Identifying targets for drug discovery using bioinformatics

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

In Vitro, In Silico, and In Vivo Studies of Cardamine hirsuta Linn as a Potential Antidiabetic Agent in a Rat Model

Diabetes mellitus (T2DM) is a multifaceted metabolic disorder with no definite treatment. In silico characterization can help to explain the interaction between molecules and predict 3D structures. The aim of the present study was to evaluate the hypoglycemic activities of the hydro-methanolic extract of Cardamine hirsuta in a rat model. In vitro antioxidant and α-amylase inhibitory assays were evaluated in the present study. Phyto-constituents were quantified using RP-UHPLC-MS analysis. Molecular docking of compounds into the binding site of different molecular targets, i.e., tumor necrosis factor (TNF-α), glycogen synthase kinase 3 β (GSK-3β), and AKT, was carried out. Acute toxicity model, in vivo antidiabetic effect, and the influence on biochemical and oxidative stress parameters were also investigated. T2DM was induced in adult male rats by streptozotocin using a high-fat diet model. Three different doses (125, 250, and 500 mg/kg BW) were orally gavaged for 30 days. Mulberrofuran-M and quercetin3-(6″caffeoylsophoroside) have demonstrated remarkable binding affinity toward TNF-α and GSK-3β, respectively. 2,2-Diphenyl-1-picrylhydrazyl and α-amylase inhibition assay exhibited IC50 values of 75.96 and 73.66 μg/mL, respectively. In vivo findings exhibited that 500 mg/kg body weight (BW) dose of the extract significantly decreased the blood glucose level, improved biochemical parameters as well as oxidative stress by reduction of lipid peroxidation, and increased high-density lipoproteins. Moreover, activities of glutathione-s-transferase, reduced glutathione, superoxide dismutase were enhanced, and cellular architecture in the histopathological examination was restored in treatment groups. The present study affirmed the antidiabetic activities of mulberrofuran-M and quercetin3-(6″caffeoylsophoroside) present in the hydro-methanolic extract of C. hirsuta, possibly due to the reduction in oxidative stress and α-amylase inhibition.

Bioinformatics approaches and big data analytics opportunities in improving fisheries and aquaculture

Aquaculture has witnessed an excellent growth rate during the last two decades and offers huge potential to provide nutritional as well as livelihood security. Genomic research has contributed significantly toward the development of beneficial technologies for aquaculture. The existing high throughput technologies like next-generation technologies generate oceanic data which requires extensive analysis using appropriate tools. Bioinformatics is a rapidly evolving science that involves integrating gene based information and computational technology to produce new knowledge for the benefit of aquaculture. Bioinformatics provides new opportunities as well as challenges for information and data processing in new generation aquaculture. Rapid technical advancements have opened up a world of possibilities for using current genomics to improve aquaculture performance. Understanding the genes that govern economically relevant characteristics, necessitates a significant amount of additional research. The various dimensions of data sources includes next-generation DNA sequencing, protein sequencing, RNA sequencing gene expression profiles, metabolic pathways, molecular markers, and so on. Appropriate bioinformatics tools are developed to mine the biologically relevant and commercially useful results. The purpose of this scoping review is to present various arms of diverse bioinformatics tools with special emphasis on practical translation to the aquaculture industry.

Biochemical and Kinetic Characterization of the Glucose-6-Phosphate Dehydrogenase from Helicobacter pylori Strain 29CaP

Helicobacter pylori (H. pylori) has been proposed as the foremost risk factor for the development of gastric cancer. We found that H. pylori express the enzyme glucose-6-phosphate dehydrogenase (HpG6PD), which participates in glucose metabolism via the pentose phosphate pathway. Thus, we hypothesized that if the biochemical and physicochemical characteristics of HpG6PD contrast with the host G6PD (human G6PD, HsG6PD), HpG6PD becomes a potential target for novel drugs against H. pylori. In this work, we characterized the biochemical properties of the HpG6PD from the H.pylori strain 29CaP and expressed the active recombinant protein, to analyze its steady-state kinetics, thermostability, and biophysical aspects. In addition, we analyzed the HpG6PD in silico structural properties to compare them with those of the HsG6PD. The optimal pH for enzyme activity was 7.5, with a T_1/2 of 46.6 °C, at an optimum stability temperature of 37 °C. The apparent K_m values calculated for G6P and NADP⁺ were 75.0 and 12.8 µM, respectively. G6P does not protect HpG6PD from trypsin digestion, but NADP⁺ does protect the enzyme from trypsin and guanidine hydrochloride (Gdn-HCl). The biochemical characterization of HpG6PD contributes to knowledge regarding H. pylori metabolism and opens up the possibility of using this enzyme as a potential target for specific and efficient treatment against this pathogen; structural alignment indicates that the three-dimensional (3D) homodimer model of the G6PD protein from H. pylori is different from the 3D G6PD of Homo sapiens.

