Network pharmacology: the next paradigm in drug discovery

The dominant paradigm in drug discovery is the concept of designing maximally selective ligands to act on individual drug targets. However, many effective drugs act via modulation of multiple proteins rather than single targets. Advances in systems biology are revealing a phenotypic robustness and a network structure that strongly suggests that exquisitely selective compounds, compared with multitarget drugs, may exhibit lower than desired clinical efficacy. This new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity. Integrating network biology and polypharmacology holds the promise of expanding the current opportunity space for druggable targets. However, the rational design of polypharmacology faces considerable challenges in the need for new methods to validate target combinations and optimize multiple structure-activity relationships while maintaining drug-like properties. Advances in these areas are creating the foundation of the next paradigm in drug discovery: network pharmacology.     

The impact of cryo-EM on determining allosteric modulator-bound structures of G protein-coupled receptors

G-protein coupled receptors (GPCRs) are important therapeutic targets for the treatment of human disease. Although GPCRs are highly successful drug targets, there are many challenges associated with the discovery and translation of small molecule ligands that target the endogenous ligand-binding site for GPCRs. Allosteric modulators are a class of ligands that target alternative binding sites known as allosteric sites and offer fresh opportunities for the development of new therapeutics. However, only a few allosteric modulators have been approved as drugs. Advances in GPCR structural biology enabled by the cryogenic electron microscopy (cryo-EM) revolution have provided new insights into the molecular mechanism and binding location of small molecule allosteric modulators. This review highlights the latest findings from allosteric modulator-bound structures of Class A, B, and C GPCRs with a focus on small molecule ligands. Emerging methods that will facilitate cryo-EM structures of more difficult ligand-bound GPCR complexes are also discussed. The results of these studies are anticipated to aid future structure-based drug discovery efforts across many different GPCRs.     

Solution- and solid-state NMR studies of GPCRs and their ligands

G protein-coupled receptors (GPCRs) represent one of the major targets of new drugs on the market given their roles as key membrane receptors in many cellular signalling pathways. Structure-based drug design has potential to be the most reliable method for novel drug discovery. Unfortunately, GPCR-ligand crystallisation for X-ray diffraction studies is very difficult to achieve. However, solution- and solid-state NMR approaches have been developed and have provided new insights, particularly focussing on the study of protein-ligand interactions which are vital for drug discovery. This review provides an introduction for new investigators of GPCRs/ligand interactions using NMR spectroscopy. The guidelines for choosing a system for efficient isotope labelling of GPCRs and their ligands for NMR studies will be presented, along with an overview of the different sample environments suitable for generation of high resolution structural information from NMR spectra.     

Structure determination of membrane proteins in five easy pieces

Rotational Alignment (RA) solid-state NMR provides the basis for a general method for determining the structures of membrane proteins in phospholipid bilayers under physiological conditions. Membrane proteins are high priority targets for structure determination, and are challenging for existing experimental methods. Because membrane proteins reside in liquid crystalline phospholipid bilayer membranes it is important to study them in this type of environment. The RA solid-state NMR approach we have developed can be summarized in five steps, and incorporates methods of molecular biology, biochemistry, sample preparation, the implementation of NMR experiments, and structure calculations. It relies on solid-state NMR spectroscopy to obtain high-resolution spectra and residue-specific structural restraints for membrane proteins that undergo rotational diffusion around the membrane normal, but whose mobility is otherwise restricted by interactions with the membrane phospholipids. High resolution spectra of membrane proteins alone and in complex with other proteins and ligands set the stage for structure determination and functional studies of these proteins in their native, functional environment.     

Protein structure: discovering selective protein kinase inhibitors

A plethora of important targets for therapeutic intervention occurs in the protein kinase superfamily, one of the most thoroughly investigated groups of drug targets. Kinases have a deep hydrophobic ATP binding site that has been successfully exploited with the discovery of potent ATP-competitive drugs. However, most features of this pocket are well conserved in all protein kinases, which explains why kinase inhibitors typically exhibit a fairly indiscriminate spectrum of activity. Crystal structures of various protein kinases bound to their ligands are described, which begin to explain the observed selectivity profiles of kinase inhibitors. The insights gained from these structures suggest several approaches to improve inhibitor specificity and these approaches are summarized. The exciting potential of new high-throughput methods in structure determination that enable the systematic atomic-resolution investigation of large numbers of inhibitors bound to their various kinase targets will be discussed.     

