Progress and problems in the exploration of therapeutic targets

An updated review of YAP: A promising therapeutic target against cardiac aging?

The transcriptional co-activator Yes-associated protein (YAP) functions as a downstream effector of the Hippo signaling pathway and plays a crucial role in cardiomyocyte survival. In its non-phosphorylated activated state, YAP binds to transcription factors, activating the transcription of downstream target genes. It also regulates cell proliferation and survival by selectively binding to enhancers and activating target genes. However, the upregulation of the Hippo pathway in human heart failure inhibits cardiac regeneration and disrupts astrogenesis, thus preventing the nuclear translocation of YAP. Existing literature indicates that the Hippo/YAP axis contributes to inflammation and fibrosis, potentially playing a role in the development of cardiac, vascular and renal injuries. Moreover, it is a key mediator of myofibroblast differentiation and fibrosis in the infarcted heart. Given these insights, can we harness YAP's regenerative potential in a targeted manner? In this review, we provide a detailed discussion of the Hippo signaling pathway and consolidate concepts for the development and intervention of cardiac anti-aging drugs to leverage YAP signaling as a pivotal target.

Do Nanodisc Assembly Conditions Affect Natural Lipid Uptake?

Lipids play critical roles in modulating membrane protein structure, interactions, and activity. Nanodiscs provide a tunable membrane mimetic that can model these endogenous protein-lipid interactions in a nanoscale lipid bilayer. However, most studies of membrane proteins with nanodiscs use simple synthetic lipids that lack the headgroup and fatty acyl diversity of natural extracts. Prior research has successfully used natural lipid extracts in nanodiscs that more accurately mimic natural environments, but it is not clear how nanodisc assembly may bias the incorporated lipid profiles. Here, we applied lipidomics to investigate how nanodisc assembly conditions affect the profile of natural lipids in nanodiscs. Specifically, we tested the effects of assembly temperature, nanodisc size, and lipidome extract complexity. Globally, our analysis demonstrates that the lipids profiles are largely unaffected by nanodisc assembly conditions. However, a few notable changes emerged within individual lipids and lipid classes, such as a differential incorporation of cardiolipin and phosphatidylglycerol lipids from the E. coli polar lipid extract at different temperatures. Conversely, some classes of brain lipids were affected by nanodisc size at higher temperatures. Collectively, these data enable the application of nanodiscs to study protein-lipid interactions in complex lipid environments.

Cationic Pd(IV)-Induced Highly Diastereoselective Arylative Cascade Cyclization of Allene-Tethered Cyclohexadienones Leading to Oxygenated Bicyclic Motifs

A cationic Pd(IV)-catalyzed arylative hydroxylation-Micheal addition of allenyl-tethered cyclohexadienones was developed. This relay reaction could afford highly diastereoselective various functionalized arylative 1,4-dioxane cis-bicyclic structural units with good to high yields. The striking features revealed from these studies is the necessity of Selectfluor and the oxidative hydroxylation originating from water initiated by F-Pd(IV) catalysis. A plausible mechanism was also proposed for this variant observation.

Structural characterization of styrene-maleic acid copolymer-lipid nanoparticles (SMALPs) using EPR spectroscopy

Spectroscopic studies of membrane proteins (MPs) are challenging due to difficulties in preparing homogenous and functional lipid membrane mimetic systems into which membrane proteins can properly fold and function. It has recently been shown that styrene-maleic acid (SMA) copolymers act as a macromolecular surfactant and therefore facilitate the formation of disk-shaped lipid bilayer nanoparticles (styrene-maleic acid copolymer-lipid nanoparticles (SMALPs)) that retain structural characteristics of native lipid membranes. We have previously reported controlled synthesis of SMA block copolymers using reversible addition-fragmentation chain transfer (RAFT) polymerization, and that alteration of the weight ratio of styrene to maleic acid affects nanoparticle size. RAFT-synthesis offers superior control over SMA polymer architecture compared to conventional radical polymerization techniques used for commercially available SMA. However, the interactions between the lipid bilayer and the solubilized RAFT-synthesized SMA polymer are currently not fully understood. In this study, EPR spectroscopy was used to detect the perturbation on the acyl chain upon introduction of the RAFT-synthesized SMA polymer by attaching PC-based nitroxide spin labels to the 5^th, 12^th, and 16^th positions along the acyl chain of the lipid bilayer. EPR spectra showed high rigidity at the 12^th position compared to the other two regions, displaying similar qualities to commercially available polymers synthesized via conventional methods. In addition, central EPR linewidths and correlation time data were obtained that are consistent with previous findings.

