deletion library of fission yeasts comprising all essential and non-essential genes for a microarray screening of target genes of the antifungal terbinafine, which inhibits ergosterol synthesis via the Erg1 enzyme. We identified 14 heterozygous strains corresponding to 10 non-essential [7 ribosomal-protein (RP) coding genes, spt7, spt20, and elp2] and 4 essential genes (tif302, rpl2501, rpl31, and erg1). Expectedly, their erg1 mRNA and protein levels had decreased compared to the control strain SP286. When we studied the action mechanism of the non-essential target genes using cognate haploid deletion strains, knockout of SAGA-subunit genes caused a down-regulation in erg1 transcription compared to the control strain ED668. However, knockout of RP genes conferred no susceptibility to ergosterol-targeting antifungals. Surprisingly, the RP genes participated in the erg1 transcription as components of repressor complexes as observed in a comparison analysis of the experimental ratio of erg1 mRNA. To understand the action mechanism of the interaction between the drug and the novel essential target genes, we performed isobologram assays with terbinafine and econazole (or cycloheximide). Terbinafine susceptibility of the tif302 heterozygous strain was attributed to both decreased erg1 mRNA levels and inhibition of translation. Moreover, Tif302 was required for efficacy of both terbinafine and cycloheximide. Based on a molecular modeling analysis, terbinafine could directly bind to Tif302 in yeasts, suggesting Tif302 as a potential off-target of terbinafine. In conclusion, this genome-wide screening system can be harnessed for the identification and characterization of target genes under any condition of interest.

Current Biochemical Applications and Future Prospects of Chlorotoxin in Cancer Diagnostics and Therapeutics

Chlorotoxin (CTX) is a minute 4 kDa protein made up of 36 amino acid residues, commonly known for its binding affinity to chloride channels and matrix metalloproteinase-2 (MMP-2) of glioma tumors of the spine and brain. This property and the possibility of conjugating this peptide to nanoparticles have enabled its diverse use in various biotechnological and biomedical applications for cancer treatment, such as in tumor imaging and radiotherapy. Because of the fascinating biological properties CTX possesses, elucidating its mechanism of action may hold promise for the development of new and effective therapeutic drugs, as well as more sensitive and highly specific cancer-screening kits. This article therefore reviews the currently known applications of CTX and suggests diverse ways in which it can be applied for the design of improved drugs and diagnostic tools for cancer.

Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs

Cells need high-sensitivity detection of non-self molecules in order to fight against pathogens. These cellular sensors are thus of significant importance to medicinal purposes, especially for treating novel emerging pathogens. RIG-I-like receptors (RLRs) are intracellular sensors for viral RNAs (vRNAs). Their active forms activate mitochondrial antiviral signaling protein (MAVS) and trigger downstream immune responses against viral infection. Functional and structural studies of the RLR-MAVS signaling pathway have revealed significant supramolecular variability in the past few years, which revealed different aspects of the functional signaling pathway. Here I will discuss the molecular events of RLR-MAVS pathway from the angle of detecting single copy or a very low copy number of vRNAs in the presence of non-specific competition from cytosolic RNAs, and review key structural variability in the RLR / vRNA complexes, the MAVS helical polymers, and the adapter-mediated interactions between the active RLR / vRNA complex and the inactive MAVS in triggering the initiation of the MAVS filaments. These structural variations may not be exclusive to each other, but instead may reflect the adaptation of the signaling pathways to different conditions or reach different levels of sensitivity in its response to exogenous vRNAs.

Revisiting the structure of a synthetic somatostatin analogue for peptide drug design

Natural or artificially manufactured peptides attract scientific interest worldwide owing to their wide array of pharmaceutical and biological activities. X-ray structural studies are used to provide a precise extraction of information, which can be used to enable a better understanding of the function and physicochemical characteristics of peptides. Although it is vulnerable to disassociation, one of the most vital human peptide hormones, somatostatin, plays a regulatory role in the endocrine system as well as in the release of numerous secondary hormones. This study reports the successful crystallization and complete structural model of octreotide, a stable octapeptide analogue of somatostatin. Common obstacles in crystallographic studies arising from the intrinsic difficulties of obtaining a suitable single-crystal specimen were efficiently overcome as polycrystalline material was employed for synchrotron and laboratory X-ray powder diffraction (XPD) measurements. Data collection and preliminary analysis led to the identification of unit-cell symmetry [orthorhombic, P212121, a = 18.5453 (15), b = 30.1766 (25), c = 39.798 (4) Å], a process which was later followed by complete structure characterization and refinement, underlying the efficacy of the suggested (XPD) approach.

Proteomics and phosphoproteomics in precision medicine: applications and challenges

Recent advances in proteomics allow the accurate measurement of abundances for thousands of proteins and phosphoproteins from multiple samples in parallel. Therefore, for the first time, we have the opportunity to measure the proteomic profiles of thousands of patient samples or disease model cell lines in a systematic way, to identify the precise underlying molecular mechanism and discover personalized biomarkers, networks and treatments. Here, we review examples of successful use of proteomics and phosphoproteomics data sets in as well as their integration other omics data sets with the aim of precision medicine. We will discuss the bioinformatics challenges posed by the generation, analysis and integration of such large data sets and present potential reasons why proteomics profiling and biomarkers are not currently widely used in the clinical setting. We will finally discuss ways to contribute to the better use of proteomics data in precision medicine and the clinical setting.

