Allo-network drugs: harnessing allostery in cellular networks

A Panel of TrpB Biocatalysts Derived from Tryptophan Synthase through the Transfer of Mutations that Mimic Allosteric Activation

Naturally occurring enzyme homologues often display highly divergent activity with non-natural substrates. Exploiting this diversity with enzymes engineered for new or altered function, however, is laborious because the engineering must be replicated for each homologue. A small set of mutations of the tryptophan synthase β-subunit (TrpB) from Pyrococcus furiosus, which mimics the activation afforded by binding of the α-subunit, was demonstrated to have a similar activating effect in different TrpB homologues with as little as 57 % sequence identity. Kinetic and spectroscopic analyses indicate that the mutations function through the same mechanism: mimicry of α-subunit binding. From these enzymes, we identified a new TrpB catalyst that displays a remarkably broad activity profile in the synthesis of 5-substituted tryptophans. This demonstrates that allosteric activation can be recapitulated throughout a protein family to explore natural sequence diversity for desirable biocatalytic transformations.

Quantitative Determination of Flexible Pharmacological Mechanisms Based On Topological Variation in Mice Anti-Ischemic Modular Networks

Targeting modules or signalings may open a new path to understanding the complex pharmacological mechanisms of reversing disease processes. However, determining how to quantify the structural alteration of these signalings or modules in pharmacological networks poses a great challenge towards realizing rational drug use in clinical medicine. Here, we explore a novel approach for dynamic comparative and quantitative analysis of the topological structural variation of modules in molecular networks, proposing the concept of allosteric modules (AMs). Based on the ischemic brain of mice, we optimize module distribution in different compound-dependent modular networks by using the minimum entropy criterion and then calculate the variation in similarity values of AMs under various conditions using a novel method of SimiNEF. The diverse pharmacological dynamic stereo-scrolls of AMs with functional gradient alteration, which consist of five types of AMs, may robustly deconstruct modular networks under the same ischemic conditions. The concept of AMs can not only integrate the responsive mechanisms of different compounds based on topological cascading variation but also obtain valuable structural information about disease and pharmacological networks beyond pathway analysis. We thereby provide a new systemic quantitative strategy for rationally determining pharmacological mechanisms of altered modular networks based on topological variation.

Biophysical and computational methods to analyze amino acid interaction networks in proteins

Globular proteins are held together by interacting networks of amino acid residues. A number of different structural and computational methods have been developed to interrogate these amino acid networks. In this review, we describe some of these methods, including analyses of X-ray crystallographic data and structures, computer simulations, NMR data, and covariation among protein sequences, and indicate the critical insights that such methods provide into protein function. This information can be leveraged towards the design of new allosteric drugs, and the engineering of new protein function and protein regulation strategies.

Pharmacological Chaperones: Design and Development of New Therapeutic Strategies for the Treatment of Conformational Diseases

Errors in protein folding may result in premature clearance of structurally aberrant proteins, or in the accumulation of toxic misfolded species or protein aggregates. These pathological events lead to a large range of conditions known as conformational diseases. Several research groups have presented possible therapeutic solutions for their treatment by developing novel compounds, known as pharmacological chaperones. These cell-permeable molecules selectively provide a molecular scaffold around which misfolded proteins can recover their native folding and, thus, their biological activities. Here, we review therapeutic strategies, clinical potentials, and cost-benefit impacts of several classes of pharmacological chaperones for the treatment of a series of conformational diseases.

Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell

All soluble proteins populate conformational ensembles that together constitute the native state. Their fluctuations in water are intrinsic thermodynamic phenomena, and the distributions of the states on the energy landscape are determined by statistical thermodynamics; however, they are optimized to perform their biological functions. In this review we briefly describe advances in free energy landscape studies of protein conformational ensembles. Experimental (nuclear magnetic resonance, small-angle X-ray scattering, single-molecule spectroscopy, and cryo-electron microscopy) and computational (replica-exchange molecular dynamics, metadynamics, and Markov state models) approaches have made great progress in recent years. These address the challenging characterization of the highly flexible and heterogeneous protein ensembles. We focus on structural aspects of protein conformational distributions, from collective motions of single- and multi-domain proteins, intrinsically disordered proteins, to multiprotein complexes. Importantly, we highlight recent studies that illustrate functional adjustment of protein conformational ensembles in the crowded cellular environment. We center on the role of the ensemble in recognition of small- and macro-molecules (protein and RNA/DNA) and emphasize emerging concepts of protein dynamics in enzyme catalysis. Overall, protein ensembles link fundamental physicochemical principles and protein behavior and the cellular network and its regulation.

Allosteric modulators of MEK1: drug design and discovery

Mitogen-activated protein kinase kinase (MAPKK, MEK) mediates signal transduction, controlling cell proliferation and survival. MEK occupies a key downstream position in the Ras-Raf-MEK-ERK signaling pathway, implying that inhibition of MEK will potently suppress tumor cell growth, with potential applications in cancer therapy. Based on the promising therapeutic effects of MEK modulators, continued efforts have been made in this class. Here, we review the discovery and development of MEK1 allosteric modulators, classifying them into four structural groups. The allosteric mechanisms and recent clinical progress involving these modulators are also reviewed.