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs

OBJECTIVE

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin).

MATERIALS AND METHODS

During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (EVHD) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server.

RESULTS

Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively.

CONCLUSION

Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Comparison of FDA Approved Kinase Targets to Clinical Trial Ones: Insights from Their System Profiles and Drug-Target Interaction Networks

Kinase is one of the most productive classes of established targets, but the majority of approved drugs against kinase were developed only for cancer. Intensive efforts were therefore exerted for releasing its therapeutic potential by discovering new therapeutic area. Kinases in clinical trial could provide great opportunities for treating various diseases. However, no systematic comparison between system profiles of established targets and those of clinical trial ones was conducted. The reveal of probable difference or shift of trend would help to identify key factors defining druggability of established targets. In this study, a comparative analysis of system profiles of both types of targets was conducted. Consequently, the systems profiles of the majority of clinical trial kinases were identified to be very similar to those of established ones, but percentages of established targets obeying the system profiles appeared to be slightly but consistently higher than those of clinical trial targets. Moreover, a shift of trend in the system profiles from the clinical trial to the established targets was identified, and popular kinase targets were discovered. In sum, this comparative study may help to facilitate the identification of the druggability of established drug targets by their system profiles and drug-target interaction networks.

Lipodisks integrated with weak affinity chromatography enable fragment screening of integral membrane proteins

Membrane proteins constitute the largest class of drug targets but they present many challenges in drug discovery.

Development of electron spin echo envelope modulation spectroscopy to probe the secondary structure of recombinant membrane proteins in a lipid bilayer

Membrane proteins conduct many important biological functions essential to the survival of organisms. However, due to their inherent hydrophobic nature, it is very difficult to obtain structural information on membrane-bound proteins using traditional biophysical techniques. We are developing a new approach to probe the secondary structure of membrane proteins using the pulsed EPR technique of Electron Spin Echo Envelope Modulation (ESEEM) Spectroscopy. This method has been successfully applied to model peptides made synthetically. However, in order for this ESEEM technique to be widely applicable to larger membrane protein systems with no size limitations, protein samples with deuterated residues need to be prepared via protein expression methods. For the first time, this study shows that the ESEEM approach can be used to probe the local secondary structure of a (2) H-labeled d8 -Val overexpressed membrane protein in a membrane mimetic environment. The membrane-bound human KCNE1 protein was used with a known solution NMR structure to demonstrate the applicability of this methodology. Three different α-helical regions of KCNE1 were probed: the extracellular domain (Val21), transmembrane domain (Val50), and cytoplasmic domain (Val95). These results indicated α-helical structures in all three segments, consistent with the micelle structure of KCNE1. Furthermore, KCNE1 was incorporated into a lipid bilayer and the secondary structure of the transmembrane domain (Val50) was shown to be α-helical in a more native-like environment. This study extends the application of this ESEEM approach to much larger membrane protein systems that are difficult to study with X-ray crystallography and/or NMR spectroscopy.

Drug-target interaction prediction: databases, web servers and computational models

Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

Production of cell surface and secreted glycoproteins in mammalian cells

Mammalian protein expression systems are becoming increasingly popular for the production of eukaryotic secreted and cell surface proteins. Here we describe methods to produce recombinant proteins in adherent or suspension human embryonic kidney cell cultures, using transient transfection or stable cell lines. The protocols are easy to scale up and cost-efficient, making them suitable for protein crystallization projects and other applications that require high protein yields.