Discovering Novel Alternaria solani Succinate Dehydrogenase Inhibitors by in Silico Modeling and Virtual Screening Strategies to Combat Early Blight

Alternaria blight is an important foliage disease caused by Alternaria solani. The enzyme Succinate dehydrogenase (SDH) is a potential drug target because of its role in tricarboxylic acid cycle. Hence targeting Alternaria solani SDH enzyme could be efficient tool to design novel fungicides against A. solani. We employed computational methodologies to design new SDH inhibitors using homology modeling; pharmacophore modeling and structure based virtual screening. The three dimensional SDH model showed good stereo-chemical and structural properties. Based on virtual screening results twelve commercially available compounds were purchased and tested in vitro and in vivo. The compounds were found to inhibit mycelial growth of A. solani. Moreover in vitro trials showed that inhibitory effects were enhanced with increase in concentrations. Similarly increased disease control was observed in pre-treated potato tubers. Hence the applied in silico strategy led us to identify novel fungicides.

Application of Traditional Chinese Medical Herbs in Prevention and Treatment of Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is a common viral pathogen of the lower respiratory tract, which, in the absence of effective management, causes millions of cases of severe illness per year. Many of these infections develop into fatal pneumonia. In a review of English and Chinese medical literature, recent traditional Chinese medical herb- (TCMH-) based progress in the area of prevention and treatment was identified, and the potential anti-RSV compounds, herbs, and formulas were explored. Traditional Chinese medical herbs have a positive effect on inhibiting viral attachment, inhibiting viral internalization, syncytial formation, alleviation of airway inflammation, and stimulation of interferon secretion and immune system; however, the anti-RSV mechanisms of TCMHs are complicated, which should be further investigated.

A Perspective on the Future of High-Throughput RNAi Screening: Will CRISPR Cut Out the Competition or Can RNAi Help Guide the Way?

For more than a decade, RNA interference (RNAi) has brought about an entirely new approach to functional genomics screening. Enabling high-throughput loss-of-function (LOF) screens against the human genome, identifying new drug targets, and significantly advancing experimental biology, RNAi is a fast, flexible technology that is compatible with existing high-throughput systems and processes; however, the recent advent of clustered regularly interspaced palindromic repeats (CRISPR)-Cas, a powerful new precise genome-editing (PGE) technology, has opened up vast possibilities for functional genomics. CRISPR-Cas is novel in its simplicity: one piece of easily engineered guide RNA (gRNA) is used to target a gene sequence, and Cas9 expression is required in the cells. The targeted double-strand break introduced by the gRNA-Cas9 complex is highly effective at removing gene expression compared to RNAi. Together with the reduced cost and complexity of CRISPR-Cas, there is the realistic opportunity to use PGE to screen for phenotypic effects in a total gene knockout background. This review summarizes the exciting development of CRISPR-Cas as a high-throughput screening tool, comparing its future potential to that of well-established RNAi screening techniques, and highlighting future challenges and opportunities within these disciplines. We conclude that the two technologies actually complement rather than compete with each other, enabling greater understanding of the genome in relation to drug discovery.

Sequence-based design of bioactive small molecules that target precursor microRNAs

Oligonucleotides are designed to target RNA using base pairing rules, but they can be hampered by poor cellular delivery and nonspecific stimulation of the immune system. Small molecules are preferred as lead drugs or probes but cannot be designed from sequence. Herein, we describe an approach termed Inforna that designs lead small molecules for RNA from solely sequence. Inforna was applied to all human microRNA hairpin precursors, and it identified bioactive small molecules that inhibit biogenesis by binding nuclease-processing sites (44% hit rate). Among 27 lead interactions, the most avid interaction is between a benzimidazole (1) and precursor microRNA-96. Compound 1 selectively inhibits biogenesis of microRNA-96, upregulating a protein target (FOXO1) and inducing apoptosis in cancer cells. Apoptosis is ablated when FOXO1 mRNA expression is knocked down by an siRNA, validating compound selectivity. Markedly, microRNA profiling shows that 1 only affects microRNA-96 biogenesis and is at least as selective as an oligonucleotide.

G-quadruplexes as potential therapeutic targets for embryonal tumors

Embryonal tumors include a heterogeneous group of highly malignant neoplasms that primarily affect infants and children and are characterized by a high rate of mortality and treatment-related morbidity, hence improved therapies are clearly needed. G-quadruplexes are special secondary structures adopted in guanine (G)-rich DNA sequences that are often present in biologically important regions, e.g. at the end of telomeres and in the regulatory regions of oncogenes such as MYC. Owing to the significant roles that both telomeres and MYC play in cancer cell biology, G-quadruplexes have been viewed as emerging therapeutic targets in oncology and as tools for novel anticancer drug design. Several compounds that target these structures have shown promising anticancer activity in tumor xenograft models and some of them have entered Phase II clinical trials. In this review we examine approaches to DNA targeted cancer therapy, summarize the recent developments of G-quadruplex ligands as anticancer drugs and speculate on the future direction of such structures as a potential novel therapeutic strategy for embryonal tumors of the nervous system.