Prediction of Drug Targets for Specific Diseases Leveraging Gene Perturbation Data: A Machine Learning Approach

Identification of the correct targets is a key element for successful drug development. However, there are limited approaches for predicting drug targets for specific diseases using omics data, and few have leveraged expression profiles from gene perturbations. We present a novel computational approach for drug target discovery based on machine learning (ML) models. ML models are first trained on drug-induced expression profiles with outcomes defined as whether the drug treats the studied disease. The goal is to “learn” the expression patterns associated with treatment. Then, the fitted ML models were applied to expression profiles from gene perturbations (overexpression (OE)/knockdown (KD)). We prioritized targets based on predicted probabilities from the ML model, which reflects treatment potential. The methodology was applied to predict targets for hypertension, diabetes mellitus (DM), rheumatoid arthritis (RA), and schizophrenia (SCZ). We validated our approach by evaluating whether the identified targets may ‘re-discover’ known drug targets from an external database (OpenTargets). Indeed, we found evidence of significant enrichment across all diseases under study. A further literature search revealed that many candidates were supported by previous studies. For example, we predicted PSMB8 inhibition to be associated with the treatment of RA, which was supported by a study showing that PSMB8 inhibitors (PR-957) ameliorated experimental RA in mice. In conclusion, we propose a new ML approach to integrate the expression profiles from drugs and gene perturbations and validated the framework. Our approach is flexible and may provide an independent source of information when prioritizing drug targets.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Inhibitory Potential of Phytochemicals on Interleukin-6-Mediated T-Cell Reduction in COVID-19 Patients: A Computational Approach

BACKGROUND

A recent COVID-19 pandemic has resulted in a large death toll rate globally and even no cure or vaccine has been successfully employed to combat this disease. Patients have been reported with multi-organ dysfunction along with acute respiratory distress syndrome which implies a critical situation for patients and made them difficult to breathe and survive. Moreover, pathology of COVID-19 is also related to cytokine storm which indicates the elevated levels of interleukin (IL)-1, IL-6, IL-12, and IL-18 along with tumor necrosis factor (TNF)-α. Among them, the proinflammatory cytokine IL-6 has been reported to be induced via binding of severe acute respiratory syndrome coronavirus 2 (SARS)-CoV-2 to the host receptors.

METHODOLOGY

Interleukin-6 blockade has been proposed to constitute novel therapeutics against COVID-19. Thus, in this study, 15 phytocompounds with known antiviral activity have been subjected to test for their inhibitory effect on IL-6. Based on the affinity prediction, top 3 compounds (isoorientin, lupeol, and andrographolide) with best scores were selected for 50 ns molecular dynamics simulation and MMGB/PBSA binding free energy analysis.

RESULTS

Three phytocompounds including isoorientin, lupeol, and andrographolide have shown strong interactions with the targeted protein IL-6 with least binding energies (-7.1 to -7.7 kcal/mol). Drug-likeness and ADMET profiles of prioritized phytocompounds are also very prominsing and can be further tested to be potential IL-6 blockers and thus benficial for COVID-19 treatment. The moelcular dynamics simulation couple with MMGB/PBSA binding free energy estimation validated conformational stability of the ligands and stronger intermolecular binding. The mean RMSD of the complexes is as: IL6-isoorientin complex (3.97 Å ± 0.77), IL6-lupeol (3.97 Å ± 0.76), and IL6-andrographolide complex (3.96 Å ± 0.77). In addition, the stability observation was affirmed by compounds mean RMSD: isoorientin (0.72 Å ± 0.32), lupeol (mean 0.38 Å ± 0.08), and andrographolide (1.09 Å ± 0.49). A similar strong agreement on systems stability was unraveled by MMGB/PBSA that found net binding net ~ -20 kcal/mol for the complexes dominated by van der Waal interaction energy.