Determining the Plasmodium vivax VCG-1 strain blood stage proteome

Plasmodium vivax is the second most prevalent parasite species causing malaria in humans living in tropical and subtropical areas throughout the world. There have been few P. vivax proteomic studies to date and they have focused on using clinical isolates, given the technical difficulties concerning how to maintain an in vitro culture of this species. This study was thus focused on identifying the P. vivax VCG-1 strain proteome during its blood lifecycle through LC-MS/MS; this led to identifying 734 proteins, thus increasing the overall number reported for P. vivax to date. Some of them have previously been related to reticulocyte invasion, parasite virulence and growth and others are new molecules possibly playing a functional role during metabolic processes, as predicted by Database for Annotation, Visualization and Integrated Discovery (DAVID) functional analysis. This is the first large-scale proteomic analysis of a P. vivax strain adapted to a non-human primate model showing the parasite protein repertoire during the blood lifecycle. Database searches facilitated the in silico prediction of proteins proposed for evaluation in further experimental assays regarding their potential as pharmacologic targets or as component of a totally efficient vaccine against malaria caused by P. vivax.

BIOLOGICAL SIGNIFICANCE

P. vivax malaria continues being a public health problem around world. Although considerable progress has been made in understanding genome- and transcriptome-related P. vivax biology, there are few proteome studies, currently representing only 8.5% of the predicted in silico proteome reported in public databases. A high-throughput proteomic assay was used for discovering new P. vivax intra-reticulocyte asexual stage molecules taken from parasites maintained in vivo in a primate model. The methodology avoided the main problem related to standardising an in vitro culture system to obtain enough samples for protein identification and annotation. This study provides a source of potential information contributing towards a basic understanding of P. vivax biology related to parasite proteins which are of significant importance for the malaria research community.

Development of fragment-based n-FABS NMR screening applied to the membrane enzyme FAAH

Despite the recognized importance of membrane proteins as pharmaceutical targets, the reliable identification of fragment hits that are able to bind these proteins is still a major challenge. Among different ¹⁹F NMR spectroscopic methods, n-fluorine atoms for biochemical screening (n-FABS) is a highly sensitive technique that has been used efficiently for fragment screening, but its application for membrane enzymes has not been reported yet. Herein, we present the first successful application of n-FABS to the discovery of novel fragment hits, targeting the membrane-bound enzyme fatty acid amide hydrolase (FAAH), using a library of fluorinated fragments generated based on the different local environment of fluorine concept. The use of the recombinant fusion protein MBP-FAAH and the design of compound 11 as a suitable novel fluorinated substrate analogue allowed n-FABS screening to be efficiently performed using a very small amount of enzyme. Notably, we have identified 19 novel fragment hits that inhibit FAAH with a median effective concentration (IC₅₀) in the low mM-μM range. To the best of our knowledge, these results represent the first application of a ¹⁹F NMR fragment-based functional assay to a membrane protein.

Proteomic and bioinformatic analysis of membrane proteome in type 2 diabetic mouse liver

Type 2 diabetes mellitus (T2DM) is the most prevalent and serious metabolic disease affecting people worldwide. T2DM results from insulin resistance of the liver, muscle, and adipose tissue. In this study, we used proteomic and bioinformatic methodologies to identify novel hepatic membrane proteins that are related to the development of hepatic insulin resistance, steatosis, and T2DM. Using FT-ICR MS, we identified 95 significantly differentially expressed proteins in the membrane fraction of normal and T2DM db/db mouse liver. These proteins are primarily involved in energy metabolism pathways, molecular transport, and cellular signaling, and many of them have not previously been reported in diabetic studies. Bioinformatic analysis revealed that 16 proteins may be related to the regulation of insulin signaling in the liver. In addition, six proteins are associated with energy stress-induced, nine proteins with inflammatory stress-induced, and 14 proteins with endoplasmic reticulum stress-induced hepatic insulin resistance. Moreover, we identified 19 proteins that may regulate hepatic insulin resistance in a c-Jun amino-terminal kinase-dependent manner. In addition, three proteins, 14-3-3 protein beta (YWHAB), Slc2a4 (GLUT4), and Dlg4 (PSD-95), are discovered by comprehensive bioinformatic analysis, which have correlations with several proteins identified by proteomics approach. The newly identified proteins in T2DM should provide additional insight into the development and pathophysiology of hepatic steatosis and insulin resistance, and they may serve as useful diagnostic markers and/or therapeutic targets for these diseases.