Innovations in studying in vivo cell behavior and pharmacology in complex tissues--microvascular endothelial cells in the spotlight

Many studies on the molecular control underlying normal cell behavior and cellular responses to disease stimuli and pharmacological intervention are conducted in single-cell culture systems, while the read-out of cellular engagement in disease and responsiveness to drugs in vivo is often based on overall tissue responses. As the majority of drugs under development aim to specifically interact with molecular targets in subsets of cells in complex tissues, this approach poses a major experimental discrepancy that prevents successful development of new therapeutics. In this review, we address the shortcomings of the use of artificial (single) cell systems and of whole tissue analyses in creating a better understanding of cell engagement in disease and of the true effects of drugs. We focus on microvascular endothelial cells that actively engage in a wide range of physiological and pathological processes. We propose a new strategy in which in vivo molecular control of cells is studied directly in the diseased endothelium instead of at a (far) distance from the site where drugs have to act, thereby accounting for tissue-controlled cell responses. The strategy uses laser microdissection-based enrichment of microvascular endothelium which, when combined with transcriptome and (phospho)proteome analyses, provides a factual view on their status in their complex microenvironment. Combining this with miniaturized sample handling using microfluidic devices enables handling the minute sample input that results from this strategy. The multidisciplinary approach proposed will enable compartmentalized analysis of cell behavior and drug effects in complex tissue to become widely implemented in daily biomedical research and drug development practice.

The fall and rise of pharmacology--(re-)defining the discipline?

Pharmacology is an integrative discipline that originated from activities, now nearly 7000 years old, to identify therapeutics from natural product sources. Research in the 19th Century that focused on the Law of Mass Action (LMA) demonstrated that compound effects were dose-/concentration-dependent eventually leading to the receptor concept, now a century old, that remains the key to understanding disease causality and drug action. As pharmacology evolved in the 20th Century through successive biochemical, molecular and genomic eras, the precision in understanding receptor function at the molecular level increased and while providing important insights, led to an overtly reductionistic emphasis. This resulted in the generation of data lacking physiological context that ignored the LMA and was not integrated at the tissue/whole organism level. As reductionism became a primary focus in biomedical research, it led to the fall of pharmacology. However, concerns regarding the disconnect between basic research efforts and the approval of new drugs to treat 21st Century disease tsunamis, e.g., neurodegeneration, metabolic syndrome, etc. has led to the reemergence of pharmacology, its rise, often in the semantic guise of systems biology. Against a background of limited training in pharmacology, this has resulted in issues in experimental replication with a bioinformatics emphasis that often has a limited relationship to reality. The integration of newer technologies within a pharmacological context where research is driven by testable hypotheses rather than technology, together with renewed efforts in teaching pharmacology, is anticipated to improve the focus and relevance of biomedical research and lead to novel therapeutics that will contain health care costs.

Single-cell transcriptomics for drug target discovery

Single cell sequencing is currently in its relative infancy although an unprecedented amount of information is already being generated. These techniques are providing new insight into intercellular variability as well as identification of previously unrecognized drug targets. As more groups are gaining an interest in this fruitful technique, new sample preparation techniques, sequencing platforms, and bioinformatics tools are being developed which only improve the quantity and quality of data generated in these studies. Great advancements in harvest (in vivo pipette), sample preparation, and sequencing (Illumina HiSeq 2500/MiSeq, Ion Torrent PGM, Pacific Biosciences RS) are allowing for previously untestable questions to be answered and for expanded accessibility of these technologies.

Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators

Mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on human samples at single-cell resolution, but instruments process only one sample at a time. Here we describe mass-tag cellular barcoding (MCB), which increases mass cytometry throughput by using n metal ion tags to multiplex up to 2n samples. We used seven tags to multiplex an entire 96-well plate, and applied MCB to characterize human peripheral blood mononuclear cell (PBMC) signaling dynamics and cell-to-cell communication, signaling variability between PBMCs from eight human donors, and the effects of 27 inhibitors on this system. For each inhibitor, we measured 14 phosphorylation sites in 14 PBMC types at 96 conditions, resulting in 18,816 quantified phosphorylation levels from each multiplexed sample. This high-dimensional, systems-level inquiry allowed analysis across cell-type and signaling space, reclassified inhibitors and revealed off-target effects. High-content, high-throughput screening with MCB should be useful for drug discovery, preclinical testing and mechanistic investigation of human disease.

Functional interrogation of breast cancer: from models to drugs

Functional genomics allows for the activity of the whole genome to be surveyed at once. Using this technology for the identification of novel targets and their validation in disease-specific contexts has profound implications for the future of drug discovery. Now researchers have the technological means to gather comprehensive data on basic biological phenomena and disease mechanisms, while monitoring the effect of drug candidates on a molecular level. Pathway analysis can facilitate the genetic profiling of patients and, in turn, predict individual responses to treatment regimes. Functional interrogation of a disease-specific phenotype at a whole genome level (through, for example, the use of whole genome RNAi libraries) allows for the identification of critical regulators in complex biological systems, and the detection of putative targets for future therapeutic intervention. The authors describe the applications of functional genomics in models of breast cancer and the integration of these disparate technologies, specifically in the context of the search for novel therapeutic targets.