Evolution of the concept of conformational dynamics of enzyme functions over half of a century: A personal view

To most physicists, it was always evident that conformational fluctuation is an inherent property of all molecules. Its existence in proteins was mentioned first by Linderström-Lang and Schellman in 1959 based on their hydrogen-deuterium exchange experiments. The "induced fit" mechanism to explain ligand-induced conformational changes was suggested by Koshland in 1958. Straub combined these two concepts in his "fluctuation fit" theory in 1964. The era of protein X-ray crystallography imposed a static view of protein structures. With proteins becoming accessible to NMR analysis, conformational dynamics could be mapped, and a new wave of dynamic interpretation of enzymatic catalysis and molecular recognition appeared. Energy landscapes, energy funnels, conformational selection, conformational distribution shifts are now frequent terms in interpreting biomolecular recognition and enzymatic catalysis. All these interpretations are based on the concept that evolution uses the conformational fluctuations of enzymes to develop efficient and dynamic catalytic machines. In a resurrection of the original "fluctuation fit" concept, it is generally recognized now that spatial and temporal events of catalysis are equally important to describe its mechanism. This special issue, dedicated to the memory of Henryk Eisenberg, prompted us to look back at the last 50 years of development of a concept that-like other important concepts-appeared, evolved and became accepted during the period covered by the scientific lifespan of Henryk.

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

Allosteric Partial Inhibition of Monomeric Proteases. Sulfated Coumarins Induce Regulation, not just Inhibition, of Thrombin

Allosteric partial inhibition of soluble, monomeric proteases can offer major regulatory advantages, but remains a concept on paper to date; although it has been routinely documented for receptors and oligomeric proteins. Thrombin, a key protease of the coagulation cascade, displays significant conformational plasticity, which presents an attractive opportunity to discover small molecule probes that induce sub-maximal allosteric inhibition. We synthesized a focused library of some 36 sulfated coumarins to discover two agents that display sub-maximal efficacy (~50%), high potency (<500 nM) and high selectivity for thrombin (>150-fold). Michaelis-Menten, competitive inhibition, and site-directed mutagenesis studies identified exosite 2 as the site of binding for the most potent sulfated coumarin. Stern-Volmer quenching of active site-labeled fluorophore suggested that the allosteric regulators induce intermediate structural changes in the active site as compared to those that display ~80–100% efficacy. Antithrombin inactivation of thrombin was impaired in the presence of the sulfated coumarins suggesting that allosteric partial inhibition arises from catalytic dysfunction of the active site. Overall, sulfated coumarins represent first-in-class, sub-maximal inhibitors of thrombin. The probes establish the concept of allosteric partial inhibition of soluble, monomeric proteins. This concept may lead to a new class of anticoagulants that are completely devoid of bleeding.

Discovery of a small-molecule binder of the oncoprotein gankyrin that modulates gankyrin activity in the cell

Gankyrin is an ankyrin-repeat oncoprotein whose overexpression has been implicated in the development of many cancer types. Elevated gankyrin levels are linked to aberrant cellular events including enhanced degradation of tumour suppressor protein p53, and inhibition of gankyrin activity has therefore been identified as an attractive anticancer strategy. Gankyrin interacts with several partner proteins, and a number of these protein-protein interactions (PPIs) are of relevance to cancer. Thus, molecules that bind the PPI interface of gankyrin and interrupt these interactions are of considerable interest. Herein, we report the discovery of a small molecule termed cjoc42 that is capable of binding to gankyrin. Cell-based experiments demonstrate that cjoc42 can inhibit gankyrin activity in a dose-dependent manner: cjoc42 prevents the decrease in p53 protein levels normally associated with high amounts of gankyrin, and it restores p53-dependent transcription and sensitivity to DNA damage. The results represent the first evidence that gankyrin is a "druggable" target with small molecules.

Controlling Allosteric Networks in Proteins

Allosteric transition, defined as conformational changes induced by ligand binding, is one of the fundamental properties of proteins. Allostery has been observed and characterized in many proteins, and has been recently utilized to control protein function via regulation of protein activity. Here, we review the physical and evolutionary origin of protein allostery, as well as its importance to protein regulation, drug discovery, and biological processes in living systems. We describe recently developed approaches to identify allosteric pathways, connected sets of pairwise interactions that are responsible for propagation of conformational change from the ligand-binding site to a distal functional site. We then present experimental and computational protein engineering approaches for control of protein function by modulation of allosteric sites. As an example of application of these approaches, we describe a synergistic computational and experimental approach to rescue the cystic-fibrosis-associated protein cystic fibrosis transmembrane conductance regulator, which upon deletion of a single residue misfolds and causes disease. This example demonstrates the power of allosteric manipulation in proteins to both elucidate mechanisms of molecular function and to develop therapeutic strategies that rescue those functions. Allosteric control of proteins provides a tool to shine a light on the complex cascades of cellular processes and facilitate unprecedented interrogation of biological systems.