CONCLUSION

It has been predicted that proposing potential IL-6 inhibitors with less side effects can help critical COVID-19 patients because it may control the cytokine storm, a major responsible factor of its pathogenesis. In this study, 3 potential phytocompounds have been proposed to have inhibitory effect on IL-6 that can be tested as potential therapeutic options against SARS-CoV-2.

Integration of pharmacogenomics and theranostics with nanotechnology as quality by design (QbD) approach for formulation development of novel dosage forms for effective drug therapy

To cater to medication needs in the future healthcare system, we need to shift from the conventional system of drug delivery to modern molecular signature-based drug delivery systems. The current drug therapies are either less effective, ineffective, or produce numerous adverse reactions. One scientific principle or discipline cannot adequately address all the problems, so we need an innovative application of the current scientific principles. Here we are proposing a novel concept of nanoformulation based on pharmacogenomics and theranostics for personalized error-free and targeted therapeutic agent delivery. The addition of more knowledge about the human genome opens the new way to study disease-gene, gene-drug, and drug-effect interactions, which is the basis of future medicines. Pharmacogenomics provides information about the disease etiology, role in genes in disease pathophysiology, disease biomarkers, drug targets, drug effects, and the fate of drugs inside the body. Theranostics approach utilizes the above information in diagnosis, treatment, and monitoring of the disease on a real-time basis. Personalized dosage forms can be formulated into a nanoformulation that provides a better therapeutic effect and minimizes adverse drug reactions. The therapeutic system needs to be shifted from the principle of one drug fits all to one drug unique population. In the present manuscript, we tried to conceptualize a modern therapeutic system by combining the three approaches viz. pharmacogenomics, theranostics, and nanotechnology applied in the area of formulation development to produce a multifunctional single tiny entity.

Neuropeptide signalling systems - An underexplored target for venom drug discovery

Neuropeptides are signalling molecules mainly secreted from neurons that act as neurotransmitters or peptide hormones to affect physiological processes and modulate behaviours. In humans, neuropeptides are implicated in numerous diseases and understanding their role in physiological processes and pathologies is important for therapeutic development. Teasing apart the (patho)physiology of neuropeptides remains difficult due to ligand and receptor promiscuity and the complexity of the signalling pathways. The current approach relies on a pharmacological toolbox of agonists and antagonists displaying high selectivity for independent receptor subtypes, with the caveat that only few selective ligands have been discovered or developed. Animal venoms represent an underexplored source for novel receptor subtype-selective ligands that could aid in dissecting human neuropeptide signalling systems. Multiple endogenous-like neuropeptides as well as peptides acting on neuropeptide receptors are present in venoms. In this review, we summarise current knowledge on neuropeptides and discuss venoms as a source for ligands targeting neuropeptide signalling systems.

Biochemical and Computational Approach of Selected Phytocompounds from Tinospora crispa in the Management of COVID-19

A pandemic caused by the novel coronavirus (SARS-CoV-2 or COVID-19) began in December 2019 in Wuhan, China, and the number of newly reported cases continues to increase. More than 19.7 million cases have been reported globally and about 728,000 have died as of this writing (10 August 2020). Recently, it has been confirmed that the SARS-CoV-2 main protease (Mpro) enzyme is responsible not only for viral reproduction but also impedes host immune responses. The Mpro provides a highly favorable pharmacological target for the discovery and design of inhibitors. Currently, no specific therapies are available, and investigations into the treatment of COVID-19 are lacking. Therefore, herein, we analyzed the bioactive phytocompounds isolated by gas chromatography-mass spectroscopy (GC-MS) from Tinospora crispa as potential COVID-19 Mpro inhibitors, using molecular docking study. Our analyses unveiled that the top nine hits might serve as potential anti-SARS-CoV-2 lead molecules, with three of them exerting biological activity and warranting further optimization and drug development to combat COVID-19.