Ligand-directed profiling: applications to target drug discovery in cancer

BACKGROUND

Generation of targeted therapy remains a major challenge in medicine. The development of drugs that can discriminate between tumor cells and non-malignant cells would improve efficacy and reduce general side effects. Phage display allows identification of specific supramolecular complexes that can target therapeutic compounds or imaging agents, both in vitro and in vivo. The use of phage display to identify molecules expressed on the surface of human cancer cells without bias, as well as to provide initial steps toward identification of a ligand/receptor-based map of the human microvasculature, has broad implications for drug discovery in general, especially for cancer therapy.

OBJECTIVE/METHOD

In this review, we discuss the use of phage display technology as a ligand-directed targeting strategy and its applications to drug discovery.

CONCLUSION

Compared to other existing drug discovery platforms, phage display technology has the advantage to provide valuable clues pointing to target proteins in an unbiased biological context. The result from various display library screenings indicates that in many cases the selected peptide motifs mimic biological ligands. Analysis of peptide motifs targeting a receptor provides a basis for rational drug design of targeted peptidomimetics.

Selecting, Acquiring, and Using Small Molecule Libraries for High-Throughput Screening

The selection, acquisition and use of high quality small molecule libraries for screening is an essential aspect of drug discovery and chemical biology programs. Screening libraries continue to evolve as researchers gain a greater appreciation of the suitability of small molecules for specific biological targets, processes and environments. The decisions surrounding the make-up of any given small molecule library is informed by a multitude of variables and opinions vary on best-practices. The fitness of any collection relies upon upfront filtering to avoiding problematic compounds, assess appropriate physicochemical properties, install the ideal level of structural uniqueness and determine the desired extent of molecular complexity. These criteria are under constant evaluation and revision as academic and industrial organizations seek out collections that yield ever improving results from their screening portfolios. Practical questions including cost, compound management, screening sophistication and assay objective also play a significant role in the choice of library composition. This overview attempts to offer advice to all organizations engaged in small molecule screening based upon current best practices and theoretical considerations in library selection and acquisition.

[Techniques for rapid production of monoclonal antibodies for use with antibody technology]

A monoclonal antibody (Mab), due to its specific binding ability to a target protein, can potentially be one of the most useful tools for the functional analysis of proteins in recent proteomics-based research. However, the production of Mab is a very time-consuming and laborious process (i.e., preparation of recombinant antigens, immunization of animals, preparation of hybridomas), making it the rate-limiting step in using Mabs in high-throughput proteomics research, which heavily relies on comprehensive and rapid methods. Therefore, there is a great demand for new methods to efficiently generate Mabs against a group of proteins identified by proteome analysis. Here, we describe a useful method called "Antibody proteomic technique" for the rapid generations of Mabs to pharmaceutical target, which were identified by proteomic analyses of disease samples (ex. tumor tissue, etc.). We also introduce another method to find profitable targets on vasculature, which is called "Vascular proteomic technique". Our results suggest that this method for the rapid generation of Mabs to proteins may be very useful in proteomics-based research as well as in clinical applications.

UniDrug-target: a computational tool to identify unique drug targets in pathogenic bacteria

Background

Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed.

Methods

A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed.

Results

The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets.

Conclusions/Significance

UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar.

Bottlenecks caused by software gaps in miRNA and RNAi research

Understanding the regulation of gene expression is critical to many areas of biology while control via RNAs has found considerable interest as a tool for scientific discovery and potential therapeutic applications. For example whole genome RNA interference (RNAi) screens and whole proteome scans provide views of how the entire transcriptome or proteome responds to biological, chemical or environmental perturbations of a gene's activity. Small RNA (sRNA) or MicroRNA (miRNA) are known to regulate pathways and bind mRNA, while the function of miRNAs discovered in experimental studies is often unknown. In both cases, RNAi and miRNA require labor intensive studies to tease out their functions within gene networks. Available software to analyze relationships is currently an ad hoc and often a manual process that can take up to several hours to analyze a single candidate RNAi or miRNA. With experiments frequently highlighting tens to hundreds of candidates this represents a considerable bottleneck. We suggest there is a gap in miRNA and RNAi research caused by inadequate current software that could be improved. For example a new software application could be created that provides interactive, comprehensive target analysis that leverages past datasets to lead to statistically stronger analyses.

Using functional genomics to identify drug targets: a Dupuytren's disease example

Research into the molecular mechanism of Dupuytren's disease (DD) illustrates all the problems common to drug discovery in orphan diseases, but also in more commonly investigated ailments. Current findings characterize DD as a disease with complex molecular pathology, with changes in expression of multiple genes and proteins as well as many contributing risk factors. Some of the observed changes include genes and proteins that have been identified in a number of other pathological processes, such as TGF-β, some which may be more specific to DD, such as ADAM12, and undoubtedly also some that have yet to be discovered in future studies. When all these results are taken into consideration, it can be deduced that DD is an end result of several pathological processes that can have many points of origin, and probably involves several subtypes that give rise to sufficiently similar clinical symptoms to be unified under a single medical term. Such breadth of view has become possible with the advent of functional genomics methods and system-wide overview of the molecular processes, which highlight molecular players and processes that might not be intuitively obvious from symptoms, as is the case with the observed parallels with wound-healing processes. As functional genomics methods allow researchers to compile a more complete image of the molecular mechanisms involved in DD pathogenesis, they also help to propose new drug targets that can be employed to develop an effective pharmacological treatment for DD. Identification of key molecular players in DD has already benefited from the integration of functional genomics and biocomputational methods, and such approach may reveal new ways how we can interfere with the emergence of the DD phenotype.