How an Inhibitor Bound to Subunit Interface Alters Triosephosphate Isomerase Dynamics

The tunnel region at triosephosphate isomerase (TIM)'s dimer interface, distant from its catalytic site, is a target site for certain benzothiazole derivatives that inhibit TIM's catalytic activity in Trypanosoma cruzi, the parasite that causes Chagas disease. We performed multiple 100-ns molecular-dynamics (MD) simulations and elastic network modeling (ENM) on both apo and complex structures to shed light on the still unclear inhibitory mechanism of one such inhibitor, named bt10. Within the time frame of our MD simulations, we observed stabilization of aromatic clusters at the dimer interface and enhancement of intersubunit hydrogen bonds in the presence of bt10, which point to an allosteric effect rather than destabilization of the dimeric structure. The collective dynamics dictated by the topology of TIM is known to facilitate the closure of its catalytic loop over the active site that is critical for substrate entrance and product release. We incorporated the ligand's effect on vibrational dynamics by applying mixed coarse-grained ENM to each one of 54,000 MD snapshots. Using this computationally efficient technique, we observed altered collective modes and positive shifts in eigenvalues due to the constraining effect of bt10 binding. Accordingly, we observed allosteric changes in the catalytic loop's dynamics, flexibility, and correlations, as well as the solvent exposure of catalytic residues. A newly (to our knowledge) introduced technique that performs residue-based ENM scanning of TIM revealed the tunnel region as a key binding site that can alter global dynamics of the enzyme.

Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations

Network-based methods are playing an increasingly important role in drug design. Our main question in this paper was whether the efficiency of drug target proteins to spread perturbations in the human interactome is larger if the binding drugs have side effects, as compared to those which have no reported side effects. Our results showed that in general, drug targets were better spreaders of perturbations than non-target proteins, and in particular, targets of drugs with side effects were also better spreaders of perturbations than targets of drugs having no reported side effects in human protein-protein interaction networks. Colorectal cancer-related proteins were good spreaders and had a high centrality, while type 2 diabetes-related proteins showed an average spreading efficiency and had an average centrality in the human interactome. Moreover, the interactome-distance between drug targets and disease-related proteins was higher in diabetes than in colorectal cancer. Our results may help a better understanding of the network position and dynamics of drug targets and disease-related proteins, and may contribute to develop additional, network-based tests to increase the potential safety of drug candidates.

Learning from the past and looking to the future: Emerging perspectives for improving the treatment of psychiatric disorders

Modern neuropsychopharmacology commenced in the 1950s with the serendipitous discovery of first-generation antipsychotics and antidepressants which were therapeutically effective yet had marked adverse effects. Today, a broader palette of safer and better-tolerated agents is available for helping people that suffer from schizophrenia, depression and other psychiatric disorders, while complementary approaches like psychotherapy also have important roles to play in their treatment, both alone and in association with medication. Nonetheless, despite considerable efforts, current management is still only partially effective, and highly-prevalent psychiatric disorders of the brain continue to represent a huge personal and socio-economic burden. The lack of success in discovering more effective pharmacotherapy has contributed, together with many other factors, to a relative disengagement by pharmaceutical firms from neuropsychiatry. Nonetheless, interest remains high, and partnerships are proliferating with academic centres which are increasingly integrating drug discovery and translational research into their traditional activities. This is, then, a time of transition and an opportune moment to thoroughly survey the field. Accordingly, the present paper, first, chronicles the discovery and development of psychotropic agents, focusing in particular on their mechanisms of action and therapeutic utility, and how problems faced were eventually overcome. Second, it discusses the lessons learned from past successes and failures, and how they are being applied to promote future progress. Third, it comprehensively surveys emerging strategies that are (1), improving our understanding of the diagnosis and classification of psychiatric disorders; (2), deepening knowledge of their underlying risk factors and pathophysiological substrates; (3), refining cellular and animal models for discovery and validation of novel therapeutic agents; (4), improving the design and outcome of clinical trials; (5), moving towards reliable biomarkers of patient subpopulations and medication efficacy and (6), promoting collaborative approaches to innovation by uniting key partners from the regulators, industry and academia to patients. Notwithstanding the challenges ahead, the many changes and ideas articulated herein provide new hope and something of a framework for progress towards the improved prevention and relief of psychiatric and other CNS disorders, an urgent mission for our Century.