Identification of a novel potassium channel (GiK) as a potential drug target in Giardia lamblia: Computational descriptions of binding sites

Background

The protozoan Giardia lamblia is the causal agent of giardiasis, one of the main diarrheal infections worldwide. Drug resistance to common antigiardial agents and incidence of treatment failures have increased in recent years. Therefore, the search for new molecular targets for drugs against Giardia infection is essential. In protozoa, ionic channels have roles in their life cycle, growth, and stress response. Thus, they are promising targets for drug design. The strategy of ligand-protein docking has demonstrated a great potential in the discovery of new targets and structure-based drug design studies.

Methods

In this work, we identify and characterize a new potassium channel, GiK, in the genome of Giardia lamblia. Characterization was performed in silico. Because its crystallographic structure remains unresolved, homology modeling was used to construct the three-dimensional model for the pore domain of GiK. The docking virtual screening approach was employed to determine whether GiK is a good target for potassium channel blockers.

Results

The GiK sequence showed 24–50% identity and 50–90% positivity with 21 different types of potassium channels. The quality assessment and validation parameters indicated the reliability of the modeled structure of GiK. We identified 110 potassium channel blockers exhibiting high affinity toward GiK. A total of 39 of these drugs bind in three specific regions.

Discussion

The GiK pore signature sequence is related to the small conductance calcium-activated potassium channels (SKCa). The predicted binding of 110 potassium blockers to GiK makes this protein an attractive target for biological testing to evaluate its role in the life cycle of Giardia lamblia and potential candidate for the design of novel antigiardial drugs.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

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.

Combination of cheminformatics and bioinformatics to explore the chemical basis of the rhizomes and aerial parts of Dioscorea nipponica Makino

OBJECTIVES

This study was aimed to explore the chemical basis of the rhizomes and aerial parts of Dioscorea nipponica Makino (DN).

METHODS

The pharmacokinetic profiles of the compounds from DN were calculated via ACD/I-Lab and PreADMET program. Their potential therapeutic and toxicity targets were screened through the DrugBank's or T3DB's ChemQuery structure search.

KEY FINDINGS

Eleven of 48 compounds in the rhizomes and over half of the compounds in the aerial parts had moderate or good human oral bioavailability. Twenty-three of 48 compounds in the rhizomes and 40/43 compounds from the aerial parts had moderate or good permeability to intestinal cells. Forty-three of 48 compounds from the rhizomes and 18/43 compounds in the aerial parts bound weakly to the plasma proteins. Eleven of 48 compounds in the rhizomes and 36/43 compounds of the aerial parts might pass across the blood-brain barrier. Forty-three 48 compounds in the rhizomes and 18/43 compounds from the aerial parts showed low renal excretion ability. The compounds in the rhizomes possessed 391 potential therapeutic targets and 216 potential toxicity targets. Additionally, the compounds from the aerial parts possessed 101 potential therapeutic targets and 183 potential toxicity targets.

CONCLUSIONS

These findings indicated that combination of cheminformatics and bioinformatics may facilitate achieving the objectives of this study.

Quality by design (QbD) approach of pharmacogenomics in drug designing and formulation development for optimization of drug delivery systems

Conventional approaches of drug discovery are very complex, costly and time consuming. But after the completion of human genome project, applications of pharmacogenomics in this area completely revolutionize the drug discovery and development process to produce a quality by design (QbD) approach based products. The applications of two areas of pharmacogenomics i.e. structural and functional pharmacogenomics excel the drug discovery process by employing genomic data in drug target identification and evaluation, lead optimization via high throughput screening, evaluation of drug metabolizing enzymes, drug transporters and drug receptors using computer aided technique and bioinformatics library data base. Pharmacogenomics also provides an important and reliable basis for evaluation and optimization of the dosage forms as well as repositioning of failed drugs for the treatment of new disease. Various dosage forms of category of drugs such as anticancer drugs, vaccines, gene and DNA delivery systems and immunological agents can be easily evaluated based on the genetic markers of the related disease. The effect of different formulation polymers on pharmacokinetic and pharmacodynamic properties of drugs can be assessed easily and therefore it plays an important role in formulation optimization. However, current applications of pharmacogenomics in drug discovery and formulation optimization are very limited because of costly and non accessible techniques for everyone, but in future, with the advancement in the technology; the application of genomic data in drug discovery will provide us with innovative, safer and more efficacious medicines.

