Chemical-protein interactome and its application in off-target identification

Schizandrin A induces non-small cell lung cancer apoptosis by suppressing the epidermal growth factor receptor activation

OBJECTIVE

The purpose of this study is to explore the biological mechanism of Schizandrin A (SchA) inducing non-small cell lung cancer (NSCLC) apoptosis.

METHODS

The reverse molecular docking tool "Swiss Target Prediction" was used to predict the targets of SchA. Protein-protein interaction analysis was performed on potential targets using the String database. Functional enrichment analyses of potential targets were performed with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. The conformation of SchA binding to target was simulated by chemical-protein interactomics and molecular docking. The effect of SchA on the expression and phosphorylation level of EGFR was detected by Western blot. Lipofectamine 3000 and EGFR plasmids were used to overexpress EGFR. Apoptosis was tested with Annexin V-FITC and propidium iodide staining, and cell cycle was detected by propidium iodide staining.

RESULTS

The "Swiss Target Prediction" database predicted 112 and 111 targets based on the 2D and 3D structures of SchA, respectively, of which kinases accounted for the most, accounting for 24%. Protein interaction network analyses showed that molecular targets such as ERBB family and SRC were at the center of the network. Functional enrichment analyses indicated that ERBB-related signaling pathways were enriched. Compound-protein interactomics and molecular docking revealed that SchA could bind to the ATP-active pocket of the EGFR tyrosine kinase domain. Laboratory results showed that SchA inhibited the phosphorylation of EGFR. Insulin could counteract the cytotoxic effect of SchA. EGFR overexpression and excess EGF or IGF-1 had limited impacts on the cytotoxicity of SchA.

CONCLUSIONS

Network pharmacology analyses suggested that ERBB family members may be the targets of SchA. SchA can inhibit NSCLC at least in part by inhibiting EGFR phosphorylation, and activating the EGFR bypass can neutralize the cytotoxicity of SchA.

Rational Construction of a Novel Bioluminescent Substrate for Sensing the Tumor-Associated Hydrolase Notum

Notum, one of the key serine hydrolases in mammals, hydrolyzes the palmitoleoyl moieties of many important proteins and modulates multiple signaling pathways including Wnt/β-catenin signaling. Notum is tightly associated with multiple human diseases, but the reliable and practical tools for sensing Notum activities in complex biological systems are rarely reported. Herein, an efficient strategy was used to rationally construct a specific bioluminescent substrate for Notum. Following computer-aided molecular design and experimental verification, octanoyl luciferin (OL) was selected as the optimum substrate for human Notum, with excellent specificity, high detection sensitivity and high signal-to-noise ratio. Under physiological conditions, OL was readily hydrolyzed by Notum or Notum-containing biological specimens to release d-luciferin that could be easily detected by various fluorescence devices in the presence of luciferase. The applicability of OL for real-time sensing native Notum was examined in living cells, extracellular matrix, and tissue preparations. OL was also used for constructing a high-throughput assay for screening of Notum inhibitors, while a natural compound (bergapten) was newly identified as a potent Notum inhibitor. Collectively, this study devises a reliable and easy-to-use tool for sensing Notum activities in biological systems, which will strongly facilitate hNotum-associated fundamental studies, disease diagnosis, and drug discovery.

A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded "objective" signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method

Human immunodeficiency virus type-1 and hepatitis C virus (HIV/HCV) coinfection occurs when a patient is simultaneously infected with both human immunodeficiency virus type-1 (HIV-1) and hepatitis C virus (HCV), which is common today in certain populations. However, the treatment of coinfection is a challenge because of the special considerations needed to ensure hepatic safety and avoid drug–drug interactions. Multitarget inhibitors with less toxicity may provide a promising therapeutic strategy for HIV/HCV coinfection. However, the identification of one molecule that acts on multiple targets simultaneously by experimental evaluation is costly and time-consuming. In silico target prediction tools provide more opportunities for the development of multitarget inhibitors. In this study, by combining Naïve Bayes (NB) and support vector machine (SVM) algorithms with two types of molecular fingerprints, MACCS and extended connectivity fingerprints 6 (ECFP6), 60 classification models were constructed to predict compounds that were active against 11 HIV-1 targets and four HCV targets based on a multiple quantitative structure–activity relationships (multiple QSAR) method. Five-fold cross-validation and test set validation were performed to measure the performance of the 60 classification models. Our results show that the 60 multiple QSAR models appeared to have high classification accuracy in terms of the area under the ROC curve (AUC) values, which ranged from 0.83 to 1 with a mean value of 0.97 for the HIV-1 models and from 0.84 to 1 with a mean value of 0.96 for the HCV models. Furthermore, the 60 models were used to comprehensively predict the potential targets of an additional 46 compounds, including 27 approved HIV-1 drugs, 10 approved HCV drugs and nine selected compounds known to be active against one or more targets of HIV-1 or HCV. Finally, 20 hits, including seven approved HIV-1 drugs, four approved HCV drugs, and nine other compounds, were predicted to be HIV/HCV coinfection multitarget inhibitors. The reported bioactivity data confirmed that seven out of nine compounds actually interacted with HIV-1 and HCV targets simultaneously with diverse binding affinities. The remaining predicted hits and chemical-protein interaction pairs with the potential ability to suppress HIV/HCV coinfection are worthy of further experimental investigation. This investigation shows that the multiple QSAR method is useful in predicting chemical-protein interactions for the discovery of multitarget inhibitors and provides a unique strategy for the treatment of HIV/HCV coinfection.