Principles of allosteric interactions in cell signaling

Linking cell signaling events to the fundamental physicochemical basis of the conformational behavior of single molecules and ultimately to cellular function is a key challenge facing the life sciences. Here we outline the emerging principles of allosteric interactions in cell signaling, with emphasis on the following points. (1) Allosteric efficacy is not a function of the chemical composition of the allosteric pocket but reflects the extent of the population shift between the inactive and active states. That is, the allosteric effect is determined by the extent of preferred binding, not by the overall binding affinity. (2) Coupling between the allosteric and active sites does not decide the allosteric effect; however, it does define the propagation pathways, the allosteric binding sites, and key on-path residues. (3) Atoms of allosteric effectors can act as "driver" or "anchor" and create attractive "pulling" or repulsive "pushing" interactions. Deciphering, quantifying, and integrating the multiple co-occurring events present daunting challenges to our scientific community.

The structural basis for cancer treatment decisions

Cancer treatment decisions rely on genetics, large data screens and clinical pharmacology. Here we point out that genetic analysis and treatment decisions may overlook critical elements in cancer development, progression and drug resistance. Two critical structural elements are missing in genetics-based decision-making: the mechanisms of oncogenic mutations and the cellular network which is rewired in cancer. These lay the foundation for the structural basis for cancer treatment decisions, which is rooted in the physical principles of the molecular conformational behavior of single molecules and their interactions. Improved tumor mutational analysis platforms and knowledge of the redundant pathways which can take over in cancer, may not only supplement known actionable findings, but forecast possible cancer progression and resistance. Such forward-looking can be powerful, endowing the oncologist with mechanistic insight and cancer prognosis, and consequently more informed treatment options. Examples include redundant pathways taking over after inhibition of EGFR constitutive activation, mutations in PIK3CA p110α and p85, and the non-hotspot AKT1 mutants conferring constitutive membrane localization.

The design of covalent allosteric drugs

A key issue in drug discovery is how to reduce drug dosage and increase specificity while retaining or increasing efficacy, as high dosage is often linked to toxicity. There are two types of drugs on the market: orthosteric and allosteric. Orthosteric drugs can be noncovalent or covalent. The latter are advantageous because they may be prescribed in lower doses, but their potential off-target toxicity is a primary concern. The chief advantages of allosteric drugs are their higher specificity and their consequently lower chance of toxic side effects. Covalent allosteric drugs combine the pharmacological merits of covalent drugs with the additional benefit of the higher specificity of allosteric drugs. In a recent promising step in therapeutic drug development, allosteric, disulfide-tethered fragments successfully modulated the activity of a protein kinase and K-Ras.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

A mechanism for the auto-inhibition of hyperpolarization-activated cyclic nucleotide-gated (HCN) channel opening and its relief by cAMP

Hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels control neuronal and cardiac electrical rhythmicity. There are four homologous isoforms (HCN1-4) sharing a common multidomain architecture that includes an N-terminal transmembrane tetrameric ion channel followed by a cytoplasmic "C-linker," which connects a more distal cAMP-binding domain (CBD) to the inner pore. Channel opening is primarily stimulated by transmembrane elements that sense membrane hyperpolarization, although cAMP reduces the voltage required for HCN activation by promoting tetramerization of the intracellular C-linker, which in turn relieves auto-inhibition of the inner pore gate. Although binding of cAMP has been proposed to relieve auto-inhibition by affecting the structure of the C-linker and CBD, the nature and extent of these cAMP-dependent changes remain limitedly explored. Here, we used NMR to probe the changes caused by the binding of cAMP and of cCMP, a partial agonist, to the apo-CBD of HCN4. Our data indicate that the CBD exists in a dynamic two-state equilibrium, whose position as gauged by NMR chemical shifts correlates with the V½ voltage measured through electrophysiology. In the absence of cAMP, the most populated CBD state leads to steric clashes with the activated or "tetrameric" C-linker, which becomes energetically unfavored. The steric clashes of the apo tetramer are eliminated either by cAMP binding, which selects for a CBD state devoid of steric clashes with the tetrameric C-linker and facilitates channel opening, or by a transition of apo-HCN to monomers or dimer of dimers, in which the C-linker becomes less structured, and channel opening is not facilitated.

Attractor Structures of Signaling Networks: Consequences of Different Conformational Barcode Dynamics and Their Relations to Network-Based Drug Design

Conformational barcodes tag functional sites of proteins and are decoded by interacting molecules transmitting the incoming signal. Conformational barcodes are modified by all co-occurring allosteric events induced by post-translational modifications, pathogen, drug binding, etc. We argue that fuzziness (plasticity) of conformational barcodes may be increased by disordered protein structures, by integrative plasticity of multi-phosphorylation events, by increased intracellular water content (decreased molecular crowding) and by increased action of molecular chaperones. This leads to increased plasticity of signaling and cellular networks. Increased plasticity is both substantiated by and inducing an increased noise level. Using the versatile network dynamics tool, Turbine (www.turbine.linkgroup.hu), here we show that the 10 % noise level expected in cellular systems shifts a cancer-related signaling network of human cells from its proliferative attractors to its largest, apoptotic attractor representing their health-preserving response in the carcinogen containing and tumor suppressor deficient environment modeled in our study. Thus, fuzzy conformational barcodes may not only make the cellular system more plastic, and therefore more adaptable, but may also stabilize the complex system allowing better access to its largest attractor.