Advances in identification and validation of protein targets of natural products without chemical modification

This review focuses on and reports case studies of the latest advances in target protein identification methods for label-free natural products. The integration of newly developed technologies will provide new insights and highlight the value of natural products for use as biological probes and new drug candidates.

Structural Dissection of Crotalicidin, a Rattlesnake Venom Cathelicidin, Retrieves a Fragment with Antimicrobial and Antitumor Activity

In silico dissection of crotalicidin (Ctn), a cathelicidin from a South American pit viper, yielded fragments Ctn[1-14] and Ctn[15-34], which were tested to ascertain to what extent they reproduced the structure and activity of the parent peptide. NMR data showing Ctn to be α-helical at the N-terminus and unstructured at the C-terminus were matched by similar data from the fragments. The peptides were tested against Gram-positive and -negative bacteria and for toxicity against both tumor and healthy cells. Despite its amphipathic α-helical structure, Ctn[1-14] was totally inert toward bacteria or eukaryotic cells. In contrast, unstructured Ctn[15-34] replicated the activity of parent Ctn against Gram-negative bacteria and tumor cells while being significantly less toxic toward eukaryotic cells. This selectivity for bacteria and tumor cells, plus a stability to serum well above that of Ctn, portrays Ctn[15-34] as an appealing candidate for further development as an anti-infective or antitumor lead.

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Computational systems biology approaches to anti-angiogenic cancer therapeutics

Angiogenesis is an exquisitely regulated process that is required for physiological processes and is also important in numerous diseases. Tumors utilize angiogenesis to generate the vascular network needed to supply the cancer cells with nutrients and oxygen, and many cancer drugs aim to inhibit tumor angiogenesis. Anti-angiogenic therapy involves inhibiting multiple cell types, molecular targets, and intracellular signaling pathways. Computational tools are useful in guiding treatment strategies, predicting the response to treatment, and identifying new targets of interest. Here, we describe progress that has been made in applying mathematical modeling and bioinformatics approaches to study anti-angiogenic therapeutics in cancer.

Determining common insertion sites based on retroviral insertion distribution across tumors

A CIS (common insertion site) indicates a genome region that is hit more frequently by retroviral insertions than expected by chance. Such a region is strongly related to cancer gene loci, which leads to the detection of cancer genes. An algorithm for detecting CISs should satisfy the following: (1) it does not require any prior knowledge of underlying insertion distribution; (2) it can resolve the insertion biases caused by hotspots; (3) it can detect CISs of any biological width; (4) it can identify noises resulting from statistic mistakes and non-CIS insertions; and (5) it can identify the widths of CISs as accurately as possible. We develop a method to resolve these difficulties. We verify a region's significance from two perspectives: distribution width and distribution depth. The former indicates how many insertions in a region while the latter evaluates the insertion distribution across the tumors in a region. We compare our method with kernel density estimation and sliding window on the simulated data, showing that our method not only identifies cancer-related insertions effectively, but also filters noises correctly. The experiments on the real data show that taking insertion distribution into account can highlight significant CISs. We detect 53 novel CISs, some of which have been proven correct by the biological literature.

Subdividing globally important zones based on data distribution across multiple genome fragments

In multiple genome fragments, a globally important mode is a zone represented by a significant change, where the change has a similar impact on every related fragment in the zone. This zone may represent the cancer related genes involved in diverse tumors. Globally important zones are characterized by two features: (1) there are more data points in globally important zones than in other areas of fragments; (2) the data points are distributed evenly on as many genome fragments as possible. Globally important zone mining needs to contain the following features: (1) independent of data distribution; (2) noise filtering; (3) pattern boundary identification; and (4) zone ranking. We have developed a hierarchical and density-based method, called GIZFinder (globally important zone finder), to detect and rank such zones based on two criteria: distribution width and distribution depth. The comparisons on the simulated data shows our method performs significantly better than the kernel framework and the sliding window. By experimenting on real cancer gene data, we identify 53 novel cancer genes, some of which have been proven correct.