A Computational Approach Using Bioinformatics to Screening Drug Targets for Leishmania infantum Species

Background

The development of new therapeutic strategies to treat patients for leishmaniasis has become a priority. The antileishmanial activity of the strychnobiflavone flavonoid was recently demonstrated against Leishmania amazonensis and Leishmania infantum amastigotes and promastigotes. The biological effect of this molecule was identified due to its capacity to interfere in the parasite mitochondrial membrane; however, the underlying molecular mechanism remains unclear.

Methods and Results

In this study, a computational approach using bioinformatics was performed to screen biological targets of strychnobiflavone in L. infantum. Computational programs, such as the target fishing approach and molecular docking assays, were used. Results showed that the putative pathway targeted by strychnobiflavone in L. infantum is the methylglyoxal degradation superpathway, and one hydrolase-like protein was predicted to be the molecular target of this flavonoid in the parasites.

Conclusion

In this context, this study provides the basis for understanding the mechanism of action of strychnobiflavone in L. infantum and presents a strategy based on bioinformatics programs to screen targets of other molecules with biological action against distinct pathogens.

Differences in reproductive toxicology between alopecia drugs: an analysis on adverse events among female and male cases

Alopecia is a dermatological condition with limited therapeutic options. Only two drugs, finasteride and minoxidil, are approved by FDA for alopecia treatment. However, little is known about the differences in adverse effects between these two drugs. We examined the clinical reports submitted to the FDA Adverse Event Reporting System (FAERS) from 2004 to 2014. For both female and males, finasteride was found to be more associated with reproductive toxicity as compared to minoxidil. Among male alopecia cases, finasteride was significantly more concurrent with several forms of sexual dysfunction. Among female alopecia cases, finasteride was significantly more concurrent with harm to fetus and disorder of uterus. In addition, drug-gene network analysis indicated that finasteride could profoundly disturb pathways related to sex hormone signaling and oocyte maturation. These findings could provide clues for subsequent toxicological research. Taken together, this analysis suggested that finasteride could be more liable to various reproductive adverse effects. Some of these adverse effects have yet to be warned in FDA-approved drug label. This information can help improve the treatment regimen of alopecia and post-marketing regulation of drug products.

Toxicological Differences Between NMDA Receptor Antagonists and Cholinesterase Inhibitors

Cholinesterase inhibitors (ChEIs), represented by donepezil, rivastigmine, and galantamine, used to be the only approved class of drugs for the treatment of Alzheimer's disease. After the approval of memantine by the Food and Drug Administration (FDA), N-methyl-d-aspartic acid (NMDA) receptor antagonists have been recognized by authorities and broadly used in the treatment of Alzheimer's disease. Along with complementary mechanisms of action, NMDA antagonists and ChEIs differ not only in therapeutic effects but also in adverse reactions, which is an important consideration in clinical drug use. And the number of patients using NMDA antagonists and ChEIs concomitantly has increased, making the matter more complicated. Here we used the FDA Adverse Event Reporting System for statistical analysis , in order to compare the adverse events of memantine and ChEIs. In general, the clinical evidence confirmed the safety advantages of memantine over ChEIs, reiterating the precautions of clinical drug use and the future direction of antidementia drug development.