Towards structural systems pharmacology to study complex diseases and personalized medicine

Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.

An automated approach to network features of protein structure ensembles

Network theory applied to protein structures provides insights into numerous problems of biological relevance. The explosion in structural data available from PDB and simulations establishes a need to introduce a standalone-efficient program that assembles network concepts/parameters under one hood in an automated manner. Herein, we discuss the development/application of an exhaustive, user-friendly, standalone program package named PSN-Ensemble, which can handle structural ensembles generated through molecular dynamics (MD) simulation/NMR studies or from multiple X-ray structures. The novelty in network construction lies in the explicit consideration of side-chain interactions among amino acids. The program evaluates network parameters dealing with topological organization and long-range allosteric communication. The introduction of a flexible weighing scheme in terms of residue pairwise cross-correlation/interaction energy in PSN-Ensemble brings in dynamical/chemical knowledge into the network representation. Also, the results are mapped on a graphical display of the structure, allowing an easy access of network analysis to a general biological community. The potential of PSN-Ensemble toward examining structural ensemble is exemplified using MD trajectories of an ubiquitin-conjugating enzyme (UbcH5b). Furthermore, insights derived from network parameters evaluated using PSN-Ensemble for single-static structures of active/inactive states of β2-adrenergic receptor and the ternary tRNA complexes of tyrosyl tRNA synthetases (from organisms across kingdoms) are discussed. PSN-Ensemble is freely available from http://vishgraph.mbu.iisc.ernet.in/PSN-Ensemble/psn_index.html.

Unraveling structural mechanisms of allosteric drug action

Orthosteric drugs block the active site to obstruct function; allosteric drugs modify the population of the active state, to modulate function. Available data lead us to propose that allosteric drugs can constitute anchors and drivers. The anchor docks into an allosteric pocket. The conformation with which it interacts is unchanged during the transition between the inactive and active states. The anchor provides the foundation that allows the driver to exert a 'pull' and/or 'push' action that shifts the receptor population from the inactive to the active state. The presence or absence of driver atom in an allosteric drug can exert opposite agonism. We map a strategy for driver identification and expect the allosteric trigger concept to transform agonist/antagonist drug discovery.

Multiple conformational selection and induced fit events take place in allosteric propagation

The fact that we observe a single conformational selection event during binding does not necessarily mean that only a single conformational selection event takes place, even though this is the common assumption. Here we suggest that conformational selection takes place not once in a given binding/allosteric event, but at every step along the allosteric pathway. This view generalizes conformational selection and makes it applicable also to other allosteric events, such as post-translational modifications (PTMs) and photon absorption. Similar to binding, at each step along a propagation pathway, conformational selection is coupled with induced fit which optimizes the interactions. Thus, as in binding, the allosteric effects induced by PTMs and light relate not only to population shift; but to conformational selection as well. Conformational selection and population shift take place conjointly.

Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence

Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".

ASD v2.0: updated content and novel features focusing on allosteric regulation

Allostery is the most direct and efficient way for regulation of biological macromolecule function and is induced by the binding of a ligand at an allosteric site topographically distinct from the orthosteric site. AlloSteric Database (ASD, http://mdl.shsmu.edu.cn/ASD) has been developed to provide comprehensive information on allostery. Owing to the inherent high receptor selectivity and lower target-based toxicity, allosteric regulation is expected to assume a more prominent role in drug discovery and bioengineering, leading to the rapid growth of allosteric findings. In this updated version, ASD v2.0 has expanded to 1286 allosteric proteins, 565 allosteric diseases and 22 008 allosteric modulators. A total of 907 allosteric site-modulator structural complexes and >200 structural pairs of orthosteric/allosteric sites in the allosteric proteins were constructed for researchers to develop allosteric site and pathway tools in response to community demands. Up-to-date allosteric pathways were manually curated in the updated version. In addition, both the front-end and the back-end of ASD have been redesigned and enhanced to allow more efficient access. Taken together, these updates are useful for facilitating the investigation of allosteric mechanisms, allosteric target identification and allosteric drug discovery.

Perturbation centrality and turbine: a novel centrality measure obtained using a versatile network dynamics tool

Analysis of network dynamics became a focal point to understand and predict changes of complex systems. Here we introduce Turbine, a generic framework enabling fast simulation of any algorithmically definable dynamics on very large networks. Using a perturbation transmission model inspired by communicating vessels, we define a novel centrality measure: perturbation centrality. Hubs and inter-modular nodes proved to be highly efficient in perturbation propagation. High perturbation centrality nodes of the Met-tRNA synthetase protein structure network were identified as amino acids involved in intra-protein communication by earlier studies. Changes in perturbation centralities of yeast interactome nodes upon various stresses well recapitulated the functional changes of stressed yeast cells. The novelty and usefulness of perturbation centrality was validated in several other model, biological and social networks. The Turbine software and the perturbation centrality measure may provide a large variety of novel options to assess signaling, drug action, environmental and social interventions.

S. Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.

Allosteric modulators for the treatment of schizophrenia: targeting glutamatergic networks

Schizophrenia is a highly debilitating mental disorder which afflicts approximately 1% of the global population. Cognitive and negative deficits account for the lifelong disability associated with schizophrenia, whose symptoms are not effectively addressed by current treatments. New medicines are needed to treat these aspects of the disease. Neurodevelopmental, neuropathological, genetic, and behavioral pharmacological data indicate that schizophrenia stems from a dysfunction of glutamate synaptic transmission, particularly in frontal cortical networks. A number of novel pre- and postsynaptic mechanisms affecting glutamatergic synaptic transmission have emerged as viable targets for schizophrenia. While developing orthosteric glutamatergic agents for these targets has proven extremely difficult, targeting allosteric sites of these targets has emerged as a promising alternative. From a medicinal chemistry perspective, allosteric sites provide an opportunity of finding agents with better drug-like properties and greater target specificity. Furthermore, allosteric modulators are better suited to maintaining the highly precise temporal and spatial aspects of glutamatergic synaptic transmission. Herein, we review neuropathological and genomic/genetic evidence underscoring the importance of glutamate synaptic dysfunction in the etiology of schizophrenia and make a case for allosteric targets for therapeutic intervention. We review progress in identifying allosteric modulators of AMPA receptors, NMDA receptors, and metabotropic glutamate receptors, all with the aim of restoring physiological glutamatergic synaptic transmission. Challenges remain given the complexity of schizophrenia and the difficulty in studying cognition in animals and humans. Nonetheless, important compounds have emerged from these efforts and promising preclinical and variable clinical validation has been achieved.

Allosteric conformational barcodes direct signaling in the cell

The cellular network is highly interconnected. Pathways merge and diverge. They proceed through shared proteins and may change directions. How are cellular pathways controlled and their directions decided, coded, and read? These questions become particularly acute when we consider that a small number of pathways, such as signaling pathways that regulate cell fates, cell proliferation, and cell death in development, are extensively exploited. This review focuses on these signaling questions from the structural standpoint and discusses the literature in this light. All co-occurring allosteric events (including posttranslational modifications, pathogen binding, and gain-of-function mutations) collectively tag the protein functional site with a unique barcode. The barcode shape is read by an interacting molecule, which transmits the signal. A conformational barcode provides an intracellular address label, which selectively favors binding to one partner and quenches binding to others, and, in this way, determines the pathway direction, and, eventually, the cell's response and fate.

Cancer-related networks: a help to understand, predict and change malignant transformation

Cancer is increasingly described as a systems-level, network phenomenon. Genetic methods, such as next generation sequencing and RNA interference uncovered the complexity tumor-specific mutation-induced effects and the identification of multiple target sets. Network analysis of cancer-specific metabolic and signaling pathways highlighted the structural features of cancer-related proteins and their complexes to develop next-generation protein kinase inhibitors, as well as the modulation of inflammatory and autophagic pathways in anti-cancer therapies. Importantly, malignant transformation can be described as a two-phase process, where an initial increase of system plasticity is followed by a decrease of plasticity at late stages of tumor development. Late-stage tumors should be attacked by an indirect network influence strategy. On the contrary, the attack of early-stage tumors may target central network nodes. Cancer stem cells need special diagnosis and targeting, since they potentially have an extremely high ability to change the rigidity/plasticity of their networks. The early warning signals of the activation of fast growing tumor cell clones are important in personalized diagnosis and therapy. Multi-target attacks are needed to perturb cancer-specific networks to exit from cancer attractors and re-enter a normal attractor. However, the dynamic non-genetic heterogeneity of cancer cell population induces the replenishment of the cancer attractor with surviving, non-responsive cells from neighboring abnormal attractors. The development of drug resistance is further complicated by interactions of tumor clones and their microenvironment. Network analysis of intercellular cooperation using game theory approaches may open new areas of understanding tumor complexity. In conclusion, the above applications of the network approach open up new, and highly promising avenues in anti-cancer drug design.