The diversity and antimicrobial activity of Preussia sp. endophytes isolated from Australian dry rainforests

Limited knowledge currently exists regarding species diversity and antimicrobial activity of endophytic isolates of Preussia within Australia. This report describes endophytic Preussia species that were identified through molecular analysis of the internal transcribed spacer region. Screening for antimicrobial secondary metabolites was determined by testing crude ethyl acetate (EtOAc) extracts derived from fungal mycelia against a panel of ATCC type strains which included Bacillus cereus, Escherichia coli, Enterococcus faecalis, Pseudomonas aeruginosa, Serratia marcescens, methicillin-resistant Staphylococcus aureus (MRSA) and the opportunist yeast pathogen Candida albicans. Subsequently, high-performance liquid chromatography generated fractions of bioactive EtOAc extracts which were subject to confirmatory testing using the Clinical and Laboratory Standards Institute reference microdilution antimicrobial activity assay. A total of 18 Preussia were isolated from nine host plants with 6/18 having a <97 % sequence similarity to other known species in Genbank, suggesting that they are new species. In preliminary screening, 13/18 Preussia isolates revealed antimicrobial activity against at least one of the microbes tested, whilst 6/18 isolates, including 4/6 putative new species showed specific antimicrobial activity against MRSA and C. albicans. These results highlight the antimicrobial potential of Australian Preussia spp. and also the importance of Australian dry rainforests as an untapped repository of potentially significant bioactive compounds.

Ligand release mechanisms and channels in histone deacetylases

Exploring the molecular channels of class I histone deacetylases (HDACs) with buried active sites are important to understand their structures and functionalities. In this work, we perform hybrid classical molecular dynamics and random acceleration molecular dynamics simulations to explore the B3N [i.e., (4-(dimethylamino)N-[7(hydroxyamino)-7-oxoheptyle] benzamide)] exit channels in the x-ray crystal structures of HDAC3 and HDAC8 enzymes. Our simulations identify B3N release through four different channels in HDAC3 (denoted as A1, A2, B1, and B2) and HDAC8 (referred as A1, B1, B2, and B3) enzymes, among which egression through channel A1 is more predominant in both the enzymes. This mechanism is similar to ligand release in HDAC1 and HDAC2 described in our previous study and can be the fingerprint ligand release mechanisms in class I HDACs. Ligand release events through B channels, on the other hand, are different among HDAC3 and HDAC8, highlighting the significances of substituted residues in controlling the access to these channels This study reveals a novel aromatic gating mechanism elicited by TYR154-TRP141-TYR111 that controls the B3N access to all the B channels in HDAC8. The TRP141 in HDAC8 is substituted by LEU133 in HDAC3, which do not hinder the access to B channels in HDAC3. However, two hydrogen bonded barricades formed as ARG28-GLY297-GLY295-GLY131 and TRP129-ARG28-ALA130-LEU29-TRP129 obstruct the B3N from exploring the B channels in HDAC3. The structural and dynamical characterizations of molecular channels and ligand unbinding mechanisms reported in this study provide novel structural insights and atomic level perspectives on HDAC3 and HDAC8 enzymes, thereby potentially aiding in the design of more specific HDAC inhibitors.

Drug target central

BACKGROUND

One of the primary pillars of drug discovery is the drug target, its relationship to both the drugs designed against it and the biological processes in which it is involved. Here we review the informatics approaches required to build a complete catalogue of known drug targets.

OBJECTIVE

Using Pfizer's internal target database as a narrative, we review the steps involved in the construction of an integrated, enterprise target-informatics system. We consider how compiling the drug target universe requires integration across several resources such as competitor intelligence and pharmacological activity databases, as well as input from techniques such as text-mining. In particular, we address data standards and the complexities of representing targets in a structured ontology as well as opportunities for future development.

CONCLUSION

Drug target-orientated databases address important areas of drug discovery such as chemogenomics, drug/candidate repurposing and business intelligence. As research in industry and academia drives continued expansion of the druggable genome, it is crucial that such systems be maintained to provide an accurate picture of the landscape. This power of this information stretches beyond drug discovery and into the wider scientific community where small molecule tool compounds can enable the dissection of complex cellular pathways.