Hypothyroidism Side Effect in Patients Treated with Sunitinib or Sorafenib: Clinical and Structural Analyses

Tyrosine kinase inhibitors (TKIs) provide more effective targeted treatments for cancer, but are subject to a variety of adverse effects, such as hypothyroidism. TKI-induced hypothyroidism is a highly complicated issue, because of not only the unrealized toxicological mechanisms, but also different incidences of individual TKI drugs. While sunitinib is suspected for causing thyroid dysfunction more often than other TKIs, sorafenib is believed to be less risky. Here we integrated clinical data and in silico drug-protein interactions to examine the pharmacological distinction between sunitinib and sorafenib. Statistical analysis on the FDA Adverse Event Reporting System (FAERS) confirmed that sunitinib is more concurrent with hypothyroidism than sorafenib, which was observed in both female and male patients. Then, we used docking method and identified 3 proteins specifically binding to sunitinib but not sorafenib, i.e., retinoid X receptor alpha, retinoic acid receptors beta and gamma. As potential off-targets of sunitinib, these proteins are well known to assemble with thyroid hormone receptors, which can explain the profound impact of sunitinib on thyroid function. Taken together, we established a strategy of integrated analysis on clinical records and drug off-targets, which can be applied to explore the molecular basis of various adverse drug reactions.

Historeceptomic Fingerprints for Drug-Like Compounds

Most drugs exert their beneficial and adverse effects through their combined action on several different molecular targets (polypharmacology). The true molecular fingerprint of the direct action of a drug has two components: the ensemble of all the receptors upon which a drug acts and their level of expression in organs/tissues. Conversely, the fingerprint of the adverse effects of a drug may derive from its action in bystander tissues. The ensemble of targets is almost always only partially known. Here we describe an approach improving upon and integrating both components: in silico identification of a more comprehensive ensemble of targets for any drug weighted by the expression of those receptors in relevant tissues. Our system combines more than 300,000 experimentally determined bioactivity values from the ChEMBL database and 4.2 billion molecular docking scores. We integrated these scores with gene expression data for human receptors across a panel of human tissues to produce drug-specific tissue-receptor (historeceptomics) scores. A statistical model was designed to identify significant scores, which define an improved fingerprint representing the unique activity of any drug. These multi-dimensional historeceptomic fingerprints describe, in a novel, intuitive, and easy to interpret style, the holistic, in vivo picture of the mechanism of any drug's action. Valuable applications in drug discovery and personalized medicine, including the identification of molecular signatures for drugs with polypharmacologic modes of action, detection of tissue-specific adverse effects of drugs, matching molecular signatures of a disease to drugs, target identification for bioactive compounds with unknown receptors, and hypothesis generation for drug/compound phenotypes may be enabled by this approach. The system has been deployed at drugable.org for access through a user-friendly web site.

Identification of a Potential Target of Capsaicin by Computational Target Fishing

Capsaicin, the component responsible for the pungency of chili peppers, shows beneficial effects in many diseases, although the underlying mechanisms remain unclear. In the present study, the potential targets of capsaicin were predicted using PharmMapper and confirmed via chemical-protein interactome (CPI) and molecular docking. Carbonic anhydrase 2 was identified as the main disease-related target, with the pharmacophore model matching well with the molecular features of capsaicin. The relation was confirmed by CPI and molecular docking and supported by previous research showing that capsaicin is a potent inhibitor of carbonic anhydrase isoenzymes. The present study provides a basis for understanding the mechanisms of action of capsaicin or those of other natural compounds.

Target-Based Drug Repositioning Using Large-Scale Chemical-Protein Interactome Data

Drug repositioning, or the identification of new indications for known drugs, is a useful strategy for drug discovery. In this study, we developed novel computational methods to predict potential drug targets and new drug indications for systematic drug repositioning using large-scale chemical-protein interactome data. We explored the target space of drugs (including primary targets and off-targets) based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. On the basis of the target profiles of drugs, we constructed statistical models to predict new drug indications for a wide range of diseases with various molecular features. The proposed method outperformed previous methods in terms of interpretability, applicability, and accuracy. Finally, we conducted a comprehensive prediction of the drug-target-disease association network for 8270 drugs and 1401 diseases and showed biologically meaningful examples of newly predicted drug targets and drug indications. The predictive model is useful to understand the mechanisms of the predicted drug indications.