Complex regulation of autophagy in cancer - integrated approaches to discover the networks that hold a double-edged sword

Autophagy, a highly regulated self-degradation process of eukaryotic cells, is a context-dependent tumor-suppressing mechanism that can also promote tumor cell survival upon stress and treatment resistance. Because of this ambiguity, autophagy is considered as a double-edged sword in oncology, making anti-cancer therapeutic approaches highly challenging. In this review, we present how systems-level knowledge on autophagy regulation can help to develop new strategies and efficiently select novel anti-cancer drug targets. We focus on the protein interactors and transcriptional/post-transcriptional regulators of autophagy as the protein and regulatory networks significantly influence the activity of core autophagy proteins during tumor progression. We list several network resources to identify interactors and regulators of autophagy proteins. As in silico analysis of such networks often necessitates experimental validation, we briefly summarize tractable model organisms to examine the role of autophagy in cancer. We also discuss fluorescence techniques for high-throughput monitoring of autophagy in humans. Finally, the challenges of pharmacological modulation of autophagy are reviewed. We suggest network-based concepts to overcome these difficulties. We point out that a context-dependent modulation of autophagy would be favored in anti-cancer therapy, where autophagy is stimulated in normal cells, while inhibited only in stressed cancer cells. To achieve this goal, we introduce the concept of regulo-network drugs targeting specific transcription factors or miRNA families identified with network analysis. The effect of regulo-network drugs propagates indirectly through transcriptional or post-transcriptional regulation of autophagy proteins, and, as a multi-directional intervention tool, they can both activate and inhibit specific proteins in the same time. The future identification and validation of such regulo-network drug targets may serve as novel intervention points, where autophagy can be effectively modulated in cancer therapy.

Allostery in disease and in drug discovery

Allostery is largely associated with conformational and functional transitions in individual proteins. This concept can be extended to consider the impact of conformational perturbations on cellular function and disease states. Here, we clarify the concept of allostery and how it controls physiological activities. We focus on the challenging questions of how allostery can both cause disease and contribute to development of new therapeutics. We aim to increase the awareness of the linkage between disease symptoms on the cellular level and specific aberrant allosteric actions on the molecular level and to emphasize the potential of allosteric drugs in innovative therapies.

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.

Structure-based network analysis of an evolved G protein-coupled receptor homodimer interface

Crystallographic structures and experimental assays of human CXC chemokine receptor type 4 (CXCR4) provide strong evidence for the capacity to homodimerize, potentially as a means of allosteric regulation. Even so, how this homodimer forms and its biological significance has yet to be fully characterized. By applying principles from network analysis, sequence-based approaches such as statistical coupling analysis to determine coevolutionary residues, can be used in conjunction with molecular dynamics simulations to identify residues relevant to dimerization. Here, the predominant coevolution sector lies along the observed dimer interface, suggesting functional relevance. Furthermore, coevolution scoring provides a basis for determining significant nodes, termed hubs, in the network formed by residues found along the interface of the homodimer. These node residues coincide with hotspots indicating potential druggability. Drug design efforts targeting such key residues could potentially result in modulation of binding and therapeutic benefits for disease states, such as lung cancers, lymphomas and latent HIV-1 infection. Furthermore, this method may be applied to any protein-protein interaction.

Multiscale modeling of the causal functional roles of nsSNPs in a genome-wide association study: application to hypoxia

BACKGROUND

It is a great challenge of modern biology to determine the functional roles of non-synonymous Single Nucleotide Polymorphisms (nsSNPs) on complex phenotypes. Statistical and machine learning techniques establish correlations between genotype and phenotype, but may fail to infer the biologically relevant mechanisms. The emerging paradigm of Network-based Association Studies aims to address this problem of statistical analysis. However, a mechanistic understanding of how individual molecular components work together in a system requires knowledge of molecular structures, and their interactions.

RESULTS

To address the challenge of understanding the genetic, molecular, and cellular basis of complex phenotypes, we have, for the first time, developed a structural systems biology approach for genome-wide multiscale modeling of nsSNPs--from the atomic details of molecular interactions to the emergent properties of biological networks. We apply our approach to determine the functional roles of nsSNPs associated with hypoxia tolerance in Drosophila melanogaster. The integrated view of the functional roles of nsSNP at both molecular and network levels allows us to identify driver mutations and their interactions (epistasis) in H, Rad51D, Ulp1, Wnt5, HDAC4, Sol, Dys, GalNAc-T2, and CG33714 genes, all of which are involved in the up-regulation of Notch and Gurken/EGFR signaling pathways. Moreover, we find that a large fraction of the driver mutations are neither located in conserved functional sites, nor responsible for structural stability, but rather regulate protein activity through allosteric transitions, protein-protein interactions, or protein-nucleic acid interactions. This finding should impact future Genome-Wide Association Studies.

CONCLUSIONS

Our studies demonstrate that the consolidation of statistical, structural, and network views of biomolecules and their interactions can provide new insight into the functional role of nsSNPs in Genome-Wide Association Studies, in a way that neither the knowledge of molecular structures nor biological networks alone could achieve. Thus, multiscale modeling of nsSNPs may prove to be a powerful tool for establishing the functional roles of sequence variants in a wide array of applications.