Current understanding of TRPM7 pharmacology and drug development for stroke

The initial excitement and countless efforts to find a pharmacological agent that disrupts the excitotoxic pathway of ischemic neuronal death have only led to disappointing clinical trials. Currently, a thrombolytic agent called recombinant tissue plasminogen activator (rt-PA) is the only pharmacological treatment available for patients with acute ischemic stroke in most countries. Even though its efficacy has been confirmed repeatedly, rt-PA is considerably underused due to reasons including a short therapeutic window and repeated complications associated with its use. A search for alternative mechanisms that may operate dependently or independently with the well-established excitotoxic mechanism has led researchers to the discovery of newly described non-glutamate mechanisms. Among the latter, transient receptor potential melastatin 7 (TRPM7) is one of the important nonglutamate mechanisms in stroke, which has been evaluated in both in-vitro and in-vivo. In this review, we will discuss the current state of pharmacological treatments of ischemic stroke and provide evidence that TRPM7 is a promising therapeutic target of stroke.

Characterizing protein domain associations by Small-molecule ligand binding

BACKGROUND

Protein domains are evolutionarily conserved building blocks for protein structure and function, which are conventionally identified based on protein sequence or structure similarity. Small molecule binding domains are of great importance for the recognition of small molecules in biological systems and drug development. Many small molecules, including drugs, have been increasingly identified to bind to multiple targets, leading to promiscuous interactions with protein domains. Thus, a large scale characterization of the protein domains and their associations with respect to small-molecule binding is of particular interest to system biology research, drug target identification, as well as drug repurposing.

METHODS

We compiled a collection of 13,822 physical interactions of small molecules and protein domains derived from the Protein Data Bank (PDB) structures. Based on the chemical similarity of these small molecules, we characterized pairwise associations of the protein domains and further investigated their global associations from a network point of view.

RESULTS

We found that protein domains, despite lack of similarity in sequence and structure, were comprehensively associated through binding the same or similar small-molecule ligands. Moreover, we identified modules in the domain network that consisted of closely related protein domains by sharing similar biochemical mechanisms, being involved in relevant biological pathways, or being regulated by the same cognate cofactors.

CONCLUSIONS

A novel protein domain relationship was identified in the context of small-molecule binding, which is complementary to those identified by traditional sequence-based or structure-based approaches. The protein domain network constructed in the present study provides a novel perspective for chemogenomic study and network pharmacology, as well as target identification for drug repurposing.

Gaining insight into membrane protein structure using isotope-edited FTIR

FTIR spectroscopy has long been used as a tool used to gain average structural information on proteins. With the advent of stable isotope editing, FTIR can be used to derive accurate information on isolated amino acids. In particular, in an anisotropic sample such as membrane layers, it is possible to measure the orientation of the peptidic carbonyl groups. Herein, we review the theory that enables one to obtain accurate restraints from FTIR spectroscopy, alongside considerations for sample suitability and general applicability. We also propose approaches that may be used to generate structural models of simple membrane proteins based on FTIR orientational restraints. This article is part of a Special Issue entitled: FTIR in membrane proteins and peptide studies.

Computational discovery of picomolar Q(o) site inhibitors of cytochrome bc1 complex

A critical challenge to the fragment-based drug discovery (FBDD) is its low-throughput nature due to the necessity of biophysical method-based fragment screening. Herein, a method of pharmacophore-linked fragment virtual screening (PFVS) was successfully developed. Its application yielded the first picomolar-range Q(o) site inhibitors of the cytochrome bc(1) complex, an important membrane protein for drug and fungicide discovery. Compared with the original hit compound 4 (K(i) = 881.80 nM, porcine bc(1)), the most potent compound 4f displayed 20 507-fold improved binding affinity (K(i) = 43.00 pM). Compound 4f was proved to be a noncompetitive inhibitor with respect to the substrate cytochrome c, but a competitive inhibitor with respect to the substrate ubiquinol. Additionally, we determined the crystal structure of compound 4e (K(i) = 83.00 pM) bound to the chicken bc(1) at 2.70 Å resolution, providing a molecular basis for understanding its ultrapotency. To our knowledge, this study is the first application of the FBDD method in the discovery of picomolar inhibitors of a membrane protein. This work demonstrates that the novel PFVS approach is a high-throughput drug discovery method, independent of biophysical screening techniques.