A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver

NADPH oxidases (NOXs) are a predominant mediator of redox homeostasis in hepatic stellate cells (HSCs), and oxidative stress plays an important role in the pathogenesis of liver fibrosis. Ursolic acid (UA) is a pentacyclic triterpenoid with various pharmacological activities, but the molecular targets and underlying mechanisms for its antifibrotic effect in the liver remain elusive. This study aimed to computationally predict the molecular interactome and mechanistically investigate the antifibrotic effect of UA on oxidative stress, with a focus on NOX4 activity and cross-linked signaling pathways in human HSCs and rat liver. Drug-drug interaction via chemical-protein interactome tool, a server that can predict drug-drug interaction via chemical-protein interactome, was used to predict the molecular targets of UA, and Database for Annotation, Visualization, and Integrated Discovery was employed to analyze the signaling pathways of the predicted targets of UA. The bioinformatic data showed that there were 611 molecular proteins possibly interacting with UA and that there were over 49 functional clusters responding to UA. The subsequential benchmarking data showed that UA significantly reduced the accumulation of type I collagen in HSCs in rat liver, increased the expression level of MMP-1, but decreased the expression level of TIMP-1 in HSC-T6 cells. UA also remarkably reduced the gene expression level of type I collagen in HSC-T6 cells. Furthermore, UA remarkably attenuated oxidative stress via negative regulation of NOX4 activity and expression in HSC-T6 cells. The employment of specific chemical inhibitors, SB203580, LY294002, PD98059, and AG490, demonstrated the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in the regulatory effect of UA on NOX4 activity and expression. Collectively, the antifibrotic effect of UA is partially due to the oxidative stress attenuating effect through manipulating NOX4 activity and expression. The results suggest that UA may act as a promising antifibrotic agent. More studies are warranted to evaluate the safety and efficacy of UA in the treatment of liver fibrosis.

A potential target of Tanshinone IIA for acute promyelocytic leukemia revealed by inverse docking and drug repurposing

Tanshinone IIA is a pharmacologically active ingredient extracted from Danshen, a Chinese traditional medicine. Its molecular mechanisms are still unclear. The present study utilized computational approaches to uncover the potential targets of this compound. In this research, PharmMapper server was used as the inverse docking tool and the results were verified by Autodock vina in PyRx 0.8, and by DRAR-CPI, a server for drug repositioning via the chemical-protein interactome. Results showed that the retinoic acid receptor alpha (RARα), a target protein in acute promyelocytic leukemia (APL), was in the top rank, with a pharmacophore model matching well the molecular features of Tanshinone IIA. Moreover, molecular docking and drug repurposing results showed that the complex was also matched in terms of structure and chemical-protein interactions. These results indicated that RARα may be a potential target of Tanshinone IIA for APL. The study can provide useful information for further biological and biochemical research on natural compounds.

Xyloketal B, a marine compound, acts on a network of molecular proteins and regulates the activity and expression of rat cytochrome P450 3a: a bioinformatic and animal study

Natural compounds are becoming popular for the treatment of illnesses and health promotion, but the mechanisms of action and safety profiles are often unknown. Xyloketal B (XKB) is a novel marine compound isolated from the mangrove fungus Xylaria sp., with potent antioxidative, neuroprotective, and cardioprotective effects. However, its molecular targets and effects on drug-metabolizing enzymes are unknown. This study aimed to investigate the potential molecular targets of XKB using bioinformatic approaches and to examine the effect of XKB on the expression and activity of rat cytochrome P450 3a (Cyp3a) subfamily members using midazolam as a model probe. DDI-CPI, a server that can predict drug–drug interactions via the chemical–protein interactome, was employed to predict the targets of XKB, and the Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to analyze the pathways of the predicted targets of XKB. Homology modeling was performed using the Discovery Studio program 3.1. The activity and expression of rat hepatic Cyp3a were examined after the rats were treated with XKB at 7 and 14 mg/kg for 8 consecutive days. Rat plasma concentrations of midazolam and its metabolite 1′-OH-midazolam were determined using a validated high-performance liquid chromatographic method. Bioinformatic analysis showed that there were over 324 functional proteins and 61 related signaling pathways that were potentially regulated by XKB. A molecular docking study showed that XKB bound to the active site of human cytochrome P450 3A4 and rat Cyp3a2 homology model via the formation of hydrogen bonds. The in vivo study showed that oral administration of XKB at 14 mg/kg to rats for 8 days significantly increased the area under the plasma concentration-time curve (AUC) of midazolam, with a concomitant decrease in the plasma clearance and AUC ratio of 1′-OH-midazolam over midazolam. Further, oral administration of 14 mg/kg XKB for 8 days markedly reduced the activity and expression of hepatic Cyp3a in rats. Taken together, the results show that XKB could regulate networks of molecular proteins and related signaling pathways and that XKB downregulated hepatic Cyp3a in rats. XKB might cause drug interactions through modulation of the activity and expression of Cyp3a members. More studies are warranted to confirm the mechanisms of action of XKB and to investigate the underlying mechanism for the regulating effect of XKB on Cyp3a subfamily members.

Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity

Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI). However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap). Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1) some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2) in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.