The structural network of inflammation and cancer: merits and challenges

Inflammation, the first line of defense against pathogens can contribute to all phases of tumorigenesis, including tumor initiation, promotion and metastasis. Within this framework, the Toll-like receptor (TLR) pathway plays a central role in inflammation and cancer. Although extremely useful, the classical representation of this, and other pathways in the cellular network in terms of nodes (proteins) and edges (interactions) is incomplete. Structural pathways can help complete missing parts of such diagrams: they demonstrate in detail how signals coming from different upstream pathways merge and propagate downstream, how parallel pathways compensate each other in drug resistant mutants, how multi-subunit signaling complexes form and in particular why they are needed and how they work, how allosteric events can control these proteins and their pathways, and intricate details of feedback loops and how kick in. They can also explain the mechanisms of some oncogenic SNP mutations. Constructing structural pathways is a challenging task. Here, our goal is to provide an overview of inflammation and cancer from the structural standpoint, focusing on the TLR pathway. We use the powerful PRISM (PRotein Interactions by Structural Matching) tool to reveal important structural information of interactions in and within key orchestrators of the TLR pathway, such as MyD88.

The molecular basis of targeting protein kinases in cancer therapeutics

In this paper, we provide an overview of targeted anticancer therapies with small molecule kinase inhibitors. First, we discuss why a single constitutively active kinase emanating from a variety of aberrant genetic alterations is capable of transforming a normal cell, leading it to acquire the hallmarks of a cancer cell. To draw attention to the fact that kinase inhibition in targeted cancer therapeutics differs from conventional cytotoxic chemotherapy, we exploit a conceptual framework explaining why suppressed kinase activity will selectively kill only the so-called oncogene 'addicted' cancer cell, while sparing the healthy cell. Second, we introduce the protein kinase superfamily in light of its common active conformation with precisely positioned structural elements, and the diversified auto-inhibitory conformations among the kinase families. Understanding the detailed activation mechanism of individual kinases is essential to relate the observed oncogenic alterations to the elevated constitutively active state, to identify the mechanism of consequent drug resistance, and to guide the development of the next-generation inhibitors. To clarify the vital importance of structural guidelines in studies of oncogenesis, we explain how somatic mutations in EGFR result in kinase constitutive activation. Third, in addition to the common theme of secondary (acquired) mutations that prevent drug binding from blocking a signaling pathway which is hijacked by the aberrant activated kinase, we discuss scenarios of drug resistance and relapse by compensating lesions that bypass the inactivated pathway in a vertical or horizontal fashion. Collectively, these suggest that the future challenge of cancer therapy with small molecule kinase inhibitors will rely on the discovery of distinct combinations of optimized drugs to target individual subtypes of different cancers.

Self-organization of progress across the century of physics

We make use of information provided in the titles and abstracts of over half a million publications that were published by the American Physical Society during the past 119 years. By identifying all unique words and phrases and determining their monthly usage patterns, we obtain quantifiable insights into the trends of physics discovery from the end of the 19th century to today. We show that the magnitudes of upward and downward trends yield heavy-tailed distributions, and that their emergence is due to the Matthew effect. This indicates that both the rise and fall of scientific paradigms is driven by robust principles of self-organization. Data also confirm that periods of war decelerate scientific progress, and that the later is very much subject to globalisation.

Tertiary and quaternary effects in the allosteric regulation of animal hemoglobins

In the last decade, protein allostery has experienced a major resurgence, boosted by the extension of the concept to systems of increasing complexity and by its exploitation for the development of drugs. Expansion of the field into new directions has not diminished the key role of hemoglobin as a test molecule for theory and experimental validation of allosteric models. Indeed, the diffusion of hemoglobins in all kingdoms of life and the variety of functions and of quaternary assemblies based on a common tertiary fold indicate that this superfamily of proteins is ideally suited for investigating the physical and molecular basis of allostery and firmly maintains its role as a main player in the field. This review is an attempt to briefly recollect common and different strategies adopted by metazoan hemoglobins, from monomeric molecules to giant complexes, exploiting homotropic and heterotropic allostery to increase their functional dynamic range. This article is part of a Special Issue entitled: Oxygen Binding and Sensing Proteins.

The role of allostery in the ubiquitin-proteasome system

The ubiquitin-proteasome system (UPS) is involved in many cellular processes including protein degradation. Degradation of a protein via this system involves two successive steps: ubiquitination and degradation. Ubiquitination tags the target protein with ubiquitin-like proteins (UBLs), such as ubiquitin, small ubiquitin-like modifier (SUMO) and NEDD8, via a cascade involving three enzymes: activating enzyme E1, conjugating enzyme E2 and E3 ubiquitin ligases. The proteasomes recognize the UBL-tagged substrate proteins and degrade them. Accumulating evidence indicates that allostery is a central player in the regulation of ubiquitination, as well as deubiquitination and degradation. Here, we provide an overview of the key mechanistic roles played by allostery in all steps of these processes, and highlight allosteric drugs targeting them. Throughout the review, we emphasize the crucial mechanistic role played by linkers in allosterically controlling the UPS action by biasing the sampling of the conformational space, which facilitate the catalytic reactions of the ubiquitination and degradation. Finally, we propose that allostery may similarly play key roles in the regulation of molecular machines in the cell, and as such allosteric drugs can be expected to be increasingly exploited in therapeutic regimes.