Assessing drug target association using semantic linked data

The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.     

Methods of studying telomere damage induced by quadruplex-ligand complexes

The burgeoning knowledge about the structure of telomeres and the roles of various factors involved in telomere maintenance provides several possible targets for pharmacological intervention. To date the area that has received major attention regarding drug discovery is the targeting the telomeric G-quadruplex (G4) structure. G4 ligands were initially designed to counteract telomerase action at telomeres. Surprisingly, their antiproliferative effects can occur in telomerase negative cells and follow kinetics, which cannot be merely explained by telomere shortening, suggesting that these compounds affect other pathways, not necessarily related to telomere biology. Impressively, it has been shown that polyaromatic compounds featuring end-stacking binding properties trigger a strong DNA damage response at telomeres. This is typical of the telomere deprotection occurring during cellular senescence or upon telomere injury. It emerged that the G4-interacting agents are more than simple telomerase inhibitors and that their direct target is rather telomere than telomerase. This review summarizes the most valid experimental approaches for studying the pharmacological telomere damage induced by G4-ligand complexes.     

From proteins to proteomics

During the second half of the 20th century, biochemistry and subsequently molecular biology blossomed into the core upon which all biological and biomedical sciences now depend. A major part of these closely related disciplines has been the study of the structure and function of proteins and the diverse biological functions that they perform. Early experimentation necessarily focused on individual entities, selected mainly for their activities, but as technology improved there developed a tendency to look at proteins as larger, interactive groups or clusters. Spurred by the recent exponential production of genomic sequence data for a rapidly increasing number of species, protein chemistry has now evolved into a new discipline, proteomics. In addition to embracing the methods and approaches that have served protein scientists well in the past, it includes, and is perhaps best defined by, high-throughput analyses based in large part on 2D gel electrophoresis, MALDI and ESI mass spectrometry and combinatorial arrays. Proteomic targets include the identification of all genome products and a mapping of their interactions and expression profiles. These hold great promise for the identification of disease markers and drug targets, but are not without their challenges and pitfalls.     

Relating protein pharmacology by ligand chemistry

The identification of protein function based on biological information is an area of intense research. Here we consider a complementary technique that quantitatively groups and relates proteins based on the chemical similarity of their ligands. We began with 65,000 ligands annotated into sets for hundreds of drug targets. The similarity score between each set was calculated using ligand topology. A statistical model was developed to rank the significance of the resulting similarity scores, which are expressed as a minimum spanning tree to map the sets together. Although these maps are connected solely by chemical similarity, biologically sensible clusters nevertheless emerged. Links among unexpected targets also emerged, among them that methadone, emetine and loperamide (Imodium) may antagonize muscarinic M3, alpha2 adrenergic and neurokinin NK2 receptors, respectively. These predictions were subsequently confirmed experimentally. Relating receptors by ligand chemistry organizes biology to reveal unexpected relationships that may be assayed using the ligands themselves.     

A pedestrian guide to membrane protein crystallization

Membrane protein structural biology is a frontier area of modern biomedical research. Twenty to thirty-five percent of the proteins encoded by an organism's genome are integral membrane proteins. Integral membrane proteins, such as channels, transporters, and receptors, are critical components of many fundamental biological processes. Also, many integral membrane proteins are important in biomedical and biotechnological applications; the majority of drug targets are integral membrane proteins. The sharp increase in the number of membrane protein structures over the last several years gives some indication that this field is poised for rather explosive growth as more and more investigators take on membrane protein projects. The purpose of this brief practical review was to take a snapshot of a field at the onset of its likely exponential growth phase, and to lay out the methods that have worked to date for obtaining membrane protein crystals suitable for structure determination by X-ray crystallography. Many of the successful experimental methods are identical to those used for soluble proteins. The major difference, and a non-trivial difference, is the necessity for inclusion of detergents above the critical micelle concentration in the purified membrane protein solution.     