Small drug molecules usually bind to unintended off-targets, leading to unexpected drug responses such as side effects or drug repositioning opportunities. Thus, identifying unintended drug-target interactions (DTI) is particularly required for understanding complicated drug actions. It remains expensive nowadays to experimentally determine DTI, so various computational methods are developed. In this study, we initiatively demonstrated that target binding is directly correlated with drug induced genomic expression profiles in Connectivity Map (CMap). By improving data quality of CMap, we illustrated three important facts: (1) Drugs binding to common targets show higher gene-expression similarity than random compounds, indicating that upstream ligand binding could be characterized by downstream gene-expression change. (2) It is found that some targets are better characterized by CMap than others. To guarantee efficiency of DTI discovery, prediction models should be specifically built for those well characterized targets. (3) It is broadly observed in the predicted DTI that ligands for the same target may collectively interact with common off-target. This observation is consistent with published experimental evidence and can help illustrate the mechanisms of unexplained drug reactions. Based on CMap, our work established an efficient pipeline of identifying potential DTI. By extending the success in CMap to other genomic data sources, we believe more DTI would be discovered.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

Finding novel pharmaceuticals in the systems biology era using multiple effective drug targets, phenotypic screening and knowledge of transporters: where drug discovery went wrong and how to fix it

Despite the sequencing of the human genome, the rate of innovative and successful drug discovery in the pharmaceutical industry has continued to decrease. Leaving aside regulatory matters, the fundamental and interlinked intellectual issues proposed to be largely responsible for this are: (a) the move from 'function-first' to 'target-first' methods of screening and drug discovery; (b) the belief that successful drugs should and do interact solely with single, individual targets, despite natural evolution's selection for biochemical networks that are robust to individual parameter changes; (c) an over-reliance on the rule-of-5 to constrain biophysical and chemical properties of drug libraries; (d) the general abandoning of natural products that do not obey the rule-of-5; (e) an incorrect belief that drugs diffuse passively into (and presumably out of) cells across the bilayers portions of membranes, according to their lipophilicity; (f) a widespread failure to recognize the overwhelmingly important role of proteinaceous transporters, as well as their expression profiles, in determining drug distribution in and between different tissues and individual patients; and (g) the general failure to use engineering principles to model biology in parallel with performing 'wet' experiments, such that 'what if?' experiments can be performed in silico to assess the likely success of any strategy. These facts/ideas are illustrated with a reasonably extensive literature review. Success in turning round drug discovery consequently requires: (a) decent systems biology models of human biochemical networks; (b) the use of these (iteratively with experiments) to model how drugs need to interact with multiple targets to have substantive effects on the phenotype; (c) the adoption of polypharmacology and/or cocktails of drugs as a desirable goal in itself; (d) the incorporation of drug transporters into systems biology models, en route to full and multiscale systems biology models that incorporate drug absorption, distribution, metabolism and excretion; (e) a return to 'function-first' or phenotypic screening; and (f) novel methods for inferring modes of action by measuring the properties on system variables at all levels of the 'omes. Such a strategy offers the opportunity of achieving a state where we can hope to predict biological processes and the effect of pharmaceutical agents upon them. Consequently, this should both lower attrition rates and raise the rates of discovery of effective drugs substantially.

Systematic investigation of protein-small molecule interactions

Cell signaling is extensively wired between cellular components to sustain cell proliferation, differentiation, and adaptation. The interaction network is often manifested in how protein function is regulated through interacting with other cellular components including small molecule metabolites. While many biochemical interactions have been established as reactions between protein enzymes and their substrates and products, much less is known at the system level about how small metabolites regulate protein functions through allosteric binding. In the past decade, study of protein-small molecule interactions has been lagging behind other types of interactions. Recent technological advances have explored several high-throughput platforms to reveal many "unexpected" protein-small molecule interactions that could have profound impact on our understanding of cell signaling. These interactions will help bridge gaps in existing regulatory loops of cell signaling and serve as new targets for medical intervention. In this review, we summarize recent advances of systematic investigation of protein-metabolite/small molecule interactions, and discuss the impact of such studies and their potential impact on both biological researches and medicine.

The promiscuous binding of pharmaceutical drugs and their transporter-mediated uptake into cells: what we (need to) know and how we can do so

A recent paper in this journal sought to counter evidence for the role of transport proteins in effecting drug uptake into cells, and questions that transporters can recognize drug molecules in addition to their endogenous substrates. However, there is abundant evidence that both drugs and proteins are highly promiscuous. Most proteins bind to many drugs and most drugs bind to multiple proteins (on average more than six), including transporters (mutations in these can determine resistance); most drugs are known to recognise at least one transporter. In this response, we alert readers to the relevant evidence that exists or is required. This needs to be acquired in cells that contain the relevant proteins, and we highlight an experimental system for simultaneous genome-wide assessment of carrier-mediated uptake in a eukaryotic cell (yeast).

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.