Combining global and local measures for structure-based druggability predictions

Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction.

Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery

Knowledge and investigation of therapeutic targets (responsible for drug efficacy) and the targeted drugs facilitate target and drug discovery and validation. Therapeutic Target Database (TTD, http://bidd.nus.edu.sg/group/ttd/ttd.asp) has been developed to provide comprehensive information about efficacy targets and the corresponding approved, clinical trial and investigative drugs. Since its last update, major improvements and updates have been made to TTD. In addition to the significant increase of data content (from 1894 targets and 5028 drugs to 2025 targets and 17,816 drugs), we added target validation information (drug potency against target, effect against disease models and effect of target knockout, knockdown or genetic variations) for 932 targets, and 841 quantitative structure activity relationship models for active compounds of 228 chemical types against 121 targets. Moreover, we added the data from our previous drug studies including 3681 multi-target agents against 108 target pairs, 116 drug combinations with their synergistic, additive, antagonistic, potentiative or reductive mechanisms, 1427 natural product-derived approved, clinical trial and pre-clinical drugs and cross-links to the clinical trial information page in the ClinicalTrials.gov database for 770 clinical trial drugs. These updates are useful for facilitating target discovery and validation, drug lead discovery and optimization, and the development of multi-target drugs and drug combinations.

TRPM7 in cerebral ischemia and potential target for drug development in stroke

Searching for effective pharmacological agents for stroke treatment has largely been unsuccessful. Despite initial excitement, antagonists for glutamate receptors, the most studied receptor channels in ischemic stroke, have shown insufficient neuroprotective effects in clinical trials. Outside the traditional glutamate-mediated excitotoxicity, recent evidence suggests few non-glutamate mechanisms, which may also cause ionic imbalance and cell death in cerebral ischemia. Transient receptor potential melastatin 7 (TRPM7) is a Ca(2+) permeable, non-selective cation channel that has recently gained attention as a potential cation influx pathway involved in ischemic events. Compelling new evidence from an in vivo study demonstrated that suppression of TRPM7 channels in adult rat brain in vivo using virally mediated gene silencing approach reduced delayed neuronal cell death and preserved neuronal functions in global cerebral ischemia. In this review, we will discuss the current understanding of the role of TRPM7 channels in physiology and pathophysiology as well as its therapeutic potential in stroke.

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.

Application of fragment-based drug discovery to membrane proteins: identification of ligands of the integral membrane enzyme DsbB

Membrane proteins are important pharmaceutical targets, but they pose significant challenges for fragment-based drug discovery approaches. Here, we present the first successful use of biophysical methods to screen for fragment ligands to an integral membrane protein. The Escherichia coli inner membrane protein DsbB was solubilized in detergent micelles and lipid bilayer nanodiscs. The solubilized protein was immobilized with retention of functionality and used to screen 1071 drug fragments for binding using target immobilized NMR Screening. Biochemical and biophysical validation of the eight most potent hits revealed an IC(50) range of 7-200 microM. The ability to insert a broad array of membrane proteins into nanodiscs, combined with the efficiency of TINS, demonstrates the feasibility of finding fragments targeting membrane proteins.

Integrated bioinformatics analysis for cancer target identification

The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work.

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.

What are next generation innovative therapeutic targets? Clues from genetic, structural, physicochemical, and systems profiles of successful targets

Low target discovery rate has been linked to inadequate consideration of multiple factors that collectively contribute to druggability. These factors include sequence, structural, physicochemical, and systems profiles. Methods individually exploring each of these profiles for target identification have been developed, but they have not been collectively used. We evaluated the collective capability of these methods in identifying promising targets from 1019 research targets based on the multiple profiles of up to 348 successful targets. The collective method combining at least three profiles identified 50, 25, 10, and 4% of the 30, 84, 41, and 864 phase III, II, I, and nonclinical trial targets as promising, including eight to nine targets of positive phase III results. This method dropped 89% of the 19 discontinued clinical trial targets and 97% of the 65 targets failed in high-throughput screening or knockout studies. Collective consideration of multiple profiles demonstrated promising potential in identifying innovative targets.