Rise and demise of bioinformatics? Promise and progress

The field of bioinformatics and computational biology has gone through a number of transformations during the past 15 years, establishing itself as a key component of new biology. This spectacular growth has been challenged by a number of disruptive changes in science and technology. Despite the apparent fatigue of the linguistic use of the term itself, bioinformatics has grown perhaps to a point beyond recognition. We explore both historical aspects and future trends and argue that as the field expands, key questions remain unanswered and acquire new meaning while at the same time the range of applications is widening to cover an ever increasing number of biological disciplines. These trends appear to be pointing to a redefinition of certain objectives, milestones, and possibly the field itself.

Molecular diagnosis using multi drug delivery network and stability

By using a pair of tweezers to generate the intense optical vortices within the PANDA ring resonator, the required molecules (drug volumes) can be trapped and moved dynamically within the molecular bus networks, in which the required diagnosis or drug delivery targets can be performed within the network. The advantage of the proposed system is that the proposed diagnostic method can perform within the tiny system (thin film device or circuit), which can be available for a human embedded device for diagnostic use. The channel spacing of the trapped volumes (molecules) within the bus molecular networks can be provided.

Reviewing ligand-based rational drug design: the search for an ATP synthase inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs.

Positron emission tomography imaging of cancer biology: current status and future prospects

Positron emission tomography (PET) is one of the most rapidly growing areas of medical imaging, with many applications in the clinical management of patients with cancer. The principal goal of PET imaging is to visualize, characterize, and measure biological processes at the cellular, subcellular, and molecular levels in living subjects using noninvasive procedures. PET imaging takes advantage of the traditional diagnostic imaging techniques and introduces positron-emitting probes to determine the expression of indicative molecular targets at different stages of cancer progression. Although [(18)F]fluorodeoxyglucose ([(18)F]FDG)-PET has been widely utilized for staging and restaging of cancer, evaluation of response to treatment, differentiation of post-therapy alterations from residual or recurrent tumor, and assessment of prognosis, [(18)F]FDG is not a target-specific PET tracer. Over the last decade, numerous target-specific PET tracers have been developed and evaluated in preclinical and clinical studies. This review provides an overview of the current status and trends in the development of non-[(18)F]FDG PET probes in oncology and their application in the investigation of cancer biology.

Design and development of molecular imaging probes

Molecular imaging, the visualization, characterization and measurement of biological processes at the cellular, subcellular level, or even molecular level in living subjects, has rapidly gained importance in the dawning era of personalized medicine. Molecular imaging takes advantage of the traditional diagnostic imaging techniques and introduces molecular imaging probes to determine the expression of indicative molecular markers at different stages of diseases and disorders. As a key component of molecular imaging, molecular imaging probe must be able to specifically reach the target of interest in vivo while retaining long enough to be detected. A desirable molecular imaging probe with clinical translation potential is expected to have unique characteristics. Therefore, design and development of molecular imaging probe is frequently a challenging endeavor for medicinal chemists. This review summarizes the general principles of molecular imaging probe design and some fundamental strategies of molecular imaging probe development with a number of illustrative examples.

Target-specific delivery of peptide-based probes for PET imaging

Positron emission tomography (PET) is one of the most rapidly growing areas of medical imaging, with many applications in the clinical management of patients with various diseases. The principal goal of PET imaging is to visualize, characterize, and measure biological processes at the cellular, subcellular, and molecular level in living subjects with non-invasive procedures. PET imaging takes advantage of the traditional diagnostic imaging techniques and introduces positron-emitting probes to determine the expression of indicative molecular targets at different stages of disease. During the last decade, advances in molecular biology have revealed an increasing number of potential molecular targets, including peptide receptors and peptide-related biomolecules. With the help of sophisticated bioconjugation and radiolabeling techniques, numerous peptide-based agents have been developed and evaluated for delivery of PET radionuclides to the specific molecular targets in preclinical and clinical studies. As compared to macromolecules, such as proteins or antibodies, low-molecular-weight peptides have their distinctive advantages and predominantly demonstrate their favorable pharmacokinetics for in vivo PET applications. This review summarizes the criteria of peptide-based PET probes design, the selection of radioisotopes, labeling methods, and provides an overview of the current status and trends in the development of target-specific peptide-based probes with respect to their unique PET imaging applications.