Receptor-based assays in screening for biologically active substances.

Molecular biology has identified new receptors and ligands which are deregulated in diseases such as cancer and autoimmune conditions and which provide rational targets for therapeutic intervention. Advances in instrumentation and methodology make it possible to screen large numbers of samples in simple receptor-ligand binding assays in the search for drug candidates. Caution must be exercised in the interpretation of data derived from such assays. This is particularly pertinent to the recently characterized receptors, such as the cytokine receptors, as we do not fully understand the relationship between the receptor type and the linkage of receptors to the appropriate or inappropriate second messenger systems that are used in the experimental screening protocols and the disease state.     

NMR screening in drug discovery

NMR methods in drug discovery have traditionally been used to obtain structural information for drug targets or target-ligand complexes. Recently, it has been shown that NMR may be used as an alternative approach for identification of ligands that bind to protein drug targets, shifting the emphasis of many NMR laboratories towards screening and design of potential drug molecules, rather than structural characterization.     

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.     

Network analysis of genome-wide association studies for drug target prioritisation

Over the past decades, genome-wide association studies (GWAS) have led to a dramatic expansion of genetic variants implicated with human traits and diseases. These advances are expected to result in new drug targets but the identification of causal genes and the cell biology underlying human diseases from GWAS remains challenging. Here, we review protein interaction network-based methods to analyse GWAS data. These approaches can rank candidate drug targets at GWAS-associated loci or among interactors of disease genes without direct genetic support. These methods identify the cell biology affected in common across diseases, offering opportunities for drug repurposing, as well as be combined with expression data to identify focal tissues and cell types. Going forward, we expect that these methods will further improve from advances in the characterisation of context specific interaction networks and the joint analysis of rare and common genetic signals.     

结核分枝杆菌膜蛋白的异源表达与纯化研究进展

膜蛋白是一类与生物膜相互作用、具有重要功能和独特结构的蛋白质。异源表达纯化一直是了解膜蛋白结构和功能的重要瓶颈。结核分枝杆菌作为典型的胞内致病菌,其膜蛋白的研究具有很好的代表性以及重要意义。目前用于表达膜蛋白的有大肠杆菌、酵母、哺乳动物细胞等表达系统,但结核菌膜蛋白的表达宿主还往往局限于大肠杆菌。异源表达需要综合考虑蛋白的来源、疏水性、跨膜区等特性。低温、加入共表达因子以及改变培养条件有助于结核菌膜蛋白的可溶性表达。另外,包涵体复性也是获得结核菌目的膜蛋白的重要途径。随着新的表达系统,新的促可溶表达策略,新的包涵体复性手段,新的纯化方法的应用,将有更多的膜蛋白异源表达纯化成功,为蛋白质功能研究奠定基础。     

Metabolomics in the fight against malaria

Metabolomics uses high-resolution mass spectrometry to provide a chemical fingerprint of thousands of metabolites present in cells, tissues or body fluids. Such metabolic phenotyping has been successfully used to study various biologic processes and disease states. High-resolution metabolomics can shed new light on the intricacies of host-parasite interactions in each stage of the Plasmodium life cycle and the downstream ramifications on the host’s metabolism, pathogenesis and disease. Such data can become integrated with other large datasets generated using top-down systems biology approaches and be utilised by computational biologists to develop and enhance models of malaria pathogenesis relevant for identifying new drug targets or intervention strategies. Here, we focus on the promise of metabolomics to complement systems biology approaches in the quest for novel interventions in the fight against malaria. We introduce the Malaria Host-Pathogen Interaction Center (MaHPIC), a new systems biology research coalition. A primary goal of the MaHPIC is to generate systems biology datasets relating to human and non-human primate (NHP) malaria parasites and their hosts making these openly available from an online relational database. Metabolomic data from NHP infections and clinical malaria infections from around the world will comprise a unique global resource.