Drugs and their molecular targets: an updated overview

About 330 targets bind approved drugs, 270 encoded by the human genome and 60 belonging to pathogenic organisms. A large number of druggable targets have been recently proposed from preclinical and first clinical data, but a huge reservoir of putative drug targets, possibly several thousands, remains to be explored. This overview considers the different types of ligands and their selectivity in the main superfamilies of drug targets, enzymes, membrane transporters and ion channels, and the various classes of membrane and nuclear receptors with their signalling pathway. Recently approved drugs such as monoclonal antibodies, tyrosine kinase and proteasome inhibitors, and major drugs under clinical studies are reviewed with their molecular target and therapeutic interest. The druggability of emerging targets is discussed, such as multidrug resistance transporters and cystic fibrosis transmembrane conductance regulator (CFTR), hyperpolarization-activated cyclic nucleotides-gated (HCN), cyclic nucleotide-gated (CNG) and transient receptor potential (TRP) ion channels, tumour necrosis factor (TNF) and receptor activator of NFkappaB (RANK) receptors, integrins, and orphan or recently deorphanized G-protein-coupled and nuclear receptors. Large advances have been made in the therapeutical use of recombinant cytokines and growth factors (i.e. tasonermin, TNFalpha-1a; becaplermin, platelet-derived growth factor (PDGF); dibotermin-alpha, bone morphogenetic proteins (BMP)2; anakinra, interleukin-1 receptor antagonist protein (IRAP), and in enzyme replacement therapy, i.e. algasidase (alpha-galactosidase) and laronidase (alpha-l-iduronidase). New receptor classes are emerging, e.g. membrane aminopeptidases, and novel concepts are stimulating drug research, e.g. epigenetic therapy, but the molecular target of some approved drugs, such as paracetamol and imidazolines, still need to be identified.

Quantitative systems-level determinants of human genes targeted by successful drugs

What makes a successful drug target? A target molecule with an appropriate (druggable) tertiary structure is a necessary but not the sufficient condition for success. Here we analyzed specific properties of human genes and proteins targeted by 919 FDA-approved drugs and identified several quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected (but far from being the most highly connected), have higher betweenness values, lower entropies of tissue expression, and lower ratios of nonsynonymous to synonymous single-nucleotide polymorphisms. Furthermore, we have identified human tissues that are significantly over- or undertargeted relative to the full spectrum of genes that are active in each tissue. Our study provides quantitative guidelines that could aid in the computational screening of new drug targets in human cells.

Quantitative analysis on the characteristics of targets with FDA approved drugs

Accumulated knowledge of genomic information, systems biology, and disease mechanisms provide an unprecedented opportunity to elucidate the genetic basis of diseases, and to discover new and novel therapeutic targets from the wealth of genomic data. With hundreds to a few thousand potential targets available in the human genome alone, target selection and validation has become a critical component of drug discovery process. The explorations on quantitative characteristics of the currently explored targets (those without any marketed drug) and successful targets (targeted by at least one marketed drug) could help discern simple rules for selecting a putative successful target. Here we use integrative in silico (computational) approaches to quantitatively analyze the characteristics of 133 targets with FDA approved drugs and 3120 human disease genes (therapeutic targets) not targeted by FDA approved drugs. This is the first attempt to comparatively analyze targets with FDA approved drugs and targets with no FDA approved drug or no drugs available for them. Our results show that proteins with 5 or fewer number of homologs outside their own family, proteins with single-exon gene architecture and proteins interacting with more than 3 partners are more likely to be targetable. These quantitative characteristics could serve as criteria to search for promising targetable disease genes.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

How many drug targets are there?

For the past decade, the number of molecular targets for approved drugs has been debated. Here, we reconcile apparently contradictory previous reports into a comprehensive survey, and propose a consensus number of current drug targets for all classes of approved therapeutic drugs. One striking feature is the relatively constant historical rate of target innovation (the rate at which drugs against new targets are launched); however, the rate of developing drugs against new families is significantly lower. The recent approval of drugs that target protein kinases highlights two additional trends: an emerging realization of the importance of polypharmacology, and also the power of a gene-family-led approach in generating novel and important therapies.