Construction of an Indonesian herbal constituents database and its use in Random Forest modelling in a search for inhibitors of aldose reductase

Data on phytochemical constituents of plants commonly used in traditional Indonesian medicine have been compiled as a computer database. This database (the Indonesian Herbal constituents database, IHD) currently contains details on ∼1,000 compounds found in 33 different plants. For each entry, the IHD gives details of chemical structure, trivial and systematic name, CAS registry number, pharmacology (where known), toxicology (LD(50)), botanical species, the part(s) of the plant(s) where the compounds are found, typical dosage(s) and reference(s). A second database has been also been compiled for plant-derived compounds with known activity against the enzyme, aldose reductase (AR). This database (the aldose reductase inhibitors database, ARID) contains the same details as the IHD, and currently comprises information on 120 different AR inhibitors. Virtual screening of all compounds in the IHD has been performed using Random Forest (RF) modelling, in a search for novel leads active against AR-to provide for new forms of symptomatic relief in diabetic patients. For the RF modelling, a set of simple 2D chemical descriptors were employed to classify all compounds in the combined ARID and IHD databases as either active or inactive as AR inhibitors. The resulting RF models (which gave misclassification rates of 21%) were used to identify putative new AR inhibitors in the IHD, with such compounds being identified as those giving RF scores >0.5 (in each of the three different RF models developed). In vitro assays were subsequently performed for four of the compounds obtained as hits in this in silico screening, to determine their inhibitory activity against human recombinant AR. The two compounds having the highest RF scores (prunetin and ononin) were shown to have the highest activities experimentally (giving ∼58% and ∼52% inhibition at a concentration of 15μM, respectively), while the compounds with lowest RF scores (vanillic acid and cinnamic acid) showed the lowest activities experimentally (giving ∼29% and ∼44% inhibition at a concentration of 15μM, respectively). These simple virtual screening studies were thus helpful in identifying novel inhibitors of AR, but yielded compounds with only very modest (micromolar) potency.

Small-molecule discovery from DNA-encoded chemical libraries

Researchers seeking to improve the efficiency and cost effectiveness of the bioactive small-molecule discovery process have recently embraced selection-based approaches, which in principle offer much higher throughput and simpler infrastructure requirements compared with traditional small-molecule screening methods. Since selection methods benefit greatly from an information-encoding molecule that can be readily amplified and decoded, several academic and industrial groups have turned to DNA as the basis for library encoding and, in some cases, library synthesis. The resulting DNA-encoded synthetic small-molecule libraries, integrated with the high sensitivity of PCR and the recent development of ultra high-throughput DNA sequencing technology, can be evaluated very rapidly for binding or bond formation with a target of interest while consuming minimal quantities of material and requiring only modest investments of time and equipment. In this tutorial review we describe the development of two classes of approaches for encoding chemical structures and reactivity with DNA: DNA-recorded library synthesis, in which encoding and library synthesis take place separately, and DNA-directed library synthesis, in which DNA both encodes and templates library synthesis. We also describe in vitro selection methods used to evaluate DNA-encoded libraries and summarize successful applications of these approaches to the discovery of bioactive small molecules and novel chemical reactivity.

Systems genetics for drug target discovery

The collection and analysis of genomic data has the potential to reveal novel druggable targets by providing insight into the genetic basis of disease. However, the number of drugs targeting new molecular entities, approved by the US Food and Drug Administration has not increased in the years since the collection of genomic data has become commonplace. The paucity of translatable results can be partly attributed to conventional analysis methods that test one gene at a time in an effort to identify disease-associated factors as candidate drug targets. By disengaging genetic factors from their position within the genetic regulatory system, much of the information stored within the genomic data set is lost. Here we discuss how genomic data is used to identify disease-associated genes or genomic regions, how disease-associated regions are validated as functional targets, and the role network analysis can play in bridging the gap between data generation and effective drug target identification.

Identification and Characterization of Cell Wall Proteins of a Toxic Dinoflagellate Alexandrium catenella Using 2-D DIGE and MALDI TOF-TOF Mass Spectrometry

The cell wall is an important subcellular component of dinoflagellate cells with regard to various aspects of cell surface-associated ecophysiology, but the full range of cell wall proteins (CWPs) and their functions remain to be elucidated. This study identified and characterized CWPs of a toxic dinoflagellate, Alexandrium catenella, using a combination of 2D fluorescence difference gel electrophoresis (DIGE) and MALDI TOF-TOF mass spectrometry approaches. Using sequential extraction and temperature shock methods, sequentially extracted CWPs and protoplast proteins, respectively, were separated from A. catenella. From the comparison between sequentially extracted CWPs labeled with Cy3 and protoplast proteins labeled with Cy5, 120 CWPs were confidently identified in the 2D DIGE gel. These proteins gave positive identification of protein orthologues in the protein database using de novo sequence analysis and homology-based search. The majority of the prominent CWPs identified were hypothetical or putative proteins with unknown function or no annotation, while cell wall modification enzymes, cell wall structural proteins, transporter/binding proteins, and signaling and defense proteins were tentatively identified in agreement with the expected role of the extracellular matrix in cell physiology. This work represents the first attempt to investigate dinoflagellate CWPs and provides a potential tool for future comprehensive characterization of dinoflagellate CWPs and elucidation of their physiological functions.