Exploring the ncRNA-ncRNA patterns based on bridging rules

ncRNAs play an important role in the regulation of gene expression. However, many of their functions have not yet been fully discovered. There are complicated relationships between ncRNAs in different categories. Finding these relationships can contribute to identify ncRNAs' functions and properties. We extend the association rule to represent the relationship between two ncRNAs. Based on this rule, we can speculate the ncRNA's function when it interacts with other ncRNAs. We propose two measures to explore the relationships between ncRNAs in different categories. Entropy theory is to calculate how close two ncRNAs are. Association rule is to represent the interactions between ncRNAs. We use three datasets from miRBase and RNAdb. Two from miRBase are designed for finding relationships between miRNAs; the other from RNAdb is designed for relationships among miRNA, snoRNA and piRNA. We evaluate our measures from both biological significance and performance perspectives. All the cross-species patterns regarding miRNA that we found are proven correct using miRNAMap 2.0. In addition, we find novel cross-genomes patterns such as (hsa-mir-190b-->hsa-mir-153-2). According to the patterns we find, we can (1) explore one ncRNA's function from another with known function and (2) speculate the functions of both of them based on the relationship even we do no understand either of them. Our methods' merits also include: (1) they are suitable for any ncRNA datasets and (2) they are not sensitive to the parameters.

Is it time to re-prioritize neuroimaging databases and digital repositories?

The development of in vivo brain imaging has lead to the collection of large quantities of digital information. In any individual research article, several tens of gigabytes-worth of data may be represented-collected across normal and patient samples. With the ease of collecting such data, there is increased desire for brain imaging datasets to be openly shared through sophisticated databases. However, very often the raw and pre-processed versions of these data are not available to researchers outside of the team that collected them. A range of neuroimaging databasing approaches has streamlined the transmission, storage, and dissemination of data from such brain imaging studies. Though early sociological and technical concerns have been addressed, they have not been ameliorated altogether for many in the field. In this article, we review the progress made in neuroimaging databases, their role in data sharing, data management, potential for the construction of brain atlases, recording data provenance, and value for re-analysis, new publication, and training. We feature the LONI IDA as an example of an archive being used as a source for brain atlas workflow construction, list several instances of other successful uses of image databases, and comment on archive sustainability. Finally, we suggest that, given these developments, now is the time for the neuroimaging community to re-prioritize large-scale databases as a valuable component of brain imaging science.

Elucidation of the molecular mechanisms of a salicylhydrazide class of compounds by proteomic analysis

Previously, we described a series of salicylhydrazide compounds with potent anti-cancer activities against a panel of human cancer cell lines derived from different origins. Preclinical evaluation showing efficacy both in vitro and in vivo in human cancer models indicated that these agents may represent a promising class of anticancer drugs. In the present study, we performed an in-depth investigation on the underlying molecular mechanisms of the most potent compounds, SC21 and SC23, using a proteomic method and bioinformatics tools. We demonstrated that SC23 induced apoptosis through multiple signaling pathways. In particular, SC23 regulated the expression of Bcl-2, p21, acetylated histone H3 and beta-tubulin and the combined modulation of these proteins may result in the induction of apoptosis. We also examined the effect of SC21 and SC23 on cell cycle progression and found that both compounds arrested cells in S-phase in most cell lines tested. To better understand the signaling networks involved, we analyzed the SC21- and SC23-treated cell lysates by the Kinexus 628 antibody microarray. The results were interpreted with the aid of Ingenuity Pathway Analysis (IPA) software. It was found that SC21 interfered with JAK/STAT signaling and elicited apoptosis through Fas and caspases pathways. Unlike SC21, SC23 induced RAR activation and caused cell cycle arrest. The signaling networks identified by this work may provide the basis for future mechanistic studies. The validation of the proposed pathways and the elucidation of the signaling cross-talk are currently under way.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.