The Therapeutic Target Database: an internet resource for the primary targets of approved, clinical trial and experimental drugs

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets. A challenge in drug discovery is to decide which targets to pursue from an increasing pool of potential targets, given the fact that few innovative targets have made it to the approval list each year. Knowledge of existing drug targets (both approved and within clinical trials) is highly useful for facilitating target discovery, selection, exploration and tool development. The Therapeutic Target Database (TTD) has been developed and updated to provide information on 358 successful targets, 251 clinical trial targets and 1254 research targets in addition to 1511 approved drugs, 1118 clinical trials drugs and 2331 experimental drugs linked to their primary targets (3257 drugs with available structure data). This review briefly describes the TTD database and illustrates how its data can be explored for facilitating target and drug searches, the study of the mechanism of multi-target drugs and the development of in silico target discovery tools.

Synergistic effect between erlotinib and MEK inhibitors in KRAS wild-type human pancreatic cancer cells

PURPOSE

The combination of erlotinib and gemcitabine has shown a small but statistically significant survival advantage when compared with gemcitabine alone in patients with advanced pancreatic cancer. However, the overall survival rate with the erlotinib and gemcitabine combination is still low. In this study, we sought to identify gene targets that, when inhibited, would enhance the activity of epidermal growth factor receptor (EGFR)-targeted therapies in pancreatic cancer cells.

EXPERIMENTAL DESIGN

A high-throughput RNA interference (RNAi) screen was carried out to identify candidate genes. Selected gene hits were further confirmed and mechanisms of action were further investigated using various assays.

RESULTS

Six gene hits from siRNA screening were confirmed to significantly sensitize BxPC-3 pancreatic cancer cells to erlotinib. One of the hits, mitogen-activated protein kinase (MAPK) 1, was selected for further mechanistic studies. Combination treatments of erlotinib and two MAP kinase kinase (MEK) inhibitors, RDEA119 and AZD6244, showed significant synergistic effect for both combinations (RDEA119-erlotinib and AZD6244-erlotinib) compared with the corresponding single drug treatments in pancreatic cancer cell lines with wild-type KRAS (BxPC-3 and Hs 700T) but not in cell lines with mutant KRAS (MIA PaCa-2 and PANC-1). The enhanced antitumor activity of the combination treatment was further verified in the BxPC-3 and MIA PaCa-2 mouse xenograft model. Examination of the MAPK signaling pathway by Western blotting indicated effective inhibition of the EGFR signaling by the drug combination in KRAS wild-type cells but not in KRAS mutant cells.

CONCLUSIONS

Overall, our results suggest that combination therapy of an EGFR and MEK inhibitors may have enhanced efficacy in patients with pancreatic cancer.

Synaptoproteomics of learned helpless rats involve energy metabolism and cellular remodeling pathways in depressive-like behavior and antidepressant response

Although depression is a severe and life-threatening psychiatric illness, its pathogenesis still is essentially unknown. Recent studies highlighted the influence of environmental stress factors on an individual's genetic predisposition to develop mood disorders. In the present study, we employed a well-validated stress-induced animal model of depression, Learned Helplessness paradigm, in rats. Learned helpless (LH) and non-learned helpless (NLH) rats were treated with nortriptyline, a tricyclic antidepressant. The resulting 4 groups (LH vs. NLH, treated vs. non-treated), were subjected to global analysis of protein expression, a powerful approach to gain insight into the molecular mechanisms underlying vulnerability to psychiatric disorders and the long-term action of drug treatments. Many of the biological targets of antidepressant drugs are localized at synapses. Thus, to reduce the complexity of the proteome analyzed and to enrich for less abundant synaptic proteins, purified nerve terminals (synaptosomes) from prefrontal/frontal cortex (P/FC) and hippocampus (HPC) of LH-NLH rats were used. Synaptosomes were purified by differential centrifugation on Percoll gradients and analyzed by two-dimensional polyacrylamide gel electrophoresis (2-DE). Protein spots differently regulated in the various comparisons were excised from gels and identified by mass spectrometry. Proteins involved in energy metabolism and cellular remodeling were primarily dysregulated, when LH and NLH rats were compared. Moreover, several proteins (aconitate hydratase, pyruvate dehydrogenase E1, dihydropyrimidinase-related protein-2 and stathmin) were found to be regulated in opposite directions by stress and drug treatment. These proteins could represent new molecular correlates of both vulnerability to stress and response to drugs, and putative targets for the development of novel drugs with antidepressant action. This article is part of a Special Issue entitled 'Trends in neuropharmacology: in memory of Erminio Costa'.

Proof of concept: network and systems biology approaches aid in the discovery of potent anticancer drug combinations

Cancer therapies that target key molecules have not fulfilled expected promises for most common malignancies. Major challenges include the incomplete understanding and validation of these targets in patients, the multiplicity and complexity of genetic and epigenetic changes in the majority of cancers, and the redundancies and cross-talk found in key signaling pathways. Collectively, the uses of single-pathway targeted approaches are not effective therapies for human malignancies. To overcome these barriers, it is important to understand the molecular cross-talk among key signaling pathways and how they may be altered by targeted agents. Innovative approaches are needed, such as understanding the global physiologic environment of target proteins and the effects of modifying them without losing key molecular details. Such strategies will aid the design of novel therapeutics and their combinations against multifaceted diseases, in which efficacious combination therapies will focus on altering multiple pathways rather than single proteins. Integrated network modeling and systems biology have emerged as powerful tools benefiting our understanding of drug mechanisms of action in real time. This review highlights the significance of the network and systems biology-based strategy and presents a proof of concept recently validated in our laboratory using the example of a combination treatment of oxaliplatin and the MDM2 inhibitor MI-219 in genetically complex and incurable pancreatic adenocarcinoma.

Structural modelling and comparative analysis of homologous, analogous and specific proteins from Trypanosoma cruzi versus Homo sapiens: putative drug targets for chagas' disease treatment

Background

Trypanosoma cruzi is the etiological agent of Chagas' disease, an endemic infection that causes thousands of deaths every year in Latin America. Therapeutic options remain inefficient, demanding the search for new drugs and/or new molecular targets. Such efforts can focus on proteins that are specific to the parasite, but analogous enzymes and enzymes with a three-dimensional (3D) structure sufficiently different from the corresponding host proteins may represent equally interesting targets. In order to find these targets we used the workflows MHOLline and AnEnΠ obtaining 3D models from homologous, analogous and specific proteins of Trypanosoma cruzi versus Homo sapiens.

Results

We applied genome wide comparative modelling techniques to obtain 3D models for 3,286 predicted proteins of T. cruzi. In combination with comparative genome analysis to Homo sapiens, we were able to identify a subset of 397 enzyme sequences, of which 356 are homologous, 3 analogous and 38 specific to the parasite.

Conclusions

In this work, we present a set of 397 enzyme models of T. cruzi that can constitute potential structure-based drug targets to be investigated for the development of new strategies to fight Chagas' disease. The strategies presented here support the concept of structural analysis in conjunction with protein functional analysis as an interesting computational methodology to detect potential targets for structure-based rational drug design. For example, 2,4-dienoyl-CoA reductase (EC 1.3.1.34) and triacylglycerol lipase (EC 3.1.1.3), classified as analogous proteins in relation to H. sapiens enzymes, were identified as new potential molecular targets.

Gene delivery in three-dimensional cell cultures by superparamagnetic nanoparticles

Three-dimensional (3D) cellular assays closely mimic the in vivo milieu, providing a rapid, inexpensive system for screening drug candidates for toxicity or efficacy in the early stages of drug discovery. However, 3D culture systems may suffer from mass transfer limitations, particularly in delivery of large polypeptide or nucleic acid compounds. Nucleic acids (e.g., genes, silencing RNA) are of particular interest both as potential therapeutics and due to a desire to modulate the gene-expression patterns of cells exposed to small-molecule pharmacological agents. In the present study, polyethylenimine (PEI)-coated superparamagnetic nanoparticles (SPMNs) were designed to deliver interfering RNA and green fluorescent protein (GFP) plasmids through a collagen-gel matrix into 3D cell cultures driven by an external magnetic field. The highest transfection efficiency achieved was 64% for siRNA and 77% for GFP plasmids. Delivery of an shRNA plasmid against GFP by PEI-coated SPMNs silenced the GFP expression with 82% efficiency. We further demonstrated that this delivery approach could be used for screening interfering RNA constructs for therapeutic or toxic effects for cells grown in 3D cultures. Four known toxic shRNA plasmids were delivered by PEI-coated SPMNs into 3D cell cultures, and significant toxicities (41-51% cell death) were obtained.

High-throughput sequencing for the identification of binding molecules from DNA-encoded chemical libraries

DNA-encoded chemical libraries are large collections of small organic molecules, individually coupled to DNA fragments that serve as amplifiable identification bar codes. The isolation of specific binders requires a quantitative analysis of the distribution of DNA fragments in the library before and after capture on an immobilized target protein of interest. Here, we show how Illumina sequencing can be applied to the analysis of DNA-encoded chemical libraries, yielding over 10 million DNA sequence tags per flow-lane. The technology can be used in a multiplex format, allowing the encoding and subsequent sequencing of multiple selections in the same experiment. The sequence distributions in DNA-encoded chemical library selections were found to be similar to the ones obtained using 454 technology, thus reinforcing the concept that DNA sequencing is an appropriate avenue for the decoding of library selections. The large number of sequences obtained with the Illumina method now enables the study of very large DNA-encoded chemical libraries (>500,000 compounds) and reduces decoding costs.

RNA interference as a method for target-site screening in the Western corn rootworm, Diabrotica virgifera virgifera

To test the efficacy of RNA interference (RNAi) as a method for target-site screening in Diabrotica virgifera virgifera LeConte (Coleptera: Chrysomelidae) larvae, genes were identified and tested for which clear RNAi phenotypes had been identified in the Coleopteran model, Tribolium castaneum. Here the cloning of the D. v. vergifera orthologs of laccase 2 (DvvLac2) and chitin synthase 2 (DvvCHS2) is reported. Injection of DvvLac2-specific double-stranded RNA resulted in prevention of post-molt cuticular tanning, while injection of DvvCHS2-specific dsRNA reduced chitin levels in midguts. Silencing of both DvvLac2 and DvvCHS2 was confirmed by RT-PCR and quantitative RT-PCR. As in T. castaneum, RNAi-mediated gene silencing is systemic in Diabrotica. The results indicate that RNAi-induced silencing of D. v. vergifera genes provides a powerful tool for identifying potential insecticide targets.

A natural products approach to drug discovery: probing modes of action of antitumor agents by genome-scale cDNA library screening

In the last few years, genomic tools have been incorporated in natural product approaches to drug discovery, including understanding mechanisms of action which cannot be elucidated from phenotypic screens such as cell viability assays. The characterization of perturbed biological pathways and target identification are important for the evaluation of the compounds' potential as drug leads and for subsequent medicinal chemistry efforts; however, general procedures to tackle this task are lacking. The combination of high-throughput screening and genomic-scale assays has proven to be a powerful tool to aid in the identification of mechanisms and potentially of protein targets, not only in yeast but also mammalian cells. Arrayed libraries of cDNAs can be transfected into cancer cell lines in a high-throughput fashion to generate variants of spatially separated cancer cells with increased gene dosages for one particular cDNA. Cells overexpressing gene products that are directly targeted by a small molecule or that lie in the perturbed pathway may be less susceptible to the effects of the compound. This fact provides the basis for drug susceptibility screens employing cDNA libraries. The general procedures to optimize and execute those screens and subsequently validate putative screening hits are discussed in detail.

Update of TTD: Therapeutic Target Database

Increasing numbers of proteins, nucleic acids and other molecular entities have been explored as therapeutic targets, hundreds of which are targets of approved and clinical trial drugs. Knowledge of these targets and corresponding drugs, particularly those in clinical uses and trials, is highly useful for facilitating drug discovery. Therapeutic Target Database (TTD) has been developed to provide information about therapeutic targets and corresponding drugs. In order to accommodate increasing demand for comprehensive knowledge about the primary targets of the approved, clinical trial and experimental drugs, numerous improvements and updates have been made to TTD. These updates include information about 348 successful, 292 clinical trial and 1254 research targets, 1514 approved, 1212 clinical trial and 2302 experimental drugs linked to their primary targets (3382 small molecule and 649 antisense drugs with available structure and sequence), new ways to access data by drug mode of action, recursive search of related targets or drugs, similarity target and drug searching, customized and whole data download, standardized target ID, and significant increase of data (1894 targets, 560 diseases and 5028 drugs compared with the 433 targets, 125 diseases and 809 drugs in the original release described in previous paper). This database can be accessed at http://bidd.nus.edu.sg/group/cjttd/TTD.asp.

Advances in Nuclear Magnetic Resonance for Drug Discovery

BACKGROUND: Drug discovery is a complex and unpredictable endeavor with a high failure rate. Current trends in the pharmaceutical industry have exasperated these challenges and are contributing to the dramatic decline in productivity observed over the last decade. The industrialization of science by forcing the drug discovery process to adhere to assembly-line protocols is imposing unnecessary restrictions, such as short project time-lines. Recent advances in nuclear magnetic resonance are responding to these self-imposed limitations and are providing opportunities to increase the success rate of drug discovery. OBJECTIVE/METHOD: A review of recent advancements in NMR technology that have the potential of significantly impacting and benefiting the drug discovery process will be presented. These include fast NMR data collection protocols and high-throughput protein structure determination, rapid protein-ligand co-structure determination, lead discovery using fragment-based NMR affinity screens, NMR metabolomics to monitor in vivo efficacy and toxicity for lead compounds, and the identification of new therapeutic targets through the functional annotation of proteins by FAST-NMR. CONCLUSION: NMR is a critical component of the drug discovery process, where the versatility of the technique enables it to continually expand and evolve its role. NMR is expected to maintain this growth over the next decade with advancements in automation, speed of structure calculation, in-cell imaging techniques, and the expansion of NMR amenable targets.

Assessing the druggability of protein-protein interactions by a supervised machine-learning method

Background

Protein-protein interactions (PPIs) are challenging but attractive targets of small molecule drugs for therapeutic interventions of human diseases. In this era of rapid accumulation of PPI data, there is great need for a methodology that can efficiently select drug target PPIs by holistically assessing the druggability of PPIs. To address this need, we propose here a novel approach based on a supervised machine-learning method, support vector machine (SVM).

Results

To assess the druggability of the PPIs, 69 attributes were selected to cover a wide range of structural, drug and chemical, and functional information on the PPIs. These attributes were used as feature vectors in the SVM-based method. Thirty PPIs known to be druggable were carefully selected from previous studies; these were used as positive instances. Our approach was applied to 1,295 human PPIs with tertiary structures of their protein complexes already solved. The best SVM model constructed discriminated the already-known target PPIs from others at an accuracy of 81% (sensitivity, 82%; specificity, 79%) in cross-validation. Among the attributes, the two with the greatest discriminative power in the best SVM model were the number of interacting proteins and the number of pathways.

Conclusion

Using the model, we predicted several promising candidates for druggable PPIs, such as SMAD4/SKI. As more PPI data are accumulated in the near future, our method will have increased ability to accelerate the discovery of druggable PPIs.